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This book offers a general overview of the physics, concepts, theories, and models underlying the discipline of aerodynamics. A particular focus is the technique of velocity field representation and modeling via source and vorticity fields and via their sheet, filament, or point-singularity idealizations. These models provide an intuitive feel for aerodynamic flow-field behavior and are the basis of aerodynamic force analysis, drag decomposition, flow interference estimation, and other important applications. The models are applied to both low speed and high speed flows. Viscous flows are also covered, with a focus on understanding boundary layer behavior and its influence on aerodynamic flows.

The book covers some topics in depth while offering introductions and summaries of others. Computational methods are indispensable for the practicing aerodynamicist, and the book covers several computational methods in detail, with a focus on vortex lattice and panel methods. The goal is to improve understanding of the physical models that underlie such methods. The book also covers the aerodynamic models that describe the forces and moments on maneuvering aircraft, and provides a good introduction to the concepts and methods used in flight dynamics. It also offers an introduction to unsteady flows and to the subject of wind tunnel measurements.

The book is based on the MIT graduate-level course “Flight Vehicle Aerodynamics” and has been developed for use not only in conventional classrooms but also in a massive open online course (or MOOC) offered on the pioneering MOOC platform edX. It will also serve as a valuable reference for professionals in the field. The text assumes that the reader is well versed in basic physics and vector calculus, has had some exposure to basic fluid dynamics and aerodynamics, and is somewhat familiar with aerodynamics and aeronautics terminology.

A Practical Guide to Making Sense of Data

In the age of Big Data, the tools of information visualization offer us a macroscope to help us make sense of the avalanche of data available on every subject. This book offers a gentle introduction to the design of insightful information visualizations. It is the only book on the subject that teaches nonprogrammers how to use open code and open data to design insightful visualizations. Readers will learn to apply advanced data mining and visualization techniques to make sense of temporal, geospatial, topical, and network data.

The book, developed for use in an information visualization MOOC, covers data analysis algorithms that enable extraction of patterns and trends in data, with chapters devoted to “when” (temporal data), “where” (geospatial data), “what” (topical data), and “with whom” (networks and trees); and to systems that drive research and development. Examples of projects undertaken for clients include an interactive visualization of the success of game player activity in World of Warcraft; a visualization of 311 number adoption that shows the diffusion of non-emergency calls in the United States; a return on investment study for two decades of HIV/AIDS research funding by NIAID; and a map showing the impact of the HiveNYC Learning Network.

Visual Insights will be an essential resource on basic information visualization techniques for scholars in many fields, students, designers, or anyone who works with data.

An Intuitive Approach

This book offers students and researchers a guide to distributed algorithms that emphasizes examples and exercises rather than the intricacies of mathematical models. It avoids mathematical argumentation, often a stumbling block for students, teaching algorithmic thought rather than proofs and logic. This approach allows the student to learn a large number of algorithms within a relatively short span of time. Algorithms are explained through brief, informal descriptions, illuminating examples, and practical exercises. The examples and exercises allow readers to understand algorithms intuitively and from different perspectives. Proof sketches, arguing the correctness of an algorithm or explaining the idea behind fundamental results, are also included. An appendix offers pseudocode descriptions of many algorithms.

Distributed algorithms are performed by a collection of computers that send messages to each other or by multiple software threads that use the same shared memory. The algorithms presented in the book are for the most part “classics,” selected because they shed light on the algorithmic design of distributed systems or on key issues in distributed computing and concurrent programming.

Distributed Algorithms can be used in courses for upper-level undergraduates or graduate students in computer science, or as a reference for researchers in the field.

Downloadable instructor resources available for this title: solution manual and slides

This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of “data science” for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT’s OpenCourseWare) and was developed for use not only in a conventional classroom but in a massive open online course (or MOOC) offered by the pioneering MIT-Harvard collaboration edX.

Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.

Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

An Interdisciplinary Reader

This book discusses some of the most critical ethical issues in mental health care today, including the moral dimensions of addiction, patient autonomy and compulsory treatment, privacy and confidentiality, and the definition of mental illness itself. Although debates over these issues are ongoing, there are few comprehensive resources for addressing such dilemmas in the practice of psychology, psychiatry, social work, and other behavioral and mental health care professions. This book meets that need, providing foundational background for undergraduate, graduate, and professional courses.

Topics include central questions such as evolving views of the morality and pathology of deviant behavior; patient competence and the decision to refuse treatment; recognizing and treating people who have suffered trauma; addiction as illness; the therapist’s responsibility to report dangerousness despite patient confidentiality; and boundaries for the therapist’s interaction with patients outside of therapy, whether in the form of tennis games, gift-giving, or social media contact. For the most part the selections address contemporary issues in contemporary terms, but the book also offers a few historic or classic essays, including Thomas S. Szasz’s controversial 1971 article “The Ethics of Addiction.” Contributors include Laura Weiss Roberts, Frederic G. Reamer, Charles P. O’Brien, and Thomas McLellan.

Beyond the Bad-Apple Approach

Federal regulations that govern research misconduct in biomedicine have not been able to prevent an ongoing series of high-profile cases of fabricating, falsifying, or plagiarizing scientific research. In this book, Barbara Redman looks critically at current research misconduct policy and proposes a new approach that emphasizes institutional context and improved oversight.

Current policy attempts to control risk at the individual level. But Redman argues that a fair and effective policy must reflect the context in which the behavior in question is embedded. As journalists who covered many research misconduct cases observed, the roots of fraud “lie in the barrel, not in the bad apples that occasionally roll into view.” Drawing on literature in related fields—including moral psychology, the policy sciences, the organizational sciences, and law—as well as analyses of misconduct cases, Redman considers research misconduct from various perspectives. She also examines in detail a series of clinical research cases in which repeated misconduct went undetected and finds laxity of oversight, little attention to harm done, and inadequate correction of the scientific record. Study questions enhance the book’s value for graduate and professional courses in research ethics.

Redman argues that the goals of any research misconduct policy should be to protect scientific capital (knowledge, scientists, institutions, norms of science), support fair competition, contain harms to end users and to the public trust, and enable science to meet its societal obligations.

Downloadable instructor resources available for this title: answers to study

A Self-Contained Approach

This unique introduction to econometrics provides undergraduate students with a command of regression analysis in one semester, enabling them to grasp the empirical literature and undertake serious quantitative projects of their own. It does not assume any previous exposure to probability and statistics but does discuss the concepts in these areas that are essential for econometrics. The bulk of the textbook is devoted to regression analysis, from simple to advanced topics. Students will gain an intuitive understanding of the mathematical concepts; Java applet simulations on the book’s website demonstrate how the algebraic equations are derived in the text and are designed to reinforce the important concepts.

After presenting the essentials of probability and statistics, the book covers simple regression analysis, multiple regression analysis, and advanced topics including heteroskedasticity, autocorrelation, large sample properties, instrumental variables, measurement error, omitted variables, panel data, simultaneous equations, and binary/truncated dependent variables. Two optional chapters treat additional probability and statistics topics. Each chapter offers examples, prep problems (bringing students “up to speed” at the beginning of a chapter), review questions, and exercises. An accompanying website offers students easy access to Java simulations and data sets (available in EViews, Stata, and Excel files). After a single semester spent mastering the material presented in this book, students will be prepared to take any of the many elective courses that use econometric techniques.

• Requires no background in probability and statistics
• Regression analysis focus
• “Econometrics lab” with Java applet simulations on accompanying Website

Downloadable instructor resources available for this title: solution manual, slides, and handouts

From Laboratory to Theory

Vision is one of the most active areas in biomedical research, and visual psychophysical techniques are a foundational methodology for this research enterprise. Visual psychophysics, which studies the relationship between the physical world and human behavior, is a classical field of study that has widespread applications in modern vision science. Bridging the gap between theory and practice, this textbook provides a comprehensive treatment of visual psychophysics, teaching not only basic techniques but also sophisticated data analysis methodologies and theoretical approaches. It begins with practical information about setting up a vision lab and goes on to discuss the creation, manipulation, and display of visual images; timing and integration of displays with measurements of brain activities and other relevant techniques; experimental designs; estimation of behavioral functions; and examples of psychophysics in applied and clinical settings.

The book's treatment of experimental designs presents the most commonly used psychophysical paradigms, theory-driven psychophysical experiments, and the analysis of these procedures in a signal-detection theory framework. The book discusses the theoretical underpinnings of data analysis and scientific interpretation, presenting data analysis techniques that include model fitting, model comparison, and a general framework for optimized adaptive testing methods. It includes many sample programs in Matlab with functions from Psychtoolbox, a free toolbox for real-time experimental control. Once students and researchers have mastered the material in this book, they will have the skills to apply visual psychophysics to cutting-edge vision science.

Beyond Gridlock

The “golden era” of American environmental lawmaking in the 1960s and 1970s saw twenty-two pieces of major environmental legislation (including the Clean Air Act, the Clean Water Act, and the Endangered Species Act) passed by bipartisan majorities in Congress and signed into law by presidents of both parties. But since then partisanship, the dramatic movement of Republicans to the right, and political brinksmanship have led to legislative gridlock on environmental issues. In this book, Christopher Klyza and David Sousa argue that the longstanding legislative stalemate at the national level has forced environmental policymaking onto other pathways.

Klyza and Sousa identify and analyze five alternative policy paths, which they illustrate with case studies from 1990 to the present: “appropriations politics” in Congress; executive authority; the role of the courts; “next-generation” collaborative experiments; and policymaking at the state and local levels. This updated edition features a new chapter discussing environmental policy developments from 2006 to 2012, including intensifying partisanship on the environment, the failure of Congress to pass climate legislation, the ramifications of Massachusetts v. EPA, and other Obama administration executive actions (some of which have reversed Bush administration executive actions). Yet, they argue, despite legislative gridlock, the legacy of 1960s and 1970s policies has created an enduring “green state” rooted in statutes, bureaucratic routines, and public expectations.

In Matter and Consciousness, Paul Churchland presents a concise and contemporary overview of the philosophical issues surrounding the mind and explains the main theories and philosophical positions that have been proposed to solve them. Making the case for the relevance of theoretical and experimental results in neuroscience, cognitive science, and artificial intelligence for the philosophy of mind, Churchland reviews current developments in the cognitive sciences and offers a clear and accessible account of the connections to philosophy of mind.

For this third edition, the text has been updated and revised throughout. The changes range from references to the iPhone's "Siri" to expanded discussions of the work of such contemporary philosophers as David Chalmers, John Searle, and Thomas Nagel. Churchland describes new research in evolution, genetics, and visual neuroscience, among other areas, arguing that the philosophical significance of these new findings lies in the support they tend to give to the reductive and eliminative versions of materialism.

Matter and Consciousness, written by the most distinguished theorist and commentator in the field, offers an authoritative summary and sourcebook for issues in philosophy of mind. It is suitable for use as an introductory undergraduate text.

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