Springer-Verlag New York Inc. – förlag

This handbook addresses the educational uses of mindfulness in schools. It summarizes the state of the science and describes current and emerging applications and challenges throughout the field. It explores mindfulness concepts in scientific, theoretical, and practical terms and examines training opportunities both as an aspect of teachers’ professional development and a means to enhance students’ social-emotional and academic skills. Chapters discuss mindfulness and contemplative pedagogy programs that have produced positive student outcomes, including stress relief, self-care, and improved classroom and institutional engagement. Featured topics include: A comprehensive view of mindfulness in the modern era.Contemplative education and the roots of resilience. Mindfulness practice and its effect on students’ social-emotional learning.A cognitive neuroscience perspective on mindfulness in education that addresses students’ academic and social skills development. Mindfulness training for teachers and administrators.Two universal mindfulness education programs for elementary and middle school students.The Handbook of Mindfulness in Education is a must-have resource for researchers, graduate students, clinicians, and practitioners in psychology, psychiatry, education, and medicine, as well as counseling, social work, and rehabilitation therapy.

This is a text in basic mathematics with multiple uses for either high school or college level courses. Readers will get a firm foundation in basic principles of mathematics which are necessary to know in order to go ahead in calculus, linear algebra or other topics. The subject matter is clearly covered and the author develops concepts so the reader can see how one subject matter can relate and grow into another.

The world is undergoing unprecedented changes in many of the factors that determine its fundamental properties and their in- ence on society. These changes include climate; the chemical c- position of the atmosphere; the demands of a growing human population for food and ?ber; and the mobility of organisms, ind- trial products, cultural perspectives, and information ?ows. The magnitude and widespread nature of these changes pose serious challenges in managing the ecosystem services on which society depends. Moreover, many of these changes are strongly in?uenced by human activities, so future patterns of change will continue to be in?uenced by society’s choices and governance. The purpose of this book is to provide a new framework for n- ural resource management—a framework based on stewardship of ecosystems for human well-being in a world dominated by unc- tainty and change. The goal of ecosystem stewardship is to respond to and shape change in social-ecological systems in order to s-tain the supply and opportunities for use of ecosystem services by society. The book links recent advances in the theory of resilience, sustainability, and vulnerability with practical issues of ecosystem management and governance. The book is aimed at advanced undergraduates and beginning graduate students of natural resource management as well as professional managers, community leaders, and policy makers with backgrounds in a wide array of d- ciplines, including ecology, policy studies, economics, sociology, and anthropology.

In this new textbook, acclaimed author John Stillwell presents a lucid introduction to Lie theory suitable for junior and senior level undergraduates. In order to achieve this, he focuses on the so-called "classical groups'' that capture the symmetries of real, complex, and quaternion spaces. These symmetry groups may be represented by matrices, which allows them to be studied by elementary methods from calculus and linear algebra.This naive approach to Lie theory is originally due to von Neumann, and it is now possible to streamline it by using standard results of undergraduate mathematics. To compensate for the limitations of the naive approach, end of chapter discussions introduce important results beyond those proved in the book, as part of an informal sketch of Lie theory and its history.John Stillwell is Professor of Mathematics at the University of San Francisco. He is the author of several highly regarded books published by Springer, including The Four Pillars of Geometry (2005), Elements of Number Theory (2003), Mathematics and Its History (Second Edition, 2002), Numbers and Geometry (1998) and Elements of Algebra (1994).

There are two major changes in the Fourth Edition of Differential Equations and Their Applications. The first concerns the computer programs in this text. In keeping with recent trends in computer science, we have replaced all the APL programs with Pascal and C programs. The Pascal programs appear in the text in place of the APL programs, where they are followed by the Fortran programs, while the C programs appear in Appendix C.Mathematics is playing an ever more important role in the physical and biological sciences, provoking a blurring of boundaries between scientific disciplines and a resurgence of interest in the modern as well as the classical techniques of applied mathematics. This renewal of interest, both in research and teaching, has led to the establishment of the series: Texts in Applied Mathematics (TAM). The development of new courses is a natural consequence of a high Ievel of excitement on the research frontieras newer techniques, such as numerical and symbolic computer systems, dynamical systems, and chaos, mix with and reinforce the traditional methods of applied mathematics. Thus, the purpose of this textbook series is to meet the current and future needs of these advances and encourage the teaching of new courses. TAM will publish textbooks suitable for use in advanced undergraduate and beginning graduate courses, and will complement the Applied Mathematical Sciences (AMS) series, which will focus on advanced textbooks and research Ievel monographs.

Imagine that we had some way to look directly at the molecules in a living organism. An x-ray microscope would do the trick, or since we’re dreaming, perhaps an Asimov-style nanosubmarine (unfortunately, neither is currently feasible). Think of the wonders we could witness firsthand: antibodies atta- ing a virus, electrical signals racing down nerve fibers, proteins building new strands of DNA. Many of the questions puzzling the current cadre of sci- tists would be answered at a glance. But the nanoscale world of molecules is separated from our everyday world of experience by a daunting million-fold difference in size, so the world of molecules is completely invisible. I created the illustrations in this book to help bridge this gulf and allow us to see the molecular structure of cells, if not directly, then in an artistic rendition. I have included two types of illustrations with this goal in mind: watercolor paintings which magnify a small portion of a living cell by one million times, showing the arrangement of molecules inside, and comput- generated pictures, which show the atomic details of individual molecules. In this second edition of The Machinery of Life, these illustrations are presented in full color, and they incorporate many of the exciting scientific advances of the 15 years since the first edition.

Investment and risk management problems are fundamental problems for  financial institutions and involve both speculative and hedging decisions. A structured approach to these problems naturally leads one to the field of applied mathematics in order to translate subjective probability beliefs and attitudes towards risk and reward into actual decisions. In Risk and Portfolio Analysis the authors present sound principles and useful methods for making investment and risk management decisions in the presence of hedgeable and non-hedgeable risks using the simplest possible principles, methods, and models that still capture the essential features of the real-world problems. They use rigorous, yet elementary mathematics, avoiding technically advanced approaches which have no clear methodological purpose and are practically irrelevant. The material progresses systematically and topics such as the pricing and hedging of derivative contracts, investment and hedging principles from portfolio theory, and risk measurement and multivariate models from risk management are covered appropriately. The theory is combined with numerous real-world examples that illustrate how the principles, methods, and models can be combined to approach concrete problems and to draw useful conclusions. Exercises are included at the end of the chapters to help reinforce the text and provide insight. This book will serve advanced undergraduate and graduate students, and practitioners in insurance, finance as well as regulators. Prerequisites include undergraduate level courses in linear algebra, analysis, statistics and probability.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing 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 colour graphics. It is 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.This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering. There is also a chapter on methods for "wide'' data (p bigger than n), including multiple testing and false discovery rates.

This greatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis. The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded this third edition to include the software package R.

This book is an introductory graduate-level textbook on the theory of smooth manifolds. Its goal is to familiarize students with the tools they will need in order to use manifolds in mathematical or scientific research--- smooth structures, tangent vectors and covectors, vector bundles, immersed and embedded submanifolds, tensors, differential forms, de Rham cohomology, vector fields, flows, foliations, Lie derivatives, Lie groups, Lie algebras, and more. The approach is as concrete as possible, with pictures and intuitive discussions of how one should think geometrically about the abstract concepts, while making full use of the powerful tools that modern mathematics has to offer.This second edition has been extensively revised and clarified, and the topics have been substantially rearranged. The book now introduces the two most important analytic tools, the rank theorem and the fundamental theorem on flows, much earlier so that they can be used throughout the book. A fewnew topics have been added, notably Sard’s theorem and transversality, a proof that infinitesimal Lie group actions generate global group actions, a more thorough study of first-order partial differential equations, a brief treatment of degree theory for smooth maps between compact manifolds, and an introduction to contact structures.Prerequisites include a solid acquaintance with general topology, the fundamental group, and covering spaces, as well as basic undergraduate linear algebra and real analysis.

This lively introductory text exposes the student to the rewards of a rigorous study of functions of a real variable. In each chapter, informal discussions of questions that give analysis its inherent fascination are followed by precise, but not overly formal, developments of the techniques needed to make sense of  them. By focusing on the unifying themes of approximation and the resolution of paradoxes that arise in the transition from the finite to the infinite, the text turns what could be a daunting cascade of definitions and theorems into a coherent and engaging progression of ideas. Acutely aware of the need for rigor, the student is much better prepared to understand what constitutes a proper mathematical proof and how to write one.Fifteen years of classroom experience with the first edition of Understanding Analysis have solidified and refined the central narrative of the second edition. Roughly 150 new exercises join a selection of the best exercises fromthe first edition, and three more project-style sections have been added. Investigations of Euler’s computation of ζ(2), the Weierstrass Approximation ­ Theorem, and the gamma function are now among the book’s cohort of seminal results serving as motivation and payoff for the beginning student to master the methods of analysis.

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

This is the sequel to my book Functions of One Complex Variable I, and probably a good opportunity to express my appreciation to the mathemat­ ical community for its reception of that work. In retrospect, writing that book was a crazy venture. As a graduate student I had had one of the worst learning experiences of my career when I took complex analysis; a truly bad teacher. As a non-tenured assistant professor, the department allowed me to teach the graduate course in complex analysis. They thought I knew the material; I wanted to learn it. I adopted a standard text and shortly after beginning to prepare my lectures I became dissatisfied. All the books in print had virtues; but I was educated as a modern analyst, not a classical one, and they failed to satisfy me. This set a pattern for me in learning new mathematics after I had become a mathematician. Some topics I found satisfactorily treated in some sources; some I read in many books and then recast in my own style. There is also the matter of philosophy and point of view. Going from a certain mathematical vantage point to another is thought by many as being independent of the path; certainly true if your only objective is getting there. But getting there is often half the fun and often there is twice the value in the journey if the path is properly chosen.

The main purpose of this book is to serve as a university textbook for a first course in astronomy. However, we believe that the audience will also include many serious ama­ teurs, who often find the popular texts too trivial. The lack of a good handbook for am­ ateurs has become a problem lately, as more and more people are buying personal com­ puters and need exact, but comprehensible, mathematical formalism for their programs. The reader of this book is assumed to have only a standard high-school knowledge of mathematics and physics (as they are taught in Finland); everything more advanced is usually derived step by step from simple basic principles. The mathematical background needed includes plane trigonometry, basic differential and integral calculus, and (only in the chapter dealing with celestial mechanics) some vector calculus. Some mathematical concepts the reader may not be familiar with are briefly explained in the appendices or can be understood by studying the numerous exercises and examples. However, most of the book can be read with very little knowledge of mathematics, and even if the reader skips the mathematically more involved sections, (s)he should get a good overview of the field of astronomy. This book has evolved in the course of many years and through the work of several authors and editors. The first version consisted of lecture notes by one of the editors (Oja).

This chapter briefly describes the general goals of the book, introduces the most fundamental features of the methodology that is employed to achieve these goals, and gives an outline of the structure of the book. A more detailed account of the goals and methodology is presented in chapters 2 and 3, respectively. What the Book Is About The main objective of this study is to attempt to answer the question: How do people understand their emotions? As we shall see in the next chapter, a large number of scholars have tried to provide answers to this question. The interest in the way people understand their emotions has led scholars to the issue of the nature of emotion concepts and emotional meaning. Since the notion of understanding involves or presupposes the notions of concept and meaning, it was only natural for scholars with an interest in the way people understand their emotions to tum their attention to emo­ tion concepts and the meaning associated with emotion terms. So the broader issue has often become more specific. For example, Davitz in his The Language of Emotion formulated the central question in the following way: "What does a person mean when he says someone is happy or angry or sad?" (Davitz 1969: 1).

The present book is meant as a text for a course on complex analysis at the advanced undergraduate level, or first-year graduate level. The first half, more or less, can be used for a one-semester course addressed to undergraduates. The second half can be used for a second semester, at either level. Somewhat more material has been included than can be covered at leisure in one or two terms, to give opportunities for the instructor to exercise individual taste, and to lead the course in whatever directions strikes the instructor's fancy at the time as well as extra read­ ing material for students on their own. A large number of routine exer­ cises are included for the more standard portions, and a few harder exercises of striking theoretical interest are also included, but may be omitted in courses addressed to less advanced students. In some sense, I think the classical German prewar texts were the best (Hurwitz-Courant, Knopp, Bieberbach, etc. ) and I would recommend to anyone to look through them. More recent texts have emphasized connections with real analysis, which is important, but at the cost of exhibiting succinctly and clearly what is peculiar about complex analysis: the power series expansion, the uniqueness of analytic continuation, and the calculus of residues.

The primary goal of these lectures is to introduce a beginner to the finite­ dimensional representations of Lie groups and Lie algebras. Since this goal is shared by quite a few other books, we should explain in this Preface how our approach differs, although the potential reader can probably see this better by a quick browse through the book. Representation theory is simple to define: it is the study of the ways in which a given group may act on vector spaces. It is almost certainly unique, however, among such clearly delineated subjects, in the breadth of its interest to mathematicians. This is not surprising: group actions are ubiquitous in 20th century mathematics, and where the object on which a group acts is not a vector space, we have learned to replace it by one that is {e. g. , a cohomology group, tangent space, etc. }. As a consequence, many mathematicians other than specialists in the field {or even those who think they might want to be} come in contact with the subject in various ways. It is for such people that this text is designed. To put it another way, we intend this as a book for beginners to learn from and not as a reference. This idea essentially determines the choice of material covered here. As simple as is the definition of representation theory given above, it fragments considerably when we try to get more specific.

Enriched and Impoverished Environments: Effects on Brain and Behaviour is the most recent review of the active area of neuronal plasticity. The question of how experience is recorded is fundamental to psychology; speculations and investigations concerning the role of the brain in this process have entered a particularly exciting phase as of the late 1980's. Manipulations of environmental complexity is one of the earliest methods utilized in the study of neural plasticity. This monograph organizes the evidence to date concerning the responsiveness of neural and behavioural systems to external manipulation of the environment. Further consideration is given to the issues of causation of the general effects of environment on brain and behaviour.

I am very pleased that my books about David Hilbert, published in 1970, and Richard Courant, published in 1976, are now being issued by Springer­ Verlag in a single volume. I have always felt that they belonged together, Courant being, as I have written, the natural and necessary sequel to Hilbert­ the rest of the story. To make the two volumes more compatible when published as one, we have combined and brought up to date the indexes of names and dates. U nfortu­ nately we have had to omit Hermann Weyl's article on "David Hilbert and his mathematical work," but the interested reader can always find it in the hard­ back edition of Hilbert and in Weyl's collected papers. At the request of a number of readers we have included a listing of all of Hilbert's famous Paris problems. It was, of course, inevitable that we would give the resulting joint volume the title Hilbert-Courant.

Since the earliest years of its development, Functional Analytic Psychotherapy (FAP) has been hailed for its clinical innovation and transformative power with clients across the range of disorders. Building on the foundations of their earlier volumes about this versatile therapy, the editors of The Practice of Functional Analytic Psychotherapy bring together noted clinicians and researchers to explain in depth how FAP can be used in conjunction with a broad spectrum of therapeutic approaches, and with diverse client populations. The hallmarks of the method, including therapist sensitivity and responding to client behavior in the moment, courage, mindfulness, acceptance, and egalitarianism, inform a wide array of interventions and strategies, among them: Integrating FAP with other treatments, including cognitive behavioral therapy, acceptance and commitment therapy, dialectical behavior therapy, behavioral activation, psychodynamic therapies, and feminist therapies.Applying FAP across cultures, nationalities, and ethnicities.Employing FAP with couples.Increasing sensitivity to and effectiveness with sexual minority clients.Modifying FAP for developmentally appropriate use with adolescents.Enhancing a team approach with severely mentally ill patients in institutional settings.Strengthening the power of interpersonal process groups.As a new tool or an enhancement to current practice, these applications of FAP will give therapists an empowering complement to their work. The Practice of Functional Analytic Psychotherapy points to compelling directions in personal growth and change—on both sides of the therapeutic bond.

For more than two decades, Functional Analytic Psychotherapy has brought new meaning – and new meaningfulness – to client/therapist relationships. And clients with disorders as varied as depression, PTSD, and fibromyalgia have benefited from its nuanced, curative power. In A Guide to Functional Analytic Psychotherapy, originators Robert Kohlenberg and Mavis Tsai join with other FAP practitioners to present a clinical framework, addressing points of convergence and divergence with other behavior therapies. Tracing FAP’s emerging evidence base, it takes readers through the deep complexities and possibilities of the therapeutic bond. And the attention to mindfulness and the self makes maximum clinical use of the uniqueness of every client – and every therapist.

For a long time I have been giving scientific lectures in different countries and on diverse topics, generally related to astronomy and to my work at the Arecibo Observatory. No matter which parti- lar topic I am talking about, the same question always comes up: Have we had any contact at Arecibo with “them”? My negative answer does not satisfy anyone. In fact, the answer either confirms their suspicions that there is a conspiracy afoot by higher autho- ties not to release information or their intentions to deceive the general public. The reasons for the deception have to do with the idea that, as in the movie Contact, the received messages contain important and useful information that will bring great advantage to whoever gets it. Many of us want to believe that extraterrestrial creatures can talk to us, that perhaps they are even living among us, as UFO fans believe. It would be fascinating if it were true, a more than extra- dinary discovery, the answer to an eternal question. There is p- sibly a deep psychological motive in this desire to know if we are alone in this huge universe, and the need to believe in something beyond our limited world, in space and time. There is no doubt, then, that this topic brings with it many scientific and philosophical discussions, as well as speculations that, on many occasions, fall into pure pseudoscience because of the lack of a reference framework.

The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data.The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly frommedicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.

There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master’s-level or Ph. D. -level statistics and computer science students. It is also suitable for researchersin statistics, machine lea- ing and data mining who want to get up to speed quickly on modern n- parametric methods. My goal is to quickly acquaint the reader with the basic concepts in many areas rather than tackling any one topic in great detail. In the interest of covering a wide range of topics, while keeping the book short, I have opted to omit most proofs. Bibliographic remarks point the reader to references that contain further details. Of course, I have had to choose topics to include andto omit,the title notwithstanding. For the mostpart,I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classi?cation or nonparametric Bayesian inference. The book developed from my lecture notes for a half-semester (20 hours) course populated mainly by master’s-level students. For Ph. D.

Taken literally, the title "All of Statistics" is an exaggeration. 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 non-parametric curve estimation, bootstrapping, and classification, 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 analysing data.

Russia’s accomplishments in planetary space exploration were not achieved easily. Formerly, the USSR experienced frustration in trying to tame unreliable Molniya and Proton upper stages and in tracking spacecraft over long distances. This book will assess the scientific haul of data from the Venus and Mars missions and look at the engineering approaches. The USSR developed several generations of planetary probes: from MV and Zond to the Phobos type. The engineering techniques used and the science packages are examined, as well as the nature of the difficulties encountered which ruined several missions. The programme’s scientific and engineering legacy is also addressed, as well as its role within the Soviet space programme as a whole.Brian Harvey concludes by looking forward to future Russian planetary exploration (e.g Phobos Grunt sample return mission). Several plans have been considered and may, with a restoration of funding, come to fruition. Soviet studies of deep space and Mars missions (e.g. TMK, Aelita) have much to offer contemporary planners in Europe and the United States. Long-duration ISS and Mir missions provide a medical record of considerable value in constructing human exploration of Mars.Illustrated with the photographs taken by Soviet Venus and Mars probes, pictures of the spacecraft, diagrams of the flight paths and landing techniques and maps of the landing sites, the book will build on the published scientific papers from the programme, archived material and memoirs and other material coming to light in recent years.

Paolo Ulivi and David Harland provide in "Robotic Exploration of the Solar System" a detailed history of unmanned missions of exploration of our Solar System As in their previous book Lunar Exploration, the subject will be treated wherever possible from an engineering and scientific standpoint. Technical descriptions of the spacecraft, of their mission designs and of instrumentations will be provided. Scientific results will be discussed in considerable depth, together with details of mission management.The books will cover missions from the 1950s until the present day, and some of the latest missions and their results will appear in a popular science book for the first time. The authors will also cover many unflown projects, providing an indication of the ideas that proved to be unfulfilled at the time but which may still be proven and useful in the future.Just like Lunar Exploration, these books will use sources only recently made available on the Soviet space program, in addition to some obscure and rarely used references on the European space program. The project will deliver three volumes totaling over 1000 pages that will provide comprehensive coverage of the topic with thousands of references to the professional literature that should make it the 'first port of call' for people seeking information on the topic.

The Fundamentals of Atomic and Molecular Physics is intended as an introduction to the field for advanced undergraduates who have taken quantum mechanics. Each chapter builds upon the previous, using the same tools and methods throughout. As the students progress through the book, their ability to use these tools will steadily increase, along with their confidence in their efficacy. The book treats the two-electron atom as the simplest example of the many-electron atom—as opposed to using techniques that are not applicable to many-electron atoms—so that it is unnecessary to develop additional equations when turning to multielectron atoms, such as carbon. External fields are treated using both perturbation theory and direct diagonalization and spontaneous emission is developed from first principles. Only diatomic molecules are considered with the hydrogen molecular ion and neutral molecule treated in some detail. This comprehensive coverage of the quantum mechanics of complex atoms and simple diatomic molecules, developed from the very basic components, is extremely useful for students considering graduate studies in any area of physics.