As this bestseller predicted, Trump has only grown more erratic and dangerous as the pressures on him mount. This new edition includes new essays bringing the book up to date—because this is still not normal. Originally released in fall 2017, The Dangerous Case of Donald Trump was a runaway bestseller. Alarmed Americans and international onlookers wanted to know: What is wrong with him? That question still plagues us. The Trump administration has proven as chaotic and destructive as its opponents feared, and the man at the center of it all remains a cipher. Constrained by the APA’s “Goldwater rule,” which inhibits mental health professionals from diagnosing public figures they have not personally examined, many of those qualified to weigh in on the issue have shied away from discussing it at all. The public has thus been left to wonder whether he is mad, bad, or both. The prestigious mental health experts who have contributed to the revised and updated version of The Dangerous Case of Donald Trump argue that their moral and civic "duty to warn" supersedes professional neutrality. Whatever affects him, affects the nation: From the trauma people have experienced under the Trump administration to the cult-like characteristics of his followers, he has created unprecedented mental health consequences across our nation and beyond. With eight new essays (about one hundred pages of new material), this edition will cover the dangerous ramifications of Trump's unnatural state. It’s not all in our heads. It’s in his.
The statistical analysis of extreme data is important for various disciplines, including hydrology, insurance, finance, engineering and environmental sciences. This book provides a self-contained introduction to the parametric modeling, exploratory analysis and statistical interference for extreme values. The entire text of this third edition has been thoroughly updated and rearranged to meet the new requirements. Additional sections and chapters, elaborated on more than 100 pages, are particularly concerned with topics like dependencies, the conditional analysis and the multivariate modeling of extreme data. Parts I–III about the basic extreme value methodology remain unchanged to some larger extent, yet notable are, e.g., the new sections about "An Overview of Reduced-Bias Estimation" (co-authored by M.I. Gomes), "The Spectral Decomposition Methodology", and "About Tail Independence" (co-authored by M. Frick), and the new chapter about "Extreme Value Statistics of Dependent Random Variables" (co-authored by H. Drees). Other new topics, e.g., a chapter about "Environmental Sciences", (co--authored by R.W. Katz), are collected within Parts IV–VI.
This book offers a guideline for "Technology Audit" exercises for the transforming innovation systems of Central and Eastern European Countries (CEECs). Further more, the book presents the results of an exemplary application of this guideline in the field of biotechnology in Hungary. The authors - a group of innovation re searchers of the Fraunhofer Institute for Systems and Innovation Research (lSI), Germany, and of the Innovation Research Centre (IKU), Hungary - provide a sound concept for the identification of technological strengths and weaknesses of the CEECs' industrially oriented research systems as a basis for the design of advanced innovation policies. After the Organisation for Economic Co-operation and Development (OECD) had proposed a "Technology Audit" of Hungary in 1993, a pilot audit was carried out under their auspices. In parallel, the German Federal Ministry of Education, Sci ence, Research and Technology (BMFT) put forward the idea of developing the audit concept further in order to make it applicable also in other Central and Eastern European Countries. They asked lSI to utilise the running OECD audits as a learn ing source and to work out a comprehensive audit approach.
Statistical analysis of extreme data is vital to many disciplines including hydrology, insurance, finance, engineering and environmental sciences. This book provides a self-contained introduction to parametric modeling, exploratory analysis and statistical interference for extreme values. For this Third Edition, the entire text has been thoroughly updated and rearranged to meet contemporary requirements, with new sections and chapters address such topics as dependencies, the conditional analysis and the multivariate modeling of extreme data. New chapters include An Overview of Reduced-Bias Estimation; The Spectral Decomposition Methodology; About Tail Independence; and Extreme Value Statistics of Dependent Random Variables.
The internationalization of research and technology is one key component of the globalization of trade and business, with potentially major impacts on patterns of economic development and public policies worldwide. Although certain aspects of this internationalization trend are well documented, and some effects can be quantified, the overall processes are extremely complex and the outcomes are highly uncertain. The existence of the phenomenon is generally accepted, but its importance and the trends are currently the topic of a lively debate. This study on "New Ways in Drug Development in Pharmaceuticals" is part of a three year project which aims at investigating how new concepts of industrial knowledge creation are implemented in the different environ ments of the innovation systems of the United States and Germany. The main focus of the overall project is a series of case studies of innovation practice in different national and sectoral contexts. The following sectors and technological fields are investigated: pharmaceuticals and new ways in drug development by the Fraunhofer Institute for Systems and Innovation Research (ISI), advanced materials by the University Hohenheim, Insti tute of International Management and Innovation (Alexander Gerybadze), financial services and home banking by the Massachusetts Institute of Tech nology (MIT), Center for Industrial Performance (Richard Lester) and the Sloan School of Management (Edward Roberts). Financially the project was supported by the German-American Academic Council, the German Federal Minstry of Education, Science Research and Technology and the Fraunhofer Society.
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