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Updated and expanded in its seventh edition, Practical Aviation & Aerospace Law and its companion workbook function as a comprehensive instructive package for undergraduate and graduate aviation law courses. This book, as a set or a stand-alone textbook, is an invaluable reference guide for aviation and aerospace business managers, pilots, maintenance personnel, aircraft owners, air traffic controllers, air safety investigators, operators of unmanned aircraft, and others involved in aviation or aerospace as a profession or hobby.Practical Aviation & Aerospace Law provides readers with the basic legal knowledge and perspective to understand how the legal system works in this industry. The authors guide you to recognize and avoid common legal pitfalls, and help you realize when you need to call a lawyer. This seventh edition reflects recent judicial decisions and changes in statutory, regulatory and international treaty law. It covers topics surrounding the burgeoning unmanned aircraft system (UAS) and commercial spaceflight segments as well, from an increasingly global viewpoint. Authors J. Scott Hamilton and Sarah Nilsson write concisely, clearly and yet conversationally about the complex field of law, including frequent examples from personal experience in practice. This combines to create for the industry a succinct foundation in understanding how to apply the law to aviation and aerospace interests and operations.

Practical, effective, evidence-based reading interventions that change students' lives Essentials of Understanding and Assessing Reading Difficulties is a practical, accessible, in-depth guide to reading assessment and intervention. It provides a detailed discussion of the nature and causes of reading difficulties, which will help develop the knowledge and confidence needed to accurately assess why a student is struggling. Readers will learn a framework for organizing testing results from current assessment batteries such as the WJ-IV, KTEA-3, and CTOPP-2. Case studies illustrate each of the concepts covered. A thorough discussion is provided on the assessment of phonics skills, phonological awareness, word recognition, reading fluency, and reading comprehension. Formatted for easy reading as well as quick reference, the text includes bullet points, icons, callout boxes, and other design elements to call attention to important information. Although a substantial amount of research has shown that most reading difficulties can be prevented or corrected, standard reading remediation efforts have proven largely ineffective. School psychologists are routinely called upon to evaluate students with reading difficulties and to make recommendations to address such difficulties. This book provides an overview of the best assessment and intervention techniques, backed by the most current research findings. Bridge the gap between research and practice Accurately assess the reason(s) why a student struggles in reading Improve reading skills using the most highly effective evidence-based techniques Reading may well be the most important thing students are taught during their school careers. It is a skill they will use every day of their lives; one that will dictate, in part, later life success. Struggling students need help now, and Essentials of Understanding and Assessing Reading Difficulties shows how to get these students on track.

We live in an age of serial asset bubbles and spectacular busts. Economists, policymakers, central bankers and most people in the financial world have been blindsided by these busts, while investors have lost trillions. Economists argue that bubbles can only be spotted after they burst and that market moves are unpredictable. Yet Marathon Asset Management, a London-based investment firm managing over $50 billion of assets has developed a relatively simple method for identifying and potentially avoiding them: follow the money, or rather the trail of investment. Bubbles whether they affect a whole economy or merely a single industry, tend to attract a splurge of capital spending. Excessive investment drives down returns and leads inexorably to a bust. This was the case with both the technology bubble at the turn of the century and the US housing bubble which followed shortly after. More recently, vast sums have been invested in mining and energy. From an investor's perspective, the trick is to avoid investing in sectors, or markets, where investment spending is unduly elevated and competition is fierce, and to put one's money to work where capital expenditure is depressed, competitive conditions are more favourable and, as a result, prospective investment returns are higher. This capital cycle strategy encourages investors to eschew the simple 'growth' and 'value' dichotomy and identify firms that can deliver superior returns either because capital has been taken out of an industry, or because the business has strong barriers to entry (what Warren Buffett refers to as a 'moat'). Some of Marathon's most successful investments have come from obscure, sometimes niche operations whose businesses are protected from the destructive forces of the capital cycle. Capital Returns is a comprehensive introduction to the theory and practical implementation of the capital cycle approach to investment. Edited and with an introduction by Edward Chancellor, the book brings together 60 of the most insightful reports written between 2002 and 2014 by Marathon portfolio managers. Capital Returns provides key insights into the capital cycle strategy, all supported with real life examples from global brewers to the semiconductor industry - showing how this approach can be usefully applied to different industry conditions and how, prior to 2008, it helped protect assets from financial catastrophe. This book will be a welcome reference for serious investors who looking to maximise portfolio returns over the long run.

A Guide to the Business Analysis Body of Knowledge® (BABOK® Guide) is the only globally recognized standard of practice for business analysis. Developed through a rigorous consensus-driven standards process, the BABOK® Guide incorporates the collective wisdom and experience of experts in the field from around the world.Previous editions have guided hundreds of thousands of professionals in their work, and it has been adopted by hundreds of enterprises as the basis of their business analysis practice. This latest version of the guide extends its scope beyond business analysis in projects to address agile development, business process management, business intelligence, and business architecture.This thoroughly revised and updated version includes:? A concept model that unifies ideas and terminology across business analysis disciplines. ? Restructured knowledge areas to support business analysis at every level from small tactical initiatives to major business transformations.? Five perspectives covering the most prominent business analysis disciplines and demonstrating how to apply the knowledge areas in different situations.? Coverage of new business analysis techniques that have gained wide acceptance in the community.? Updated and revised content in every knowledge area and more!Whether you are considering starting a career in business analysis, or you are an experienced professional in the field,the BABOK® Guide is your key resource to help you and your stakeholders discover opportunities for business success, deliver successful organizational change, and create business value.

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 book covers both classical and modern models in deep learning. The primary focus is on the theory and algorithms of deep learning. The theory and algorithms of neural networks are particularly important for understanding important concepts, so that one can understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than off-the-shelf machine-learning models? When is depth useful? Why is training neural networks so hard? What are the pitfalls? The book  is also rich in discussing different applications in order to give the practitioner a flavor of how neural architectures are designed for different types of problems. Applications associated with many different areas like recommender systems, machine translation, image captioning, image classification, reinforcement-learning based gaming, and text analytics are covered. The chapters of this book span three categories:The basics of neural networks:  Many traditional machine learning models can be understood as special cases of neural networks.  An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10.The book is written for graduate students, researchers, and practitioners.   Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.