On the road and off-road, on and under water and even in the air - micro models can do everything like the big ones! Since miniaturised electronic components, tiny motors and the smallest batteries have been available, it is possible to build remote-controlled models even in H0 scale. They can drive, fly or swim, can be steered, have indicators, headlights and blue lights, compete in four-wheel drive truck trials or dive in the aquarium. The authors, all pioneers of this new fascinating hobby, show here how to get from the simple conversion of a commercially available model to the realisation of a wide range of special functions to the high school of micromodelling. There are detailed building descriptions, richly illustrated with more than 200 illustrations, for a truck with rafinesses, a bulldozer with crawler tracks, a Unimog for road and rail, a landing ship, a research submarine, a twin-engine aircraft and several more. In addition, there are explanations of the basic electrical and electronic components as well as sources of supply and useful tips and tricks for the workshop - because micromodelling requires special knowledge and skills. Discover a new challenge for talented tinkerers: micromodelling!

Forecasting from multi-equation models has very rarely been the focus in econometric literature. In response, this book presents a range of methodologies to approach this complex field and offers readers essential information on forecasting from multi-equation econometric micromodels. In the twentieth century, significant interest in econometric macromodels emerged. These multi-equation models are mostly systems of interdependent equations, most often used to describe the national economies of various countries. The book analyzes econometric forecasting procedures and illustrates them with empirical examples that are based on real economic (mostly business-derived) data. The procedure of forecast building from systems of interdependent equations is presented for two categories of econometric models: models with a feedback effect and models with closed-loop links between interdependent variables. The forecasts obtained via this technique are compared with the results derived from reduced-form equations of the respective econometric model. The author also generalizes the rules of the reduced-recursive (helical, iterative) procedure application, against the backdrop of the proposed method of forecast building from reduced-form equations of systems of interdependent equations. Given its scope, the book will appeal not only to PhD students and researchers, but also undergraduate students and academics in general.

The great success of the first book on remote-controlled miniature models has encouraged the team of authors around Thorsten Feuchter to build more functional road vehicles in 1:87 scale and to describe their experiences in detail again. Their preference is for functional models such as the heavy-duty tractor with manual gearbox, the wheel loader that can do some proper excavating, and the road sweeper that will make the battery hoover superfluous for model railways in the future. In addition to teaching the basics such as the use of LEDs and muscular wire, this book is particularly concerned with the steering, switching and lighting of H0 vehicles. The solutions presented here require a well-equipped hobby workshop, but some of the projects can also be realised without a lathe or milling machine. Over 140 illustrations and numerous sketches and tables supplement the text.

Ships, submarines, amphibious vehicles - functional in 1:87/H0 Models in small scales are particularly fascinating - micromodels on water even more so! In this book, the four authors - most of them already known from other books on the subject of micromodels - present ships, submarines and amphibious vehicles almost completely in 1:87 scale, also known as H0. Of course, they do not limit themselves to the pure driving functions. The models in this book are packed with special functions of all kinds - by the way, many of them can also be used for models in larger scales. The technology presented here makes almost everything possible and the instructions show that a replica is also feasible with normal means. Take up the challenge and put a micro-functional model on the water! From the content • Introduction – a few thoughts in advance • Henry Morgan – a small harbour tugboat to warm up • Little helpers – simple tools with great effect • Arngast – a plastic model learns to dive • Wulf Isebrand – deep drawing technology in miniature • Soldering aid – hole grid board to help with soldering • LED – Handling LEDs in Model Making • DUKW – a ship with wheels • ROV – maximum fun with minimal effort • Calypso – a handy functional model • Hermann Marwede – conversion of a Revell - model in 1/72nd scale • Friesland – a crane ship with play value

Deals with specialized but interrelated problems in oil recovery in which the effect of interfacial behaviors is the dominant factor. Describes approaches to improving the understanding of the fundamentals of displacement, with the goal of simplifying systems sufficiently to enable measurements and

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.