This book presents a range of recent advances concerning industrial restructuring strategies, industrial organization, industrial policy, departmental economic research, industrial competitiveness, regional industrial structure, national industrial economic security theory and empirical research. Successfully combining theory and practice, the book gathers the outcomes of the "6th International Conference on Industrial Economics System and Industrial Security Engineering", which was held at the University of Maryland, USA.

This book presents a range of recent advances concerning industrial restructuring strategies, industrial organization, industrial policy, departmental economic research, industrial competitiveness, regional industrial structure, national industrial economic security theory and empirical research. Successfully combining theory and practice, the book gathers the outcomes of the "6th International Conference on Industrial Economics System and Industrial Security Engineering", which was held at the University of Maryland, USA.

Institutional weaknesses limit the capacity of local governments to support efficient urbanization in developing countries. They also lead to the emergence of large developers with the clout to build entire cities. This paper analyzes the urbanization process when local governments are weak and large developers are powerful. Results from a non-cooperative game setting with minimal assumptions show that multiple equilibria can emerge depending on key institutional parameters of the model and the nature of the game, but all of them are inefficient. In this simple setting, increasing the capacity of the local government may not lead to better outcomes, because it may crowd out urban land development by the more effective private investor. Subsidizing the large investor can ensure efficiency, but it makes the rest of society worse off. Selling the rights to the city can be Pareto efficient, but only provided that the price at which the rights are sold are sufficiently high. However, more analytical and empirical work is needed before these analyses can be deemed relevant in practice. Competition among jurisdictions, time consistency challenges, and the social implications of private cities deserve special attention.

Electromagnetic imaging has been a powerful technique in various civil and military applications across medical imaging, geophysics, and space exploration. The Nyquist-Shannon theory has formed the basis for processing the signals in such systems. The advent of Compressive Sensing techniques has enabled low-dimension-model-based techniques to be used to break many of the bottlenecks of the earlier technologies. Low-dimensional-model-based electromagnetic imaging remains at its early stage, and many important issues relevant to practical applications need to be carefully investigated. In particular, this is the era of big data with booming electromagnetic sensing, by which massive data are being collected for retrieving very detailed information of probed objects. This monograph gives an overview of the low-dimensional models of structure signals, along with its relevant theories and low-complexity algorithms of signal recovery. It further reviews the recent advancements of low-dimensional-model-based electromagnetic imaging in various applied areas. It is a comprehensive introduction for researchers and engineers wishing to understand the state-of-the-art of electromagnetic imaging.

This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.