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The contributions to this volume attempt to apply different aspects of Ilya Prigogine's Nobel-prize-winning work on dissipative structures to nonchemical systems as a way of linking the natural and social sciences. They address both the mathematical methods for description of pattern and form as they evolve in biological systems and the mechanisms of the evolution of social systems, containing many variables responding to subjective, qualitative stimuli.

The mathematical modeling of human systems, especially those far from thermodynamic equilibrium, must involve both chance and determinism, aspects both quantitative and qualitative. Such systems (and the physical states of matter which they resemble) are referred to as self-organized or dissipative structures in order to emphasize their dependence on the flows of matter and energy to and from their surroundings. Some such systems evolve along lines of inevitable change, but there occur instances of choice, or bifurcation, when chance is an important factor in the qualitative modification of structure. Such systems suggest that evolution is not a system moving toward equilibrium but instead is one which most aptly evokes the patterns of the living world.

The volume is truly interdisciplinary and should appeal to researchers in both the physical and social sciences. Based on a workshop on dissipative structures held in 1978 at the University of Texas, contributors include Prigogine, A. G. Wilson, Andre de Palma, D. Kahn, J. L. Deneubourgh, J. W. Stucki, Richard N. Adams, and Erick Jantsch.

The papers presented include Allen, "Self-Organization in the Urban System"; Robert Herman, "Remarks on Traffic Flow Theories and the Characterization of Traffic in Cities"; W. H. Zurek and Schieve, "Nucleation Paradigm: Survival Threshold in Population Dynamics"; De Palma et al., "Boolean Equations with Temporal Delays"; Nicholas Georgescu-Roegin, "Energy Analysis and Technology Assessment"; Magoroh Maruyama, "Four Different Causal Meta-types in Biological and Social Sciences"; and Jantsch, "From Self-Reference to Self-Transcendence: The Evolution of Self-Organization Dynamics."

The IoT is the inter-networking of connected and smart devices, buildings, vehicles and other items which are embedded with electronics, software, sensors, actuators, and network connectivity that enable these objects to collect and exchange data. A sensor is a detection device that measures, records, or responds to a physical property. Sensors represent the front end of information processing. Progress in communication technologies is part of the multi-factorial advances in electronics, sensors, embedded computing, signal processing and machine learning methods that has led to the development of new capabilities in the IoT.

The shadowing function is an important element in the simulation and calculation of how electromagnetic waves scatter at randomly rough surfaces. Its derivation and use is an active, interdisciplinary area of research with practical applications in fields such as radar, optics, acoustics, geoscience, computer graphics and remote sensing.

Semiconductor power switches are essential to the operation of power electronic converters and systems. Traditional silicon-based semiconductor power switches have intrinsic physical limits to their performance. Wide Bandgap (WBG) devices deliver higher efficiency, smaller size, and/or lighter weight. Applications in power grid electronics as well as in electromobility are on the rise, but WBG devices also pose new challenges, in particular due to their fast switching speed and the need for protection.

When digitized entities, connected devices and microservices interact purposefully, we end up with a massive amount of multi-structured streaming (real-time) data that is continuously generated by different sources at high speed. Streaming analytics allows the management, monitoring, and real-time analytics of live streaming data. The topic has grown in importance due to the emergence of online analytics and edge and IoT platforms. A real digital transformation is being achieved across industry verticals through meticulous data collection, cleansing and crunching in real time. Capturing and subjecting those value-adding events is considered to be the prime task for achieving trustworthy and timely insights.

Swarm Intelligence (SI) is one of the most important and challenging paradigms under the umbrella of computational intelligence. It focuses on the research of collective behaviours of a swarm in nature and/or social phenomenon to solve complicated and difficult problems which cannot be handled by traditional approaches. Thousands of papers are published each year presenting new algorithms, new improvements and numerous real world applications. This makes it hard for researchers and students to share their ideas with other colleagues; follow up the works from other researchers with common interests; and to follow new developments and innovative approaches. This complete and timely collection fills this gap by presenting the latest research systematically and thoroughly to provide readers with a full view of the field of swarm. Students will learn the principles and theories of typical swarm intelligence algorithms; scholars will be inspired with promising research directions; and practitioners will find suitable methods for their applications of interest along with useful instructions.

Reluctance motors induce non-permanent magnetic poles on the ferromagnetic rotor; the rotor does not have any windings and torque is generated through magnetic reluctance. Synchronous reluctance motors (SyRMs) have an equal number of stator and rotor poles. Reluctance motors can deliver high power density at low cost, so they are finding increasing application in the transport sector. Disadvantages include high torque ripple and the complexity of designing and controlling them. Advances in theory, computer design, and control electronics can overcome these issues.