This book presents detailed descriptions of chaos for continuous-time systems. It is the first-ever book to consider chaos as an input for differential and hybrid equations. Chaotic sets and chaotic functions are used as inputs for systems with attractors: equilibrium points, cycles and tori. The findings strongly suggest that chaos theory can proceed from the theory of differential equations to a higher level than previously thought. The approach selected is conducive to the in-depth analysis of different types of chaos. The appearance of deterministic chaos in neural networks, economics and mechanical systems is discussed theoretically and supported by simulations. As such, the book offers a valuable resource for mathematicians, physicists, engineers and economists studying nonlinear chaotic dynamics.

The book is concerned with the concepts of chaos and fractals, which are within the scopes of dynamical systems, geometry, measure theory, topology, and numerical analysis during the last several decades. It is revealed that a special kind of Poisson stable point, which we call an unpredictable point, gives rise to the existence of chaos in the quasi-minimal set. This is the first time in the literature that the description of chaos is initiated from a single motion. Chaos is now placed on the line of oscillations, and therefore, it is a subject of study in the framework of the theories of dynamical systems and differential equations, as in this book. The techniques introduced in the book make it possible to develop continuous and discrete dynamics which admit fractals as points of trajectories as well as orbits themselves. To provide strong arguments for the genericity of chaos in the real and abstract universe, the concept of abstract similarity is suggested.

The current literature on dynamic systems is quite comprehensive, and system theory’s mathematical jargon can remain quite complicated. Thus, there is a need for a compendium of accessible research that involves the broad range of fields that dynamic systems can cover, including engineering, life sciences, and the environment, and which can connect researchers in these fields. The Handbook of Research on Modeling, Analysis, and Control of Complex Systems is a comprehensive reference book that describes the recent developments in a wide range of areas including the modeling, analysis, and control of dynamic systems, as well as explores related applications. The book acts as a forum for researchers seeking to understand the latest theory findings and software problem experiments. Covering topics that include chaotic maps, predictive modeling, random bit generation, and software bug prediction, this book is ideal for professionals, academicians, researchers, and students in the fields of electrical engineering, computer science, control engineering, robotics, power systems, and biomedical engineering.

This edited volume gathers selected, peer-reviewed contributions presented at the fourth International Conference on Differential & Difference Equations Applications (ICDDEA), which was held in Lisbon, Portugal, in July 2019. First organized in 2011, the ICDDEA conferences bring together mathematicians from various countries in order to promote cooperation in the field, with a particular focus on applications. The book includes studies on boundary value problems; Markov models; time scales; non-linear difference equations; multi-scale modeling; and myriad applications.

Text introducing a new empirically-based model of financial market dynamics.

the origin-of-life problem and the proteinoid theory molecular biology and the reconstruction of microbial phylogenies: des liaisons dangereuses? prebiotic chirality and life concentration and organization of precursors at inorganic interplayers and interfaces THE CHEMICAL ORIGINS OF LIFE AND MIND symmetry breaking and the origin of life THE WEAK FORCE AND SETH: THE SEARCH FOR EXTRA-TERRESTRIAL HOMOCHIRALITY COMMUNICATION BETWEEN ACTION AND REACTION UNDERLYING THE PHYSICAL ORIGIN OF LIFE randomness, determinism, and evolution

The interest of physicists in economic and social questions is not new: for over four decades, we have witnessed the emergence of what is called nowadays “sociophysics” and “econophysics”, vigorous and challenging areas within the wider “Interdisciplinary Physics”. With tools borrowed from Statistical Physics and Complexity, this new area of study have already made important contributions, which in turn have fostered the development of novel theoretical foundations in Social Science and Economics, via mathematical approaches, agent-based modelling and numerical simulations. From these foundations, Computational Social Science has grown to incorporate as well the empirical component --aided by the recent data deluge from the Web 2.0 and 3.0--, closing in this way the experiment-theory cycle in the best tradition of Physics.