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Electron swarms --- Electron-molecule collisions

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Earthquake swarms --- Yellowstone Volcano Observatory --- Yellowstone National Park

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Swarming has become a fundamental cultural technique related to dynamic processes and an effective metaphor for the collaborative efforts of society. This book examines the media history of swarm research and its significance to current socio-technological processes. It shows that the hype about collective intelligence is based on a reciprocal computerization of biology and biologization of computer science: After decades of painstaking biological observations in the ocean, experiments in aquariums, and mathematical model-making, it was swarms-inspired computer simulation which provided biological researchers with enduring knowledge about animal collectives. At the same time, a turn to biological principles of self-organization made it possible to adapt to unclearly delineated sets of problems and clarify the operation of opaque systems - from logistics to architecture, or from crowd control to robot collectives. As zootechnologies, swarms offer performative, synthetic, and approximate solutions in cases where analytical approaches are doomed to fail.

Swarming (Zoology) --- Zoology --- Biology --- Natural history --- Animals --- Swarms (Zoology) --- Herding behavior in animals --- Technological innovations. --- Swarm intelligence. --- Computer simulation. --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- Collective intelligence --- Cellular automata --- Distributed artificial intelligence --- Media History, Media Theory Swarm Intelligence, Computer Simulation, Animal Collectives. --- Methods in social research (general) --- Artificial intelligence. Robotics. Simulation. Graphics --- Knowledge, Theory of.

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This book summarizes the efforts of ten papers collected by the Special Issue "Condensed-Matter-Principia Based Information & Statistical Measures: From Classical to Quantum". It calls for papers which deal with condensed-matter systems, or their interdisciplinary analogs, for which well-defined classical–statistical vs. quantum information measures can be inferred while based on the entropy concept. The contents have mainly been rested upon objectives addressed by an international colloquium held on October 2019, in UTP Bydgoszcz, Poland (see http://zmpf.imif.utp.edu.pl/rci-jcs/rci-jcs-4/), with an emphasis placed on the achievements of Professor Gerard Czajkowski, who commenced his research activity with open diffusion–reaction systems under the supervision of Roman S. Ingarden (Toruń), a father of Polish synergetics, and original thermodynamic approaches to self-organization. The active cooperation of Professor Czajkowski, mainly with German physicists (Friedrich Schloegl, Aachen; Werner Ebeling, Berlin), ought to be highlighted. In light of this, a development of his research, as it has moved from statistical thermodynamics to solid state theory, pursued in terms of nonlinear solid-state optics (Franco Bassani, Pisa), and culminated very recently with large quasiparticles termed Rydberg excitons, and their coherent interactions with light, is worth delineating.

entropy --- second law --- thermodynamics --- Shannon measure of information --- information theory --- surface plasmons --- fractals --- quantum plasmonics --- beyond dipole --- entanglement --- electromagnetically induced transparency --- cross-Kerr nonlinearity --- Gazeau–Klauder coherent states --- Helstrom bound --- chemical computing --- oscillatory reaction --- genetic optimization --- classification problem --- interacting oscillators --- Flory–De Gennes exponent --- conformation of protein --- albumin --- non-gaussian chain --- non-isothermal characteristics --- Fisher’s test --- Kullback–Leibler divergence --- network --- flow --- channel --- probability distribution --- Shannon information measure --- cross-entropy --- drones --- swarms --- robustness --- information --- classical vs. quantum system --- condensed matter --- soft matter --- complex systems

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Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties:

n/a --- self-organization --- signal source localization --- multi-robot system --- sensor deployment --- parallel technique --- shape normalization --- genetic algorithm --- multiple robots --- optimization --- improved potential field --- optimal configuration --- autonomous docking --- asymmetrical interaction --- comparison --- behaviors --- patterns --- self-assembly robots --- congestion control --- surface-water environment --- target recognition --- coordinate motion --- UAV swarms --- formation reconfiguration --- swarm robotics --- swarm intelligence --- artificial bee colony algorithm --- obstacle avoidance --- fish swarm optimization --- search algorithm --- robotics --- time-difference-of-arrival (TDOA) --- formation --- mobile robots --- formation control --- meta-heuristic --- event-triggered communication --- search --- virtual structure --- 3D model identification --- surveillance --- event-driven coverage --- scale-invariant feature transform --- system stability --- Swarm intelligence algorithm --- bionic intelligent algorithm --- unmanned aerial vehicle --- underwater environment --- artificial flora (AF) algorithm --- swarm behavior --- weighted implicit shape representation --- Cramer–Rao low bound (CRLB) --- environmental perception --- particle swarm optimization --- modular robots --- cooperative target hunting --- virtual linkage --- multi-AUV --- consensus control --- panoramic view --- nonlinear disturbance observer --- sliding mode controller --- path optimization --- Swarm Chemistry --- multi-agents --- Cramer-Rao low bound (CRLB)