In February of 2020, [Constantino] Tsallis, mathematician Nihat Ay, and physicist Ugur Tirnakli convened a small SFI working group to focus on the mathematical counterpart of the third signature of nonextensive statistical mechanics, namely the distribution of energies. They have published their results in a recent paper in the journal Nonlinear Dynamics.

In the paper, they demonstrate how non-extensive statistical mechanics mirrors the large deviation theory, related to the long-awaited third signature in these statistics for complex systems. They go on to computationally test their approach on iconic models of complex systems that describe earthquakes, avalanches, and extinction of biological species.

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A P Riascos, José L Mateos
Journal of Complex Networks, Volume 9, Issue 5, October 2021, cnab032,

In this article, we present a survey of different types of random walk models with local and non-local transitions on undirected weighted networks. We present a general approach by defining the dynamics as a discrete-time Markovian process with transition probabilities expressed in terms of a symmetric matrix of weights. In the first part, we describe the matrices of weights that define local random walk dynamics like the normal random walk, biased random walks and preferential navigation, random walks in the context of digital image processing and maximum entropy random walks. In addition, we explore non-local random walks, like Lévy flights on networks, fractional transport through the new formalism of fractional graph Laplacians, and applications in the context of human mobility. Explicit relations for the stationary probability distribution, the mean first passage time and global times to characterize random walks are obtained in terms of the elements of the matrix of weights and its respective eigenvalues and eigenvectors. Finally, we apply the results to the analysis of particular local and non-local random walk dynamics, and we discuss their capacity to explore several types of networks. Our results allow us to study and compare the global dynamics of different types of random walk models.

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Arshed Nabeel, Danny Raj M
In the study of collective motion, it is common practice to collect movement information at the level of the group to infer the characteristics of the individual agents and their interactions. However, it is not clear whether one can always correctly infer individual characteristics from movement data of the collective. We investigate this question in the context of a composite crowd with two groups of agents, each with its own desired direction of motion. A simple observer attempts to classify an agent into its group based on its movement information. However, collective effects such as collisions, entrainment of agents, formation of lanes and clusters, etc. render the classification problem non-trivial, and lead to misclassifications. Based on our understanding of these effects, we propose a new observer algorithm that infers, based only on observed movement information, how the local neighborhood aids or hinders agent movement. Unlike a traditional supervised learning approach, this algorithm is based on physical insights and scaling arguments, and does not rely on training-data. This new observer improves classification performance and is able to differentiate agents belonging to different groups even when their motion is identical. Data-agnostic approaches like this have relevance to a large class of real-world problems where clean, labeled data is difficult to obtain, and is a step towards hybrid approaches that integrate both data and domain knowledge.

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Denis Sh. Sabirov and Igor S. Shepelevich

Entropy 2021, 23(10), 1240

Basic applications of the information entropy concept to chemical objects are reviewed. These applications deal with quantifying chemical and electronic structures of molecules, signal processing, structural studies on crystals, and molecular ensembles. Recent advances in the mentioned areas make information entropy a central concept in interdisciplinary studies on digitalizing chemical reactions, chemico-information synthesis, crystal engineering, as well as digitally rethinking basic notions of structural chemistry in terms of informatics.

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Heiko Hamann; Julian Schwarzat; Ingo Thomsen and Sven Tomforde

Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems – VEHITS

Platooning is an approach to coordinate the driving behaviour of vehicles on major roads such as motorways. The aim is to take advantage of, e.g., slipstream effects to reduce cost. We present an approach to transfer the platooning concept to urban road networks of cities. The reduced slipstream effect is compensated by integration with the signalisation infrastructure to dynamically allow for prioritisation of platoons using progressive signal systems (i.e., “green waves”). We define the scenario and derive a research road map towards fully self-organised platoon operations and integrated coordination with self-adaptive and self-organising urban traffic control systems. Starting from both directions, that is, self-organised urban platooning as well as self-organised progressive signal systems in urban road networks, we define the scenario, identify main challenges, and present first results to demonstrate the feasibility of our research agenda.

Read the full article at: www.scitepress.org