# gst: hyperchaos and complex dynamical regimes in N-d neuron lattices.

AA << study the dynamics of N-dimensional lattices of nonchaotic Rulkov neurons coupled with a flow of electrical current. (They) consider both nearest-neighbor and next-nearest-neighbor couplings, homogeneous and heterogeneous neurons, and small and large lattices over a wide range of electrical coupling strengths. >>

<< As the coupling strength is varied, the neurons exhibit a number of complex dynamical regimes, including unsynchronized chaotic spiking, local quasi-bursting, synchronized chaotic bursting, and synchronized hyperchaos. >>

<< For lattices in higher spatial dimensions, (AA) discover dynamical effects arising from the ``destructive interference'' of many connected neurons and miniature ``phase transitions'' from coordinated spiking threshold crossings. In large two- and three-dimensional neuron lattices, (They) observe emergent dynamics such as local synchronization, quasi-synchronization, and lag synchronization. >>

<< These results illustrate the rich dynamics that emerge from coupled neurons in multiple spatial dimensions, highlighting how dimensionality, connectivity, and heterogeneity critically shape the collective behavior of neuronal systems. >>

Brandon B. Le, Dima Watkins. Hyperchaos and complex dynamical regimes in N-dimensional neuron lattices. arXiv: 2505.03051v1 [nlin.CD]. May 5, 2025.

https://arxiv.org/abs/2505.03051

Also: brain, network, behavior, chaos, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, network, behavior, cooperation, cooperative behavior, chaos, hyperchaos, transitions, phase transitions, transition thresholds,  synchrony, dimensionality, topology of connectivity, intermittent bursting activity, interference, destructive interference.

# gst: apropos of critical transitions, a new approach to extreme events.

FIG. 1. Dynamics of excitable complex networks [coupling topologies: random (RN); small-world (SW); scale-free (SF); all-to-all (complete; CP)]. 

<< Unexpected and often irreversible shifts in the state or the dynamics of a complex system often accumulate in extreme events with likely disastrous impact on the system and its environment. Detection, understanding, and possible prediction of such critical transitions are thus of paramount importance across a variety of scientific fields. >>

<< The rather modest improvement achieved so far may be due previous research mostly concentrating on either particular subsystems, considered to be of vital importance for the generating mechanism of a critical transition, or on the system as a whole. These approaches only rarely take into account the intricate, time-dependent interrelatedness of subsystems that can essentially determine emerging behaviors underlying critical transitions. >> 

AA << uncover subsystems, network vertices, and the interrelatedness of certain subsystems, network edges, as tipping elements in a networked dynamical system, forming a time-evolving tipping subnetwork. (They)  demonstrate the existence of tipping subnetworks in excitable complex networks and in human epileptic brains. These systems can repeatedly undergo critical transitions that result in extreme events. >>

AA << findings reveal that tipping subnetworks encapsulate key properties of mechanisms involved in critical transitions. >>

Timo Bröhl, Klaus Lehnertz. Emergence of a tipping subnetwork during a critical transition in networked systems: A new avenue to extreme events. Phys. Rev. Research 7, 023109. May 1, 2025.

https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.023109

Also: network, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, networks, excitable complex networks, network edges, network vertices, subnetwork, tipping subnetworks, small-worlds, unexpected shifts, transitions, critical transition, extreme events, interrelatedness, time-dependent interrelatedness.

# gst: emergent oscillations and chaos in noncompliant microfluidic networks.

<< Incompressible fluids in microfluidic networks with nonrigid channels can exhibit flow rate oscillations analogous to electric current oscillations in RLC (resistor, inductor, capacitor) circuits. This is due to the elastic deformation of channel walls that can store and release fluid, as electric capacitors can store and release electric charges. This property is quantified through the compliance of the system, defined as the volume change relative to the pressure change. >>

<< In systems with rigid walls and incompressible fluid, compliance vanishes, and no oscillations can occur through this mechanism. >>

Here, AA << show that not only oscillations but also chaos can emerge in the flow-rate dynamics of noncompliant microfluidic networks with incompressible fluid. Notably, these dynamics emerge spontaneously, even under time-independent driving pressures. The underlying mechanism is governed by the effect of fluid inertia, which becomes relevant at moderate Reynolds numbers observed in microfluidic systems exhibiting complex flow patterns. >>

<< The results are established using a combination of direct numerical simulations and a reduced model derived from modal analysis. This approach enables (AA) to determine the onset of oscillations, the associated bifurcations, the oscillation frequencies and amplitudes, and their dependence on the driving pressures. >>

Yanxuan Shao, Jean-Regis Angilella, Adilson E. Motter. Emergent oscillations and chaos in noncompliant microfluidic networks. Phys. Rev. Fluids 10, 054401. May 1, 2025.

https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.10.054401

arXiv: 2505.00068v1 [physics.flu-dyn]. 

https://arxiv.org/abs/2505.00068

Also: network, elastic, chaos, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, networks, microfluidic networks, noncompliant networks with incompressible fluid, fluid inertia, 

driving pressures, elasticity, chaos.

# gst: how noise affects memory in linear recurrent networks

<< The effects of noise on memory in a linear recurrent network are theoretically investigated. Memory is characterized by its ability to store previous inputs in its instantaneous state of network, which receives a correlated or uncorrelated noise. >>

<< Two major properties are revealed: First, the memory reduced by noise is uniquely determined by the noise's power spectral density (PSD). Second, the memory will not decrease regardless of noise intensity if the PSD is in a certain class of distribution (including power law). >>

JingChuan Guan, Tomoyuki Kubota, et al. How noise affects memory in linear recurrent networks. Phys. Rev. Research 7, 023049. Apr 14, 2025.

https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.023049

Also: noise, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, noise, network, memory

# gst: strange attractors in complex networks

<< Disorder and noise in physical systems often disrupt spatial and temporal regularity, yet chaotic systems reveal how order can emerge from unpredictable behavior. Complex networks, spatial analogs of chaos, exhibit disordered, non-Euclidean architectures with hidden symmetries, hinting at spontaneous order. Finding low-dimensional embeddings that reveal network patterns and link them to dimensionality that governs universal behavior remains a fundamental open challenge, as it needs to bridge the gap between microscopic disorder and macroscopic regularities. >>

<< Here, the minimal space revealing key network properties is introduced, showing that non-integer dimensions produce chaotic-like attractors. >>

Pablo Villegas. Strange attractors in complex networks. Phys. Rev. E 111, L042301. Apr 15, 2025. 

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.111.L042301.  

arXiv: 2504.08629v1 [cond-mat.stat-mech] . Apr 11, 2025.

https://arxiv.org/abs/2504.08629

Also: disorder, disorder & fluctuations, noise, network, attractor, chaos, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, disorder, disorder & fluctuations, noise, networks, attractors, self-similarity, chaos 

# gst: switching from active Brownian motion to stationary rotation of Janus particles in a viscoelastic fluid.

<< Swimming micro-objects exist in viscoelastic fluids. Elucidating the effect of viscoelasticity on the motion of these objects is important for understanding their behavior. >>

AA << examined the motion of Janus particles self-propelled by induced charge electrophoresis over a wide range of speeds in semidilute polymer solutions. In (Their) system, the motion of Janus particles changed from active Brownian motion to stationary rotation as the speed increased. The torque for stationary rotation originates from the difference between the direction of self-propulsion and that of the time-delayed restoring force from the polymer solution, which has been reported in another self-propelled particle system. The switch from active Brownian motion to stationary rotation at different polymer concentrations can be explained by the Weisenberg number, which is defined as the ratio of the relaxation time of the polymer network to the travel time of the Janus particle to its size. >>

Keita Saito, Ryunosuke Kawano, et al. Self-propelled motion of induced-charge electrophoretic Janus particles in viscoelastic fluids. Phys. Rev. E 111, 045409. Apr 10, 2025.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.111.045409

️

Also: Janus, transition, particle, in FonT:

Janus https://flashontrack.blogspot.com/search?q=janus 

transition https://flashontrack.blogspot.com/search?q=transition 

particle https://flashontrack.blogspot.com/search?q=particle

Keywords: gst, Janus, transitions, particles, self-propelled particles

# gst: evolution of robust cell differentiation mechanisms under epigenetic feedback

<< In multi-cellular organisms, cells differentiate into multiple types as they divide. States of these cell types, as well as their numbers, are known to be robust to external perturbations; as conceptualized by Waddington's epigenetic landscape where cells embed themselves in valleys corresponding with final cell types. >>

<< How is such robustness achieved by developmental dynamics and evolution? To address this question, (AA) consider a model of cells with gene expression dynamics and epigenetic feedback, governed by a gene regulation network. By evolving the network to achieve more cell types, (They) identified three major differentiation mechanisms exhibiting different properties regarding their variance, attractors, stability, and robustness. >>

<< The first of these mechanisms, type A, exhibits chaos and long-lived oscillatory dynamics that slowly transition until reaching a steady state. The second, type B, follows a channeled annealing process where the epigenetic changes in combination with noise shift the stable landscape of the cells towards varying final cell states. Lastly, type C exhibits a quenching process where cell fate is quickly decided by falling into pre-existing fixed points while cell trajectories are separated through periodic attractors or saddle points. >>

AA << find types A and B to correspond well with Waddington's landscape while being robust. Finally, the dynamics of type B demonstrate a novel method through dimensional reduction of gene-expression states during differentiation. >>

Davey Plugers, Kunihiko Kaneko. Evolution of robust cell differentiation mechanisms under epigenetic feedback. arXiv: 2503.20651v1 [physics.bio-ph]. Mar 26, 2025. 

https://arxiv.org/abs/2503.20651

Also: evolution, noise, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, evolution, noise, epigenetics, epigenetic feedback, differentiation

# gst: synchronization and chaos in complex systems with delayed interactions.

<< Explaining the wide range of dynamics observed in ecological communities is challenging due to the large number of species involved, the complex network of interactions among them, and the influence of multiple environmental variables. >>

AA << consider a general framework to model the dynamics of species-rich communities under the effects of external environmental factors, showing that it naturally leads to delayed interactions between species, and analyze the impact of such memory effects on population dynamics. >>

<< Employing the generalized Lotka-Volterra equations with time delays and random interactions, (AA) characterize the resulting dynamical phases in terms of the statistical properties of community interactions. (Their) findings reveal that memory effects can generate persistent and synchronized oscillations in species abundances in sufficiently competitive communities. This provides an additional explanation for synchronization in large communities, complementing known mechanisms such as predator-prey cycles and environmental periodic variability. >>

<< Furthermore, (AA) show that when reciprocal interactions are negatively correlated, time delays alone can induce chaotic behavior. This suggests that ecological complexity is not a prerequisite for unpredictable population dynamics, as intrinsic memory effects are sufficient to generate long-term fluctuations in species abundances. >>

Francesco Ferraro, Christian Grilletta, et al. Synchronization and chaos in complex ecological communities with delayed interactions. arXiv: 2503.21551v1 [q-bio.PE]. Mar 27, 2025.

https://arxiv.org/abs/2503.21551

Also: pause, silence, random, chaos, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, pause, silence, random, chaos, chaotic behavior, network, delay, time delay, delayed interactions, random interactions, memory effect 

# behav: the benefit of ignorance for traffic through a random congestible network.

<< When traffic is routed through a network that is susceptible to congestion, the self-interested decisions made by individual users do not, in general, produce the optimal flow. This discrepancy is quantified by the so-called "price of anarchy." >>

AA << consider whether the traffic produced by self-interested users is made better or worse when users have uncertain knowledge about the cost functions of the links in the network, and define a parallel concept that (They) call the "price of ignorance."  >>

AA << introduce a simple model in which fast, congestible links and slow, incongestible links are mixed randomly in a large network and users plan their routes with finite uncertainty about which of the two cost functions describes each link. >>

<< One of (Their) key findings is that a small level of user ignorance universally improves traffic, regardless of the network composition. Further, there is an optimal level of ignorance which, in (the) model, causes the self-interested user behavior to coincide with the optimum. Many features of (AA) model can be understood analytically, including the optimal level of user ignorance and the existence of critical scaling near the percolation threshold for fast links, where the potential benefit of user ignorance is greatest. >>️

Alican Saray, Calvin Pozderac, et al. The benefit of ignorance for traffic through a random congestible network. arXiv: 2503.09684v1 [cond-mat.dis-nn]. Mar 12, 2025.

https://arxiv.org/abs/2503.09684

Also:  network, behav, random, uncertainty, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, networks, behavior,  randomness, uncertainty, price of anarchy, price of ignorance

# gst: apropos of nuanced dependencies in intricately coupled network, attractive-repulsive challenge in swarmalators with time-dependent speed.

AA << examine a network of entities whose internal and external dynamics are intricately coupled, modeled through the concept of ``swarmalators'' as introduced by O'Keeffe et al. (Nat. Commun., 8(1):1–13, 2017). (They) investigate how the entities' natural velocities impact the network's collective dynamics and path to synchronization. >>

<< Specifically, (AA) analyze two scenarios: one in which each entity has an individual natural velocity, and another where a group velocity is defined by the average of all velocities. >> 

Their << findings reveal two distinct forms of phase synchronization -- static and rotational -- each preceded by a complex state of attractive-repulsive interactions between entities. This interaction phase, which depends sensitively on initial conditions, allows for selective modulation within the network. By adjusting initial parameters, (AA) can isolate specific entities to experience attractive-repulsive interactions distinct from the group, prior to the onset of full synchronization. >>

<< This nuanced dependency on initial conditions offers valuable insights into the role of natural velocities in tuning synchronization behavior within coupled dynamic networks. >>️

Steve J. Kongni, Thierry Njougouo, et al. Attractive-repulsive challenge in swarmalators with time-dependent speed. arXiv: 2501.06048v1 [nlin.AO]. Jan 10, 2025. 

https://arxiv.org/abs/2501.06048

Also: swarm, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, swarm, swarmalators, network

# gst: multiple Pareto-optimal solutions of the dissipation-adaptation trade-off

<< Adaptation refers to the ability to recover and maintain “normal” function on perturbations of internal or external conditions and is essential for sustaining life. Biological adaptation mechanisms are dissipative, i.e., they require a supply of energy such as the coupling to the hydrolysis of ATP. Via evolution the underlying biochemical machinery of living organisms evolved into highly optimized states. However, in the case of adaptation processes two quantities are optimized simultaneously, the adaptation speed or accuracy and the thermodynamic cost. In such cases one typically faces a trade-off, where improving one quantity implies worsening the other. The solution is no longer unique but rather a Pareto set—the set of all physically attainable protocols along which no quantity can be improved without worsening another. >> 

AA << investigate Pareto fronts in adaptation-dissipation trade-offs for a cellular thermostat and a minimal ATP-driven receptor-ligand reaction network. (They) find convex sections of Pareto fronts to be interrupted by concave regions, implying the coexistence of distinct optimization mechanisms. (They) discuss the implications of such “compromise-optimal” solutions and argue that they may endow biological systems with a superior flexibility to evolve, resist, and adapt to different environments. >>️

Jorge Tabanera-Bravo, Aljaz Godec. Multiple Pareto-optimal solutions of the dissipation-adaptation trade-off. 

Phys. Rev. Research 7, 013020. Jan 7, 2025.

https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.013020

Also: 'dissipation' in FonT  https://flashontrack.blogspot.com/search?q=dissipation

Also: 'adaptation' in FonT  https://flashontrack.blogspot.com/search?q=adaptation   in Notes (quasi-stochastic poetry) (a) https://inkpi.blogspot.com/search?q=adaptation   

(b) https://inkpi.blogspot.com/search?q=adattamento

Keywords: gst, adaptation, dissipation

# gst: phenomenology of cracks in thin colloidal films (undergoing desiccation)

<< A number of geometric and topological properties of samples of crack-template based conductive films are examined to assess the degree to which Voronoi diagrams can successfully model structure and conductivity in such networks. >>

AA << analysis suggests that although Poisson-Voronoi diagrams are only partially successful in modeling structural features of real-world crack patterns formed in films undergoing desiccation, such diagrams can nevertheless be useful in situations where topological characteristics are more important than geometric ones. A phenomenological model is proposed that is more accurate at capturing features of the real-world crack patterns. >>️

Yuri Yu. Tarasevich, Andrei V. Eserkepov, et al. Phenomenological model of crack patterns in thin colloidal films undergoing desiccation. arXiv: 2501.07303v1 [cond-mat.dis-nn]. Jan 13, 2025.

https://arxiv.org/abs/2501.07303

Also: crack, particle, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, crack, particles, network, crack pattern networks, Voronoi tessellations 

# gst: apropos of transitions, towards a theory for the formation of chimera patterns in complex networks

This AA work << formalizes a systematic method by evoking pattern formation theory to explain the emergence of chimera states in complex networks. >>

They << show that the randomness of network topology, as reflected in the localization of the graph Laplacian eigenvectors, determines the emergence of chimera patterns, underscoring the critical role of network structure. In particular, this approach explains how amplitude and phase chimeras arise separately and explores whether phase chimeras can be chaotic or not. (AA) findings suggest that chimeras result from the interplay between local and global dynamics at different time scales. >>

Malbor Asllani, Alex Arenas. Towards a Theory for the Formation of Chimera Patterns in Complex Networks. arXiv: 2412.05504v1 [nlin.AO]. Dec 7, 2024.

https://arxiv.org/abs/2412.05504

Also: chimera, network, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chimera, network, transition

# gst: self-organized chimera states in pulse-coupled oscillator systems.

<< Coupled oscillator systems can lead to states in which synchrony and chaos coexist. These states are called “chimera states.” >>

️

AA << study a variation of a pulse-coupled oscillator (PCO) model that has been shown to produce chimera states, demonstrate that it reproduces several of the expected chimera properties, like the formation of multiple heads and the ability to control the natural drift that Kuramoto's chimera states experience in a ring, and explain how chimera states emerge. >>️

<< Three notable aspects of chimeras in our PCO networks (with time-discrete coupling) are the absence of firing events from the tail (which still almost synchronize their phases), the reliable onset of the phenomenon from virtually any initial configuration, and the lack of a superimposed structure (e.g., artificially splitting the population into subgroups) and thus the self-organized nature of the phenomenon. >>️

Arke Vogell, Udo Schilcher, et al. Chimera states in pulse-coupled oscillator systems. Phys. Rev. E 110, 054214. Nov 26, 2024.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.054214 

Also: chimera, self-assembly, chaos, network,  in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chimera, self-assembly, chaos, network 

# game: balance exploration and exploitation, making decisions cooperatively without sharing information.

<< Multiagent reinforcement learning (MARL) studies crucial principles that are applicable to a variety of fields, including wireless networking and autonomous driving. (AA) propose a photonic-based decision-making algorithm to address one of the most fundamental problems in MARL, called the competitive multiarmed bandit (CMAB) problem. >>

AA << demonstrate that chaotic oscillations and cluster synchronization of optically coupled lasers, along with (their) proposed decentralized coupling adjustment, efficiently balance exploration and exploitation while facilitating cooperative decision making without explicitly sharing information among agents. >>

AA << study demonstrates how decentralized reinforcement learning can be achieved by exploiting complex physical processes controlled by simple algorithms. >>

Shun Kotoku, Takatomo Mihana, et al. Decentralized multiagent reinforcement learning algorithm using a cluster-synchronized laser network. Phys. Rev. E 110, 064212. Dec 11, 2024.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.064212

Img: https://x.com/PhysRevE/status/1866889144657465632

Also: game, chaos, ai (artificial intell), in https://www.inkgmr.net/kwrds.html 

Keywords: game, cooperation, chaos, exploration, exploitation, ai, artificial intelligence, MARL, CMAB.

# gst: apropos of diffusive anomalies, anomalous diffusion of active Brownian particles in responsive elastic gels.

Here, AA << examine via extensive computer simulations the dynamics of SPPs (self-propelled particles) in deformable gellike structures responsive to thermal fluctuations. (AA) treat tracer particles comparable to and larger than the mesh size of the gel. (They) observe distinct trapping events of active tracers at relatively short times, leading to subdiffusion; it is followed by an escape from meshwork-induced traps due to the flexibility of the network, resulting in superdiffusion. >>

AA << thus find crossovers between different transport regimes. (They) also find pronounced nonergodicity in the dynamics of SPPs and non-Gaussianity at intermediate times. The distributions of trapping times of the tracers escaping from “cages” in (..)  quasiperiodic gel often reveal the existence of two distinct timescales in the dynamics. At high activity of the tracers these timescales become comparable. >>

<< Furthermore, (AA) find that the mean waiting time exhibits a power-law dependence on the activity of SPPs (in terms of their Péclet number). (Their) results additionally showcase both exponential and nonexponential trapping events at high activities. Extensions of this setup are possible, with the factors such as anisotropy of the particles, different topologies of the gel network, and various interactions between the particles (also of a nonlocal nature) to be considered. >>

Koushik Goswami, Andrey G. Cherstvy, et al. Anomalous diffusion of active Brownian particles in responsive elastic gels: Nonergodicity, non-Gaussianity, and distributions of trapping times. Phys. Rev. E 110, 044609. Oct 29, 2024.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.044609

Also: particle, random, escape, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, particle, random, random walks, escape, network

# gst: protected chaos in a topological lattice.

<< The erratic nature of chaotic behavior is thought to erode the stability of periodic behavior, including topological oscillations. However, (AA) discover that in the presence of chaos, non-trivial topology not only endures but also provides robust protection to chaotic dynamics within a topological lattice hosting non-linear oscillators. >>

<< Despite the difficulty in defining topological invariants in non-linear settings, non-trivial topological robustness still persists in the parametric state of chaotic boundary oscillations. (AA) demonstrate this interplay between chaos and topology by incorporating chaotic Chua's circuits into a topological Su-Schrieffer-Heeger (SSH) circuit. >>

<< By extrapolating from the linear limit to deep into the non-linear regime, (AA) find that distinctive correlations in the bulk and edge scroll dynamics effectively capture the topological origin of the protected chaos. (Their)  findings suggest that topologically protected chaos can be robustly achieved across a broad spectrum of periodically-driven systems, thereby offering new avenues for the design of resilient and adaptable non-linear networks. >>️

Haydar Sahin, Hakan Akgün, et al. Protected chaos in a topological lattice. arXiv: 2411.07522v1 [cond-mat.mes-hall]. Nov 12, 2024.

https://arxiv.org/abs/2411.07522

Also: chaos, random, instability, transition, network, ai (artificial intell), in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chaos, random,  instability, transition, network, AI, Artificial Intelligence

# gst: apropos of transverse instabilities, from chimeras to extensive chaos

<< Populations of coupled oscillators can exhibit a wide range of complex dynamical behavior, from complete synchronization to chimera and chaotic states. We can thus expect complex dynamics to arise in networks of such populations. >>️

Here AA << analyze the dynamics of networks of populations of heterogeneous mean-field coupled Kuramoto-Sakaguchi oscillators, and show that the instability that leads to chimera states in a simple two-population model also leads to extensive chaos in large networks of coupled populations. >>️

Pol Floriach, Jordi Garcia-Ojalvo, Pau Clusella. From chimeras to extensive chaos in networks of heterogeneous Kuramoto oscillator populations. arXiv: 2407.20408v2 [nlin.CD]. Oct 11, 2024.

https://arxiv.org/abs/2407.20408

Also: chimera, instability, chaos, network, in 

https://www.inkgmr.net/kwrds.html 

Keywords: gst, chimera, instability, chaos, network

# life: don't worry folks! Mr Donald will not run again in 2028. Anzicheforse?

<< In September, Donald Trump said he would run for president again in 2028 if he didn't win this week's general election. >>️

<< But on Tuesday, Donald Trump won the vote to become the 47th president of the United States. So, can he still run for office in 2028? >>️

<< And just a few months before that, at the NRA's annual meeting in May, Trump mentioned running for a third term. >>️

<< Here's what to know about term limits and if Trump can run for president a third time, now that he's won twice. >>

<< The 22nd Amendment to the Constitution ... >>️️

Lianna Norman, Joyce Orlando. Can Trump run for president again in 2028? Here’s what to know about term limits. USA TODAY NETWORK, Florida. Nov 7, 2024. 

https://eu.floridatoday.com/story/news/2024/11/07/trump-run-again-term-limits-22nd-amendment-constitution/76087581007/

Also: Mr. Donald, in https://www.inkgmr.net/kwrds.html 

Keywords: life, Donald, potus, potus race

# gst: apropos of noise-assisted phenomena, self-organized transport in noisy dynamic networks.

AA << present a numerical study of multicommodity transport in a noisy, nonlinear network. The nonlinearity determines the dynamics of the edge capacities, which can be amplified or suppressed depending on the local current flowing across an edge. (AA) consider network self-organization for three different nonlinear functions: For all three (They) identify parameter regimes where noise leads to self-organization into more robust topologies, that are not found by the sole noiseless dynamics. Moreover, the interplay between noise and specific functional behavior of the nonlinearity gives rise to different features, such as (i) continuous or discontinuous responses to the demand strength and (ii) either single or multistable solutions. (AA) study shows the crucial role of the activation function on noise-assisted phenomena. >>️

Frederic Folz, Kurt Mehlhorn, Giovanna Morigi. Self-organized transport in noisy dynamic networks. Phys. Rev. E 110, 044310. Oct 21, 2024. 

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.044310

Also: network, noise, behavior, self-assembly, instability, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, network, noise, behavior, self-assembly, stability