Structural glasses form through various out-of-equilibrium processes, including temperature quenches, rapid compression (crunches), and shear. Although each of these processes should be formally understandable within the recently formulated dynamical mean-field theory (DMFT) of glasses, the numerical tools needed to solve the DMFT equations up to the relevant physical regime do not yet exist. In this context, numerical simulations of minimally structured (and therefore mean-field-like) model glass formers can aid the search for and understanding of such solutions, thanks to their ability to disentangle structural from dimensional effects. We study here the infinite-range Mari-Kurchan model under simple out-of-equilibrium processes, and we compare results with the random Lorentz gas [J. Phys. A 55, 334001 (2022)]. Because both models are mean-field-like and formally equivalent in the limit of infinite spatial dimensions, robust features are expected to appear in the DMFT as well. The comparison provides insight into temperature and density onsets, memory, as well as anomalous relaxation. This work also further enriches the algorithmic understanding of the jamming density.

We consider a quantum Otto cycle with a $q$-deformed quantum oscillator working substance and classical thermal baths. We investigate the influence of the quantum statistical deformation parameter $q$ on the work and efficiency of the cycle. In usual quantum Otto cycle, a Hamiltonian parameter is varied during the quantum adiabatic stages while the quantum statistical character of the working substance remains fixed. We point out that even if the Hamiltonian parameters are not changing, work can be harvested by quantum statistical changes of the working substance. Work extraction from thermal resources using quantum statistical mutations of the working substance makes a quantum Otto cycle without any classical analog.

Synchronization is a critical phenomenon that displays a pivotal role in a wealth of dynamical processes ranging from natural to artificial systems. Here, we untangle the synchronization optimization in a system of globally coupled phase oscillators incorporating heterogeneous interactions encoded by the deterministic-random coupling. We uncover that, within the given restriction, the added deterministic correlations can profoundly enhance the synchronizability in comparison with the uncorrelated scenario. The critical points manifesting the onset of synchronization and desynchronization transitions, as well as the level of phase coherence, are significantly shaped by the increment of deterministic correlations. In particular, we provide an analytical treatment to properly ground the mechanism underlying synchronization enhancement and substantiate that the analytical predictions are in fair agreement with the numerical simulations. This study is a step forward in highlighting the importance of heterogeneous coupling among dynamical agents, which provides insights for control strategies of synchronization in complex systems.

A densely packed colloidal suspension, called a paste, is known to remember the direction of its motion because of its plasticity. Because the memory in the paste determines the preferential direction for crack propagation, the desiccation crack pattern morphology depends on memory of its motions (memory effect of paste). Two types of memory effects are memory of vibration and memory of flow. When a paste is dried, it usually shows an “isotropic and random cellular” desiccation crack pattern. However, when a paste is vibrated before drying and it remembers the direction of its vibrational motion, primary desiccation cracks propagate in a direction perpendicular to its vibrational motion before drying (memory of vibration). Once it flows and remembers the direction of its flow motion, primary desiccation cracks propagate in the direction parallel to its flow motion (memory of flow). Anisotropic network formation via interparticle attraction among colloidal particles in a suspension is the dominant factor affecting a paste's memory of its motion. Calcium carbonate $\left({\mathrm{CaCO}}_3\right)$ paste remembers the direction of its vibrational motion, but not its own flow direction because Coulombic repulsion among charged ${\mathrm{CaCO}}_3$ colloidal particles prevents the formation of a network structure in a flow. For this study, we strove to assist and eliminate ${\mathrm{CaCO}}_3$ paste memory effects by adding polysaccharides. First, to characterize memory in paste, we propose a method of image analysis to quantify the strength and the direction of the anisotropy of desiccation crack patterns using Shannon's information entropy. Next, we conduct experiments to add polysaccharide to ${\mathrm{CaCO}}_3$ paste, revealing that the addition of a small amount of polysaccharide to ${\mathrm{CaCO}}_3$ paste assists the paste in remembering its own flow motion. Findings also indicate that the addition of a large amount of polysaccharide prevents the formation of both memories of its flow and vibrational motion and eliminates the memory effects of paste. We then perform “flocculation and sedimentation” experiments to investigate the interaction among ${\mathrm{CaCO}}_3$ colloidal particles in a solution. Results show that, in an aqueous solution with low polysaccharide concentration, ${\mathrm{CaCO}}_3$ colloidal particles flocculate each other and quickly form a sediment in a short time, whereas, in an aqueous solution with high polysaccharide concentration, a longer time is necessary for flocculation and sedimentation. Because the addition of small amounts of polysaccharides to ${\mathrm{CaCO}}_3$ paste induces polymer bridging between colloidal particles as interparticle attraction, it helps to produce a macroscopic network structure which retains memory of its flow motion and thereby assists the formation of memory of flow, whereas the addition of large amounts of polysaccharides induces interparticle repulsion, which prevents the formation of memory effects of all types.

We explore the high-dimensional chaos of a one-dimensional lattice of diffusively coupled tent maps using the covariant Lyapunov vectors (CLVs). We investigate the connection between the dynamics of the maps in the physical space and the dynamics of the covariant Lyapunov vectors and covariant Lyapunov exponents that describe the direction and growth (or decay) of small perturbations in the tangent space. We explore the tangent space splitting into physical and transient modes and find that the splitting persists for all of the conditions we explore. In general, the leading CLVs are highly localized in space and the CLVs become less localized with increasing Lyapunov index. We consider the dynamics with a conservation law whose strength is controlled by a parameter that can be continuously varied. Our results indicate that a conservation law delocalizes the spatial variation of the CLVs. We find that when a conservation law is present, the leading CLVs are entangled with fewer of their neighboring CLVs than in the absence of a conservation law.

The dynamics of competing opinions in social network plays an important role in society, with many applications in diverse social contexts such as consensus, election, morality, and so on. Here, we study a model of interacting agents connected in networks in order to analyze their decision stochastic process. We consider a first-neighbor interaction between agents in a one-dimensional network with the shape of ring topology. Moreover, some agents are also connected to a hub, or master node, who has preferential choice or bias. Such connections are quenched. As the main results, we observed a continuous nonequilibrium phase transition to an absorbing state as a function of control parameters. By using the finite-size scaling method we analyzed the static and dynamic critical exponents to show that this model probably cannot match any universality class already known.

Motivated by recent asymptotic results in atmosphere-ocean fluid dynamics, we present an idealized numerical and theoretical study of two-dimensional dispersive waves propagating through a small-amplitude random mean flow. The objective is to delineate clearly the conditions under which the cumulative Doppler shifting and refraction by the mean flow can change the group velocity of the waves not only in direction, but also in magnitude. The latter effect enables a possible transition from fast to slow waves, which behave very differently. Within our model we find the conditions on the dispersion relation and the mean flow amplitude that allow or rule out such fast-slow transitions. For steady mean flows we determine a finite mean flow amplitude threshold below which such transitions can be ruled out indefinitely. For unsteady mean flows a sufficiently rapid rate of change means that this threshold goes to zero, i.e., in this scenario all waves eventually undergo a fast-slow transition regardless of mean flow amplitude, with corresponding implications for the long-term fate of these waves.

The copropagation of two relativistic intense laser beams with orthogonal polarization in a parabolic plasma channel is studied analytically and numerically. A set of coupled equations for the evolution of the laser spot sizes and transverse centroids are derived by use of the variational approach. It is shown that the centroids of the two beams can spiral and oscillate around each other along the channel axis, where the characteristic frequency is determined both by the laser and plasma parameters. The results are verified by direct numerical solution of the relativistic nonlinear Schrödinger equations for the laser envelopes as well as three-dimensional particle-in-cell simulations. In the case with two ultrashort laser pulses when laser wakefields are excited, it is shown that the two wake bubbles driven by the laser pulses can spiral and oscillate around each other in a way similar to the two pulses. This can be well controlled by adjusting the incidence angle and separation distance between the two laser pulses. Preliminary studies show that externally injected electron beams can follow the trajectories of the oscillating bubbles. Our studies suggest a new way to control the coupling of two intense lasers in plasma for various applications, such as electron acceleration and radiation generation.

We study the dispersion properties of electron plasma waves, or plasmons, which can be excited in quantum plasmas in the nonlinear regime. In order to describe nonlinear electron response to finite amplitude plasmons, we apply the Volkov approach to nonrelativistic electrons. For that purpose, we use the Schrödinger equation and describe the electron population of a quantum plasma as a mixture of quantum states. Within the kinetic framework that we are able to derive from the Volkov solutions, we discuss the role of the wave amplitude on the nonlinear plasma response. Finally, we focus on the quantum properties of nonlinear Landau damping and study the contributions of multiplasmon absorption and emission processes.

Does the brain optimize itself for storage and transmission of information, and if so, how? The critical brain hypothesis is based in statistical physics and posits that the brain self-tunes its dynamics to a critical point or regime to maximize the repertoire of neuronal responses. Yet, the robustness of this regime, especially with respect to changes in the functional connectivity, remains an unsolved fundamental challenge. Here, we show that both scale-free neuronal dynamics and self-similar features of behavioral dynamics persist following significant changes in functional connectivity. Specifically, we find that the psychedelic compound ibogaine that is associated with an altered state of consciousness fundamentally alters the functional connectivity in the retrosplenial cortex of mice. Yet, the scale-free statistics of movement and of neuronal avalanches among behaviorally related neurons remain largely unaltered. This indicates that the propagation of information within biological neural networks is robust to changes in functional organization of subpopulations of neurons, opening up a new perspective on how the adaptive nature of functional networks may lead to optimality of information transmission in the brain.

We present an investigation of excitability effects on the dynamics of scroll waves partially pinned to inert cylindrical obstacles in three-dimensional Belousov-Zhabotinsky excitable media. We also report on corresponding numerical simulations with the Oregonator model. The excitability varies according to the concentration of sulfuric acid $\left[{\mathrm{H}}_2\mathrm{S}{\mathrm{O}}_4\right]$ in the Belousov-Zhabotinsky (BZ) reaction and the parameter ${\varepsilon }^{-1}$ in the Oregonator model. Initially, the freely rotating scroll segment rotates faster than the pinned one. The difference in the frequency of the two parts results in a transition from a straight pinned scroll wave to a twisted one, which helically wraps around the entire obstacle. The wave frequency in the whole volume is equal to that of the freely rotating scroll wave. When the excitability is increased, the time for the transition to the twisted wave structure decreases while the average speed $s$ of the development increases. After the transition, the twisted wave remains stable. In media with higher excitability, the helical pitch is shorter but the twist rate $\omega$ is higher. Analysis presented in this study together with our previous findings of the effect of the cylindrical obstacle diameter on the wave dynamics results in common features: The average speed $s$ and the twist rate $\omega$ of both studies fit well to functions of the difference in the initial frequency $\mathrm{\Delta }f$ of the freely rotating and untwisted pinned waves. We also demonstrate the robustness of the partially pinned scroll waves against perturbations from spontaneous waves emerging during the wave generation in the BZ medium with high $\left[{\mathrm{H}}_2\mathrm{S}{\mathrm{O}}_4\right]$. Even though the scroll wave is partly disturbed at the beginning of the experiment, the spontaneous waves are gradually suppressed and the typical wave structure is finally developed.

At least two different approaches to define and solve statistical models for the analysis of economic systems exist: the typical, econometric one, interpreting the gravity model specification as the expected link weight of an arbitrary probability distribution, and the one rooted in statistical physics, constructing maximum-entropy distributions constrained to satisfy certain network properties. In a couple of recent companion papers, they have been successfully integrated within the framework induced by the constrained minimization of the Kullback-Leibler divergence: specifically, two broad classes of models have been devised, i.e., the integrated and conditional ones, defined by different, probabilistic rules to place links, load them with weights and turn them into proper, econometric prescriptions. Still, the recipes adopted by the two approaches to estimate the parameters entering into the definition of each model differ. In econometrics, a likelihood that decouples the binary and weighted parts of a model, treating a network as deterministic, is typically maximized; to restore its random character, two alternatives exist: either solving the likelihood maximization on each configuration of the ensemble and taking the average of the parameters afterwards or taking the average of the likelihood function and maximizing the latter one. The difference between these approaches lies in the order in which the operations of averaging and maximization are taken—a difference that is reminiscent of the quenched and annealed ways of averaging out the disorder in spin glasses. The results of the present contribution, devoted to comparing these recipes in the case of continuous, conditional network models, indicate that the annealed estimation recipe represents the best alternative to the deterministic one.

Communities often represent key structural and functional clusters in networks. To preserve such communities, it is important to understand their robustness under network perturbations. Previous work in community robustness analysis has focused on studying changes in the community structure as a response of edge rewiring and node or edge removal. However, the impact of increasing connectivity on the robustness of communities in networked systems is relatively unexplored. Studying the limits of community robustness under edge addition is crucial to better understanding the cases in which density expands or false edges erroneously appear. In this paper, we analyze the effect of edge addition on community robustness in synthetic and empirical temporal networks. We study two scenarios of edge addition: random and targeted. We use four community detection algorithms, Infomap, Label Propagation, Leiden, and Louvain, and demonstrate the results in community similarity metrics. The experiments on synthetic networks show that communities are more robust when the initial partition is stronger or the edge addition is random, and the experiments on empirical data also indicate that robustness performance can be affected by the community similarity metric. Overall, our results suggest that the communities identified by the different types of community detection algorithms exhibit different levels of robustness, and so the robustness of communities depends strongly on the choice of detection method.

Microrobots for, e.g., biomedical applications, need to be equipped with motility strategies that enable them to navigate through complex environments. Inspired by biological microorganisms we re-create motility patterns such as run-and-reverse, run-and-tumble, or run-reverse-flick applied to active rodlike particles in silico. We investigate their capability to efficiently explore disordered porous environments with various porosities and mean pore sizes ranging down to the scale of the active particle. By calculating the effective diffusivity for the different patterns, we can predict the optimal one for each porous sample geometry. We find that providing the agent with very basic sensing and decision-making capabilities yields a motility pattern outperforming the biologically inspired patterns for all investigated porous samples.

The idea of creating polymer-like structures by crosslinking magnetic nanoparticles (MNPs) opened an alternative perspective on controlling the rheological properties of magnetoresponsive systems, because unlike suspensions of self-assembled MNPs, whose cluster sizes are sensitive to temperature, magnetic filaments (MFs) preserve their initial topology. Considering the length scales characteristic of single-domain nanoparticles used to create MFs, the MNPs can be both ferro- and superparamagnetic. Moreover, steric or electrostatic stabilization might not fully screen van der Waals interactions. In this paper, using coarse-grained molecular dynamics simulations, we investigate the influence of susceptibility of superparamagnetic MNPs—their number and central attraction forces between them—on the polymeric, structural, and magnetic properties of MFs with varied backbone rigidity. We find that, due to the general tendency of MFs with superparamagnetic monomers to bend, reinforced for colloids with a high susceptibility, properties of MFs vary greatly with chain length.

By means of two-dimensional numerical simulations based on contact dynamics, we present a systematic analysis of the joint effects of grain shape (i.e., grain elongation) and system size on silo discharge for increasing orifice sizes $D$. Grains are rounded-cap rectangles whose aspect ratio are varied from 1 (disks) to 7. In order to clearly isolate the effect of grain shape, the mass of the grains is keeping constant as well as the condition of the discharge by reintroducing the exiting grains at the top of the silo. In order to quantify the possible size effects, the thickness $W$ of the silos is varied from 7 to 70 grains diameter, while keeping the silos aspect ratio always equal to 2. We find that, as long as size effects are negligible, the flow rate $Q$ increases as a Beverloo-like function with $D$, also for the most elongated grains. In contrast, the effects of grain elongation on the flow rate depend on orifice size. For small normalized orifice sizes, the flow rate is nearly independent with grain elongation. For intermediate normalized orifice sizes the flow rate first increases with grain elongation up to a maximum value that depends on the normalized size of the orifice and saturates as the grains become more elongated. For larger normalized orifice size, the flow rate is an increasing function of grains' aspect ratio. Velocity profiles and packing fraction profiles close to the orifice turn out to be self-similar for all grain shapes and for the whole range of orifice and system sizes studied. Following the methodology introduced by Janda et al. [Phys. Rev. Lett. 108, 248001 (2012)], we explain the nonlinear variation of $Q$ with grain elongation, and for all orifice sizes, from compensation mechanisms between the velocity and packing fraction measured at the center of the orifice. Finally, an equation to predict the evolution of $Q$ as a function of the aspect ratio of the grains is deduced.

The investigation of the converging shock-induced Richtmyer-Meshkov instability, which arises from the interaction of converging shocks with the interface between materials of differing densities in cylindrical capsules, is of significant importance in the field of inertial confinement fusion (ICF). The use of converging shocks, which exhibit higher efficiency than planar shocks in the development of the RMI due to the Bell-Plesset effects, is particularly relevant to energy production in the ICF. Moreover, external magnetic fields are often utilized to mitigate the development of the RMI. This paper presents a systematic investigation of the anisotropic nature of the Richtmyer-Meshkov instability in magnetohydrodynamic induced by the interaction between converging shocks and perturbed semicylindrical density interfaces (DI) based on numerical simulations using $\text{Athena}++$. The results reveal that magnetic fields with $\beta =1000$, 100, and 10 ($\beta$ is defined as the ratio of the plasma pressure to the magnetic pressure) lead to an anisotropic intensification of magnetic fields, anisotropic accelerations of various shock waves [including the converging incident shock (CIS), transmitted shock (TS), and reflected shock (RS)], and anisotropic growth of the DI with subsequent anisotropic vorticity distribution. Upon closer inspection, it becomes evident that these phenomena are strongly interconnected. In particular, the region where the wave front of the CIS impacts the middle point of semicylindrical DI, where the magnetic field is more perpendicular to the fluid motion, experiences a more significant amplification of the magnetic fields. This generates higher-pressure jumps, which in turn accelerates the shock wave near this region. Furthermore, the anisotropic amplification of the magnetic fields reduces the movement of the RMI near the middle point of semicylindrical DI and leads to the anisotropic formation of RMI-induced bubbles and spikes, as well as vortices. By examining vorticity distributions, the results underscore the crucial role of magnetic tension forces in inhibiting fluid rotation.

We propose a distinguishable-particle glassy model suitable for the molecular dynamics simulation of structural glasses. This model can sensitively tune the kinetic fragility of supercooled liquids in a wide range by simply changing the distribution of particle interactions. In the model liquid, we observe the occurrence of thermodynamic liquid-liquid phase transitions above glass transition. The phase transition is facilitated by lowering fragility. Prior to the liquid-liquid phase transition, our simulations verify the existence of a constant-volume heat capacity maximum varying with fragility. We reveal the characteristics of the equilibrium potential energy landscape in liquids with different fragility. Within the Gaussian excitation model, the liquid-liquid transition as well as the response to fragility is reasonably interpreted in configuration space.

Using stochastic simulations supported by analytics we determine the degree of nonergodicity of box-confined fractional Brownian motion for both sub- and superdiffusive Hurst exponents $H$. At $H>1/2$ the nonequivalence of the ensemble- and time-averaged mean-squared displacements (TAMSDs) is found to be most pronounced (with a giant spread of individual TAMSDs at $H\to 1$), with two distinct short-lag-time TAMSD exponents.