Understanding the mechanisms of firing propagation in brain networks has been a long-standing problem in the fields of nonlinear dynamics and network science. In general, it is believed that a specific firing in a brain network may be gradually propagated from a source node to its neighbors and then to the neighbors' neighbors and so on. Here, we explore firing propagation in the neural network of Caenorhabditis elegans and surprisingly find an abnormal phenomenon, i.e., remote firing propagation between two distant and indirectly connected nodes with the intermediate nodes being inactivated. This finding is robust to source nodes but depends on the topology of network such as the unidirectional couplings and heterogeneity of network. Further, a brief theoretical analysis is provided to explain its mechanism and a principle for remote firing propagation is figured out. This finding provides insights for us to understand how those cognitive subnetworks emerge in a brain network.

Starch suspensions are often used as model systems to demonstrate extreme shear-thickening effects. We study the aging of cornstarch particles in aqueous suspensions at room temperature by granulometry and rheological measurements. When starch is diluted in glycerol, no long-term changes are observed. The situation differs when water is used as solvent. For volume fractions up to 20 vol %, when the cornstarch suspensions in water are stored under continual agitation, we observe an increase in viscosity. When the cornstarch suspension is aged under quiescent conditions, no evolution of the particle size is observed. In the concentrated situation, the rheological properties vary independent of the storage condition. We show that the increase in viscosity is related to air trapped in the pore space and to the swelling of the granules and leakage of the amylopectin component of the starch into the surrounding water. The relative importance of the two processes depends upon the particle concentration and upon the energy brought to the system.

Convective transport in low-permeability rocks can be enhanced by the injection of a pressurized fluid to activate preexisting weak planes (fractures). These fractures are initially closed, but fluid-pressure-induced slippage creates void space that allows for fluid flow. The Coulomb-Mohr criterion yields a critical pressure required to open each of the fractures. Due to the intrinsic porosity of the rock, the injected fluid can flow from the fractures' surfaces to the rock matrix through a process referred to as leakoff. Following this activation mechanism, the connectivity of the cluster of activated fractures is strongly dependent on the ratio $F_N$ of the standard deviation of the critical pressures to the viscous pressure drop over a fracture's length. Recently, we proposed a continuum model to predict the effects of fluid transport on the morphology of the cluster of activated fractures formed by this process over a specific intermediate range of values of $F_N$ [Alhashim and Koch, J. Fluid Mech. 847, 286 (2018)]. In this paper, the activation process of a discrete well-connected network of preexisting fractures embedded in a highly heterogeneous rock is modeled to analyze the effects of fluid transport on the resulting cluster's morphology for a wider range of $F_N$ and show how the idealized averaged equation solution arises in a discrete system. We derive a length scale ${\xi }_{ch}$, which is a function of $F_N$ above which the viscous pressure drop is important. This length scale, along with the radius of the cluster $R$ and the average separation between the preexisting fractures ${\xi }_0$, can be used to define distinct growth regimes where different models can be used to describe the growth dynamics and predict the connectivity of the active network. When ${\xi }_0\sim {\xi }_{ch}\ll R$, the cluster is well connected and a linear pressure diffusion equation can be used to describe the cluster's growth. When ${\xi }_{ch}\gg R\gg {\xi }_0$, a fractal network is formed by an invasion percolation process. In an intermediate regime ${\xi }_0\ll {\xi }_{ch}\ll R$, percolation theory relates the porosity and permeability of the network to the local fluid pressure. For this regime, we validate the predictions of the continuum theory we recently developed to describe the cluster growth on length scales larger than ${\xi }_{ch}$.

This paper presents a detailed description of a molecular velocity distribution-based mesoscopic kinetic approach that enables a better understanding of various nonequilibrium hydrodynamic and thermodynamic effects in shock waves, contact discontinuities, and rarefaction waves. This builds on the mesoscopic kinetic approach in a previous investigation into regular reflection shocks by further addressing the mesoscopic physical meaning of kinetic moments from the view of kinetics and the implications of the magnitude and sign of nonequilibrium kinetic moments. To deepen understanding of nonequilibrium effects, this work focuses on the one-dimensional unsteady shock tube problem, which contains the typical and essential features of the discontinuous flows, and has no interference of two-dimensional flow direction. The approach uses a lattice Boltzmann method to solve the flow field, and describes nonequilibrium effects through the nonequilibrium kinetic moments of molecular velocity distribution functions. The mechanism of nonequilibrium effect in discontinuous flows is further probed. This work develops the mesoscopic kinetic approach and clarifies the mesoscopic physics of shock waves, contact discontinuities, and rarefaction waves.

Single-shot wide-angle diffraction imaging is a widely used method to investigate the structure of noncrystallizing objects such as nanoclusters, large proteins, or even viruses. Its main advantage is that information about the three-dimensional structure of the object is already contained in a single image. This makes it useful for the reconstruction of fragile and nonreproducible particles without the need for tomographic measurements. However, currently there is no efficient numerical inversion algorithm available that is capable of determining the object's structure in real time. Neural networks, on the other hand, excel in image processing tasks suited for such purpose. Here we show how a physics-informed deep neural network can be used to reconstruct complete three-dimensional object models of uniform, convex particles on a voxel grid from single two-dimensional wide-angle scattering patterns. We demonstrate its universal reconstruction capabilities for silver nanoclusters, where the network uncovers novel geometric structures that reproduce the experimental scattering data with very high precision.

The breakup pathway of Rayleigh fission of a charged drop is unequivocally demonstrated by continuous, high-speed imaging of a drop levitated in an AC quadrupole trap. The experimental observations consistently exhibited asymmetric, subcritical Rayleigh breakup with an upward (i.e., opposite to the direction of gravity) ejection of a jet from the levitated drop. These experiments supported by numerical calculations show that the gravity induced downward shift of the equilibrium position of the drop in the trap causes significant, large amplitude shape oscillations superimposed over the center-of-mass oscillations. The shape oscillations result in sufficient deformations to act as triggers for the onset of instability below the Rayleigh limit (a subcritical instability). The concurrently occurring, center-of-mass oscillations, which are out of phase with the applied voltage, are shown to lead to an asymmetric breakup such that the Rayleigh fission occurs upwards via the ejection of a jet at the pole of the deformed drop. As an important application, it follows by inference that the nanodrop generation in electrospray devices will occur, more as a rule rather than as an exception, via asymmetric, subcritical Rayleigh fission events of microdrops due to inherent directionality provided by the external electric fields.

We report on recent results that show that the pair correlation function of systems with exponentially decaying interactions can fail to exhibit Ornstein-Zernike asymptotics at all sufficiently high temperatures and all sufficiently small densities. This turns out to be related to a lack of analyticity of the correlation length as a function of temperature and/or density and even occurs for one-dimensional systems.

We consider the phenomenon of condensation of a globally conserved quantity $H={\sum }_{i=1}^N{\epsilon }_i$ distributed on $N$ sites, occurring when the density $h=H/N$ exceeds a critical density $h_c$. We numerically study the dependence of the participation ratio $Y_2=\langle {\epsilon }_i^2\rangle /\left(N{h}^2\right)$ on the size $N$ of the system and on the control parameter $\delta =(h-h_c)$, for various models: (i) a model with two conservation laws, derived from the discrete nonlinear Schrödinger equation; (ii) the continuous version of the zero-range process class, for different forms of the function $f\left(\epsilon \right)$ defining the factorized steady state. Our results show that various localization scenarios may appear for finite $N$ and close to the transition point. These scenarios are characterized by the presence or the absence of a minimum of $Y_2$ when plotted against $N$ and by an exponent $\gamma \ge 2$ defined through the relation $N^{*}\simeq {\delta }^{-\gamma }$, where $N^{*}$ separates the delocalized region ($N\ll N^{*}$, $Y_2$ vanishes with increasing $N$) from the localized region ($N\gg N^{*}$, $Y_2$ is approximately constant). We finally compare our results with the structure of the condensate obtained through the single-site marginal distribution.

Optimization of heat engines at the microscale has applications in biological and artificial nanotechnology and stimulates theoretical research in nonequilibrium statistical physics. Here we consider noninteracting overdamped particles confined by an external harmonic potential, in contact with either a thermal reservoir or a stochastic self-propulsion force (active Ornstein-Uhlenbeck model). A cyclical machine is produced by periodic variation of the parameters of the potential and of the noise. An exact mapping between the passive and the active model allows us to define the effective temperature $T_{\mathrm{eff}}\left(t\right)$, which is meaningful for the thermodynamic performance of the engine. We show that $T_{\mathrm{eff}}\left(t\right)$ is different from all other known active temperatures, typically used in static situations. The mapping allows us to optimize the active engine, regardless of the values of the persistence time or self-propulsion velocity. In particular, through linear irreversible thermodynamics (small amplitude of the cycle), we give an explicit formula for the optimal cycle period and phase delay (between the two modulated parameters, stiffness and temperature) achieving maximum power with Curzon-Ahlborn efficiency. In the quasistatic limit, the formula for $T_{\mathrm{eff}}\left(t\right)$ simplifies and coincides with a recently proposed temperature for stochastic thermodynamics, bearing a compact expression for the maximum efficiency. A point, which has been overlooked in recent literature, is made about the difficulty in defining efficiency without a consistent definition of effective temperature.

Ballistic thermal rectification is of significance for the management of thermal transport at the nanoscale since the size of thermal devices shrinks down to the phonon mean free path. By using the single-particle Lorentz gas model, the ballistic thermal transport in asymmetric homojunctions is investigated. The ballistic thermal rectification of the asymmetric rectangular homojunction is enhanced by the increasing structural asymmetry. A hyperbolic tangent profile is introduced to the interface to study the effect of interface steepness on thermal transport. We find that the thermal rectification ratio increases with the decreasing interface steepness, indicating that a gradual interface is of benefit to increase the thermal rectification. Moreover, the thermal rectification of the asymmetric homojunction can be improved by either increasing the temperature gradient or decreasing the average temperature of two heat sources.

When faced with an imminent risk of predation, many animals react to escape consumption. Antipredator strategies are performed by individuals acting as a group to intimidate predators and minimize the damage when attacked. We study the antipredator prey response in spatial tritrophic systems with cyclic species dominance using the rock-paper-scissors game. The impact of the antipredator behavior is local, with the predation probability reducing exponentially with the number of prey in the predator's neighborhood. In contrast to the standard Lotka-Volterra implementation of the rock-paper-scissors model, where no spiral waves appear, our outcomes show that the antipredator behavior leads to spiral patterns from random initial conditions. The results show that the predation risk decreases exponentially with the level of antipredator strength. Finally, we investigate the coexistence probability and verify that antipredator behavior may jeopardize biodiversity for high mobility. Our findings may help biologists to understand ecosystems formed by species whose individuals behave strategically to resist predation.

Many theoretical studies of the voter model (or variations thereupon) involve order parameters that are population-averaged. While enlightening, such quantities may obscure important statistical features that are only apparent on the level of the individual. In this work, we ask which factors contribute to a single voter maintaining a long-term statistical bias for one opinion over the other in the face of social influence. To this end, a modified version of the network voter model is proposed, which also incorporates quenched disorder in the interaction strengths between individuals and the possibility of antagonistic relationships. We find that a sparse interaction network and heterogeneity in interaction strengths give rise to the possibility of arbitrarily long-lived individual biases, even when there is no population-averaged bias for one opinion over the other. This is demonstrated by calculating the eigenvalue spectrum of the weighted network Laplacian using the theory of sparse random matrices.

The lattice Boltzmann method (LBM) has gained increasing popularity in incompressible viscous flow simulations, but it uses many distribution functions (far more than the flow variables) and is often memory demanding. This disadvantage was overcome by a recent approach that solves the more actual macroscopic equations obtained through Taylor series expansion analysis of the lattice Boltzmann equations [Lu et al., J. Comput. Phys. 415, 109546 (2020)]. The key is to keep some small additional terms (SATs) to stabilize the numerical solution of the weakly compressible Navier-Stokes equations. However, there are many SATs that complicate the implementation of their method. Based on some analyses and numerous tests, we ultimately pinpoint two essential ingredients for stable simulations: (1) suitable density (pressure) diffusion added to the continuity equation and (2) proper numerical dissipation related to the velocity divergence added to the momentum equations. Then we propose a simplified method that is not only easier to implement but noticeably faster than the original method and the LBM. It contains much simpler SATs that only involve the density (pressure) derivatives, and it requires no intermediate steps or variables. As well, it is extended for thermal flows with small temperature variations and for two-phase flows with uniform density and viscosity. Several test cases, including some two-phase problems under two-dimensional, axisymmetric, and three-dimensional geometries, are presented to demonstrate its capability. This work may help pave the way for the simplest simulation of incompressible viscous flows on collocated grids based on the artificial compressibility methodology.

A concise operator form of the Fokker-Planck equation agreeing with that proposed by Weizenecker [Phys. Med. Biol. 63, 035004 (2018)] for the joint orientational distribution of the coupled physical and magnetodynamic rotational diffusion of a single-domain ferromagnetic nanoparticle suspended in a liquid is written from the postulated Langevin equations for the stochastic dynamics. Series expansion of its solution in a complete set yields, using the theory of angular momentum, differential-recurrence equations for statistical moments for coupled motion with uniaxial symmetry of the internal anisotropy-Zeeman energy of a nanoparticle. The numerical results via the matrix iteration method suggest that the susceptibility is adequately approximated by a single Lorentzian with peak frequency given by the inverse integral relaxation time and are discussed in relation to those of the well-known “egg model”.

First passage under restart has recently emerged as a conceptual framework to study various stochastic processes under restart mechanism. Emanating from the canonical diffusion problem by Evans and Majumdar, restart has been shown to outperform the completion of many first-passage processes which otherwise would take longer time to finish. However, most of the studies so far assumed continuous time underlying first-passage time processes and moreover considered continuous time resetting restricting out restart processes broken up into synchronized time steps. To bridge this gap, in this paper, we study discrete space and time first-passage processes under discrete time resetting in a general setup without specifying their forms. We sketch out the steps to compute the moments and the probability density function which is often intractable in the continuous time restarted process. A criterion that dictates when restart remains beneficial is then derived. We apply our results to a symmetric and a biased random walker in one-dimensional lattice confined within two absorbing boundaries. Numerical simulations are found to be in excellent agreement with the theoretical results. Our method can be useful to understand the effect of restart on the spatiotemporal dynamics of confined lattice random walks in arbitrary dimensions.

Harmonic oscillator chains connecting two harmonic reservoirs at different constant temperatures cannot act as thermal diodes, irrespective of structural asymmetry. However, here we prove that perfectly harmonic junctions can rectify heat once the reservoirs (described by white Langevin noise) are placed under temperature gradients, which are asymmetric at the two sides, an effect that we term “temperature-gradient harmonic oscillator diodes.” This nonlinear diode effect results from the additional constraint—the imposed thermal gradient at the boundaries. We demonstrate the rectification behavior based on the exact analytical formulation of steady-state heat transport in harmonic systems coupled to Langevin baths, which can describe quantum and classical transport, both regimes realizing the diode effect under the involved boundary conditions. Our study shows that asymmetric harmonic systems, such as room-temperature hydrocarbon molecules with varying side groups and end groups, or a linear lattice of trapped ions may rectify heat by going beyond simple boundary conditions.

We introduce the “leaking elastic capacitor” (LEC) model, a nonconservative dynamical system that combines simple electrical and mechanical degrees of freedom. We show that an LEC connected to an external voltage source can be destabilized (Hopf bifurcation) due to positive feedback between the mechanical separation of the plates and their electrical charging. Numerical simulation finds regimes in which the LEC exhibits a limit cycle (regular self-oscillation) or strange attractors (chaos). The LEC acts as an autonomous engine, cyclically performing work at the expense of the constant voltage source. We show that this mechanical work can be used to pump current, generating an electromotive force without any time-varying magnetic flux and in a thermodynamically irreversible way. We consider how this mechanism can sustain electromechanical waves propagating along flexible plates. We argue that the LEC model can offer a qualitatively new and more realistic description of important properties of active systems with electrical double layers in condensed-matter physics, chemistry, and biology.

In systems of diffusing particles, we investigate large deviations of a time-averaged measure of clustering around one particle. We focus on biased ensembles of trajectories, which realize large-deviation events. The bias acts on a single particle, but elicits a response that spans the whole system. We analyze this effect through the lens of macroscopic fluctuation theory, focusing on the coupling of the bias to hydrodynamic modes. This explains that the dynamical free energy has nontrivial scaling relationships with the system size, in 1 and 2 spatial dimensions. We show that the long-ranged response to a bias on one particle also has consequences when biasing two particles.

We present a spatially extended version of the Wood–Van den Broeck–Kawai–Lindenberg stochastic phase-coupled oscillator model. Our model is embedded in two-dimensional (2d) array with a range-dependent interaction. The Wood–Van den Broeck–Kawai–Lindenberg model is known to present a phase transition from a disordered state to a globally oscillatory phase in which the majority of the units are in the same discrete phase. Here we address a parameter combination in which such global oscillations are not present. We explore the role of the interaction range from a nearest neighbor coupling in which a disordered phase is observed and the global coupling in which the population concentrate in a single phase. We find that for intermediate interaction range the system presents spiral wave patterns that are strongly influenced by the initial conditions and can spontaneously emerge from the stochastic nature of the model. Our results present a spatial oscillatory pattern not observed previously in the Wood–Van den Broeck–Kawai–Lindenberg model and are corroborated by a spatially extended mean-field calculation.