Motivated by vehicular traffic phenomena, we study a bidirectional two-lane open totally asymmetric simple exclusion process with extended symmetric coupling conditions in the presence of Langmuir kinetics. The phase diagrams and density profiles are calculated utilizing mean-field theory for different lane-changing rates and are found to be in a good match with Monte Carlo simulation results. It has been observed that the qualitative topology of phase diagrams depends on the lane-switching rate significantly, resulting in nonmonotonic variations in the number of steady-state phases. The proposed model provides various mixed phases leading to bulk induced phase transitions. The interplay between bidirectional movement, extended coupling conditions, and Langmuir kinetics produces unusual phenomena, including a back-and-forth phase transition and partial phase division of the shock region for comparatively smaller values of the lane-changing rate. Moreover, we analyze the shock dynamics and calculate critical values for the lane-changing rate at which the phases appear or disappear.

We introduce a variant of the voter model in which agents may have different degrees of confidence in their opinions. Those with low confidence are normal voters whose state can change upon a single contact with a different neighboring opinion. However, confidence increases with opinion reinforcement, and above a certain threshold, these agents become zealots, irreducible agents who do not change their opinion. We show that both strategies, normal voters and zealots, may coexist (in the thermodynamical limit), leading to competition between two different kinetic mechanisms: curvature-driven growth and interfacial noise. The kinetically constrained zealots are formed well inside the clusters, away from the different opinions at the surfaces that help limit their confidence. Normal voters concentrate in a region around the interfaces, and their number, which is related to the distance between the surface and the zealotry bulk, depends on the rate at which the confidence changes. Despite this interface being rough and fragmented, typical of the voter model, the presence of zealots in the bulk of these domains induces a curvature-driven dynamics, similar to the low temperature coarsening behavior of the nonconserved Ising model after a temperature quench.

Thermalization in open systems coupled to macroscopic environments is usually analyzed from the perspective of relaxation of the reduced state of the system to the equilibrium state. Less emphasis is given to the change of the state of the bath. However, as previously shown for some specific models, during the thermalization the environment may undergo a nontrivial dynamics, indicated by the the change of its von Neumann entropy, at timescales even longer than the relaxation time of the system; here such a behavior is called post-thermalization. We provide a more detailed analysis of this phenomenon by simulating the full dynamics of a variety of systems together with their environment. In particular, the post-thermalization is qualitatively explained as a result of reconversion of the initially built up correlation between the system and the bath into the correlation between the degrees of freedom in the environment. We also present exemplary systems in which such a reconversion is suppressed due to non-Markovian dynamics or the presence of interactions.

In this paper, we developed a coupled diffuse-interface lattice Boltzmann method (DI-LBM) to study the transport of a charged particle in the Poiseuille flow, which is governed by the Navier-Stokes equations for fluid field and the Poisson-Boltzmann equation for electric potential field. We first validated the present DI-LBM through some classical benchmark problems, and then investigated the effect of electric field on the lateral migration of the particle in the Poiseuille flow. The numerical results show that the electric field has a significant influence on the particle migration. When an electric field in the vertical direction is applied to the charged particle initially located above the centerline of the channel, the equilibrium position of the particle would drop suddenly as the electric field is larger than a critical value. This is caused by the wall repulsion due to lubrication, the inertial lift related to shear slip, the lift owing to particle rotation, the lift due to the curvature of the undisturbed velocity profile, and the electric force. On the other hand, when an electric field in the horizontal direction is adopted, the equilibrium position of the particle would move toward the centerline of the channel with the increase of the electric field.

We study fire-ant columns, an active version of passive granular columns, and find that, despite the inherent activity of the ants and their natural tendency to rearrange, the ants develop force-chain structures that help support the weight of the column. Hence, the apparent mass at the bottom of the column saturates with added mass in a Janssen-like fashion, reminiscent of what is seen in passive-grain columns in wide containers. Activity-induced rearrangements within the column, however, lead to changes in the force-chain structure that slightly reduce the supportive nature of the force-chains over time and to fluctuations in the pressure at the bottom of the column that scale like the law of large numbers. We capture the experimental results in simulations that include not only friction with the walls, but also a fluctuating force that introduces activity and that effectively affects the force-chain structure of the ant collective.

Temporal fluctuation scaling (TFS) is a power-law relation between the variance ($\mathrm{\Xi }$) and the mean ($\mathrm{{\rm Y}}$) which is present in cumulative time series. Taking into account that Theil index ($T$) can be assumed as a measure of dispersion and considering diffusive trajectory time series, we find a power-law relation between $T$ and $\mathrm{{\rm Y}}$ of the form $T\sim {(1-c\mathrm{{\rm Y}})}^{\beta }$, which we call temporal Theil scaling (TTS). Specifically, by analyzing data of volatility and absolute log-return for 24 nonstationary time series of financial markets, meteorology, and COVID-19 spread, we find that TTS is present in diffusive trajectory time series, while TFS is not present. Furthermore, we show that the power-law relation of TTS has a form that is similar to the relation between order parameter and temperature, which is found in the Ginzburg-Landau theory when the nontrivial critical points of an energy functional ${\mathcal{F}}_{\eta ,\delta }$ containing arbitrary powers $\eta$ and $\delta$ of the order parameter are calculated.

Scrambling in many-body quantum systems causes initially local observables to spread uniformly over the whole available Hilbert space under unitary dynamics, which in lattice systems causes exponential suppression of dynamical correlation functions with system size. Here, we present a perturbed free quantum circuit model, in which ergodicity is induced by an impurity interaction placed on the system's boundary, that allows for demonstrating the underlying mechanism. This is achieved by mapping dynamical correlation functions of local operators acting at the boundary to a partition function with complex weights defined on a two-dimensional lattice with a helical topology. We evaluate this partition function in terms of transfer matrices, which allow for numerically treating system sizes far beyond what is accessible by exact diagonalization and whose spectral properties determine the asymptotic scaling of correlations. Combining analytical arguments with numerical results, we show that for impurities which remain unitary under partial transpose correlations are exponentially suppressed with system size in a particular scaling limit. In contrast, for generic impurities or generic locations of the local operators, correlations show persistent revivals with a period given by the system size.

By Molecular Dynamics simulation, we investigate the dynamics of isotropic fluids of colloidal nanotrimers whose interactions are described by varying the strength of attractive and repulsive terms of the Mie potential. To provide a consistent comparison between the systems described by different force fields, we determine the phase diagram and critical points of each system, characterize the morphology of high-density liquid phases at the same reduced temperature and density, and finally investigate their long-time relaxation dynamics. In particular, we detect an especially complex dynamics that reveals the existence of slow and fast nanotrimers and the resulting occurrence of non-Gaussianity, which develops at intermediate timescales. Deviations from Gaussianity are temporary and vanish within the timescales of the system's density fluctuations decay, when a Fickian-like diffusion regime is eventually observed.

A detailed understanding of drop-wall interactions at high-temperature and high-pressure conditions can help optimize fuel injection, engine operation, and material design. The existing formulas developed for simulating drop-wall interactions are either valid only for a small range of operating conditions or based on the single-drop impact scenarios neglecting the effect of the nonstationary liquid film on the wall. The Leidenfrost temperature is a critical parameter in determining the impact outcome and needs to be considered in characterizing the impact behavior at extreme conditions. In this study, the smoothed particle hydrodynamics method, a Lagrangian-based method, is used to study the impact of an $n$-heptane droplet stream on a heated wall near and above the Leidenfrost temperature. The impingement frequency and wall temperature are varied to understand the impact dynamics and outcomes. Visualizations of the impact outcomes are provided to explain the interaction between the succeeding drops and the liquid film created by the preceding drops. To further characterize the shift in the Leidenfrost behaviors and the corresponding impact outcomes caused by the change in ambient pressure, simulations are also conducted at the corresponding fluid states for ambient pressures of 5 and 20 bar. Results show that the increase in ambient pressure impedes splashing and the film is concentrated inwards near the impingement point.

Directional depinning dynamics of a two-dimensional (2D) dusty plasma solid modulated by a 2D square periodic substrate are investigated using Langevin dynamical simulations. We observe prominent directional locking effects when the direction of the external driving force is varied relative to the underlying square substrate. These locking steps appear when the direction of the driving force is close to the symmetry direction of the substrate, corresponding to the different dynamical flow patterns and the structures. In the conditions between the adjacent locking steps, moving ordered states are observed. Although the discontinuous transitions often occur between the locking steps and the nonlocking portion, the continuous transitions are also found around the locking step associated with the disordered plastic flow close to its termini. Our results show that directional locking also occurs for underdamped systems, which could be tested experimentally in dusty plasmas modulated by 2D substrates.

The active Brownian particle (ABP) model describes a swimmer, synthetic or living, whose direction of swimming is a Brownian motion. The swimming is due to a propulsion force, and the fluctuations are typically thermal in origin. We present a two-dimensional model where the fluctuations arise from nonthermal noise in a propelling force acting at a single point, such as that due to a flagellum. We take the overdamped limit and find several modifications to the traditional ABP model. Since the fluctuating force causes a fluctuating torque, the diffusion tensor describing the process has a coupling between translational and rotational degrees of freedom. An anisotropic particle also exhibits a mass-dependent noise-induced drift, which does not disappear in the overdamped limit. We show that these effects have measurable consequences for the long-time diffusivity of active particles, in particular adding a contribution that is independent of where the force acts.

In laser-driven implosions for laboratory fusion, the comparison of hot-spot x-ray yield to neutron production can serve to infer hot-spot mix. For high-performance direct-drive implosions, this ratio depends sensitively on the degree of equilibration between the ion and electron fluids. A scaling for x-ray yield as a function of neutron yield and characteristic ion and electron hot-spot temperatures is developed on the basis of simulations with varying degrees of equilibration. We apply this model to hot-spot x-ray measurements of direct-drive cryogenic implosions typical of the direct-drive designs with best ignition metrics. The comparison of the measured x-ray and neutron yields indicates that hot-spot mix, if present, is below a sensitivity estimated as $\sim 2\%$ by-atom mix of ablator plastic into the hot spot.

We revisit the problem of quantum bi- and multistability by considering the dissipative double resonance model. For a large driving frequency, this system has a simpler phase structure than the driven dissipative nonlinear oscillator, the paradigm model for classical and quantum bistability. This allows us to obtain an analytical estimate for the lifetime of quantum limit cycles. On the other hand, for a small driving frequency, the system is much richer than the nonlinear oscillator. This allows us to address a novel phenomenon of dissipation- and chaos-assisted tunneling between quantum limits cycles.

In the present paper, we have extended the simulation technique Brownian cluster dynamics (BCD) to analyze the dynamics of the binary mixture of hard ellipsoids and spheres. The shape dependent diffusional properties have been incorporated into BCD using Perrin's factor and compared with analytical results of a one-component ellipsoidal system. We have investigated pathways to enhance the diffusivity of spheroids in the binary mixture by manipulating the phase behavior of the system through varying the fraction of spheres in the binary mixture. We show that at low volume fraction the spherical particles have a higher diffusion coefficient than the ellipsoids due to the higher friction coefficient. However, at a higher volume fraction, we show that the diffusion coefficient of the ellipsoids increases irrespective of the aspect ratio due to the anisotropic shape.

We investigate the formation of cohesive groups and the collective diffusion of colloidal spherocylinders with a motility driven by a simple visual perception model. For this, we perform Brownian dynamics simulations without hydrodynamic interactions. The visual perception is based on sight cones attached to the spherocylinders and perception functions quantifying the visual stimuli. If the perception function of a particle reaches a predefined threshold, an active component is added to its motion. We find that, in addition to the opening angle of the cone of sight, the aspect ratio of the particles plays an important role for the formation of cohesive groups. If the elongation of the particles is increased, the maximum angle for which the rods organize themselves into such groups decreases distinctly. After a system forms a cohesive group, it performs a diffusive motion, which can be quantified by an effective diffusion coefficient. For increasing aspect ratios, the spatial expansion of the cohesive groups and the effective diffusion coefficient of the collective motion increase, while the number of active group members decreases. We also find that a larger particle number, a smaller propulsion velocity of the group members, and a smaller threshold for the visual stimulus increase the maximum opening angle for which cohesive groups form. Based on our results, we expect anisotropic particles to be of great relevance for the adjustability of visual perception-dependent motility.

We report on a linear Langevin model that describes the evolution of the roughness of two interfaces that move towards each other and are coupled by a diffusion field. This model aims at describing the closing of the gap between two 2D material domains during growth, and the subsequent formation of a rough grain boundary. We assume that deposition occurs in the gap between the two domains and that the growth units diffuse and may attach to the edges of the domains. These units can also detach from edges, diffuse, and reattach elsewhere. For slow growth, the edge roughness increases monotonously and then saturates at some equilibrium value. For fast growth, the roughness exhibits a maximum just before the collision between the two interfaces, which is followed by a minimum. The peak of the roughness can be dominated by statistical fluctuations or by edge instabilities. A phase diagram with three regimes is obtained: Slow growth without peak, peak dominated by statistical fluctuations, and peak dominated by instabilities. These results reproduce the main features observed in kinetic Monte Carlo simulations.

We breakdown complex projects into activities and their logical dependencies. We estimate the project finish time based on the activity durations and relations. However, adverse events trigger delay cascades shifting the finish time. Here I derive a tropical algebraic equation for the finish time of activity networks, encapsulating the principle of linear superposition of exogenous perturbations in the tropical sense. From the tropical algebraic equation I derive the finish time distribution with explicit reference to the distribution of exogenous delays and the network topology and geometry.

Current deep neural networks are highly overparameterized (up to billions of connection weights) and nonlinear. Yet they can fit data almost perfectly through variants of gradient descent algorithms and achieve unexpected levels of prediction accuracy without overfitting. These are formidable results that defy predictions of statistical learning and pose conceptual challenges for nonconvex optimization. In this paper, we use methods from statistical physics of disordered systems to analytically study the computational fallout of overparameterization in nonconvex binary neural network models, trained on data generated from a structurally simpler but “hidden” network. As the number of connection weights increases, we follow the changes of the geometrical structure of different minima of the error loss function and relate them to learning and generalization performance. A first transition happens at the so-called interpolation point, when solutions begin to exist (perfect fitting becomes possible). This transition reflects the properties of typical solutions, which however are in sharp minima and hard to sample. After a gap, a second transition occurs, with the discontinuous appearance of a different kind of “atypical” structures: wide regions of the weight space that are particularly solution dense and have good generalization properties. The two kinds of solutions coexist, with the typical ones being exponentially more numerous, but empirically we find that efficient algorithms sample the atypical, rare ones. This suggests that the atypical phase transition is the relevant one for learning. The results of numerical tests with realistic networks on observables suggested by the theory are consistent with this scenario.

Focal conic structure formed near the transition from the cholesteric liquid crystal phase into isotropic liquid is studied in planar cells with a combination of optical methods. Employing a procedure with slow variation of temperature, we manage to obtain regions of periodic focal conic structure with two-dimensional square ordering in the plane of the cell. Using ordered samples and materials with selective reflection in the visible spectral range, we obtain spectral and microscopic optical data which deepen our knowledge of the focal conic structure.

Twist-bend nematic liquid crystals ($N_{\mathrm{TB}}$ LCs), although consisting of achiral molecules, possess a spontaneous conic helix. They have been intensively studied and utilized in many applications in recent years. Herein we add chiral molecules to $N_{\mathrm{TB}}$ LCs and study their effects on the structure of the conic helix. We observe that the system is in the regular chiral nematic phase at high temperature and is still in the twist-bend nematic phase at low temperature. The addition of the chiral molecules does not induce a twist of the conic helical axis. The main effect of the chiral molecules is increasing the cone angle of the conic helix. We show that the structural chirality parameters in the chiral nematic phase and the twist-bend chiral nematic phase can be calculated from the same intrinsic chirality parameter, which only depends on the molecular structure and concentrations of the chiral molecule. We also observe a pretransitional phenomenon that the helical pitch of the chiral nematic phase increases dramatically when temperature is decreased toward the chiral nematic to twist-bend nematic phase transition temperature.