Physical review. E最新文献

Previous studies on oscillator populations with two-simplex interaction have reported novel phenomena such as discontinuous desynchronization transitions and multistability of synchronized states. However, the noise effect is not well understood. Here, we study a higher-order network of noisy oscillators with generic interactions consisting of one-simplex and two types of two-simplex interactions. We observe that when a type of two-simplex interaction is dominant, synchrony is eroded and eventually disappears even for infinitesimally weak noise. Nevertheless, synchronized states may persist for extended periods, with the lifetime increasing approximately exponentially with the strength of the two-simplex interaction. When one-simplex or another type of two-simplex interaction is sufficiently strong, noise erosion is prevented, and synchronized states become persistent. A weakly nonlinear analysis reveals that as one-simplex coupling increases, the synchronized state appears supercritically or subscritically, depending on the interaction strength. Furthermore, assuming weak noise and using Kramers' rate theory, we derive a closed dynamical equation for the Kuramoto order parameter, from which the time scale of the erosion process is derived. Our study elucidates the synchronization and desynchronization of oscillator assemblies in higher-order networks and is expected to provide insights into such systems' design and control principles.

We develop the off-lattice Eden model to simulate the growth of bacterial colonies in the three-dimensional geometry of a Petri dish. In contrast to its two-dimensional counterpart, our model takes a three-dimensional set of possible growth directions and employs additional constraints on growth, which are limited by access to the nutrient layer. We rigorously test the basic off-lattice Eden implementation against literature data for a planar cluster. We then extend it to three-dimensional growth. Our model successfully demonstrates the nontrivial dependency of the cluster morphology, nonmonotonous dependency of the cluster density, and power law of the thickness of the boundary layer of clusters as a function of the nutrient layer's height. Moreover, we reveal the fractal nature of all the clusters by investigating their fractal dimensions. Our density results allow us to estimate the basic transport properties, namely, the permeability and tortuosity of the bacterial colonies.

The connection between measure theoretic optimal transport and dissipative nonequilibrium dynamics provides a language for quantifying nonequilibrium control costs, leading to a collection of thermodynamic speed limits, which rely on the assumption that the target probability distribution is perfectly realized. This is almost never the case in experiments or numerical simulations, so here we address the situation in which the external controller is imperfect. We obtain a lower bound for the dissipated work in generic nonequilibrium control problems that (1) is asymptotically tight and (2) matches the thermodynamic speed limit in the case of optimal driving. Along with analytically solvable examples, we refine this imperfect driving notion to systems in which the controlled degrees of freedom are slow relative to the nonequilibrium relaxation rate, and identify independent energy contributions from fast and slow degrees of freedom. Furthermore, we develop a strategy for optimizing minimally dissipative protocols based on optimal transport flow matching, a generative machine learning technique. This latter approach ensures the scalability of both the theoretical and computational framework we put forth. Crucially, we demonstrate that we can compute the terms in our bound numerically using efficient algorithms from the computational optimal transport literature and that the protocols we learn saturate the bound.

We show that connectivity within the high-dimensional recurrent layer of a reservoir network is crucial for its performance. To this end, we systematically investigate the impact of network connectivity on its performance, i.e., we examine the symmetry and structure of the reservoir in relation to its computational power. Reservoirs with random and asymmetric connections are found to perform better for an exemplary Mackey-Glass time series than all structured reservoirs, including biologically inspired connectivities, such as small-world topologies. This result is quantified by the information processing capacity of the different network topologies which becomes highest for asymmetric and randomly connected networks.

In our previous paper [N. Tsutsumi et al., Chaos 32, 091101 (2022)10.1063/5.0100166], we proposed a method for constructing a system of differential equations of chaotic behavior from only observable deterministic time series, which we call the radial function-based regression (RfR) method. However, when the targeted variable's behavior is rather complex, the direct application of the RfR method does not function well. In this study, we propose a method of modeling such dynamics, including the high-frequency intermittent behavior of a fluid flow, by considering another variable (base variable) showing relatively simple, less intermittent behavior. We construct an autonomous joint model composed of two parts: the first is an autonomous system of a base variable, and the other concerns the targeted variable being affected by a term involving the base variable to demonstrate complex dynamics. The constructed joint model succeeded in not only inferring a short trajectory but also reconstructing chaotic sets and statistical properties obtained from a long trajectory such as the density distributions of the actual dynamics.

We experimentally and computationally analyze impact-shock-induced stress wave propagation in packings of disordered flexible fibers. We find that dispersive wave propagation, associated with large stress attenuation, occurs much more prevalently in systems with larger fiber aspect ratios and moderate fiber flexibility. We trace these features to the microstructural properties of fiber contact chains and the energy-trapping abilities of deformable fibers. These findings provide insights into physics of the shock-impacted flexible fiber packings and open the way toward an improved granular-material-based damping technology.

By introducing appropriate surfactants to nonpolar solvents, charged inverse micelles can be incorporated as charge carriers, facilitating stable particle suspensions via electrostatic interactions. The presence of these charge carriers enables electric-field-induced transport phenomena, notably electrophoresis and electro-osmosis, to occur in these systems. As a consequence, these nonpolar-solvent systems are used in a wide range of applications, such as electronic paper displays and smart windows. In previously reported experimental work, we found that, under the right circumstances, electrophoresis and electro-osmosis act synergistically to transport particles unexpectedly fast. This work aims to uncover the underlying physics of experimentally observed particle velocity fields and trajectories driven by an applied electric field in a nonpolar solvent. Our approach involves a comprehensive numerical model to analyze particle motion in nonpolar solvents. By comparing simulation results of particle velocity fields and trajectories with experimental data obtained through astigmatism microparticle tracking velocimetry, we find that both electrophoresis and electro-osmosis contribute to particle motion. By quantifying the contributions of electrophoresis and electro-osmosis based on average particle velocities, we further confirm that electro-osmosis contributes significantly to particle transport. Two modes of electro-osmosis are considered, one that is caused by the electrical double layer near the glass surfaces and the other that is caused by the induced space charge in the vicinity of the driving electrodes. Additionally, enhanced particle velocities are found mainly in the center of the cell and result from the superposition of electrophoresis and electro-osmosis. Finally, we propose a scheme that explains how particle trajectories emerge as a result of the interplay between electrophoresis and electro-osmotic flows generated near the glass surface and in the vicinity of the driving electrodes. This study contributes to the fundamental understanding of the interplay between electrophoresis and electro-osmosis in nonpolar solvents and offers insights for advancing the design of enhanced electrokinetic displays.

This corrects the article DOI: 10.1103/PhysRevE.103.043105.

We computed the equation of state of iron using extended first-principles molecular dynamics simulations, ranging from 7.874g/cm^{3} and 5500 K up to 47.2g/cm^{3} and 10^{9}K. We compared the principal Hugoniot curve with semiempirical models, average atom-based model predictions, and shock experiment results. We derived the Helmholtz free energy and the entropy via thermodynamic integration along isochores. We explore the ionization processes at play along the Hugoniot curve by analyzing the evolution of the electronic densities of states in the gigabar regime.

We show that classical DNA unzipping transition, which is equivalently described by quantum mechanical localization-delocalization transition in the ground state of non-Hermitian single impurity Hatano-Nelson Hamiltonian, is underpinned by generalized parity (P)-time reversal (T) symmetry breaking transition. We also study one-dimensional discretized version of the Hatano-Nelson model in the presence of single impurity and random disorder on a finite-size lattice. These discrete models are useful to study unzipping of a single adsorbed polymer from a surface. Our results show that the discrete models also undergo a phase transition from a PT unbroken phase to a broken phase. Interestingly, the generalized PT phase transition points coincide with the localization-delocalization transition for continuum as well as lattice models.