Physical Review E最新文献

Random walks on lattices with preferential relocation to previously visited sites provide a simple framework for modeling the displacements of animals and humans. When the lattice contains a few impurities or resource sites where the walker spends more time on average at each visit than on the other sites, the long-range memory can suppress diffusion and induce by reinforcement a steady state localized around a resource. This phenomenon can be identified with a spatial learning process. Here we study theoretically and numerically how the decay of memory impacts learning in a model with one impurity. If memory decays as 1/τ or slower, where τ is the time backward into the past, the localized solution is the same as with perfect, nondecaying memory and it is linearly stable. If forgetting is faster than 1/τ, for instance exponential, an unusual regime of intermittent localization is observed, where well-localized periods of exponentially distributed duration are disrupted by possibly long intervals of diffusive motion. At the transition between the two regimes, for a kernel in 1/τ, the approach to the stable localized state is the fastest, opposite to the expected critical slowing down effect. Hence, forgetting can allow the walker to save a lot of memory without compromising learning and to achieve a faster learning process. These findings agree with biological evidence on the benefits of forgetting.

We investigated thermal transport in aqueous solutions containing short amphiphilic and purely hydrophilic oligomers using hybrid molecular dynamics and multiparticle collision dynamics simulations. By systematically varying the simulation box length, we observed a dimensional crossover from three-dimensional to quasi-one-dimensional behavior, accompanied by a nonlinear increase in thermal conductivity. This transition was characterized by deviations from theoretical temperature profiles and a prolonged decay of heat flux autocorrelation functions, indicating the emergence of anomalous thermal transport. The addition of oligomers, whether amphiphilic or hydrophilic, promoted the emergence of anomalous behavior compared with pure solvent systems. While the formation of spherical micelles by amphiphilic oligomers increased thermal conductivity, it had minimal influence on the scaling behavior associated with anomalous transport, such as κ∼ξ^{1/3}. These results suggest that geometric anisotropy and the presence of oligomeric solutes are the primary drivers of anomalous thermal transport, whereas self-assembled structures mainly improve thermal transport efficiency. Overall, our findings offer microscopic insights into non-Fourier thermal transport in nanofluids and confined liquid systems and provide a foundation for the rational design of thermally functional materials, where structural organization and transport properties can be tuned synergistically.

Shear transformations (STs), as the fundamental events of plastic deformation of amorphous solids, remain a great deal of uncertainty regarding their basic characteristics. Here we propose an effective method for extracting these basic characteristics by means of fitting the spatial correlation of strain fields to the solutions of Eshelby's inclusion theory. Our method identifies individual STs from plastic events and eliminates the influence of local fluctuations, which greatly improves the effectiveness of the fitting results. Using this method, the Eshelby characteristics of STs and the relationship between STs and atomic structure are investigated in the atomistic simulations of a Cu_{50}Zr_{50} model glass. The broad distributions of Eshelby characteristics confirm the heterogeneous and anisotropic nature of STs. The shape of STs closely approximates a sphere, with the inclusion radius approaching 1.5 times the atomic size. Except for the component along the macroscopic loading direction, the other eigenstrain components generally follow a Gaussian distribution. The shear deformation of STs is much smaller than previously believed. The volumetric deformation of STs can be either dilatational or compressive, with a slight preference for dilatation. The further softening induced by STs is due to the fact that the promoting effect of local dilatation overweighs the suppressive effect of structural ordering. Our method can extract key information of STs for establishing a constitutive model that bridges the gap between microscopic atomic rearrangements and macroscopic plastic response.

We study an information engine operating in an active bath, where a Brownian particle confined in a harmonic trap undergoes feedback-driven displacement cycles. Unlike thermal environments, active baths exhibit temporally correlated fluctuations, introducing memory effects that challenge conventional feedback strategies. Extending the framework of stochastic thermodynamics to account for such memory, we analyze a feedback protocol that periodically shifts the potential minimum based on noisy measurements of the particle's position. We show that conventional feedback schemes, optimized for memoryless thermal baths, can degrade performance in active media due to the disruption of bath-particle memory by abrupt resetting. To overcome this degradation, we introduce a class of memory-preserving feedback protocols that partially retain the covariance between the particle's displacement and active noise, thereby exploiting the temporal persistence of active fluctuations. Through asymptotic analysis, we show how the feedback gain-which quantifies the strength of positional shifts-nontrivially shapes the engine's work and power profiles. In particular, we demonstrate that in active media, intermediate gains outperform full-shift resetting. Our results reveal the critical interplay between bath memory, measurement noise, and feedback gain, offering guiding principles for designing high-performance information engines in nonequilibrium environments.

Hydrodynamic interactions play a crucial role in the formation of flagellated microswimmer clusters, yet they are still not clearly understood. In this article we try to elucidate the influence mechanism of the flagellum elasticity on the clustering-separation process of two flagellated microswimmers. We first systematically derive an active sphere-rod model from the undulating flagellum model. Based on this simplified model, we then construct autonomous two-dimensional dynamical systems to describe the clustering-separation process of two identical flagellated microswimmers in Newtonian fluid. The theoretical predictions without considering the flagellum elasticity are compared with results from smoothed dissipative particle dynamics simulations in which fluid-structure interactions are fully resolved. The flagellum elasticity is identified to be the key factor that alters the phase portrait and even triggers a clustering-separation transition. The elastic flagella are found to bend under the influence of nonuniform hydrodynamic forces induced by the nearby microswimmer. This bending leads to curved mean centerlines which generate rotational torques on the microswimmers thereby causing them to separate. With the inclusion of the bending effect, the theoretical model is also able to reproduce the clustering-separation transition, and predicts the appearance of a saddle point in a specific phase regime. However, fluid viscoelasticity has a much smaller impact on the phase portrait, it only considerably affects the temporal trajectory of the system. Our results highlight the critical roles that flagellum elasticity may play in the cluster formation of flagellated microswimmers.

Recent separated reactant experiments for thin-shell (6µm) shock-driven implosions on OMEGA have demonstrated significant mix from a buried deuterated layer of the shell into the hot spot. Time resolved D^{3}He-p reaction history data demonstrate a (50±20)ps shift earlier in peak nuclear emission for separated reactant experiments relative to control, in contrast to past experimental data for thicker, 20µm shells with no laser burn through that show a 75ps delay due to the time required for hydrodynamic instabilities to develop. This contrast suggests that the mix mechanism was not hydrodynamic. Ion kinetic simulations utilizing fall line analyses show much closer agreement with mix yield and temperature than diffusion models, predicting a D^{3}He-p mix yield of 1.7×10^{9} as compared to the experimental value of 9.3(±2.1)×10^{8}. This is three orders of magnitude closer than the fall line analysis from a hydrodynamic simulation with an inline diffusive mix model, which suggests minimal mix and D^{3}He-p yields of 5×10^{5}. This makes kinetic mechanisms the only feasible explanation for the mix seen, demonstrating impact of a non-standard mix mechanism. An analytical model of this kinetic mix mechanism suggests that it can remain significant in situations when the shell expands significantly to low densities, and diffusive models predict negligible mix. Kinetic mix will impact multiple types of high energy density, laser-driven fusion experiments including high-adiabat direct drive cryoexperiments, nuclear cross section experiments, and thin-shell polar direct drive experiments used to tune heat conduction models.

In both classical and quantum physics, irreversible processes are described by maps that contract the space of states. The change in volume has often been taken as a natural quantifier of the amount of irreversibility. In Bayesian inference, loss of information results in the retrodiction for the initial state becoming increasingly influenced by the choice of reference prior. In this paper, we import this latter perspective into physics, by quantifying the irreversibility of any process with its Bayesian subjectivity-that is, the sensitivity of its retrodiction to one's prior. From this perspective, we review analytical and numerical results that highlight both intuitive and subtle insights that this measure sheds on irreversible processes.

In realistic neural circuits, both neurons and synapses are coupled in dynamics with separate time scales. The circuit functions are intimately related to these coupled dynamics. However, it remains challenging to understand the intrinsic properties of the coupled dynamics. Here, we develop the neuron-synapse coupled quasi-potential method to demonstrate how learning induces a qualitative change in the macroscopic behaviors of recurrent neural networks. We find that under the Hebbian learning, a large Hebbian strength will alter the nature of the chaos transition, from a continuous type to a discontinuous type, where the onset of chaos requires a smaller synaptic gain compared to the nonplastic counterpart network. In addition, our theory predicts that under feedback and homeostatic learning, the location and type of chaos transition are retained, and only the chaotic fluctuation is adjusted. Our theoretical calculations are supported by numerical simulations.

This study explores the application of elongated particle interaction models, traditionally used in liquid crystal phase research, in the context of early bacterial biofilm development. Through computer simulations using an agent-based model, we have investigated the possibilities and limitations of modeling biofilm formation and growth using different models for interaction between bacteria, such as the Hertz model, soft repulsive spherocylindrical model, and attractive Kihara model. Our approach focuses on understanding how mechanical forces due to the interaction between cells, in addition to growth and diffusive parameters, influence the formation of complex bacterial communities. By comparing such force models, we evaluate their impact on the structural properties of bacterial microcolonies. The results indicate that, although the specific force model has some effect on biofilm properties, the intensity of the interaction between bacteria is the most important determinant. This study highlights the importance of properly selecting interaction strength in simulations to obtain realistic representations of biofilm growth and suggests which adapted models of rod-shaped bacterial systems may offer a valid approach to study the dynamics of complex biofilms.

Active matter represents a class of nonequilibrium systems that constantly dissipate energy to produce directed motion. Controlling active matter to achieve a target state holds great potential for advancements in synthetic molecular motors, targeted drug delivery, and adaptive smart materials. However, the inherently nonequilibrium nature of active matter poses a significant challenge in achieving optimal control with minimal energy cost. In this work, we extend the concept of thermodynamic geometry, originally developed to provide geometric representations of energy cost in passive systems, to active systems. We propose a systematic geometric framework for minimizing energy cost in active matter with interparticle interactions. Specifically, we derive a cost metric that defines a Riemannian manifold for control parameters, enabling the application of powerful geometric tools to design optimal control protocols. The geometric perspective reveals that, unlike in passive systems, minimizing energy cost in active systems entails a universal trade-off scaling relation, leading to an optimal transportation speed in the geometric space that intriguingly coincides with the self-propulsion speed of an active Brownian particle. This insight enriches the broader concept of thermodynamic geometry. Furthermore, the derived scaling relation suggests an optimal protocol duration that aligns with the general expectation proposed by Davis et al. [Phys. Rev. X 14, 011012 (2024)2160-330810.1103/PhysRevX.14.011012] for active matter. We illustrate the utility of this approach by optimizing the performance of an active monothermal engine within the geometric framework.