Physical review. E最新文献

We introduce and analyze a class of heat engines composed of interacting units, in which the thermal reservoir is associated with the neighborhood surrounding each unit. These systems can be mapped onto stochastic opinion models and are characterized by collective behavior at low temperatures, displaying different types of phase transitions, marked by spontaneous symmetry breaking and classifications that depend on topology, the neighborhood, and other ingredients. For the case of contact with two thermal baths-equivalent to each unit having k=4 nearest neighbors-the system can be tuned to operate at maximum power without sacrificing the efficiency η and/or increasing dissipation σ[over ¯]. All of them are related by a general expression when the worksource stems from different interaction energies. The heat engine placed in contact with more than three reservoirs is more revealing, showing that the intermediate thermal reservoir can be conveniently adjusted to achieve the desired compromise between power P, efficiency, and dissipation. The influence of lattice topology (regular and random-regular networks), its relationship with collective operation, and distinct ratios between the temperatures of the thermal baths, has also been investigated.

Multilayer networks offer a powerful framework for modeling complex systems across diverse domains, effectively capturing multiple types of connections and interdependent subsystems commonly found in real-world scenarios. To analyze these networks, embedding techniques that project nodes into a lower-dimensional geometric space are essential. This paper introduces a novel hyperbolic embedding framework that advances the state of the art in multilayer network analysis. Our method, which supports heterogeneous node sets across networks and interlayer connections, generates layer-specific hyperbolic embeddings, enabling detailed intralayer analysis and interlayer comparisons, while simultaneously preserving the global multilayer structure within hyperbolic space-a capability that sets it apart from existing approaches, which typically rely on independent embedding of layers. Through experiments on synthetic multilayer stochastic block models, we demonstrate that our approach effectively preserves community structure, even when layers consist of different node sets. When applied to real brain networks, the method successfully clusters disease-related brain regions from different patients, outperforming layer-independent approaches and highlighting its relevance for comparative analysis. Overall, this work provides a robust tool for multilayer network analysis, enhancing interpretability and offering new insights into the structure and function of complex systems.

Higher-order dynamics are essential for maintaining species diversity in ecological systems. In this paper, we examine how intraspecific competition affects interspecific interactions within spatially embedded hyperlattice frameworks. We thoroughly analyze their impact on conserving biodiversity by including higher-order competitive processes. Extensive numerical simulations show that increased higher-order competition significantly boosts biodiversity, even with higher mobility. The snapshot analyses further identify four distinct spatial patterns resulting from species interactions: regular pattern formation, two-species dominance, spiral wave structures, and single-species dominance. These results emphasize the key role of higher-order competitive interactions in shaping and stabilizing ecological diversity.

We study single-variable approaches for describing stochastic dynamics with small inertia. The basic models we deal with describe passive Brownian particles and phase elements (phase oscillators, rotators, superconducting Josephson junctions) with an effective inertia in the case of a linear dissipation term and active Brownian particles in the case of a nonlinear dissipation. Elimination of a fast variable (velocity) reduces the characterization of the system state to a single variable and is formulated in four representations: moments, cumulants, the basis of Hermite functions, and the formal cumulant variant of the last. This elimination provides rigorous mathematical description for the overdamped limit in the case of linear dissipation and the overactive limit of active Brownian particles. For the former, we derive a low-dimensional equation system which generalizes the Ott-Antonsen Ansatz to systems with small effective inertia. In the latter case, we derive a Fokker-Planck-type equation with a forced drift term and an effective diffusion in one dimension, where the standard two- and three-dimensional mechanism is impossible. In the four considered representations, truncated equation chains are demonstrated to be utilitary for numerical simulation for a small finite inertia.

Growing evidence suggests that the macroscopic functional states of urban road networks exhibit multistability and hysteresis, but microscopic mechanisms underlying these phenomena remain elusive. Here, we demonstrate that in real-world road networks, the recovery process of congested roads is not spontaneous, as assumed in existing models, but is hindered by connected congested roads, and such hindered recovery can lead to the emergence of multistability and hysteresis in urban congestion dynamics. By analyzing real-world urban traffic data, we observed that congestion propagation between individual roads is well described by a simple contagion process as an epidemic, but the recovery rate of a congested road decreases drastically by the congestion of the adjacent roads unlike an epidemic. Based on this microscopic observation, we proposed a simple model of congestion propagation and dissipation, and found that our model shows a discontinuous phase transition between macroscopic functional states of road networks when the recovery hindrance is strong enough through a mean-field approach and numerical simulations. Our findings shed light on an overlooked role of recovery processes in the collective dynamics of failures in networked systems.

Scale invariance is a hallmark of many natural systems, including solar flares, where energy release spans a vast range of scales. Recent computational advances, at the level of both algorithmics and hardware, have enabled high-resolution magnetohydrodynamical (MHD) simulations to span multiple scales, offering new insights into magnetic energy dissipation processes. Here, we study the scale invariance of magnetic energy dissipation in two distinct MHD simulations. Current sheets are identified and analyzed over time. Results demonstrate that dissipative events exhibit scale invariance, with power-law distributions characterizing their energy dissipation and lifetimes. Remarkably, these distributions are consistent across the two simulations, despite differing numerical and physical setups, suggesting universality in the process of magnetic energy dissipation. Comparisons between the evolution of dissipation regions reveal distinct growth behaviors in high plasma-β regions (convective zone) and low plasma-β regions (atmosphere). The latter display spatiotemporal dynamics similar to those of avalanche models, suggesting self-organized criticality and a common universality class.

We conduct standard dimensional analysis (Vaschy-Buckingham Π theorem) for the mean avalanche size 〈s〉 when particles flow through, and clog at, a small orifice on the base of a flat-bottomed silo. We consider the effect of particle diameter d, orifice diameter D, particle density ρ, particle Young's modulus E, and acceleration of gravity g. We both perform discrete element method simulations and compile available data in the literature in order to sample the parameter space. We find that our simulations and data across many experiments and simulations of frictional grains are consistent with the scaling equation ln(〈s〉+1)=A_{α}[(D/d)^{α}-1]+B_{α}sqrt[ρgd/E^{1}], where A_{α} and B_{α} are empirical constants and α is the dimensionality of the system (α=2 and α=3 for two dimensions and three dimensions, respectively). This expression successfully synthesizes the clogging behavior of a number of related clogging systems and motivates future extensions to more complex configurations involving, for example, very low friction particles or external vibrations.

We study the dynamics of an overdamped Brownian particle in a repulsive scale-invariant potential V(x)∼-x^{n+1}. For n>1, a particle starting at position x reaches infinity in a finite, randomly distributed time. We focus on the short-time tail T→0 of the probability distribution P(T,x,n) of the blowup time T for integer n>1. Krapivsky and Meerson [Phys. Rev. E 112, 024128 (2025)2470-004510.1103/1hds-9ttg] recently evaluated the leading-order asymptotics of this tail, which exhibits an n-dependent essential singularity at T=0. Here we provide a more accurate description of the T→0 tail by calculating, for all n=2,3,⋯, the previously unknown large preexponential factor of the blowup-time probability distribution. To this end, we apply a WKB (after Wentzel, Kramers and Brillouin) approximation-at both leading and subleading orders-to the Laplace-transformed backward Fokker-Planck equation governing P(T,x,n). For even n, the WKB solution alone suffices. For odd n, however, the WKB solution breaks down in a narrow boundary layer around x=0. In this case, it must be supplemented by an "internal" solution and a matching procedure between the two solutions in their common region of validity.

In standard rotating Hele-Shaw cell flows, an initially circular fluid drop, surrounded by an outer fluid of negligible density and viscosity, is centered at the rotation axis of the cell. The interplay of centrifugal and surface tension forces leads to the emergence of intricate interfacial patterns, markedly characterized by intense competition among the inward-moving fingers of the outer fluid as they penetrate the inner one. In this work, we study a variation of this traditional rotating flow problem, considering that the center of the initially circular drop is at a distance d from the cell's rotation axis. We explore this off-centered situation by employing numerical simulations based on the level set method. Our numerical results show that, at fully nonlinear stages of the flow, the off-center parameter d plays a key role in determining the morphology and dynamic competition among fingers, leading to the development of asymmetric interfacial patterns that drift away from the rotation axis of the cell. The impact of the effective surface tension parameter B (measure of the relative strength of centrifugal and capillary effects) on the main features of these complex, centrifugally driven translating patterns is also discussed.

High-quality quantum oscillators are preferred for precision sensing of external physical parameters because if the noise level due to interactions with the environment is too high, metrological information can be lost due to quantum decoherence. On the other hand, stronger interactions with a thermal environment could be seen as a resource for new types of metrological schemes. We present a general amplification strategy that enables the detection of zero-point fluctuations using low-quality quantum oscillators at finite temperature. We show that by injecting a controllable level of multiplicative frequency noise in a Brownian oscillator, quantum deviations from the virial theorem can be amplified by a parameter proportional to the strength of the frequency noise at constant temperature. As an application, we suggest a scheme in which the virial ratio is used as a witness of the quantum fluctuations of an unknown thermal bath, either by measuring the oscillator energy or the heat current flowing into an ancilla bath. Our work expands the metrological capacity of low-quality oscillators and can enable new measurements of the quantum properties of thermal environments by sensing their zero-point contributions to system variables.