Physical review. E最新文献

We highlight the importance of long-range correlations in active matter systems of self-propelling particles even in the absence of global order or steric interactions by demonstrating that long-range density fluctuations are reduced. We show this analytically for a one-dimensional lattice process employing a Poisson representation. Within this framework, we are able to derive the fluctuating hydrodynamics for the Poisson fields. The emergent imaginary noise indicates the non-Poissonian nature of the number fluctuations and manifests in a nontrivial structure factor S(k), which we are computing analytically. Numerically, we corroborate the relevance of these findings for off-lattice Vicsek-type models with antialigning interactions for which we observe apparent nonuniversal hyperuniformity, which we suggest to interpret as a reduction with integer power law to a finite value.

A growing body of theoretical and empirical evidence shows that the global steady-state distributions of many equilibrium and nonequilibrium systems approximately satisfy an analog of the Boltzmann distribution, with a local dynamical property of states playing the role of energy. The correlation between the effective potential of the steady-state distribution and the logarithm of the exit rates determines the quality of this approximation. We demonstrate and explain this phenomenon in a simple one-dimensional particle system and in random dynamics of the Sherrington-Kirkpatrick spin glass by providing the first explicit estimates of this correlation. We find that, as parameters of the dynamics vary, each system exhibits a threshold above and below which the correlation dramatically differs. We explain how these thresholds arise from underlying transitions in the relationship between the local and global "parts" of the effective potential.

The brain's synaptic network, characterized by parallel connections and feedback loops, drives interaction pathways between neurons through a large system with infinitely many degrees of freedom. This system is best modeled by the graph C*-algebra of the underlying directed graph, the Toeplitz-Cuntz-Krieger (TCK) algebra, which captures the diversity of path-structured flow connectivity. Equipped with the gauge action, the TCK algebra defines an algebraic quantum system, and here we demonstrate that its thermodynamic properties provide a natural framework for describing the dynamic mappings of potential flow pathways within the network. Specifically, the KMS states of this system represent the stationary distributions of a non-Markovian stochastic process with memory decay, capturing how influence propagates along exponentially weighted paths, and yield global statistical measures of neuronal interactions. Applied to the C. elegans synaptic network, our framework reveals that neurolocomotor neurons emerge as the primary hubs of incoming path-structured flow at inverse temperatures where the entropy of KMS states peaks. This finding aligns with experimental evidence of the foundational role of locomotion in C. elegans behavior, suggesting that functional centrality may arise from the topological embedding of neurons rather than solely from local physiological properties. Our results highlight the potential of algebraic quantum methods and graph algebras to uncover patterns of functional organization in complex systems and neuroscience.

The phase-field lattice Boltzmann (LB) method is a powerful tool for simulating two-phase flows. However, for gas-liquid-solid systems with large density ratios, the accurate and systematic treatment of three-dimensional curved solid surfaces remains a challenge. In this work, we propose a wetting boundary scheme within the phase-field LB framework to address this problem. First, a phase-field LB model is developed for simulating multiphase flows with the density ratio up to 1000. Then, based on the free-energy approach, a method is introduced to determine the location of solid boundaries and their normal vectors. Finally, depending on the spatial position of the solid surface, the wetting condition is imposed using different interpolation schemes. The proposed method is validated through three benchmark cases: a droplet resting on a spherical surface, capillary rise in cylindrical tubes, and self-propelled droplet on conical fibers. Simulation results demonstrate that the method achieves high accuracy in both static and dynamic processes and is capable of handling arbitrarily complex curved surfaces in three-dimensional space. Moreover, to the best of our knowledge, two new power-law behaviors are observed in the third case, indicating the potential of the model for discovering novel wetting dynamics.

Recently, a paradoxical effect has been demonstrated in which transport of a free Brownian particle driven by active fluctuations in the form of white Poisson shot noise can be significantly enhanced when it is additionally subjected to a periodic potential. This phenomenon can emerge in an overdamped system, but it may also be inertia-induced. Here, we considerably extend previous studies and comprehensively investigate the impact of inertia on the effect of free transport enhancement observed in the overdamped system. We detect that inertia cannot only induce this phenomenon, but, depending on a parameter regime, it may also strengthen, weaken, or even destroy it. We exemplify these different scenarios and explore the parameter space to identify the corresponding regions where they emerge. The variance of the active fluctuations amplitude distribution is a key determinant of the inertia influence on the effect of free transport amplification. Our results are relevant not only for microscopic physical systems, but also for biological ones, such as living cells, where fluctuations generated by metabolic activities are active by default.

Understanding how higher-order interactions shape the energy landscape of coupled oscillator networks is crucial for characterizing complex synchronization phenomena. Here we investigate a generalized Kuramoto model with triadic interactions, combining deterministic basin analysis, noise-induced transitions, and quantum annealing methods. We uncover a dual effect of higher-order interactions: They simultaneously expand basins for nontwisted states while contracting those of twisted states yet modify potential well depths for both. As triadic coupling strengthens, higher-winding-number states and nontwisted states gain stability relative to synchronized states. The system exhibits remarkable stability asymmetry, where states with small basins can possess deep potential wells, making them highly resistant to noise-induced transitions once formed. These findings extend quasipotential theory to high-dimensional networked systems and offer new insights for controlling synchronization in complex systems.

We discuss synchrotron absorption of a short electromagnetic pulse that propagates in a cold magnetized pair plasma. We show that the pulse can be absorbed when ω_{B}/a_{0}<ω1 is the strength parameter of the pulse, and ω and ω_{B}, respectively, are the frequency of the wave and the cyclotron frequency in the background magnetic field (all quantities are defined in the reference frame where the particles are at rest before being illuminated by the pulse). The condition ω_{B}/a_{0}<ω1. When ω_{B}/a_{0}<ω

Active particles are nonequilibrium entities that uptake energy and convert it into self-propulsion. A dynamically rich class of inertial active particles having features of wave-particle coupling and wave memory are walking/superwalking droplets. Such classical, active wave-particle entities (WPEs) have previously been shown to exhibit hydrodynamic analogs of many single-particle quantum systems. Inspired by the rich dynamics of strongly interacting superwalking droplets in experiments, we numerically investigate the dynamics of WPE clusters using a stroboscopic model. We find that several interacting WPEs self-organize into a stable bound cluster, reminiscent of an atomic nucleus. This active cluster exhibits a rich spectrum of collective excitations, including shape oscillations and chiral rotating modes, akin to vibrational and rotational modes of nuclear excitations, as the spatial extent of the waves and their temporal decay rate (memory) are varied. Dynamically distinct excitation modes create a common time-averaged collective wave field potential, bearing qualitative similarities with the nuclear shell model and the bag model of hadrons. For high memory and rapid spatial decay of waves, the active cluster becomes unstable and disintegrates; however, within a narrow regime of the parameter space, the cluster ejects single particles whose decay statistics follow exponential laws, reminiscent of radioactive nuclear decay. Our study uncovers a rich spectrum of dynamical behaviors in clusters of active particles, opening new avenues for exploring hydrodynamic quantum analogs in active matter systems.

We revisit the infinite variance problem in fermionic Monte Carlo simulations, which is widely encountered in areas ranging from condensed matter to nuclear and high-energy physics. The different algorithms, which we broadly refer to as determinantal quantum Monte Carlo (DQMC), are applied in many situations and differ in details, but they share a foundation in field theory, and often involve fermion determinants whose symmetry properties make the algorithm sign-problem-free. We show that the infinite variance problem arises as the observables computed in DQMC tend to form heavy-tailed distributions. To remedy this issue retrospectively, we introduce a tail-aware error estimation method to correct the otherwise unreliable estimates of confidence intervals. Furthermore, we demonstrate how to perform DQMC calculations that eliminate the infinite variance problem for a broad class of observables. Our approach is an exact bridge link method, which involves a simple and efficient modification to the standard DQMC algorithm. The method introduces no systematic bias and is straightforward to implement with minimal computational overhead. Our results establish a practical and robust solution to the infinite variance problem, with broad implications for improving the reliability of a variety of fundamental fermion simulations.

Intermittent locomotion represents a significant area of research. This type of motion can be exhibited by individual species or groups of species, contingent upon their morphology, environmental factors, and physiological requirements. In some instances, this phenomenon contributes to the enhancement of group dynamics within a system. Intermittent motion is not limited to living systems but can also be observed in inanimate active systems. Previous research has extensively documented that camphor-based systems can generate intermittent motion characterized by irregular movements interspersed with periods of stasis. This particular type of motion has been observed in both single-camphor disk and multiple-disk systems. However, the underlying mechanisms responsible for the intermittent motion remain elusive. Here we address this question using the combination of experimental and theoretical modeling. We visualize the dissolved, local concentration of camphor released by the disk in the liquid. The concentration field shows interesting dynamic patterns as it is advected by the ambient liquid within a confinement. Intermittent motion results from formation and opening of dynamic traps that set up the evolving camphor cloud. Moreover, our findings indicate that the size of the confinement and the rate of camphor release are the key factors governing this behavior. To validate our findings, we present a mathematical model that captures the dynamics of the surrounding camphor cloud. This model successfully reproduces the ability of the disk to switch between two distinct states: the "burst" and "pause." The feedback mechanism between the surrounding camphor cloud and the disk plays a crucial role in governing these transitions.