Physical review. E最新文献

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.

We present molecular dynamics simulation results for the static nonlocal dielectric function, ɛ(k), and ion-ion correlations in aqueous electrolytes across various concentrations, analyzed within the framework of statistical mechanical theory. We investigate the complicated behavior of ɛ(k) and its influence on the screening patterns of ionic electric fields. At large to moderate values of the wave number k, ɛ(k) is primarily governed by the molecular structure of water, even at concentrations up to 1 M. However, at higher concentrations, ionic structuring begins to diminish water's influence. In agreement with the theoretical analysis presented, both Debye-type plain exponential and exponentially decaying oscillatory contributions to the electrostatic interactions are obtained for low electrolyte concentrations of NaCl in water. The latter originate from the water structure, while the former can be quantitatively described up to at least 2 M solutions by using a Debye length calculated with a concentration-dependent "dielectric constant" obtained by simulations. The theoretical basis for the use of such a "constant" is examined. At higher concentrations, there are only oscillatory contributions to the interactions. In our simulations, we do not have long-range monotonic interactions of the type reported in surface force measurements at concentrations above 1 M NaCl in water, often referred to as "anomalous underscreening." Instead, we find a Debye-type decay, modified by an electrolyte-concentration-dependent effective dielectric constant. This behavior is seen up to at least 2 M concentrations, with decay lengths significantly shorter than those associated with underscreening.

Thermalization times of light ions in fusion plasmas are calculated using quantum and semiclassical dielectric models for the interaction of external particles with plasma electrons and ions. An accurate analytical approximation is obtained, which combines a classical Bohr-type description for the interaction with plasma ions with a quantum description for the interaction with plasma electrons. Scaling laws for thermalization times are found for different light ions in a characteristic range of intermediate energies of special interest for tokamak systems composed of pure deuterium and deuterium-tritium plasmas. The study covers a range of temperatures between 10^{7} and 10^{9}K and electron densities from 10^{13} to 10^{15}cm^{-3}. A useful fitting formula for the thermalization time is proposed and applied to the cases of plasma heating using energetic deuterium beams and the thermalization of alpha particles produced by D-T fusion reactions. Finally, a set of useful tables with reference values for these cases is provided.

Janus colloidal particles have spin vectors normal to their Janus equator and resulting spin-orientational order that differs from the orientational order considered in traditional models of hexatic melting. Based on imaging their monolayers experimentally, we describe their topological spin defects and show that the Berezinskii-Kosterlitz-Thouless model describes their orientational order when spin orientations are driven into two dimensions by an external AC electric field. This is nontrivial, as the model assumes isotropic nearest-neighbor interactions, which is not the case for this system. These experiments reveal the predictive power of this idealized model beyond the strict validity of its assumptions.

We study the efficacy of strategies aimed at controlling the spread of deception-based cyber-threats unfolding on online social networks. We model directed and temporal interactions between users using a family of activity-driven networks featuring tunable homophily levels among gullibility classes. We simulate the spreading of cyber-threats using classic susceptible-infected-susceptible (SIS) models. We explore and quantify the effectiveness of four control strategies. Akin to vaccination campaigns with a limited budget, each strategy selects a fraction of nodes with the aim to increase their awareness and provide protection from cyber-threats. The first strategy picks nodes randomly. The second assumes global knowledge of the system selecting nodes based on their activity. The third picks nodes via egocentric sampling. The fourth selects nodes based on the outcome of standard security awareness tests, customarily used by institutions to probe, estimate, and raise the awareness of their workforce. We quantify the impact of each strategy by deriving analytically how they affect the spreading threshold. Analytical expressions are validated via large-scale numerical simulations. Interestingly, we find that targeted strategies, focusing on key features of the population such as the activity, are extremely effective. Egocentric sampling strategies, though not as effective, emerge as a clear second best despite not assuming any knowledge about the system. In addition, we find that networks characterized by highly homophilic interactions linked to gullibility might expand the range of transmissibility parameters that allows for macroscopic outbreaks. At the same time, they reduce the reach of these spreading events. Hence, rather isolated patches of the network formed by highly gullible individuals might provide fertile grounds for the propagation and survival of cyber-threats.

The anomalous surface mobility exhibited by many slowly vapor-deposited molecular solids, and their simulated counterparts, allows annealing their structure and increasing their density down to T_{dep}≈0.85T_{g}. This work describes the design and characteristics of a simulation that uses a "swept swapping" technique designed to extend structural annealing to lower temperatures. Films are grown using size-dispersed attractive Lennard-Jones (L-J) potential spheres deposited onto their free surface and then equilibrated with a surface-following Monte Carlo swap region. The motion of that region combined with sphere size dispersion speeds structural relaxation by orders of magnitude compared to bulk swapping. Sphere size span and size step (25% and ∼2.5%, respectively) are sufficient to completely suppress crystallization and maintain a high swap acceptance rate. The regime revealed by this technique attains a high packing fraction, which is near that of an hexagonal-close-packed L-J crystal with no size-dispersion. Additional swapping refines its structure, at near constant density, maximizing short-range icosahedral order to levels higher than seen previously; the levels apparently limited only by the computing budget.

Feedback in cellular processes is typically inferred through cellular responses to experimental perturbations. Modular response analysis provides a theoretical framework for translating specific perturbations into feedback sensitivities between cellular modules. However, in large-scale drug perturbation studies the effect of any given drug may not be known and may not only affect one module at a time. Here, we analyze the response of gene expression models to random perturbations that affect multiple modules simultaneously. In the deterministic regime we analytically show how cellular responses to infinitesimal random perturbations can be used to infer the nature of feedback regulation in gene expression, as long as the effects of perturbations are statistically independent between modules. We numerically extend this deterministic analysis to the response of average abundances of stochastic gene expression models to finite perturbations. By sampling simple models of stochastic gene expression, we identify example systems that violate the bounds predicted within a deterministic framework. We show these violations persist even in the limit of infinitesimal perturbations and are due to the inherently stochastic dynamics of biochemical feedback circuits. These discrepancies demonstrate how deterministic analyses can fail to correctly describe the response of cellular averages to perturbations even in the linear response regime.

We study the susceptible-infected-susceptible (SIS) model on directed complex networks within the quenched mean-field approximation. Combining results from random matrix theory with an analytic approach to the distribution of fixed-point infection probabilities, we derive the phase diagram and show that the model exhibits a nonequilibrium phase transition between the absorbing and endemic phases for c≥λ^{-1}, where c is the mean degree and λ is the average infection rate. Interestingly, the critical line is independent of the degree distribution but is highly sensitive to the form of the infection-rate distribution. We further show that the inverse participation ratio of infection probabilities diverges near the epidemic threshold, indicating that the disease may become localized on a small fraction of nodes. These results provide a systematic characterization of how network heterogeneities shape epidemic spreading on directed contact networks within the quenched mean-field approximation.

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.