Physical review. E最新文献

We introduce an ensemble of spatial networks built from the junctions of hindered-rotation chains, incorporating directional correlations between bonds, an aspect ignored in the standard network modeling paradigm. The emergent random networks support geodesics with a wandering exponent ξ=1/2, and a travel-time fluctuation exponent χ=0, consistent with the KPZ relation, yet violating the bound χ≥1/8 predicted in the Poissonian framework. Transverse deviations follow the Kolmogorov distribution, indicating similarities between Brownian bridge excursions and geodesics in a random medium with correlated edges orientations. These results reveal a new universality class of Euclidean first-passage percolation, where local orientational memory reshapes transport properties and challenges existing bounds for random spatial networks.

Radiation therapy is one of the most common cancer treatments, and dose optimization and targeting of radiation are crucial since both cancerous and healthy cells are affected. Different mathematical and computational approaches have been developed for this task. The most common mathematical approach, dating back to the late 1970s, is the linear-quadratic (LQ) model for the survival probability given the radiation dose. Most simulation models consider tissue as a continuum rather than consisting of discrete cells. While reasonable for large-scale models (e.g., human organs), continuum approaches necessarily neglect cellular-scale effects, which may play a role in growth, morphology, and metastasis of tumors. Here we propose a method for modeling the effect of radiation on cells based on the mechanobiological CellSim3D simulation model for growth, division, and proliferation of cells. To model the effect of a radiation beam, we incorporate a Monte Carlo procedure into CellSim3D with the LQ model by introducing a survival probability at each beam delivery. Effective removal of dead cells by phagocytosis was also implemented. Systems with two types of cells were simulated: stiff slowly proliferating healthy cells and soft rapidly proliferating cancer cells. For model verification, the results were compared to prostate cancer (PC-3 cell line) data for different doses, and we found good agreement. In addition, we simulated proliferating systems and analyzed the probability density of the contact forces. We determined the state of the system with respect to the jamming transition and found very good agreement with experiments.

The active transfer of phospholipids between membrane leaflets (flip-flop), mediated by (adenosine triphosphate) ATP-dependent enzymes such as flippases and floppases, is a key regulator of membrane asymmetry and curvature. However, the theoretical understanding of curvature generation driven by flip-flop under external perturbations remains incomplete. Here we present a mesoscopic thermodynamic model in which lipid asymmetry couples to membrane curvature via a Landau-type free energy, with curvature as the order parameter and transmembrane voltage as the control parameter. This framework predicts an electrically driven phase transition analogous to a ferroelectric transition. The model reproduces field-induced bistability, critical susceptibility divergence, and hysteresis, with numerical simulations revealing thickness-dependent curvature thresholds and robust curvature-memory effects. These results clarify how electric-field-driven lipid redistribution governs membrane shape and suggest strategies for voltage-controlled nanoscale memory and shape encoding.

Critically elastic materials-those that are rigid with a single state of self-stress-can be generated from parent systems with two states of self-stress by the removal of one of many constraints. We show that the elastic moduli of the resulting homogeneous and isotropic daughter systems are interrelated by a universal functional form parametrized by properties of the parent. In simulations of both spring networks and packings of soft spheres, judicious choice of parent systems and bond removal allows for the selection of a wide variety of moduli and Poisson's ratios in the critically elastic systems, providing a framework for versatile deterministic selection of mechanical properties.

Polar flocks in discrete active systems are often assumed to be robust, yet recent studies reveal their fragility under both imposed and spontaneous fluctuations. Here, we revisit the four-state active Potts model and show that its globally ordered phase is metastable across a broad swath of parameter space. Small counterpropagating droplets disrupt the flocking phase by inducing a persistent sandwich state, where the droplet-induced opposite-polarity lane remains embedded within the original flock, particularly pronounced at low noise, influenced by spatial anisotropy. In contrast, small transversely propagating droplets, when introduced into the flock, can trigger complete phase reversal due to their alignment orthogonal to the dominant flow and their enhanced persistence. At low diffusion and strong self-propulsion, such transverse droplets also emerge spontaneously, fragmenting the flock into multiple traveling domains and giving rise to a short-range order (SRO) regime. We further identify a motility-induced pinning (MIP) transition in small diffusion and low-temperature regimes when particles of opposite polarity interact, flip their state, hop, and pin an interface. Our comprehensive phase diagrams, encompassing full reversal, sandwich coexistence, stripe bands, SRO, and MIP, delineate how thermal fluctuations, self-propulsion strength, and diffusion govern flock stability in discrete active matter systems.

We use a robust methodology that enables us to detect synchronous regions in networks of coupled dynamical systems and identify their collective behaviors. Our method employs ordinal patterns of spatial configuration of neighbor oscillators at each time point to ascertain whether or not neighboring nodes in a network are synchronized. We then use permutation entropy and forbidden sequence cardinality to classify collective behavior. We first demonstrate the effectiveness of our method on a time series of coupled identical logistic maps that are located on a ring. Our method not only confirms previous findings of collective behavior identification but also shows the borders of synchronous regions when oscillators of a network are partially synchronized. Then we apply our findings to a network of logistic maps with random connections to demonstrate the method's efficacy in situations where the network's spatiotemporal plots are not feasible.

Pickering emulsions are attracting growing attention as carriers of active ingredients or micronutrients in pharmaceutical and food applications, due to their high stability and low toxicity. The adsorption process of Pickering particles significantly affects emulsion properties and can be described using the random sequential adsorption (RSA) approach. While most studies focus on small, amorphous, spherical particles, the common use of elongated, crystalline, micron-sized particles in Pickering emulsions makes it necessary to consider the curvature and finite size of emulsion droplet surfaces to correctly understand and predict the interfacial adsorption behavior. The present study employs a Monte Carlo (MC) method to simulate the RSA process of both spherical and elongated micron-sized particles. Key factors such as particle polydispersity, emulsion droplet-particle size ratio, contact angle, and particle number are investigated. From the MC simulations, a new expression for the available surface function, ASF(ϕ), with coverage-dependent exponent is proposed. Based on this, generalized coverage evolution models are established using response surface methodology to relate RSA conditions to ASF(ϕ) parameters. For spherical particles, jamming coverage and desorption energy under various conditions are reported. For capsule-shaped particles, an aspect ratio of ε=2 is found to yield higher coverage and faster adsorption. The proposed ASF(ϕ) expression outperforms existing fixed-exponent expressions. The generalized coverage evolution models show good agreement with MC testing simulations. The mean absolute percentage errors are less than 2.63% for spherical particles and 6.58% for elongated ones in the validation cases.

This paper investigates absorbing-state phase transitions in opinion dynamics through a master-node network model analyzed using annealing approximation. We develop a theoretical framework examining three fundamental regimes: systems converging to complete disagreement, complete consensus, or both states depending on initial conditions. The phase behavior is governed by two key chiral parameters: R measuring right-oriented influence and L measuring left-oriented influence in the network interactions. Our analysis reveals a rich phase diagram featuring both continuous and discontinuous transitions between disordered and ordered phases. The discontinuous transition emerges in systems with two absorbing states, where the final configuration depends critically on initial opinion distributions. The annealing approximation provides fundamental insights into how asymmetric social influences (chirality) shape collective opinion formation, acting as a symmetry-breaking element that drives the system toward polarization or consensus.

We study the transition from classical radiation reaction, described by the Landau-Lifshitz model, to the quantum mechanical regime. The plasma is subject to a circularly polarized field where the self-consistent plasma current is the source of the electromagnetic field through Ampere's law. The radiation reaction implies wave energy loss, frequency up-conversion, and a modified distribution function. Increasing the value of the quantum χ-parameter, the quantum results gradually differ from the classical ones. Moreover, the deviation between models also depends on the plasma parameters, including density and temperature. We discuss the implications of our findings.

Creep tests on heterogeneous materials under subcritical loading typically show a power-law decay in strain rate before failure, with the exponent often considered material dependent but independent of applied stress. By imposing successive small stress relaxations through a displacement feedback loop, we probe creep dynamics and show experimentally that this exponent varies with both applied load and loading direction. Simulations of a disordered fiber bundle model reproduce this load dependence, demonstrating that such models capture essential features of delayed rupture dynamics.