Physical review. E最新文献

The vertex model with homogeneous cell properties is known to exhibit a parameter degeneracy in which the system's dynamics is independent of the target area. Here, we show, for the heterogeneous vertex model where cells differ in size and stiffness, that degeneracy is also present with the average product of target areas and stiffness becoming dynamically irrelevant. Fixing this quantity is equivalent to fixing the global internal tissue pressure. Unless properly treated, this degeneracy undermines the physical relevance of key observables' numerical values, such as cell target shape index, cell pressure, and cell stress tensor. We present methods to resolve the degeneracy and to correctly set the gauge pressure via symmetry transformations applied to the cells' target areas. We further demonstrate that the degeneracy is removed under certain boundary conditions and partially lifted when spherical tissues are modeled using a locally planar approximation, leading to numerical consequences when fitting model parameters to experimental data. The approach extends beyond vertex models and provides a framework for testing whether the parameter spaces of other physical models are free from degeneracy.

We present the results of the molecular dynamics simulation of ultracold Xe plasma in constant homogeneous crossed electric and magnetic fields. A Simulation shows that there are two electron loss mechanisms. The first one is that the initial electron cloud splits into two lobes. The second one is that electrons leave the plasma as a beam. The beam is deformed due to the diocotron instability and drifts along the [E×B] direction with a constant velocity. A qualitative analysis of the conditions for observing the two mechanisms is provided. The result obtained may be of interest for electron microscopy.

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. Compared with other recurrent neural networks, one great advantage of ESN is the simplicity of its training process. Yet, despite the seemingly restricted learnable parameters, ESN has been shown to successfully capture the spatial-temporal dynamics of complex patterns. Here we build an ESN to model the coarsening dynamics of charge-density waves (CDWs) in a semiclassical Holstein model, which exhibits a checkerboard electron density modulation at half-filling stabilized by a commensurate lattice distortion. The inputs to the ESN are local CDW order parameters in a finite neighborhood centered around a given site, while the output is the predicted CDW order of the center site at the next time step. Special care is taken in the design of couplings between hidden layer and input nodes to ensure lattice symmetries are properly incorporated into the ESN model. Since the model predictions depend only on CDW configurations of a finite domain, the ESN is scalable and transferrable in the sense that a model trained on dataset from a small system can be directly applied to dynamical simulations on larger lattices. Our work opens avenues for efficient dynamical modeling of pattern formations in functional electron materials.

This work investigates the influence of convective transport within a sedimenting drop on the exit time of a colloidal particle. Using Brownian dynamics simulations, we compute exit times for particles originating from various locations inside the drop over a range of Péclet numbers (Pe). The Péclet number quantifies the balance between the convective transport, caused by the Hadamard-Rybczynski flow field within a sedimenting drop, and the thermal fluctuations in the system. Additionally, we model the exit time as a first-passage process governed by the backward Kolmogorov equation, solving it asymptotically for Pe≪1 and Pe≫1, as well as numerically, to determine the mean exit time as a function of Pe.

Efficient allocation is important in nature and human society, where individuals frequently compete for limited resources. The Minority Game (MG) is perhaps the simplest toy model to address this issue. However, most previous solutions assume that the strategies are provided a priori and static, failing to capture their adaptive nature. Here we adopt the reinforcement learning paradigm to the MG, where individuals' decision-making is guided by their experience and the expectation of future rewards. By regulating the balance between exploration and exploitation in individual decision-making, the study reveals diverse collective behaviors. We find that the optimal allocation is reached when individuals appreciate both the experience and the rewards in the future and can balance the exploitation of their experiences with exploration by randomly acting. When the balance of the exploitation and exploration is broken, only partial coordination is observed; in some scenarios, anticoordination may occur, a phenomenon where the coordination is even worse than the scenario where every individual acts purely randomly. Mechanism analysis reveals a symmetry-breaking of action preferences that underlines optimal coordination, where resource utilization reaches its maximum, and the dynamics behind its destabilization. These findings are robust to the population size and the resource capacity. Our work thus provides a different solution to the MG and valuable insights into the resource allocation problems in general.

Protein nanoclustering is a characteristic feature of their activated state and is essential for forming numerous subcellular structures. The formation of these nanoclusters is highly dependent on a series of posttranslational modifications, such as mono- and multiphosphorylation and dephosphorylation of residues. We theoretically simulate how a protein can be either mono- or multiphosphorylated on several residues in functional nanoclusters, depending on effective biophysical parameters (diffusion, dwell time, etc.). Moving beyond a binary view of phosphorylation, this approach highlights the interplay between mono- and multiphosphorylation, the cooperative effects generally associated with multiphosphorylation networks, and stresses the role of phosphatases in transforming graded phosphorylation signals into almost switchlike responses. The results are discussed in light of experiments that probe the distribution of phospho-residues.

Computed tomography (CT) is essential for studying rock microstructures and macroscopic properties, yet its imaging quality is often compromised by complex degradation factors in real-world scenarios. Superresolution (SR) reconstruction techniques for rock CT images aim to enhance image quality significantly and overcome low-resolution limitations. However, most existing SR methods rely on difficult-to-acquire paired training data or employ simulated data that inadequately reflect actual degradation processes, leading to suboptimal performance on real rock CT images. To address the challenge of high-quality three-dimensional (3D) rock CT reconstruction under real degradation conditions, this paper proposes an innovative approach that integrates a physics-driven stochastic degradation model with a lightweight network architecture. Key innovations comprise a physics-driven stochastic degradation model that dynamically utilizes randomized selection and permutation of degradation operations to emulate real-system degradation diversity-enhancing synthetic-to-real distribution alignment; synergistically integrated with a batch normalization-free lightweight 3D network where strategic elimination of batch normalization layers achieves extreme computational efficiency while preserving microstructure-critical spatial fidelity; collectively enabling a robust synthetic-to-real framework trained solely on physics-compliant synthetic data, validated through superior reconstruction quality and physical characteristic preservation. This work provides an efficient and reliable image enhancement solution for high-precision digital core analysis in petroleum exploration and related fields while offering valuable insights for geoscientific image reconstruction.

In 2016, x-ray microcalorimeter spectroscopy with the Hitomi soft x-ray spectrometer observed strong spectrum lines of Fe XXIV-XXVI in galaxy NGC 1275 at the center of the Perseus cluster of galaxies. Special attention was paid to the Kα lines of the Fe plasmas [Aharonian et al., Nature (London) 535, 117 (2016)0028-083610.1038/nature18627]. The dominant spectral component was treated as a collisional ionization equilibrium plasma using the SPEX code. However, it was found that the ratio of Fe XXV Heα resonant and forbidden lines is lower than the observed one. This difference could be attributed to the insufficient consideration of radiation field effects in the spectral modeling approach. To address this issue, we recalculated the emission spectra under astrophysical conditions using the nonlocal thermodynamic equilibrium collisional-radiative code. Our analysis reveals a pronounced sensitivity of the emission spectra to the radiative temperature and distribution. Furthermore, we performed a detailed investigation of the atomic processes involving photon interactions and their effects on the emission spectra of Fe plasmas. The recalculated spectra show excellent agreement with the measured spectra, highlighting the significant role of the radiation field in shaping the spectral characteristics of astrophysical plasmas.

This work presents a theoretical and numerical investigation of the modulational instability and rogue wave triplets of dust-acoustic waves in a dusty plasma composed of warm adiabatic dust grains with opposite polarity, q-nonextensive electrons, and nonthermal ions. The dynamics are modeled by deriving a nonlinear Schrödinger (NLS) equation using the reductive perturbation method. This equation leads to the growth rate of modulational instability of dust-acoustic waves. The analysis reveals that positively charged dust grains, the degree of electron nonextensivity, and the distribution of electrons (protons) on negatively (positively) charged dust grains critically influence the growth rate of instability. The ratio of dispersion to nonlinear coefficients in the NLS equation demarcates stable and unstable regions, distinguishing bright and dark solitons. This novel mechanism reveals a second stability regime in opposite polarity dusty plasma for the fast acoustic mode. We also explore the impacts of multiple physical parameters, which are sensitive in forming rogue wave triplets. These parameters result in three distinct peaks arranged in a triangular pattern, with unique rotational behavior that offers a new perspective on the dynamical behaviors of localized nonlinear waves. To validate the model, we benchmark the exact analytical solutions for rogue wave triplets with numerical results. This comparison demonstrates the accuracy of the model and provides deeper insight into nonlinear localized waves. This analysis has significant implications for rogue wave triplet formation in both space and laboratory plasma environments. These triplets may coalesce into super freak waves under specific conditions, particularly when key physical parameters approach zero.

Disordered network materials abound in both nature and synthetic situations while rigorous analysis of their nonlinear mechanical behaviors remains challenging. The purpose of this paper is to connect the mathematical framework of the sweeping process originally proposed by Moreau to the generic class of lattice spring models that incorporate plasticity. We derive the equations of quasistatic evolution of an elastic-perfectly plastic lattice and relate them to concepts from rigidity theory and structural mechanics. Then we explicitly construct a sweeping process and provide numerical schemes to find the evolution of stresses in the model. In particular, we develop a highly efficient "leapfrog" computational framework that allows us to rigorously track the progression of plastic events in the system based on the sweeping process theory. The utility of our framework is demonstrated by analyzing the elastoplastic stresses in a novel class of disordered network materials exhibiting the property of hyperuniformity, in which the (normalized) infinite-wavelength density fluctuations associated with the distribution of network nodes are completely suppressed. We find enhanced mechanical properties such as increasing stiffness, yield strength, and tensile strength as the degree of hyperuniformity of the material system increases. Our results have implications for optimal network material design and our event-based framework can be readily generalized to nonlinear stress analysis of other heterogeneous material systems.