Physical Review E最新文献

This Letter examines the full scope of long-standing conjectures identifying the invariant thermodynamic curvature R as the correlation volume ξ^{d} and also as a measure of underlying statistical interactions. To this end, we set up a two-parameter axial next nearest neighbor Ising (ANNNI) chain featuring two next nearest neighbor (nnn) and a nearest neighbor (nn) interaction. Competing interactions and resulting frustrations engender a rich phase behavior including a crossover between two ferrimagnetic subphases. We show that R attests to all its conjectured attributes with valuable physical insights into the character of mesoscopic fluctuating substructures. In a remarkable demonstration of its relevance at a far-from-critical point, R is shown to resolve a hitherto unnoticed tricky issue involving ξ. A physically transparent expression for the zero field R helps bring into focus the pivotal role played by some third order fluctuation moments.

Fracture propagation is highly sensitive to the conditions at the crack tip. In heterogeneous materials, microscale obstacles can cause propagation instabilities. Macroscopic heterogeneities modify the stress field over scales larger than the tip region. Here we experimentally investigate the propagation of fluid-driven fractures through multilayered materials. We focus on analyzing fracture profiles formed upon injection of a low-viscosity fluid into a two-layer hydrogel block. Experimental observations highlight the influence of the originating layer on fracture dynamics. Fractures that form in the softer layer are confined, with no penetration in the stiffer layer. Conversely, fractures initiated within the stiffer layer experience rapid fluid transfer into the softer layer when reaching the interface. We report the propagation dynamics and show that it is controlled by the toughness contrast between neighboring layers, which drives fluid flow. We model the coupling between elastic deformation, material toughness, and volume conservation. After a short transient regime, scaling arguments capture the dependence of the fracture geometry on material properties, injection parameters, and time. These results show that stiffness contrast can modify fracture propagation over large length scales and demonstrate the importance of macroscopic scale heterogeneities on fracture dynamics. These results have implications for climate change mitigation strategies involving the storage of heat and carbon dioxide in stratified underground rock formations.

Coulomb collision is a fundamental diffusion process in plasmas that can be described by the Landau-Fokker-Planck (LFP) equation or the stochastic differential equation (SDE). While energy and momentum are conserved exactly in the LFP equation, they are conserved only on average by the conventional corresponding SDEs, suggesting that the underlying stochastic process may not be well defined by such SDEs. In this study, we derive new SDEs with exact energy-momentum conservation for the Coulomb collision by factorizing the collective effect of field particles into individual particles and enforcing Newton's third law. These SDEs, when interpreted in the Stratonovich sense, have a particularly simple form that represents pure diffusion between particles without drag. To demonstrate that the new SDEs correspond to the LFP equation, we develop numerical algorithms that converge to the SDEs and preserve discrete conservation laws. Simulation results are presented in a benchmark of various relaxation processes.

Substrate-based cell motility is crucial for biological processes, and heterogeneity in the physical properties of the substrate can influence the outcomes of these processes. We demonstrate numerically the impact of different adhesion strengths on one substrate, achieved by adjusting the drag coefficients of different regions on the substrate, on cellular dynamics. We observed that, given the same initial cell location relative to the interface between two regions with different adhesion strengths, the behavior of a cell differs depending on whether it is initially a static cell or a stationary moving cell. Furthermore, we also introduced external stimulation to the cell. The cellular motility behavior around the interface can also be affected by adjusting the magnitude, range, and duration of the external stimulation.

Most of the analyses concerning signed networks have focused on balance theory, hence identifying frustration with undirected, triadic motifs having an odd number of negative edges; much less attention has been paid to their directed counterparts. To fill this gap, we focus on signed, directed connections, with the aim of exploring the notion of frustration in such a context. When dealing with signed, directed edges, frustration is a multifaceted concept, admitting different definitions at different scales: if we limit ourselves to consider cycles of length 2, frustration is related to reciprocity, i.e., the tendency of edges to admit the presence of partners pointing in the opposite direction. As the reciprocity of signed networks is still poorly understood, we adopt a principled approach for its study, defining quantities and introducing models to consistently capture empirical patterns of the kind. In order to quantify the tendency of empirical networks to form either mutualistic or antagonistic cycles of length 2, we extend the exponential random graph framework to binary, directed, signed networks with global and local constraints and then compare the empirical abundance of the aforementioned patterns with the one expected under each model. We find that the (directed extension of the) balance theory is not capable of providing a consistent explanation of the patterns characterizing the directed, signed networks considered in this work. Although part of the ambiguities can be solved by adopting a coarser definition of balance, our results call for a different theory, accounting for the directionality of edges in a coherent manner. In any case, the evidence that the empirical, signed networks can be highly reciprocated leads us to recommend to explicitly account for the role played by bidirectional dyads in determining frustration at higher levels (e.g., the triadic one).

Ocean currents exhibit strong time dependence at all scales that influences physical and biochemical dynamics. Network approaches to fluid transport permit to address explicitly how connectivity across the seascape is affected by the spatiotemporal variability of currents. However, such temporal aspect is mostly neglected, relying on a static representation of the flow. We here investigate the role of current variability on networks describing physical transport across the Mediterranean basin. We first focus on degree distributions and community structure comparing ensembles of temporal networks that explicitly resolve time dependence and their aggregated, i.e., time-averaged, counterparts. Furthermore, we explore the implications of the two approaches in a simple reaction dispersal model for a generic tracer. Our analysis evidences that aggregation induces structural network changes that cannot be easily avoided, not even introducing a pruning of the aggregated adjacency matrix. We also highlight that, depending on the time scales considered, the importance of the temporal features of the networks can vary significantly. Finally, we find that the tracer evolution obtained from a temporal dispersal kernel cannot be always approximated by aggregated adjacency matrices, in particular during transients of the dynamics.

The spread of disinformation (maliciously spread false information) in online social networks has become an important problem in today's society. Disinformation's spread is facilitated by the fact that individuals often accept false information based on cognitive biases which predispose them to believe information that they have heard repeatedly or that aligns with their beliefs. Moreover, disinformation often spreads in direct competition with corresponding true information. To model these phenomena, we develop a model for two competing beliefs spreading on a social network, where individuals have an internal opinion that models their cognitive biases and modulates their likelihood of adopting one of the competing beliefs. By numerical simulations of an agent-based model and a mean-field description of the dynamics, we study how the long-term dynamics of the spreading process depend on the initial conditions for the number of spreaders and the initial opinion of the population. We find that the addition of cognitive biases enriches the transient dynamics of the spreading process, facilitating behavior such as the revival of a dying belief and the overturning of an initially widespread opinion. Finally, we study how external recruitment of spreaders can lead to the eventual dominance of one of the two beliefs.

The dynamic-shell concept for inertial confinement fusion (ICF) uses an initially homogeneous target and a carefully shaped laser pulse to form a shell and implode it. The laser pulse consists of a series of pickets that drive shocks into the target. The first few shocks converge inwards and rebound from the center of the target, creating an expanding, low-density plasma. Subsequent shocks are launched into the expanding plasma and eventually coalesce to form a shell, which is then imploded with a traditional ICF laser pulse. This study describes radiation-hydrodynamic simulations that investigate the sensitivity of dynamic-shell targets to imperfections in the laser drive. A one-dimensional (1D) study looks at mistiming and power variations in the pickets and a two-dimensional (2D) study examines irradiation perturbations imposed by the laser-beam geometry. Simulations show that less than ∼2% power imbalance or 200 ps timing variation in the pickets is sufficient to keep the yield above 90% of the maximum. Additionally, the 2D simulations show that 72 or more beams are required to keep irradiation nonuniformities low enough to obtain fusion yields close to that of 1D simulations.

Can the concept of self-organized criticality, exemplified by models such as the sandpile model, be described within the framework of continuous phase transitions? In this paper, we provide extensive numerical evidence supporting an affirmative answer. Specifically, we explore the Bak, Tang, and Wiesenfeld (BTW) and Manna sandpile models as instances of percolation transitions from disordered to ordered phases. To facilitate this analysis, we introduce the concept of drop density-a continuously adjustable control variable that quantifies the average number of particles added to a site. By tuning this variable, we observe a transition in the sandpile from a subcritical to a critical phase. Additionally, we define the scaled size of the largest avalanche occurring from the beginning of the sandpile as the order parameter for the self-organized critical transition and analyze its scaling behavior. Furthermore, we calculate the correlation length exponent and note its divergence as the critical point is approached. The finite-size scaling analysis of the avalanche size distribution works quite well at the critical point of the BTW sandpile.

The time-evolution operator corresponding to the fractional-time Schrödinger equation is nonunitary because it fails to preserve the norm of the vector state in the course of its evolution. However, in the context of the time-dependent non-Hermitian quantum formalism applied to the time-fractional dynamics, it has been demonstrated that a unitary evolution can be achieved for a traceless two-level Hamiltonian. This is accomplished by considering a dynamical Hilbert space embedding a time-dependent metric operator concerning which the system unitarily evolves in time. This allows for a suitable description of a quantum system consistent with the standard quantum mechanical principles. In this work, we investigate the Jaynes-Cummings model in the fractional-time scenario taking into account the fractional-order parameter α and its effect in unitary quantum dynamics. We analyze the well-known dynamical properties, such as the atomic population inversion and the atom-field entanglement, when the atom starts in its excited state and the field in a coherent state.