Intermittent phenomena have long been studied in the context of nondiffusive transport across a variety of fields. In the TORPEX device, the cross-field spreading of an injected suprathermal ion beam by electrostatic plasma turbulence can access different nondiffusive transport regimes. A comprehensive set of suprathermal ion time series has been acquired, and time intermittency quantified by their skewness. Values distinctly above background level are found across all observed transport regimes. Intermittency tends to increase toward quasi- and superdiffusion and for longer propagation times of the suprathermal ions. The specific prevalence of intermittency is determined by the meandering motion of the instantaneous ion beam. We demonstrate the effectiveness of an analytical model developed to predict local intermittency from the time-average beam. This model might thus be of direct interest for similar systems, e.g., in beam physics, or meandering flux-rope models for solar energetic particle propagation. More generally, it illustrates the importance of identifying the system-specific sources of time-intermittent behavior when analyzing nondiffusive transport.

Multiple-point geostatistics (MPS) is a competitive algorithm that produces a set of geologically realistic models. Viewing a training image (TI) as a prior model, MPS extracts patterns from the TI and reproduces patterns which are compatible with the hard data (HD). However, two challenges within the MPS framework are the geologically complex simulation and the TI evaluation. With the objective to achieve a high-quality simulation, we explore a way to address these two issues. First, correlation-driven direct sampling (CDS) is proposed to realize geostatistical simulation. We introduce the correlation-driven distance as a measure of similarity between two patterns. The weights in our distance measurement are derived by the concepts of the ellipse, correlation coefficient, Gaussian function, and affine transformation. Second, we fulfill TI evaluation on the basis of the consistency between TI and HD. Inspired by CDS, the minimum correlation-driven distance (MCD) is proposed to improve the evaluation accuracy. We suggest a conditioning pattern extraction history strategy to speed up the evaluation program. Third, the local consistency is presented to address nonstationarity. The program automatically divides the simulation domain into several subareas. A two-dimensional (2D) channelized reservoir image and a three-dimensional (3D) rock image are used to validate our proposed method. In comparison with previous methods, CDS yields better simulation quality. The further applications include a set of 2D TI evaluations and a 3D simulation domain segmentation. MCD exhibits sensible evaluation accuracy, excellent computational efficiency, and the ability to deal with nonstationarity.

Using stochastic thermodynamics, we determine the entropy production and the dynamic heat capacity of systems subject to a sinusoidally time-dependent temperature, in which case the systems are permanently out of thermodynamic equilibrium, inducing a continuous generation of entropy. The systems evolve in time according to a Fokker-Planck or a Fokker-Planck-Kramers equation. Solutions of these equations, for the case of harmonic forces, are found exactly, from which the heat flux, the production of entropy, and the dynamic heat capacity are obtained as functions of the frequency of the temperature modulation. These last two quantities are shown to be related to the real and imaginary parts of the complex heat capacity.

Disordered magnets, martensitic mixed crystals, and glassy solids can be irreversibly deformed by subjecting them to external deformation. The deformation produces a smooth, reversible response punctuated by abrupt relaxation “glitches.” Under appropriate repeated forward and reverse deformation producing multiple glitches, a strict repetition of a single sequence of microscopic configurations often emerges. We exhibit these features by describing the evolution of the system configuration from glitch to glitch as a mapping of $\mathcal{N}$ states into one another. A map $\mathbf{U}$ controls forward deformation; a second map $\mathbf{D}$ controls reverse deformation. Iteration of a given sequence of forward and reverse maps, e.g., $\mathbf{D}\mathbf{D}\mathbf{D}\mathbf{D}\mathbf{U}\mathbf{U}\mathbf{U}\mathbf{U}$ necessarily produces a convergence to a fixed cyclic repetition of states covering multiple glitches. The repetition may have a period of more than one strain cycle, as recently observed in simulations. Using numerical sampling, we characterize the convergence properties of four types of random maps implementing successive physical restrictions. The most restrictive is the much-studied Preisach model. These maps show only the most qualitative resemblance to annealing simulations. However, they suggest further properties needed for a realistic mapping scheme.

We investigate the dynamics of the asymmetric exclusion process at a junction. When two input roads are initially fully occupied and a single output road is initially empty, the ensuing rarefaction wave has a rich spatial structure. The density profile also changes dramatically as the initial densities are varied. Related phenomenology arises when one road feeds into two. Finally, we determine the phase diagram of the open system, where particles are fed into two roads at rate $\alpha$ for each road, the two roads merge into one, and particles are extracted from the single output road at rate $\beta$.

In previous publications [Piron and Blenski, Phys. Rev. E 94, 062128 (2016); Blenski and Piron, High Energy Density Phys. 24, 28 (2017)], the authors have proposed Debye-Hückel-approximate free-energy functionals of the pair distribution functions for one-component fluids and two-component plasmas. These functionals yield the corresponding Debye-Hückel integral equations when they are minimized with respect to the pair distribution functions, lead to correct thermodynamic relations, and fulfill the virial theorem. In the present paper, we update our results by providing simpler functionals that have the same properties. We relate these functionals to the approaches of Lado [Phys. Rev. A 8, 2548 (1973)] and of Olivares and McQuarrie [J. Chem. Phys. 65, 3604 (1976)]. We also discuss briefly the nonuniqueness issue that is raised by these results.

The sedimentation of a two-dimensional suspension containing rods was studied by means of Monte Carlo (MC) simulations. An off-lattice model with continuous positional and orientational degrees of freedom was considered. The initial state before sedimentation was produced using a model of random sequential adsorption. During such sedimentation, the rods undergo translational and rotational Brownian motions. The MC simulations were run at different initial number densities (the numbers of rods per unit area), ${\rho }_i$, and sedimentation rates, $u$. For sediment films, the spatial distributions of the rods, the order parameters, and the electrical conductivities were examined. Different types of sedimentation-driven self-assembly and anisotropy of the electrical conductivity were revealed inside the sediment films. This anisotropy can be finely regulated by changes in the values of ${\rho }_i$ and $u$.

The dissipative curve-crossing problem provides a paradigm for electron-transfer (ET) processes in condensed media. It establishes the simplest conceptual test bed to study the influence of the medium's dynamics on ET kinetics both on the ensemble level, and on the level of single particles. Single electron description is particularly important for nanoscaled systems like proteins, or molecular wires. Especially insightful is this framework in the semiclassical limit, where the environment can be treated classically, and an exact analytical treatment becomes feasible. Slow medium's dynamics is capable of enslaving ET and bringing it on the ensemble level from a quantum regime of nonadiabatic tunneling to the classical adiabatic regime, where electrons follow the nuclei rearrangements. This classical adiabatic textbook picture contradicts, however, in a very spectacular fashion to the statistics of single electron transitions, even in the Debye, memoryless media, also named Ohmic in the parlance of the famed spin-boson model. On the single particle level, ET always remains quantum, and this was named a quantum breaking of ergodicity in the adiabatic ET regime. What happens in the case of subdiffusive, fractional, or sub-Ohmic medium's dynamics, which is featured by power-law decaying dynamical memory effects typical, e.g., for protein macromolecules, and other viscoelastic media? Such a memory is vividly manifested by anomalous Cole-Cole dielectric response in such media. We address this question based both on accurate numerics and analytical theory. The ensemble theory remarkably agrees with the numerical dynamics of electronic populations, revealing a power-law relaxation tail even in a profoundly nonadiabatic electron transfer regime. In other words, ET in such media should typically display fractional kinetics. However, a profound difference with the numerically accurate results occurs for the distribution of residence times in the electronic states, both on the ensemble level and the level of single trajectories. Ergodicity is broken dynamically even in a more spectacular way than in the memoryless case. Our results question the applicability of all the existing and widely accepted ensemble theories of electron transfer in fractional, sub-Ohmic environments, on the level of single molecules, and provide a real challenge to face, both for theorists and experimentalists.

In this paper, we study pairs of oscillators that are indirectly coupled via active (excitable) cells. We introduce a scalar phase model for coupled oscillators and excitable cells. We first show that one excitable and one oscillatory cell will exhibit phase locking at a variety of $m:n$ patterns. We next introduce a second oscillatory cell and show that the only attractor is synchrony between the oscillators. We will also study the robustness to heterogeneity when the excitable cell fires or is quiescent. We next examine the dynamics when the oscillators are coupled via two excitable cells. In this case, the dynamics are very complicated with many forms of bistability and, in some cases, chaotic behavior. We also apply weak-coupling analysis to this case and explain some of the degeneracies observed in the bifurcation diagram. Further, we look at pairs of oscillators coupled via long chains of excitable cells and show that small differences in the frequency of the oscillators makes their locking more robust. Finally, we demonstrate many of the same phenomena seen in the phase model for a gap-junction coupled system of Morris-Lecar neurons.

We study the effects of individual perceptions of payoffs in two-player games. In particular we consider the setting in which individuals' perceptions of the game are influenced by their previous experiences and outcomes. Accordingly, we introduce a framework based on evolutionary games where individuals have the capacity to perceive their interactions in different ways. Starting from the narrative of social behaviors in a pub as an illustration, we first study the combination of the Prisoner's Dilemma and Harmony Game as two alternative perceptions of the same situation. Considering a selection of game pairs, our results show that the interplay between perception dynamics and game payoffs gives rise to nonlinear phenomena unexpected in each of the games separately, such as catastrophic phase transitions in the cooperation basin of attraction, Hopf bifurcations and cycles of cooperation and defection. Combining analytical techniques with multiagent simulations, we also show how introducing individual perceptions can cause nontrivial dynamical behaviors to emerge, which cannot be obtained by analyzing the system at a macroscopic level. Specifically, initial perception heterogeneities at the microscopic level can yield a polarization effect that is unpredictable at the macroscopic level. This framework opens the door to the exploration of new ways of understanding the link between the emergence of cooperation and individual preferences and perceptions, with potential applications beyond social interactions.

Compression stiffening, or an increase in shear modulus with increasing compressive strain, has been observed in recent rheometry experiments on brain, liver, and fat tissues. Here we extend the known types of biomaterials exhibiting this phenomenon to include agarose gel and fruit flesh. The data reveal a linear relationship between shear storage modulus and uniaxial prestress, even up to 40% strain in some cases. We focus on this less-familiar linear relationship to show that two different results from classic elasticity theory can account for the phenomenon of linear compression stiffening. One result is due to Barron and Klein, extended here to the relevant geometry and prestresses; the other is due to Birch. For incompressible materials, there are no adjustable parameters in either theory. Which one applies to a given situation is a matter of reference state, suggesting that the reference state is determined by the tendency of the material to develop, or not develop, axial stress (in excess of the applied prestress) when subjected to torsion at constant axial strain. Our experiments and analysis also strengthen the notion that seemingly distinct animal and plant tissues can have mechanically similar behavior at the quantitative level under certain conditions.

Understanding the magnitude and structure of interneuronal correlations and their relationship to synaptic connectivity structure is an important and difficult problem in computational neuroscience. Early studies show that neuronal network models with excitatory-inhibitory balance naturally create very weak spike train correlations, defining the “asynchronous state.” Later work showed that, under some connectivity structures, balanced networks can produce larger correlations between some neuron pairs, even when the average correlation is very small. All of these previous studies assume that the local network receives feedforward synaptic input from a population of uncorrelated spike trains. We show that when spike trains providing feedforward input are correlated, the downstream recurrent network produces much larger correlations. We provide an in-depth analysis of the resulting “correlated state” in balanced networks and show that, unlike the asynchronous state, it produces a tight excitatory-inhibitory balance consistent with in vivo cortical recordings.

Phospholipid vesicle membranes are simple models used to study the mechanical properties of cell membranes. The shapes of flaccid vesicles can exhibit very diverse forms. When researching very flaccid vesicles, axisymmetrical vesicles with the membranes adhered to an annular region can also be observed. A phase diagram of such shapes was studied for different values of the vesicle parameters, i.e., the adhesion constant, the vesicle volume-to-membrane ratio, the volume ratio between the polar and the equatorial parts, and the equilibrium difference between the membrane monolayers. The energies of the annular shapes with respect to the vesicle parameters were closely examined and compared with the energies of the discocyte and stomatocyte shapes. The requirements for the existence of such annular shapes were also given for adhesion-free vesicle membranes. The results show that the adhesion between the lipid bilayers stabilizes the observed shapes, which belong to the locally stable branch of the annular vesicles. The value obtained for the adhesion constant of the SOPC membrane is $3\times {10}^{-9}\mathrm{J}/{\mathrm{m}}^2$.

It is well known that the kinetics of an intracellular biochemical network is stochastic. This is due to intrinsic noise arising from the random timing of biochemical reactions in the network as well as due to extrinsic noise stemming from the interaction of unknown molecular components with the network and from the cell's changing environment. While there are many methods to study the effect of intrinsic noise on the system dynamics, few exist to study the influence of both types of noise. Here we show how one can extend the conventional linear-noise approximation to allow for the rapid evaluation of the molecule numbers statistics of a biochemical network influenced by intrinsic noise and by slow lognormally distributed extrinsic noise. The theory is applied to simple models of gene regulatory networks and its validity confirmed by comparison with exact stochastic simulations. In particular, we consider three important biological examples. First, we investigate how extrinsic noise modifies the dependence of the variance of the molecule number fluctuations on the rate constants. Second, we show how the mutual information between input and output of a network motif is affected by extrinsic noise. And third, we study the robustness of the ubiquitously found feed-forward loop motifs when subjected to extrinsic noise.

We numerically explore the dynamics of an incompressible fluid heated from below, bounded by free-slip horizontal plates and periodic lateral boundary conditions, subject to rapid rotation about a distant axis that is tilted with respect to the gravity vector. The angle $\varphi$ between the rotation axis and the horizontal plane measures the tilting of the rotation axis; it can be taken as a proxy for latitude if we think of a local Cartesian representation of the convective dynamics in a rotating fluid shell. The results of the simulations indicate the existence of three different convective regimes, depending on the value of $\varphi$: (1) sheared, intermittent large-scale winds in the direction perpendicular to the plane defined by the gravity and rotation vectors, when rotation is “horizontal” ($\varphi =0^{\circ }$); (2) a large-scale cyclonic vortex tilted along the rotation axis, when the angle between the rotation axis and the gravity vector is relatively small ($\varphi$ between about ${45}^{\circ }$ and ${90}^{\circ }$); and (3) a new intermediate regime characterized by vertically sheared large-scale winds perpendicular to both gravity and rotation. In this regime, the winds are organized in bands that are tilted along the rotation axis, with unit horizontal wave number in the plane defined by gravity and rotation at values of $\varphi$ less than about ${60}^{\circ }$. This intermediate solution, studied for the first time in this work, is characterized by weaker vertical heat transport than the cases with large-scale vortices. For intermediate values of $\varphi$ (between about ${45}^{\circ }$ and ${60}^{\circ }$), the banded, sheared solution coexists with the large-scale vortex solution, with different initial conditions leading to one or the other dynamical behavior. A discussion of the possible implications of these results for the dynamics of rapidly rotating planetary atmospheres is provided.

Proton radiography experiments of laser-irradiated hohlraums performed at the OMEGA laser facility are analyzed using three-dimensional (3D) hydrodynamic simulations coupled to a proton trajectography package. Experiments with three different laser irradiation patterns were performed, and each produced a distinct proton image. By comparing these results with synthetic proton images obtained by sending protons through plasma profiles in the hohlraum obtained from 3D radiation hydrodynamic simulations, it is found that the simulated images agree favorably with the experimental images when electric fields, due to the electron pressure gradients that arise from 3D structures occurring during plasma expansion, are included. These comparisons provide quantitative estimates of the electric field present inside the hohlraums.

Many methodological approaches have been proposed to improve systematic or bottom-up coarse-graining techniques to enhance the representability and transferability of the derived interaction potentials. Transferability describes the ability of a coarse-grained (CG) model to be predictive, i.e., to describe a system at state points different from those chosen for parametrization. Whereas the representability characterizes the accuracy of a CG model to reproduce target properties of the underlying reference or fine-grained model at a given state point. In this article, we shift the focus away from methodological aspects and rather raise the question whether we can overcome the disadvantages of a given method in terms of representability and transferability by systematically selecting the state point at which the CG model gets parametrized. We answer this question by applying the inverse Monte Carlo (IMC) approach—a structure-based coarse-graining method—to derive effective interactions for binary mixtures of simple Lennard-Jones (LJ) particles, which are different in size. For such simple systems we indeed can identify a concentration where the derived potentials show the best performance in terms of structural representability and transferability. This specific concentration is identified by computing the relative entropy which quantifies the information loss between different IMC models and the reference LJ model at varying mixture compositions. Further, we show that an IMC model for mixtures of $n$-hexane and $n$-perfluorohexane shows the same trend in transferability as the IMC models for the LJ system. All derived models are more transferable in the direction of increasing concentration of the larger-sized compound.

Mathematical symmetries of the Beliaev-Budker kernel are the most important structure of the relativistic Landau-Fokker-Planck equation. In most numerical simulations, however, one of the symmetries is not preserved in the discrete level resulting in a violation of the energy conservation. Recently, we proposed a charge-momentum-energy-conserving relativistic Vlasov-Maxwell scheme by preserving mathematical formulas in discrete form, and here we apply the concept to the relativistic Landau-Fokker-Planck equation. Through a numerical experiment of relativistic collisional relaxation, a mass-momentum-energy-conserving simulation has been demonstrated without any artificial constraints.