We present a density functional study of Lennard-Jones liquids in contact with a nanocorrugated wall. The corresponding substrate potential is taken to exhibit a repulsive hard core and a Van der Waals attraction. The corrugation is modeled by a periodic array of square nanopits. We have used the modified Rosenfeld density functional in order to study the interfacial structure of these liquids which with respect to their thermodynamic bulk state are considered to be deep inside their liquid phase. We find that already considerably below the packing fraction of bulk freezing of these liquids, inside the nanopits a three-dimensional-like density localization sets in. If the sizes of the pits are commensurate with the packing requirements, we observe high-density spots separated from each other in all spatial directions by liquid of comparatively very low density. The number, shape, size, and density of these high-density spots depend sensitively on the depth and width of the pits. Outside the pits, only layering is observed; above the pit openings these layers are distorted with the distortion reaching up to a few molecular diameters. We discuss quantitatively how this density localization is affected by the geometrical features of the pits and how it evolves upon increasing the bulk packing fraction. Our results are transferable to colloidal systems and pit dimensions corresponding to several diameters of the colloidal particles. For such systems the predicted unfolding of these structural changes can be studied experimentally on much larger length scales and more directly (e.g., optically) than for molecular fluids which typically call for sophisticated x-ray scattering.

The spatial propagation of Hantavirus-infected mice is considered a serious threat for public health. We analyze the spatial spread of the infected mice by including diffusion in the stage-dependent model for Hantavirus infection recently proposed by Reinoso and de la Rubia [Phys. Rev. E 87, 042706 (2013)]. We consider a general scenario in which mice propagate in fronts from their refugia to the surroundings and find an expression for the speed of the front of infected mice. We also introduce a depletion time that measures the time scale for an appreciable impoverishment of the environment conditions and show how this new situation may change the spreading of the infection significantly.

There are many controversial and challenging discussions about quantum effects in microscopic structures in neurons of the brain and their role in cognitive processing. In this paper, we focus on a small, nanoscale part of ion channels which is called the “selectivity filter” and plays a key role in the operation of an ion channel. Our results for superposition states of potassium ions indicate that decoherence times are of the order of picoseconds. This decoherence time is not long enough for cognitive processing in the brain, however, it may be adequate for quantum superposition states of ions in the filter to leave their quantum traces on the selectivity filter and action potentials.

We have performed a systematic study quantifying the variation of solitary wave behavior from that of an ordered cloud resembling a “crystalline” configuration to that of a disordered state that can be characterized as a soliton “gas.” As our illustrative examples, we use both one-component, as well as two-component, one-dimensional atomic gases very close to zero temperature, where in the presence of repulsive interatomic interactions and of a parabolic trap, a cloud of dark (dark-bright) solitons can form in the one- (two-) component system. We corroborate our findings through three distinct types of approaches, namely a Gross-Pitaevskii type of partial differential equation, particle-based ordinary differential equations describing the soliton dynamical system, and Monte Carlo simulations for the particle system. We define an “empirical” order parameter to characterize the order of the soliton lattices and study how this changes as a function of the strength of the “thermally” (i.e., kinetically) induced perturbations. As may be anticipated by the one-dimensional nature of our system, the transition from order to disorder is gradual without, apparently, a genuine phase transition ensuing in the intermediate regime.

The influence of fluid dispersion on the Saffman-Taylor instability in miscible fluids has been investigated in both the linear and the nonlinear regimes. The convective characteristic scales are used for the dimensionless formulation that incorporates the Péclet number (Pe) into the governing equations as a measure for the fluid dispersion. A linear stability analysis (LSA) is performed in a similarity transformation domain using the quasi-steady-state approximation. LSA results confirm that a flow with a large Pe has a higher growth rate than a flow with a small Pe. The critical Péclet number (${\text{Pe}}_c$) for the onset of instability for all possible wave numbers and also a power-law relation of the onset time and most unstable wave number with Pe are observed. Unlike the radial source flow, ${\text{Pe}}_c$ is found to vary with $t_0$. A Fourier spectral method is used for direct numerical simulations (DNS) of the fully nonlinear system. The power-law dependence of the onset of instability $t_{\mathrm{on}}$ on Pe is obtained from the DNS and found to be inversely proportional to Pe and it is in good agreement with that obtained from the LSA. The influence of the anisotropic dispersion is analyzed in both the linear and the nonlinear regimes. The results obtained confirm that for a weak transverse dispersion merging happens slowly and hence the small wave perturbations become unstable. We also observ that the onset of instability sets in early when the transverse dispersion is weak and varies with the anisotropic dispersion coefficient, $\epsilon$, as $\sim \sqrt{\epsilon }$, in compliance with the LSA. The combined effect of the Korteweg stress and Pe in the linear regime is pursued. It is observed that, depending on various flow parameters, a fluid system with a larger Pe exhibits a lower instantaneous growth rate than a system with a smaller Pe, which is contrary to the results when such stresses are absent.

A model is presented for the linear Rayleigh-Taylor instability taking place at the early stage of acceleration of an elastic-plastic solid, when the shock wave is still running into the solid and is driven by a time varying pressure on the interface. When the the shock is formed sufficiently close to the interface, this stage is considered to follow a previous initial phase controlled by the Ritchmyer-Meshkov instability that settles new initial conditions. The model reproduces the behavior of the instability observed in former numerical simulation results and provides a relatively simpler physical picture than the currently existing one for this stage of the instability evolution.

We consider the mean first passage time (MFPT) of a two-dimensional diffusing particle to a small trap with a distribution of absorbing and reflecting sections. High-order asymptotic formulas for the MFPT and the fundamental eigenvalue of the Laplacian are derived which extend previously obtained results and show how the orientation of the trap affects the mean time to capture. We obtain a simple geometric condition which gives the optimal trap alignment in terms of the gradient of the regular part of a regular part of a Green's function and a certain alignment vector. We find that subdividing the absorbing portions of the trap reduces the mean first passage time of the diffusing particle. In the scenario where the trap undergoes prescribed motion in the domain, the MFPT is seen to be particularly sensitive to the orientation of the trap.

We study Anderson localization of the classical lattice waves in a chain with mass impurities distributed randomly through a power-law relation $s^{-(1+\alpha )}$ with $s$ as the distance between two successive impurities and $\alpha >0$. This model of disorder is long-range correlated and is inspired by the peculiar structure of the complex optical systems known as Lévy glasses. Using theoretical arguments and numerics, we show that in the regime in which the average distance between impurities is finite with infinite variance, the small-frequency behavior of the localization length is ${\xi }_{\alpha }\left(\omega \right)\sim {\omega }^{-\alpha }$. The physical interpretation of this result is that, for small frequencies and long wavelengths, the waves feel an effective disorder whose fluctuations are scale dependent. Numerical simulations show that an initially localized wave-packet attains, at large times, a characteristic inverse power-law front with an $\alpha$-dependent exponent which can be estimated analytically.

The deterministic dynamics of randomly connected neural networks are studied, where a state of binary neurons evolves according to a discrete-time synchronous update rule. We give theoretical support that the overlap of systems' states between the current and a previous time develops in time according to a Markovian stochastic process in large networks. This Markovian process predicts how often a network revisits one of the previously visited states, depending on the system size. The state concentration probability, i.e., the probability that two distinct states coevolve to the same state, is utilized to analytically derive various characteristics that quantify attractors' structure. The analytical predictions about the total number of attractors, the typical cycle length, and the number of states belonging to all attractive cycles match well with numerical simulations for relatively large system sizes.

Many networks can be usefully decomposed into a dense core plus an outlying, loosely connected periphery. Here we propose an algorithm for performing such a decomposition on empirical network data using methods of statistical inference. Our method fits a generative model of core-periphery structure to observed data using a combination of an expectation-maximization algorithm for calculating the parameters of the model and a belief propagation algorithm for calculating the decomposition itself. We find the method to be efficient, scaling easily to networks with a million or more nodes, and we test it on a range of networks, including real-world examples as well as computer-generated benchmarks, for which it successfully identifies known core-periphery structure with low error rate. We also demonstrate that the method is immune to the detectability transition observed in the related community detection problem, which prevents the detection of community structure when that structure is too weak. There is no such transition for core-periphery structure, which is detectable, albeit with some statistical error, no matter how weak it is.

We prove the existence of an asymptotic phase-transition threshold on community detectability for the spectral modularity method [M. E. J. Newman, Phys. Rev. E 74, 036104 (2006) and Proc. Natl. Acad. Sci. (USA) 103, 8577 (2006)] under a stochastic block model. The phase transition on community detectability occurs as the intercommunity edge connection probability $p$ grows. This phase transition separates a subcritical regime of small $p$, where modularity-based community detection successfully identifies the communities, from a supercritical regime of large $p$ where successful community detection is impossible. We show that, as the community sizes become large, the asymptotic phase-transition threshold $p^{*}$ is equal to $\sqrt{p_1{p}_2}$, where $p_i(i=1,2)$ is the within-community edge connection probability. Thus the phase-transition threshold is universal in the sense that it does not depend on the ratio of community sizes. The universal phase-transition phenomenon is validated by simulations for moderately sized communities. Using the derived expression for the phase-transition threshold, we propose an empirical method for estimating this threshold from real-world data.

In the study of complex systems from observed multivariate time series, insight into the evolution of one system may be under investigation, which can be explained by the information storage of the system and the information transfer from other interacting systems. We present a framework for the model-free estimation of information storage and information transfer computed as the terms composing the predictive information about the target of a multivariate dynamical process. The approach tackles the curse of dimensionality employing a nonuniform embedding scheme that selects progressively, among the past components of the multivariate process, only those that contribute most, in terms of conditional mutual information, to the present target process. Moreover, it computes all information-theoretic quantities using a nearest-neighbor technique designed to compensate the bias due to the different dimensionality of individual entropy terms. The resulting estimators of prediction entropy, storage entropy, transfer entropy, and partial transfer entropy are tested on simulations of coupled linear stochastic and nonlinear deterministic dynamic processes, demonstrating the superiority of the proposed approach over the traditional estimators based on uniform embedding. The framework is then applied to multivariate physiologic time series, resulting in physiologically well-interpretable information decompositions of cardiovascular and cardiorespiratory interactions during head-up tilt and of joint brain-heart dynamics during sleep.

A new phase-field model is developed for studying partial wetting. The introduction of a third phase representing a solid wall allows for the derivation of a new surface tension force that accounts for energy changes at the contact line. In contrast to other multi-phase-field formulations, the present model does not need the introduction of surface energies for the fluid-wall interactions. Instead, all wetting properties are included in a unique parameter known as the equilibrium contact angle ${\theta }_{\mathrm{eq}}$. The model requires the solution of a single elliptic phase-field equation, which, coupled to conservation laws for mass and linear momentum, admits the existence of steady and unsteady compact solutions (compactons). The representation of the wall by an additional phase field allows for the study of wetting phenomena on flat, rough, or patterned surfaces in a straightforward manner. The model contains only two free parameters, a measure of interface thickness $W$ and $\beta$, which is used in the definition of the mixture viscosity $\mu ={\mu }_l{\varphi }_l+{\mu }_v{\varphi }_v+\beta {\mu }_l{\varphi }_w$. The former controls the convergence towards the sharp interface limit and the latter the energy dissipation at the contact line. Simulations on rough surfaces show that by taking values for $\beta$ higher than 1, the model can reproduce, on average, the effects of pinning events of the contact line during its dynamic motion. The model is able to capture, in good agreement with experimental observations, many physical phenomena fundamental to wetting science, such as the wetting transition on micro-structured surfaces and droplet dynamics on solid substrates.

Electron screening of ions is among the most fundamental properties of plasmas, determining the effective ionic interactions that impact all properties of a plasma. With the development of new experimental facilities that probe high-energy-density physics regimes ranging from warm dense matter to hot dense matter, a unified framework for describing dense plasma screening has become essential. Such a unified framework is presented here based on finite-temperature orbital-free density functional theory, including gradient corrections and exchange-correlation effects. We find a new analytic pair potential for the ion-ion interaction that incorporates moderate electronic coupling, quantum degeneracy, gradient corrections to the free energy, and finite temperatures. This potential can be used in large-scale “classical” molecular dynamics simulations, as well as in simpler theoretical models (e.g., integral equations and Monte Carlo), with no additional computational complexity. The new potential theoretically connects limits of Debye-Hückel–Yukawa, Lindhard, Thomas-Fermi, and Bohmian quantum hydrodynamics descriptions. Based on this new potential, we predict ionic static structure factors that can be validated using x-ray Thomson scattering data.

The complex pattern and propagation characteristics of nonlinear periodic ion-acoustic waves, namely, ion-acoustic cnoidal waves, in a dense relativistic degenerate magnetoplasma consisting of relativistic degenerate electrons and nondegenerate cold ions are investigated. By means of the reductive perturbation method and appropriate boundary conditions for nonlinear periodic waves, a nonlinear modified Korteweg–de Vries (KdV) equation is derived and its cnoidal wave is analyzed. The various solutions of nonlinear ion-acoustic cnoidal and solitary waves are presented numerically with the Sagdeev potential approach. The analytical solution and numerical simulation of nonlinear ion-acoustic cnoidal waves of the nonlinear modified KdV equation are studied. Clearly, it is found that the features (amplitude and width) of nonlinear ion-acoustic cnoidal waves are proportional to plasma number density, ion cyclotron frequency, and direction cosines. The numerical results are applied to high density astrophysical situations, such as in superdense white dwarfs. This research will be helpful in understanding the properties of compact astrophysical objects containing cold ions with relativistic degenerate electrons.

A lattice Boltzmann (LB) model for the convection-diffusion equation (CDE) with divergence-free velocity field is proposed, and the Chapman-Enskog analysis shows that the CDE can be recovered correctly. In the present model, the convection term is treated as a source term in the lattice Boltzmann equation (LBE) rather than being directly recovered by LBE; thus the CDE is intrinsically solved as a pure diffusion equation with a corresponding source term. To avoid the adoption of a nonlocal finite-difference scheme for computing the convection term, a local scheme is developed based on the Chapman-Enskog analysis. Most importantly, by properly specifying the discrete source term in the moment space, the local scheme can reach the same order $\left({\varepsilon }^2\right)$ at which the CDE is recovered by a LB model. Numerical tests, including a one-dimensional periodic problem, diffusion of a Gaussian hill, diffusion of a rectangular pulse, and natural convection in a square cavity, are carried out to verify the present model. Numerical results are satisfactorily consistent with analytical solutions or previous numerical results, and show higher accuracy due to the correct recovery of CDE.

This paper is devoted to the crumpling of thin matter. The Edwards-like statistical mechanics of crumpling networks in a crushed self-avoiding sheet with finite bending rigidity is developed. The statistical distribution of crease lengths is derived. The relationship between sheet packing density and hydrostatic pressure is established. The entropic contribution to the crumpling network rigidity is outlined. The effects of plastic deformations and sheet self-contacts on crumpling mechanics are discussed. Theoretical predictions are in good agreement with available experimental data and results of numerical simulations. Thus, the findings of this work provide further insight into the physics of crumpling and mechanical properties of crumpled soft matter.

Evolutionary social dilemma games are extended by an additional matching-pennies game that modifies the collected payoffs. In a spatial version players are distributed on a square lattice and interact with their neighbors. First, we show that the matching-pennies game can be considered as the microscopic force of the Red Queen effect that breaks the detailed balance and induces eddies in the microscopic probability currents if the strategy update is analogous to the Glauber dynamics for the kinetic Ising models. The resulting loops in probability current breaks symmetry between the chessboardlike arrangements of strategies via a bottleneck effect occurring along the four-edge loops in the microscopic states. The impact of this congestion is analogous to the application of a staggered magnetic field in the Ising model; that is, the order-disorder critical transition is wiped out by noise. It is illustrated that the congestion induced symmetry breaking can be beneficial for the whole community within a certain region of parameters.

We present a numerical method for simulations of spinodal decomposition of liquid-vapor systems. The results are in excellent agreement with theoretical predictions for all expected time regimes from the initial growth of “homophase fluctuations” up to the inertial hydrodynamics regime. The numerical approach follows a modern formulation of the smoothed particle hydrodynamics method with a van der Waals equation of state and thermal conduction. The dynamics and thermal evolution of instantaneously temperature-quenched systems are investigated. Therefore, we introduce a simple scaling thermostat that allows thermal fluctuations at a constant predicted mean temperature. We find that the initial stage spinodal decomposition is strongly affected by the temperature field. The separated phases react on density changes with a change in temperature. Although, the thermal conduction acts very slowly, thermal deviations are eventually compensated. The domain growth in the late stage of demixing is found to be rather unaffected by thermal fluctuations. We observe a transition from the Lifshitz-Slyozov growth rate with $1/3$ exponent to the inertial hydrodynamics regime with a rate of $2/3$, only excepted from simulations near the critical point where the liquid droplets are observed to nucleate directly in a spherical shape. The transition between the growth regimes is found to occur earlier for higher initial temperatures. We explain this time dependency with the phase interfaces that become more diffuse and overlap with approaching the critical point. A prolonging behavior of the demixing process is observed and also expected to depend on temperature. It is further found that the observations can excellently explain the growth behavior for pure nonisothermal simulations that are performed without thermostat.

We study in experiment and with computer simulation the free energy and the kinetics of vacancy and interstitial defects in two-dimensional dipolar crystals. The defects appear in different local topologies, which we characterize by their point group symmetry; $C_n$ is the $n$-fold cyclic group and $D_n$ is the dihedral group, including reflections. The frequency of different local topologies is not determined by their almost degenerate energies but is dominated by entropy for symmetric configurations. The kinetics of the defects is fully reproduced by a master equation in a multistate Markov model. In this model, the system is described by the state of the defect and the time evolution is given by transitions occurring with particular rates. These transition rate constants are extracted from experiments and simulations using an optimization procedure. The good agreement between experiment, simulation, and master equation thus provides evidence for the accuracy of the model.