The curvature elasticity of a polyelectrolyte brush monolayer attached to curved surface is investigated theoretically. An analytical method based on the strong-stretching theory for a Gaussian chain is developed to calculate the elastic modulus induced by a polyelectrolyte brush. In particular, the scaling relations for the bending or Gaussian modulus with respect to system parameters related to the electrostatic interaction (degree of ionization and salt concentration) are derived. Using the numerical self-consistent-field theory, the inner structural, free-energy, and elastic moduli are computed for the polyelectrolyte brush with excluded-volume interactions. Compared to the analytical result, the curvature elasticity has a weaker dependence on the system parameters, which is attributed to the linearization for the Poisson-Boltzmann equation in the analytical treatment. Furthermore, our results are compared to the curvature elasticity of a bare charged surface, wherefrom the unique polyelectrolyte brush effect on the surface elasticity is clarified clearly. The scaling relations derived in our paper can serve as a guide to experimental studies on the related systems.

The mean-squared displacement (MSD) and velocity autocorrelation (VAC) of tracked single particles or molecules are ubiquitous metrics for extracting parameters that describe the object's motion, but they are both corrupted by experimental errors that hinder the quantitative extraction of underlying parameters. For the simple case of pure Brownian motion, the effects of localization error due to photon statistics (“static error”) and motion blur due to finite exposure time (“dynamic error”) on the MSD and VAC are already routinely treated. However, particles moving through complex environments such as cells, nuclei, or polymers often exhibit anomalous diffusion, for which the effects of these errors are less often sufficiently treated. We present data from tracked chromosomal loci in yeast that demonstrate the necessity of properly accounting for both static and dynamic error in the context of an anomalous diffusion that is consistent with a fractional Brownian motion (FBM). We compare these data to analytical forms of the expected values of the MSD and VAC for a general FBM in the presence of these errors.

The behavior of long DNA molecules in a cell-sized confined space was investigated. We prepared water-in-oil droplets covered by phospholipids, which mimic the inner space of a cell, following the encapsulation of DNA molecules with unfolded coil and folded globule conformations. Microscopic observation revealed that the adsorption of coiled DNA onto the membrane surface depended on the size of the vesicular space. Globular DNA showed a cell-size-dependent unfolding transition after adsorption on the membrane. Furthermore, when DNA interacted with a two-phase membrane surface, DNA selectively adsorbed on the membrane phase, such as an ordered or disordered phase, depending on its conformation. We discuss the mechanism of these trends by considering the free energy of DNA together with a polyamine in the solution. The free energy of our model was consistent with the present experimental data. The cooperative interaction of DNA and polyamines with a membrane surface leads to the size-dependent behavior of molecular systems in a small space. These findings may contribute to a better understanding of the physical mechanism of molecular events and reactions inside a cell.

In spite of the extensive previous efforts on traffic dynamics and epidemic spreading in complex networks, the problem of traffic-driven epidemic spreading on correlated networks has not been addressed. Interestingly, we find that the epidemic threshold, a fundamental quantity underlying the spreading dynamics, exhibits a nonmonotonic behavior in that it can be minimized for some critical value of the assortativity coefficient, a parameter characterizing the network correlation. To understand this phenomenon, we use the degree-based mean-field theory to calculate the traffic-driven epidemic threshold for correlated networks. The theory predicts that the threshold is inversely proportional to the packet-generation rate and the largest eigenvalue of the betweenness matrix. We obtain consistency between theory and numerics. Our results may provide insights into the important problem of controlling and/or harnessing real-world epidemic spreading dynamics driven by traffic flows.

In a forked road, the provision of inappropriate information to car drivers sometimes leads to undesirable situations such as one-sided congestion, which is called the hunting phenomenon in real traffic. To address such problems, we propose a forked exclusion model and investigate the behavior of traffic flow in two routes, providing various types of information to a limited number of traveling particles according to the share rate of information. To analytically understand the phenomena, we develop a coarse-grained representation of the model. By analyzing the model, we find the most effective types of information to minimize particles' travel time and the existence of an optimal share rate according to route conditions.

We study a hyperbolic version of the FitzHugh-Nagumo (also known as the Bonhoeffer-van der Pol) reaction-diffusion system. To be able to obtain analytical results, we employ a piecewise linear approximation of the nonlinear kinetic term. The hyperbolic version is compared with the standard parabolic FitzHugh-Nagumo system. We completely describe the dynamics of wavefronts and discuss the properties of the speed equation. The nonequilibrium Ising-Bloch bifurcation of traveling fronts is found to occur in the hyperbolic case as well as in the parabolic system. Waves in the hyperbolic case typically propagate with lower speeds, in absolute value, than waves in the parabolic one. We find the interesting feature that the hyperbolic and parabolic front trajectories coincide in the phase plane for the FitzHugh-Nagumo model with a diagonal diffusion matrix, which is the case of self-diffusion, and differ for the system with cross diffusion.

We investigate the scaling behavior of the maximal Lyapunov exponent in chaotic systems with time delay. In the large-delay limit, it is known that one can distinguish between strong and weak chaos depending on the delay scaling, analogously to strong and weak instabilities for steady states and periodic orbits. Here we show that the Lyapunov exponent of chaotic systems shows significant differences in its scaling behavior compared to constant or periodic dynamics due to fluctuations in the linearized equations of motion. We reproduce the chaotic scaling properties with a linear delay system with multiplicative noise. We further derive analytic limit cases for the stochastic model illustrating the mechanisms of the emerging scaling laws.

We show that the addition of pairwise repulsive inhibition to excitatory networks of bursting neurons induces synchrony, in contrast to one's expectations. Through stability analysis, we reveal the mechanism underlying this purely synergistic phenomenon and demonstrate that it originates from the transition between different types of bursting, caused by excitatory-inhibitory synaptic coupling. This effect is generic and observed in different models of bursting neurons and fast synaptic interactions. We also find a universal scaling law for the synchronization stability condition for large networks in terms of the number of excitatory and inhibitory inputs each neuron receives, regardless of the network size and topology. This general law is in sharp contrast with linearly coupled networks with positive (attractive) and negative (repulsive) coupling where the placement and structure of negative connections heavily affect synchronization.

We present results for the equilibrium statistics and dynamic evolution of moderately large $[n=O({10}^2-{10}^3)]$ numbers of interacting point vortices on the sphere under the constraint of zero mean angular momentum. For systems with equal numbers of positive and negative identical circulations, the density of rescaled energies, $p\left(E\right)$, converges rapidly with $n$ to a function with a single maximum with maximum entropy. Ensemble-averaged wave-number spectra of the nonsingular velocity field induced by the vortices exhibit the expected $k^{-1}$ behavior at small scales for all energies. Spectra at the largest scales vary continuously with the inverse temperature of the system. For positive temperatures, spectra peak at finite intermediate wave numbers; for negative temperatures, spectra decrease everywhere. Comparisons of time and ensemble averages, over a large range of energies, strongly support ergodicity in the dynamics even for highly atypical initial vortex configurations. Crucially, rapid relaxation of spectra toward the microcanonical average implies that the direction of any spectral cascade process depends only on the relative difference between the initial spectrum and the ensemble mean spectrum at that energy, not on the energy, or temperature, of the system.

Flow structure of a dust medium electrostatically suspended and confined in a plasma presents a unique setup where the spatial scale of a volumetric drive by the plasma flow might exceed that of the boundaries confining the dust. By means of a formal implementation of a two-dimensional hydrodynamic model to a confined dust flow and its analytic curvilinear solutions, it is shown that the eigenmode spectrum of the dust vortex flow can lose correlations with the driving field even at the low dust Reynolds numbers as a result of strong shear and finer scales introduced in the equilibrium dust vorticity spectrum by the boundaries. While the boundary effects can replace the desired turbulent processes unavailable in this regime, the shear observable in most of the dust vortex flows is identified to have a definite exponent of dependence on the dust viscosity over a substantially large range of the latter. These results and scalings allow quantification of the notion of dusty plasma medium as a paradigm for a wide range of natural flow processes having scales inaccessible to ordinary laboratory experiments.

In this work, minimal kinetic theories based on unconventional entropy functions, $H\sim \text{ln}f$ (Burg entropy) for 2D and $H\sim f^{1-\frac{2}{n}}$ (Tsallis entropy) for $n\text{D}$ with $n\ge 3$, are studied. These entropy functions were originally derived by Boghosian et al. [Phys. Rev. E 68, 025103 (2003)] as a basis for discrete-velocity and lattice Boltzmann models for incompressible fluid dynamics. The present paper extends the entropic models of Boghosian et al. and shows that the explicit form of the equilibrium distribution function (EDF) of their models, in the continuous-velocity limit, can be identified with the Poisson kernel of the Poisson integral formula. The conservation and Navier-Stokes equations are recovered at low Mach numbers, and it is shown that rest particles can be used to rectify the speed of sound of the extended models. Fourier series expansion of the EDF is used to evaluate the discretization errors of the model. It is shown that the expansion coefficients of the Fourier series coincide with the velocity moments of the model. Employing two-, three-, and four-dimensional (2D, 3D, and 4D) complex systems, the real velocity space is mapped into the hypercomplex spaces and it is shown that the velocity moments can be evaluated, using the Poisson integral formula, in the hypercomplex space. For the practical applications, a 3D projection of the 4D model is presented, and the existence of an $H$ theorem for the discrete model is investigated. The theoretical results have been verified by simulating the following benchmark problems: (1) the Kelvin-Helmholtz instability of thin shear layers in a doubly periodic domain and (2) the 3D flow of incompressible fluid in a lid-driven cubic cavity. The present results are in agreement with the previous works, while they show better stability of the proposed kinetic model, as compared with the BGK type (with single relaxation time) lattice Boltzmann models.

We study the distribution of the mean radial displacement of charges of a two-dimensional (2D) one-component plasma in the thermodynamic limit $N\to \infty$ at finite temperature $\beta >0$. We compute explicitly the large deviation functions showing the emergence of a fourth-order phase transition as a consequence of a change of topology in the plasma distribution. This weak phase transition occurs exactly at the ground state of the plasma. These results have been compared with the integrable case (finite $N$) of plasma parameter $\beta q^2=2$. In this case the problem can be mapped to the stationary properties of 2D Dyson Brownian particles and to a non-Hermitian matrix model.

When DNA molecules are heated they denature. This occurs locally so that loops of molten single DNA strands form, connected by intact double-stranded DNA pieces. The properties of this “melting” transition have been intensively investigated. Recently there has been a surge of interest in this question, in part caused by experiments determining the properties of partially bound DNA confined to nanochannels. But how does such confinement affect the melting transition? To answer this question we introduce and solve a model predicting how confinement affects the melting transition for a simple model system by first disregarding the effect of self-avoidance. We find that the transition is smoother for narrower channels. By means of Monte Carlo simulations we then show that a model incorporating self-avoidance shows qualitatively the same behavior and that the effect of confinement is stronger than in the ideal case.

In many complex systems, states and interaction structure coevolve towards a dynamic equilibrium. For the adaptive contact process, we obtain approximate expressions for the degree distributions that characterize the interaction network in such active steady states. These distributions are shown to agree quantitatively with simulations except when rewiring is much faster than state update and used to predict and to explain general properties of steady-state topologies. The method generalizes easily to other coevolutionary dynamics.

A granular gas in gravity heated from below develops a certain stationary density profile. When the heating is switched off, the granular gas collapses. We investigate the process of sedimentation using computational hydrodynamics, based on the Jenkins-Richman theory, and find that the process is significantly more complex than generally acknowledged. In particular, during its evolution, the system passes several stages which reveal distinct spatial regions of inertial (supersonic) and diffusive (subsonic) dynamics. During the supersonic stages, characterized by $\text{Mach}>1$, the system develops supersonic shocks which are followed by a steep front of the hydrodynamic fields of temperature and density, traveling upward.

In recent years there has been a growing interest in the study of shape formation using modern responsive materials that can be preprogrammed to undergo spatially inhomogeneous local deformations. In particular, nematic liquid crystalline solids offer exciting possibilities in this context. Considerable recent progress has been made in achieving a variety of shape transitions in thin sheets of nematic solids by engineering isolated points of concentrated Gaussian curvature using topological defects in the nematic director field across textured surfaces. In this paper, we consider ways of achieving shape transitions in thin sheets of nematic glass by generation of nonlocalized Gaussian curvature in the absence of topological defects in the director field. We show how one can blueprint any desired Gaussian curvature in a thin nematic sheet by controlling the nematic alignment angle across the surface and highlight specific patterns which present feasible initial targets for experimental verification of the theory.

When common factors strongly influence two power-law cross-correlated time series recorded in complex natural or social systems, using detrended cross-correlation analysis (DCCA) without considering these common factors will bias the results. We use detrended partial cross-correlation analysis (DPXA) to uncover the intrinsic power-law cross correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis that takes into account partial correlation analysis. We demonstrate the method by using bivariate fractional Brownian motions contaminated with a fractional Brownian motion. We find that the DPXA is able to recover the analytical cross Hurst indices, and thus the multiscale DPXA coefficients are a viable alternative to the conventional cross-correlation coefficient. We demonstrate the advantage of the DPXA coefficients over the DCCA coefficients by analyzing contaminated bivariate fractional Brownian motions. We calculate the DPXA coefficients and use them to extract the intrinsic cross correlation between crude oil and gold futures by taking into consideration the impact of the U.S. dollar index. We develop the multifractal DPXA (MF-DPXA) method in order to generalize the DPXA method and investigate multifractal time series. We analyze multifractal binomial measures masked with strong white noises and find that the MF-DPXA method quantifies the hidden multifractal nature while the multifractal DCCA method fails.

The dynamics of two-dimensional bubbles ascending under the influence of buoyant forces is numerically studied with a one-fluid model coupled with the front-tracking technique. The bubble dynamics are described by recording the position, shape, and orientation of the bubbles as functions of time. The qualitative properties of the bubbles and their terminal velocities are described in terms of the Eötvos (ratio of buoyancy to surface tension) and Archimedes numbers (ratio of buoyancy to viscous forces). The terminal Reynolds number result from the balance of buoyancy and drag forces and, consequently, is not an externally fixed parameter. In the cases that yield small Reynolds numbers, the bubbles follow straight paths and the wake is steady. A more interesting behavior is found at high Reynolds numbers where the bubbles follow an approximately periodic zigzag trajectory and an unstable wake with properties similar to the Von Karman vortex street is formed. The dynamical features of the motion of single bubbles are compared to experimental observations of air bubbles ascending in a water-filled Hele-Shaw cell. Although the comparison is not strictly valid in the sense that the effect of the lateral walls is not incorporated in the model, most of the dynamical properties observed are in good qualitative agreement with the numerical calculations. Hele-Shaw cells with different gaps have been used to determine the degree of approximation of the numerical calculation. It is found that for the relation between the terminal Reynolds number and the Archimedes number, the numerical calculations are closer to the observations of bubble dynamics in Hele-Shaw cells of larger gaps.

We ask what happens when two nonequilibrium systems in steady state are kept in contact and allowed to exchange a quantity, say mass, which is conserved in the combined system. Will the systems eventually evolve to a new stationary state where a certain intensive thermodynamic variable, like equilibrium chemical potential, equalizes following the zeroth law of thermodynamics and, if so, under what conditions is it possible? We argue that an equilibriumlike thermodynamic structure can be extended to nonequilibrium steady states having short-ranged spatial correlations, provided that the systems interact weakly to exchange mass with rates satisfying a balance condition—reminiscent of a detailed balance condition in equilibrium. The short-ranged correlations would lead to subsystem factorization on a coarse-grained level and the balance condition ensures both equalization of an intensive thermodynamic variable as well as ensemble equivalence, which are crucial for construction of a well-defined nonequilibrium thermodynamics. This proposition is proved and demonstrated in various conserved-mass transport processes having nonzero spatial correlations.