The nonlinear fluid theory developed by Schamel suggests a modified KdV equation to describe the temporal evolution of ion acoustic (IA) solitons in the presence of trapped electrons. The validity of this theory is studied here by verifying solitons' main characteristic, i.e., stability against successive mutual collisions. We have employed a kinetic model as a more comprehensive theory than the fluid one, and utilized a fully kinetic simulation approach (both ions and electrons are treated based on the Vlasov equation). In the simulation approach, these solitons are excited self-consistently by employing the nonlinear process of IA solitons formation from an initial density perturbation (IDP). The effect of the size of IDPs on the chain formation is proved by the simulation code as a benchmark test. It is shown that the IA solitons, in the presence of trapped electrons, can retain their features (both in spatial and velocity direction) after successive mutual collisions. The collisions here include encounters of IA solitons with the same trapping parameter, while differing in size. Kinetic simulation results reveal a complicated behavior during a collision between IA solitons in contrast to the fluid theory predictions and simulations. In the range of parameters considered here, two oppositely propagating solitons rotate around their collective center in the phase space during a collision, independent of their trapping parameters. Furthermore, they exchange some portions of their trapped populations.

Herbert Simon's classic rich-get-richer model is one of the simplest empirically supported mechanisms capable of generating heavy-tail size distributions for complex systems. Simon argued analytically that a population of flavored elements growing by either adding a novel element or randomly replicating an existing one would afford a distribution of group sizes with a power-law tail. Here, we show that, in fact, Simon's model does not produce a simple power-law size distribution as the initial element has a dominant first-mover advantage, and will be overrepresented by a factor proportional to the inverse of the innovation probability. The first group's size discrepancy cannot be explained away as a transient of the model, and may therefore be many orders of magnitude greater than expected. We demonstrate how Simon's analysis was correct but incomplete, and expand our alternate analysis to quantify the variability of long term rankings for all groups. We find that the expected time for a first replication is infinite, and show how an incipient group must break the mechanism to improve their odds of success. We present an example of citation counts for a specific field that demonstrates a first-mover advantage consistent with our revised view of the rich-get-richer mechanism. Our findings call for a reexamination of preceding work invoking Simon's model and provide an expanded understanding going forward.

In this paper, we study the evolution of cooperation in structured populations (individuals are located on either a regular lattice or a scale-free network) in the context of repeated games by involving three types of strategies, namely, unconditional cooperation, unconditional defection, and extortion. The strategy updating of the players is ruled by the replicator-like dynamics. We find that extortion strategies can act as catalysts to promote the emergence of cooperation in structured populations via different mechanisms. Specifically, on regular lattice, extortioners behave as both a shield, which can enwrap cooperators inside and keep them away from defectors, and a spear, which can defeat those surrounding defectors with the help of the neighboring cooperators. Particularly, the enhancement of cooperation displays a resonance-like behavior, suggesting the existence of optimal extortion strength mostly favoring the evolution of cooperation, which is in good agreement with the predictions from the generalized mean-field approximation theory. On scale-free network, the hubs, who are likely occupied by extortioners or defectors at the very beginning, are then prone to be conquered by cooperators on small-degree nodes as time elapses, thus establishing a bottom-up mechanism for the emergence and maintenance of cooperation.

We present a statistical-mechanical model for the behavior of intertwined DNAs, with a focus on their torque and extension as a function of their catenation (linking) number and applied force, as studied in magnetic tweezers experiments. Our model produces results in good agreement with available experimental data and predicts a catenation-dependent effective twist modulus distinct from what is observed for twisted individual double-helix DNAs. We find that buckling occurs near the point where experiments have observed a kink in the extension versus linking number, and that the subsequent “supercoiled braid” state corresponds to a proliferation of multiple small plectoneme structures. We predict a discontinuity in extension at the buckling transition corresponding to nucleation of the first plectoneme domain. We also find that buckling occurs for lower linking number at lower salt; the opposite trend is observed for supercoiled single DNAs.

The equilibrium structure and dynamics of magnetorheological (MR) fluids are studied in this work by simulations, where particles are modeled as dipoles with a quasihard spherical core. Upon increasing the interaction strength, controlled experimentally by the magnetic field, elongated clusters grow and, for intense fields, thick columns form, aligned with the field. The dynamics of the system is monitored by the mean-squared displacement and density correlation functions, which show an increasing slowing down with the attraction strength. The correlation function shows a two-step decay, with a separation between microscopic and long time dynamics, a typical hallmark of undercooled fluids. We have therefore analyzed the dynamics of this MR fluid using the typical concepts for undercooled fluids. Thus, the second decay of the density correlation function is fitted with a stretched exponential, and the wave-vector dependence of the fitting parameters studied. Both the amplitude and the time scale oscillate in phase with the structure factor. Our results support the idea that the magnetorheological effect is in fact the manifestation of a colloidal system approaching an attractive glass transition (or gel transition).

The stability of the gravity-driven flow of a viscous film coating the inside of a tube with a porous wall is studied theoretically. We used Darcy's law to describe the motion of fluids in a porous medium. The Beaver-Joseph condition is used to describe the discontinuity of velocity at the porous-fluid interface. We derived an evolution equation for the film thickness using a long-wave approximation. The effect of velocity slip at the porous wall is identified by a parameter $\beta$. We examine the effect of $\beta$ on the temporal stability, the absolute-convective instability (AI-CI), and the nonlinear evolution of the interface deformation. The results of the temporal stability reveal that the effect of velocity slip at the porous wall is destabilizing. The parameter $\beta$ plays an important role in determining the AI-CI behavior and the nonlinear evolution of the interface. The presence of the porous wall promotes the absolute instability and the formation of the plug in the tube.

The available lattice Boltzmann (LB) models for combustion or phase change are focused on either single-phase flow combustion or two-phase flow with evaporation assuming a constant density for both liquid and gas phases. To pave the way towards simulation of spray combustion, we propose a two-phase LB method for modeling combustion of liquid fuel droplets. We develop an LB scheme to model phase change and combustion by taking into account the density variation in the gas phase and accounting for the chemical reaction based on the Cahn-Hilliard free-energy approach. Evaporation of liquid fuel is modeled by adding a source term, which is due to the divergence of the velocity field being nontrivial, in the continuity equation. The low-Mach-number approximation in the governing Navier-Stokes and energy equations is used to incorporate source terms due to heat release from chemical reactions, density variation, and nonluminous radiative heat loss. Additionally, the conservation equation for chemical species is formulated by including a source term due to chemical reaction. To validate the model, we consider the combustion of n-heptane and $n$-butanol droplets in stagnant air using overall single-step reactions. The diameter history and flame standoff ratio obtained from the proposed LB method are found to be in good agreement with available numerical and experimental data. The present LB scheme is believed to be a promising approach for modeling spray combustion.

The state of a stochastic process evolving over a time $t$ is typically assumed to lie on a normal distribution whose width scales like $t^{\frac{1}{2}}$. However, processes in which the probability distribution is not normal and the scaling exponent differs from $\frac{1}{2}$ are known. The search for possible origins of such “anomalous” scaling and approaches to quantify them are the motivations for the work reported here. In processes with stationary increments, where the stochastic process is time-independent, autocorrelations between increments and infinite variance of increments can cause anomalous scaling. These sources have been referred to as the Joseph effect and the Noah effect, respectively. If the increments are nonstationary, then scaling of increments with $t$ can also lead to anomalous scaling, a mechanism we refer to as the Moses effect. Scaling exponents quantifying the three effects are defined and related to the Hurst exponent that characterizes the overall scaling of the stochastic process. Methods of time series analysis that enable accurate independent measurement of each exponent are presented. Simple stochastic processes are used to illustrate each effect. Intraday financial time series data are analyzed, revealing that their anomalous scaling is due only to the Moses effect. In the context of financial market data, we reiterate that the Joseph exponent, not the Hurst exponent, is the appropriate measure to test the efficient market hypothesis.

The four-wave interaction in quantum nonlinear Schrödinger lattices with disorder is shown to destroy the Anderson localization of waves, giving rise to unlimited spreading of the nonlinear field to large distances. Moreover, the process is not thresholded in the quantum domain, contrary to its “classical” counterpart, and leads to an accelerated spreading of the subdiffusive type, with the dispersion $\langle {\left(\mathrm{\Delta }n\right)}^2\rangle \sim t^{1/2}$ for $t\to +\infty$. The results, presented here, shed light on the origin of subdiffusion in systems with a broad distribution of relaxation times.

Several subdiffusive stochastic processes in nature, e.g., the motion of a tagged monomer in polymers, the height fluctuation of interfaces, particle dynamics in single-file diffusion, etc., can be described rigorously or approximately by the superposition of various modes whose relaxation times are broadly distributed. In this paper, we propose a mode analysis generating superdiffusion, which is paired with or complementary to subdiffusion. The key point in our discussion lies in the identification of a pair of conjugated variables, which undergo sub- and superdiffusion, respectively. We provide a simple interpretation for the sub- and superdiffusion duality for these variables using the language of polymer physics. The analysis also suggests the usefulness of looking at the force fluctuation in experiments, where a polymer is driven by a constant velocity.

We present a weakly coupled map lattice model for patterning that explores the effects exerted by weakening the local dynamic rules on model biological and artificial networks composed of two-state building blocks (cells). To this end, we use two cellular automata models based on (i) a smooth majority rule (model I) and (ii) a set of rules similar to those of Conway's Game of Life (model II). The normal and abnormal cell states evolve according to local rules that are modulated by a parameter $\kappa$. This parameter quantifies the effective weakening of the prescribed rules due to the limited coupling of each cell to its neighborhood and can be experimentally controlled by appropriate external agents. The emergent spatiotemporal maps of single-cell states should be of significance for positional information processes as well as for intercellular communication in tumorigenesis, where the collective normalization of abnormal single-cell states by a predominantly normal neighborhood may be crucial.

Theories that are used to extract energy-landscape information from single-molecule pulling experiments in biophysics are all invariably based on Kramers' theory of the thermally activated escape rate from a potential well. As is well known, this theory recovers the Arrhenius dependence of the rate on the barrier energy and crucially relies on the assumption that the barrier energy is much larger than $k_{\mathrm{B}}T$ (limit of comparatively low thermal fluctuations). As was shown already in Dudko et al. [Phys. Rev. Lett. 96, 108101 (2006)], this approach leads to the unphysical prediction of dissociation time increasing with decreasing binding energy when the latter is lowered to values comparable to $k_{\mathrm{B}}T$ (limit of large thermal fluctuations). We propose a theoretical framework (fully supported by numerical simulations) which amends Kramers' theory in this limit and use it to extract the dissociation rate from single-molecule experiments where now predictions are physically meaningful and in agreement with simulations over the whole range of applied forces (binding energies). These results are expected to be relevant for a large number of experimental settings in single-molecule biophysics.

Ecological communities in heterogeneous environments assemble through the combined effect of species interaction and migration. Understanding the effect of these processes on the community properties is central to ecology. Here we study these processes for a single community subject to migration from a pool of species, with population dynamics described by the generalized Lotka-Volterra equations. We derive exact results for the phase diagram describing the dynamical behaviors, and for the diversity and species abundance distributions. A phase transition is found from a phase where a unique globally attractive fixed point exists to a phase where multiple dynamical attractors exist, leading to history-dependent community properties. The model is shown to possess a symmetry that also establishes a connection with other well-known models.

Minimization of the low-temperature elastic free-energy functional of orientationlly ordered membranes involves independent variation of the membrane-shape, while keeping the orientational order on it (its texture) fixed. We propose an operational, coordinate-independent method for implementing such a variation. Using the Nelson-Peliti formulation of elasticity that emphasizes the interplay between geometry, topology, and thermal fluctuations of orientationally ordered membranes, we minimize the elastic free energy to obtain equations governing their equilibrium shape, together with associated free boundary conditions. Our results are essential for understanding and predicting equilibrium shapes as well as textures of membranes and vesicles; particularly under conditions in which shape deformations are large.

We study the time evolution of the compaction of a noncohesive or cohesive granular material submitted to shaking through experiments and a stochastic model. Beyond well-known empirical expressions, we show that the characteristic time scales depend on the number of objects in the assembly. For a noncohesive granular material, the compaction time scale is governed by the number of individual grains in the system. In the case of a cohesive granular material, a two-scale model (individual particles and clusters) allows one to mimic the time evolution of the compaction of an actual cohesive powder driven by horizontal vibrations. In this case, the two time scales are associated with the numbers of clusters and grains, respectively.

The collective nonideal effects on the nuclear fusion reaction process are investigated in partially ionized classical nonideal hydrogen plasmas. The effective pseudopotential model taking into account the collective and plasma shielding effects is applied to describe the interaction potential in nonideal plasmas. The analytic expressions of the Sommerfeld parameter, the fusion penetration factor, and the cross section for the nuclear fusion reaction in nonideal plasmas are obtained as functions of the nonideality parameter, Debye length, and relative kinetic energy. It is found that the Sommerfeld parameter is suppressed due to the influence of collective nonideal shielding. However, the collective nonideal shielding is found to enhance the fusion penetration factor in partially ionized classical nonideal plasmas. It is also found that the fusion penetration factors in nonideal plasmas represented by the pseudopotential model are always greater than those in ideal plasmas represented by the Debye-Hückel model. In addition, it is shown that the collective nonideal shielding effect on the fusion penetration factor decreases with an increase of the kinetic energy.

In this study, alternative symmetry boundary implementations for the axisymmetric lattice Boltzmann (LB) method are proposed based on the nonequilibrium extrapolation and the direct extrapolation schemes. The proposed boundary schemes are directly implemented on the symmetry axis, and the postcollision distribution function and the macroscopic variables at the boundary nodes are extrapolated from the inner fluid nodes; thereby, the singularities arising at the symmetry axis $(r=0)$ during the collision and the macroscopic variable calculations are completely avoided. The accuracy of the present schemes is consistent with the well-established axisymmetric LB model. Moreover, in comparison with previous symmetry boundary schemes, the present implementations are slightly more accurate than the symmetry scheme by Guo et al. [Phys. Rev. E 79, 046708 (2009)] and numerically more stable than the specular reflection-based schemes.

The mathematical framework of multiplex networks has been increasingly realized as a more suitable framework for modeling real-world complex systems. In this work, we investigate the optimization of synchronizability in multiplex networks by evolving only one layer while keeping other layers fixed. Our main finding is to show the conditions under which the efficiency of convergence to the most optimal structure is almost as good as the case where both layers are rewired during an optimization process. In particular, interlayer coupling strength responsible for the integration between the layers turns out to be a crucial factor governing the efficiency of optimization even for the cases when the layer going through the evolution has nodes interacting much more weakly than those in the fixed layer. Additionally, we investigate the dependency of synchronizability on the rewiring probability which governs the network structure from a regular lattice to the random networks. The efficiency of the optimization process preceding evolution driven by the optimization process is maximum when the fixed layer has regular architecture, whereas the optimized network is more synchronizable for the fixed layer having the rewiring probability lying between the small-world transition and the random structure.

The principle of maximum entropy provides a useful method for inferring statistical mechanics models from observations in correlated systems, and is widely used in a variety of fields where accurate data are available. While the assumptions underlying maximum entropy are intuitive and appealing, its adequacy for describing complex empirical data has been little studied in comparison to alternative approaches. Here, data from the collective spiking activity of retinal neurons is reanalyzed. The accuracy of the maximum entropy distribution constrained by mean firing rates and pairwise correlations is compared to a random ensemble of distributions constrained by the same observables. For most of the tested networks, maximum entropy approximates the true distribution better than the typical or mean distribution from that ensemble. This advantage improves with population size, with groups as small as eight being almost always better described by maximum entropy. Failure of maximum entropy to outperform random models is found to be associated with strong correlations in the population.

Message passing equations yield a sharp percolation transition in finite graphs, as an artifact of the locally treelike approximation. For an arbitrary finite, connected, undirected graph we construct an infinite tree having the same local structural properties as this finite graph, when observed by a nonbacktracking walker. Formally excluding the boundary, this infinite tree is a generalization of the Bethe lattice. We indicate an infinite, locally treelike, random network whose local structure is exactly given by this infinite tree. Message passing equations for various cooperative models on this construction are the same as for the original finite graph, but here they provide the exact solutions of the corresponding cooperative problems. These solutions are good approximations to observables for the models on the original graph when it is sufficiently large and not strongly correlated. We show how to express these solutions in the critical region in terms of the principal eigenvector components of the nonbacktracking matrix. As representative examples we formulate the problems of the random and optimal destruction of a connected graph in terms of our construction, the nonbacktracking expansion. We analyze the limitations and the accuracy of the message passing algorithms for different classes of networks and compare the complexity of the message passing calculations to that of direct numerical simulations. Notably, in a range of important cases, simulations turn out to be more efficient computationally than the message passing.