We elaborate on the methodology to simulate bulk systems in the absence of time-reversal symmetry by the phase-fixed path-integral Monte Carlo method under (possibly twisted) periodic boundary conditions. Such systems include two-dimensional electrons in the quantum Hall regime and rotating ultracold Bose and Fermi gases; time-reversal symmetry is broken by an external magnetic field and the Coriolis force, respectively. We provide closed-form expressions in terms of Jacobi elliptic functions for the thermal density matrix (or the Euclidean propagator) of a single particle on a flat torus under very general conditions. We then modify the multislice sampling method in order to sample paths by the magnitude of the complex-valued thermal density matrix. Finally, we demonstrate that these inventions let us study the vortex melting process of a two-dimensional Yukawa gas in terms of the de Boer interaction strength parameter, temperature, and rotation (Coriolis force). The bosonic case is relevant to ultracold Fermi-Fermi mixtures of widely different masses under rotation.

The diffusion of molecules and clusters under nanoscale confinement or absorbed on surfaces is the key controlling factor in dynamical processes such as transport, chemical reaction, or filtration. Enhancing diffusion could benefit these processes by increasing their transport efficiency. Using a nonlinear Langevin equation with an extensive number of simulations, we find a large enhancement in diffusion through surface oscillation. For helium confined in a narrow carbon nanotube, the diffusion enhancement is estimated to be over three orders of magnitude. A synchronization mechanism between the kinetics of the particles and the oscillating surface is revealed. Interestingly, a highly nonlinear negative correlation between diffusion coefficient and temperature is predicted based on this mechanism, and further validated by simulations. Our results provide a general and efficient method for enhancing diffusion, especially at low temperatures.

Delay-coordinate mapping is an effective and widely used technique for reconstructing and analyzing the dynamics of a nonlinear system based on time-series outputs. The efficacy of delay-coordinate mapping has long been supported by Takens' embedding theorem, which guarantees that delay-coordinate maps use the time-series output to provide a reconstruction of the hidden state space that is a one-to-one embedding of the system's attractor. While this topological guarantee ensures that distinct points in the reconstruction correspond to distinct points in the original state space, it does not characterize the quality of this embedding or illuminate how the specific parameters affect the reconstruction. In this paper, we extend Takens' result by establishing conditions under which delay-coordinate mapping is guaranteed to provide a stable embedding of a system's attractor. Beyond only preserving the attractor topology, a stable embedding preserves the attractor geometry by ensuring that distances between points in the state space are approximately preserved. In particular, we find that delay-coordinate mapping stably embeds an attractor of a dynamical system if the stable rank of the system is large enough to be proportional to the dimension of the attractor. The stable rank reflects the relation between the sampling interval and the number of delays in delay-coordinate mapping. Our theoretical findings give guidance to choosing system parameters, echoing the tradeoff between irrelevancy and redundancy that has been heuristically investigated in the literature. Our initial result is stated for attractors that are smooth submanifolds of Euclidean space, with extensions provided for the case of strange attractors.

Flow patterns surrounding particles suspended near electrodes within an electrolyte solution can be induced with an electric field due to an electrohydrodynamic (EHD) force. Depending on electrolyte, particle, and field properties, a variety of particle packing and stability states have been observed in EHD flow. In this work, we report evidence of EHD flow-induced aggregation of 2-μm sulfonated latex beads in NaCl and NaOH electrolyte solutions, in inverted particle-electrode orientations. Experimental conditions were chosen to match previous work where aggregation was observed at a bottom electrode. Particles remain stable at the top electrode for times greater than 1 h, and aggregation behavior is quantified in terms of growth rate and particle packing density and order. Similar aggregation behavior is seen at both top and bottom electrodes, with aggregate growth occurring more quickly within NaCl solutions than NaOH solutions at both the bottom and top electrode. In addition, an observed secondary location of stability for the vertical position of particles in NaOH electrolyte at the bottom electrode is not seen at the top electrode. Comparing these metrics to predictions made by a scaling model for EHD flow, particle aggregation behavior is successfully predicted at both top and bottom electrodes, but some of the observed differences in aggregate packing are not. Thus we suggest that modifications to existing models or their interpretation may be needed to improve predictions of the behavior of such systems.

A model for describing growing length scale accompanying the vitrification is introduced, in which we assume that in a subsystem whose density is above a certain threshold value, ${\rho }_c$, due to steric constraints, particle rearrangements are highly suppressed for a sufficiently long time period ($\sim$structural relaxation time). We regard such a subsystem as a glassy cluster. With this assumption and without introducing any complicated thermodynamic arguments, we predict that with compression (increasing average density $\rho$) at a fixed temperature $T$ in supercooled states, the characteristic length of the clusters, $\xi$, diverges as $\xi \sim {({\rho }_c-\rho )}^{-2/d}$, where $d$ is the spatial dimensionality. This $\xi$ measures the average persistence length of the steric constraints in blocking the rearrangement motions and is determined by the subsystem density. Additionally, with decreasing $T$ at a fixed $\rho$, the length scale diverges in the same manner as $\xi \sim {(T-T_c)}^{-2/d}$, for which $\rho$ is identical to ${\rho }_c$ at $T=T_c$. The exponent describing the diverging length scale is the same as the one predicted by some theoretical models and indeed has been observed in some simulations and experiments. However, the basic mechanism for this divergence is different; that is, we do not invoke thermodynamic anomalies associated with the thermodynamic phase transition as the origin of the growing length scale. We further present arguements for the cooperative properties of the structural relaxation based on the clusters.

In their study, Duncan and Duncan [Proc. R. Soc. London A 383, 191 (1982)] calculate the shape of an inextensible surface folded in two about a general curve. They find the analytical relationships between pairs of generators linked across the fold curve, the shape of the original path, and the fold angle variation along it. They present two special cases of generator layouts for which the fold angle is uniform or the folded curve remains planar, for simplifying practical folding in sheet-metal processes. We verify their special cases by a graphical treatment according to a method of Gauss. We replace the fold curve by a piecewise linear path, which connects vertices of intersecting pairs of hinge lines. Inspired by the d-cone analysis by Farmer and Calladine [Int. J. Mech. Sci. 47, 509 (2005)], we construct the spherical images for developable folding of successive vertices: the operating conditions of the special cases in Duncan and Duncan are then revealed straightforwardly by the geometric relationships between the images. Our approach may be used to synthesize folding patterns for novel deployable and shape-changing surfaces without need of complex calculation.

This paper reports on numerical investigations of the effect of a slight inclination $\alpha$ on pattern formation in a shallow vertical cylindrical cell heated from below for binary mixtures with a positive value of the Soret coefficient. By using direct numerical simulation of the three-dimensional Boussinesq equations with Soret effect in cylindrical geometry, we show that a slight inclination of the cell in the range $\alpha \approx 0.036\mathrm{rad}=2^{\circ }$ strongly influences pattern selection. The large-scale shear flow (LSSF) induced by the small tilt of gravity overcomes the squarelike arrangements observed in noninclined cylinders in the Soret regime, stratifies the fluid along the direction of inclination, and produces an enhanced separation of the two components of the mixture. The competition between shear effects and horizontal and vertical buoyancy alters significantly the dynamics observed in noninclined convection. Additional unexpected time-dependent patterns coexist with the basic LSSF. We focus on an unsual periodic state recently discovered in an experiment, the so-called superhighway convection state (SHC), in which ascending and descending regions of fluid move in opposite directions. We provide numerical confirmation that Boussinesq Navier-Stokes equations with standard boundary conditions contain the essential ingredients that allow for the existence of such a state. Also, we obtain a persistent heteroclinic structure where regular oscillations between a SHC pattern and a state of nearly stationary longitudinal rolls take place. We characterize numerically these time-dependent patterns and investigate the dynamics around the threshold of convection.

This paper presents a numerical and theoretical study of multistability in two stochastic models of transitional wall flows. An algorithm dedicated to the computation of rare events is adapted on these two stochastic models. The main focus is placed on a stochastic partial differential equation model proposed by Barkley. Three types of events are computed in a systematic and reproducible manner: (i) the collapse of isolated puffs and domains initially containing their steady turbulent fraction; (ii) the puff splitting; (iii) the build-up of turbulence from the laminar base flow under a noise perturbation of vanishing variance. For build-up events, an extreme realization of the vanishing variance noise pushes the state from the laminar base flow to the most probable germ of turbulence which in turn develops into a full blown puff. For collapse events, the Reynolds number and length ranges of the two regimes of collapse of laminar-turbulent pipes, independent collapse or global collapse of puffs, is determined. The mean first passage time before each event is then systematically computed as a function of the Reynolds number $r$ and pipe length $L$ in the laminar-turbulent coexistence range of Reynolds number. In the case of isolated puffs, the faster-than-linear growth with Reynolds number of the logarithm of mean first passage time $T$ before collapse is separated in two. One finds that $ln\left(T\right)=A_{p}r-B_p$, with $A_p$ and $B_p$ positive. Moreover, $A_p$ and $B_p$ are affine in the spatial integral of turbulence intensity of the puff, with the same slope. In the case of pipes initially containing the steady turbulent fraction, the length $L$ and Reynolds number $r$ dependence of the mean first passage time $T$ before collapse is also separated. The author finds that $T\asymp exp\left[L\right(Ar-B\left)\right]$ with $A$ and $B$ positive. The length and Reynolds number dependence of $T$ are then discussed in view of the large deviations theoretical approaches of the study of mean first passage times and multistability, where $ln\left(T\right)$ in the limit of small variance noise is studied. Two points of view, local noise of small variance and large length, can be used to discuss the exponential dependence in $L$ of $T$. In particular, it is shown how a $T\asymp exp\left[L(A^{\prime }R-B^{\prime })\right]$ can be derived in a conceptual two degrees of freedom model of a transitional wall flow proposed by Dauchot and Manneville. This is done by identifying a quasipotential in low variance noise, large length limit. This pinpoints the physical effects controlling collapse and build-up trajectories and corresponding passage times with an emphasis on the saddle points between laminar and turbulent states. This analytical analysis also shows that these effects lead to the asymmetric probability density function of kinetic energy of turbulence.

This study investigated the typical performance of approximation algorithms known as belief propagation, the greedy algorithm, and linear-programming relaxation for maximum coverage problems in sparse biregular random graphs. After we used the cavity method for a corresponding hard-core lattice-gas model, results showed that two distinct thresholds of replica-symmetry and its breaking exist in the typical performance threshold of belief propagation. In the low-density region, the superiority of three algorithms in terms of a typical performance threshold is obtained by some theoretical analyses. Although the greedy algorithm and linear-programming relaxation have the same approximation ratio in worst-case performance, their typical performance thresholds are mutually different, indicating the importance of typical performance. Results of numerical simulations validate the theoretical analyses and imply further mutual relations of approximation algorithms.

We present a general classification of one-soliton solutions as well as families of rogue-wave solutions for $F=1$ spinor Bose-Einstein condensates (BECs). These solutions are obtained from the inverse scattering transform for a focusing matrix nonlinear Schrödinger equation which models condensates in the case of attractive mean-field interactions and ferromagnetic spin-exchange interactions. In particular, we show that when no background is present, all one-soliton solutions are reducible via unitary transformations to a combination of oppositely polarized solitonic solutions of single-component BECs. On the other hand, we show that when a nonzero background is present, not all matrix one-soliton solutions are reducible to a simple combination of scalar solutions. Finally, by taking suitable limits of all the solutions on a nonzero background we also obtain three families of rogue-wave (i.e., rational) solutions.

Percolation theory characterizing the robustness of a network has applications ranging from biology, to epidemic spreading, and complex infrastructures. Percolation theory, however, only concerns the average response of a network to random damage of its nodes while in real finite networks, fluctuations around this average behavior are observable. Consequently, for finite networks, there is an urgent need to evaluate the risk of collapse in response to rare configurations of the initial damage. Here, we build a large deviation theory of percolation characterizing the response of a sparse network to rare events. This general theory includes the second-order phase transition observed typically for random configurations of the initial damage, but reveals also discontinuous transitions corresponding to rare configurations of the initial damage for which the size of the giant component is suppressed.

We analyze an axisymmetric magnetohydrodynamics configuration, describing the morphology of a purely differentially rotating thin plasma disk, in which linear and nonlinear perturbations are triggered associated with microscopic magnetic structures. We study the evolution of the nonstationary correction in the limit in which the corotation condition (i.e., the dependence of the disk angular velocity on the magnetic flux function) is preserved and the poloidal velocity components are neglected. The main feature we address here is the influence of ideal (finite electron inertia) and collisional (resistivity, viscosity, and thermal conductivity) effects on the behavior of the flux function perturbation and of the associated small-scale modifications in the disk. We analyze two different regimes in which resistivity or viscosity dominates and study the corresponding linear and nonlinear behaviors of the perturbation evolution, i.e., when the backreaction magnetic field is negligible or comparable to the background one, respectively. We demonstrate that when resistivity dominates, a radial oscillating morphology (crystalline structure) emerges and it turns out to be damped in time, in both the linear and nonlinear regimes, but in such a way that the resulting transient can be implemented in the description of relevant astrophysical processes, for instance, associated with jet formation or cataclysmic variables. When the viscosity effect dominates the dynamics, only the nonlinear regime is available and a very fast instability is triggered.

We examine a new kind of lattice gas that closely resembles modern lattice Boltzmann methods. This new kind of lattice gas, which we call a Monte Carlo lattice gas, has interesting properties that shed light on the origin of the multirelaxation time collision operator, and it derives the equilibrium distribution for an entropic lattice Boltzmann. Furthermore these lattice gas methods have Galilean invariant fluctuations given by a Poisson statistics, giving further insight into the properties that we should expect for fluctuating lattice Boltzmann methods.

Explosive synchronization in networked second-order Kuramoto oscillators has been well studied recently and it is revealed that the synchronization process is featured by cluster explosive synchronization. However, little attention has been paid to the influence of noise or perturbation. We here study this problem and discuss the influences of noise and perturbation. For the former, we interestingly find that noise has significant influence on the cluster explosive synchronization of those nodes with smaller degrees, i.e., their synchronization will change from the first-order to second-order transition and the critical points for both the forward and backward synchronization depend on the strength of noise. Especially, when the strength of noise is in an optimal range, a synchronization of the nodes with smaller degrees will be induced in the region of coupling strength where they do not display synchronization in the absence of noise. For the latter, we find that the effect of perturbation is similar to that of noise when its duration $W$ is small. However, the perturbation will induce a change from cluster explosive synchronization to explosive synchronization when $W$ is large. Furthermore, a brief theory is provided to explain the influence of perturbations on the critical points.

In the social, behavioral, and economic sciences, it is important to predict which individual opinions eventually dominate in a large population, whether there will be a consensus, and how long it takes for a consensus to form. Such ideas have been studied heavily both in physics and in other disciplines, and the answers depend strongly both on how one models opinions and on the network structure on which opinions evolve. One model that was created to study consensus formation quantitatively is the Deffuant model, in which the opinion distribution of a population evolves via sequential random pairwise encounters. To consider heterogeneity of interactions in a population along with social influence, we study the Deffuant model on various network structures (deterministic synthetic networks, random synthetic networks, and social networks constructed from Facebook data). We numerically simulate the Deffuant model and conduct regression analyses to investigate the dependence of the time to reach steady states on various model parameters, including a confidence bound for opinion updates, the number of participating entities, and their willingness to compromise. We find that network structure and parameter values both have important effects on the convergence time and the number of steady-state opinion groups. For some network architectures, we observe that the relationship between the convergence time and model parameters undergoes a transition at a critical value of the confidence bound. For some networks, the steady-state opinion distribution also changes from consensus to multiple opinion groups at this critical value.

The general mechanisms behind self-organized criticality (SOC) are still unknown. Several microscopic and mean-field theory approaches have been suggested, but they do not explain the dependence of the exponents on the underlying network topology of the SOC system. Here, we first report the phenomena that in the Bak-Tang-Wiesenfeld (BTW) model, sites inside an avalanche area largely return to their original state after the passing of an avalanche, forming, effectively, critically arranged clusters of sites. Then, we hypothesize that SOC relies on the formation process of these clusters, and present a model of such formation. For low-dimensional networks, we show theoretically and in simulation that the exponent of the cluster-size distribution is proportional to the ratio of the fractal dimension of the cluster boundary and the dimensionality of the network. For the BTW model, in our simulations, the exponent of the avalanche-area distribution matched approximately our prediction based on this ratio for two-dimensional networks, but deviated for higher dimensions. We hypothesize a transition from cluster formation to the mean-field theory process with increasing dimensionality. This work sheds light onto the mechanisms behind SOC, particularly, the impact of the network topology.

In this paper, we systematically derive a fourth-order continuum theory capable of reproducing mesoscale turbulence in a three-dimensional suspension of microswimmers. We start from overdamped Langevin equations for a generic microscopic model (pushers or pullers), which include hydrodynamic interactions on both small length scales (polar alignment of neighboring swimmers) and large length scales, where the solvent flow interacts with the order parameter field. The flow field is determined via the Stokes equation supplemented by an ansatz for the stress tensor. In addition to hydrodynamic interactions, we allow for nematic pair interactions stemming from excluded-volume effects. The results here substantially extend and generalize earlier findings [S. Heidenreich et al., Phys. Rev. E 94, 020601 (2016)], in which we derived a two-dimensional hydrodynamic theory. From the corresponding mean-field Fokker-Planck equation combined with a self-consistent closure scheme, we derive nonlinear field equations for the polar and the nematic order parameter, involving gradient terms of up to fourth order. We find that the effective microswimmer dynamics depends on the coupling between solvent flow and orientational order. For very weak coupling corresponding to a high viscosity of the suspension, the dynamics of mesoscale turbulence can be described by a simplified model containing only an effective microswimmer velocity.

In this work, the frequency dependence of the known mechano-optical effect which occurs in the micellar isotropic phases ($I$) of mixtures of potassium laurate (KL), decanol (DeOH), and water is investigated in the range from $200\mathrm{mHz}$ to $200\mathrm{Hz}$. In order to fit the experimental data, a model of superimposed damped harmonic oscillators is proposed. In this phenomenological approach, the micelles (microscopic oscillators) interact very weakly with their neighbors. Due to shape anisotropy of the basic structures, each oscillator $i$ ($i=1,2,3,...,N$) remains in its natural oscillatory rotational movement around its axes of symmetry with a frequency ${\omega }_{0i}$. The system will be in the resonance state when the frequency of the driving force $\omega$ reaches a value near ${\omega }_{0i}$. This phenomenological approach shows excellent agreement with the experimental data. One can find $f\sim 2.5$, 9.0, and $4.0\mathrm{Hz}$ as fundamental frequencies of the micellar isotropic phases $I$, $I_1$, and $I_2$, respectively. The different micellar isotropic phases $I$, $I_1$, and $I_2$ that we find in the phase diagram of the KL-DeOH-water mixture are a consequence of possible differences in the intermicellar correlation lengths. This work reinforces the possibilities of technological applications of these phases in devices such as mechanical vibration sensors.

Power-law–type probability density functions spanning several orders of magnitude are found for different avalanche properties. We propose a methodology to overcome empirical constraints that limit the range of truncated power-law distributions. By considering catalogs of events that cover different observation windows, the maximum likelihood estimation of a global power-law exponent is computed. This methodology is applied to amplitude and energy distributions of acoustic emission avalanches in failure-under-compression experiments of a nanoporous silica glass, finding in some cases global exponents in an unprecedented broad range: 4.5 decades for amplitudes and 9.5 decades for energies. In the latter case, however, strict statistical analysis suggests experimental limitations might alter the power-law behavior.

Here we demonstrate that the dynamics of few-electron states in a correlated quantum-dot system coupled to an electronic reservoir is governed by the symmetry properties of the total system leading to the collective behavior of all the electrons. Time evolution of two-electron states in a correlated double quantum dot after coupling to the reservoir has been analyzed by means of kinetic equations for pseudoparticle occupation numbers with constraint on possible physical states. It was revealed that the absolute value of the spin correlation function and the degree of entanglement for two-electron states could considerably increase after coupling to the reservoir. The obtained results demonstrate the possibility of a controllable tuning of both the spin correlation function and the concurrence value in a coupled quantum-dot system by changing of the gate voltage applied to the barrier separating the dots.