In this article, in addition to the characterization of geometrical state spaces for the passive states, an operational approach has been introduced to distinguish them on their charging capabilities of a quantum battery. Unlike the thermal states, the structural instability of passive states assures the existence of a natural number $n$, for which $n+1$ copies of the state can charge a quantum battery while $n$ copies cannot. This phenomenon can be presented in an $n$ copy resource-theoretic approach, for which the free states are unable to charge the battery in $n$ copies. Here we have exhibited the single copy scenario explicitly. We also show that general ordering of the passive states on the basis of their charging capabilities is not possible and even the macroscopic entities (viz. energy and entropy) are unable to order them precisely. Interestingly, for some of the passive states, the majorization criterion gives sufficient order to the charging and discharging capabilities. However, the charging capacity for the set of thermal states (for which charging is possible) is directly proportional to their temperature.

Progress has been recently made, both theoretical and experimental, regarding the thermostatistics of complex systems of interacting particles or agents (species) obeying a nonlinear Fokker-Planck dynamics. However, major advances along these lines have been restricted to systems consisting of only one type of species. The aim of the present contribution is to overcome that limitation, going beyond single-species scenarios. We investigate the dynamics of overdamped motion in interacting and confined many-body systems having two or more species that experience different intra- and interspecific forces in a regime where forces arising from standard thermal noise can be neglected. Even though these forces are neglected, the behavior of the system can be analyzed in terms of an appropriate thermostatistical formalism. By recourse to a mean-field treatment, we derive a set of coupled nonlinear Fokker-Planck equations governing the behavior of these systems. We obtain an $H$ theorem for this Fokker-Planck dynamics and discuss in detail an example admitting an exact, analytical stationary solution.

This article presents a one-dimensional model of diffusion in a heterogeneous environment, which qualitatively reflects the transport properties of a polymeric membrane with carbon nanotube areas. We derive the Fokker-Planck equation from a system of stochastic equations using a diffusion regime in polymers and a ballistic diffusion regime in nanotube areas. We demonstrate how the probability density function changes in the presence of nanotubes. The mean-square displacement of nonlinear time dependence is observed, indicating anomalous diffusion. This model explains the mechanism of anomalous diffusion in a ballistic-diffusion regime. Our approach does not suppose any type of distribution and does not use a fractional differentiate apparatus.

Using our proposed approach to describe extreme matrices, we find an explicit exponentiation formula linking the classical extreme laws of Fréchet, Gumbel, and Weibull given by the Fisher-Tippet-Gnedenko classification and free extreme laws of free Fréchet, free Gumbel, and free Weibull of Ben Arous and Voiculescu. We also develop an extreme random matrix formalism, in which refined questions about extreme matrices can be answered. In particular, we demonstrate explicit calculations for several more or less known random matrix ensembles, providing examples of all three free extreme laws. Finally, we present an exact mapping, showing the equivalence of free extreme laws to the Peak-over-Threshold method in classical probability.

The thermodynamic definition of entropy can be extended to nonequilibrium systems based on its relation to information. To apply this definition in practice requires access to the physical system's microstates, which may be prohibitively inefficient to sample or difficult to obtain experimentally. It is beneficial, therefore, to relate the entropy to other integrated properties which are accessible out of equilibrium. We focus on the structure factor, which describes the spatial correlations of density fluctuations and can be directly measured by scattering. The information gained by a given structure factor regarding an otherwise unknown system provides an upper bound for the system's entropy. We find that the maximum-entropy model corresponds to an equilibrium system with an effective pair interaction. Approximate closed-form relations for the effective pair potential and the resulting entropy in terms of the structure factor are obtained. As examples, the relations are used to estimate the entropy of an exactly solvable model and two simulated systems out of equilibrium. The focus is on low-dimensional examples, where our method, as well as a recently proposed compression-based one, can be tested against a rigorous direct-sampling technique. The entropy inferred from the structure factor is found to be consistent with the other methods, superior for larger system sizes, and accurate in identifying global transitions. Our approach allows for extensions of the theory to more complex systems and to higher-order correlations.

We study stochastic radiation transport through random media in one dimension, in particular for pure absorbing cases. The statistical model to calculate the ensemble-averaged transmission for a binary random mixture is derived based on the cumulative probability density function (PDF) of optical depth, which is numerically simulated for both Markovian and non-Markovian mixtures by Monte Carlo calculations. We present systematic results about the influence of mixtures' stochasticity on the radiation transport. It is found that mixing statistics affects the ensemble-averaged intensities mainly due to the distribution of cumulative PDF at small optical depths, which explains well why the ensemble-averaged transmission is observed to be sensitive to chord length distribution and its variances. The effect of the particle size is substantial when the mixtures' correlation length is comparable to the mean free path of photons, which imprints a moderately broad transition region into the cumulative PDF. With the mixing probability increasing, the intensity decreases nearly exponentially, from which the mixing zone length can be approximately estimated. The impact of mixed configuration is also discussed, which is in line with previous results.

The Langevin description of systems with two symmetric absorbing states yields a phase diagram with three different phases (disordered and active, ordered and active, absorbing) separated by critical lines belonging to three different universality classes (generalized voter, Ising, and directed percolation). In this paper we present a microscopic spin model with two symmetric absorbing states that has the property that the model parameters can be varied in a continuous way. Our results, obtained through extensive numerical simulations, indicate that all features of the Langevin description are encountered for our two-dimensional microscopic spin model. Thus the Ising and direction percolation lines merge into a generalized voter critical line at a point in parameter space that is not identical to the classical voter model. A vast range of different quantities are used to determine the universality classes of the order-disorder and absorbing phase transitions. The investigation of time-dependent quantities at a critical point belonging to the generalized voter universality class reveals a more complicated picture than previously discussed in the literature.

We study the position distribution of a single active Brownian particle (ABP) on the plane. We show that this distribution has a compact support, the boundary of which is an expanding circle. We focus on a short-time regime and employ the optimal fluctuation method to study large deviations of the particle position coordinates $x$ and $y$. We determine the optimal paths of the ABP, conditioned on reaching specified values of $x$ and $y$, and the large deviation functions of the marginal distributions of $x$ and of $y$. These marginal distributions match continuously with “near tails” of the $x$ and $y$ distributions of typical fluctuations, studied earlier. We also calculate the large deviation function of the joint $x$ and $y$ distribution $P(x,y,t)$ in a vicinity of a special “zero-noise” point, and show that $lnP(x,y,t)$ has a nontrivial self-similar structure as a function of $x, y,$ and $t$. The joint distribution vanishes extremely fast at the expanding circle, exhibiting an essential singularity there. This singularity is inherited by the marginal $x$- and $y$-distributions. We argue that this fingerprint of the short-time dynamics remains there at all times.

Frustration can contribute to very slow relaxation times in large open chains, as in spin glasses and in biopolymers. However, frustration may not be sufficient to produce broken ergodicity in finite systems. Here we employ a system-plus-reservoir approach to investigate how strongly inhomogeneous environments and frustration compete in the relaxation of finite open chains. We find a sufficient condition for our inhomogeneous environments to break ergodicity. We use the microscopic model to derive a Markovian quantum master equation for a generic chain with ultrastrong intrachain couplings. We show that this microscopic model avoids a spurious broken ergodicity we find in the phenomenological model. We work out an explicit example of broken ergodicity due to the inhomogeneous environment of an unfrustrated spin chain as far as simulating a recent experiment on protein denaturation (where environment inhomogeneity is especially relevant). We finally show that an inhomogeneous environment can mitigate the effects of frustration-induced degeneracies.

Multiscale phenomena that evolve on multiple distinct timescales are prevalent throughout the sciences. It is often the case that the governing equations of the persistent and approximately periodic fast scales are prescribed, while the emergent slow scale evolution is unknown. Yet the course-grained, slow scale dynamics is often of greatest interest in practice. In this work we present an accurate and efficient method for extracting the slow timescale dynamics from signals exhibiting multiple timescales that are amenable to averaging. The method relies on tracking the signal at evenly spaced intervals with length given by the period of the fast timescale, which is discovered by using clustering techniques in conjunction with the dynamic mode decomposition. Sparse regression techniques are then used to discover a mapping which describes iterations from one data point to the next. We show that, for sufficiently disparate timescales, this discovered mapping can be used to discover the continuous-time slow dynamics, thus providing a novel tool for extracting dynamics on multiple timescales.

The various dynamics of many complex systems, both natural and artificial, can be well described as random walks on a newly proposed yet powerful object known as a complex network. Here we consider random walks on a special kind of network, a tree. Using the methods of probability-generating functions, we derive a close connection between the widely studied parameters mean first-passage time $\mathcal{M}$ and mean shortest path length $\mathcal{A}$ on trees. This suggests that for a given tree, the analytic value for $\mathcal{M}$ can be easily obtained by calculating the value $\mathcal{A}$ when determining the latter is more convenient and vice versa. As a result, the well-known $T$ graph is selected as one of various applications of our methods, and we then obtain an exact solution to its quantity $\mathcal{M}$. On the one hand, the result addressed here is in perfect agreement with previous ones. On the other hand, our method is easier to manipulate than most preexisting ones, for instance, methods from spectral graph theory, since no complicated techniques are involved.

We present a statistical mechanical model to describe the dynamics of an arbitrary cotransport system. Our starting point was the alternating access mechanism, which suggests the existence of six states for the cotransport cycle. Then we determined the 14 transition probabilities between these states, including a leak pathway, and used them to write a set of Master Equations for describing the time evolution of the system. The agreement between the asymptotic behavior of this set of equations and the result obtained from thermodynamics is a confirmation that leakage is compatible with the static head equilibrium condition and that our model has captured the essential physics of cotransport. In addition, the model correctly reproduced the transport dynamics found in the literature.

The diffuse-interface model (DIM) is a widely used tool for modeling fluid phenomena involving interfaces, such as sessile drops (liquid drops on a solid substrate, surrounded by saturated vapor) and liquid ridges (two-dimensional sessile drops). In this work, it is proved that, surprisingly, the DIM does not admit solutions describing static liquid ridges. If, however, the vapor-to-liquid density ratio is small—for example, for water at room temperature—the ridges can still be observed as quasistatic states, as their evolution is too slow to be distinguishable from evaporation. Interestingly, the nonexistence theorem cannot be extended to axisymmetric sessile drops and ridges near a vertical wall, which are not ruled out.

A polyatomic gas with slow relaxation of the internal modes is considered, and the Navier-Stokes equations with two temperatures, the translational and internal temperatures, are derived for such a gas on the basis of the ellipsoidal-statistical (ES) model of the Boltzmann equation for a polyatomic gas, proposed by Andries et al. [Eur. J. Mech. B, Fluids 19, 813 (2000)], by the Chapman-Enskog procedure. Then, the derived equations are applied to numerically investigate the structure of a plane shock wave in ${\mathrm{CO}}_2$ gas, which is known to have slowly relaxing internal modes. The results show good agreement with those obtained by the direct numerical analysis of the ES model for moderately strong shock waves. In particular, the results perfectly reproduce the double-layer structure of the shock profiles consisting of a thin front layer with rapid change and a thick rear layer with slow relaxation of the internal modes.

We experimentally study the viscous fingering problem of viscoplastic fluids in channels of rectangular cross section. We find that a yield stress-dependent capillary number (${\text{Ca}}^{*}$) and an aspect ratio-dependent Bond number (${\text{Bo}}^{*}$) can classify the finger shape into ramified and unified fingering patterns, and the finger flow regime into yield stress, viscosity, and aspect ratio-buoyancy-dominated regimes. For these regimes, we provide the transition boundaries using ${\text{Ca}}^{*}$ and ${\text{Bo}}^{*}$ and propose simple relations to predict the finger width, for a wide range of flow parameters, versus the capillary number, the channel aspect ratio, and the rheology of the viscoplastic fluid.

The properties of water in confinement are very different from those under bulk conditions. In some cases the melting point of ice may be shifted and one may find either ice, icelike water, or a state in which freezing is completely inhibited. Understanding the dynamics and rheology of water in confined media, such as small nanotubes, is of fundamental importance to the biological properties of micro-organisms at low temperatures, to the development of new devices for preserving DNA samples, and for other biological materials and fluids, lubrication, and development of nanostructured materials. We study rheology and dynamics of water in small nanotubes using extensive equilibrium and nonequilibrium molecular dynamics simulations. The results demonstrate that in strong confinement in nanotubes at temperatures significantly below and above bulk freezing temperature water behaves as a shear-thinning fluid at shear rates smaller than the inverse of the relaxation time in the confined medium. In addition, our results indicate the presence of regions in which the local density of water varies significantly over the same range of temperature in the nanotube. These findings may also have important implications for the design of nanofluidic systems.

We study conformal invariance of vorticity clusters in weakly compressible two-dimensional turbulence at low Mach numbers. On the basis of very high resolution direct numerical simulation we demonstrate the scaling invariance of the inverse cascade with scaling close to Kolmogorov prediction. In this range of scales, the statistics of zero-vorticity isolines are found to be compatible with those of critical percolation, thus generalizing the results obtained in incompressible Navier-Stokes turbulence.

We consider a quantum multicomponent plasma made with $\mathcal{S}$ species of point charged particles interacting via the Coulomb potential. We derive the screened activity series for the pressure in the grand-canonical ensemble within the Feynman-Kac path integral representation of the system in terms of a classical gas of loops. This series is useful for computing equations of state for it is nonperturbative with respect to the strength of the interaction and it involves relatively few diagrams at a given order. The known screened activity series for the particle densities can be recovered by differentiation. The particle densities satisfy local charge neutrality because of a Debye-dressing mechanism of the diagrams in these series. We introduce a new general neutralization prescription, based on this mechanism, for deriving approximate equations of state where consistency with electroneutrality is automatically ensured. This prescription is compared to other ones, including a neutralization scheme inspired by the Lieb-Lebowitz theorem and based on the introduction of $(\mathcal{S}-1)$ suitable independent combinations of the activities. Eventually, we briefly argue how the activity series for the pressure, combined with the Debye-dressing prescription, can be used for deriving approximate equations of state at moderate densities, which include the contributions of recombined entities made with three or more particles.

Accommodation coefficients (ACs) are the phenomenological parameters used to evaluate gas-wall interactions. The gas transport through a finite length nanochannel will confront the variation of properties along the length of the channel. A three-dimensional molecular dynamics simulation has been carried out to examine this streamwise inhomogeneity of flow characteristics in a nanochannel. The rarefaction of the flow to the downstream direction is a crucial behavior in a pressure-driven nanochannel flow. This is manifested as the variation in velocity and temperature along the length of the channel. Subsequently, the interactions between the gas and wall particles will get reduced considerably. Moreover, the characteristics near the wall are examined in detail. A nonhomogeneous behavior in density and velocity profile near the wall is reported. Further, the momentum accommodation coefficient (MAC) in both the tangential and normal directions is examined along the lengthwise sections of the channel. The results show a significant variation of tangential and normal MACs along the length. Further, three channels with different length-to-characteristic dimension ($L/H$) ratios are considered to investigate the effect of $L/H$ ratio. All three channels are subjected to the same pressure drop along the length. It is observed that the MACs and slip length show distinct behavior for different ($L/H$) ratios. The work establishes that the variation of MAC along the length of the channel has to be considered in modeling the nano- and microtransport systems.

A reversible diffusion process is initialized at position $x_0$ and run until it first hits any of several targets. What is the probability that it terminates at a particular target? We propose a computationally efficient approach for estimating this probability, focused on those situations in which it takes a long time to hit any target. In these cases, direct simulation of the hitting probabilities becomes prohibitively expensive. On the other hand, if the timescales are sufficiently long, then the system will essentially “forget” its initial condition before it encounters a target. In these cases the hitting probabilities can be accurately approximated using only local simulations around each target, obviating the need for direct simulations. In empirical tests, we find that these local estimates can be computed in the same time it would take to compute a single direct simulation, but that they achieve an accuracy that would require thousands of direct simulation runs.