It is well known that collisionless systems are dissipation free from the perspective of particle collision and thus conserve entropy. However, processes such as magnetic reconnection and turbulence appear to convert large-scale magnetic energy into heat. In this paper, we investigate the energization and heating of collisionless plasma. The dissipation process is discussed in terms of fluid entropy in both isotropic and gyrotropic forms. Evolution equations for the entropy are derived and they reveal mechanisms that lead to changes in fluid entropy. These equations are verified by a collisionless particle-in-cell simulation of multiple reconnecting current sheets. In addition to previous findings regarding the pressure tensor, we emphasize the role of heat flux in the dissipation process.

It is difficult to characterize by experiment the structural features of liquids and glasses which lack long-range translational periodicity in the structure. Here, we suggest that the height and shape of the first peak of the structure function $S\left(Q\right)$ carry significant information about the nature of the medium-range order and the coherence of density correlations. It is further proposed that they indicate how ideal the liquid structure is. Here, the ideal state is defined by long-range density correlations, not by structural coherence at the atomic level. The analysis is applied to the $S\left(Q\right)$ of metallic alloy liquids determined by x-ray diffraction and simulation. The ideality index defined here may provide a common parameter to characterize structural coherence among various disparate groups of liquids and glasses.

The inversion of the electron energy distribution function (EEDF) at low energies and the absolute negative electron conductivity are predicted and confirmed by numerical modeling of a direct current glow discharge in argon. It is shown that, in contrast to the local approximation used earlier for searching the inverse EEDF, in a real gas-discharge plasma, the formation of the EEDF is significantly affected by the terms with spatial gradients in the Boltzmann kinetic equation. In analogy with the inverse population of excited states in lasers, such a medium will amplify electromagnetic waves.

The fully connected Ising $p$-spin model has for $p>2$ a discontinuous phase transition from the paramagnetic phase to a stable state with one-step replica symmetry breaking (1RSB). However, simulations in three dimension do not look like these mean-field results and have features more like those which would arise with full replica symmetry breaking (FRSB). To help understand how this might come about we have studied in the fully connected $p$-spin model the state of two-step replica symmetry breaking (2RSB). It has a free energy degenerate with that of 1RSB, but the weight of the additional peak in $P\left(q\right)$ vanishes. We expect that the state with full replica symmetry breaking (FRSB) is also degenerate with that of 1RSB. We suggest that finite-size effects will give a nonvanishing weight to the FRSB features, as also will fluctuations about the mean-field solution. Our conclusion is that outside the fully connected model in the thermodynamic limit, FRSB is to be expected rather than 1RSB.

Recently, it was shown that certain systems with large time-varying delay exhibit different types of chaos, which are related to two types of time-varying delay: conservative and dissipative delays. The known high-dimensional turbulent chaos is characterized by strong fluctuations. In contrast, the recently discovered low-dimensional laminar chaos is characterized by nearly constant laminar phases with periodic durations and a chaotic variation of the intensity from phase to phase. In this paper we extend our results from our preceding publication [Hart, Roy, Müller-Bender, Otto, and Radons, Phys. Rev. Lett. 123, 154101 (2019)], where it is demonstrated that laminar chaos is a robust phenomenon, which can be observed in experimental systems. We provide a time series analysis toolbox for the detection of robust features of laminar chaos. We benchmark our toolbox by experimental time series and time series of a model system which is described by a nonlinear Langevin equation with time-varying delay. The benchmark is done for different noise strengths for both the experimental system and the model system, where laminar chaos can be detected, even if it is hard to distinguish from turbulent chaos by a visual analysis of the trajectory.

In the past few decades, the frequency of pandemics has been increased due to the growth of urbanization and mobility among countries. Since a disease spreading in one country could become a pandemic with a potential worldwide humanitarian and economic impact, it is important to develop models to estimate the probability of a worldwide pandemic. In this paper, we propose a model of disease spreading in a structural modular complex network (having communities) and study how the number of bridge nodes $n$ that connect communities affects disease spread. We find that our model can be described at a global scale as an infectious transmission process between communities with global infectious and recovery time distributions that depend on the internal structure of each community and $n$. We find that near the critical point as $n$ increases, the disease reaches most of the communities, but each community has only a small fraction of recovered nodes. In addition, we obtain that in the limit $n\to \infty$, the probability of a pandemic increases abruptly at the critical point. This scenario could make the decision on whether to launch a pandemic alert or not more difficult. Finally, we show that link percolation theory can be used at a global scale to estimate the probability of a pandemic since the global transmissibility between communities has a weak dependence on the global recovery time.

We present a full kinetic model of a hydrogel that undergoes phase separation during swelling and deswelling. The model accounts for the interfacial energy of coexisting phases, finite strain of the polymer network, and solvent transport across free boundaries. For the geometry of an initially dry layer bonded to a rigid substrate, the model predicts that forcing solvent into the gel at a fixed rate can induce a volume phase transition, which gives rise to coexisting phases with different degrees of swelling, in systems where this cannot occur in the free-swelling case. While a nonzero shear modulus assists in the propagation of the transition front separating these phases in the driven-swelling case, increasing it beyond a critical threshold suppresses its formation. Quenching a swollen hydrogel induces spinodal decomposition, which produces several highly localized, highly swollen phases which coarsen and are then ejected from free boundary. The wealth of dynamic scenarios of this system is discussed using phase-plane analysis and numerical solutions in a one-dimensional setting.

The equilibrium properties of a finite-length linear polymer chain confined in an infinite wedge composed of two perfectly reflecting hard walls meeting at a variable apex angle ($\alpha$) are presented. One end of the polymer is anchored a distance $y$ from the apex on the conical axis of symmetry, while the other end is free. We report here, the nonmonotonic behavior of $\theta$ temperature as a function of $y$ for a finite-length chain. Data collapse for different chain lengths indicates that such behavior will exist for all finite lengths. We delineate the origin of such nonmonotonic behavior, which may have potential applications in understanding the cellular process occurring in nanoconfined geometries.

Identifying protein-protein interactions is crucial for a systems-level understanding of the cell. Recently, algorithms based on inverse statistical physics, e.g., direct coupling analysis (DCA), have allowed to use evolutionarily related sequences to address two conceptually related inference tasks: finding pairs of interacting proteins and identifying pairs of residues which form contacts between interacting proteins. Here we address two underlying questions: How are the performances of both inference tasks related? How does performance depend on dataset size and the quality? To this end, we formalize both tasks using Ising models defined over stochastic block models, with individual blocks representing single proteins and interblock couplings protein-protein interactions; controlled synthetic sequence data are generated by Monte Carlo simulations. We show that DCA is able to address both inference tasks accurately when sufficiently large training sets of known interaction partners are available and that an iterative pairing algorithm allows to make predictions even without a training set. Noise in the training data deteriorates performance. In both tasks we find a quadratic scaling relating dataset quality and size that is consistent with noise adding in square-root fashion and signal adding linearly when increasing the dataset. This implies that it is generally good to incorporate more data even if their quality are imperfect, thereby shedding light on the empirically observed performance of DCA applied to natural protein sequences.

We consider two-dimensional turbulence in the presence of a condensate. The nondiagonal correlation functions of the Lagrangian accelerations are calculated, and it is shown that they have the same universality properties as the nondiagonal correlation functions of the velocity fluctuations.

We study, numerically, a system of active particles with either a single noise value or a mixture of equal proportions of particles with two noise values under the influence of an attractive periodic background potential, and we observe their diffusion regimes and trapping states. For the single noise system, we show that the slow diffusion is correlated to a significant particle trapping, while normal diffusion is seen for partial or no trapping. Our results indicate that low noise particles are less susceptible to the background, i.e., they have a smaller chance to be trapped as compared to higher noise particles for the same background, and that denser systems achieve a no-trapping state, unless for the largest noise value we studied. For the mixtures, we study the sorting of particles based on their noise value differences and observe that particles with distinct noises are trapped at distinct radii compared to a trap minimum, and, since these radii depend on the density, the latter should be well tuned in order to have an efficient sorting.

Emergence and collapse of coherent motions of self-propelled particles are affected more by particle motions and interactions than by their material or biological details. In the reconstructed systems of biofilaments and molecular motors, several types of collective motion including a global-order pattern emerge due to the alignment interaction. Meanwhile, earlier studies show that the alignment interaction of a binary collision of biofilaments is too weak to form the global order. The multiple collision is revealed to be important to achieve global order, but it is still unclear what kind of multifilament collision is actually involved. In this study, we demonstrate that not only alignment but also crossing of two filaments is essential to produce an effective multiple-particle interaction and the global order. We design the reconstructed system of biofilaments and molecular motors to vary a probability of the crossing of biofilaments on a collision and thus control the effect of volume exclusion. In this system, biofilaments glide along their polar strands on the turf of molecular motors and can align themselves nematically when they collide with each other. Our experiments show the counterintuitive result, in which the global order is achieved only when the crossing is allowed. When the crossing is prohibited, the cluster pattern emerges instead. We also investigate the numerical model in which we can change the strength of the volume exclusion effect and find that the global orientational order and clusters emerge with weak and strong volume exclusion effects, respectively. With those results and simple theory, we conclude that not only alignment but also finite crossing probability are necessary for the effective multiple-particles interaction forming the global order. Additionally, we describe the chiral symmetry breaking of a microtubule motion which causes a rotation of global alignment.

We apply time-dependent Ginzburg-Landau (TDGL) numerical simulations to study the finite frequency electrodynamics of superconductors subjected to an intense rf magnetic field. Much recent TDGL work has focused on spatially uniform external magnetic fields and largely ignores the Meissner state screening response of the superconductor. In this paper, we solve the TGDL equations for a spatially nonuniform magnetic field created by a point magnetic dipole in the vicinity of a semi-infinite superconductor. A two-domain simulation is performed to accurately capture the effect of the inhomogeneous applied fields and the resulting screening currents. The creation and dynamics of vortex semiloops penetrating deep into the superconductor domain are observed and studied, and the resulting third-harmonic nonlinear response of the sample is calculated. The effect of pointlike defects on vortex semi-loop behavior is also studied. This simulation method will assist our understanding of the limits of superconducting response to intense rf magnetic fields.

In this paper, we describe the unification and extension of multiple kinematic theories on the advection of colloidal particles through periodic obstacle lattices of arbitrary geometry and infinitesimally small obstacle size. We focus specifically on the particle displacement lateral to the flow direction (termed “deterministic lateral displacement”) and the particle-obstacle interaction frequency, and develop methods for describing these as a function of particle size and lattice parameters for arbitrary lattice geometries. We then demonstrate design algorithms for microfluidic devices consisting of chained obstacle lattices of this type that approximate any lateral displacement function of size to arbitrary accuracy with respect to multiple optimization metrics, prove their validity mathematically, and compare the generated results favorably to designs in the literature with respect to metrics such as accuracy, device size, and complexity.

Many real-world networks exhibit degree-degree correlations between nodes separated by more than one step. Such long-range degree correlations (LRDCs) can be fully described by one joint and four conditional probability distributions with respect to degrees of two randomly chosen nodes and shortest path distance between them. While LRDCs are induced by nearest-neighbor degree correlations (NNDCs) between adjacent nodes, some networks possess intrinsic LRDCs which cannot be generated by NNDCs. Here we develop a method to extract intrinsic LRDC in a correlated network by comparing the probability distributions for the given network with those for nearest-neighbor correlated random networks. We also demonstrate the utility of our method by applying it to several real-world networks.

A model of physiological age, accompanied by nonlinear diffusion in space, is studied analytically and numerically, and is shown to develop nonstationary traveling population waves. A window of intermediate growth rates is found where collective supercycles are formed from individual (stochastic) life cycles. Supercycle periods can be considerably different (larger or smaller) than the average longevities of contributing individuals, while the time-averaged spatial expansion rate has a local maximum in the supercycling mode. A method of adiabatic similarity solutions is used to derive dependencies of the solution parameters on source and sink inhomogeneities, and obtain closed coupled dynamic equations for the age structure and leading and trailing fronts. Analytical results are compared with numerically computed similarity and full solutions for several types of population waves. We discuss possible model applications to development of lichen thallus, multiyear patterns of agricultural crop yields, and autocorrelation of locust swarming.

Segmental dynamics is considered as a major factor governing ionic conductivity of polymerized ionic liquids (PILs), envisioned as potential electrolytes in fuel cells and batteries. Our dielectric studies performed in $T-P$ thermodynamic space on ionene, composed of the positively charged polymer backbone and freely moving anions, indicate that other relaxation modes, completely ignored so far, can affect the charge transport in PILs as well. We found that fast mobility manifested by a secondary β process promotes segmental dynamics and thereby increases ionic conductivity making the studied material a first coupled PIL of superionic properties. The molecular mechanism underlying such a β process has been identified as Johari-Goldstein relaxation giving experimental proof that fast secondary relaxations of intermolecular origin exist also in PILs and thereby reveal a universal character.

The effect of confinement on the behavior of liquid crystals is interesting from a fundamental and practical standpoint. In this work, we report Monte Carlo simulations of hard rods in an array of hard nanoposts, where the surface-to-surface separations between nanoposts are comparable to or less than the length of hard rods. This particular system shows promise as a means of generating large-scale organization of the nematic liquid by introducing an entropic external field set by the alignment of nanoposts. The simulations show that nematic ordering of hard rods is enhanced in the nanopost arrays compared with that in bulk, in the sense that the nematic order is significant even at low concentrations at which hard rods remain isotropic in bulk, and the enhancement becomes more significant as the passage width between two nearest nanoposts decreases. An analysis of local distribution of hard-rod orientations at low concentrations with weak nematic ordering reveals that hard rods are preferentially aligned along nanoposts in the narrowing regions between two curved surfaces of nearest nanoposts; hard rods are less ordered in the passages and in the centers of interpost spaces. It is concluded that at low concentrations the confinement in a dense array of nanoposts induces the localized nematic order first in the narrowing regions and, as the concentration further increases, the nematic order spreads over the whole region. The formation of a well-ordered phase at low concentrations of hard rods in a dense array of nanoposts can provide a new route to the low-concentration preparation of nematic liquid crystals that can be used as anisotropic dispersion media.

We use numerical simulations to study the flow of athermal, frictionless, soft-core two-dimensional spherocylinders driven by a uniform steady-state simple shear applied at a fixed volume and a fixed finite strain rate $\dot{\gamma }$. Energy dissipation is via a viscous drag with respect to a uniformly sheared host fluid, giving a simple model for flow in a non-Brownian suspension with Newtonian rheology. We study the resulting spatial structure of the sheared system, and compute correlation functions of the velocity, the particle density, the nematic order parameter, and the particle angular velocity. Correlations of density, nematic order, and angular velocity are shown to be short ranged both below and above jamming. We compare a system of size-bidisperse particles with a system of size-monodisperse particles, and argue how differences in spatial order as the packing increases lead to differences in the global nematic order parameter. We consider the effect of shearing on initially well ordered configurations, and show that in many cases the shearing acts to destroy the order, leading to the same steady-state ensemble as found when starting from random initial configurations.