Topological defects are increasingly being identified in various biological systems, where their characteristic flow fields and stress patterns are associated with continuous active stress generation by biological entities. Here, using numerical simulations of continuum fluctuating nematohydrodynamics, we show that even in the absence of any specific form of active stresses associated with self-propulsion, mesoscopic fluctuations in either orientational alignment or hydrodynamics can independently result in flow patterns around topological defects that resemble the ones observed in active systems. Our simulations further show the possibility of extensile- and contractile-like motion of fluctuation-induced positive half-integer topological defects. Remarkably, isotropic stress fields also reproduce the experimentally measured stress patterns around topological defects in epithelia. Our findings further reveal that extensile- or contractile-like flow and stress patterns around fluctuation-induced defects are governed by passive elastic stresses and flow-aligning behavior of the nematics.

Modeling the electrowetting process of a liquid droplet placed on a hydrophobic surface in an ambient environment has several challenges over and above those of basic spreading [F. Mugele, Soft Matter 5, 3377 (2009)]. At an external voltage below the value that causes contact angle saturation, transient spreading is augmented by contact angle reduction defined by the Young-Lippmann equation. In addition, the macroscopic equilibrium contact angle and, therefore, the spreading rate could be altered by the surface hysteresis. Beyond the saturation point, spreading reveals additional features of higher complexity [Q. Vo and T. Tran, J. Fluid Mech. 925, A19 (2021)]. These details have been examined from experiments as well as numerical simulation in the present work. Below the saturation point, the contact angle model of Dwivedi et al. [Phys. Rev. Fluids 7, 034002 (2022)] with the correction related to the electric field is seen to be applicable. Beyond saturation, the experimentally determined instantaneous contact angle distribution shows two distinct functionalities with respect to the contact line velocity. The first prevails from the onset of spreading until the spreading factor attains a peak value. The second trend is initiated with the retraction of the contact line. Except for differences in parametric values, the form of the contact angle model remains unchanged. Simulations in the postsaturation regime are shown to match experimental data in terms of the transient spreading factor, drop shapes, and the instantaneous contact angle. The role of the ground wire is found to be important and the three-phase contact line formed on it has been included in simulations. Spreading dynamics of the droplet have also been studied when the ground wire is kept at a distance of 40 μm from the apex of the drop. Simulations as well as experiments, show the propagation of a capillary wave between the ground wire and the three-phase contact line. For spreading over an uncoated polydimethylsiloxane (PDMS) surface, the contact line is trapped at local pinning sites, leading to additional distortions in the instantaneous shapes acquired by the interface.

We provide comprehensive data on the spectral and temporal characteristics of low-frequency (LF) (MHz) and high-frequency (HF) (GHz) radio emissions and investigate their correlation with the streamer formation. We show that the propagation of streamers from the cathode is accompanied only by the LF radio emission (10–150 MHz). In contrast, the HF radio emission (1–4 GHz) arises during the travel of counterstreamers from the anode, which is also indicated by radio interferometric measurements. The power of the LF radio emission sharply increases almost synchronously with that of the HF radio emission. We find that the HF radio emission has a complex spectral and temporal structure and appears as multiple short (less than 1 ns) bursts characterized by various frequency components, existing in subnanosecond time intervals.

Reservoir computing is a machine learning paradigm that uses a high-dimensional dynamical system, or $reservoir$, to approximate and predict time series data. The scale, speed, and power usage of reservoir computers could be enhanced by constructing reservoirs out of electronic circuits, and several experimental studies have demonstrated promise in this direction. However, designing quality reservoirs requires a precise understanding of how such circuits process and store information. We analyze the feasibility and optimal design of electronic reservoirs that include both linear elements (resistors, inductors, and capacitors) and nonlinear memory elements called memristors. We provide analytic results regarding the feasibility of these reservoirs and give a systematic characterization of their computational properties by examining the types of input-output relationships that they can approximate. This allows us to design reservoirs with optimal properties. By introducing measures of the total linear and nonlinear computational capacities of the reservoir, we are able to design electronic circuits whose total computational capacity scales extensively with the system size. Our electronic reservoirs can match or exceed the performance of conventional “echo state network” reservoirs in a form that may be directly implemented in hardware.

We obtain exact closed-form expressions for the partition function of the one-dimensional Ising model in the fixed-$M$ ensemble, for three commonly used boundary conditions: periodic, antiperiodic, and Dirichlet. These expressions allow for the determination of fluctuation-induced forces in the canonical ensemble, which we term Helmholtz forces. The thermodynamic expressions and the calculations flowing from them should provide insights into the nature and behavior of fluctuation-induced forces in interesting and as-yet unexplored regimes.

We study the interplay of chaos and tunneling between two weakly coupled Bose-Josephson junctions. The classical phase space of the composite system has a mixed structure including quasi-integrable self-trapping islands for particles and excitations, separated by a chaotic sea. We show that the many-body dynamical tunneling gap between macroscopic Schrödinger cat states supported by these islands is chaos-enhanced. The many-body tunneling rate fluctuates over several orders of magnitude with small variations of the system parameters or the particle number.

Antiferromagnetic skyrmions are considered to be promising information carriers due to their attractive properties. Therefore, the pinning phenomenon of antiferromagnetic skyrmions is of great significance. With the Landau-Lifshitz-Gilbert equation, we simulate the nonstationary dynamic behaviors of skyrmions driven by currents in a chiral antiferromagnetic thin film with quenched disorder. Based on the dynamic scaling forms, the critical current and static and dynamic critical exponents of the depinning phase transition are accurately determined. A theoretical analysis using Thiele's approach is presented in comparison with the numerical simulation. Unlike the ferromagnetic skyrmions, the critical current of the antiferromagnetic skyrmions is very sensitive to a small nonadiabatic coefficient. This is important for manipulating antiferromagnetic skyrmions and designing novel information processing devices.

Considering the higher-order nonlinearity is essential in a broad range of real physical media as it significantly influences the wave dynamics in these systems. We study the propagation of femtosecond light pulses inside an optical fiber medium exhibiting higher-order dispersion and cubic-quintic nonlinearities. Pulse evolution in such a system is governed by a higher-order nonlinear Schrödinger equation incorporating second-, third-, and fourth-order dispersions as well as cubic and quintic nonlinearities. The periodic and solitary wave solutions are identified using the equation method. Results presented indicated the potentially rich set of periodic waves in the system under the combined influence of higher-order dispersive effects and cubic-quintic nonlinearity. The velocity of these structures is uniquely dependent on all orders of dispersion. Conditions on the optical fiber parameters for the existence of these exact stable solutions are found by analytical stability analysis.

Intracellular pathogens, including all viruses and many bacteria, enter a host cell through either passive endocytosis or active self-propulsion. Though the cellular uptake of passive particles via endocytic process has been studied extensively, little work has been done on the active entry of self-propelled pathogens, such as Listeria monocytogenes. Here, we present a theoretical model to investigate the adhesive wrapping of a self-propelled particle by a plasma membrane, and find a type of first-order wrapping transition from a small partial wrapping state to a large partial wrapping state triggered by the active force. The phase diagram displays more complex behaviors compared with the passive wrapping mediated merely by adhesion. We also find that a tubular protrusion can be formed if the active force exceeds a force barrier. These results may provide a useful guidance to the study of activity-driven cellular entry of active particles into cells.

We examine the long-term evolution of a random wind wave field generated by constant forcing, by comparing numerical simulations of the kinetic equation and direct numerical simulations (DNS) of the dynamical equations. While the integral characteristics of the spectra are in reasonably good agreement, the spectral shapes differ considerably at large times, the DNS spectral shape being in much better agreement with field observations. Varying the number of resonant and approximately resonant wave interactions in the DNS numerical scheme, we show that when the ratio of nonlinear and linear parts of the Hamiltonian tends to zero, the DNS spectral shape approaches the shape predicted by the kinetic equation. We attribute the discrepancies between the kinetic equation modeling, on one side, and the DNS and observations, on the other, to the neglect of non-Gaussianity in the derivation of the kinetic equation.

Recent studies have explored finite-time dissipation-minimizing protocols for stochastic thermodynamic systems driven arbitrarily far from equilibrium, when granted full external control to drive the system. However, in both simulation and experimental contexts, systems often may only be controlled with a limited set of degrees of freedom. Here, going beyond slow- and fast-driving approximations employed in previous studies, we obtain exact finite-time optimal protocols for this limited-control setting. By working with deterministic Fokker-Planck probability density time evolution, we can frame the work-minimizing protocol problem in the standard form of an optimal control theory problem. We demonstrate that finding the exact optimal protocol is equivalent to solving a system of Hamiltonian partial differential equations, which in many cases admit efficiently calculable numerical solutions. Within this framework, we reproduce analytical results for the optimal control of harmonic potentials and numerically devise optimal protocols for two anharmonic examples: varying the stiffness of a quartic potential and linearly biasing a double-well potential. We confirm that these optimal protocols outperform other protocols produced through previous methods, in some cases by a substantial amount. We find that for the linearly biased double-well problem, the mean position under the optimal protocol travels at a near-constant velocity. Surprisingly, for a certain timescale and barrier height regime, the optimal protocol is also nonmonotonic in time.

Elementary cellular automata provide one of the simplest ways to generally describe the phenomena of pattern formation. However, they are considered too simple to be able to describe in detail the more complex phenomena occurring in real experimental systems. In this article, we demonstrate the an application of these methods to optical systems, providing an understanding of the mechanisms behind the formation of periodic patterns in nanoparticle-doped liquid crystals. Our extremely simplified model also explains the observed linear relationship between periodicity and system size.

We introduce a short-range repulsive force model to tackle near-contact interactions when the collision occurs between bubbles. In contrast to the previous numerical method adopting the adaptive mesh refinement technique, such a mesoscale model can be applied to a relatively coarse mesh, which can prevent the nonphysical coalescence between bubbles without excessive mesh refinement. We assume that the repulsive force is inversely proportional to the third power of distance, as a reasonable approximation to the short-range phase interactions. The model is validated against two different experiments. In both experiments, two identical bubbles rising side by side were considered. First, the experiment performed in a water-glycerol mixture helps determine the model parameter $K$. Second, three typical combinations of bubble size and initial separation distance are simulated, presenting different types of interactions, i.e., coalescence, bouncing coalescence, and bouncing separation, agreeing well with the second experiment, which was performed in pure water. Owing to its simplicity, this model can be easily implemented into existing codes, and it can be extended to the case with multiple bubbles or droplets.

A correlation between density maximization and size segregation for packings of polydisperse particles in geometric confinement has been discovered, through the derivation of a general solution for the densest-packed zigzag arrangements of polydisperse particles. This solution is a size-graded structure in which the larger a particle the closer it is located to either end of the system, such that the smaller particles in the interior are encapsulated by the larger ones away from it. Any pair of different-sized adjacent particles is a grain-boundary-like configuration that reduces the overall packing efficiency of the system, and this solution corresponds to a minimization of excess-volume contributions from grain-boundary-like configurations of different-sized particles as a result of the clustering of equal- or like-sized particles. Our findings provide new insights into how structural order of polydisperse particles emerges in confined settings.

We construct an information-geometric structure for chemical thermodynamics, applicable to a wide range of chemical reaction systems including nonideal and open systems. For this purpose, we explicitly construct dual affine coordinate systems, which completely designate an information-geometric structure, using the extent of reactions and the affinities of reactions as coordinates on a linearly constrained space of amounts of substances. The resulting structure induces a metric and a divergence (a function of two distributions of amounts), both expressed with chemical potentials. These quantities have been partially known for ideal-dilute solutions, but their extensions for nonideal solutions and the complete underlying structure are novel. The constructed geometry is a generalization of dual affine coordinates for stochastic thermodynamics. For example, the metric and the divergence are generalizations of the Fisher information and the Kullback–Leibler divergence. As an application, we identify the chemical-thermodynamic analog of the Hatano–Sasa excess entropy production using our divergence.

Despite their formal simplicity, most lattice spin models cannot be easily solved, even under the simplifying assumptions of mean field theory. In this paper, we present a method for generating mean field solutions to classical continuous spins. We focus our attention on systems with nonlocal interactions and nonperiodic boundaries, which require careful handling with existing approaches, such as Monte Carlo sampling. Our approach utilizes functional optimization to derive a closed-form optimality condition and arrive at self-consistent mean field equations. We show that this approach significantly outperforms conventional Monte Carlo sampling in convergence speed and accuracy. To convey the general concept behind the approach, we first demonstrate its application to a simple system: a finite one-dimensional dipolar chain in an external electric field. We then describe how the approach naturally extends to more complicated spin systems and to continuum field theories. Furthermore, we numerically illustrate the efficacy of our approach by highlighting its utility on nonperiodic spin models of various dimensionality.

We introduce a generalized Langevin model system for different non-Markovian effects in the well and barrier regions of a potential, and use it to numerically study the barrier-crossing time. In the appropriate limits, our model interpolates between the theoretical barrier-crossing-time predictions by Grote and Hynes (GH), as well as by Pollak et al., which for a single barrier memory time can differ by several orders of magnitude. Our model furthermore allows one to test an analytic rate theory for space-inhomogeneous memory, which disagrees with our numerical results in the long well-memory regime. In this regime, we find that short barrier memory decreases the barrier-crossing time as compared to long barrier memory. This is in contrast with the short well-memory regime, where both our numerical results and the GH theory predict an acceleration of the barrier crossing time with increasing barrier memory time. Both effects, the “Markovian-barrier acceleration” and GH “non-Markovian-barrier acceleration,” can be understood from a committor analysis. Our model combines finite relaxation times of orthogonal degrees of freedom with a space-inhomogeneous coupling to such degrees and represents a step towards more realistic modeling of reaction coordinates.