In this work, a mixed bounce-back boundary scheme of general propagation lattice Boltzmann (GPLB) model is proposed for isotropic advection-diffusion equations (ADEs) with Robin boundary condition, and a detailed asymptotic analysis is also conducted to show that the present boundary scheme for the straight walls has a second-order accuracy in space. In addition, several numerical examples, including the Helmholtz equation in a square domain, the diffusion equation with sinusoidal concentration gradient, one-dimensional transient ADE with Robin boundary and an ADE with a source term, are also considered. The results indicate that the numerical solutions agree well with the analytical ones, and the convergence rate is close to 2.0. Furthermore, through adjusting the two parameters in the GPLB model properly, the present boundary scheme can be more accurate than some existing lattice Boltzmann boundary schemes.

For the simulation of disruptions in tokamak fusion plasmas, a fluid model describing the evolution of relativistic runaway electrons and their interaction with the background plasma is presented. The overall aim of the model is to self-consistently describe the nonlinear coupled evolution of runaway electrons (REs) and plasma instabilities during disruptions. In this model, the runaway electrons are considered as a separate fluid species in which the initial seed is generated through the Dreicer source, which eventually grows by the avalanche mechanism (further relevant source mechanisms can easily be added). Advection of the runaway electrons is considered primarily along field lines, but also taking into account the $\mathit{E}\times \mathit{B}$ drift. The model is implemented in the nonlinear magnetohydrodynamic (MHD) code jorek based on Bezier finite elements, with current coupling to the thermal plasma. Benchmarking of the code with the one-dimensional runaway electron code go is done using an artificial thermal quench on a circular plasma. As a first demonstration, the code is applied to the problem of an axisymmetric cold vertical displacement event in an ITER plasma, revealing significantly different dynamics between cases computed with and without runaway electrons. Though it is not yet feasible to achieve fully realistic runaway electron velocities close to the speed of light in complete simulations of slowly evolving plasma instabilities, the code is demonstrated to be suitable to study various kinds of MHD-RE interactions in MHD-active and disruption relevant plasmas.

Electrospraying in the cone-jet mode has being regarded an isothermal process, and the laws obtained under this assumption are considered valid throughout the operational range. However, self-heating due to dissipation is expected to be significant at sufficiently high electrical conductivities. This is confirmed by solving the isothermal leaky-dielectric model to evaluate the Ohmic and viscous dissipation, and estimate the temperature increase. Self-heating is important at conductivities $\gtrsim 0.05\mathrm{S}/\mathrm{m}$, increases rapidly at larger values, and requires the nonisothermal modeling of this regime.

In cases in which an original image is blind, a decoding method where both the image and the messages can be estimated simultaneously is desirable. We propose a spread spectrum watermarking model with image restoration based on Bayes estimation. We therefore need to assume some prior probabilities. The probability for estimating the messages is given by the uniform distribution, and the ones for the image are given by the infinite-range model and two-dimensional (2D) Ising model. Any attacks from unauthorized users can be represented by channel models. We can obtain the estimated messages and image by maximizing the posterior probability. We analyzed the performance of the proposed method by the replica method in the case of the infinite-range model. We first calculated the theoretical values of the bit error rate from obtained saddle-point equations and then verified them by computer simulations. For this purpose, we assumed that the image is binary and is generated from a given prior probability. We also assume that attacks can be represented by the Gaussian channel. The computer simulation retults agreed with the theoretical values. In the case of prior probability given by the 2D Ising model, in which each pixel is statically connected with four-neighbors, we evaluated the decoding performance by computer simulations, since the replica theory could not be applied. Results using the 2D Ising model showed that the proposed method with image restoration is as effective as the infinite-range model for decoding messages. We compared the performances in a case in which the image was blind and one in which it was informed. The difference between these cases was small as long as the embedding and attack rates were small. This demonstrates that the proposed method with simultaneous estimation is effective as a watermarking decoder.

We reconsider the tricritical Blume-Capel model on the square lattice with a magnetic field acting on the open boundaries in one direction. Periodic boundary conditions are applied in the other direction. We apply three types of Monte Carlo algorithms, local Metropolis updates, and cluster algorithms of the Wolff and geometric type, adapted to the symmetry properties of the model. Statistical analyses of the bulk magnetization, the bulk Binder ratio, the edge magnetization, and the connected product of the edge and bulk magnetizations lead to new results confirming the presence of a singular edge transition at $H_{\mathrm{sc}}\approx 0.68$, as we reported earlier [Phys. Rev. E 71, 026109 (2005)]. We provide a plausible answer concerning a discrepancy between the behavior of the edge Binder ratio reported in that work and our new results.

Internal feedback is commonly present in biological populations and can play a crucial role in the emergence of collective behavior. To describe the temporal evolution of the distribution of a single-species population, we consider a generalization of the Fisher-KPP equation. This equation includes the elementary processes of random motion, reproduction, and, importantly, nonlocal interspecific competition, which introduces a spatial scale of interaction. In addition, we take into account feedback mechanisms in diffusion and growth processes, mimicked by power-law density dependencies. This feedback includes, for instance, anomalous diffusion, reaction to overcrowding or to the rarefaction of the population, as well as Allee-like effects. We show that, depending on the kind of feedback that takes place, the population can self-organize splitting into disconnected subpopulations, in the absence of external constraints. Through extensive numerical simulations, we investigate the temporal evolution and the characteristics of the stationary population distribution in the one-dimensional case. We discuss the crucial role that density-dependence has on pattern formation, particularly on fragmentation, which can bring important consequences to processes such as epidemic spread and speciation.

The paper presents a detailed analysis of the effects of chiral doping on antiferroelectric $B_2$ subphases in bent core systems. The studied system exhibits the phase sequence $\text{Sm}A\leftrightarrow \text{Sm}A{P}_A\leftrightarrow \text{Sm}{C}_S{P}_A$. The chiral dopant induces the tilt in the $\text{Sm}A{P}_A$ phase or the tilt modulation in the $\text{Sm}{C}_S{P}_A$ phase, which is known as the chiroclinic effect, but it also influences the stability ranges and the order of the transition between the phases. The order of the phase transition changes from a continuous for weak effects of chiral doping to discontinuous for strong effects. Competition of interactions results in changes of the phase sequences, which are resumed in the phase diagram. The method that leads to these findings, which could be used for the analysis of other similar systems, is briefly presented as well.

Almost all available results in elasticity on curved topographies are obtained within either a small curvature expansion or an empirical covariant generalization that accounts for screening between Gaussian curvature and disclinations. In this paper, we present a formulation of elasticity theory in curved geometries that unifies its underlying geometric and topological content with the theory of defects. The two different linear approximations widely used in the literature are shown to arise as systematic expansions in reference and actual space. Taking the concrete example of a two-dimensional crystal, with and without a central disclination, constrained on a spherical cap, we compare the exact results with different approximations and evaluate their range of validity. We conclude with some general discussion about the universality of nonlinear elasticity.

Numerical investigations are presented for an ionic liquid meniscus undergoing evaporation of ions in a regime of high electric field. A detailed model is developed to simulate the behavior of a stationary meniscus attached to a liquid feed system. The latter serves as a proxy for commonly utilized electrospray emitters such as needles and capillary tubes. Two solution families are identified for prototype liquid analogous to the ionic liquid 1-ethyl-3-methylimidazolium tetrafluoroborate (EMI-BF4). The first belongs to a regime of low electric field in which the meniscus is blunt and does not emit charge. The second belongs to a regime of high electric field in which a conelike meniscus produces charge from a sharp tip. Electrohydrodynamic features of the meniscus in this regime are presented. These reveal that the meniscus is Stokesian and hydrostatic and governed by conduction. The applied electric field influences both the shape of the meniscus and the current that it produces while the impedance of the feed system—which must be above a threshold value in order to ensure that the current, and therefore the flow, remains below a maximum value—influences the meniscus current but not the macroscopic shape. In general, this shape deviates from Taylor's idealized cone.

In the present study, we use the dynamic mesh method based on the radial basis function interpolation for the two-dimensional simulation of harmonically oscillating NACA0015 airfoil. Under various flapping frequencies, heaving and pitching amplitudes, the observed wake flows can be divided into seven types, including the Bénard-von Kármán (BvK) vortex street, the reversed BvK (RBvK) vortex street, the $1P$ wake, the $mP$ wake, the $2P+mS$ wake, the $2S+mS$ wake, and the $mS$ wake, where $m$ is around 4 and $xS+yP$ signifies $x$ single vortices and $y$ vortex pairs shedding per oscillation period. Then we have constructed two phase diagrams of the wake types in terms of the flapping frequency, heaving and pitching amplitudes. Importantly, we have combined the propulsion performance of the flapping airfoil with the wake map and found that $\alpha \left(\frac{T}{4}\right)$, the angle of attack at $t=T/4$, can determine the wake type: negative value corresponding to drag-dominated wakes, while positive value corresponding to thrust dominated flow wakes. With the increase of $\alpha \left(\frac{T}{4}\right)$, the wake transforms from the $mP$ to $2S+mS$ then to RBvK and eventually to $1P$ wake. Furthermore, the coherent structure analysis and spectral analysis are conducted for all the types of wakes by using dynamic mode decomposition. And there is a positive correlation between the strengths of vortices shedding at $i$ times flapping frequency and the modulus of the $i\text{th}$ dynamic mode decomposition mode, which can further reveal the differences among different types of wakes.

Many physical systems are described by probability distributions that evolve in both time and space. Modeling these systems is often challenging due to their large state space and analytically intractable or computationally expensive dynamics. To address these problems, we study a machine-learning approach to model reduction based on the Boltzmann machine. Given the form of the reduced model Boltzmann distribution, we introduce an autonomous differential equation system for the interactions appearing in the energy function. The reduced model can treat systems in continuous space (described by continuous random variables), for which we formulate a variational learning problem using the adjoint method to determine the right-hand sides of the differential equations. This approach can be used to enforce a reduced physical model by a suitable parametrization of the differential equations. The parametrization we employ uses the basis functions from finite-element methods, which can be used to model any physical system. One application domain for such physics-informed learning algorithms is to modeling reaction-diffusion systems. We study a lattice version of the Rössler chaotic oscillator, which illustrates the accuracy of the moment closure approximation made by the method and its dimensionality reduction power.

We examine a two-dimensional nonequilibrium lattice model where particles adsorb at empty sites and desorb when the number of neighboring particles is greater than a given threshold. In a certain range of parameters the model exhibits entropic ordering similar to some hard-core systems. However, contrary to hard-core systems, on increasing the density of particles the ordering is destroyed. In the heterogenous version of our model, a regime with slow dynamics appears that might indicate formation of some kind of glassy structures.

The possibility of a controlled manipulation with molecules at the nanoscale allows us to gain net work from thermal energy, although this seems to be in contradiction to the second law of thermodynamics. Any manipulation, however, causes some memory records somewhere in the system's surroundings. To complete the thermodynamic cycle, these records must be reset, which costs energy that cancels the previous gain. An important memory record may also be the final state of the work reservoir. This memory record is not reset. Nevertheless, it is rewritten and the associated memory erased whenever the state of the work reservoir is changed during the cycle repeating. The question is, what is the cost of this memory erasure. To answer it, we study a hypothetical cycle in which all memory records are reset except the state of the work reservoir alone, and the ensemble average of the work extracted from an equilibrium heat reservoir during this cycle, $\langle W\rangle$, is positive. It is shown that a strong information coupling of the system and the work reservoir then influences the system's dynamics so much that the cycle repeat may exhibit quite different behavior. Especially, it can run reversely and decrease energy in the work reservoir. It implies that even if the memory erasure is a natural part of the process, it costs energy in accord with information thermodynamics. At the nanoscale, this energy cost may manifest as a process obeying the fluctuation theorem.

The exchange of energy between a classical open system and its environment can be analyzed for a single run of an experiment using the phase-space trajectory of the system. By contrast, in the quantum regime such energy exchange processes must be defined for an ensemble of runs of the same experiment based on the reduced system density matrix. Single-shot approaches based on stochastic wave functions have been proposed for quantum systems that are continuously monitored or weakly coupled to a heat bath. However, for systems strongly coupled to the environment and not continuously monitored, a single-shot analysis has not been attempted because no system wave function exists for such systems within the standard formulation of quantum theory. Using the notion of the conditional wave function of a quantum system, we derive here an exact formula for the rate of total energy change in an open quantum system, valid for arbitrary coupling between the system and the environment. In particular, this allows us to identify three distinct contributions to the total energy flow: an external contribution coming from the explicit time dependence of the Hamiltonian, an interaction contribution associated with the interaction part of the Hamiltonian, and an entanglement contribution, directly related to the presence of entanglement between the system and its environment. Given the close connection between weak values and the conditional wave function, the approach presented here provides a new avenue for experimental studies of energy fluctuations in open quantum systems.

We have found “bright and dark” solutions of the Gardner equation which can model internal rogue waves in three-layer fluids. We provide the first four “bright” and “dark” exact rational solutions to the Gardner equation. These are the lowest-order solutions of the corresponding hierarchies of rogue-wave solutions of this equation. They have been obtained from the rogue-wave solutions of a modified Korteweg-de Vries equation by using a Lorentz-type transformation. The maximal (and minimal) amplitudes and the background levels of these solutions for arbitrary order are deduced, based on the lowest-order examples. These solutions can be useful for explanations of extremely large amplitude internal waves in the ocean, as well as for abnormally large-amplitude waves in other areas of nonlinear physics, such as optics and dusty plasmas.

We introduce a model to study the interplay between information spreading and opinion formation in social systems. Our framework consists in a two-layer multiplex network where opinion dynamics takes place in one layer, while information spreads on the other one. The two dynamical processes are mutually coupled in such a way that the control parameters governing the dynamics of the node states at one layer depend on the dynamical states at the other layer. In particular, we consider the case in which consensus is favored by the common adoption of information, while information spreading is boosted between agents sharing similar opinions. Numerical simulations of the model point out that, when the coupling between the dynamics of the two layers is strong enough, a double explosive transition, i.e., a discontinuous transition both in consensus dynamics and in information spreading appears. Such explosive transitions lead to bi-stability regions in which the consensus-informed states and the disagreement-uninformed states are both stable solutions of the intertwined dynamics.

Diffusive public goods (PG) games are difficult to analyze due to population assortment affecting growth rates of cooperators (producers) and free-riders. We study these growth rates using spectral decomposition of cellular densities and derive a finite cell-size correction of the growth rate advantage which exactly describes the dynamics of a randomly assorted population and approximates the dynamics under limited dispersal. The resulting effective benefit-to-cost ratio relates the physical parameters of PG dynamics to the persistence of cooperation, and our findings provide a powerful tool for the analysis of diffusive PG games, explaining commonly observed patterns of cooperation.

There are numerous biological scenarios in which populations of cells migrate in crowded environments. Typical examples include wound healing, cancer growth, and embryo development. In these crowded environments cells are able to interact with each other in a variety of ways. These include excluded-volume interactions, adhesion, repulsion, cell signaling, pushing, and pulling. One popular way to understand the behavior of a group of interacting cells is through an agent-based mathematical model. A typical aim of modellers using such representations is to elucidate how the microscopic interactions at the cell-level impact on the macroscopic behavior of the population. At the very least, such models typically incorporate volume-exclusion. The more complex cell-cell interactions listed above have also been incorporated into such models; all apart from cell-cell pulling. In this paper we consider this under-represented cell-cell interaction, in which an active cell is able to “pull” a nearby neighbor as it moves. We incorporate a variety of potential cell-cell pulling mechanisms into on- and off-lattice agent-based volume exclusion models of cell movement. For each of these agent-based models we derive a continuum partial differential equation which describes the evolution of the cells at a population level. We study the agreement between the agent-based models and the continuum, population-based models and compare and contrast a range of agent-based models (accounting for the different pulling mechanisms) with each other. We find generally good agreement between the agent-based models and the corresponding continuum models that worsens as the agent-based models become more complex. Interestingly, we observe that the partial differential equations that we derive differ significantly, depending on whether they were derived from on- or off-lattice agent-based models of pulling. This hints that it is important to employ the appropriate agent-based model when representing pulling cell-cell interactions.

This paper deals with the theoretical study of magnetic hyperthermia, induced by a system of immobilized single-domain ferro- (ferri-) magnetic particles under the action of an oscillating magnetic field. It is supposed that the particles are randomly distributed in a host medium. The effect of magnetic interparticle interaction on the intensity of the heat production is the focus of our attention. The results show that interparticle interaction enhances the thermal effect.