We consider a preferential cluster growth in a stochastic model describing the dynamics of a binary Markov chain with an additional long-range memory. The model is driven by data describing emotional patterns observed in online community discussions with binary states corresponding to emotional valences. Numerical simulations and approximate analytical calculations show that the pattern of frequencies depends on a preference exponent related to the memory strength in our model. For low values of this exponent in the majority of simulated discussion threads both emotions are observed with similar frequencies. When the exponent increases an ordered phase emerges in the majority of threads, i.e., only one emotion is represented from a certain moment. Similar changes are observed with increase of a single-step Markov memory value. The transition becomes discontinuous in the thermodynamical limit when discussions are infinitely long and even an infinitely small preference exponent leads to ordered behavior in each discussion thread. Numerical simulations are in a good agreement with the approximated analytical formula. The model resembles a dynamical phase transition observed in other Markov models with a long memory where persistent dynamics follows from a transition to a superdiffusion phase. The ordered patterns predicted by our model have been found in the Blog06 dataset although their number is limited by fluctuations and sentiment classification errors.

Natural earthquake fault systems are highly nonhomogeneous. The inhomogeneities occur because the earth is made of a variety of materials which hold and dissipate stress differently. In this work, we study scaling in earthquake fault models which are variations of the Olami-Feder-Christensen and Rundle-Jackson-Brown models. We use the scaling to explore the effect of spatial inhomogeneities due to damage and inhomogeneous stress dissipation in the earthquake-fault-like systems when the stress transfer range is long, but not necessarily longer than the length scale associated with the inhomogeneities of the system. We find that the scaling depends not only on the amount of damage, but also on the spatial distribution of that damage.

Accurate determination of three macroscopic parameters governing reactive transport in porous media, namely, the apparent solute velocity, the dispersion, and the apparent reaction rate, is of key importance for predicting solute migration through reservoir aquifers. Two methods are proposed to calculate these parameters as functions of the Péclet and the Péclet-Dahmköhler numbers. In the first method called the pore-scale model (PSM), the porous medium is discretized by the level set method; the Stokes and convection-diffusion equations with reaction at the wall are solved by a finite-difference scheme. In the second method, called the pore-network model (PNM), the void space of the porous medium is represented by an idealized geometry of pore bodies joined by pore throats; the flow field is computed by solving Kirchhoff's laws and transport calculations are performed in the asymptotic regime where the solute concentration undergoes an exponential evolution with time. Two synthetic geometries of porous media are addressed by using both numerical codes. The first geometry is constructed in order to validate the hypotheses implemented in PNM. PSM is also used for a better understanding of the various reaction patterns observed in the asymptotic regime. Despite the PNM approximations, a very good agreement between the models is obtained, which shows that PNM is an accurate description of reactive transport. PNM, which can address much larger pore volumes than PSM, is used to evaluate the influence of the concentration distribution on macroscopic properties of a large irregular network reconstructed from microtomography images. The role of the dimensionless numbers and of the location and size of the largest pore bodies is highlighted.

We present two unconditionally stable finite-difference time-domain (FDTD) methods for modeling the Sagnac effect in rotating optical microsensors. The methods are based on the implicit Crank-Nicolson scheme, adapted to hold in the rotating system reference frame—the rotating Crank-Nicolson (RCN) methods. The first method (RCN-2) is second order accurate in space whereas the second method (RCN-4) is fourth order accurate. Both methods are second order accurate in time. We show that the RCN-4 scheme is more accurate and has better dispersion isotropy. The numerical results show good correspondence with the expression for the classical Sagnac resonant frequency splitting when using group refractive indices of the resonant modes of a microresonator. Also we show that the numerical results are consistent with the perturbation theory for the rotating degenerate microcavities. We apply our method to simulate the effect of rotation on an entire Coupled Resonator Optical Waveguide (CROW) consisting of a set of coupled microresonators. Preliminary results validate the formation of a rotation-induced gap at the center of a transfer function of a CROW.

The lattice Boltzmann method for immiscible multiphase flows with large density ratio is extended to high Reynolds number flows using a multiple-relaxation-time (MRT) collision operator, and its stability and accuracy are assessed by simulating the Kelvin-Helmholtz instability. The MRT model is successful at damping high-frequency oscillations in the kinetic energy emerging from traveling waves generated by the inclusion of curvature. Numerical results are shown to be in good agreement with prior studies using adaptive mesh refinement techniques applied to the Navier-Stokes equations. Effects of viscosity and surface tension, as well as density ratio, are investigated in terms of the Reynolds and Weber numbers. It is shown that increasing the Reynolds number results in a more chaotic interface evolution and eventually shattering of the interface, while surface tension is shown to have a stabilizing effect.

A model of clustering dynamics is proposed for a population of spatially distributed active rotators. A transition from excitable to oscillatory dynamics is induced by the increase of the local density of active rotators. It is interpreted as dynamical quorum sensing. In the oscillation regime, phase waves propagate without decay, which generates an effectively long-range interaction in the clustering dynamics. The clustering process becomes facilitated and only one dominant cluster appears rapidly as a result of the dynamical quorum sensing. An exact localized solution is found to a simplified model equation, and the competitive dynamics between two localized states is studied numerically.

Memristive systems, namely, resistive systems with memory, are currently attracting considerable attention. Here we show that even the simplest one-dimensional network formed by the most common memristive elements with voltage threshold bears nontrivial physical properties. In particular, by taking into account the single element variability we find (1) dynamical acceleration and slowing down of the total resistance in adiabatic processes, (2) dependence of the final state on the history of the input signal with same initial conditions, (3) existence of switching avalanches in memristive ladders, and (4) independence of the dynamics voltage threshold with respect to the number of memristive elements in the network (scale invariance). An important criterion for this scale invariance is the presence of memristive systems with very small threshold voltages in the ensemble. These results elucidate the role of memory in complex networks and are relevant to technological applications of these systems.

Spatiotemporal Turing-Hopf pinning solutions near the codimension-two Turing-Hopf point of the one-dimensional Brusselator model are studied. Both the Turing and Hopf bifurcations are supercritical and stable. The pinning solutions exhibit coexistence of stationary stripes of near critical wavelength and time-periodic oscillations near the characteristic Hopf frequency. Such solutions of this nonvariational problem are in contrast to the stationary pinning solutions found in the subcritical Turing regime for the variational Swift-Hohenberg equations, characterized by a spatially periodic pattern embedded in a spatially homogeneous background state. Numerical continuation was used to solve periodic boundary value problems in time for the Fourier amplitudes of the spatiotemporal Turing-Hopf pinning solutions. The solution branches are organized in a series of saddle-node bifurcations similar to the known snaking structures of stationary pinning solutions. We find two intertwined pairs of such branches, one with a defect in the middle of the striped region, and one without. Solutions on one branch of one pair differ from those on the other branch by a $\pi$ phase shift in the spatially periodic region, i.e., locations of local minima of solutions on one branch correspond to locations of maxima of solutions on the other branch. These branches are connected to branches exhibiting collapsed snaking behavior, where the snaking region collapses to almost a single value in the bifurcation parameter. Solutions along various parts of the branches are described in detail. Time dependent depinning dynamics outside the saddle nodes are illustrated, and a time scale for the depinning transitions is numerically established. Wavelength variation within the snaking region is discussed, and reasons for the variation are given in the context of amplitude equations. Finally, we compare the pinning region to the Maxwell line found numerically by time evolving the amplitude equations.

In this work, we study the onset of sequential activity in ensembles of neuronlike oscillators with inhibitorylike coupling between them. The winnerless competition (WLC) principle is a dynamical concept underlying sequential activity generation. According to the WLC principle, stable heteroclinic sequences in the phase space of a network model represent sequential metastable dynamics. We show that stable heteroclinic sequences and stable heteroclinic channels, connecting saddle limit cycles, can appear in oscillatory models of neural activity. We find the key bifurcations which lead to the occurrence of sequential activity as well as heteroclinic sequences and channels.

The smoothed-particle-hydrodynamics (SPH) method has been used to study the evolution of free-surface Newtonian viscous flows specifically focusing on dissipation mechanisms in gravity waves. The numerical results have been compared with an analytical solution of the linearized Navier-Stokes equations for Reynolds numbers in the range 50–5000. We found that a correct choice of the number of neighboring particles is of fundamental importance in order to obtain convergence towards the analytical solution. This number has to increase with higher Reynolds numbers in order to prevent the onset of spurious vorticity inside the bulk of the fluid, leading to an unphysical overdamping of the wave amplitude. This generation of spurious vorticity strongly depends on the specific kernel function used in the SPH model.

We calculate the average volumetric flux versus pressure drop of bubbles moving in a single capillary tube with varying diameter, finding a square-root relation from mapping the flow equations onto that of a driven overdamped pendulum. The calculation is based on a derivation of the equation of motion of a bubble train, considering the capillary forces and the entropy production associated with the viscous flow. We also calculate the configurational probability of the positions of the bubbles.

We present exact results on the dynamics of a biased, by an external force $\mathbf{F}$, intruder (BI) in a two-dimensional lattice gas of unbiased, randomly moving hard-core particles. Going beyond the usual analysis of the force-velocity relation, we study the probability distribution $P\left({\mathbf{R}}_n\right)$ of the BI displacement ${\mathbf{R}}_n$ at time $n$. We show that despite the fact that the BI drives the gas to a nonequilibrium steady state, $P\left({\mathbf{R}}_n\right)$ converges to a Gaussian distribution as $n\to \infty$. We find that the variance ${\sigma }_x^2$ of $P\left({\mathbf{R}}_n\right)$ along $\mathbf{F}$ exhibits a weakly superdiffusive growth ${\sigma }_x^2\sim {\nu }_{1}n\ln \left(n\right)$, and a usual diffusive growth, ${\sigma }_y^2\sim {\nu }_{2}n$, in the perpendicular direction. We determine ${\nu }_1$ and ${\nu }_2$ exactly for arbitrary bias, in the lowest order in the density of vacancies, and show that ${\nu }_1\sim {\left|\mathbf{F}\right|}^2$ for small bias, which signifies that superdiffusive behavior emerges beyond the linear-response approximation. We also present analytical arguments predicting a striking field-induced superdiffusive behavior ${\sigma }_x^2\sim n^{3/2}$ for two-dimensional stripes and three-dimensional capillaries, which is confirmed by Monte Carlo simulations.

Conventional wisdom indicates that initial memory should decay away exponentially in time for general (noncritial) equilibration processes. In particular, time-integrated quantities such as heat are presumed to lose initial memory in a sufficiently long-time limit. However, we show that the large deviation function of time-integrated quantities may exhibit initial memory effect even in the infinite-time limit, if the system is initially prepared sufficiently far away from equilibrium. For a Brownian particle dynamics, as an example, we found a sharp finite threshold rigorously, beyond which the corresponding large deviation function contains everlasting initial memory. The physical origin for this phenomenon is explored with an intuitive argument and also from a toy model analysis. Our results can be applied to general nonequilibrium relaxation processes reaching (non)equilibrium steady states.

We present a new method based on the scattering technique to investigate fluctuation-induced forces at a fluid interface. The scattering approach, well suited to the study of many-body systems of arbitrary geometries, is augmented to include boundary fluctuations. Using this method, we study the deviation of the total fluctuation-induced interaction from the sum of pairwise energies for three colloidal particles. We consider both frozen and fluctuating colloids and obtain a very good agreement between analytical and numerical results. We find a marked difference in the three-body fluctuation-induced free energy between the frozen and fluctuating colloids, both in sign and relative size.

Using $p$-aminothiophenol (PATP) molecules on a gold substrate and high-vacuum tip-enhanced Raman spectroscopy (HV-TERS), we show that the vibrational spectra of these molecules are distinctly different from those in typical surface-enhanced Raman spectroscopy. Detailed first-principles calculations help to assign the Raman peaks in the TERS measurements as Raman-active and IR-active vibrational modes of dimercaptoazobenzene (DMAB), providing strong spectroscopic evidence for the dimerization of PATP molecules to DMAB under the TERS setup. The activation of the IR-active modes is due to enhanced electromagnetic field gradient effects within the gap region of the highly asymmetric tip-surface geometry. Fermi resonances are also observed in HV-TERS. These findings help to broaden the versatility of TERS as a promising technique for ultrasensitive molecular spectroscopy.

We study the mechanism of the polydomain-monodomain transition in liquid crystalline elastomers at the molecular scale. A coarse-grained model is proposed in which mesogens are described as ellipsoidal particles. Molecular dynamics simulations are used to examine the transition from a polydomain state to a monodomain state in the presence of uniaxial strain. Our model demonstrates soft elasticity, similar to that exhibited by side-chain elastomers in the literature. By analyzing the growth dynamics of nematic domains during uniaxial extension, we provide direct evidence that at a molecular level the polydomain-monodomain transition proceeds through cluster rotation and domain growth.

The dynamics of a spin system is usually calculated using the density matrix. However, the usual formulation in terms of the density matrix predicts that the signal will decay to zero, and does not address the issue of individual spin dynamics. Using stochastic calculus, we develop a dynamical theory of spin relaxation, the origins of which lie in the component spin fluctuations. This entails consideration of random pure states for individual protons, and how these pure states are correctly combined when the density matrix is formulated. Both the lattice and the spins are treated quantum mechanically. Such treatment incorporates both the processes of spin-spin and (finite temperature) spin-lattice relaxation. Our results reveal the intimate connections between spin noise and conventional spin relaxation.

We study the effect of dimensionality on the percolation threshold ${\eta }_c$ of identical overlapping nonspherical convex hyperparticles in $d$-dimensional Euclidean space ${\mathbb{R}}^d$. This is done by formulating a scaling relation for ${\eta }_c$ that is based on a rigorous lower bound [Torquato, J. Chem. Phys. 136, 054106 (2012)] and a conjecture that hyperspheres provide the highest threshold, for any $d$, among all convex hyperparticle shapes (that are not a trivial affine transformation of a hypersphere). This scaling relation also exploits the recently discovered principle that low-dimensional continuum percolation behavior encodes high-dimensional information. We derive an explicit formula for the exclusion volume $v_{\text{ex}}$ of a hyperparticle of arbitrary shape in terms of its $d$-dimensional volume $v$, surface area $s$, and radius of mean curvature $\overline{R}$ (or, equivalently, mean width). These basic geometrical properties are computed for a wide variety of nonspherical hyperparticle shapes with random orientations across all dimensions, including, among other shapes, various polygons for $d=2$, Platonic solids, spherocylinders, parallepipeds, and zero-volume plates for $d=3$ and their appropriate generalizations for $d\ge 4$. Using this information, we compute the lower bound and scaling relation for ${\eta }_c$ for this comprehensive set of continuum percolation models across dimensions. We demonstrate that the scaling relation provides accurate upper-bound estimates of the threshold ${\eta }_c$ across dimensions and becomes increasingly accurate as the space dimension increases.

In this paper we study the validity of the total power fluctuation theorem spent on a Brownian harmonic oscillator when the system is driven out of equilibrium through the drag of the potential minimum. The theorem is first proved for an ordinary harmonic oscillator in two cases: The first one considers the particle in a thermal bath under the action of Gaussian white noise, and in the second one the drift is provided by an additional external Gaussian colored noise satisfying the characteristics of an Ornstein-Uhlenbeck process. We go further, by considering a charged harmonic oscillator under the action of an electromagnetic field. The theorem is also proven as in the two cases given above. In both of those cases, we illustrate the theorem for a uniform motion of the trap potential minimum and show that in the presence of external colored noise, the theorem is only valid in the stationary state.

The recently discovered iron-based superconductors $A$${}_y$Fe${}_{2-x}$Se${}_2$ ($A=\text{K}$, Rb, Cs, Tl) show a long-range antiferromagnetic order with an unexpectedly high transition temperature $T_N\sim 550$ K and a unique $\sqrt{5}\times \sqrt{5}$ vacancy order. Taking the extended $J_1$-$J_2$ model as a minimal model, we investigate the finite-temperature magnetic phase transitions in a square lattice with a $\sqrt{5}\times \sqrt{5}$ vacancy superstructure by using large-scale Monte Carlo simulations. By the parallel tempering technique, the block spin checkerboard and stripe antiferromagnetic states are detected to be the ground states for three representative sets of model parameters. The short-time dynamic approach is applied to accurately determine the critical temperature as well as the static and dynamic exponents. Our results indicate that the dramatic enhancement of the critical temperature as observed in experiments should be mainly due to a combination of the vacancy order and the block lattice contraction.