We conduct numerical simulations of a noisy generalized Kuramoto-Sivashinsky system under optimal control. The cost functions are kinetic energy and permutation entropy, which are designed to reduce the kinetic energy and the permutation entropy, respectively. The results demonstrate that kinetic-energy-based control strongly suppresses the structures and drives the system into a trivial state. In contrast, the permutation-entropy-based control preserves nontrivial structures, although the reduction in kinetic energy is smaller and requires only slight actuation energy. These findings provide insight into designing control strategies for complex systems where both efficiency and functional richness are desired.

We investigate dust-acoustic rogue waves in a one-dimensional binary dusty plasma using analytical nonlinear Schrödinger equation theory and fully kinetic particle-in-cell simulations. Modulational instability initially drives nonlinear focusing and rogue-wave formation, consistent with fluid predictions. However, after the rogue-wave peak, the coherent structure collapses irreversibly due to kinetic effects instead of exhibiting periodic recurrence. The breakdown is caused by asymmetric kinetic heating between the two dust species. The lighter dust grains, owing to their lower inertia, absorb most of the wave energy through strong wave-particle interactions and develop non-Maxwellian energetic tails, while the heavy dust experiences only weak heating. In addition, the rogue-wave excitation time is highly sensitive to small changes in the imposed perturbation amplitude. These results demonstrate the kinetic and irreversible nature of rogue waves in realistic binary dusty plasmas.

Laser wakefield acceleration (LWFA) provides a compact platform for generating bright, ultrashort, and tunable x-ray betatron radiation. When the betatron oscillation wavelength matches the laser centroid oscillation period in a preformed plasma channel, resonance occurs, leading to a rapid amplification of the electron betatron oscillation amplitude and significantly increasing the photon energy and yield of the sources. However, nonideal conditions from laser-plasma interactions may drive electrons out of resonance rapidly. In this work we use particle-in-cell simulations and theoretical analysis to identify four distinct mechanisms that disrupt the resonant growth: the nonlinear focusing force at large radii, the dephasing between the wakefield and the electron beam, the acceleration or deceleration of the electrons, and the decay of the laser centroid oscillation amplitude caused by phase mixing among different laser slices. Detailed particle tracking reveals that a delicate balance between dephasing and acceleration can sustain electrons in resonance for an extended period. Building upon these physical insights, we optimize laser, electron beam, and plasma channels to significantly enhance the emitted radiation. These results provide valuable guidance for developing high-yield and high-energy betatron radiation sources based on LWFA.

We theoretically and experimentally investigate wave scattering in a single-column woodpile structure with impurities. This structure consists of slender cylindrical beams that support flexural bending modes, which are coupled strongly with propagating waves in the low-frequency regime (i.e., local resonance coupling). We examine the linear scattering of plane waves from single and double impurities in periodic configurations. When local resonance coupling is neglected, the double-impurity configuration exhibits reflectionless transmission, resembling the Ramsauer-Townsend (RT) resonance. Further analysis reveals that, under strong local resonance coupling, this behavior evolves into a Fano resonance, which features an asymmetric line shape with a sharp transition between transmission and reflection. However, as the RT resonance and the local resonance become spectrally closer, an analog of electromagnetically induced transparency (EIT) emerges, resulting from the balanced interference between the two modes. These distinct resonant behaviors can be tuned by varying the local resonance coupling strength, providing a controllable platform for macroscopic analogs of quantum wave interference. Potential applications include vibration isolation and wave-based switching devices.

The pore-surface and surface-surface correlation functions are structural characteristics that play an important role in theoretical materials science and in small-angle scattering theory. Symbolic expressions for the surface correlation functions are available only for very few models, and we here derive such expressions for the general class of dead-leaves models. Within these models, a two-phase pore-solid structure is created by sequentially and randomly filling space with porelike or solidlike grains that overlap any preexisting structure, in the same way that dead leaves fall on the ground. The obtained mathematical expressions are valid for any grain shape, in arbitrary dimension. The results are illustrated with monodispersed spherical grains as well as with a dead-leaves realization of a Debye random medium. In the latter case, the size distribution of the grains is designed to produce a structure having an exponential two-point correlation function. Compared to Debye random media obtained by numerical reconstruction, the dead-leaves structure has an almost identical surface-surface correlation function, but a distinctly different pore-surface correlation function with different average mean curvature of the pore-solid surface. Using the concepts introduced in the paper, we submit a general analytic expression for the pore-surface and surface-surface correlation functions of the Boolean model of interpenetrating grains, valid for arbitrary grains. We also derive general expressions for the surface correlation functions of mosaic structures.

One-dimensional alternating particle systems are widely used to study interconnections between the hydrodynamics of blast waves in a gas-like medium and the Newtonian dynamics of its corpuscular constituents. In this article, we study the model in which point particles with masses $m,\mu ,m,\mu ,\cdots , (m\ge \mu )$ are distributed on the positive half-line ${\mathbb{R}}_{+}$. Their dynamics are initiated by giving a positive velocity to the leftmost particle; in its course, the particles undergo elastic collisions. For this model with $m/\mu =2$, it has been established that the dynamics that start from random initial positions are consistent with predictions based on Euler's hydrodynamic equation. In particular, they have the following properties: (i) the position of the rightmost particle (shock front) evolves as $t^{\delta }$ with $\delta <1$; (ii) recoiled particles behind the front enter the negative half-axis; and (iii) particles with locations $x\le 0$ move ballistically and eventually take over the total energy of the system. In this paper, we present numerical and analytical results for the dynamics of this model with nonrandom (typically equidistant) initial positions and various values of $m/\mu$. For $m/\mu =2$ and equidistant initial positions, our results qualitatively agree with those just mentioned. At the same time, we found an infinite family of numbers $\left\{{\mathcal{M}}_k\right\}$ such that, for $m/\mu ={\mathcal{M}}_k$, the hydrodynamic behavior mentioned changes drastically to the following. At each moment, only a single triplet $m,\mu ,m$ is in motion, whereas all other particles are at rest. In the moving triplet, two heavy particles move to the right, while the lighter particle oscillates between them, transferring energy and momentum to the right. As a result, the shock front moves ballistically with an average velocity equal to the initial one. Such a ‘staggering domino-like’ picture is obtained as an exact solution, which yields, in particular, explicit formulas for ${\mathcal{M}}_k$ and the particle velocities and positions. Its details and importance in a wider context are also discussed.

We extend the canonical Gibbs distribution, originally formulated for systems at equilibrium, to systems driven out of equilibrium. The stochastic dynamics of a small system are described by a probability distribution over discrete energy levels. Within this framework, we derive a microscopic expression for work and introduce a microscopic definition of entropy production (defined here in terms of the uncompensated heat of Clausius) during a nonequilibrium stochastic process. Work and entropy production share a common origin arising from variations of the system energy. The proposed framework allows us to recover the nonequilibrium work relation and to establish an equivalent identity for the heat exchanged during a work protocol. Finally, we show that the fluctuations of work and heat governed by the extended canonical distribution follow directly from the fluctuation theorem for entropy production.

It is an important physical property for a diffusion process to respond to an external force or a spatial confinement in experiments. This paper investigates the random diffusivity processes coupled with an inverse $\alpha$-stable subordinator which characterizes the trapping events in crowded environment, and studies its dynamics under the harmonic potential by evaluating the ensemble-averaged and time-averaged mean-squared displacements (MSDs). We build three kinds of coupled Langevin equations based on different physical realities of the external force in crowded environment, e.g., the harmonic potential only modifies the particle's motion at the moments of particle-making jumps, and that exerts an effect throughout the whole physical time, and the case with the validity of fluctuation-dissipation theorem. By comparing these three models, we find that the crowding trapping effect and temporal dynamic fluctuation play different roles in the MSDs. Nevertheless, we also observe an interesting phenomenon that the ratio between two MSDs exhibits the same scaling form, which perhaps reveals an underlying mechanism of the ergodicity breaking within the subordinated diffusion processes.

Entanglement entropy is a fundamental diagnostic for quantum chaos, typically exhibiting volume-law scaling in highly excited eigenstates of chaotic many-body systems. In this work, we present a striking counterexample: a Floquet-driven quantum many-body system with a Rydberg-like blockade that, despite being fully chaotic as indicated by its Wigner-Dyson level statistics and local thermalization, exhibits a strict area-law entanglement entropy. Specifically, the entanglement entropy of every Floquet eigenstate is bounded by $ln2$, independent of system size. We trace this anomaly to the specific Hilbert space structure imposed by the blockades, which restricts the Schmidt rank across a bipartition. Furthermore, we generalize this discovery by establishing a duality between constrained many-body Hamiltonians and single-particle quantum walks on median graphs, and we outline a general procedure for constructing systems with an entanglement entropy bounded by a predetermined constant. Our results demonstrate that entanglement entropy alone is an insufficient diagnostic of many-body quantum chaos and highlight the profound impact of Hilbert space geometry on quantum dynamics and thermalization.

We study the kinetics of two-temperature induced phase separation in dilute binary mixtures of active (“hot”) and passive (“cold”) particles using molecular dynamics simulations and a coarse-grained hydrodynamic model. Following a temperature quench, cold particles nucleate into mobile clusters that move ballistically and merge through successive coalescence events. The resulting domain growth exhibits dynamic scaling with a growth exponent $\approx 0.7$, markedly faster than diffusive coarsening. We identify this regime as ballistic agglomeration of cold clusters, demonstrating a distinct nonequilibrium growth mechanism in low-density scalar active systems.

We experimentally investigate the dynamics of a spherical intruder driven horizontally at a constant force in a granular medium subjected to a localized surface overload. While intruder motion beneath a free surface exhibits constant acceleration in the quasistatic regime, the presence of a surface load induces a pronounced transient deceleration when the intruder passes below the loaded region. The magnitude of this deceleration increases with the applied overload and saturates at large overloads, while it decreases with intruder depth. Introducing a characteristic timescale and an overload-based Froude number, we show that the deceleration dynamics collapse onto master curves. We further develop a model incorporating stress transmission from the surface, which partially captures the intruder deceleration. In this approach, this deceleration is shown to depend on two parameters: the overload and the area on which this overload is applied. These results provide a framework to quantify how localized surface stresses influence subsurface intruder dynamics, with implications for locomotion, root growth, and underground transport in granular media.

A state-based ergodic approach is introduced using a descriptor constructed from instantaneous, spatially resolved measurements to quantify the spatiotemporal evolution of system states in far-from-equilibrium systems. This method is demonstrated for the mixing process in confined water-glycerol flows driven across three milli-scale geometries, by combining experiments with lattice Boltzmann simulations that uniquely incorporate colloids as passive flow obstacles. This generalizable framework distinguishes nonequilibrium pathways by tracking information about accessed system states.

We study the dynamic critical behavior of the two-dimensional random-bond three-state Potts model using large-scale Monte Carlo simulations with the Wolff and Swendsen-Wang cluster algorithms. At the disorder-dependent critical temperature, we compute integrated and exponential autocorrelation times and extract the dynamic critical exponent $z$ via finite-size scaling. Critical slowing down is significantly weakened by bond randomness, with the dynamic exponent decreasing from $z\approx 0.54$ in the pure system to $z\approx 0.24$ at strong disorder. From the finite-size scaling of the Wolff cluster size, we obtain $\gamma /\nu \approx 1.73$, where $\gamma$ and $\nu$ are the susceptibility and correlation-length exponents, respectively, consistent with the universality class of the two-dimensional three-state Potts model. These results indicate that bond randomness strongly affects the critical dynamics but leaves the underlying static universality class unchanged. Furthermore, both static and dynamic observables, including the specific heat and the autocorrelation times, show a lack of self-averaging.

We perform large-scale numerical simulations to investigate the critical behavior of $k$-core percolation in two dimensions with an extended interaction range $r$. By systematically varying both the core index $k$ and the interaction range $r$, we construct a comprehensive phase diagram in the $(k,r)$ plane. In contrast to $k$-core percolation in infinite dimensions, no hybrid transition is observed in two dimensions: The phase diagram contains only a continuous transition regime and a strictly first-order regime, separated by a tricritical or critical end point $(k_s,r_s)$. For $k<k_s$ and $r<r_s$, the transition is continuous and belongs to the universality class of standard two-dimensional percolation. For $k>k_s$ and finite $r>r_s$, the transition is discontinuous, with no hybrid features or critical singularities. In this first-order regime, the pseudocritical point approaches the critical point as $1/lnL$, where $L$ is the linear system size, distinct from the $L^{-d}$ scaling typical of conventional thermodynamic first-order transitions in $d$ dimensions. This logarithmic finite-size drift is consistent with a nucleation-driven mechanism, in which rare voids trigger the collapse of the finite-range $k$-core. These results demonstrate that geometric constraints can fundamentally alter the nature of $k$-core percolation found in finite dimensions.

Standard affine jump-diffusion models treat shocks as increments to the system's state, whereas conventional stochastic resetting models reduce their impact to a full reset of the system's state. Neither model is adequate when diffusion along one of the state components persists, while shocks affect another component. Our study examines a linear system perturbed by two independent additive Markovian components. We demonstrate that in this model the solutions to the generalized Feynman-Kac (GFK) equations describing the expected evolution operator admit an exact factorization to solutions corresponding to separate noise components. Factorizable two-component models are applicable in the real world, particularly in systems where an endogenous background noise persists even when exogenous shocks occur. We show that this decoupling enables a comprehensive description of stylized yield-curve shapes. Furthermore, it allows for the prediction of deviations from exponential Moore's Law trajectories for novel technologies.

We introduce a solvable spherical model of coupled oscillators with fully random interactions and distributed natural frequencies. Using the dynamical mean-field theory, we derive self-consistent equations for the stationary response and correlation functions. We show that the finite-temperature spin-glass transition is suppressed for any nonmonodisperse natural-frequency distribution, including continuous, discrete, and asymmetric distributions, because the resulting low-frequency singularity of the correlation function is incompatible with the spherical constraint. At zero temperature, however, a spin-glass phase persists for arbitrary frequency dispersion. This residual zero-temperature glassiness is likely a special feature of the spherical dynamics and would be destroyed by local nonlinearities. The model thus provides a solvable oscillator framework for studying how nonequilibrium perturbations suppress finite-temperature glassy freezing.

Network community detection is usually considered as an unsupervised learning problem. Given a network, the aim is to partition it using some general-purpose algorithm. In this paper, we instead treat community detection as a hypothesis testing problem. Given a network, we examine the evidence for community structure in the observed network compared to a network generated by a null model. To do this, we define an appropriate test statistic, analogous to a z-score, and discuss several null models derived from maximizing entropy under different constraints. These constraints are imposed in expectation, giving a canonical ensemble rather than the usual microcanonical ensemble. We demonstrate the application of this method on real and synthetic data and contrast our method to Bayesian approaches based on the stochastic block model. We demonstrate that this method gives rigorous answers to concrete questions, which can be more useful to analysts than the output of a generic algorithm.

We analyze the protein secondary structure using the Frenet frame to describe the curvature and torsion of the discrete curve formed by the protein $\alpha$-carbons. We show how a simple criterion based on the evaluation of Frenet frame rotation along the discrete curve supports identification of alpha-helices, beta-strands, and selected supersecondary structures in proteins. Moreover, the description of proteins as fixed points of an effective action inspired by a $U\left(1\right)$ gauge model is supported by the analysis of a large dataset of proteins in the Protein Data Bank.

Theoretical expressions used to study the assembly of magnetic particles under the action of external magnetic fields are generalized to incorporate carrier fluids with varying viscosity (shear thinning fluids). The theory is essentially local, and the drag force assigned to each particle considers local viscosity and flow changes due to pseudoplastic behavior. A benchmark is performed with finite element simulations for the case of pairwise interactions and static external fields. General agreement is observed, although simulations reveal the complexity that arises in these systems due to nonlinear hydrodynamic interactions. In the case of external rotating fields, the generalized theory predicts the transition from chains to cluster-like assemblies as the angular frequency of the field increases, similar to what happens in Newtonian media. The difference lies in that particles forming clusters in shear thinning fluids exhibit a higher degree of orientational ordering and are larger than those formed in Newtonian media. An experimental test is performed using magnetite dispersed in a power-law fluid made with xanthan gum. Image analysis reveals that the clusters formed in the power-law fluid are larger than those formed in a reference Newtonian fluid. Further analysis indicates that aggregation in the non-Newtonian fluid is primarily a dynamical process and is little influenced by direct interactions between the magnetite and the polymer.

A key principle underlying most turbulence theories is that the mean energy dissipation rate remains finite even as viscosity vanishes: the so-called zeroth law of turbulence. Although this property has been established for hydrodynamic and magnetohydrodynamic (MHD) turbulence, its validity in weakly collisional plasmas has remained uncertain, where viscous and resistive closures fail. Using fully kinetic numerical simulations, we provide confirmation that the mean dissipation rate approaches a finite, system-independent value when expressed in terms of an effective Reynolds number derived from a heuristic collisionless viscosity. This result demonstrates a finite dissipation anomaly in collisionless plasma turbulence, establishing that the zeroth law extends beyond the collisional MHD regime.