The theoretical understanding of pattern formation in active systems remains a central problem of interest. Heterogeneous flocks made up of multiple species can exhibit a remarkable diversity of collective states that cannot be obtained from single-species models. In this paper, we derive a kinetic theory for multispecies systems of self-propelled particles with (anti)alignment interactions. We summarize the numerical results for the binary system before employing linear stability analysis on the coarse-grained system. We find good agreement between theoretical predictions and particle simulations, and our kinetic theory is able to capture the correct lengthscale in the emergent coexistence phases through a Turing-Hopf instability. Extending the kinetic framework to multispecies systems with cyclic alignment interactions, we recover precisely the same emergent ordering as corresponding simulations of the microscopic model. More generally, our kinetic theory provides an extensible framework for analyzing pattern formation and collective order in multispecies active matter systems.

We study a two-species Vicsek model with intraspecies alignment and asymmetric interspecies couplings, where one species aligns with the other while the latter antialigns. Motivated by recent results showing that globally coherent chiral motion is not a generic large-scale state of finite-range nonreciprocal flocking, we ask whether a chiral state can nevertheless be stabilized in the discrete-time, metric, nonreciprocal two-species Vicsek model, and if so, under what conditions. For equal populations and motilities, we find that the global chiral state is a long-lived finite-time state rather than an asymptotically stable phase at nonzero motility. Its breaking time increases rapidly as the self-propulsion speed is reduced or the strength of the nonreciprocal coupling is enhanced, explaining why systems with very low motility can appear stably chiral over conventional simulation timescales. This finite-time chiral regime is further limited to high-density systems and to system sizes that are small relative to the interaction range. Within this window, we also find that chirality appears primarily when aligning interactions dominate over antialignment, whereas stronger antialignment leads to species segregation and suppresses chirality. Conversely, introducing species asymmetry through population imbalance drives transitions from the finite-time chiral regime to porous parallel-flocking or antiparallel-flocking liquids; motility imbalance induces asynchronous oscillations and, in extreme cases, leads to segregation into moving clusters of the faster species within a more dispersed background of slower particles. Overall, these results indicate that chirality in the nonreciprocal two-species Vicsek model arises within a restricted regime set by density, motility, interspecies coupling, and system size, rather than being a generic outcome of nonreciprocal interactions.

The aggregation or clustering of proteins plays an important role in the formation of postsynaptic domains (PSDs) at excitatory and inhibitory synapses in neurons. PSDs are rich in scaffolding proteins that can transiently trap transmembrane neurotransmitter receptors, allowing them to regulate the strength of synaptic connections during learning and memory. Recently, a two-dimensional diffusion-mediated aggregation model of PSD formation was developed in which the spatial locations of the clusters are determined by a set of fixed anchoring sites. The system is kept out of equilibrium by the recycling of particles between the cell membrane and interior. This results in a nontrivial stationary state consisting of multiple stable protein clusters. In this paper, we use matched asymptotic methods to reduce the underlying reaction-diffusion model with moving interior boundaries to a corresponding finite-dimensional nonlinear system. The latter couples the cluster radii to the spatially averaged protein concentration in the bulk domain. We assume that the diffusivity $D$ is $O(1/\nu )$, where $\nu =-1/ln\epsilon$ and $\epsilon$ is a small parameter that characterizes the size of the clusters relative to the size of the bulk domain. The reduced dynamical system allows us to explore both the existence and stability of the multicluster stationary state while maintaining the effects of diffusion-mediated interactions between clusters.

Many morphogenetic processes in living systems are driven by active stresses within epithelial sheets that undergo simultaneous changes in thickness, curvature, and internal order. Existing theoretical descriptions of active nematics have largely focused on effectively two-dimensional layers of fixed thickness or prescribed geometry, limiting their ability to capture the coupled thickness-shape dynamics that are central to epithelial morphogenesis. Here, starting from a three-dimensional active nematohydrodynamic description, we derive a hierarchy of low-dimensional continuum models for thin active layers with finite thickness that can deform, flow, and reorient. Using an asymptotic long-wavelength theory, we show how activity couples nematic order to thickness variations and surface geometry in both flat and curved films. In flat layers, allowing thickness to evolve qualitatively modifies classical bend and splay instabilities, producing activity-driven modes that are not directionally constrained. In curved geometries, such as cylindrical films relevant to tubular epithelia and cnidarian body columns, thickness and shape instabilities become intrinsically coupled. Notably, we find that in isotropic phases both extensile and contractile activity can induce nematic order when thickness and shape are dynamic—contrasting sharply with active nematics on fixed substrates. Our results suggest experimentally testable mechanisms by which active stresses in epithelia can drive invagination, thickening, and curvature generation during development. More broadly, this framework provides a minimal physical theory linking active nematic order, thickness regulation, and morphogenesis in living tissues.

Non-neutral plasmas can be confined for long times in electromagnetic traps. Adiabatic cooling of such plasmas can be achieved by slowly decreasing the effective trap stiffness and letting the plasma occupy a larger volume. Here, we propose an optimal control strategy to cool an antiproton plasma in a time much shorter than that required by an adiabatic process. The optimal cooling protocol is found by minimizing an appropriate cost functional that measures the distance of the system from the desired “cold” state, while keeping the stiffness amplitude within prescribed limits. The proposed optimal protocol is shown to outperform other expansion-based cooling methods in terms of stability of the plasma, duration of the process, and volume occupied by the final plasma.

Lacking a universal law for granular flows across gravitational environments, fundamental processes such as hopper discharge remain vulnerable to failure in low gravity environments. A central challenge is clogging, the spontaneous arrest of flow through a constriction; yet previous studies report contradictory results on its dependence on gravitational acceleration. We identify the granular Bond number as the missing control parameter, defined as the ratio of intrinsic cohesive interactions among particles to gravity. Based on an in-bulk measurement of this quantity, we propose to rescale Earth-measured data for predicting granular behavior in low gravity. We present experiments of granular flow through an orifice under true reduced gravity (Moon and Mars), using an active drop tower, and extraterrestrial soil simulants as model cohesive materials. Our experiments reveal substantial increases in clogging probability, contrary to previously predictions, which depends on the properties of the material itself. When rescaled by the Bond number, seemingly conflicting results can be explained and collapse into a unified state diagram, predicting clogging across materials and gravitational accelerations. This establishes a general framework for the cohesion-to-gravity competition. Future space missions to the Moon, Mars, and asteroids will rely on such predictions of granular behavior in low gravity.

We employ a variational neural annealing approach for solving large-scale constrained portfolio optimization problems, mapped onto classical Ising-like Hamiltonians, and study its performance under finite annealing times. The method enables efficient exploration of high-dimensional feasible spaces and achieves competitive solutions on standard financial indices. To characterize the underlying optimization dynamics, we analyze the decay of the residual energy as a function of annealing steps and observe a power-law behavior for fixed problem size. Extending this analysis across different portfolio sizes, we examine whether the optimization dynamics are consistent with a dynamical finite-size scaling form and find that the residual energy data collapse onto a single scaling curve within the studied range. This scaling analysis yields an effective annealing-time exponent that quantifies how computational effort grows with problem size. Our results demonstrate that finite-size scaling concepts provide a useful framework for understanding and forecasting the performance of neural annealing-based optimization methods in realistic, constrained settings.

Suspensions of paramagnetic colloids exhibit kinetic arrest in strong magnetic fields. Through a dissipative process of toggling the field on and off, suspensions self-assemble into dense and dynamic steady-state phases. We construct a phase diagram using $k$-means clustering analysis based on domain elongation, α-shapes, contour metrics, and the degree of phase separation. We identify six characteristic structural regimes: a structureless phase, an arrested structure, sheets, ribbons, a spiky phase, and a transient fluid-fluid regime. We further report the distribution and alignment of domains and the generality of the results. We model self-assembled domain shapes using an equilibrium mean-field magnetostatic energy calculation, which predicts the surprising emergence of highly anisotropic structures driven by the sample's confinement.

The ability to optimize the properties of a material is particularly advantageous for applications. Here the tuneable properties of side-chain liquid crystal elastomers (LCEs) are investigated. LCEs were fabricated using the same acrylate monomers, varying the proportion of mesogenic groups to influence the phase, density, and mechanical properties. The density increases with mesogenic content, varying from $1.11\mathrm{g}/\mathrm{c}{\mathrm{m}}^3$ to $1.19\mathrm{g}/\mathrm{c}{\mathrm{m}}^3$ as the mesogenic content is increased from 51 mol% to 99 mol%. The 7% increase in density was attributed to closer packing of the nanoscale network, observed directly through Small- and Wide-Angle X-ray Scattering. A 22% reduction in the network end-to-end spacing was measured for LCEs as the mesogenic content was changed from 56 mol% to 75 mol%. LCEs with above 70 mol% mesogenic content were revealed to exhibit a frustrated smectic phase, with nematic phases confirmed for the lower mesogenic concentrations. The frustrated smectic materials exhibit a higher correlation length parallel to the director ($>125\AA$) than the nematic LCEs ($<100\AA$) and demonstrate significantly higher Young's moduli, with the greatest moduli perpendicular to the director measured at $21{}^{\circ }\mathrm{C}$ as 8.3 MPa for the smectic LCEs and as a constant value of 2.1 MPa for the nematic LCEs. Crucially, this demonstrates that only a small change in the mesogenic content can induce a transition from a nematic LCE to a frustrated smectic LCE. This series of materials reveals how the nanoscale packing, the macroscopic material properties, and the phase can be controlled via the mesogenic content in side-chain acrylate LCEs, suggesting design approaches that allow for the potential customization of the material properties for applications.

Neuromodulatory levels in the brain change moment to moment, yet are typically ignored in studies of neural criticality, where empirical support remains varied. Here we show in a biophysical network of bursting neurons that arousal acts as a mechanistic control parameter for a directed percolation phase transition. At intermediate arousal, neuronal activity exhibits peaked susceptibility, power-law avalanche size and duration distributions, universal shape collapse, and anomalous diffusion, jointly satisfying the hyperscaling relation of the 2+1-dimensional directed percolation universality class. Reanalyzing extracellular recordings in awake mice, we find that time-resolved susceptibility tracks pupil-inferred arousal as the model predicts. These results identify arousal as a physiological control parameter for neural criticality, offering a statistical-physics basis for the Yerkes-Dodson effect, and imply that arousal should be tracked at fine temporal resolution to obtain reliable empirical estimates of neural criticality.

Numerical implementation of a theory yields acoustic wave packets whose peak-to-peak speeds from source to receiver are supersonic in a dispersionless medium due to temporal interference between direct and boundary-reflected paths. The effect occurs when the source and receiver are near each other and at least one is within $c\widetilde{\delta t}/2$ of the boundary, where $c$ is the phase speed of propagation in the medium, and $\widetilde{\delta t}$ is the smallest temporal separation between the paths at which interference first occurs. This $\mathrm{direct}+\mathrm{reflected}$ path mechanism is distinct from mechanisms yielding superluminal speeds for electromagnetic waves (EM) including quantum tunneling and anomalous dispersion. For temporally interfering $\mathrm{direct}+\mathrm{reflected}$ acoustic paths, simulations yield a speed of information less than $c$. The speed of information from the interfering paths can exceed the speed derived from propagation only along the direct path. We conjecture these results will also hold for EM waves. If so, we prove the speed of information is less than or equal to the speed of light in a vacuum, so the effect does not violate special relativity. These theoretical and simulation results, as well as their conjectured EM extension, should be readily accessible to experimental verification.

Granular gases are commonly characterized through their velocity distribution, which provides access to the granular temperature. In experiments, velocity distributions are typically obtained by particle tracking, which becomes, however, limited at moderate and high particle densities. As a way forward, we propose a new technique for measuring particle velocities in situ by using a fiber Bragg grating (FBG) sensor, which remains applicable at significantly higher particle densities. The FBG sensor detects strain pulses induced by particle-fiber collisions, from which the velocity of the impacting particle can be derived. Applying this method to an ensemble of granular particles allows us to extract its velocity distributions, as we present for a granular system excited by a vibrational shaker. We validate the extracted velocity distribution against conventional particle-tracking measurements, confirming the reliability of the FBG-based technique.

We demonstrate that the thermodynamic friction metric governing dissipation in slowly driven continuous-time Markov chains is equivalent to the commute-time embedding and the resistance distance. This equivalence yields complementary insights: The commute-time embedding demonstrates the intrinsic cost of transporting probability across dynamical bottlenecks, while the resistance distance maps thermodynamic dissipation to Joule heating in an electrical network. We further demonstrate that the linear-response thermodynamic distance is a discrete $L^2$-Wasserstein optimal transport cost evaluated along paths of equilibrium distributions, extending a continuous-state correspondence to discrete networks. This conceptual synthesis of linear-response thermodynamics, random walks on graphs, electrical circuits, and optimal-transport theory connects independently developed geometric frameworks, reduces complex metric calculations to simple circuit algebra, and provides a clear physical picture of dissipation as the energetic cost of routing probability through the state space network.

The achievement of fusion ignition at the National Ignition Facility opens new opportunities for foundational studies in opacity, radiation transport, and high-flux astrophysics in regimes previously unattainable. We have succeeded in the critical enabling step of demonstrating ignition in a hohlraum modified with diagnostic windows that burn through prior to ignition, granting experimental access to the brightest radiation source in the laboratory. This Letter reports the results of a deuterium-tritium-layered capsule implosion that achieved $2.4\pm 0.09$ MJ neutron yield in a hohlraum with windows designed to allow the escape of radiation energy from the capsule without significant loss of implosion symmetry, thus establishing a validated platform for next-generation radiation-driven experiments.

Stochastic dynamical systems allow modeling of transitions induced by random disturbances, in particular from an attracting equilibrium and crossing the stable manifold of a saddle. While the small-noise limit is well-described by the large deviation principle, existing computational methods often struggle with stochastic forcings any more complicated than nondegenerate Gaussian white noise, and with unbounded time intervals. The primary innovations of this work are extending the framework to cater for colored and degenerate forcing and unbounded time horizons—scenarios that are physically realistic but numerically challenging. We cater for degenerate noise by using the Hamiltonian optimal control method. We cater for a class of colored noises by using linear filters on white noise. We cater for infinite time horizon by introducing the method of division (MOD), a computational approach for approximating rare transition events, including their most likely paths and the exponential scaling laws of their transition rates. The effectiveness of MOD and the above approaches to colored noise, is demonstrated by illustration on two examples: an inverted double-well potential and a simplified roll-heave model for ship capsize.

We present an explicit construction of the Freidlin-Wentzell quasipotential of a stochastic system with two degrees of freedom and nonreciprocal interactions. This model undergoes noise-induced transitions between four metastable attractors, forming recurrent but aperiodic “Escher cycles,” similar to the cyclic nucleation dynamics observed in the nonreciprocal Ising model. We calculate the quasipotential analytically to first order in nonreciprocality. We characterize it along a one-dimensional reaction coordinate that connects the attractors, and we also obtain the full two-dimensional landscape, at leading order in perturbation theory. The resulting landscapes feature flat regions and extended plateaus, together with nondifferentiable switching lines. These singular structures arise from two geometric mechanisms: the handover of dominance between competing transition paths, and the competition between basins of attraction. The system provides a rare case where the geometry of nonequilibrium rare events can be fully resolved, and a simple analytically tractable example of a quasipotential in more than one coordinate that captures a rich set of nonequilibrium features.

Quasimonoenergetic proton beams have long been anticipated in laser-driven ion acceleration, particularly via multispecies targets in radiation pressure and target normal sheath acceleration, yet experimental results are far from expectations, and the underlying cause remains elusive. Here we reveal that even under ideal conditions, only a power-law energy spectrum $d{\mathcal{N}}_p/d{\mathcal{E}}_p\propto {\mathcal{E}}_p^{-\eta }$, rather than a quasimonoenergetic one, is predicted by theory when full spatial dynamics are considered, where ${\mathcal{N}}_p$, ${\mathcal{E}}_p$, and $\eta$ represent the proton number, energy, and power-law index, respectively. Our theory and three-dimensional particle-in-cell simulations demonstrate that the spectral shape and index $\eta$ are independent of the specific acceleration mechanism, but are correlated with the target composition and laser focal spot profile. This provides a plausible explanation for the power-law spectra observed in experiments across a wide range of laser and plasma parameters. Possible paths to breaking the universality and achieving quasimonoenergetic proton beams are discussed.

Polymer mixtures fractionate between phases depending on their molecular weight. Consequently, by varying solvent conditions, a polydisperse polymer sample can be separated between phases to achieve a particular molecular weight distribution in each phase. In principle, predictive physics-based theories can help guide separation design and interpret experimental phase-diagram and fractionation measurements. Even so, applying the standard Flory-Huggins model can require numerical computations that hamper the predictions considering the full molecular weight distribution. Here, we apply a recently derived exact analytical solution of multicomponent Flory-Huggins theory for polydisperse homopolymers to understand the principles of polymer fractionation for common molecular weight distributions. Consistent with previous studies, the method highlights the sensitivity of polymer fractionation to the shape, and in particular the tails, of this distribution. Our results provide a systematic approach to evaluate the full molecular weight distribution in phase coexistence calculations over the possible composition space.

During gas breakdown between two parallel-plate electrodes excited by radio frequency (RF), a time-dependent striated optical emission structure is experimentally observed. Its formation process is reproduced and explained using particle-based simulations. The striations appear due to significantly different mobilities of ions and electrons, which lead to the formation of space charge in the vicinity of the extrema in the charged-particle densities under a strong RF electric field. The striated structures of the charge density as well as other parameters are amplified by locally enhanced ionization rates associated with the growing charged-particle density, and are eventually suppressed as the RF electric field is progressively screened from the discharge central region.

We derive the exact nonequilibrium steady state of a run-and-tumble particle (RTP) in $d$ dimensions confined in an isotropic harmonic trap $V\left(\mathbf{r}\right)=\mu r^2/2$, with $r=\parallel \mathbf{r}\parallel$. Rotational invariance reduces the problem to the stationary single-coordinate marginal $p_X\left(x\right)$, from which the radial distribution $p_R\left(r\right)$ and the full joint stationary density follow by explicit integral transforms. We first focus on a generalized trapped RTP in one dimension, where post-tumble velocities are drawn from an arbitrary distribution $W\left(v\right)$. Using a Kesten-type recursion, we represent its stationary position in terms of a stick-breaking (or Dirichlet) process, yielding closed-form expressions for its distribution and its moments. Specializing $W\left(v\right)$ to the projected velocity law of an isotropic RTP, we reconstruct $p_R\left(r\right)$ and the full joint distribution of all the coordinates in $d=1,2,3$. In $d=1$ and $d=2$, the radial law simplifies to a beta distribution, while in $d=3$, we derive closed-form expressions for $p_R\left(r\right)$ and the stationary joint distribution $P(x,y,z)$, which differ from a beta distribution. In all cases, we characterize a persistence-controlled shape transition at the turning surface $r=v_0/\mu$, where $v_0$ is the self-propulsion speed. We further include thermal noise characterized by a diffusion coefficient $D>0$, showing that the stationary law is a Gaussian convolution of the $D=0$ result, which regularizes turning-point singularities and controls the crossover between persistence- and diffusion-dominated regimes as $D\to 0$ and $D\to \infty$, respectively. All analytical predictions are systematically validated against numerical simulations.