In network-based susceptible-infected-susceptible (SIS) models of infectious disease transmission, infection can only occur between directly connected individuals. This constraint naturally gives rise to spatial correlations between the states of neighboring nodes, as the infection status of connected individuals becomes interdependent. Although mean-field approximations and the standard pairwise model are commonly used to simplify disease forecasting on networks, they inadequately capture spatial correlations; mean-field frameworks assume that populations are well mixed, while the pairwise model neglects correlations beyond nearest-neighbor connections, which leads to inaccurate predictions of infection numbers over time. As such, the development of approximations that account for higher-order spatially correlated infections is of great interest, as they offer a compromise between accurate disease forecasting and analytic tractability. Here we use existing corrections to mean-field theory on the regular lattice to construct a more general framework for equivalent corrections on random regular graph topologies. We derive and simulate a hierarchical system of ordinary differential equations for the time evolution of the spatial correlation function at various geodesic distances on random networks. Solving these equations allows us to predict the time-dependent global infection density, which agrees well with numerical simulations. Our results substantially improve on existing corrections to mean-field theory for infectious individuals in SIS processes and provide an in-depth characterization of how structural randomness in networks affects the dynamical trajectories of infectious diseases on networks.

The reoriented potential mixer (RPM) can enhance mixing and reaction during in situ remediation of contaminated groundwater, in which a chemical or biological amendment is introduced into a contaminant plume to react with and degrade the contaminant. Each step of the RPM consists of dipole flow between a pair of wells—one for injection and one for extraction—that is active for a dimensionless time $\tau$. Subsequently, a different pair of wells is activated at a reorientation angle $\mathrm{\Theta }$. Certain $\{\tau ,\mathrm{\Theta }\}$ pairs lead to chaotic advection, with elliptic islands embedded in a chaotic sea. Importantly, a central elliptic island encompasses a “trapped” region containing fluid that does not reach an extraction well during typical groundwater remediation timescales. Indeed for groundwater remediation, the RPM must be designed so that the contaminant remains within such a trapped region, eliminating the possibility of degrading previously uncontaminated regions of the aquifer. In our model, this trapped region consists of two parts: an inner region surrounding an elliptic fixed point within which the fluid is minimally mixed by weak shear, and an outer region with stronger shear that can produce substantial stretching of the interface between the contaminant and amendment plumes. An RPM designed to contain the contaminant and amendment plumes within the trapped region has the potential to enhance reaction during in situ remediation of contaminated groundwater, while preventing the spread of contaminated groundwater into previously uncontaminated regions of the aquifer.

We propose a time-series paradigm named ChronoCast for molecular dynamics simulation using an advanced autoregressive Equivariant Graph Neural Network, which reformulates the conventionally time-consuming integration process into a forecasting task. This reformulation in turn offers a promising solution to overcome the challenge of tremendous computational cost for studying complex physical problems based on traditional algorithms. By incorporating velocity as a node feature and enforcing momentum conservation, the proposed model achieves exceptionally high accuracy in reproducing diverse physical properties. Using the simple Si crystal and complex van der Waals ${\mathrm{NbSe}}_3$ nanowires as two examples, we demonstrate that the radial distribution function, mean-squared displacement, and vibrational density of states in both systems can be accurately reproduced by long-term autoregressive forecasting. More importantly, ChronoCast significantly reduces the trajectory generation time by orders of magnitude compared to the ab initio method and by more than half compared to state-of-the-art machine learning potential, demonstrating superior efficiency over these prevailing molecular dynamics simulations. This work offers an accurate and efficient time-series approach for studying the statistical physics of complex dynamical processes.

Wave propagation in multiply scattering media is a fundamental and long-standing topic that has undergone renewed interest with the advent of metamaterials. In particular, disorder control is an efficient way to alter wave propagation. Stealth hyperuniform (SHU) media, characterized by the cancellation of large-scale density fluctuations, have proven particularly relevant in this context. The objective of this article is to show that the remarkable properties of these media are robust to many material constraints and are therefore a very relevant avenue for the control of mechanical waves, i.e., acoustic waves in fluid media and elastic waves in isotropic solids. Furthermore, this article provides theoretical insights by reformulating multiple scattering correlation integrals through the radial structure factor $S\left(q\right)$. This spectral approach explicitly demonstrates that transparency in SHU media arises from the exact cancellation of the monopole scattering mode by positional correlations. We formally demonstrate the existence of a transparency regime for elastic waves in solids. We show that this phenomenon is remarkably robust against mode conversions and, crucially, is governed by two distinct cutoff frequencies for longitudinal and transverse waves. Our results establish a comprehensive framework for wave control in correlated disorder, from transparency to mechanical waves to the emergence of Bragg-induced attenuation peaks.

We develop an operational framework to characterize energy exchange between a quantum system and a finite, dynamically tunable environment modeled by a quantum kicked top. Instead of relying on external driving or equilibrium assumptions, heat and work are defined through a weighted decomposition of the reservoir energy change, where the weighting factor quantifies the degree of thermalization based on long-time fluctuations. This approach provides a continuous interpolation between coherent (worklike) and incoherent (heatlike) energy transfer in finite quantum systems. By tuning the dynamical regime of the quantum kicked top from regular to chaotic, we show that increasing chaos enhances effective thermalization and converts structured energy flow into stochastic behavior. As an illustration, we consider an Otto-like energy conversion protocol. We demonstrate that both the apparent efficiency and fluctuations depend sensitively on the reservoir dynamics. This example highlights how partial thermalization can act as a resource while emphasizing that the resulting efficiency is an operational quantity rather than a thermodynamic bound.

Control of transcription presides over a vast array of biological processes, including those mediated by gene regulatory circuits that exhibit multistability. Within these circuits, two- and three-gene network motifs are particularly critical to the repertoire of metabolic and developmental pathways. Theoretical models of these circuits, however, often vary parameters such as dissociation constants, transcription rates, and degradation rates without specifying precisely how these parameters are controlled biologically. In this study, we examine the role of effector molecules, which can alter the concentrations of the active transcription factors that control regulation, and are ubiquitous in regulatory processes across many biological settings. We specifically consider allosteric regulation in the context of extending the standard bistable switch to three-gene networks, and explore the rich multistable dynamics exhibited in these architectures as a function of effector concentrations. We then analyze how the dynamics evolve under various interpretations of regulatory circuit mechanics, underlying inducer activity, and perturbations thereof. Notably, the biological mechanism by which we model effector control over dual-function proteins transforms not only the phenotypic trend of dynamic tuning but also the set of available dynamic regimes. In this way, we determine key parameters and regulatory features that drive phenotypic decisions, and offer an experimentally tunable structure for encoding inducible multistable behavior arising from both single- and dual-function allosteric transcription factors.

This study investigates the influence of visual field offset—a restricted field of vision positioned at a fixed angle from an agent's heading direction—on collective behavior using the Vicsek model. This offset shapes an agent's ability to perceive and respond to neighbors, thereby impacting overall group dynamics. Our results reveal that introducing an offset leads to a diverse range of emergent behaviors, some of which are counterintuitive. Notably, in the case of flipped sensing, where particles perceive their surroundings from directly behind their heading direction, we observe near-perfect alignment and clustering at the center of mass, even under high noise conditions. This outcome contrasts with the conventional Vicsek model, where increased noise typically disrupts group behavior. Drawing inspiration from biological observations, where animals often process information beyond their immediate heading, these findings highlight the benefits of offset perception in natural systems. Furthermore, they suggest potential applications in artificial systems, such as multiple-robot teams, where implementing offset sensing could enhance coordinated group behavior.

Knotted proteins embed a physical (i.e., open) knot within their native structures. For decades, significant effort has been devoted to elucidating the functional role of knots in proteins, yet no consensus has been reached. Here, using extensive Monte Carlo off-lattice simulations of a simple structure-based model, we isolate the effect of topology by comparing simulations that preserve the linear topology of the chain with simulations that allow chain crossings. This controlled framework enables us to isolate topological effects from sequence, structure, and energetic contributions. We show that protein kinetic stability, defined as resistance to unfolding at a fixed temperature, is higher in knotted proteins. Additionally, kinetic stability increases significantly with knot depth, whereas foldability (or folding efficiency) is comparatively less affected. By considering a simple model of protein evolution in which amino-acid alphabet size is used as a proxy for evolutionary time, we find that increasing primary-sequence complexity through the addition of biotic amino acids predominantly enhances kinetic stability. Taken together, these results indicate that kinetic stability is a functional advantage conferred by protein knots and suggest that evolutionary pressure for kinetic stability could contribute to the persistence of knotted proteins.

We derive the asymptotic behavior of the radial distribution function $g\left(x\right)$ for one-dimensional (1D) hard-rod systems and related quasi-one-dimensional geometries at high packing fractions using Laplace transform techniques and pole analysis. By identifying the poles and residues of the Laplace transform in the limit of small void fraction, we obtain compact representations of $g\left(x\right)$ in terms of the Jacobi elliptic theta function ${\theta }_3$. This formulation naturally captures the two regimes governing the oscillatory decay toward unity: an intermediate algebraic decay and a long-distance exponential decay, consistent with previous results for the Tonks gas. Our approach provides a unified framework that (i) expresses $g\left(x\right)$ in a single well-tabulated special function, (ii) links spatial correlations directly to the pole structure in complex Laplace space, offering clear physical insight into decay rates and oscillation frequencies, and (iii) generalizes straightforwardly to 1D binary mixtures and confined hard-disk systems, where direct Gaussian decompositions are cumbersome. The equivalence between the theta-function representation and the Gaussian superposition of [L. Bouzar et al., Phys. Rev. E 112, L042105 (2025)] is established via the Poisson summation formula, highlighting the versatility and conceptual advantages of the Laplace-pole framework.

Exact equilibrium constraints in quantum many-body systems, such as sum rules and Ward identities, can be hard to expose because local currents are distributional and equal-time commutators may contain anomaly-like contact terms. We present a simple geometric organization of the recently identified quantum shift symmetry. The local shifting superoperator is viewed as a statistical-gauge connection acting on observables, so hyperforces are covariant derivatives and force balance becomes a thermal Ward identity expressed with the Kubo-Mori inner product. For compact smearings we prove bulk flatness: smeared shift generators close on the Lie bracket of vector fields with no bulk Schwinger term. This closure yields an iterative, all-orders Ward-Bianchi hierarchy of equal-time constraints that closes on a finite set of force insertions and a bilinear force-gradient kernel, leading to general antisymmetry relations and positivity bounds. We fix the microscopic generator by quantizing the diffeomorphism moment map, where Weyl and half-density quantization agree and give a self-adjoint operator without bulk ordering anomalies. Although the bulk connection is flat, global topology remains through holonomy, which links naturally to twisted boundary conditions and flux threading. Background electromagnetic fields deform the algebra through explicit density-weighted curvature insertions rather than a bulk central extension. As an application we treat fractional quantum Hall fluids on a torus, connect the deformation to the guiding-center GMP-$W_{\infty }$ algebra, and verify the key identities with exact diagonalization.

Many biophysical processes begin when the fastest searcher finds a target out of many random searchers, which is called an extreme or fastest first passage time (fFPT). In some models (i) the fFPT vanishes logarithmically as the number of searchers grows and (ii) the fFPT can be faster for subdiffusive search compared to normal diffusion. Though mathematically rigorous, the relevance of (i) and (ii) to actual physical systems is suspect since their derivations involve searchers which move with unbounded speed. Indeed, we previously proved that the fFPT for searchers with bounded speed converges exponentially to a strictly positive minimal search time as the number of searchers grows. In this paper we study fFPTs for a broad class of anomalous and normal diffusion models with bounded or unbounded speed. These models include scaled Brownian motion, Riemann-Liouville fractional Brownian motion, and fractional Brownian motion. For all of these models, we show that the fFPT decays logarithmically in the number of searchers and that subdiffusion can be faster than normal diffusion (we further show that superdiffusion can be slower than normal diffusion). In this sense features (i) and (ii) are rather universal. On the other hand, we show that the parameter regimes in which (i) and (ii) are valid depend on the particulars of the individual model, and thus ambiguities remain in the relevance of these features to specific physical systems.

Nonreciprocal interactions that violate Newton's law “actio = reactio” are ubiquitous in nature and are currently intensively investigated in active matter, chemical reaction networks, population dynamics, and many other fields. An outstanding challenge is the thermodynamically consistent formulation of the underlying stochastic dynamics that obeys local detailed balance and allows for a rigorous analysis of the stochastic thermodynamics of nonreciprocally interacting particles. Here, we present such a framework for a broad class of active systems and derive, by systematic coarse-graining, exact expressions for the macroscopic entropy production. Four independent contributions to the thermodynamic dissipation can be identified, among which the energy flux sustaining vorticity currents manifests the presence of nonreciprocal interactions. Then, Onsager's nonreciprocal relations, the fluctuation-response relation, the fluctuation relation, and the thermodynamic uncertainty relations for nonreciprocal systems are derived. Finally, we demonstrate that our general framework is applicable to a plethora of active matter systems and chemical reaction networks and opens new paths to understand the stochastic thermodynamics of nonreciprocally interacting many-body systems.

Two-dimensional systems of nonlocally coupled oscillators exhibit spiral wave chimeras, a phenomenon that has garnered significant attention in nonlinear dynamics. These chimeras are characterized by the simultaneous presence of synchronized spiral regions and desynchronized phase-randomized cores. In contrast, biological pacemakers have been shown to entrain organic cells, producing regular rhythmic patterns. Here, we report on a rather surprising finding that the spiral chimeras emerge from a spatially homogeneous state due to an external pacemaker in a sphere of all-to-all coupled oscillators interacting with distance-dependent time delays. Our results reveal that the emergent spiral chimeras exhibit an exotic type of dynamics that presents the spiral arms with phase kinks. Notably, our analysis demonstrates that such a kink-spiral chimera state is universally observed in both pure-phase and amplitude-phase oscillator systems. Moreover, we show that adjusting the forcing strength and frequency of the pacemaker enables the switching between multiple types of kink-spiral chimeras. Furthermore, for the phase oscillator system, we uncover the intricate structure of the Arnold tongue, illustrating a resonant-type interplay between the pacemaker's frequency and interaction time delay that governs spiral chimera formation. This study sheds light on the dynamics of coupled oscillators and highlights the role of external drivers in generating complex spatiotemporal patterns.

Adaptive chaos control has been studied extensively for autonomous systems. For real-world, nonautonomous systems, such as planetary weather, observations of the system state in response to seasonally and diurnally varying forcing are available only at discrete times and locations. We consider a stochastic representation of such systems to account for observational and process noise, as a building block for adaptive control, and develop and test control strategies in an idealized low-dimensional setting. We present the first example of finite-time adaptive chaos control for a seasonally forced and noise-perturbed Lorenz-84 model. We consider two strategies for triggering control: (1) Stochastic: based on transition probabilities for the latent states of a nonhomogeneous hidden Markov model (NHMM); and (2) Dynamical: using local Lyapunov exponents (LLE). The NHMM triggers coincide with strongly positive LLE regimes, confirming their dynamical interpretability. While both approaches enable selective, small energy interventions and achieve significant suppression of extreme excursions, the NHMM and related state-space methods may generalize to interpretable stochastic control models for meteorological applications.

We study the flow of a Bingham yield stress fluid in a pore network model where the throats have radii drawn from a uniform distribution. We consider the case in which a fraction of the largest radii is blocked. The fluid can flow only through the percolating cluster that exists when the fraction is above the percolation threshold. Two distinct flow regimes are identified: above the percolation threshold the flow curve can be characterized by deterministic values of the critical pressure drop, permeability, and other observables, with subleading fluctuations that we quantify. At the percolation threshold these quantities become non-self-averaging, and their scaling is governed exclusively by the critical percolation backbone, independent of the specific realization of the radii.

Dust clusters with crystalline structure, formed by charged microparticles in complex plasmas, can undergo phase transitions under external influences. While many studies have focused on the effects of temperature and pressure on crystallization, it is worthwhile to investigate a different factor such as the influence of an electron beam on the structural properties of the cluster. This study investigates the melting of a dust cluster with crystalline symmetry composed of micrometer-size plastic spheres, suspended in a radio-frequency argon plasma, induced by a high-energy pulsed electron beam with a tunable energy range of 9–14 keV. Imaging at 60 frames per second provided high-resolution particle trajectories, enabling structural and dynamical analyses via Voronoi diagrams, pair-correlation functions $g\left(r\right)$, and particle tracking velocimetry. The results show that the electron-beam energy governs the melting process. At 9–10 keV, gradual disordering and hexaticlike behavior suggest a continuous transition, with $g\left(r\right)$ peaks broadening continuously and particle velocities increasing incrementally. At 11–14 keV, rapid destabilization, chaotic trajectories, and abrupt $g\left(r\right)$ collapse indicate a sharp transition, with melting times dropping from 0.6 s to 0.1 s. The process is characterized by the induced rotation of the entire crystal irradiated with 9–10 keV electrons, exhibiting minimal structural deformation, followed by a shear-induced destabilization of the rotating layers and eventual melting of the cluster. Velocity and kinetic-energy distributions further confirm distinct transition regimes. These findings highlight the electron beam's role in controlling phase behavior, offering insights into the dynamics of the crystalline structure with potential applications in astrophysics, semiconductor processing, and fusion research.

Shock-augmented ignition (SAI) [R. H. H. Scott et al., Phys. Rev. Lett. 129, 195001 (2022)] is an alternative inertial confinement fusion concept that is designed to achieve high energy gain by combining improved resilience to instabilities with enhanced fuel compression. In SAI, lower implosion velocities can improve hydrodynamic stability and limited laser intensities reduce the excitation of detrimental laser-plasma instabilities, enabling greater areal density accumulation. Here we report an experimental investigation of SAI using a series of warm ${\mathrm{D}}_2$ implosions. By timing the augmenting shock to arrive at the implosion center immediately prior to peak compression, the areal density and hot-spot pressure increase by 50% and 112%, respectively, relative to unoptimized designs. Ignition-scale simulations reproduce the observed timing dependence and demonstrate the potential of SAI for inertial fusion energy.

Partitioning of (bio)materials in polymeric mixtures is a key phenomenon both in cellular environments, as well as in industrial applications. In cells, macromolecules are distributed among distinct biomolecular phases. The coexistence of polymeric aqueous phases in multicomponent systems has been widely exploited for the extraction and purification of (bio)materials from aqueous environments. Despite their relevance, key physical and chemical properties controlling the phase behavior of these complex systems are still lacking. Here, we have developed a classical density functional theory approach for describing the phase coexistence and partitioning of an arbitrary number of polymers and suspended materials. As a case example, we focus on a binary mixture of phase separating polymers in which a third material is dispersed. We explore the effect of size ratios and affinities between the different materials and address their distribution and coexisting densities, and find optimal conditions for partitioning.

Nanoparticle-protein dispersions constitute complex soft materials in which competing attractive and repulsive interactions can strongly influence their phase behavior, including gelation. In this work, we demonstrate a strategy to utilize interaction between anionic silica nanoparticles and anionic protein bovine serum albumin to achieve heat-induced gels with tunable physical properties. Upon heating, the protein molecules in solution undergo unfolding followed by hydrophobic aggregation, leading to the formation of a three-dimensional gel network. The introduction of negatively charged nanoparticles generates additional electrostatic repulsion that competes with the attractive hydrophobic interactions between partially unfolded proteins. This competition modifies both the structure and mechanical properties of the resulting gels. In particular, nanoparticle-protein gels exhibit markedly enhanced optical transparency ($\sim 90\%$) compared with gels formed from pure protein solutions ($<1\%$). Rheological measurements further show shear-thinning behavior, with the gel strength decreasing systematically with increasing nanoparticle concentration, leading to progressively softer gels. At sufficiently high nanoparticle content, gelation is completely suppressed, thereby stabilizing the protein dispersion against thermal aggregation. The underlying mechanism is elucidated in terms of interaction potentials obtained by modeling small-angle neutron scattering data measured in situ during gel formation. Finally, we demonstrate that nanoparticle concentration and ionic strength serve as effective parameters to control gel opacity and mechanical rigidity, enabling the formation of both soft and rigid gels. These results demonstrate how nanoparticle-mediated interactions can regulate aggregation and gelation in protein-based soft matter systems.