We present a systematic numerical study of the ferromagnetic Ising model on complex networks with tunable degree-degree correlations. The networks are constructed with a truncated power-law degree distribution, ensuring finite moments, and correlations are introduced via the Xulvi-Brunet-Sokolov rewiring algorithm, allowing continuous control from assortative to disassortative mixing. Using finite-size scaling analysis, we find that the system undergoes second-order phase transitions for all correlation levels. Assortative correlations increase the critical temperature, whereas disassortative correlations decrease it. For strong correlations, the network exhibits a segregation into degree-based subgroups, leading to multiple cascading transitions before the system fully loses magnetic order. For the continuous transitions, we compute the critical exponents and find that they remain robust across all correlation regimes, taking values consistent with mean-field predictions for the Ising model. This result establishes that the universality class of the system is preserved despite significant changes in network structure. Our results highlight the distinct roles of degree correlations in shaping nonuniversal features, such as transition temperatures and structural organization, while leaving the underlying universality class unchanged. This work provides insights into the interplay between topology and critical phenomena in heterogeneous networks.

Contagion processes in social systems often involve interactions that go beyond pairwise contacts. Higher-order networks, represented as hypergraphs, have been widely used to model multibody interactions, and their presence can drastically alter contagion dynamics compared to traditional network models. However, existing analytical approaches typically assume independence between pairwise and higher-order degrees, and thus study their roles in isolation. In this paper, we develop an effective hyperdegree model to describe susceptible-infected-susceptible dynamics on hypergraphs that explicitly captures correlations between the distribution of groups with different sizes. Our effective hyperdegree model shows excellent agreement with stochastic simulations across different types of higher-order networks, including those with heterogeneous degree distributions. We explore the critical role of cross-order degree correlations, specifically, whether nodes that are hubs in pairwise interactions also serve as hubs in higher-order interactions. We show that positive correlation decreases the epidemic threshold and anticorrelation temporally desynchronizes infection pathways (pairwise and group interactions). Finally, we demonstrate that, depending on the level of correlation, the optimal control strategy shifts—from one that is purely pairwise or higher-order focused to one in which a mixed strategy becomes optimal.

The height of viral particles adsorbed on solid substrates is governed by the equilibrium between adhesion energy and capsid elasticity. While the resulting height distribution has been proposed as a noninvasive proxy for viral stiffness, the physical origin of its broadening is unknown. In this work, we combine atomic force microscopy (AFM) topography measurements of adenoassociated virus (AAV8) and Hepatitis B virus (HBV) with a theoretical shell-deformation model to identify the determinants of height dispersion. By modeling the viral shell as an elastic body under adhesive load, we evaluate the relative contributions of thermal fluctuations and mechanical heterogeneity to the observed height dispersion. We demonstrate that thermal noise is insufficient to explain the width of the distribution. Instead, the data support a model where the dispersion in height arises from the intrinsic variability of capsid stiffness. This variability is associated to the surface inhomogeneity of identical capsids. Our results validate that, when this inhomogeneity is accounted for, the height distribution of adsorbed particles provides a quantitative measure of viral mechanics without the need for individual nanoindentation.

Cells interpret noisy biochemical signals with remarkable precision, raising fundamental questions about the limits of sensing. While receptor-level constraints in chemosensing are well characterized, the role of intracellular signaling remains underexplored. We develop a theoretical framework combining stochastic simulations and analytical methods to quantify the accuracy of spatial gradient detection via readout of the cellular response triggered by a linear signaling cascade. Our analysis reveals striking robustness: directional sensing accuracy is unaffected by cytoplasmic activation rates and remains stable under deactivation rates that are much faster or slower than the rest, a regime frequently observed in natural signaling cascades. This robustness persists under nonlinear output transformations that mimic amplification processes such as actin polymerization. Counterintuitively, under certain conditions, downstream readouts improve directional accuracy by extending the effective integration time set by intracellular dynamics.

Protein complexes play crucial roles in cellular functions. The work focuses on the prediction of interchain ternary residue contacts in protein complexes. We first constructed a homotrimeric dataset from the PDB and developed DTC (Deep Ternary Contact predictor), a hybrid deep learning framework for ternary contact prediction. DTC does not require experimentally resolved structures as input, and integrates PLM embeddings with MSA-dependent representations extracted from the Evoformer module of AlphaFold2. DTC breaks through the challenge faced by existing residue-residue contact prediction methods, namely the inability to identify ternary contacts. On an independent test set, DTC achieves a Top-1 accuracy of 0.6. Furthermore, we propose the Top-$k$ coverage metric, which provides a more reasonable standard for evaluating model performance under extreme class imbalance. Application results demonstrate that the sites predicted by DTC can be used as constraints to significantly improve the success rate of protein docking and show good generalization capability in viral trimers. The study provides a new computational tool and insights for understanding the cooperative assembly mechanisms of multimeric proteins.

We present an experimental investigation demonstrating that the suppression of parametric instabilities in laser-plasma interactions under conditions relevant to direct-drive inertial confinement fusion, specifically backward stimulated Raman scattering (SRS) and two-plasmon decay (TPD), shows the effects of spatial beam coherence and plasma-density scale length. Experiments were performed with the GEKKO-XII kilojoule laser, both with and without random phase plates. Results indicate that SRS suppression is enhanced under conditions of initial spatial incoherence, whereas TPD is influenced primarily by the plasma scale length. A comparison of the relationship between the SRS and TPD signals and hot-electron measurements suggests that TPD is the primary source of hot electrons across all experimental conditions. These findings underscore the importance of beam coherence in determining instability dominance and carry implications for mitigating hot-electron preheat in ignition-scale inertial-confinement implosions as well as for developing hot-electron-driven schemes such as shock ignition.

Equilibrium molecular dynamical simulations of three-dimensional (3D) Yukawa fluids are performed to investigate thermodynamics and supercritical transition of 3D dusty plasma fluids. The normalized reduced excess entropy $s_{\mathrm{ex}}/s_{\mathrm{ex}}^{\mathrm{m}}$ of 3D Yukawa fluids (where $s_{\mathrm{ex}}^{\mathrm{m}}$ is the excess entropy at the melting point) is calculated from their reduced excess internal energy $u_{\mathrm{ex}}$, so that different variation trends of $s_{\mathrm{ex}}/s_{\mathrm{ex}}^{\mathrm{m}}\propto {\mathrm{\Gamma }}^{0.5}$ and $\propto {\mathrm{\Gamma }}^2$ are obtained at lower and higher temperatures, respectively. A fitting expression for $s_{\mathrm{ex}}/s_{\mathrm{ex}}^{\mathrm{m}}$ of 3D Yukawa fluids for all temperatures is proposed here, leading to the general expression of the excess internal energy. The relative deviation of $s_{\mathrm{ex}}/s_{\mathrm{ex}}^{\mathrm{m}}$ with respect to the power law scaling is introduced as $\mathrm{\Delta }$, so that the criterion of $\mathrm{\Delta }=10\%$ is proposed to determine the supercritical transition of 3D Yukawa fluids between the liquidlike and gaslike states. It is found that the supercritical transition points of 3D Yukawa fluids identified from $\mathrm{\Delta }=10\%$ well agree with the previous results from other criteria.

The aim of this work is to predict the opacity of plasmas under stellar conditions. We focus on iron and nickel, as these elements have been extensively investigated both theoretically and experimentally. In certain regimes, notably under nonlocal thermodynamic equilibrium, calculating the spectral opacity can be computationally demanding. To mitigate this difficulty, we employ deep learning models to predict accurate opacities while achieving a substantial reduction in computational cost. Specifically, we develop and train a hybrid model that combines a convolutional neural network with a multilayer perceptron. The proposed approach provides both the spectral opacity over the 0–10 000 eV energy range, and the corresponding mean opacities for large sets of temperatures and mass densities, in close agreement with standard computational methods. The agreement is very satisfactory, except in the L- and M-shell energy ranges for nickel at high temperatures, where a very large number of emerging transitions leads to substantial discrepancies.

We investigate the emergence of cooperative and competitive behaviors in multiagent reinforcement learning systems, where autonomous agents, modeled as active particles, learn to capture targets dynamically regenerated in geometrically distinct arenas. Using proximal policy optimization, we study circular, elliptical, and square geometries with varying target distributions. Our results show that cooperation frequently induces spontaneous symmetry breaking with agents exploring different arena regions, whereas competition tends to preserve symmetry and produce overlapping space distributions. In both fixed and mobile-target simulations, symmetry breaking occurs. The ability to break symmetry and reach optimal strategies depends sensitively on the arena geometry, agent number, and location of target-rich regions. Moreover, in a “blind” situation where agents lose direct target perception, symmetry breaking can still emerge in rare instances, highlighting that memory and interagent interactions alone may suffice to induce asymmetry. Introducing stochasticity through reward noise further facilitates this process, allowing agents to escape metastable symmetric states with moderate performance and converge toward fully asymmetric configurations with optimal performance. Together, these findings establish symmetry breaking as a key organizing principle in learning-based multiagent systems and reveal how geometry, reward structure, and stochasticity jointly govern the emergence of collective intelligence.

Self-propelled particles (SPPs) model diverse transport phenomena, including T-cell motility within crowded lymph nodes. Efficient scanning of antigen-presenting cells (APCs) by T cells remains poorly understood under dense conditions. Here we simulate a two-dimensional system of radius-oscillating SPPs with random propulsion directions and short-range interactions to mimic T-cell scanning of a low-mobility APC. We find that scanning rate peaks at an optimal oscillation frequency and intermediate area fraction, with maximal efficiency in the processive limit of motion. This optimum arises from local inflow dynamics near the target and coincides with a percolation transition in the particle contact network. Active deformation enhances scanning efficiency compared to nonoscillating particles, especially at higher densities. These findings suggest that mechanical interactions and active shape changes facilitate effective target exploration in crowded environments, providing insights relevant to cellular transport and active matter systems.

We investigate the nonequilibrium compression of a confined colloidal fluid driven by a mobile boundary within dynamical density functional theory. The system consists of a hard-sphere fluid confined between two parallel walls, one of which acts as an overdamped piston subjected to a sudden increase in external pressure. The piston motion is characterized by a mobility parameter $K$. By varying $K$ over several orders of magnitude, we identify a crossover from quasistatic compression to a diffusion-limited strongly driven regime. For small $K$, the system evolves through near-equilibrium states and the total injected work attains its minimal value, equal to the equilibrium free-energy difference. In contrast, for large $K$, the piston rapidly adjusts and the dynamics becomes controlled by the intrinsic diffusive relaxation of the confined colloidal fluid, leading to universal saturation behavior of the piston trajectory, pressure-position relation, particle currents, and center-of-mass velocity. In this regime, the total injected work and entropy production are bounded, reflecting fundamental constraints imposed by diffusive transport. We find that the maximum injected power scales linearly with $K$, while the entropy-production peak exhibits a crossover from quadratic growth to saturation. The corresponding peak times display distinct $1/K$ asymptotic regimes separated by an intermediate crossover. The entropy change of the thermal bath is computed explicitly and shown to interpolate between the reversible limit, where it exactly compensates the configurational entropy loss of the fluid, and a strongly driven regime dominated by irreversible dissipation. Finally, the time evolution of the configurational entropy and the external potential energy reveals a dynamical decoupling between geometric confinement and structural relaxation, including transient nonmonotonic behavior in the high-mobility regime. These results provide a quantitative thermodynamic characterization of boundary-driven compression and uncover generic nonequilibrium features governed by a single mobility parameter.

Geometrical design endows the curved beam with asymmetric bistability without prestress, contributing to its widespread applications in integrated forming and microscale fabrication. However, the gravity bias inherent in these applications is rarely considered, undermining the accurate prediction of its real-world dynamics. To address this, a gravity-biased nonlinear dynamic model is established via Hamilton's principle and reduced to a Duffing-type equation with a constant term using Galerkin's method. Numerical simulations show that both the interwell oscillations and the contained vacillating behavior manifest as V-shaped regions in the excitation amplitude-frequency parameter space. Bifurcation analysis reveals that the left and right boundaries of the vacillating region correspond to saddle-node and period-doubling bifurcations, respectively, both of which can be theoretically predicted. Furthermore, gravity bias is shown to deepen the lower potential well. For an upward-curved beam, it enlarges the excitation range for interwell motion when initiated from the upper equilibrium, while narrowing the range when starting from the lower one. The influence of key geometric parameters is also systematically examined. Base excitation experiments were conducted to equivalently simulate distributed force excitation, and the results validate the theoretical predictions. This work elucidates the effects of practical environmental conditions and structural parameters on the nonlinear dynamics of curved beams, thereby providing a foundation for the design of mechanical metamaterials and related engineered systems.

The linear response of a symmetric binary ionic liquid confined between two parallel, blocking electrodes is analyzed under the influence of electrostatic correlations $L_c$ while systematically comparing the effects of two distinct interfacial boundary conditions: the Bazant–Storey–Kornyshev (BSK) boundary condition, corresponding to the vanishing correlation limit, and the de Souza-Bazant (dSB) boundary condition, which incorporates finite electrostatic correlation effects at charged electrode surfaces. In contrast to conventional weakly nonlinear models involving thin-double-layer scalings based on small parameters ${\lambda }_D/L\ll 1$ or $\sqrt{{\lambda }_D{L}_c}/L\ll 1$, where ${\lambda }_D$ denotes the Debye screening length and $L$ is the half-cell thickness, the present formulation accommodates double layers of arbitrary width. Using matched asymptotic expansions at leading order, both the frequency-dependent response and the equilibrium response are derived in the asymptotic limit $\varepsilon =L_c/L\ll 1$. This separation of length scales gives rise to interfacial dynamics and equilibrium characteristics that are not captured by standard Debye-based asymptotic models. In particular, the frequency response exhibits a low-frequency inductive behavior manifested by a negative real impedance, a negative complex capacitance, and a nonmonotonic phase response, with these effects being significantly enhanced under the dSB boundary condition. The origin of the inductive regime is linked to the emergence of a low-frequency chemical-inductor contribution, which introduces an intrinsic slow dynamical process. Together with the fast Debye timescale serving as the reference scale, this leads to a coupled fast–slow dynamical structure that becomes increasingly pronounced with growing electrostatic correlation length $L_c$. A scaling analysis in the low-frequency regime further identifies a distinguished intermediate relaxation timescale $L_c^2/D$ in the strong-correlation regime, where $D$ denotes the diffusion coefficient of the ion species. At equilibrium, the dSB boundary condition likewise predicts a nonclassical negative differential capacitance that is absent under the BSK boundary condition.

Fracture processes in multiphase solids are inherently complex due to multiple competing mechanisms. Here, we investigate the elastic and fracture behavior of two-phase solids comprising a fragile phase and a tough phase using a disordered spring network model. As the proportion of the tough phase increases, the system undergoes a reentrant phase transition in fracture behavior: from brittle to ductilelike and back to brittle. These transitions and the physical interpretation of the underlying mechanisms are identified through avalanche statistics and cluster-size characteristics of broken springs. Notably, the avalanche exponent associated with the majority phase changes universality class during the brittle-to-ductile transition. In the brittle regime, dominant clusters rapidly absorb other large clusters. In contrast, the ductile regime is characterized by more gradual coalescence, leading to a decrease in the total number of clusters over time while their average size increases.

We introduce a constructive framework to learn effective Langevin equations from stationary time series. Unlike conventional approaches that require iterative calibration to match target statistics, our construction guarantees the observed steady-state density by design and enforces short-lag coordinate-correlation constraints directly from data—so that the surrogate satisfies the targeted invariant measure and short-lag coordinate-correlation constraints from the outset, without trial-and-error tuning. The drift is parametrized in terms of the score function—the gradient of the logarithm of the steady-state distribution—and a constant mobility matrix whose symmetric part controls dissipation and diffusion and whose antisymmetric part encodes mean nonequilibrium circulation. The score is learned from samples using denoising score matching, while the mobility coefficients are inferred from short-lag correlation identities estimated via a clustering-based finite-volume discretization on a data-adaptive state-space partition. We validate the approach on low-dimensional stochastic benchmarks and on partially observed Kuramoto-Sivashinsky dynamics, where the resulting Markovian surrogate preserves the marginal invariant measure and captures the temporal correlations of the resolved variables. The resulting Langevin models define explicit reduced generators that enable efficient sampling and generation of synthetic trajectories with the imposed statistical and dynamical constraints, without direct simulation of the underlying full dynamics.

The Jarzynski equality, which relates equilibrium free-energy difference to an average of nonequilibrium work, plays a central role in modern nonequilibrium statistical thermodynamics. In this paper we study a weaker consequence of this relation, known as Jarzynski's inequality, which can be formally obtained from the Jarzynski equality via Jensen's inequality. We identify and analyze several extensions of Jarzynski's inequality that go beyond its direct derivation from the Jarzynski equality. In particular, we consider chemical systems both in the linear regime and away from linear thermodynamics. Furthermore, by employing functional-integral techniques, we extend Jarzynski's inequality to many-body statistical systems described by quantum field theory. Salient issues, such as connections of the Jarzynski inequality with the maximum work theorem and the Landau-Lifshitz theory of fluctuations, are also discussed.

This work extends the universal finite-size scaling framework for continuum percolation from two-dimensional (2D) to quasi-three-dimensional (Q3D) stick systems, in which sequentially deposited wires of finite diameter stack vertically on a flat substrate. Using Monte Carlo simulation, the percolation threshold is determined for isotropic Q3D stick systems as $N_c{l}^2=6.850923\pm 0.000014$, approximately $21.5\%$ above the established 2D value of 5.6373. The threshold is shown to be independent of the wire diameter-to-length ratio $d/l$, reflecting the scale invariance of the contact topology under sequential deposition. Simulation results indicate that, as for 2D networks, the spanning probability of Q3D stick percolation on square systems with free boundary conditions collapses onto a single universal scaling function once a nonuniversal metric factor is introduced. This collapse is consistent with the universal scaling function established for 2D continuum and lattice percolation.

The Boltzmann distribution for an equilibrium system constrains the statistics of the system by the energetics. Despite the nonequilibrium generalization of the Boltzmann distribution being studied extensively, a unified framework valid for far-from-equilibrium discrete-state systems is lacking. Here, we derive an exact path-integral representation for discrete-state processes and represent it using the exponential of the action for stochastic transition dynamics. Solving the variational problem, the effective action is shown to be equal to the inferred entropy production rate (a thermodynamic quantity) and a nonquadratic dissipation function of the thermodynamic length (TL) defined for microscopic stochastic currents (a dynamic quantity). This formulates a far-from-equilibrium analog of the Boltzmann distribution, namely, the minimum action principle. The nonquadratic dissipation function is physically attributed to incorporating non-Gaussian fluctuations or far-from-equilibrium nonconservative driving. Further, an exact large deviation dynamical rate functional is derived. The equivalence of the variational formulation with the information geometric formulation is proved. The nonquadratic TL recovers the nonquadratic thermodynamic-kinetic uncertainty relation (TKUR) and the speed limits, which are tighter than the close-to-equilibrium quadratic formulations. Moreover, if the transition affinities are known, then the nonquadratic TL recovers the fluctuation relation (FR). The minimum action principle manifests the nonquadratic TKUR and FR as two faces corresponding to the thermodynamic inference and partial control descriptions, respectively. In addition, the validity of these results is extended to coarse-grained observable currents, strengthening the experimental/numerical applicability of them.

We investigate collective dynamics in a pulse-coupled adaptive Winfree network under the influence of a frustration (phase-lag) parameter. The coupling strengths coevolve according to a Hebbian adaptation rule and self-organize to support a wide variety of collective states. We observe frequency-clustered states, entrainment, bump states, bump-frequency cluster states, antipodal and multiantipodal cluster states, chimera states, and incoherent dynamics. Notably, we report the spontaneous emergence of entrainment, bump, and bump-frequency cluster states in an adaptive network without any external forcing. To systematically characterize these regimes, we introduce three complementary measures of incoherence based on (i) time-averaged frequencies, (ii) instantaneous phases, and (iii) mean frequencies per bin. These measures enable the construction of one- and two-parameter phase diagrams that clearly delineate transitions between distinct dynamical states. Furthermore, we analytically derive the stability condition for the frequency-entrained state, which shows excellent agreement with numerical simulations. Our results highlight the crucial role of frustration-mediated plasticity in shaping rich self-organized dynamics in pulse-coupled adaptive networks.