Understanding the spread of infectious diseases requires integrating movement, physical constraints, and spatial configurations into epidemiological models. In this study, we investigate how particle diffusivity, hardcore interactions, and nonequilibrium initial conditions influence infection dynamics within a system of Brownian particles. Using numerical simulations and theoretical analysis, we reveal a nontrivial relationship between diffusivity and the speed of infection spread. Specifically, when particles are initially positioned at uniform distances greater than the infection radius—a nonequilibrium configuration—there exists an optimal diffusion coefficient that minimizes the infection propagation speed. This counterintuitive result arises from the competition between diffusive timescales and the rate of infection transmission. The presence of an optimal diffusivity is observed both in systems with and without hardcore interactions, provided that the infection radius exceeds the mean lattice spacing. Our findings provide a theoretical framework for understanding and controlling the spread of infections in confined and diffusive environments, with potential implications for designing movement-based strategies for infection control.

We consider neighbor-induced damage percolation, a model describing systems where the inactivation of some elements may damage their neighboring active ones, making them unusable. We present an exact solution for the size of the giant usable component (GUC) and the giant damaged component (GDC) in uncorrelated random graphs. We show that even for strongly heterogeneous distributions, the GUC always appears at a finite threshold and its formation is characterized by homogeneous mean-field percolation critical exponents. The threshold is a nonmonotonic function of connectivity: robustness is maximized by networks with finite optimal average degree. We also show that if the average degree is large enough, a damaged phase appears, characterized by the existence of a GDC, bounded by two distinct percolation transitions. The birth and the dismantling of the GDC are characterized by standard percolation critical exponents in networks, except for the dismantling in scale-free networks where new critical exponents are found. Numerical simulations on regular lattices in $D=2$ show that the existence of a GDC depends not only on the spatial dimension but also on the lattice coordination number.

This study explores the systemic interactions between cancer stem cell markers (CSCMs) and cancer-affected organs through a hypergraph-based approach. By modeling organs as nodes and CSCMs as hyperedges, we capture multiway relationships beyond conventional pairwise analyses. Using the Louvain algorithm, we identify clusters of organs with shared CSCM profiles, revealing potential tumorigenic pathways and functional networks. Additionally, O-information quantifies redundancy and synergy, highlighting CSCMs with systemic influence on tumor progression and metastasis. These findings provide a structured framework for understanding CSCM dynamics across organ systems, offering insights that may inform targeted, multiorgan therapeutic strategies.

In the brain, fine-scale correlations combine to produce macroscopic patterns of activity. However, as experiments record from larger and larger populations, we approach a fundamental bottleneck: the number of correlations one would like to include in a model grows larger than the available data. In this undersampled regime, one must focus on a sparse subset of correlations; the optimal choice contains the maximum information about patterns of activity or, equivalently, minimizes the entropy of the inferred maximum entropy model. Applying this “minimax entropy” principle is generally intractable, but here we present an exact and scalable solution for pairwise correlations that combine to form a tree (a network without loops). Applying our method to over 1000 neurons in the mouse hippocampus, we find that the optimal tree of correlations reduces our uncertainty about the population activity by 14% (over 50 times more than a random tree). Despite containing only 0.1% of all pairwise correlations, this minimax entropy model accurately predicts the observed large-scale synchrony in neural activity and becomes even more accurate as the population grows. The inferred Ising model is almost entirely ferromagnetic (with positive interactions) and exhibits signatures of thermodynamic criticality. Together, these results suggest that a large amount of information may be compressed into a small number of correlations between neurons, and provide the tools for identifying the most important correlations in other complex living systems.

The $\varepsilon \text{−}{\mathrm{ZrH}}_2$ species act as an important component in zirconium-based composite hydrides, which have various applications in nuclear energy, hydrogen storage, and catalysis. In this work, deep-learning interatomic potentials for $\varepsilon \text{−}{\mathrm{ZrH}}_2$ have been developed by training density functional theory (DFT) data. The results indicate that the developed deep-learning interatomic potentials (DP) can accurately predict the structural, mechanical, and thermodynamic properties of $\varepsilon \text{−}{\mathrm{ZrH}}_2$ with DFT level accuracy. These deep-learning interatomic potentials are shown to be superior to the conventional modified embedded atom method potential. The H-isotope effect was also taken into account in constructing the deep-learning interatomic potentials, which facilitates molecular dynamic (MD) simulations under irradiation conditions. The development of these deep-learning interatomic potentials offers improved options for MD simulations of $\varepsilon \text{−}\mathrm{Zr}{X}_2$ $(X=\mathrm{H}, \mathrm{D}, \text{and} \mathrm{T})$.

In this paper, an alternative multiple-relaxation-time lattice Boltzmann (LB) scheme for conjugate heat transfer is proposed. We have derived the correct energy governing equation from the first law of thermodynamics and clarified that the thermal boundary for conjugate heat transfer should be the continuity of the conductive heat flux, which is essential to guarantee Galilean invariance. The development of the present model has two distinct features. First, the temperature evolution equation is modified to include the effect of the specific heat capacity. Second, the convection term is considered as a source term and is calculated in moment space, thus preserving the inherent advantages of the LB method without finite difference calculations. To verify the thermal governing equations, we conduct numerical simulations of heat conduction in a moving rod configuration with varying thermal capacities. Furthermore, to validate the present model's in accuracy, we examine several benchmark problems, including the effective thermal conductivity of parallel and series dual-component materials, steady and unsteady coupled heat transfer in horizontal interface channels, stable heat conduction in a two-layer annulus, natural convection in a square cavity with 16 solid blocks, and thermocapillary flow with two superimposed planar fluids. In the realm of application research, we apply our model to simulate the cooling process in random porous media. The results demonstrate that our model achieves satisfactory accuracy and exhibits a second-order convergence rate in space. The simulation results show the potential of the current LB method to simulate complex applications, highlighting its versatility and accuracy.

We consider the driven dynamics of a probe particle moving through an assembly of particles with competing long-range repulsive and short-range attractive interactions, which form crystal, stripe, labyrinth, and bubble states as the ratio of attraction to repulsion is varied. We show that the probe particle exhibits a depinning-like threshold from an elastic regime, where the probe particle is trapped by interactions with the other particles, to a plastic flow regime, where the probe particle can break bonds in the surrounding medium. For a fixed particle density, the depinning threshold and sliding velocity of the probe particle vary nonmonotonically as the attraction term is increased. A velocity minimum appears near the crystal to stripe crossover, and there is a significant increase in the depinning threshold in the bubble regime when the probe particle is strongly confined inside the bubbles. For fixed attractive interaction but increasing particle density, the behavior is also nonmonotonic, and there are jumps and drops in the velocity and depinning threshold corresponding to points at which the system transitions between different structures. There are also several distinct flow states that can be characterized by the amount of plastic deformation induced by the probe particle in the surrounding medium. Each flow state generates a different amount of effective drag on the probe particle, and there can be jumps in the velocity-force curve at transitions between the states. We also find that when oriented stripes are present, the probe particle can move along the stripe in an edge transport state that has a finite Hall angle.

This paper theoretically analyzes the diffusiophoretic motion of a soft particle comprising a surface charge-dependent hydrophobic rigid core coated with a polyelectrolyte layer. The surface hydrophobicity of the inner core is considered to depend on the lateral mobility of the surface charge ions. A modified slip boundary condition is employed at the inner core interface to account for the hydrodynamic frictional and tangential electric forces on the flow due to the mobile adsorbed ions. The dielectric permittivity of the porous layer is assumed to be lower than the background electrolyte's permittivity constant, yielding the ion partitioning effect. The electrolyte concentration is considered sufficiently high so that the electric double layer around the particle becomes very thin compared to the porous layer thickness, and the inner core surface can be regarded as a flat plate. Considering the particle to be weakly charged, for which the Debye-Hückel approximation can be applied, a closed-form general diffusiophoretic mobility expression is derived, valid to a thin Debye layer. Analytic expressions for the electrophoretic and chemiphoretic components are also reported separately. Asymptotic mobility solutions are further deduced for various limiting situations. Results show that the lateral mobility of the surface charge ions significantly influences the diffusiophoretic motion of the particle, and the impact is extensive for a highly hydrophobic surface. Additionally, the ion partitioning effect strengthens the influence of the surface charge mobility on the flow by augmenting the effective charge of the particle. The contribution of the surface charge-dependent hydrophobic inner core and the ion partitioning effect is substantial for a highly permeable porous layer and insignificant for a thinner hydrodynamic screening length.

Polar nematic liquid crystals have two special features, compared with conventional nematic liquid crystals. First, because of flexoelectricity, the combination of polar order and splay reduces the free energy. Second, because of electrostatics, any splay generates a bound charge density, which increases the free energy. To assess the competition between these two effects, we develop a theory that combines flexoelectricity and electrostatics. The theory predicts a phase diagram that includes ferroelectric, antiferroelectric, and conventional nematic phases.

We use integral representations and analytical solutions for the propagator of the $(1+1)$-dimensional Salpeter Hamiltonian to describe the wave propagation of a non-spin-relativistic quantum particle. An explicit expression of the propagator, or Green function, is derived in terms of special functions. The exact Green function is convoluted with the initial wave function by using suitable boundary conditions to find the wave propagation for both massive and massless particles. The analytical extension of the Hamiltonian in the complex plane allows us to formulate the equivalent stochastic problem, namely the Bäumer equation. This equation describes relativistic stochastic processes with time-changing anomalous diffusion. Given the known crossover between Cauchy and Gaussian diffusion, we analytically calculate the propagator to interpolate between these regimes. This Bäumer propagator corresponds to the Green function of a relativistic diffusion process that interpolates between Cauchy distributions for small times and Gaussian diffusion for large times, providing a framework for stochastic processes where anomalous diffusion is time-dependent.

Although the importance of quantum-classical correspondence has been recognized in numerous studies of quantum chaos, its usefulness in understanding quantum chaos through finite-time classical dynamics remains less well understood. We address this question in this work by performing a detailed analysis of how the quantum chaotic measure relates to the chaoticity of the finite-time classical trajectories. A good correspondence between them has been revealed in time- dependent and many-body systems, both of them being of the mixed type. In particular, we show that the dependence of the quantum chaotic measure on the chaoticity of finite-time trajectories can be well captured by a function that is independent of the system. This strongly implies the universal validity of the finite-time quantum-classical correspondence. Our findings enhance the understanding of quantum-classical correspondence and provide a promising approach to explore the ergodic hierarchy in quantum systems.

The donation game is an instance of a social dilemma with a single parameter given by the cost-to-benefit ratio of cooperation, $r$. In spatial settings, limited local interactions and clustering are capable of supporting cooperation by reducing exploitation from defectors. Traditionally, the interaction and competition neighborhoods are identical. Here we discuss intriguing differences in the dynamics that arise when separating the neighborhoods. On the square lattice, disjoint interaction and competition neighborhoods are easily realized by considering nearest-neighbor interactions and second-nearest-neighbor competition. Incidentally, the number of first and second neighbors is the same. More importantly, this separates the population into two competing subpopulations, with interactions solely between subpopulations but competition within subpopulations. For negative cost-to-benefit ratios, $r$, the donation game turns into a harmony game, and defection becomes an act of spite. In the traditional setup, the extinction of cooperators under harsh conditions, large $r$, and that of spiteful defectors, $r<0$, exhibits critical phase transitions with characteristics of directed percolation. In contrast, with two subpopulations spiteful behavior cannot persist, while the extinction of cooperators exhibits the same characteristics. Most intriguingly, however, for smaller $r$ spontaneous symmetry breaking in the levels of cooperation between the two subpopulations is observed. The symmetry breaking resembles the sublattice ordering occurring in the antiferromagnetic Ising model. Within the twofold-degenerate phases, decreasing the cost-to-benefit ratio induces extremely large fluctuations (bursts) in the frequencies of cooperation. These bursts eventually drive the system into one of the absorbing states: occasionally homogeneous defection in both sublattices, but usually only in one and homogeneous cooperation in the other, achieving perfect asymmetry.

This study explores biased random walk dynamics with stochastic resetting on general networks. We show that the combination of biased random walks and stochastic resetting makes significant contributions by analyzing the search efficiency. We derive two analytical expressions for the stationary distribution and the mean first passage time, which are related to the spectral representation of the probability transition matrix of a biased random walk without resetting. These expressions can be used to determine the capacity of a random walker to reach the specific target and probe a finite network. We apply the analytical results to two types of networks, pseudofractal scale-free webs and T-fractals, which are constructed through an iterative process. We also extend a strategy to explore other complex structure networks or larger networks by leveraging the spectral properties.

For many purposes, classical plasma dynamics models can work surprisingly well, even for strong electromagnetic fields, approaching the Schwinger critical fields, and high frequencies, approaching the Compton frequency. However, the applicability of classical models tends to depend rather sensitively on the details of the problem. In the present paper, we study the specific case of plasma oscillations to draw a line between the classical and quantum relativistic regimes. Due to the field geometry of study, mechanisms like radiation reaction and Breit-Wheeler pair production, which tend to be important for electromagnetic fields, are rather effectively suppressed. Moreover, we find that the polarization current due to the electron spin is generally negligible for frequencies below the Compton frequency, compared with the free current, whose magnitude is well-approximated by the classical Vlasov theory. However, we show that pair creation due to the Schwinger mechanism can sometimes be important for surprisingly modest field strengths, of the order of 10% of the critical field or even smaller. A rough guideline for when the classical Vlasov theory can be applied is given.

Cells and cellular organelles are encapsulated by nanometrically thin membranes whose main component is a lipid bilayer. In the presence of electric fields, the ion-impermeable lipid bilayer acts as a capacitor and supports a potential difference across the membrane. We analyze the charging dynamics of a planar membrane separating bulk solutions with different electrolyte concentrations upon the application of an applied uniform dc electric field. The membrane is modeled as a zero-thickness capacitive interface. The evolution of the electric potential and ion distributions in the bulk are solved for using the Poisson-Nernst-Planck equations. Asymptotic solutions are derived in the limit of thin Debye layers and weak fields (compared to the thermal electric potential).

We analyze, both analytically and numerically, the electrokinetic behavior of highly charged dielectric microparticles suspended in aqueous electrolytes. Specifically, we consider small particles subjected to nonuniform AC electric fields and describe their motion as resulting from the interplay between electrical forces—dielectrophoresis (DEP)—and steady electroosmotic flows on the particle surface, a phenomenon known as concentration-polarization electroosmosis (CPEO). The net particle motion is known as dipolophoresis (DIP). We derive analytical expressions for the induced motion in the limit of small Dukhin numbers (i.e., relatively low surface conductance) and validate these results by comparing them with numerical simulations. This comparison confirms the reliability of our numerical approach, which we then extend to explore dipolophoresis for arbitrary Dukhin numbers. Our findings indicate that at low AC field frequencies, the contribution of CPEO to particle motion becomes significant for Dukhin numbers around 0.1 or higher, eventually surpassing the effect of DEP when the Dukhin number is on the order of one.

Social dynamics are often driven by both pairwise (i.e., dyadic) relationships and higher-order (i.e., polyadic) group relationships, which one can describe using hypergraphs. To gain insight into the impact of polyadic relationships on dynamical processes on networks, we formulate and study a polyadic voter process, which we call the group-driven voter model (GVM), that incorporates the effects of group dynamics through nonlinear interactions that are subject to a group (i.e., hyperedge) constraint. By examining the competition between nonlinearity and group sizes, we show that the GVM achieves consensus faster than standard voter-model dynamics, with an optimal minimizing exit time. We substantiate this finding by using mean-field theory on annealed uniform hypergraphs with $N$ nodes, for which the exit time scales as $\mathcal{A}lnN$, where the prefactor $\mathcal{A}$ depends both on the nonlinearity and on group-constraint factors. Our results reveal how competition between group interactions and nonlinearity shapes GVM dynamics. We thereby highlight the importance of such competing effects in complex systems with polyadic interactions.

We review a conjecture by Ruppeiner that relates the nature of interparticle interactions to the sign of the thermodynamic curvature scalar $R$, paying special attention to the case of zero curvature. We highlight the underappreciated fact that there are two Ruppeiner metrics, and corresponding curvature scalars, that are equally viable in principle, obtained by restricting to systems of constant volume and constant particle number, respectively. We then demonstrate the existence of thermodynamic systems with vanishing curvature scalar but nontrivial interactions. Information about interactions in these systems is obtained by carrying out an inversion procedure on the virial coefficients. We demonstrate that these nontrivial zero-curvature systems are not always spurious or unphysical, as they include the well-known hard-sphere potential, as well as a system whose virial expansion matches, up to the second virial coefficient, that of a particular inverse-power potential. Finally, we argue that the ideal gas is the unique physical system for which both curvature scalars vanish. This leads us to propose an extension to Ruppeiner's conjecture.

Finding the model that best describes a high-dimensional dataset is a daunting task, even more so if one aims to consider all possible high-order patterns of the data (i.e., correlation patterns between three or more variables), going beyond pairwise models. For binary data, we show that this task becomes feasible when restricting the search to a family of simple models, which we call minimally complex models (MCMs). MCMs are maximum entropy models that have interactions of arbitrarily high order grouped into independent components of minimal complexity. They are simple in information-theoretic terms, which means they can only fit well certain types of data patterns and are therefore easy to falsify. We show that Bayesian model selection restricted to these models is computationally feasible and has many advantages. First, the model evidence, which balances goodness of fit against complexity, can be computed efficiently without any parameter fitting, enabling very fast explorations of the space of MCMs. Second, the family of MCMs is invariant under gauge transformations, which can be used to develop a representation-independent approach to statistical modeling. For small systems (up to 15 variables), combining these two results allows us to select the best MCM among all, even though the number of models is already extremely large (${10}^{68}$ models for 15 variables). For larger systems, we propose simple heuristics to find optimal MCMs in reasonable times. Also, inference and sampling can be performed without any computational effort. Finally, because MCMs have interactions of any order, they can reveal the presence of important high-order dependencies in the data, providing an alternative approach to explore high-order dependencies in complex systems. We apply our method to synthetic data and real-world examples, illustrating how MCMs portray the structure of dependencies among variables in a simple manner, extracting falsifiable predictions on symmetries and invariance from the data.

Spiral waves in cardiac tissue are believed to be closely related to cardiac diseases such as arrhythmias. Accurately identifying the phase singularity (PS, the tip of a spiral wave) is important in electrophysiological studies of cardiac arrhythmias, for which various PS detection methods have been proposed. However, clinical data are typically low resolution, resulting in insufficient spatial information about spiral waves. Direct application of conventional PS detection methods to such data often results in false points. Here we find a characteristic in the PS distribution within low-resolution data: false points tend to appear in adjacent pairs, exhibiting opposite chirality. Based on this noteworthy finding, we propose the topological charge-weighted method, which is suitable for a single spiral wave system within low-resolution data. This method calculates a weighted PS and then generates a spatial continuous PS trajectory, reducing PS-detecting error rates while improving localization accuracy.