Networks of coupled nonlinear elements can amplify weak periodic signals when unit heterogeneity is intermediate—a phenomenon known as diversity-induced resonance (DIR). We test this mechanism at surfactant-laden interfaces by modeling the interface as a two-dimensional lattice of laterally coupled patches obeying Frumkin adsorption-desorption kinetics, driven by sinusoidal modulation of the monomer concentration; diversity enters as patchwise variability in the adsorption drive. Using linear analysis and numerical simulations, we compute the first-harmonic gain of the mean surface coverage while sweeping diversity and other parameters, and connect the model to experimental observables. Intermediate diversity maximizes the collective response, increasing mean coverage by $\sim 3–4\times$ versus a homogeneous interface and improving robustness to frequency variations, consistent with DIR. Because the effect emerges from network coupling, the model requires no empirical time constants or ad hoc relaxation pathways.

The shear response of polyelectrolytes in solutions drives distinct microscopic conformational dynamics whose correlation with macroscopic rheological properties remains an open question. Using large-scale, fully atomistic molecular dynamics simulations of aqueous solutions of fully sulfonated polystyrene polyelectrolytes, we probe their conformational dynamics under shear. In contrast to their quiescent extended configuration, we find that the chains exhibit a wide variety of conformational fluctuations, folding back with minimal extension in the transverse direction, often resulting in tumbling at a characteristic time ${\tau }_r$ that scales with the shear rate as ${\dot{\gamma }}^{-2/3}$. These results, together with similar scaling previously observed experimentally for biomolecules, have established that tumbling dynamics transcends many orders of magnitude in shear rate.

In this work, we propose a methodology for robustly estimating particle-size distributions from optical scattering measurements using constrained Gaussian process regression. The estimation of particle-size distributions is commonly formulated as a Fredholm integral equation of the first kind, an ill-posed inverse problem characterized by instability due to measurement noise and limited data. To address this, we use a Gaussian process prior to regularize the solution and integrate a normalization constraint into the Gaussian process via two approaches: By constraining the Gaussian process using a pseudomeasurement and by using Lagrange multipliers in the equivalent optimization problem. To improve computational efficiency, we employ a spectral expansion of the covariance kernel using eigenfunctions of the Laplace operator, resulting in a computationally tractable low-rank representation without sacrificing accuracy. Additionally, we investigate two complementary strategies for hyperparameter estimation: a data-driven approach based on maximizing the unconstrained log-marginal likelihood, and an alternative approach where the physical constraints are taken into account. Numerical experiments demonstrate that the proposed constrained Gaussian process regression framework accurately reconstructs particle-size distributions, producing numerically stable, smooth, and physically interpretable results. This methodology provides a principled and efficient solution for addressing inverse scattering problems and related ill-posed integral equations.

Complex systems exhibit macroscopic behaviors that emerge from the coordinated interactions of their individual components. Understanding the microscopic origins of these emergent properties remains a significant challenge, especially in less-understood systems, due to the absence of a generalized framework for identifying the governing degrees of freedom. The multiscale complexity formalism, developed to address this challenge, consists of a set of information-theoretic indices designed to identify the scales at which collective behaviors emerge. In this article, we evaluate one such index, the complexity profile, by applying it to the two-dimensional Ising model with finite-range interactions. In particular, we show that the complexity profile captures the transition between the disordered and ordered phases by detecting the emergence of multiscale structure exclusively in the critical region, and therefore offering insights into the formation of magnetic domains from an information-theoretic perspective. Additionally, we show that the pairwise complexity exhibits a maximum in the disordered phase that remains bounded in the thermodynamic limit. These results highlight the potential of the multiscale complexity formalism to probe emergent behaviors and detect hidden features of critical phenomena in interacting systems beyond the classical characterization of correlations.

The $q$-state Potts model on a square lattice and in the presence of an external magnetic field that couples to only one state is studied by using extensive Monte Carlo simulations. A hybrid algorithm that combines the single spin-flip Metropolis method with Wolff cluster updates is employed. Single-histogram reweighting techniques and the field-mixing approach are also used to obtain the corresponding universal probability distributions. By analyzing the finite-size scaling behavior of the order parameters and universal probability distributions, the phase diagram in the temperature–versus–external field plane is obtained in the thermodynamic limit for some values of state $q\ge 5$. For positive fields and higher values of $q$, there is a clear first-order transition line that starts at the zero-field transition point and ends at an isolated critical point, which is in the two-dimensional Ising universality class. As the value of state $q$ decreases, the corresponding critical field also decreases. The present results strongly suggest that the critical field vanishes when $q\to 5^{+}$. However, for negative fields, another first-order transition line occurs without any critical or multicritical behavior for $q\ge 6$. The special case with $q=5$ undergoes a weak first-order transition at zero field and a line of second-order transitions for negative fields. This second-order transition line is in the same universality class as the $q=4$ Potts model.

This paper explores the catastrophic energy transformations, in particular the ones leading to the generation of a flow in a weakly rotating self-gravitating fluid/gas found, for instance, in the vicinity of a massive compact object. Because of the similarity in the governing equations, the system dynamics is worked out exactly in parallel to the methods developed for investigating catastrophic relaxation in stellar plasmas [S. Ohsaki et al., Astrophys. J. 559, L61 (2001); Astrophys. J. 570, 395 (2002); E. Saralidze et al., Astrophys. Space Sci. 370, 64 (2025)]. In the latter, a more “complex” equilibrium state, on slow changes in the environment, can lose its equilibrium (catastrophe), and transform to a less complex state with a very different energy mix from the original. It is shown that a similar transformation in the weakly rotating self-gravitating fluid/gas will convert much of its gravitation energy into kinetic energy in the flow. Since flows are a perennial ingredient of high-energy astrophysical systems, the energy transformation processes revealed in the present study, can advance our understanding of a variety of them. Some particularly relevant examples are macroscale flows / structures in galaxies, accretion discs, and the dynamics and stability of a rotating star / its atmosphere.

In this work, we investigate the competition among collective modes and single-beam side scatter (SSRS) of stimulated Raman scattering (SRS) in multibeam configuration, utilizing theoretical analysis and particle simulations. The shared common electrostatic (SCE) wave is excited, when the crossing angle is smaller than a critical angle. Besides the SCE mode, two electrostatic submodes are developed, which results in a higher growth rate of SCE than the other collective and single-beam SRS modes. The SCE excites an overall intense electrostatic field, which produces a large number of hot electrons compared to SSRS. The SCE mode remains dominant with damping considered. Compared to plane waves, the beams with speckle can enhance the SCE, thus increasing hot-electron generation. In contrast to SSRS, the wavelength of SCE scattered light increases with the exit angle and is concentrated in a small exit angle range. The collective SCE mode is significantly mitigated by two incident beams with a 0.5% frequency difference, and then single-beam SRS dominates, which leads to the suppression of hot-electron generation. When the crossing angle is larger than the critical angle, SSRS is the dominant mode under direct-drive conditions. The shared common scattered light is significantly developed in the kinetic regime of quasi-homogeneous plasmas with density scale length above $1800{\lambda }_0$, where ${\lambda }_0$ is the wavelength of the incident laser. Additionally, at a high laser intensity or a large crossing angle, SSRS is the dominant mode.

Machine learning, however popular and accessible, is computationally intensive and highly power-consuming, prompting interest in alternative physical implementations of learning tasks. We introduce a training scheme for physical systems that minimize power dissipation in which only boundary parameters (inputs and outputs) are externally controlled. Using this scheme, these Boundary-Enabled Adaptive State Tuning Systems (BEASTS) learn by exploiting local physical rules. Our scheme, BEASTAL (BEAST-Adaline), is the closest analog of the Adaline (ADAptive LInear NEuron) algorithm for such systems. We demonstrate this autonomous learning in silico for regression and classification tasks. Our approach advances previous physical learning schemes by using intuitive, local evolution rules without requiring large-scale memory or complex internal architectures. BEASTAL can be applied to any physical system with a linear input-output map, achieving best performance when the local evolution rule is nonlinear.

This paper documents trapped Scholte modes; nonradiative surface acoustic modes that exist on fluid-loaded elastic cylindrical rods. Unlike previously reported surfacelike waves on cylindrical rods, the Scholte modes identified here are localized to the interface between the rod and the surrounding fluid. The study investigates plastic rods submerged in water, which allows exploration of the domain where the Scholte velocity is significantly slower than the speed of sound in water. The supported Scholte modes are shown to become increasingly localized to the solid-fluid interface at high frequencies, as their phase velocities tend toward that of the Scholte wave on a planar interface. This was modeled numerically using the finite element method and experimentally verified using cast acrylic rods underwater.

We introduce the random permutation sorting system (rpss), a software-defined framework that generates high-quality, uniform randomness by leveraging a statistical model with properties analogous to quantum ensembles. The RPSS operates as a self-contained entropy engine based on a fundamental pair of conjugate observables: the permutation count $\left({\widehat{N}}_p\right)$ and the elapsed sorting time $\left(\widehat{T}\right)$. While the raw distributions of these observables are highly right-skewed and heavy-tailed, we provide a mathematical proof of their synchronous convergence to uniformity. The permutation count distributions follow a negative binomial model, and the observed modes for different repetition parameters $m$ closely match the theoretical predictions, confirming the validity of the model. This convergence arises from the compounded effect of permutation counts on elapsed time and a process of modular uniformization, in which a vast number of microstates—stemming from combinatorial complexity and intrinsic system-level jitter—map onto a finite set of uniform symbols. This mechanism enables the RPSS to function as a true uniform random number generator. We implemented the RPSS as the qpp-rng, a practical software realization that autonomously harvests entropy from a platform's intrinsic microarchitectural dynamics. Unlike prior benchmark-focused studies, this work provides a deep theoretical and experimental investigation of the underlying physical mechanism. Empirical validation confirms our predictions, demonstrating rapid entropy convergence and statistically uniform outputs, as assessed by NIST SP 800-90B min-entropy and χ-squared statistics, consistent with central-limit-theorem expectations. The RPSS establishes a class of quantum-inspired ecocryptosystems, in which randomness is simultaneously harvested from unpredictable hardware behavior and amplified through a combinatorial process. This dual-source approach provides a compact, self-stabilizing entropy engine suitable for foundational applications in cryptography, blockchain protocols, and digital currency systems, offering a robust, platform-agnostic alternative to conventional entropy sources.

From an entropy-based formulation of the first law of thermodynamics in the quantum regime, we investigate the performance of Otto-like and Carnot-like engines for a single-qubit working medium. Within this framework, the first law includes an additional contribution—coherence work—that quantifies the energetic cost of deviating the quantum trajectory from its natural unitary evolution. We focus on the efficiency of the heat-to-coherence work conversion and show that the Carnot cycle achieves the classical Carnot efficiency, while the performance of the Otto cycle is upper-bounded by the Carnot efficiency corresponding to the extreme temperatures of the cycle. We identify entropy generation during the isochoric stages as the key source of irreversibility limiting the Otto cycle's efficiency.

Triadic percolation turns bond percolation into a dynamical problem governed by an effective one-dimensional unimodal map. We show that the geometry of superstable cycles provides a direct, map-agnostic probe of local nonlinearity: specifically, the distance from the map's maximum to a distinguished next-to-maximum point on the attracting $2^n$ cycle (which coincides with a preimage of the maximum at $2^n$ superstability) scales as ${\left|\mathrm{\Delta }p\right|}^{\gamma }$, with $\gamma =1/z$, where $z$ is the nonflat order of the maximum. This prediction is verified across canonical unimodal families and heterogeneous triadic ensembles, with Lyapunov spectra corroborating the one-dimensional reduction. A derivative condition on the activation kernel fixes the local nonlinearity order $z$ (and thus, under standard unimodal-map hypotheses, the associated $z$-logistic universality class) and gives conditions under which $z>2$ can be realized. The diagnostic operates directly on orbit data under standard regularity assumptions, providing a practical tool to classify universality in higher-order networks.

The growth of biological tissue is sensitive to the physical properties of the environment. For example, the growth rate of contractile tissue under geometric confinement is proportional to local curvature as a result of tissue surface tension. It is not known how local cell behavior is coordinated to give rise to such behavior. Here we use computer simulations based on the cellular Potts model to investigate the role of cell adhesion and cortical contractility for curvature-dependent tissue growth. Our results show that tension-dependent local proliferation leads to the observed macroscopic curvature-driven growth kinetics via tissue surface tension, independent of soluble growth factors.

Time-varying media enable electromagnetic nonreciprocity, frequency conversion, and the production of momentum space bandgaps in time crystals. However, most theoretical descriptions contain fundamental omissions in their treatment of how the medium properties are modulated, notably in the creation and removal of polarizable constituents. Neglecting the consequences of the momentum that these particles contribute to the medium plays a significant role in the outcome, and in some cases gives rise to nonphysical amplification of the waves. This is a problem traceable to the methods by which the medium's properties are modulated, and in this article, we give a specific example for the case in which the polarizable constituents are free-electron carriers injected and removed by ionization and recombination. We show that when momentum conservation is accounted for consistently in the modulation process, one avoids spurious postcollapse field growth commonly interpreted as amplification, or gain. This analysis applies broadly to other polarizable constituents and points to the importance of addressing how these constituents of the medium are introduced or altered; otherwise one encounters questions about how the surroundings perform work on the medium to account for this predicted gain.

In this study, we employ molecular dynamics simulations to investigate the anomalous diffusion of ${\mathrm{Na}}^{+}$ and ${\mathrm{Cl}}^{-}$ ions in water at the microscopic scale. By carefully distinguishing the migration (drift) component from the diffusive motion, we analyze the influence of ion concentration and applied electric fields on ionic transport. Our results reveal clear deviations from classical Brownian dynamics under nonzero electric fields, highlighting the emergence of anomalous diffusion regimes. We further show that the degree of anomalous behavior strongly depends on the ionic chemical activity and the strength of Coulombic interactions between ions.

We investigate the behavior of dynamical systems with nonholonomic constraints when coupled to a thermal bath, focusing on the paradigmatic case of the Chaplygin sleigh. A straightforward Langevin-type approach—obtained by naively adding stochastic and dissipative terms to the equations of motion—predicts a regime in which useful work can be extracted, violating the second law of thermodynamics. To resolve this paradox, we resort to a physically motivated implementation of the nonholonomic constraint as the limiting case of a viscous interaction. However, at finite temperature, fluctuation-dissipation relations imply that the viscous force has to be complemented with stochastic forces acting at the contact. We show that their incorporation restores compliance with the second law. Therefore, our results place fundamental limits on the physical realizability of idealized nonholonomic constraints.