A complex study of Ti hybrid X-pinch (HXP) radiation, including spectroscopic studies with temporal and spatial resolutions, source size measurements, and relative intensities of the spectral lines, was carried out. The time-resolved spectra recorded by the x-ray streak camera were calibrated in intensity using the time-integrated spectra recorded on imaging plates with a linear response. A numerical modeling using the PrismSPECT program was used to estimate the parameters of Ti HXP plasma and their changes in time. The radiation source size determined by point-projection radiography was taken into account in the spectral analysis. The results show that radiation collapse is most likely the mechanism responsible for the formation of hot spots in X pinches.

We explore how minimal neural networks can invert the renormalization group (RG) coarse-graining procedure in the two-dimensional Ising model, effectively dreaming up microscopic configurations from coarse-grained states. This task, formally impossible at the level of configurations, can be approached probabilistically, allowing machine-learning models to reconstruct scale-invariant distributions without relying on microscopic input. We demonstrate that even neural networks with as few as three trainable parameters can learn to generate critical configurations, reproducing the scaling behavior of observables such as magnetic susceptibility, heat capacity, and Binder ratios. A real-space renormalization group analysis of the generated configurations confirms that the models capture not only scale invariance but also reproduce nontrivial eigenvalues of the RG transformation. While the inversion is necessarily imperfect, these minimal models robustly reproduce the RG-relevant structure of the critical distribution. Surprisingly, we find that increasing network complexity by introducing multiple layers offers no significant benefit. These findings suggest that simple local rules, akin to those generating fractal structures, are sufficient to encode the universality of critical phenomena, creating an opportunity for efficient generative models of statistical ensembles in physics.

In this study, we developed a system of active particles equipped with visual perception and alignment abilities and explored their collective behavior in different flow environments. An intelligent-like behavior has been observed in strong shear flows: The active particles split into two separate groups, one swimming upstream in the high-velocity region and another moving downstream in the low-velocity region. By introducing the maximizing downstream flux efficiency, we identified the physical mechanism behind the autonomous choices of the active particles. Using nonequilibrium molecular dynamics simulations and statistical analysis, we also displayed the relationship between unusual behaviors and the order parameters such as dynamical entropy, orientation, and particle concentration. This work not only clarifies the complex physics of active matter but also offers theoretical insights and design guidelines for creating smart microswimmer systems.

In biological systems, active agents such as actomyosin and cells move and interact on curved surfaces, exhibiting diverse phenomena. These observations have motivated studies of how curvature shapes their collective behavior. Here, using a geometric framework, a minimal model is presented for interacting active agents on curved surfaces with Vicsek-like polar alignment. A transition between disordered and ordered states occurs on spheres as well as on oblate and prolate spheroids. As the deviation from sphericity increases, the transition point shifts to higher alignment strengths, and swarming localizes to an equatorial belt away from the poles, indicating that curvature heterogeneity influences the emergence of the polar-ordered state.

Reservoir computing has emerged as a powerful framework for time-series modeling and forecasting, including the prediction of discontinuous transitions. However, a mechanistic understanding of how reservoir computing reproduces discontinuous dynamical phenomena remains unexplored. This Letter elucidates the functioning of reservoir computing by analyzing its successful reproduction of boundary and attractor-merging crises. By examining the internal dynamics of the trained reservoir map, we reveal how a reservoir that is dynamically distinct from the target system nonetheless undergoes the same crisis mechanism and reproduces the associated scaling exponent with exact statistical correspondence. We establish this across distinct systems, exemplified by the logistic and Gauss maps. The Letter contributes to a broader understanding of the internal dynamics that enable learning algorithms to anticipate critical transitions.

We present a theory for the effects of osmotic pressure exerted by macromolecular crowders on a double-stranded DNA or other semiflexible polymer extended by tension. Our results predict the force and crowder-density dependence of the polymer extension. The lowest-order effect is a crowder-dependent compression that counteracts the external stretching force, which can collapse the polymer at a crowder-dependent critical force $f^{*}$. This compression is dependent on crowder radius $r$ and density $\varphi$, with higher densities and smaller radii having greater effects on the force-extension curve. At first order in perturbation theory, we also find a fluctuation-dependent correction to the depletion volume, which can overwhelm the simple compression effect for large crowders, leading to expansion of the polymer.

Promoter-proximal pausing is a key regulatory checkpoint in RNA polymerase II transcription, yet current models lack a unified description of how arrival-state-dependent pause heterogeneity shapes nascent transcriptional occupancy, completion flux, and steady-state mature mRNA levels. We introduce a mechanistic framework in which the promoter-proximal pause gate is a continuous-time Markov chain that modulates an $M/G/\infty$ queue. Each initiation event samples a gate-at-initiation state that fixes the entire episode lifetime—pause waiting, early elongation, and termination—yielding exact analytical expressions for transient and steady-state nascent concurrency, and the completion flux. From this structure, we derive an explicit gate-induced pause-initiation limit: a maximal productive initiation rate determined solely by the finite capacity and residence time of the promoter-proximal window. This bound provides a mechanistic explanation for experimentally inferred ceilings on transcriptional activation and clarifies how perturbations affecting pause duration, elongation speed, or termination latency move genes along the same constraint. We further obtain interpretable formulas for the promoter-proximal pausing index and its dependence on pause-state composition. Together, our results establish a compact mathematical theory linking microscopic pausing kinetics to macroscopic transcriptional outputs, offering general principles for how promoter-proximal regulation shapes gene expression dynamics.

A fluid with broken time-reversal symmetry would exhibit odd transport coefficients, such as odd viscosity, thermal conductivity, and diffusion coefficient, which may fundamentally alter the fluid properties and significantly influence the structure and dynamics of immersed objects. Here, we develop an efficient coarse-grained simulation approach for the odd fluid that captures all essential features of real odd fluids. Based on microscopic kinetic theory, we analytically derive the transport coefficients of the mesoscale odd fluid. We validate this approach by performing both simulations and theoretical calculations to explore the intricate transport phenomena of the odd fluid under various external drivings. Furthermore, we demonstrate that colloidal particles in the odd fluid exhibit unusual dynamic behavior.

We present a method for generating a narrowband terahertz (THz) emission using a radially oscillating plasma oscillator formed in a plasma slab with a longitudinal paraboliclike density profile. Through quasi-three-dimensional particle-in-cell simulations, we observe more stable, longer-lived plasma oscillations near the vertex of the density profile, resulting in temporally coherent THz emission. In contrast, a linear density gradient leads to faster decoherence of the plasma oscillator. The radiated THz pulse has a narrow spectral width of approximately 0.66 THz ($\sim 3.36\%$ relative bandwidth), with a spectral energy density efficiency of $0.01\%$ per THz, in good agreement with analytical results. These findings demonstrate the potential of this scheme to efficiently concentrate THz energy into a compact, narrower spectral band, offering a promising route toward the development of next-generation narrowband THz sources for applications in THz-driven particle acceleration and ultrafast wireless communication.

We study a quasi-one-dimensional fluid of hard dumbbells with continuous orientational degrees of freedom using an exact transfer-matrix formulation. The model allows for a complete analytical characterization of thermodynamic properties, orientational ordering, and correlation functions in terms of the spectral properties of an integral operator. We derive exact expressions for the equation of state, the orientational distribution function, and both partial and total radial distribution functions. Their asymptotic behavior is governed by the complex poles of the Laplace-transformed correlation functions, which determine the positional and orientational correlation lengths. At high densities, an extended intermediate regime with an approximate algebraic decay of the radial distribution function precedes the pole-dominated asymptotic exponential behavior. As density increases, the system exhibits a continuous crossover from a weakly ordered regime with a unimodal orientational distribution to a strongly constrained regime characterized by bimodal orientational ordering. This crossover is accompanied by a nonmonotonic behavior of the pressure relative to the Tonks gas and by a qualitative change in the decay of correlation functions from oscillatory to monotonic. In the high-pressure limit, we show that orientational and positional fluctuations contribute equally to the pressure, leading to a universal ratio of twice the Tonks pressure. The theoretical predictions are supported by numerical solutions of the discretized transfer operator and by scaling arguments that elucidate the high-pressure behavior of ordering and correlation lengths.

Two influential exact results in classical one-dimensional diffusive transport are about current statistics for the symmetric simple exclusion process: one in the stationary state on a finite line coupled with two unequal reservoirs at the boundaries, and the other in the nonstationary state on an infinite line. We present the corresponding result for the intermediate geometry of a semi-infinite line coupled with a single reservoir. This result is obtained using the fluctuating hydrodynamics approach of macroscopic fluctuation theory and confirmed by rare event simulations using a cloning algorithm. We apply our exact result for solving several related challenging problems, namely, the full counting statistics in presence of a defect bond, exclusion process with localized injection, survival of a tagged particle in presence of an absorbing boundary, and the stretched exponential decay in a kinetically constrained model.

We study the statistics of extinction and blowup times in well-mixed systems of stochastically reacting particles. We focus on the short-time tail, $T\to 0$, of the extinction- or blowup-time distribution ${\mathcal{P}}_m\left(T\right)$, where $m$ is the number of particles at $t=0$. This tail often exhibits an essential singularity at $T=0$, and we show that the singularity is captured by a time-dependent WKB (Wentzel-Kramers-Brillouin) approximation applied directly to the master equation. This approximation, however, leaves undetermined a large pre-exponential factor. We show how to calculate this factor by applying a leading- and a subleading-order WKB approximation to the Laplace-transformed backward master equation. Accurate asymptotic results can be obtained when this WKB solution can be matched to another approximate solution (the “inner” solution), valid for not too large $m$. We demonstrate and verify this method on three examples of reactions which are also solvable without approximations.

We introduce a technique for calculating the density operator time evolution along the lines of Heisenberg representation of quantum mechanics. Using this technique, we find the exact solution for the evolution of two and three coupled harmonic oscillators initially prepared in thermal states at different temperatures. We show that such systems exhibit interesting quantum dynamics in which oscillators swap their thermal states due to correlation induced in the process of energy exchange and yield noise-induced coherence. A photonic quantum heat engine (QHE) composed of two optical cavities can be modeled as coupled harmonic oscillators with time-dependent frequencies. Photons in the cavities become correlated during the engine operation. We show that the work done by such an engine is maximum if at the end of the cycle the oscillators swap numbers of excitations which can be achieved when the engine operates under the condition of parametric resonance. We also show that Carnot formula yields limiting efficiency for QHEs under general assumptions. Moreover, we show that, by making a canonical transformation, density operator of arbitrary $n$-mode Gaussian state can be written as a product of $n$ thermal density operators describing independent collective excitations with different temperatures. Thus, operation of QHEs based on the correlated Gaussian states is equivalent to that based on uncorrelated thermal reservoirs. Our results deepen understanding of quantum evolution of mixed states which could be useful to design quantum machines with better performance.

Antiferromagnetic Ising models on frustrated lattices can realize classical spin liquids, with highly degenerate ground states and, possibly, fractionalized excitations and emergent gauge fields. Motivated by the recent interest in many-body systems in negatively curved space, we study hyperbolic frustrated Ising models. Specifically, we consider nearest-neighbor Ising models on tesselations with odd-length loops in two-dimensional hyperbolic space. For finite systems with open boundaries we determine the ground-state degeneracy exactly, and we perform extensive finite-temperature Monte Carlo simulations to obtain thermodynamic data as well as correlation functions. We show that the shape of the boundary, constituting an extensive part of the system, can be used to control low-energy states: Depending on the boundary, we find ordered or disordered ground states. Our results demonstrate how geometric frustration acts in curved space to produce classical spin liquids.

Optical tweezers can confine position as well as orientation of a Brownian particle by simultaneously exerting restoring force and torque on it. Here we propose the theoretical model of a microscopic Stirling engine, using a Brownian ellipsoid as its working substance. The position and orientation degrees of freedom of the ellipsoid (in two dimensions), both being confined harmonically by the tweezers, are coupled to a hot and a cold thermal bath time-periodically. The stiffness of the force confinement is also time-periodic such that it resembles a pistonlike protocol, driving the Brownian ellipsoid through the strokes of a Stirling cycle. The ellipsoid takes heat from the hot bath and partially converts it into useful thermodynamic work. The extracted work and input heat shows explicit dependence on the geometry and orientation of the ellipsoid. The operational characteristics of the anisotropic Stirling engine is analyzed using the variance in work and average efficiency in the quasistatic regime. Several ways have been proposed to yield maximum efficiency at a minimum fluctuation in the output. The dissipative coupling between the position and orientation of the ellipsoid, arising due to its spherical-asymmetry and orientational bias, plays an important role in optimizing the engine characteristics. Finally, we have analytically explored the slightly anisotropic regime, where the equations of motion can be linearized. Average extracted work has been calculated in this regime, exhibiting an excellent agreement with the numerical results of the fully anisotropic system, when subjected to the stipulated range of parameters.

This paper introduces a data-driven framework for constructing accurate and general equivariant models of multiscale phenomena which does not rely on specific assumptions about the underlying physics. This framework is illustrated using incompressible fluid turbulence as an example that is representative, practically important, reasonably simple, and exceedingly well studied. We use direct numerical simulations of freely decaying turbulence in two spatial dimensions to infer an effective field theory comprising explicit, interpretable evolution equations for both the large (resolved) and small (modeled) scales. The resulting closed system of equations is capable of accurately describing the effect of small scales, including backscatter—the flow of energy from small to large scales, which is particularly pronounced in two dimensions—which is an outstanding challenge.

Plasma disruption in tokamaks is one of the most serious challenges in fusion research. Recent studies suggest that microturbulence may play an important role in facilitating thermal quench (TQ) and resultant disruptions, but experimental evidence on the impact of turbulence on the dynamic evolution of TQ is lacking. In this paper, we present a comprehensive observation using high spatiotemporal resolution, two-dimensional images of electron temperature fluctuations, which shows that before TQ for the initial heat transfer passing through the island $X$ point, turbulence magnitudes, correlation lengths, and propagation characteristics are all significantly enhanced, revealing crucial effects of turbulence on propelling energy quench. Our findings provide direct evidence that ambient turbulence may play a role in facilitating TQ and subsequent disruption in tokamak plasmas.

Physics-informed neural networks (PINNs) recast partial differential equations (PDEs) solving as an optimization problem in function space by minimizing a residual-based objective, yet many applications require additional derivative-based relations that are just as fundamental as the governing equations. In this paper, we present derivative-constrained PINNs (DC-PINNs), a general framework that treats constrained PDE solving as an optimization guided by a minimum objective function criterion where the physics resides in the minimum principle. DC-PINNs embed general nonlinear constraints on states and derivatives, e.g., bounds, monotonicity, convexity, incompressibility, computed efficiently via automatic differentiation, and they employ self-adaptive loss balancing to tune the influence of each objective, reducing reliance on manual hyperparameters and problem-specific architectures. DC-PINNs consistently reduce constraint violations and improve physical fidelity versus baseline PINN variants, representative hard-constraint formulations on benchmarks, including heat diffusion with bounds, financial volatilities with arbitrage-free, and fluid flow with vortices shed. Explicitly encoding derivative constraints stabilizes training and steers optimization toward physically admissible minima even when the PDE residual alone is small, providing reliable solutions of constrained PDEs grounded in energy minimum principles.

A colloidal monolayer embedded in the bulk of a fluid experiences a “compressible”, long-range hydrodynamic interaction which, far from boundaries, leads to a breakdown of Fick's law above a well-defined length scale, showing up as anomalous collective diffusion. We extend the model to study the effect of the hydrodynamic interaction on a monolayer formed by two types of particles. The most interesting finding is a new regime, in the limit of very dissimilar kinds of particles, where the effective dynamics of the concentration of “big” (slow) particles appears to obey Fick's law at large scales, but the corresponding collective diffusivity is completely determined, through hydrodynamic coupling, by the diffusivity of the “small” (fast) particles.