arXiv preprints from January 1, 2026 through September 24, 2026 — 09:16:55 EST
Ultra-low-frequency electromagnetic waves Pc1 (0.2-5 Hz), widely known in the literature as pearls, are excited in the outer radiation belt and propagate to the Earth along the geomagnetic field lines in the form of Alfven waves. The study of pearls is of considerable interest for magnetospheric physics. Both spontaneous and...
Diffusion-Limited Aggregation (DLA), the canonical model for non-equilibrium fractal growth, emerges from the simple rule of irreversible attachment by random walkers. Despite four decades of study, a unified computational framework reconciling its stochastic algorithm, universal fractal dimension, multifractal growth measure, and...
We present STENSOR, a new computational tool integrated into the Bilbao Crystallographic Server, designed for the automatic calculation of symmetry-adapted tensors under spin group symmetry. The program requires either a file containing the structural data of the magnetic compound or the generators of the oriented spin point group,...
Fluctuation dynamos provide a robust mechanism for amplifying weak seed magnetic fields in turbulent astrophysical plasmas. However, their behaviour in the highly compressible regimes characteristic of the interstellar medium remains incompletely understood. Using high-resolution 3D magnetohydrodynamic simulations of supersonic...
We present a complete analytic and semi-analytic study of gravitational collapse and primordial black hole (PBH) formation in the quadratic $f(R)$ model $f(R)=R+αR^2$. We first derive the perturbative expansion around General Relativity (GR), working to first order in the small parameter $α$. For a collapsing flat FLRW dust interior...
We delineate the scope of research on the completeness of eigenstates in quantum mechanics. Based on the limit of the potential function at infinity, the proof of completeness is divided into eight cases, and theoretical proofs or numerical simulations are provided for each case. We present the definition of orthonormalization for...
Mechanosensitive ion nanochannels regulate transport by undergoing conformational changes within nanopores. However, achieving precise control over these conformational states remains a major challenge for both artificial soft or solid pores. Here, we propose an alternative mechanism that modulates the charge carrier density inside...
Random matrix ensembles with Dyson index $β=L^{2}$ describe systems of $M$ charge-$L$ particles interacting logarithmically in the presence of an external potential, yet exact formulas for their physical observables have remained elusive for $L\neq 1,2$. We show that, for $L$ even, $β=L^{2}$ ensembles are governed by the KP hierarchy...
LaRh2X2 (X = Al, Ga, In) compounds crystallize in a tetragonal layered ThCr2Si2-type structure and belong to a family of low critical temperature superconductors. Using first-principles density functional theory calculations implemented in the CASTEP code, we systematically investigated their structural, mechanical, elastic,...
Cross-country soaring flights rely on intermittent atmospheric updrafts to cover long distances, producing trajectories that alternate between rapid relocation and local exploration. From a large dataset of paraglider, hang glider, and sailplane flights, we uncover a universal transport law: beyond short ballistic times, horizontal...
Quantum entanglement serves as a key phenomenon in understanding correlations in many-body systems, but analytical results remain scarce for coupled three-body oscillators. In this work, we address this gap by introducing a geometrical diagonalization approach that constrains Euler angles, thereby reducing the degrees of freedom in...
We present studies of electron identification (eID) in the MPD experiment at NICA using machine learning techniques. The goal is to improve electron identification efficiency while preserving high purity, which is crucial for dielectron analyses. We compare electron identification performance between traditional cut-based approach and...
Ohm's law provides a fundamental framework for understanding charge transport in conductors and underpins the concept of electrical scaling that has enabled the continuous advancement of modern CMOS technologies. As transistors are scaled to even smaller dimensions, device channels inevitably enter low-dimensional regimes to achieve...
Cellular automata (CA) are discrete-time dynamical systems with local update rules on a lattice. Despite their elementary definition, CA support a wide spectrum of macroscopic phenomena central to statistical physics: equilibrium and nonequilibrium phase transitions, transport and hydrodynamic limits, kinetic roughening,...
In this paper we study the properties of Kasner cosmological solutions in Lovelock gravity. Recent progress in the investigation of flat cosmological models in Lovelock gravity unveiled the fact that in quadratic (Gauss--Bonnet) and cubic Lovelock gravities, the higher-order power-law solutions could play the role of both future and...
We demonstrate that the thermodynamic friction metric governing dissipation in slowly driven continuous-time Markov chains is equivalent to the commute-time embedding and the resistance distance. This equivalence yields complementary insights: The commute-time embedding demonstrates the intrinsic cost of transporting probability...
Traditional quantum thermodynamic frameworks associate work to energy exchanges induced by unitary transformations generated by external controls, and heat to energy exchanges induced by bath interaction. Recently, a framework was introduced aiming at extending the thermodynamic formalism to genuine quantum settings, also referred to...
Plasma activated water (PAW) offers a sustainable, nonthermal solution for degrading persistent organic pollutants in industrial effluents. This study employed a gliding arc plasma system to generate PAW for treating diluted waste water containing dyes, pesticides, and pharmaceuticals. Experimental parameters such as exposure time,...
Primary human cells offer the most faithful representation of native human physiology, yet their practical utility is constrained by the difficulty of introducing exogenous genetic material. Electroporation provides a promising non-viral gene delivery approach; however, conventional bulk systems lack the uniformity and integration...
We report on a high-fidelity digital quantum simulation of Wigner localisation in a quasi-one-dimensional (quasi-1D) electron system using a 6-qubit segment of the state-of-the-art \textbf{IBM\,Heron\,2} quantum processor. By mapping the Coulomb interaction Hamiltonian onto a 6-qubit ring lattice, we reconstruct the ground-state...
Variational principles are a unifying mathematical framework across many areas of physics, yet their instruction at the undergraduate level remains primarily analytical. This work presents a pedagogically oriented and computationally enhanced approach to variational modeling that integrates contemporary tools including gradient...
The structural and electronic properties of the doubly calcium-doped boron cluster B$_{18}$Ca$_2$ have been systematically investigated using density functional theory calculations. Basin-hopping searches reveal that B$_{18}$Ca$_2$ adopts a double-ring geometry as its global minimum, consisting of two fused B$_9$ rings symmetrically...
We experimentally investigate quasi-two-dimensional (Q2D) forced shallow flows in the presence of solid boundaries and analyze the deviation from the Kolmogorov-Kraichnan (KK) theory. Complex motion is generated using a thin electrolyte subject to electromagnetic forces, and we employ particle tracking velocimetry to resolve the flow...
Dense Associative Memory networks (DenseAMs) unify several popular paradigms in Artificial Intelligence (AI), such as Hopfield Networks, transformers, and diffusion models, while casting their computational properties into the language of dynamical systems and energy landscapes. This formulation provides a natural setting for studying...
Non-Markovian memory effects in open quantum systems provide valuable resources for preserving coherence and enhancing controllability. However, exploiting them requires strategies adapted to history-dependent dynamics. We introduce a reinforcement-learning framework that autonomously learns to amplify information backflow in a driven...