arXiv Physics @arxiv_physics@qoto.org

In this work, we present an analytical study of the electrostatic potential generated by a charged disk with a surface charge distribution that possesses radial axial symmetry. We express the potential in cylindrical coordinates and apply a Bessel function representation for the Coulomb kernel to simplify its calculation. We consider a broad family of surface charge densities and derive an exact expression for the potential based on a classical integral identity involving Bessel functions. This general formulation includes several important special cases such as the uniformly charged disk and edge-concentrated charge density distributions. We explore how variations in various parameters affect the resulting potential, thereby illustrating the influence of radial charge modulation. It is found that the potential undergoes a qualitative change of behavior when the chosen set of parameters is varied. The results are analytically elegant and computationally efficient as all functions involved are implemented in standard scientific computing libraries.

Permutation time irreversibility is an important method to quantify nonequilibrium characteristics of complex systems; however, ordinal pattern is a coarse-graining alternative of temporal structure and cannot accurately represent detailed structural information. This study aims to propose a fuzzy permutation time irreversibility (fpTIR) by measuring the difference between vector elements based on a negative exponential function. The amplitude permutation of vector is constructed and its membership degree is calculated; then, the difference in probability distribution between the forward and backward sequences is measured for fpTIR. To compare and measure the system's complexity, the Shannon entropy is calculated as the average amount of information in the fuzzy permutation probability distribution, i.e., fuzzy permutation entropy (fPEn). According to the surrogate theory, mode series are generated using logistic, Henon, and first-order autoregressive systems to verify the fpTIR, which is then used to analyze the heartbeats of patients with congestive heart failure and healthy elderly and young participants from the PhysioNet database. Results suggest that the fpTIR effectively measures the system's nonequilibrium characteristics, thus improving the accuracy of heartbeat analysis. However, in analyzing probability distributions, the fpTIR and fPEn exhibit discrepancies in the chaotic series and even opposite results in the heartbeats, wherein the results of fpTIR are consistent with the theory of complexity loss in aging and disease. Overall, the fpTIR accurately characterizes the structure of the sequences and enhances the accuracy of the nonequilibrium analysis of complex systems, providing a theoretical basis for exploring complex systems from the perspectives of nonequilibrium dynamics and entropy complexity.

Sonification can be part of educational resources that can be accessible to those who prefer, or require, non-visual learning methods. Furthermore, sonification can contribute to an engaging multi-sensory learning experience, which are known to benefit general learners. Whilst some sonification can be relatively agnostic to musical culture, many sonifications are subject to culturally influenced choices, such as the chosen harmonies, rhythmic structures, and instrumentation. This is important when considering how universally inclusive and relatable sonification-based educational resources will be. Here we present a case study of translating a sonification-based educational show about the Solar System, that was originally designed with influences from Euro-American (Western-classical) music, to be more culturally relevant to the Caribbean region. We describe the motivation, approach, some of the challenges, and the initial feedback of the resulting output of the project. Finally, we provide reflections on the importance of further work exploring how educational sonifications can transcend international borders and musical cultures.

Input-output analysis has been widely used to predict the transition to turbulence in wall-bounded shear flows, but it typically does not capture the full nonlinear effects involved. This work employs the Small-Signal Finite-Gain (SSFG) Lp stability theorem to assess nonlinear input-output amplification and demonstrates its applicability using a nine-mode shear flow model with a random body force. Our analysis employs Linear Matrix Inequalities (LMI) and Sum-of-Squares (SOS) as the primary tools to search for a quadratic Lyapunov function of an unforced nonlinear system certifying the SSFG Lp stability of a nonlinear input-output system. The predicted nonlinear input-output Lp gain (amplification) is consistent with numerical simulations; the Lp norm of the output from numerical simulations remains bounded by the theoretical prediction from the SSFG Lp stability theorem, with the gap between simulated and theoretical bounds narrowing as p approaches infinity. The input-output gain obtained from the nonlinear SSFG Lp stability theorem is higher than the linear Lp gain, which suggests that nonlinearity can significantly amplify small disturbances. The SSFG Lp stability theorem requires the input forcing to be smaller than a permissible forcing amplitude to maintain finite input-output gain, which is an inherently nonlinear behavior that cannot be predicted by linear input-output analysis. We also identify such permissible forcing amplitude using numerical simulations and bisection search, where below such forcing amplitude, the output norm at any time will be lower than a given threshold value. The permissible forcing amplitude identified from the SSFG Lp stability theorem is conservative but also consistent with that obtained by numerical simulations and bisection search.

The process launched by Lobachevsky. The movement of the Kazan school of geometry towards physics. Personal memories of Alexei Zinovievich Petrov, the great Kazan geometer and theoretical physicist, who became the Author's Guiding Star. KeyWords: Alexey Zinovievich Petrov, geometry, general theory of relativity, Kazan University, Department of Relativity and Gravitation Theory, methods of teaching exact sciences.

In this work, we investigate the impact of Fabry-Perot (FP) cavities formed between a fiber array and a silicon chip during edge-coupled testing of photonic integrated circuits (PICs). Our results show that subwavelength variations in the distance between the fiber array and the device under test induce coupling efficiency modulations of several tenths of a decibel - challenging current alignment precision and undermining measurement repeatability in wafer-level probing. To address these FP-induced fluctuations, we introduce a complementary dual-measurement technique that shifts the chip-fiber separation by a quarter wavelength, thereby generating phase-opposite modulation artifacts that cancel upon averaging. This approach allows a higher tolerance in probe-to-PIC positioning by substantially eliminating the FP modulation. We also demonstrate how it can be leveraged to enhance repeatability and accurate positioning during alignment, reducing the insertion loss variability between multiple measurements from 0.34 to 0.04 dB. Such precision is essential for emerging applications like quantum photonics, silicon photonic sensors, and high-bandwidth optical interconnects where measurement accuracy directly impacts device performance characterization. Ultimately, our method offers a robust and accurate solution for high-throughput PIC characterization without requiring index-matching gel, which is impractical for wafer-level testing.

We've developed a scalable and sustainable online atomic data portal with an automated interface for easy update and addition of new data. The current portal provides energies, transition matrix elements, transition rates, radiative lifetimes, branching ratios, polarizabilities, hyperfine constants, and other data, for 28 atoms and ions. It also features an interactive polarizability plotting interface for neutral atoms and singly-charged ions. The data production is supported by recent developments of open-access atomic software based on our research codes, including new workflow algorithms, which allow large volumes of such data to be generated with automated accuracy assessments. This entails a new method of comparing our calculated values with data from the NIST Atomic Spectra Database. All calculated values include estimated uncertainties. Data for more systems will be added in the future. Experimental values are included with references, where high-precision data are available.

Optical tweezers equipped with position detection allow for application of piconewton-scale forces and high-temporal-resolution measurements of nanometer-scale motion. While typically used for trapping microscopic objects, the optical tweezer detection pathway can also be used for a microscopy technique sensitive to nanometer-sized structures. Optical tweezers most commonly use back-focal-plane detection to determine the position of the trapped object. This technique involves analyzing the interference pattern between scattered and unscattered light. Despite the reliance on interference, we show that an image of an extended object can be understood as a sum of the images of individual point-like particles that make up the object. This allows for optical tweezers with back-focal-plane detection to be used as an imaging tool capable of determining the mass distribution of scanned structures. Furthermore, the sample-orientation-dependent detector response allows for a unique method of background subtraction. We demonstrate the quantitative imaging capabilities of optical tweezer microscopy by determining the size distributions multiple nearby particles and measuring the changing diameter of collagen fibrils. The background subtraction technique is demonstrated by imaging surface-bound microtubules with a strong background from protein aggregate co-adsorption. Optical tweezer microscopy allows for quantitative imaging of objects far from the coverslip surface. This makes it an excellent tool for studying the link between the structure and mechanics of microscopic systems.

Highly segmented calorimeters represent a modern trend in experimental particle physics aimed at improving the energy resolution with the particle flow reconstruction. The widely used and cost-effective solution is the structure assembled from small scintillator elements readout by tiny photosensors. The improvement of signal to noise ratio can be achieved by operating at low temperatures. The paper presents studies of temperature dependence of response to muons for elements comprised of plastic scintillator tiles with different options of reflective coverage and read out by silicon photomultipliers. No temperature dependence of response for bare tiles has been detected. With reflective film, the reduction of response with decreasing temperature is observed. The measured effect amounts to 10% in the range from +30oC to -30oC.

Using nonequilibrium Monte Carlo simulations of the phonon Boltzmann transport equation, we study transient heat transfer in five indium-based two-dimensional monolayers: Janus monolayers In$_2$SeTe and In$_2$SSe, pristine InSe, and InSe under 4$\%$ and 6$\%$ tensile strain. In this work, the potential of these materials for energy conversion in thermoelectric generators and hotspot control in metal-oxide-semiconductor field-effect transistors is investigated. A promising option for an effective heat dissipation and enhanced transistor reliability is found to be a strained InSe, which shows the lowest peak temperature during the heating among the studied materials. On the other hand, with a high Seebeck coefficient, low thermal conductivity, and an improved figure of merit, the Janus In$_2$SeTe monolayer, compensates for its increased phonon scattering to reach the maximum temperature, making it a potent thermoelectric material. Our findings emphasis the importance of strain engineering and structural asymmetry in tuning phonon transport, enabling material optimization for next-generation nanoelectronic and energy-harvesting devices.

Scientific machine learning (SciML) provides a structured approach to integrating physical knowledge into data-driven modeling, offering significant potential for advancing hydrological research. In recent years, multiple methodological families have emerged, including physics-informed machine learning, physics-guided machine learning, hybrid physics-machine learning, and data-driven physics discovery. Within each of these families, a proliferation of heterogeneous approaches has developed independently, often without conceptual coordination. This fragmentation complicates the assessment of methodological novelty and makes it difficult to identify where meaningful advances can still be made in the absence of a unified conceptual framework. This review, the first focused overview of SciML in hydrology, addresses these limitations by proposing a unified methodological framework for each SciML family, bringing together representative contributions into a coherent structure that fosters conceptual clarity and supports cumulative progress in hydrological modeling. Finally, we highlight the limitations and future opportunities of each unified family to guide systematic research in hydrology, where these methods remain underutilized.

Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However, running these simulations remains challenging due to the wide range of computational methods, diverse software ecosystems, and the need for expert knowledge and manual effort for the setup, execution, and validation stages. In this work, we present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. Users can perform tasks such as molecular structure generation, single-point energy, geometry optimization, vibrational analysis, and thermochemistry calculations with methods ranging from tight-binding and machine learning interatomic potentials to density functional theory or wave function theory-based methods. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models like GPT-4o. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables smaller LLM models to match or exceed GPT-4o's performance in specific scenarios.

Social relations have been shown to impact individual and group success in farm animal populations. Fundamental to addressing these relationships is an understanding of the social network structure resulting from the co-habitation and co-movement of relationships between individuals in a group. Here, we investigate the social network of a group of around 210 lactating dairy cows on a dutch farm during a 14 days period. A positioning system called \emph{Cowview} collected positional data for the whole period. We make the assumption that spatial proximity can be used as a proxy for social interaction. The data is processed to get adjacency matrices. Then social networks are identified based on these matrices. Community detection techniques are applied to the networks. We measure metrics of different dimensions to test community structure, centralization, and similarity of network structure over time. Our study show that there is no evidence that cows are subdivided into stable social communities when looking at interaction in the whole barn. We, however, notice relatively clear communities when dividing the barn into areas with different activities. The social network is characterized by significant centralization, low connectivity, and a hierarchy.

Atomic force microscopy (AFM) enables high-resolution imaging and quantitative force measurement, which is critical for understanding nanoscale mechanical, chemical, and biological interactions. In dynamic AFM modes, however, interaction forces are not directly measured; they must be mathematically reconstructed from observables such as amplitude, phase, or frequency shift. Many reconstruction techniques have been proposed over the last two decades, but they rely on different assumptions and have been applied inconsistently, limiting reproducibility and cross-study comparison. Here, we systematically evaluate major force reconstruction methods in both frequency- and amplitude-modulation AFM, detailing their theoretical foundations, performance regimes, and sources of error. To support benchmarking and reproducibility, we introduce an open-source software package that unifies all widely used methods, enabling side-by-side comparisons across different formulations. This work represents a critical step toward achieving consistent and interpretable AFM force spectroscopy, thereby supporting the more reliable application of AFM in fields ranging from materials science to biophysics.

This paper presents three novel algorithms for calculating geodesic intersections on an ellipsoid. These algorithms are applied in a case study analyzing real-time transit data in California to assess vehicle position drift. The analysis reveals that while certain data anomalies can be corrected, large-scale discrepancies persist. The paper concludes by proposing a set of practical solutions that can be implemented by either data producers or consumers to significantly improve positional accuracy.

Molecular polaritons are hybrid light-matter states that enable the exploration of potential cavity-modified chemistry. The development of dynamical, first-principles approaches for simulating molecular polaritons is important for understanding their origins and properties. Herein, we present a hierarchy of first-principles methods to simulate the real-time dynamics of molecular polaritons in the strong coupling regime. These methods are based on real-time time-dependent density functional theory (RT-TDDFT) and the corresponding real-time nuclear-electronic orbital (RT-NEO) approach, in which specified nuclei are treated quantum mechanically on the same level as the electrons. The hierarchy spans semiclassical, mean-field-quantum, and full-quantum approaches to simulate polariton dynamics under both electronic strong coupling and vibrational strong coupling. In the semiclassical approaches, the cavity mode is treated classically, whereas in the full-quantum approaches, the cavity mode is treated quantum mechanically with propagation of a joint molecule-mode density matrix. The semiclassical and full-quantum approaches produce virtually identical Rabi splittings and polariton peak locations for the systems studied. However, the full-quantum approaches allow exploration of molecule-mode quantum entanglement in the real-time dynamics. Although the degree of light-matter entanglement is relatively small in the systems considered, the oscillations of the von Neumann entropy reveal an entanglement Rabi splitting that differs from the Rabi splitting computed from the time-dependent dipole moment. These results suggest that a classical treatment of the cavity mode may provide an excellent description of polariton dynamics for macroscopic observables such as the Rabi splitting, but novel physics may be detectable by considering molecule-mode entanglement.

The paper provides a synoptic overview of a series of works carried out by a group of researchers at the Institute of Physics of the Earth RAS with the aim of finding new approaches to the problems of earthquake physics. The fundamental laws of Omori, Gutenberg-Richter and Bath have served as a constant support and reference point in the course of many years of research. The concept of the tectonic earthquake triad as a natural trinity of foreshocks, main shocks and aftershocks is used in the article to organise the thematic material. A classification of main shocks into six types of triads found in experience is given. The parameters appearing in the three laws for different types of triads are given. The axiomatic theory of the evolution of aftershocks is outlined. The concepts of source deactivation, Omori epoch and source bifurcation are introduced, and the notion of the proper time of unsteady lithospheric processes is introduced. Convergence of foreshocks and divergence of aftershocks are mentioned. The general conclusion is that the Omori, Gutenberg-Richter and Bath laws are reliable tools in the experimental and theoretical study of earthquakes. The laws have a depth of content that has been demonstrated by the ability to enrich the original formulations of the discoverers with interesting and important additional statements.

Understanding the high-pressure phase behavior of carbon dioxide-hydrocarbon mixtures is of considerable interest owing to their wide range of applications. Under certain conditions, these systems are not amenable to direct visual monitoring, and experimentalists often rely on spectrophotometric data to infer phase behavior. Consequently, developing computationally efficient and robust methods to leverage such data is crucial. Here, we combine nearest neighbor permutation entropy, computed directly from in situ near-infrared absorbance spectra acquired during depressurization trials of mixtures of carbon dioxide and a distilled petroleum fraction, with an anomaly detection approach to identify phase transitions. We show that changes in nearest neighbor entropy effectively signal transitions from initially homogeneous mixtures to two-phase equilibria, thereby enabling accurate out-of-sample online predictions of transition pressures. Our approach requires minimum data preprocessing, no specialized detection techniques or visual inspection of the spectra, and is sufficiently general to be adapted for studying phase behavior in other high-pressure systems monitored via spectrophotometry.

Photon acceleration (PA) driven by ultra-relativistic electron beams offers a promising approach to generating high-power, high-frequency coherent radiation sources. While current methods typically rely on external optical laser pulses injected into beam-driven plasma wakefields, they face significant challenges in synchronization and alignment between electron accelerators and laser systems. We propose utilizing transition radiation (TR) generated by the drive electron bunch transversing the vacuum-gas interface as the seed photons of PA. Using a 1 GeV electron bunch, we demonstrate acceleration of TR from 4.4 μm to 184 nm in 1.6 mm of two-stage uniform plasma, achieving more than a 20-fold frequency boost. Further frequency increases can be achieved with optimized setups. This scheme addresses the synchronization and alignment issues present in previous approaches, providing a practical path toward beam-driven photon acceleration.

Tritium, predominantly produced through spallation reactions caused by cosmic ray interactions, is a significant radioactive background for silicon-based rare event detection experiments, such as dark matter searches. We have investigated the feasibility of removing cosmogenic tritium from high-purity silicon intended for use in low-background experiments. We demonstrate that significant tritium removal is possible through diffusion by subjecting silicon to high-temperature (> 400C) baking. Using an analytical model for the de-trapping and diffusion of tritium in silicon, our measurements indicate that cosmogenic tritium diffusion constants are comparable to previous measurements of thermally-introduced tritium, with complete de-trapping and removal achievable above 750C. This approach has the potential to alleviate the stringent constraints of cosmic ray exposure prior to device fabrication and significantly reduce the cosmogenic tritium backgrounds of silicon-based detectors for next-generation rare event searches.