General Physics
The role of anisotropy in $f(Q)$ gravity: insights from cosmological observations
Ghulam Murtaza, Nandan Roy and Avik De
We investigate the cosmological dynamics of Bianchi-I spacetime in symmetric teleparallel $f(Q)$
gravity through a dynamical system approach to analyse observational constraints. By reformulating the modified field equations into an autonomous system, we analyse two representative $f(Q)$ models and constrain their parameters using Pantheon Plus, DES Y5, DESI DR2, and compressed CMB data. The observational analysis yields consistent constraints across all
dataset combinations and tightly bounds the anisotropic contribution, indicating that deviations from isotropy remain small. Both models reproduce the standard matter-dominated
evolution and the observed late-time accelerated expansion while exhibiting distinct dark-energy dynamics. Model I undergoes a smooth phantom-divide crossing and approaches a de Sitter phase in the asymptotic future, whereas Model II evolves from an early phantom regime toward a cosmological-constant-like state around the present epoch, closely mimicking the late-time
evolution of the $\Lambda$CDM model. These results indicate that anisotropic $f(Q)$ cosmology remains consistent with current background observations while admitting characteristic dark-energy
evolution that may be testable with future cosmological surveys.
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General Physics
Investigating the coincidence problem with Interacting Dark Energy Models
Kshitiz Singh and Savita Gahlaut
Interacting dark energy (IDE) models within the framework of general relativity (GR) provide a possible extension of the standard $\Lambda$CDM model and may alleviate the coincidence problem. In this paper, we investigate three phenomenological interacting dark energy models using a combination of
cosmic chronometer $H(z)$ measurements and two standard
cosmic rulers:
baryon acoustic oscillations (BAO) and
quasars (QSO). The observational constraints indicate a mild preference for energy transfer from dark energy to dark matter, a scenario that may help alleviate the
cosmic coincidence problem. However, the standard $\Lambda$CDM model, corresponding to the absence of interaction, remains fully consistent with the observational data, as the no-interaction case lies within the $1\sigma$ confidence interval of the inferred interaction parameter for all three IDE models. Model comparison, using Akaike Information Criterion (AIC) and the
Bayesian Information Criterion (BIC), further indicates that the current data do not provide statistically significant evidence favouring any IDE model over the standard $\Lambda$CDM scenario or over one another. These results suggest that, while a weak dark-sector interaction is permitted by the data, present observations do not provide compelling evidence for departures from the standard $\Lambda$CDM model.
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General Physics
Stability analysis of de Sitter solution in the Starobinsky-Grisaru-Zanon gravity using the dynamical system method
Tuan Q. Do
We study whether the so-called Starobinsky-Grisaru-Zanon
gravity, which is a novel fourth-order
gravity model involving the so-called Grisaru-Zanon term, admits a stable de Sitter solution. First, we derive the corresponding field equations of the Starobinsky-Grisaru-Zanon
gravity for the spatially flat Friedmann-Lemaitre-Robertson-Walker metric by using an effective method based on the Euler-Lagrange equations. Then, we figure out an exact de Sitter solution of these field equations for the first time. Finally, we point out, through the dynamical system method, that the obtained de Sitter solution is always unstable. Interestingly, although the Starobinsky $R^2$ term does not contribute to the value of the obtained de Sitter solution, it does affect on the instability of this solution.
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Instrumentation and Detectors
Radiotracer photoluminescence for element-specific identification of color centers
Brecht Biesmans, Afonso Lamelas, Kirill Danilov et al. (+8 more)
We report on the implementation of a radiotracer photoluminescence spectroscopy setup at the ISOLDE radioactive ion beam facility at CERN, enabling element-specific identification of optically active defects in solids. The method combines radioactive ion implantation with optical spectroscopy, allowing the temporal
evolution of photoluminescence signals to be correlated directly with
nuclear decay. The setup is currently optimized for color centers in diamond and related wide-bandgap materials and enables room-temperature measurements. The system consists of an optical microscope coupled to a fiber-fed Czerny-Turner spectrometer with a
liquid-nitrogen-cooled CCD detector, providing the stability required for long-duration measurements. As a proof-of-principle, radioactive $^{75}$Ga was implanted into diamond as a precursor to produce $^{75}$Ge
impurities. The photoluminescence band extending from 600 nm, corresponding to the well-known GeV$^{-}$ center, exhibits an exponential decay with a half-life of $82.3^{+2.5}_{-2.3} \mathrm{min}$, in agreement with the known $\beta^{-}$ decay half-life of $^{75}$Ge of $82.78(4) \mathrm{min}$. This establishes a direct and unambiguous correlation between the observed spectral feature and its germanium origin. These results demonstrate the capability of the setup to perform element-specific optical spectroscopy and extend radiotracer methods to color centers in wide-bandgap materials, taking advantage of the uniquely broad range of radioactive isotopes available at ISOLDE.
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All authors Brecht Biesmans, Afonso Lamelas, Kirill Danilov, Aleksandr Seliverstov, Ângelo Costa, Vítor Amaral, André Vantomme, João Guilherme Correia, Ulrich Wahl, Lino M. C. Pereira, the ISOLDE Collaboration
Atmospheric and Oceanic Physics
Borey: A High-Resolution Regional Atmosphere-Ocean-Sea Ice-Wave Forecasting System and Hindcast Dataset for the Barents and Kara Seas
Vasily Ivanov, Polina Verezemskaya, Alexander Gavrikov et al. (+5 more)
Borey is a high-resolution regional modeling and operational
forecasting system for the Barents and Kara Seas. It combines WRF for the atmosphere, NEMO-SI3 for the ocean and sea ice, and WW3 for waves on approximately 3–6\,km grids, and generates daily forecasts to 72 hours. We describe the model chain and production workflow and present an accompanying hourly hindcast of surface conditions from August 2015 to August 2023. The archive provides aligned atmosphere, ocean, sea ice, and wave fields for regional marine studies and a baseline for evaluating the operational system. Comparisons with observations and observation-based products show that Borey captures much of the variability in near-surface atmospheric conditions and ocean temperature. Skill in the evaluated WRF, NEMO, and SI3 forecasts changes only modestly across the three-day window. The main limitations are persistent rather than rapidly growing errors: sea surface temperature is generally too cold, sea ice concentration and occurrence are overestimated during seasonal retreat, and significant wave height is underestimated. Borey should therefore complement observation-constrained products. The planned public release will provide hourly surface fields, native grids, provenance information, and validation outputs for regional analysis, model development, and carefully evaluated data-driven
forecasting and data-assimilation research.
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All authors Vasily Ivanov, Polina Verezemskaya, Alexander Gavrikov, Vitaliy Sharmar, Mikhail Krinitskiy, Timofey Grigoryev, Vladimir Vanovskiy, Evgeny Burnaev
Geophysics · Cross-listed from cs.CV
Fast Fourier Convolutional GAN for 30 m Clear-Sky Land Surface Temperature Gap-Free Reconstruction
Marwa Alfouly, Smajil Halilovic, Nils Bochow et al. (+3 more)
Satellite-derived Land Surface Temperature (LST) provides spatially comprehensive data that ground stations cannot match. However, its utility is frequently limited by severe data gaps due to the presence of clouds. As LST is essential for understanding land-atmosphere interactions, numerous methods have been proposed to address this challenge. Yet, the development of a scalable and adaptable pipeline for generating gap-free LST
datasets and reconstructing cloud-contaminated pixels remains challenging. Moreover, the reconstruction of extensive missing regions in fine-spatial-resolution observations is particularly difficult. To address this challenge, we propose a
Multimodal Fast
Fourier Convolutional GAN for reconstructing cloud-contaminated pixels in fine-resolution (30 m) Landsat imagery to generate gap-free clear-sky LST products. The method leverages Fast
Fourier Convolution to enable a global receptive field across the image, and is guided by a stack of data consisting of satellite observations and Synthetic Aperture Radar (SAR) data. Across all LST
quantiles, the interquartile range of scene-averaged RMSE (computed over reconstructed pixels) is consistently between 0.8 K and 1.8 K. The proposed approach enables the recovery of extensive missing regions, including scenes with more than 70% cloud-induced gaps, while relying on auxiliary data that are readily available at a near-global scale.
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All authors Marwa Alfouly, Smajil Halilovic, Nils Bochow, Thomas Hamacher, Niklas Boers, Konrad Schindler
Data Analysis and Statistics · Cross-listed from q-bio.NC
Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation
Zag ElSayed, Nathan Suer, Grace Westerkamp et al. (+4 more)
The electroencephalogram (
EEG) is a valuable and widely applied tool for investigating
brain disorders and behavioral changes. It offers a minimally restrictive and non-invasive method. However, challenges in using
EEG for cognitive development studies include temporal resolution, signal source localization, and
EEG artifacts. Careful consideration of these factors is essential for informed application of
EEG technology. Independent component analysis (ICA) effectively isolates source generator processes from signals recorded by multiple, adjacent
EEG scalp electrodes. Although ICA decomposition requires manual inspection, selection, and interpretation of independent components (ICs), this process is time consuming and demands expertise. Automated IC classification can achieve sufficient accuracy, expediting large scale
EEG research and enabling near real time applications in conjunction with
brain activity rejection tasks, which are crucial for medical specialists. This study introduces an automated computer
vision based ICA rejection labeling tool compatible with widely used software interfaces like ICLabel and EEGLab. By automating the manual task, the proposed system reduces processing time by 7200 fold and achieves an accuracy of 89.45%.
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All authors Zag ElSayed, Nathan Suer, Grace Westerkamp, Jack Yanchen Liu, Makoto Miyakoshi, Craig Erickson, Ernest Pedapati
Physics and Society
Service Ecosystem Evolution: A Comprehensive Survey from Complex Network Perspectives
Shuiguang Deng, Xingliang Wang, Guochang Li et al. (+5 more)
Digital society increasingly relies on complex service ecosystems, formed by interconnected services from technology
giants. However, the growing scale and intricate dependencies of these ecosystems pose significant challenges to their
evolution, frequently leading to systemic failures during upgrades or restructuring. To address these challenges, current research is shifting from the perspective of single services to the ecosystem. Drawing upon the synthesis and induction of current research, we present a comprehensive survey focused on the study of service ecosystem
evolution, employing a novel three-stage analytical framework. This framework structures the evolutionary lifecycle and provides a systematic way to organize and review existing research, filling the gap caused by the lack of surveys specifically focused on service ecosystem
evolution. Additionally, we pioneer the application of complex network theory to analyze service ecosystem
evolution, providing a novel perspective to capture the inherent connectivity, dynamics, and emergent properties often missed by traditional approaches. Building on our analysis, we point out critical research gaps and propose three specific future directions. We provide a robust theoretical foundation and methodological guidance for understanding and guiding the sustainable
evolution of modern service ecosystems.
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All authors Shuiguang Deng, Xingliang Wang, Guochang Li, Yunkun Wang, Haoran Xu, Chen Zhi, Junxiao Han, Jianwei Yin
History and Philosophy of Physics
Quantum nature of gravity and spacetime: Fundamental insights from the Hoyle-Narlikar theory
Dawood Kothawala
The Hoyle-Narlikar action-at-a-distance formulation for
gravity remains one of the most mathematically rigorous attempts to incorporate Mach principle into physics. A lesser known, but no less significant, is the fact that it is also one of the first in which two point functions play a key role in determining spacetime structure, and hence, are more fundamental than the metric. We argue that these ingredients have direct relevance for modern attempts to understand fundamental, emergent, and informational aspects of
quantum spacetime. The non-local description of
quantum spacetime, with Planck scale $\ell_0$ as a zero-point length, provides a mechanism through which the entire universe can inform local causal structure, `a la Mach, Hoyle, and Narlikar. In this chapter, we highlight certain key aspects of the HN theory and then discuss case studies from modern research that illustrate how the
quantum spacetime might realize a version of the
quantum gravitational Mach principle.
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Applied Physics · Cross-listed from cond-mat.supr-con
Geometric Superconducting Diode Effect in an NbN Nanoring
Tianyu Li, Peiyuan Huang, Jiong Li et al. (+14 more)
Superconducting diodes, which exhibit nonreciprocal critical currents, are promising building blocks for low-power cryogenic electronics and superconducting
circuits. Existing superconducting diode platforms commonly rely on Josephson junctions, multilayer
heterostructures,
ferromagnetic elements, gate-difined structures. Here, we demonstrate a geometrically induced superconducting diode effect realized in a structurally minimal, single-materials NbN nanoring, where inversion-
symmetry breaking is introduced solely by the asymmetric geometry. The device exhibits pronounced and polarity-switchable critical-current nonreciprocity. Systematic
magnetic-field and temperature-dependent measurements reveal that, at low fields, the applied
magnetic field redistributes the critical current asymmetrically between opposite bias directions without significantly reducing the overall superconducting current-carrying capability. Moreover, the maximal nonreciprocity and diode efficiency exhibit distinct temperature dependence: the maximal diode efficiency follows the
evolution of the energy gap, whereas the maximal nonreciprocity is more closely associated with the superfluid density. These results establish asymmetric superconducting nanorings as a minimal
geometric platform for studying nonreciprocal superconducting transport and provide a simple design principle for future superconducting electronics.
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All authors Tianyu Li, Peiyuan Huang, Jiong Li, Jiyao Shang, Nuo-Zhou Yang, Wuyue Xu, Wen-Cheng Yue, Yang-Yang Lyu, Chong Li, Yihuang Xiong, Xuecou Tu, Tao Tao, Xiaoqing Jia, Qing-Hu Chen, Huabing Wang, Peiheng Wu, Yong-Lei Wang
Optics
Emergent Non-Equilibrium Emission Profiles via Luminescent Angle Restriction for Photovoltaic Energy Conversion
Jonathan McTague, Rivi Ratnaweera and Matthew Sheldon
The detailed-balance method established by Shockley and Queisser determines photovoltaic (PV) conversion efficiencies by relating non-
equilibrium converter operation to an
equilibrium reference state. Conventionally, the angular and spectral radiative channels during power conversion are assumed to be defined by the
equilibrium absorptivity and emissivity, with the
photon chemical potential setting their occupation. This assumption is well motivated for flat-plate
cells, but requires closer examination when radiative energy is redistributed within a more complex nanophotonic architecture. We analyze this distinction with a proposed luminescent angle restrictor (LAR), in which a flat-plate PV absorber is coupled to an overlayer with vertically aligned nanorods (NR). The NR layer is nearly transparent to near-normal sunlight, while oblique PV luminescence can be absorbed, re-emitted, and partially returned to the absorber. To calculate the operating emission profile, we develop an ergodic Markov chain transport model enforcing microscopic reversibility for local optical transitions without imposing a fixed macroscopic emissivity. The model recovers the flat-plate detailed-balance limit and predicts voltage-dependent external emission profiles from the LAR/PV architecture. Under idealized conditions, this non-
equilibrium angular redistribution reduces the angular
entropy of luminescent emission and increases efficiency relative to the conventional flat-plate reference. We also evaluate NR photoluminescence
quantum yield,
solar misalignment, orientational disorder, and Auger recombination to identify practical constraints on the proposed architecture. These results show how detailed-balance limits for PV structures can depend on internal radiative energy redistribution and the non-
equilibrium emission profile, beyond what can be inferred from the macroscopic
equilibrium optical response.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Toward Mechanistic Interpretability of an AI Foundation Model Fine-Tuned for Atmospheric Chemistry
Jason Y. Hu, Ivan Higuera-Mendieta, Patrick Obin Sturm et al. (+1 more)
Weather
forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models. These FMs are typically trained on reanalysis data and generate forecasts through autoregressive rollout. They do not explicitly represent governing physical or chemical processes. Therefore, high forecast skill does not reveal whether a model has learned physical mechanisms or exploits statistical regularities in its training data. Here, we present the first study of what a FM fine-tuned for atmospheric chemistry has learned by examining Microsoft's Aurora model. We impose controlled chemical perturbations on its forecasts and test them against known photochemical relationships. We then examine the internal representations that generate these forecasts. We find that Aurora captures a first-order ozone response to reactive nitrogen but does not enforce the chemical constraints that a process-based model encodes. It generates chemically inconsistent combinations of related species and relaxes localized emission features such as wildfire plumes toward background. Internally, its representations remain largely organized around the meteorology inherited during pretraining, with little structure specific to chemistry. Using sparse autoencoders, we identify internal components that causally control the chemical forecast but do not map cleanly onto individual atmospheric processes. This work provides a framework for testing whether AI
forecasting systems learn atmospheric chemistry from reanalysis data. As these models are increasingly positioned to inform environmental policy decisions, we argue that composition forecasts should also be judged by their internal mechanisms rather than by
benchmark skill alone.
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All authors Jason Y. Hu, Ivan Higuera-Mendieta, Patrick Obin Sturm, Makoto M. Kelp
Optics
Digitally Programmable Photochromic Hydrogel Contact Lenses as Light-Adaptive Artificial Irises
Asad Nauman, Tristen Moncada, Guli Gulinihali et al. (+3 more)
Excessive exposure to ultraviolet (UV) radiation is associated with a range of ocular pathologies, motivating the development of soft optical devices that can dynamically regulate incident light. In the human eye, this adaptive optical functionality is performed by the iris, which modulates pupil size to control retinal irradiance in response to ambient illumination. Here we present a photochromic contact lens based artificial iris that mimics this biological light-adaptation mechanism through reversible, spatially programmable modulation of optical transmission with intrinsic UV blocking. Photochromic dyes are embedded within a biocompatible hydrogel
matrix, while the cross-linked network is patterned using a digital micromirror device (DMD)based grayscale UV lithography to encode controlled radial gradients in dye switching. This approach generates iris-like attenuation profiles that emulate pupil-dependent light regulation while enabling customizable iris geometries and transmission patterns. The resulting lenses exhibit rapid and reversible UV-induced darkening with position-dependent kinetics, enabling continuous modulation of transmitted light. The photoresponse remains stable over repeated activation cycles without measurable fatigue. The patterned lenses maintain mechanical stability, controlled swelling, and wettability suitable for contact lens applications. This platform combines programmable photochromism and hydrogel optics to enable light-adaptive lenses that mimic key functions of the human iris.
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All authors Asad Nauman, Tristen Moncada, Guli Gulinihali, Muhammad Waleed Khalid, Yeshaiahu Fainman, Abdoulaye Ndao
Chemical Physics
Importance-Sampling Estimation of Gaussian Molecular Shape Overlap: Exact Union Volumes and Confidence-Bounded Virtual Screening
Egor I. Tuzharov, Alexandra V. Bochenkova and Yury Maximov
Gaussian descriptions of molecular shape underpin 3D shape-based virtual screening, but existing methods evaluate Gaussian overlap analytically. The widely used first-order approximation is fast but systematically overestimates overlap, whereas the exact molecular volume requires a combinatorial inclusion-exclusion expansion. We introduce the first
stochastic estimator of Gaussian shape overlap: an unbiased Monte Carlo method that importance-samples directly from a molecule's Gaussian mixture. The
estimator reproduces analytic overlap without bias and extends to the exact union volume of all inclusion-exclusion orders with O(N) cost per sample. On drug-like molecules, the union
estimator matches high-resolution grid quadrature with a mean relative error of 0.07 percent, while the first-order approximation overestimates the true union volume by 3.4x on average. The
estimator provides analytic standard errors, enabling confidence-bounded screening that reduces sampling by 94 percent while preserving ranking. Implemented in JAX, it is fully differentiable and supports gradient-based rigid
alignment on CPU, GPU, and TPU. On the DUD-E and LIT-PCBA
benchmarks, the method achieves shape-only enrichment comparable to existing single-conformer approaches while additionally providing unbiased absolute volumes and uncertainty estimates.
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Physics Education
Interactive and accessible quantum key distribution modules for introducing students to quantum science and engineering
John M. Donohue, Silas Ifeanyi, Andrew Chisholm et al. (+11 more)
Teaching key concepts in
quantum information science and technology benefits from hands-on activities that can engage students at all levels, especially high-school and undergraduate students. In this paper, we report on an educational kit for student exploration of
quantum key distribution (QKD) via optical analogies that provides an affordable and highly interactive role-playing experience for students with minimal abstraction and direct connections to applications, key
quantum concepts like superposition and measurement uncertainty, and student's pre-existing knowledge like light polarization. Manual and automated versions of the kit are detailed, as well as feedback from workshops with high-school students, undergraduates, and educators.
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All authors John M. Donohue, Silas Ifeanyi, Andrew Chisholm, Julien Côté, Quazell Cunningham, Jamiel Nasser, Aaron Xayvongsa, Evangeline Dryburgh, Fiona Dang, Gabriel Ghrayeb, Ian Jinzo Macpherson, Fareed Rasheed, Andi Zhao, Simarjeet S. Saini
Data Analysis and Statistics · Cross-listed from stat.ME
The Well-Tempered Likelihood: Honest Confidence Intervals for Misspecified Models
Benjamin Nachman and Jesse Thaler
Likelihood-based inference in particle physics, and in the physical sciences more broadly, relies on the assumption that the model accurately describes the data. When the model is misspecified, though, standard confidence intervals shrink to zero width with increasing data, producing overconfident and potentially misleading constraints. We propose the well-tempered likelihood, which divides the likelihood-ratio test statistic by a goodness-of-fit (GOF) statistic evaluated at the best-fit point. Under correct specification, the GOF is $\mathcal{O}(1)$ and standard inference is recovered. Under misspecification, both the likelihood and the GOF scale as $\mathcal{O}(N)$ for $N$ data points, so their ratio remains $\mathcal{O}(1)$ and the resulting well-tempered confidence interval self-limits at a floor determined by the model's inadequacy, essentially reducing the effective sample size. In other words, all models are correct, as long as your
dataset is small enough. We present binned and unbinned formulations of the well-tempered likelihood—the latter based on a classifier two-sample test—and demonstrate our method on a Gaussian example and on a measurement of the strong coupling constant using synthetic electron-positron collisions.
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Atmospheric and Oceanic Physics
Spatial Generalization Tests for Machine Learning-based Weather Models to Assess Physical Consistency
Maren Höver, Milan Klöwer, Christian Schroeder de Witt et al. (+1 more)
Machine learning-based weather prediction is revolutionizing weather
forecasting by learning from weather data in present-day
climate. However, generalization to other climates remains a major challenge. With melting sea ice, land-use change, and increasing ocean temperatures, boundary conditions are changing. Therefore, generalization in time depends on generalization in space. Here, we present three test cases to evaluate whether machine learning-based weather and
climate models generalize in space and apply them to GraphCast and NeuralGCM. We reverse or rotate the
planet in longitude or latitude under the model's coordinate system and adapt all boundary conditions and forcings accordingly. Physics-based general circulation models simulate a rotated/reversed
planet with only rounding errors, but GraphCast and NeuralGCM fail these tests. The analyses furthermore revealed unphysical variable mappings based on correlation rather than causation. We argue that machine learning-based
climate models should be designed to pass generalization tests to prevent overfitting on present-day regional
climate.
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All authors Maren Höver, Milan Klöwer, Christian Schroeder de Witt, Hannah M. Christensen
Atomic and Molecular Clusters · Cross-listed from quant-ph
Bistability of Exciton-Photon Microcavities in the Ultrastrong-Coupling Regime
Rashed Aljasmi, Hisham Sati and Hichem Eleuch
We investigate a coherently driven
exciton–
photon microcavity with Kerr nonlinearity in the ultrastrong-coupling regime. When the lower and upper polariton branches are well separated in energy, the full Hopfield–Rabi–Kerr model reduces to an effective single-mode description of the lower polariton. We analyze the stability of the lower-polariton steady states. We show that the resulting bistability is qualitatively similar to that in the strong-coupling regime. However, in the ultrastrong-coupling regime counter-rotating processes and the diamagnetic $A^{2}$ term renormalize the polariton spectrum and composition, changing the effective detuning $\tilde{\Delta}_{1}$ and nonlinearity $U_{\mathrm{LP}}$ $g$-dependency beyond the strong-coupling (RWA) picture. As a result, although the semiclassical bistability criterion keeps its standard Kerr–oscillator form, the turning points and hysteresis window are shifted relative to the strong-coupling prediction.
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Atmospheric and Oceanic Physics
Seasonality of hail outbreaks in the United States and its link to weather regimes
Matthew Graber, Robert J. Trapp and Zhuo Wang
Hailstorms in the United States produce immense economic losses and have an occurrence frequency that appears to have a long-term positive trend. Here we contribute an updated examination of such hail activity and the relevant environmental parameters over the period 1990–2024. We define a hail outbreak as any day with $\gt 6$ significant hail reports ($hail\geq 2$” diameter) and find a positive trend in hail outbreaks since 1990 ($slope=0.8$ days $yr^{-1}$) with the largest increases occurring in May and June and in the Southern Great Plains. Analysis of environmental parameters shows that the long-term increase in hail outbreaks correlates with a long-term increase in the significant hail parameter (SHIP, $cc=0.67$, $p\lt 0.01$). The interannual variability and long-term trend of SHIP are driven both by thermodynamic and kinematic processes, though kinematic processes play a more important role than thermodynamic processes in the interannual variability of hail outbreaks. Using warm-season (April–July) weather regimes (WRs), we find that large-scale circulation modulates the interannual variability of hail outbreak frequency. An empirical model using WR frequency captures the interannual variability in warm-season hail outbreaks reasonably well ($cc=0.38$, $p=0.03$). Our study is the first to relate hail activity to WRs and presents a better understanding of the trend and year-to-year variability of hail outbreaks.
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Geophysics
Barchan-barchan and barchan-obstacle interactions: insights from grain-scale studies
Erick de Moraes Franklin, Willian Righi Assis, Danilo da Silva Borges et al. (+1 more)
Sand dunes are bedforms that grow due to the action of a fluid flow over a sand bed or pile. Whenever the fluid flow is mainly in one direction and the availability of sand is limited, crescent-shaped dunes known as barchans appear. These dunes are a strong attractor, being found on Earth, Mars, and other celestial bodies, usually in dune fields where they interact with each other, the terrain, and dune-size obstacles. In this review, we discuss the processes and outcomes of the different barchan-barchan and barchan-obstacle interactions, based on grain-scale subaqueous experiments and numerical simulations. We propose that those interactions depend basically on the Shields and Stokes numbers (that are two dimensionless parameters), the transverse position of bedforms, and the size ratio between the interacting objects. In addition, we show in detail the fluid flow, the trajectories of grains, and the resultant force acting on each grain, explaining the mechanisms for the different behaviors observed. Finally, we discuss the implications for the aeolian case, and put into perspective the current findings.
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All authors Erick de Moraes Franklin, Willian Righi Assis, Danilo da Silva Borges, Nicolao Cerqueira Lima
Computational Physics
A Single-Trace Surface Integral Equation Solver for Simulation of Open Bianisotropic Metasurfaces Described by Generalized Sheet Transition Conditions
Sebastian Celis Sierra, Junze Shao, Ran Zhao et al. (+3 more)
A single-trace surface integral equation (SIE) solver incorporating generalized sheet transition conditions (GSTCs) is presented for the simulation of three-dimensional (3D) open bianisotropic metasurfaces. The metasurface is modeled as an infinitesimally thin, non-enclosing sheet across which the GSTCs enforce the electromagnetic field discontinuities through four surface susceptibility tensors. The proposed solver uses a single set of equivalent surface currents on the sheet, in place of the two sets used by prior multi-trace formulations. The scattered fields on both faces of the sheet, expressed through SIE operators acting on these currents, are substituted into the GSTCs. The resulting system of equations is then discretized using Rao–Wilton–Glisson basis functions. This solver models an open metasurface directly, without an artificial closure, and applies to both planar and curved geometries. It is validated against analytical solutions for polarization rotation and perfect reflection, and is used to model a realistic broadband absorber whose susceptibility tensors are retrieved from full-wave simulation data. A direct comparison shows that the single-trace formulation attains lower error than a multi-trace formulation while using significantly fewer unknowns.
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All authors Sebastian Celis Sierra, Junze Shao, Ran Zhao, Rui Chen, Partha Mondal, Hakan Bagci