Earthquake detection and seismic phase picking are fundamental yet challenging tasks in seismology due to low signal-to-noise ratios, waveform variability, and overlapping events. Recent deep-learning models achieve strong results but rely on large datasets and heavy backpropagation training, raising concerns over efficiency, interpretability, and sustainability. We propose GreenPhase, a multi-resolution, feed-forward, and mathematically interpretable model based on the Green Learning framework. GreenPhase comprises three resolution levels, each integrating unsupervised representation learning, supervised feature learning, and decision learning. Its feed-forward design eliminates backpropagation, enabling independent module optimization with stable training and clear interpretability. Predictions are refined from coarse to fine resolutions while computation is restricted to candidate regions. On the Stanford Earthquake Dataset (STEAD), GreenPhase achieves excellent performance with F1 scores of 1.0 for detection, 0.98 for P-wave picking, and 0.96 for S-wave picking. This is accomplished while reducing the computational cost (FLOPs) for inference by approximately 83% compared to state-of-the-art models. These results demonstrate that the proposed model provides an efficient, interpretable, and sustainable alternative for large-scale seismic monitoring.

Modeling the unsaturated behavior of porous materials with multimodal pore size distributions presents significant challenges, as standard hydraulic models often fail to capture their complex, multi-scale characteristics. A common workaround involves superposing unimodal retention functions, each tailored to a specific pore size range; however, this approach requires separate parameter identification for each mode, which limits interpretability and generalizability, especially in data-sparse scenarios. In this work, we introduce a fundamentally different approach: a physics-constrained machine learning framework designed for meta-modeling, enabling the automatic discovery of closed-form mathematical expressions for multimodal water retention curves directly from experimental data. Mathematical expressions are represented as binary trees and evolved via genetic programming, while physical constraints are embedded into the loss function to guide the symbolic regressor toward solutions that are physically consistent and mathematically robust. Our results demonstrate that the proposed framework can discover closed-form equations that effectively represent the water retention characteristics of porous materials with varying pore structures. To support third-party validation, application, and extension, we make the full implementation publicly available in an open-source repository.

The rapid development of artificial intelligence technology is driving the transformation of physics education from traditional models to intelligent and data-driven approaches. To explore the evolution and cutting-edge hotspots in this field, this study conducted a systematic bibliometric analysis of 138 core literature published between 2021 and 2025 using VOSViewer and CiteSpace, based on the Web of Science Core Collection database. Research shows that the number of related publications will increase exponentially from 2023, with the United States, China, and Germany being the main research forces. The research focus has rapidly evolved from early machine learning assisted data analysis to the application of generative AI in teaching, the integration of physics information neural networks in computational physics courses, and the exploration of intelligent medical physics education. At present, this field is in the early stages of explosive growth and exhibits significant interdisciplinary characteristics. Future research should focus on building an adaptive learning ecosystem, reconstructing evaluation systems, and cultivating students' AI ethics and physical intuition.

Accurate subgrid-scale closures are essential for weather/climate models, where predicting extreme events is critical. Traditional closures have structural errors, e.g., producing excessive diffusion that dampens extremes. Artificial intelligence has gained attention for closure modeling, but the prediction of extreme events remains challenging. Supervised offline learning needs abundant high-fidelity training data and can lead to instabilities. Online learning algorithms are emerging as an alternative, but reliance on differentiable numerical solvers or scalable optimizers hinders broad use. Here, we introduce SMARL to develop closures for canonical prototypes of atmospheric/oceanic turbulence, using only the enstrophy spectrum, estimated from a few high-fidelity samples, as reward. This reward ensures that the model captures the cascades of scales in these simulations. These online-learned closures enable stable simulations, with up to five orders of magnitude fewer degrees of freedom, that reproduce high-fidelity simulation statistics and capture in particular extremes. We interpret the closures by analyzing the SMARL policy and demonstrate generalization to other flows. The results highlight SMARL as a potent tool for developing closures capable of capturing extremes in atmospheric/oceanic flows, opening new capabilities for effective climate modeling.

The International Physics Olympiad (IPhO) is the world's most prestigious and renowned physics competition for pre-university students. IPhO problems require complex reasoning based on deep understanding of physical principles in a standard general physics curriculum. On IPhO 2025 theory problems, while gold medal performance by AI models was reported previously, it falls behind the best human contestant. Here we build a simple agent with Gemini 3.1 Pro Preview. We run it five times and it achieved a perfect score every time. However, data contamination could occur because Gemini 3.1 Pro Preview was released after the competition.

Accurate identification of charged pions and kaons is essential for precision measurements in relativistic heavy-ion collisions, but becomes increasingly challenging at intermediate and high transverse momentum due to the overlap between time-of-flight mass-square ($m^{2}$) and ionization energy loss ($n\sigma$) distributions. In this work, we present a two-dimensional shift and rotation method that exploits the correlated information between $m^{2}$ and $n\sigma$ to enhance particle identification performance. The method is validated using Au+Au collision events generated with the AMPT model, where detector response effects are incorporated through a data-driven smearing procedure tuned to reproduce the particle identification performance of the STAR experiment. The reconstructed pion and kaon transverse momentum distributions show excellent agreement with the AMPT input, maintaining a purity exceeding 98\% at high $p_T$ and extend the reliable identification range up to $p_T \approx$ 3 GeV/$c$. The extracted elliptic flow $v_2$ remains consistent with the input over the extended $p_T$ range, demonstrating that the proposed method provides a robust framework for high precision identified hadron measurements.

Incorporating nanomaterials into food products provides key benefits, including extended shelf life, improved safety, and enhanced quality and texture. These innovations could help tackle major challenges in modern food systems, such as reducing waste and enhancing food quality and safety. However, potential toxicity remains a concern, compounded by the lack of physiologically relevant models for assessing ingested nanomaterials. Traditional in vitro and in vivo approaches often fail to mimic gastrointestinal complexity, resulting in inconsistent and non predictive nanotoxicity data that hinder accurate risk assessment of nano enabled foods. To address this gap, this review evaluates the potential of microphysiological systems (MPS), particularly gut-targeted MPS, for modeling gastrointestinal nanoparticle exposure. It examines how MPS technologies replicate key physiological processes relevant to food specific risk assessment, including intestinal barrier function, microbiota immune interactions, and gut organ communication. A comparative analysis of technological advances and their applications in nanotoxicology explores how MPS can be better adapted for nanofood safety evaluation.

Carbonaceous Chondrites have special significance in the stellar evolution and in particular in the evolution of life on earth. The carbonaceous meteorite that fell in Mukundpura village, Jaipur, Rajasthan on 6th June 2017 is one such rare CM2 (Carbonaceous Chondrite) carbonaceous meteorite. We carried out high resolution scanning and transmission electron microscopic (TEM) studies on typical thin sections, showing abundant grains of iridium (Ir), pentlandite (NiS), and more interestingly crystalline carbon (C). These crystallite carbon grains resemble nanodiamond like signature in the freshest Mukundpura meteorite. The high-resolution Raman spectroscopic measurements are carried out on the crystalline carbon grains, showing well resolved three distinct peaks with a vibrational mode at 1315 cm-1, with the onset of a weak vibrational mode at 1150 cm^-1, substantiating the observation of nanocrystalline diamond in Mukundpura meteorite. The broad peak centered at 1360 cm^-1 and 1575 cm^-1 (as an average), suggest the presence of graphitic carbon as well together with apparent presence of nanocrystalline diamond. The average size of nanocrystalline diamond is ~ 3-5 nm. High iridium content in this meteorite supports the meteoric impact related iridium anomaly in geological stratigraphic boundaries (this http URL-Tertiary boundary) that has caused mass extinction of flora and fauna.

In hot arid and semi-arid regions, substantial irrigation water is lost through surface evaporation under intense solar irradiation and high temperatures, limiting freshwater sustainability and crop productivity. Superhydrophobic Sand (SHS) mulch, a plastic-free, bio-inspired technology, has been proposed as a dry diffusion barrier to suppress evaporative losses. Here, we combine controlled column experiments with heat and mass transfer modeling to quantify how SHS thickness and soil properties govern evaporation under fixed irradiation. Relative to unmulched controls, a 5 mm SHS layer reduced evaporative flux by 65$\%$ in fine sand and 63$\%$ in coarse sand, while a 10 mm layer reduced flux by 83$\%$ and 70$\%$, respectively. Notably, soil-type trends reversed after mulching: although unmulched fine sand exhibited 37.5$\%$ higher evaporation than coarse sand, application of a 10 mm SHS layer reduced fine-sand evaporation to 40$\%$ below that of coarse sand. To explain this counterintuitive behavior, we developed a coupled heat and vapor transport model incorporating soil thermophysical properties and diffusion through the porous mulch layer. The model accurately predicted steady-state temperature profiles and evaporation rates for both mulched and unmulched systems. Our results show that SHS mulch shifts evaporation from a surface-temperature-controlled regime to a diffusion-limited regime governed by mulch thickness and soil thermal conductivity. This mechanistic understanding clarifies the performance of SHS and supports its potential to enhance irrigation efficiency in arid agricultural and landscaping applications.

We consider the implementation of optimization techniques within the study of tectonic plate motion. Specifically, we examine the optimization underlying optAPM, a leading code for modeling absolute plate motion. We highlight that modifications in the construction of the objective function, composed of individual cost functions, can improve modelling performance. In particular, we propose a simpler and more intuitive formulation of the hotspot cost function. A key part of the new hotspot analysis is the pre-interpolation of hotspot trail data, crucial geological markers for validating absolute plate motion over O(100) Myr timescales. By reducing the propagation of modeling errors, our refined model provides more precise reconstructions of historical plate movements. Our modified hotspot modelling improves the accuracy and reliability of the optAPM outputs.

Global food production must reduce environmental impact while meeting rising demand for dietary protein. Plant-based meats aim to preserve the sensory and cultural role of animal meat, while lowering greenhouse gas emissions, land use, and health risks. Advances in protein structure and flavor chemistry have improved product quality; yet, consumers continue to prioritize taste and texture over sustainability and systematic large-scale consumer surveys are scarce. It remains unclear how plant-based products rank against animal benchmarks and which product attributes most strongly influence overall liking. Here we show, in a large-scale, blinded, in-person sensory evaluation across 14 product categories, 2,684 consumers, more than 11,000 product evaluations and 800,000 data points, that plant-based products still trail animal benchmarks at the category average level, but approach parity in selected formats: Plant-based unbreaded chicken filets, chicken nuggets, and burgers achieved mean overall liking scores of 5.1, 4.9, and 5.2, differing from the animal benchmark by only 0.1, 0.2, and 0.3 points on a seven-point scale. For unbreaded chicken filets and burgers, 48% and 47% of participants rated the plant-based product the same as or better than the animal benchmark. Categories with higher sensory parity captured 5-14% market share compared with less than 1% for low-parity categories. Penalty analysis identified savoriness, aftertaste, juiciness, and tenderness as the strongest determinants of liking. These findings show that sensory parity is technically achievable, but not yet consistent across product types. By publicly sharing all data, we establish an open benchmark for alternative protein performance to democratize research and accelerate principled, data-driven innovation. All data are freely available at this https URL.

Because of the special angular distribution of excited electrons by the photoelectric effect, the Gas Pixel Detector (GPD) is effective in measuring keV X-ray polarization of astrophysical events (e.g. gamma-ray bursts), by capturing ionization tracks of excited electrons as polarized images. Traditionally, the emission angles of photoelectrons are extracted from polarized images first, and statistics are then performed on these angles to infer the polarization direction and intensity. However, observation with the wide field of view requires the incident angle of X-rays not directly attainable through the traditional analysis process. In this paper, we propose using the generalized sliced Wasserstein (GSW) distance, projected by neural networks with random weights, as a completely data-driven approach to analyze X-ray polarization based on two-dimensional polarized images. We find the structures of the randomized neural networks matter when focusing on different aspects of the polarized images, and take advantage of the discrimination abilities by different neural network structures. The proposed method, named the structured GSW distance, successfully distinguishes polarized images with different configurations of incident angles and polarization directions. Furthermore, we build a simplified statistical model based on the von Mises distribution and the circular Wasserstein distance and compare the model against the proposed method, showing their high consistency. The computational method reported in this paper may benefit GPD-based polarimetry in astroparticle experiments and also pattern analysis on raw data from pixel detectors.

Oil slicks are widely distributed in the ocean today, as a kind of coverage on sea surface, they became a part of ocean environment and affect their surroundings. A stochastic-dynamic theoretical model proposed in this work to illustrate how oil slicks affect global climate from micro scale relation between a piece of oil slick and sea surface temperature (SST) of its surrounding unit area, for SST is an important index of global climate. The model indicate that oil slicks make the sea surface warmer in the future, and the temperature series of the sea surface covered by oil slicks will have greater variance and fatter tails for its distribution and reduce SST predictability from a microcosmic perspective. Thus, more oil infused into the ocean makes the air-sea system more uncertain. These findings indicate that the present air-sea coupled models may lack of sufficient attention to oil slicks floating on the sea surface.

The El Niño-Southern Oscillation (ENSO) is a fluctuation in sea surface temperature (SST) and pressure across the equatorial Pacific Ocean with a period of 2-7 years. As the largest mode of interannual variability on Earth, ENSO shapes global weather and climate patterns ranging from monsoons in southern Asia to hurricanes in the Atlantic and droughts in South America. Predicting and understanding ENSO's response to greenhouse warming is essential for mitigating the impacts of climate change, yet model ensemble projections are prohibitively expensive to generate across emission scenarios and remain incompletely understood. Here, we use a hierarchy of models to show that ENSO strength undergoes a transient rise followed by a long-term fall under greenhouse warming. An East Pacific energy budget reveals that the initial increase in ENSO variability is due to enhanced upper-ocean stratification, while its subsequent decrease arises from a slowing Walker circulation and stronger surface flux damping. Building on these mechanisms, we derive a linear model which predicts the evolution of ENSO variability from only East Pacific temperature and stratification. We further show that subsurface warming, and therefore stratification, is connected to surface warming with a lag, enabling us to create a lag-linear model that explains $\sim$90\% of simulated changes in ENSO variability from only global mean SST and its history. This efficient predictor can forecast ENSO strength over time in any warming scenario, and reveals that faster emissions lead to stronger peak ENSO variability even with identical total emissions.

Radiation detectors deployed as part of a large urban network or for homeland security monitoring must maintain reliable energy calibration even when subjected to substantial variations in temperature and ambient background radiation. Traditional calibration methods often rely on power-intensive temperature stabilization or peak-locking algorithms that are susceptible to environmental changes. This publication presents a novel software-based calibration method that eliminates the need for active temperature control by utilizing full-spectrum analysis. The method continuously updates the calibration parameters by fitting the spectral data with a series of background radiation contributions (K, U, Th series, radon progeny and cosmics) combined with a Monte-Carlo-based physical detector model that incorporates light yield non-proportionality and photomultiplier tube saturation. Performance was validated using simulated data, measurements in an environmental chamber across a wide temperature range (-25C to +50C), and data from a multi-day outdoor field deployment. Results demonstrate that the method successfully maintains stable energy calibration despite significant ambient temperature variations and precipitation events. The technique effectively decouples instrumental drift from spectral changes caused by environmental background fluctuations. This approach provides a robust, automated, and low-power alternative to conventional calibration techniques, enabling the practical deployment of large-scale, unattended networked detector systems.

*The abstract of this article is too long to be included in the arXiv metadata; please see the paper for the full abstract.* ...In this paper, we introduce the detailed theory of cubic-in-magnetization magneto-optic Kerr effect (CMOKE) by deriving the magneto-optic tensor of third order in magnetization, denoted as $\bm{H}$, and comparing the strength of CMOKE for different crystal orientations theoretically and experimentally. In crystals with cubic symmetry, the tensor $\bm{H}$ is described by two independent parameters $H_{123}$ and $H_{125}$. Together with the linear magneto-optic tensor $\bm{K}$ and quadratic magento-optic tensor $\bm{G}$, the permittivity tensor is described up to third order in magnetization. We analytically describe equations of the MOKE including the contribution of QMOKE and CMOKE itself for (001)- and (111)-oriented cubic crystal structures. Those are compared to experimental measurements of two samples with an (001)- and (111)-oriented fcc Ni layer, respectively. Further, we use Yeh's 4$\times$4 transfer matrix calculus to simulate and describe the experimental measurements phenomenologically from the permittivity tensor up to third order in $\bm{M}$. We find that the MOKE anisotropy that stems from the magneto-optic tensor $\bm{H}$ described as $\Delta H = H_{123}-3H_{125}$, is much more pronounced for the (111)-oriented cubic crystal structure, for which it manifests as three-fold in-plane angular dependencies of MOKE with longitudinal and also with transversal magnetization direction, respectively.

Traditionally, the Planck constant $h$ makes its debut appearance in quantum physics textbooks in the context of the blackbody radiation law and subsequently as a fundamental ingredient of the physics of the photoelectric effect and the Bohr atom. In this paper we consider an alternative timeline path (which could have taken place several years before the proposal of the Bohr atom) where a classical physics hydrogen atom is studied with dimensional analysis techniques in combination with empirical laws of blackbody radiation. The outcome of this ``classical physicist's'' approach is the identification of the correct fundamental Planck constant and the reconstruction of the energy and size scales of the Bohr atom.

Near-surface extreme winds profoundly affect human society, yet process-based understanding of their changes under climate forcings remains limited. This study systematically investigates the responses of high (HWE) and low (LWE) wind extremes (10-meter) to climate forcings using a hierarchy of climate model experiments from multiple general circulation models that participated in the Cloud Feedback Model Intercomparison Project. We analyze idealized atmosphere-only aquaplanet (Aqua) simulations and more realistic land-atmosphere (AMIP) simulations to identify robust responses to climate forcings and trace the sources of structural uncertainty. In Aqua simulations, tropical LWE changes exhibit large inter-model spread, which can be traced to dynamically distinct representations of low-pressure systems between models. In contrast, extratropical HWE intensify robustly with surface warming, linked to the strengthening of high-latitude extratropical cyclones. The AMIP simulations confirm the robust intensification of extratropical HWE. The more realistic boundary conditions in AMIP simulations act as a constraint, reducing inter-model spread in tropical zonal means compared to Aqua simulations. A comparison of uniform and patterned 4-K warming experiments suggests that the global magnitude of warming, rather than the specific warming pattern, dominates the large-scale responses of wind extremes. However, regional projections of extreme wind changes, especially over land, remain highly uncertain due to divergences in model physics. Case studies reveal that major disagreements in HWE changes can stem from fundamental differences in representing the type and seasonality of extreme-producing weather systems. Our results underscore that reducing uncertainty in regional wind projections requires constraining the physical representation of weather systems in climate models.

The interaction of a shock wave with a localized constriction in a straight conduit is investigated by systematically varying the blockage ratio in the range 0.35-0.75, the normalized constriction length in the range 0.25-2, and the incident Mach numbers of 1.4 and 1.8. Abrupt rectangular constrictions and smoothly contoured sinusoidal constrictions are considered, as they provide two limiting configurations. Validated Large-eddy simulations resolve both the transient start-up dynamics and the subsequent propagation of reflected and transmitted shock waves. The results show that, for rectangular constrictions, the reflected shock strength depends primarily on the blockage ratio and is largely independent of length, whereas the transmitted shock exhibits measurable sensitivity to constriction length. In contrast, sinusoidal constrictions display a strong coupling between blockage and length, with the reflection process governed by the local contour slope and evolving reflection topology. The start-up process within the constriction occurs over time scales one to two orders of magnitude longer than the shock passage time and is characterized by a sequence of reflection, separation, and flow reorganization events that determine the eventual steady shock configuration. At later times, the reflected shock Mach number scales linearly with blockage ratio, while the transmitted shock strength decreases monotonically with increasing blockage. Based on these trends, semi-empirical models are developed to predict the strengths of both reflected and transmitted shocks across the parameter space considered. These results provide a unified framework for understanding and predicting shock propagation in conduits with localized geometric variations, with direct relevance to compressible internal flows in engineering and natural systems.

From soil to the gut, communities composed of thousands of microbes perform functions such as carbon sequestration and immune system regulation. Here, we introduce a data-driven approach that explains how community function can be traced to just a few groups of microbes or genes. In gut communities, our neural-network based clustering algorithm correctly recovers known functional groups. In the ocean metagenome, it distills ~500 gene modules down to three sparse groups highlighting survival strategies at different depths. In soils, it distills ~4400 bacterial species into two groups that enter a mathematical model of nitrate metabolism. By combining interpretable ML with strain isolation and sequencing experiments, we connect the metabolic specialization of each group to community-wide responses to perturbations. This integrated approach yields simple structure-function maps of microbiomes, allowing the discovery of molecular mechanisms underlying human and environmental health. More broadly, we illustrate how to do function-informed dimensionality reduction in biology.

Metasurfaces leveraging nonlocal resonances enable narrowband spectral control and strong near-fields, with applications spanning augmented reality, biosensing, and nonlinear optics. However, the large spa- tial extent of these modes also poses new challenges: finite-size effects often deteriorate the performance of practical, footprint-limited devices. Here, we develop a spatiotemporal coupled-mode theory model that intuitively and quantitatively captures how finite size affects the scattering response of nonlocal metasurfaces. This reveals that, when the modal propagation length becomes constrained by the phys- ical interaction length, the scattered field shows strong interference fringes and linewidth broadening. We derive an expression for the quality factor that incorporates an additional edge-loss channel, demon- strating that the stored energy and effective lifetime scale exponentially with the interaction length. We validate these predictions experimentally using position- and momentum-resolved spectroscopy on a 30-micron-wide metasurface. Overall, this work formalizes the impact of finite size on the scattering re- sponse of nonlocal photonic systems, and provides handles on how to minimize the impact of finite-size effects in metasurface design.

We present two independent optical methods for absolute primary thermometry using rare-earth-doped nanoparticles. Both approaches rely exclusively on the internal energy levels and population dynamics of the dopant ions, eliminating the need for external temperature references. We experimentally demonstrate the concepts by using Y$_2$O$_3$: Yb$^{3+}$/Er$^{3+}$ nanoparticles, exploiting Boltzmann distribution between individual Stark sublevels of the Er$^{3+}$ ions, emitting in the green spectral region ($\sim$550 nm) and in the near-infrared spectral region ($\sim$1600 nm). Our strategy establishes rare-earth-based luminescence thermometers as genuine absolute primary probes, conceptually comparable to Johnson noise and acoustic gas thermometers, but with the fundamental advantage of possibly being employed at the nanoscale, potentially down to the single-ion limit, with optical readout and over wide temperature ranges.

Foreshock transients upstream of the Earth's bow shock, such as foreshock bubbles and hot flow anomalies, are often characterized by reduced-density cores and strong plasma fluctuations. These conditions provide environments where electrostatic wave activity and localized density structures can coexist. Using high-time-resolution measurements from the Magnetospheric Multiscale (MMS) mission, we investigate the relationship between bursty electrostatic wave activity and localized electron density depletions within foreshock transients. A representative case study reveals a clear scaling between wave activity and density depletion, and a statistical analysis across multiple events shows that this scaling persists when the wave activity, with characteristics consistent with ion-acoustic-like waves, is represented in terms of electrostatic potential fluctuations normalized by electron temperature. In contrast, representations based on electric field amplitude, even when similarly normalized, exhibit substantial event-to-event variability. These results provide observational evidence for a causal relationship between ion-acoustic-like electrostatic wave activity and cavitons in foreshock plasmas.

This study examines the decay of Hurricane Melissa (2025) as the storm crossed the mountainous terrain of Jamaica, focusing on changes in inner-core energetics. Using NOAA P-3 reconnaissance observations near the 700 hPa level, integrated kinetic energy within 100 km of the storm center was computed before and after land interaction to quantify vortex weakening. During the approximately four hour period in which the center remained over Jamaica, the storm experienced rapid degradation, including a 48 percent reduction in peak tangential wind, a 58 hPa rise in central pressure, and a 41 percent decrease in integrated kinetic energy. To investigate the mechanisms responsible for this decay, the observations were compared with results from an idealized axisymmetric tangential momentum diffusion model that isolates the effects of vertical turbulent mixing and enhanced surface drag. Despite its simplified physics, the model reproduces many leading order features of the observed weakening, including substantial reductions in tangential wind and a 36 percent decline in integrated kinetic energy. These results indicate that enhanced friction and turbulent mixing associated with extremely rough mountainous terrain can account for a large fraction of the rapid spin down observed during land interaction. Differences between the model and observations suggest that additional processes such as asymmetric dynamics, terrain induced flow distortion, and thermodynamic feedbacks further amplify the weakening of intense tropical cyclones over land.

Rapid urban population growth drives car travel demand, increasing transport carbon emissions and posing a critical challenge to sustainable development. Although existing studies have demonstrated that eco-routing can reduce individual emissions, research gaps remain. On the one hand, such personal reductions have a negligible impact on overall emissions, and cannot be simply aggregated to capture the complex effects of large-scale eco-routing. On the other hand, under population growth, the long-term effectiveness of eco-routing, as well as the evolution of its efficiency and traveler route choice, remain underexplored. To address these limitations, this study proposes Time-Only and Time-Carbon user equilibrium (UE) models, integrates them with a demand forecasting method for simulating future network traffic, and designs multi-dimensional metrics to characterize urban dynamics. Using real-world road networks, commuting origin-destination (OD) demand, and population projections under various shared socioeconomic pathways (SSPs) for six representative U.S. cities as a case study, we conduct a comprehensive analysis of urban dynamics across different routing strategies and population sizes. The results reveal that while eco-routing mitigates total emissions, emissions in most cities scale superlinearly with population, a scaling order that remains invariant regardless of routing and construction strategies. Moreover, under population growth, travelers using eco-routing tend to increasingly select shorter routes, giving rise to carbon bottlenecks. A strategy of targeted capacity expansion on these critical bottlenecks (0.46% of links) significantly reduces both emissions (3%) and travel time (28%) without compromising eco-routing efficiency. This study provides a foundation for formulating low-carbon urban transport planning and emission reduction policies.

We consider a laser cooling and trapping of alkaline-earth and similar atoms in a bichromatic field resonant to a closed optical transition $^1S_0 \to \, ^1P_1$ or $^1S_0 \to \, ^3P_1$. It is shown that new kinetic effects emerge compared to monochromatic fields, enabling the formation of a deep macroscopic trap capable of capturing and cooling neutral atoms to sub-Doppler temperatures. Such a purely optical macroscopic trap can serve as an alternative to the well-known magneto-optical trap and can be used in applications requiring minimization of the magnetic field in the cold atom cloud region. The obtained results are of interest for the new generation of quantum sensors and optical frequency standards.

In non-axisymmetric plasmas, the ambipolar condition may have multiple roots. In such cases, the evolution of the ambipolar electric field can be described by the dynamics in a bistable potential, where the relative depth of the potential wells primarily determines the realized root. In this study, we show that the inclusion of the magnetic drift in the orbit model can significantly modify the potential landscape and affect root selection. This effect provides a possible explanation for discrepancies between simulation results obtained using different orbit models, as well as between simulations and experimental observations of ambipolar radial electric field profiles. Further, the analysis suggests that the ambipolar electric field may be more susceptible to fluctuations than previously expected, indicating the potential relevance of noise-induced state transitions.

The mesosphere and lower thermosphere (MLT), spanning approximately 80--100~km in altitude, is a region of intense dynamical activity where atmospheric gravity waves amplify due to decreasing air density. These waves often undergo breaking, inducing energy and momentum dissipation as well as turbulent mixing. These processes can be described by atmospheric instabilities -- including convective and shear instabilities -- that regulate the vertical coupling between atmospheric layers. Ripple-like structures observed in mesospheric airglow imagery represent small-scale, short-lived signatures of such instabilities. Their occurrence often reflects localized instabilities, critical wave interactions, or wave ducting phenomena. Therefore, detecting and analyzing these features across broad spatial and temporal domains remains a challenge. In this study, we develop a deep learning framework using convolutional neural networks (CNNs) to automatically detect ripple-scale wave structures in all-sky airglow images. Trained on labeled datasets with recognized ripples, the model learns the spatial morphology associated with instability-driven gravity waves and achieves high accuracy in identifying ripple events. Beyond detection, we perform a statistical characterization of the detected ripples to examine their frequency of occurrence, orientation distributions, scales, and geographic and seasonal variability. These statistics are used to infer underlying physical mechanisms, such as localized instability conditions and background wind filtering. This work advances our understanding of instability-driven dynamics in the upper atmosphere through AI-powered detection, while also highlighting the potential of deep learning in scientific research.

We propose to implement an optical analogue of the tautochrone property of the cycloid to allow the focusing of ultrashort pulses inside photonic systems. This allows to enhance nonlinear effects, resulting in orders of magnitude increase of nonlinearity-induced phase shifts, while employing low irradiances. Building upon the optical-mechanical analogy, we show how to produce optical limiters for temporal light pulses, and how to implement temporal bistability and even multistability with large numbers of states. Finally, we move this concept to the quantum realm and predict a tautochrone quantum blockade regime with a stronger antibunching.

The present study investigates influence of compressibility on separation induced transition in a low pressure turbine cascade using high fidelity direct numerical simulations of the T106A blade. Simulations are performed for inlet Mach numbers, Ms ranging from 0.15 to 0.35 at a fixed Reynolds number and high incidence, representative of off design LPT operation. A dispersion relation preserving numerical framework is employed to accurately capture instability waves, separation bubbles, and separation induced transition to turbulence. A comprehensive analysis is carried out using surface pressure and skin friction distributions, boundary layer integral parameters, spectral analyses, and budgets of compressible enstrophy. Increasing Ms systematically reduces streamwise extent of both leading edge and trailing edge separation bubbles and promotes earlier transition and reattachment, consistent with trends observed under increased free stream disturbances. Despite shorter separation regions, suction side momentum thickness at trailing edge increases from Ms = 0.15 to 0.35, indicating higher profile losses at elevated Ms. Spectral analyses demonstrate a redistribution of turbulent spatial and temporal scales, with energy injection occurring at progressively larger scales as Ms increases. Flow field visualizations reveal a transition pathway that shifts from two dimensional spanwise rolls and intermittent turbulent spots at low Ms to streak dominated, bypass like transition at higher Ms.

The inverse design of nonlocal metasurfaces requires the precise optimization of lattice geometry to engineer spatial dispersion and high-Q resonances. However, gradient-based optimization is frequently bottle-necked by the evaluation of the periodic Dyadic Green's Function (DGF), where traditional Finite Difference (FD) methods suffer from an inherent trade-off between truncation error and numerical instability near spectral singularities. In this work, we present an end-to-end Analytic Gradient Engine for 2D Bravais lattices. By mapping the spectral lattice sums of the Coupled Dipole Approximation (CDA) to the theory of Quasi-Modular Forms (QMF), we derive exact, closed-form expressions for the gradients of the interaction matrix with respect to the modular lattice parameter $\tau$. Our framework explicitly handles conditionally convergent terms via regularization and addresses the non-holomorphic outlier $\sigma_4^{(2)}$ via a hybrid numerical strategy. We further introduce a robust evaluation scheme combining $SL(2, \mathbb{Z})$ domain reduction with automatic error certificates. Experimental validation demonstrates that our engine achieves machine-precision derivatives ($10^{-15}$) and a 6.5$\times$ speedup in optimization convergence compared to finite-difference baselines, enabling the robust design of giant anisotropy in regimes where traditional methods fail.

For window applications in hot climates, it is desirable to have windows with high visible transparency, while maintaining strong reflectance in both the ultraviolet and near infrared, to minimize unwanted heat gain. Given that more than 70% of incident solar energy is at wavelengths shorter than 1000 nm, achieving spectrally abrupt transitions from transparent to reflecting at the boundaries of the visible is essential. Such abrupt transitions at multiple wavelengths typically would require tens of dielectric layers, which is impractical for most window applications. Here, we propose and realize a structure comprising only eight planar layers that achieves sharp reflectance changes at ~390 and ~680 nm, resulting in high visible transmittance (>70%), and high near-infrared (>80%) and UV (>60%) reflectance, as well as high mid-infrared emissivity (>90%) for additional radiative cooling. We demonstrate an air temperature reduction of up to 3.8 °C with our engineered window compared to a reference window.

A simple numerical method for loading of a relativistic Maxwellian-type distribution is proposed based on inverse transform sampling. The relativistic Maxwellian energy distribution is introduced as an alternative to the Maxwell-Jüttner distribution. The cumulative distribution of the shifted-Maxwellian energy distribution is approximated by an invertible function. Random variates of energy is transformed from uniformly distributed random variables. Then, the energy variates are converted to momentum vector variates. Numerical tests are presented to show that the present method successfully reproduce the relativistic Maxwellian energy distribution.

We report a phase-sensitive nonlinear x-ray response in the charge-density-wave material 1T-TaS2, revealed through x-ray parametric down-conversion into the ultraviolet. Extending nonlinear x-ray wave mixing beyond conventional crystalline systems to a correlated quantum material, we employ reciprocal-lattice phase matching to isolate distinct Fourier components of the nonlinear susceptibility. By selecting a fundamental reciprocal-lattice vector and a stacking-sensitive half-integer reciprocal-lattice vector, we probe the response across the nearly commensurate and incommensurate charge-density-wave phases. Tuning the ultraviolet photon energy through Ta O-shell resonances uncovers pronounced Fourier-component and phase-dependent resonant structure, indicating that the stacking-related nonlinear susceptibility couples differently to Ta-centered resonant states than the average lattice response. Remarkably, the nonlinear signal is strongly enhanced in the nearly commensurate phase despite weaker Bragg diffraction, demonstrating that the nonlinear susceptibility provides information inaccessible to linear probes. These results establish nonlinear x-ray spectroscopy as a phase-sensitive and orbital-selective probe of electronic reconstruction in quantum materials.

Label-free vibrational microscopy provides chemically specific access to cellular structure, yet quantitative volumetric chemical dynamics in living cells remain largely inaccessible, particularly on subsecond timescales relevant to intracellular transport and structural reorganization. This limitation arises because most high-speed vibrational techniques rely on raster scanning, which constrains volumetric throughput to approximately one volume per second (vps). Although mid-infrared photothermal (MIP) imaging offers a pathway toward spatially parallel chemical detection, existing implementations have remained far below video-rate volumetric operation, reflecting a fundamental trade-off between imaging speed and signal-to-noise ratio. Here, we overcome this trade-off in MIP tomography and realize video-rate volumetric chemical imaging using mid-infrared photothermal optical diffraction tomography (MIP-ODT), achieving high photothermal sensitivity while maintaining quantitative measurement fidelity. High per-angle detectability supports volumetric reconstruction without temporal averaging, yielding a signal-to-noise ratio exceeding 70 under video-rate acquisition conditions. Consequently, volumetric imaging at 19.2 vps is achieved, representing a nearly 400-fold improvement over prior implementations. Using this capability, we performed video-rate three-dimensional tracking of lipid droplets in living cells and quantified anomalous diffusion from full volumetric trajectories, revealing heterogeneous intracellular transport behaviors that are obscured in two-dimensional measurements. We further demonstrate high-speed hyperspectral volumetric chemical imaging across a 300 cm-1 spectral window within 1 s through rapid MIR wavenumber sweeping, paving the way for real-time three-dimensional organelle-specific chemical phenotyping.

This study investigates the influence of a perturbed eddy-viscosity model on linear stability and shape sensitivity of the global vortex rope mode arising in a hydro-turbine flow under fully turbulent conditions. The framework is based on the Reynolds-averaged Navier--Stokes equations with a standard $k$-$\varepsilon$ turbulence closure, linearized around a base-flow state. This base state is tuned to match the vortex-rope bifurcation predicted from three-dimensional unsteady simulations. The shape sensitivity of the global mode is derived, accounting for perturbations of both the base flow and the linear operator. We show that although the perturbed eddy-viscosity model has only a marginal effect on the eigenvalues and eigenmodes of interest, it substantially alters the resulting shape sensitivities. These differences arise primarily through the base-flow contribution to the total sensitivity, which dominates the sensitivity to shape deformations. Although both models identify coherent-velocity production and advection as the leading contributors, the linearized model captures additional mechanisms associated with eddy-viscosity perturbations. Comparison with experiments shows that only the perturbed eddy-viscosity model reproduces the correct trends in shape sensitivity, whereas the frozen model fails to do so. These findings highlight the importance of consistently linearizing turbulence models for sensitivity-based control of turbulent global instabilities.

Cardiac arrhythmogenesis is governed by complex electromechanical interactions that are not directly observable in vivo, motivating the development of non-invasive computational approaches for reconstructing three-dimensional activation dynamics. We present a physics-informed neural network framework for recovering cardiac activation patterns, active tension propagation, deformation fields, and hydrostatic pressure from measurable deformation data in simplified left ventricular geometries. Our approach integrates nonlinear anisotropic constitutive modeling, heterogeneous fiber orientation, weak formulations of the governing mechanics, and finite-element-based loss functions to embed physical constraints directly into training. We demonstrate that the proposed framework accurately reconstructs spatiotemporal activation dynamics under varying levels of measurement noise and reduced spatial resolution, while preserving global propagation patterns and activation timing. By coupling mechanistic modeling with data-driven inference, this method establishes a pathway toward patient-specific, non-invasive reconstruction of cardiac activation, with potential applications in digital phenotyping and computational support for arrhythmia assessment.

Planar metasurfaces can profoundly control electromagnetic scattering. At microwave frequencies, such devices are typically implemented using multilayer cascades of patterned metallic sheets, whose design often requires time-consuming full-wave optimization. Here, we extend analytical models originally developed for sparse loaded-wire metagratings to accurately describe densely packed Jerusalem-cross meta-atoms embedded in standard printed circuit board (PCB) dielectric stacks. The model captures both near- and far-field coupling within and between layers, enabling efficient prediction of the dual-polarized response. Using this framework, we identify highly transmissive meta-atoms whose phase is controlled by the leg lengths of the Jerusalem crosses (microscopic design stage). This (phase)-(leg-length) "lookup table" allows rapid synthesis of Huygens' metasurfaces (macroscopic design stage), demonstrated through a full-wave-validated metalens exhibiting low-reflection beam manipulation. Notably, we implement a judicious scaling method to further extend the model to predict wideband meta-atom responses. In the companion paper (Part II), a hybrid machine-learning approach leverages this semianalytical framework to enhance accuracy without requiring the conventional exhaustive full-wave training, enabling ultrafast inverse design across the full parameter space. Overall, the presented methodology -- the standalone semianlytical scheme (Part I) and the machine-learning enhanced version (Part II) -- establishes an effective open-source toolkit for versatile, rapid, and highly accurate synthesis of fabrication-ready dual-polarized transmissive Huygens' meta-atoms and metasurfaces.

Global climate projections rely on computationally demanding Earth System Models (ESMs), which are typically limited to coarse spatial resolutions due to their high cost. To obtain high-resolution projections for regions of interest, it is common to use Regional Climate Models (RCMs), which are driven by data produced by ESMs as boundary conditions. While more efficient than running ESMs at fine resolution, RCMs remain expensive and restrict the size of ensemble simulations. Inspired by recent advances in probabilistic machine learning for weather and climate, we introduce a data-driven climate downscaling method based on stochastic interpolants. Our approach efficiently transforms coarse ESM output into high-resolution regional climate projections at a fraction of the computational cost of traditional RCMs. Through extensive validation, we demonstrate that our method generates accurate regional ensembles, enabling both improved uncertainty quantification and broader use of high-resolution climate information.

Femtosecond pulsed laser systems constitute powerful tools for the high-precision structuring of materials at micro/nano-scale resolutions. A critical parameter influencing the efficacy of ultrafast laser-material interactions is the laser-induced damage threshold (LIDT), which is defined as the minimum laser fluence required to induce irreversible modification to the material surface. While extensive studies have addressed single-pulse damage mechanisms, the response of thin metallic films to double-pulse femtosecond irradiation, particularly when the film thickness is of the order of the optical penetration depth, remains, generally, unexplored. In this work, we present a rigorous theoretical investigation into the spatiotemporal evolution of energy deposition, thermalization processes and optical parameter changes under double-pulse excitation conditions. The analysis considers key parameters including the inter-pulse delay and the film thickness to evaluate their influence on the LIDT for a range of technologically relevant metals: Au, Ag, Cu, Al, Ni, Ti, Cr, Pt, W, Mo and Stainless Steel (100Cr6). ). A comparative analysis highlights the potential of controlled double-pulse irradiation schemes to manipulate energy coupling efficiency, improve the spatial selectivity of laser-induced modifications and compile a comprehensive LIDT database for commonly used industrial materials. The approach is aimed to provide a robust foundation for the design and optimization of advanced laser micromachining and nanofabrication protocols across a broad spectrum of metallic systems.

Radiative near-field (RNF) beamforming is vulnerable to blockages that disrupt Fresnel zones. This letter proposes an obstruction-unaware wavefront shaping strategy inspired by catastrophe optics. By superimposing a calibrated quartic phase, we generate a Pearcey-like wave packet that exhibits structural stability against perturbations. We establish a fair comparison protocol where the quartic beam is calibrated in free space to avoid exploiting obstruction knowledge. Numerical results demonstrate up to 8.5~dB SINR gain over conventional focusing for multi-user scenarios near the depth-of-focus limit. Crucially, this gain stems from improved channel conditioning under partial blockage, which mitigates the severe noise amplification inherent to zero-forcing precoding.

Modeling inviscid compressible flows with shocks and vortex dominated dynamics remains challenging for particle grid methods due to moving discontinuities, cell crossing noise, and quadrature degradation under strong deformation. Building on a FLIP/APIC framework with vorticity aware tensor artificial viscosity, we identify a long time RTI failure mode: particle depletion at spike heads degrades quadrature and particle grid coupling, producing nonphysical, void-like dents. Standard mitigations (CPDI lite and subcell-jittered seeding) reduce but do not eliminate this artifact. We therefore add two sampling-aware controls: (i) conservative split resampling that replenishes depleted cells while exactly conserving mass, momentum, and internal energy; and (ii) a soft-switch that attenuates only the APIC affine term when local support is insufficient. Tests on the Sod shock tube and single/multi mode RTI show that the method removes spike head voids in long-time RTI while preserving vortex roll up, and matches reference Euler growth metrics

We present a generative framework for inverse design of five-layer transmissive Huygens' metasurfaces (HMSs), addressing a longstanding challenge in achieving full-phase, high-efficiency unit cell designs with minimal full-wave simulations. The key to achieving this is our reliance on the field-based semianalytical (SA) scheme developed in Part I of this paper, which allows rapid and highly effective synthesis of such multilayer composites, however with limited accuracy. To overcome the prohibitive data demands of traditional pipelines, we employ Mamba, a selective state space model well suited for long-range sequence modeling as the backbone of our learning framework. A bidirectional Mamba (Bi-Mamba) forward surrogate is first trained on SA-generated data and subsequently fine-tuned with full-wave CST samples. An ablation over a 1080-sample CST pool shows that as few as 270 full-wave calibration samples suffice to reach near-CST-level agreement at a fraction of the simulation cost. An autoregressive Mamba inverse generator is subsequently trained on surrogate-augmented data, treating unit-cell synthesis as a sequential generation task. The resulting one-to-many generative model produces diverse unit cell geometries conditioned on target scattering responses. It achieves CST-validated designs with field transmission magnitude 0.9 across the full 0-$2\pi$ phase range at 20 GHz. Moreover, a CST-calibrated surrogate trained to accurately predict frequency responses (18-22 GHz) enables functional post-selection of inverse generated designs. Together, the hybrid SA-generative methodology in this two-part compilation establishes a scalable and data-efficient solution for multilayer HMS synthesis, with natural extensions toward broadband, oblique-incidence, and higher-dimensional electromagnetic inverse-design problems.

The integrity of plasma-facing components (PFCs) in tokamaks is critically challenged by transient events such as runaway electron (RE) impacts. We report the first systematic analysis of the thermal damage to tungsten-based PFC tiles comprising the SPARC outboard off-midplane limiters that is induced by RE beams formed during vertical displacement events. Parametric scans in RE impacting characteristics as well as energy-pitch distribution functions from the Dream code are employed for calculations of the volumetric heat loads. A realistic panel design is adopted to enhance the fidelity of the thermal analysis. The PFC thermal responses are compared in terms of in-depth temperature profiles and damage characteristics, such as melt depth and vaporization losses.

We present experimental study of nonradiative double electron capture processes in collisions of 95 and 146 MeV/u bare xenon ions with krypton and xenon gaseous atoms at the HIRFL-CSR storage ring. Angular distributions of the characteristic K{\alpha} radiation of the down-charged projectile ions Xe52+* are measured, which are closely related to the magnetic sublevel population of the excited 1s2l_j states of Xe52+*. It was found that the K{\alpha}1 radiation shows pronounced anisotropic and is sensitive to the collision energies and the target atoms, whereas the K{\alpha}2 radiation gives rise to isotropic. Moreover, obviously difference in the anisotropy parameters of Lyman-{\alpha}1 of Xe53+* ions and K{\alpha} transitions of Xe52+* ions separately following nonradiative single and double electron capture into the L-shell levels of projectiles is obtained and discussed.

Objective: Real-time adaptive proton range verification systems based on produced neutrons require accurate information on their non-isotropic momentum distributions within short times, for which Monte Carlo (MC) methods are too computationally expensive. We present a surrogate model based on Fourier Neural Operators (FNO) for fast prediction of angle- and energy-resolved proton transport and neutron production within proton therapy. Approach: We treat the irradiated phantom and the proton beam's state as depth-evolving series, respectively of different materials, and of spatial, angular and energy phase space density distributions. The task is solved auto-regressively by learning changes in the distributions of protons and those of produced neutrons. For training and evaluation, two datasets of 47 MC simulations featuring different primary intensities were produced. Simulated geometries were extracted from a thoracic CT scan as series of laterally homogeneous materials. Main Results: An average relative $L^2$ discrepancy of 0.067 and 0.137 was achieved by the predicted proton and neutron distributions, respectively. This corresponded to an average gamma passing rate in the spatial distributions of 99.95$\%$ and 99.40$\%$. Training with higher primary intensities led to improvements between 12$\%$ and 30$\%$ in density metrics. Inference over depths of 40 cm at a resolution of 0.5 mm required on average 23.17 s per beam. Significance: The proposed proton beam surrogate generates accurate spatial and momentum distributions of neutrons at MC-level accuracy within seconds, while demonstrating robust generalization with respect to irradiated geometry and beam characteristics. This approach can be used for prototyping and operation of range verification systems, other tasks such as neutron dose estimation, and can be extended to include other kinds of secondary emissions.

Despite advancements in surgical techniques, hernia recurrence rates remain high, underscoring the need for improved understanding of abdominal wall behaviour. While surgeons are aware of many factors contributing to hernia occurrence (e.g obesity, smoking, surgical technique or site infection), it would be of interest to consider it as a biomechanical pathology. Indeed, an abdominal hernia arises from an imbalance between abdominal wall deformability and applied forces. This review article discusses how biomechanics offer a quantitative framework for assessing healthy and damaged tissue behaviour, guiding personalised surgical strategies throughout the pre-, intra-, and post-operative periods. The abdominal wall is a dynamic, load-bearing structure, continuously subjected to intra-abdominal pressure and mechanical stress. Its biomechanical properties, including elasticity and resistance to loading forces, dictate its function and response to surgical intervention. The linea alba is the stiffest component experiencing the highest stress, while the abdominal wall's anisotropic nature influences deformation patterns. Various experimental and computational methods enable biomechanical characterisation. Hernias represent mechanical failures at anatomical weak points. While surgeons qualitatively evaluate abdominal wall's biomechanics by estimating deformation and closure forces, functional imaging (elastography, dynamic acquisitions) could provide objective biomechanical insights. Hernia formation alters abdominal wall biomechanics, inducing greater mobility and elasticity. Surgical repair fundamentally alters the biomechanics of the abdominal wall. The choice of defect's suturing technique, mesh properties, placement, overlap and fixation methods (e.g. suture, tacks) significantly influence mechanical outcomes. Surgical repair tends to restore physiological biomechanics by re-establishing force transmission and hernia-induced excessive mobility. Suturing techniques, mesh selection and placement influence mechanical outcomes. However, optimal results require implants with mechanical properties mimicking native tissue. Lightweight meshes (<70 g/m2) placed in a retrorectus position, combined with a small-bite suture technique, have been associated with lower recurrence rates and improved post-operative function. By bridging biomechanics with surgical practice, this review highlights how mechanical principles shape hernia formation, diagnosis, and repair. A deeper integration of biomechanical principles into surgical decision-making could refine hernia management and lead to patient-specific, mechanics-informed strategies. For surgeons, this knowledge is not just academic - it is practical and can make a difference to patient outcomes.

State-of-the-art ensemble Kalman filtering (EnKF) algorithms require incorporating localization techniques to cope with the rank deficiency and the inherited spurious correlations in their error covariance matrices. Localization techniques are mostly ad-hoc, based on some distances between the state and observation variables, requiring demanding manual tuning. This work introduces a new ensemble filtering approach, which is inherently localized, avoiding the need for any auxiliary localization technique. Instead of explicitly applying localization on ensembles, the idea is to first localize the continuous analysis probability density function (pdf) before ensemble sampling. The localization of the analysis pdf is performed through an approximation by a product of independent marginal pdfs corresponding to small partitions of the state vector, using the variational Bayesian optimization. These marginals are then sampled following stochastic EnKF and deterministic ensemble transform Kalman filtering (ETKF) procedures, using ensembles larger than the partitions' size. The resulting filters involve the same forecast steps as their standard EnKF and ETKF counterparts but different analysis steps, iteratively adjusting the EnKF and ETKF updates of each partition based on the ensemble means of the other partitions. Numerical experiments are conducted with the Lorenz-96 model under different scenarios to demonstrate the potential of the proposed filters. The new filters' performances are comparable to those of the EnKF and ETKF with already tuned localization, both in terms of computational burden and estimation accuracy.

We propose an SIS competition model describing the propagation of conflicting rumors, such as fake news and its corrections. This simple model captures the interaction between rumor propagation and opinion dynamics, where rumors drive opinion changes and, conversely, individuals' opinions determine the infection rates of rumors. We analytically derive all steady states and their stability. These results uncover a novel coexistence mechanism. This coexistence corresponds to a scenario where belief in one rumor (e.g., fake news) paradoxically aids the spread of the opposing rumor (e.g., corrective information). Due to this mechanism, a nontrivial but realistic phenomenon occurs where a lower infection rate actually enhances the spread of a rumor. Furthermore, although the model does not explicitly incorporate majority conformity, a phenomenon where the majority gains an advantage emerges spontaneously. Consequently, even if one rumor has a higher infection rate, it may be eliminated by the other if its initial share fails to exceed a critical threshold. We analytically derive this threshold using the singular perturbation method.

Effects of neoclassical toroidal viscosity (NTV) on plasma flow evolution in the presence of resonant magnetic perturbation (RMP) in a tokamak have been evaluated using a cylindrical theory model. Calculations show that the introduction of NTV has almost no effect on the flow on the resonant surface, so the locked or unlocked state on the resonant surface remains unchanged, but it impacts the rotation profile in the core region. The toroidal, poloidal, and parallel flows in the core region are slightly reduced with uniform pressure. For non-uniform pressure profiles, elevated $\beta$ enhances the global amplitude of NTV torque but suppresses that of electromagnetic (EM) torque. These two driving terms collectively maintain the locked mode state.

An anomalous back discharge movement phenomenon is induced by a set of dielectric barrier corona discharges (DBCD) at unipolar half-sine voltage waveforms, where the back discharge has a time delay that relates to the applied voltage level. An ionic wind model is employed to analyze the physical behavior. Theoretical explanation and quantitative analysis are presented in this study based on abundant experimental results of 5 typical insulating materials and a FEP insulating cable. A numerical model is derived, which indicates that the back discharge can be activated under a relatively low potential voltage level in this study. The results highlight that the back discharge movement phenomenon behaves distinctly under half-sine voltage with negative polarity, yielding a significantly different partial discharge (PD) pattern with positive polarity. Besides, PD amplitude dependent on dielectric thickness is demonstrated by plotting in phase resolved partial discharge (PRPD) pattern. Furthermore, comparative experiments are conducted with respect to the variation of air gap length and dielectric geometry, manifesting different influences on PD amplitude.

Active topological photonic systems enable robust light control and new pathways for semiconductor lasing. However, their intrinsically non-Hermitian nature, combining gain, radiation leakage, and material loss, makes the underlying physics more complex, and prior studies have mostly focused on gain competition while the influence of loss channels is less examined. Here, we experimentally demonstrate radiative-channel-driven topological lasing in a valley photonic crystal consisting of isolated InP nanorods on an insulator, achieving room-temperature single-mode operation within an about 4 lambda scale cavity. Loss-included simulations show that material absorption and radiative leakage can be exploited to establish the lasing pathway. Local off-edge pumping provides spatial evidence of topological edge-guided lasing. Berry-curvature calculations, reflecting unit-cell symmetry breaking, verify the valley-Hall interface in a triangular lattice. Geometry and temperature tuning identify a narrow spectral window where the above-light-line edge branch matches the gain-loss balance and remains decoupled from bulk bands. These results clarify how the loss landscape governs topological lasing and establish a radiative-edge design framework for active topological photonics. The substrate-supported nanorod architecture additionally offers a scalable route for on-chip implementation.

We consider the inverse problem of calculating a refracting surface that generates a prescribed irradiance distribution in the far field for a collimated incident beam. This problem can be formulated as a mass transportation problem (MTP) with a quadratic cost function. To solve this problem, we propose a version of the supporting quadric method (SQM), in which the calculation of the quadric parameters is reduced to the problem of minimizing a convex function. We obtain simple analytical expressions for the second derivatives of this function, making it possible to calculate the quadric parameters using second-order optimization methods. This allows us to refer to the proposed method as the second-order SQM. We demonstrate high efficiency of this approach by designing several optical surfaces that generate complex irradiance distributions. We also consider the application of the second-order SQM to nonimaging optics problems described by MTPs with a non-quadratic cost function.

In-situ plasma processing of superconducting radio frequency (SRF) cavities is a performance recovery technique used to mitigate the field emission limiting phenomenon. It has been proved very effective at major particle accelerator facilities such as SNS, CEBAF, FRIB, FNAL and C-ADS. This technique is based on the ignition of a noble-gas/oxygen plasma inside the cavity over several hours to remove hydrocarbon-based contamination, responsible for the parasitic field emission degradation observed after several years of operation. Despite a large experimental R\&D effort from the community, plasma parameters and cleaning rates under various experimental conditions have never been directly evaluated. In this study, plasma parameters were measured using a Langmuir probe and cleaning rates thanks to a quartz crystal microbalance (QCM) coated with an amorphous carbon film to simulate a carbon-based contamination. In this article, the main results from a large parameter space are discussed along with guidelines for improving the plasma processing effectiveness in SRF cavities. The encountered technical challenges are also discussed, as the SRF cavity is by design not intended to be a plasma reactor.

Contact tracing is one of the most important control measures deployed during epidemics. Relying on the identification of contacts of known infected individuals, it necessitates a network perspective. Although pairwise models have been used extensively to study contact tracing, their analysis typically depends on a decoupling assumption-most commonly that contact tracing operates on a much faster timescale than disease transmission. Furthermore, contact tracing models often assume that all infected individuals become contact tracing-triggering, which is unrealistic given partial compliance to treatment. We relax both of these restrictive assumptions and provide a full analytical characterisation of the epidemic threshold in the pairwise mean-field model. Our analysis uses a fast-variables approach that captures the rapid early stabilisation of key network quantities. Inspired by mechanisms from social adoption dynamics, we introduce triplewise contact tracing in which an infected individual can be traced not only through direct contact with a single tracing-triggering neighbor (pairwise tracing), but also indirectly when connected to two tracing-triggering nodes simultaneously. For pure pairwise and pure triplewise contact tracing, we derive analytical expressions for critical contact tracing levels and demonstrate that when many infected individuals bypass treatment, the epidemic can become uncontrollable. When both contact tracing mechanisms operate together, we map out their combined contribution and relative impact on epidemic control. This unified framework yields rigorous and tractable threshold conditions for contact tracing dynamics on networks, extending the applicability of pairwise models beyond the fast-tracing regime and providing new insight into the interplay between disease progression, partial treatment compliance, and higher-order tracing processes.

Van der Waals (vdW) ferroelectrics are emerging nonlinear photonic materials that combine large second-order susceptibility \c{hi}(2) with heterostructure compatibility, offering an attractive route toward miniaturized spontaneous parametric down-conversion (SPDC) sources. However, vdW SPDC sources operating under continuous irradiation in air remain limited in low brightness and poor operational stability, as oxygen and moisture exposure, together with pump-induced heating, lead to material degradation and permanent damage. Here we demonstrate an air-stable, bright SPDC source based on ferroelectric NbOI2 enabled by graphene encapsulation. Graphene provides robust environmental protection and can effectively supress pump induced degradation by enhancing heat dissipation. We report a record photon-pair generation absolute rate of 258 Hz and a normalized brightness of 19,900 Hz/(this http URL). Leveraging this stabilized platform, we further generate polarization entangled photon pairs with 94% fidelity with respect to the maximally entangled Bell states from graphene-encapsulated 90° twisted bilayer NbOI2. Our results establish a practical and air-stable vdW ferroelectric SPDC platform that overcomes key limitations of existing vdW quantum light sources and provides a viable pathway toward scalable, integrated entangled photon sources for on chip quantum photonics.

In stellarator design, the coil winding surfaces $\Sigma\subset\mathbb R^3$ support current distributions $j$ that shape the magnetic field. This work provides a theoretical framework explaining the emergence of centre and saddle point regions, a key feature in coil optimisation. For coil winding surfaces with a toroidal shape, we prove a dichotomy principle: the current distribution has both centre and saddle point regions or is no-where vanishing. For coil winding surfaces that consist of piecewise cylinders, we show that if $j$ is oppositely oriented on the two boundary circles, centre and saddle points appear, and all but finitely many field lines of $j$ are periodic. When $j$ admits a harmonic potential, all field lines are closed poloidal orbits. These results offer insights into current patterns on winding surfaces, with implications for coil design strategies and their simplification.

Multiconfigurational short-range density functional theory (MC-srDFT) rigorously combines ground state wavefunction theory with DFT. Unlike single-reference range-separated hybrid functionals, MC-srDFT has lacked theoretically grounded protocols for choosing the system-specific range-separation parameter. To address this problem, we introduce an optimal-tuning scheme based on enforcing the correct exponential decay of the electron density. We show that the range-separation parameter can be determined from the ionization potential given by the smallest-magnitude eigenvalue of the Extended Koopmans' Theorem matrix constructed for the model Hamiltonian. We validate this approach for static and dynamic dipole polarizabilities of ground-state molecular systems using MC-srDFT within both full linear response and its extended random phase approximation (ERPA) variant. Optimal tuning substantially improves polarizabilities relative to the commonly used universal $\mu = 0.4\,\mathrm{bohr}^{-1}$ parameter.

The CMS muon system is undergoing substantial upgrades to meet the challenges of the High-Luminosity LHC (HL-LHC), including the installation of the new Muon Endcap 0 (ME0) detector. Large-scale production started in 2024. ME0 is a six-layer station designed to extend pseudo-rapidity coverage to |{\eta}| = 2.8 from the previous maximum of |{\eta}| = 2.4, enhancing sensitivity to forward physics processes. Each endcap will host 18 ME0 stacks, with each stack comprising six triple-layer gas electron multiplier (GEM) chambers. The system adds up to six additional hits per track, which significantly improves muon identification, spatial resolution, and robust track reconstruction at the first trigger level. Chamber production and quality control across multiple international sites ensure scalability and timely delivery. The ME0 design incorporates lessons learned from earlier GEM deployments, with improvements in electronics robustness, grounding, and segmentation to withstand high background rates and minimize damage from discharges. This contribution provides a comprehensive overview of the ME0 detector concept, assembly strategy, quality assurance procedures, current production status, and its pivotal role in strengthening CMS muon reconstruction during HL-LHC operations.

Impacts of liquid droplets on wind turbine blade surfaces, for example sea sprays, can result in material damage through erosion. In this study, we derive an explicit, closed-form analytical approximation for droplet impact and subsequent spreading on a solid surface in inertia-dominated regimes of large Reynolds and Weber numbers. The formulation extends an existing theoretical framework based on inviscid potential flow for a rising expanding disk in an infinite liquid domain. The modified solution provides full spatio-temporal pressure distributions and impact force histories on the impact surface over the entire impact duration, capturing both the early-time self-similar flow and the inertia-driven lamella spreading following the peak impact force. The predicted pressure and force profiles show good agreement with analytical, numerical and experimental results reported in the literature, including accurate reproduction of the well-known ring-shaped pressure distribution. Key quantities, such as the radial location and magnitude of peak pressure, as well as the timing and magnitude of the peak impact force, are predicted analytically with reasonable accuracy. To enable solid material erosion analysis, the analytical liquid-phase solution is coupled with a finite-element (FE) simulation for the solid response. This analytical-numerical coupled method (ANCM) eliminates the need to explicitly simulate droplet fluid dynamics, which is conventionally performed using smoothed particle hydrodynamics (SPH). As a result, for the purpose of material response analysis, the proposed approach achieves grid independence at substantially lower mesh resolutions and reduces computational cost by more than 97% compared to SPH-based simulations, while maintaining or improving numerical accuracy.

This paper evaluates the radiation tolerance of commercial off-the-shelf (COTS) electronics components for use in the Thin Gap Chamber (TGC) frontend electronics of the ATLAS experiment at the High-Luminosity LHC (HL-LHC). The ATLAS experiment has accumulated more than 450 fb^-1 of data as of 2025. Its luminosity upgrade, the HL-LHC scheduled to begin operation in 2030, will deliver 3000-4000 fb^-1 over ten years and lead to substantially higher radiation levels in detector electronics. The radiation levels for the TGC frontend electronics are estimated to be 4.1-7.3 Gy in terms of Total Ionizing Dose (TID) and 1.1-2.2 x 10^11 n_1MeV cm^-2 in terms of Non-Ionizing Energy Loss (NIEL). To evaluate component suitability under these conditions, TID tests were conducted using Cobalt-60 gamma rays at Nagoya University, and NIEL tests were performed with the Tandem Accelerator at Kobe University. Various COTS components, including SFP+ optical transceivers, clock jitter cleaners, optical fibers, voltage references, operational amplifiers, analog-to-digital converters, digital-to-analog converters, SD cards, flash memories, and low-dropout regulators, were tested and evaluated against the required radiation levels. The results demonstrate that all evaluated components meet the TID and NIEL tolerance requirements for application in the TGC frontend electronics at the HL-LHC.

Impacts between droplets and solid surfaces can commonly cause erosion problem in Engineering applications, including aircraft surface erosion, wind blade leading-edge erosion and steam turbine blade erosion. In practice, the impacted solid surfaces have varied material softness, ranging from stiff metallic coatings to soft materials. An analytical-numerical coupled model (ANCM) for simulating droplet impacts on surfaces, and corresponding material analysis, has been developed in the literature. However, the analytical impact pressure solution of the ANCM model has been derived assuming rigid solid surface. In the current study, we investigate the performance of the ANCM model for droplet impacts on soft materials made of urethane gel phantom, by comparing the ANCM computations to lab-based experiments and numerical simulations based on Smoothed Particle Hydrodynamics (SPH). Parametric studies explore the applicability limit of the ANCM model for droplet impacts on very soft materials at low Young's modulus. It was found that for materials at Young's modulus of $47,400$ Pa or stiffer, which covers most engineering applications, the developed ANCM model performs as expected for an assumed rigid surface. For softer solid materials, the SPH-modeled liquid interacts with the evolving surface geometry and mitigates impact intensity as deformation occurs. The analytical impact loads estimated by ANCM are independent of surface geometry, and hence provide conserved impact impulse in a non-physical way. Results show a critical value of Young's modulus at $E=10,000$ pa for the ANCM model, below which the model exhibits overshoot in total contact force and surface deformation, leading to the formation of steep wall craters.

In conventional optical Stark-shift spectroscopy, molecules are exposed to spatially homogeneous static electric fields that shift the energies of their spectral lines. These shifts are attributed to the molecular electronic properties, such as variation of dipolar moment and polarizability of the molecule associated with photo(de)excitation. In realistic environments containing structural defects and nanoscale heterogeneities, however, molecules experience internal electric fields that vary strongly on the molecular scale, rendering the standard Stark selection rules inapplicable. Here we develop an extended theory of atomic-scale Stark shift, addressing such scenarios. Specifically, we present a detailed theoretical analysis of an experimentally relevant configuration where the atomically sharp tip of a light-assisted scanning tunneling microscope is used to controllably apply inhomogeneous electrostatic fields to representative molecular dyes spanning several molecular families. We decompose the total Stark shift into linear and quadratic contributions and show that they contain different information about the molecular properties. Concretely, spatial variations of the linear Stark shift as the tip scans across the molecule enable subnanometric mapping of the charge redistribution between ground and excited electronic states, with high sensitivity to molecular composition and chemical functionalization. The quadratic Stark contribution, in contrast, reflects changes in the conventional dipolar polarizability upon excitation. Together, these results establish nanoscale Stark-shift spectroscopy as a powerful tool for resolving excited-state charge dynamics in single molecules under realistic, strongly inhomogeneous electric fields.

We systematically investigate the thermodynamic landscape of the 38-atom Lennard--Jones cluster LJ$_{38}$ using Population Annealing (PA), a method suited for systems with challenging double-funnel energy landscapes. By employing an adaptive temperature schedule, we demonstrate that thermodynamic observables, such as internal energy and heat capacity, converge robustly when the population size is sufficiently large. To gain deeper insights into the competing basins, we introduce an integrated framework that combines PA reweighting factors with structure-resolved analysis. Using quenched configurations characterized by potential energy and Steinhardt's bond-orientational order parameters, we identify three structural basins, FCC-like, icosahedral, and liquid-like, via dimensionality reduction and clustering. This framework enables the direct computation of structure-resolved free energy differences from population fractions, providing a quantitative mapping of the thermodynamic competition between the funnels. The resulting structural crossovers are consistent with the heat-capacity peak, demonstrating PA as a promising and scalable framework for structure-resolved thermodynamics in complex molecular systems.

Can we infer whether all of physical reality began to exist? Several novel results are offered suggesting a negative verdict. First, a common strategy for defending a cosmic beginning involves showing that individual beginningless cosmological models are implausible. This strategy is shown to make an elementary error in confirmation theory. Second, two necessary (but not necessarily sufficient) conditions are offered for a cosmic beginning. Third, three extensions are offered to the Malament-Manchak theorems. The three extensions show that in almost all classical spacetimes, observers cannot collect sufficient data to determine whether the application conditions for the classic singularity theorems are satisfied or whether their spacetime satisfies the two necessary conditions for a cosmic beginning. Lastly, a reply is offered to the objection that the skeptical consequences of the three extensions can be overcome with induction. Importantly, all past singular dust FLRW spacetimes have observationally indistinguishable counterparts which, while sharing a number of important local properties, either do not include a singularity to the past of every point or else do not have the sort of time ordering intuitively required for a cosmic beginning.

Cell migration plays a fundamental role in numerous physiological processes, including embryonic development, wound healing, and cancer metastasis. While cell-cell adhesion is known to regulate motion by shaping cell morphology and intercellular force balance, its dynamic, rate-dependent contributions to tissue behavior remain poorly understood. In this study, we examine how the dissipative nature of cell-cell adhesion influences tissue dynamics and collective migration using an extended vertex model with explicit junctional viscosity. Our findings reveal a nontrivial interplay between two distinct components of adhesion: an interfacial adhesion energy (energetic, rate-independent) contribution, which sets the effective junctional tension, and a dissipative (rate-dependent) contribution, which controls resistance to relative motion during cell rearrangements. We show that increasing the energetic component promotes migration by modifying cell shape and lowering the barrier to neighbor exchanges, whereas strengthening the dissipative component induces jamming and suppresses cell motion. Linear rheological analysis further demonstrates that, in the unjammed regime, vertex-model tissues exhibit power-law viscoelastic behavior, with adhesion modulating the power-law exponent and thereby controlling the spread of relaxation timescales. Together, these findings clarify the dual role of adhesion in governing tissue mechanics and rheology and provide a mechanistic framework for understanding the balance between fluidity and rigidity in epithelial monolayers.

Coupled beam optics can be geometrically described in terms of invariant eigenmode planes of a stable symplectic ``one-turn'' map $\mathcal M\in Sp(4)$. We show that the non-uniqueness of symplectically normalized bases within each eigenmode plane constitutes an in-plane gauge freedom $Sp(2)\times Sp(2)$, and that many coupled-optics parametrizations differ primarily by gauge choice. Building on this fact, we identify basis-independent descriptors of lattice and beam optics and introduce bounded, gauge-invariant coupling parameters or fractions $u_{k,\mathrm{inv}}$ computed from orthogonal projectors onto the eigenmode planes. To obtain smooth $s$-dependent optics functions and consistent mode labeling, we present a unifying and practical approach based on an $SO(2)$ continuity gauge (Procrustes alignment), together with diagnostics for stability and invariance. We further relate Edwards--Teng, Mais--Ripken, Lebedev--Bogacz, Wolski, and Sagan--Rubin parametrizations as gauge-equivalent representations within the respective $Sp(2)\times Sp(2)$ gauge freedom. Numerical examples of coupled lattices and beam optics illustrate the proposed invariants and show how representation-dependent scalar coupling parameters (e.g.\ in the Lebedev--Bogacz gauge) can leave their nominal bounds while $u_{k,\mathrm{inv}}$, defined here, remain bounded and physically interpretable.

Collimation systems are essential in particle accelerators to safely and efficiently manage unavoidable beam losses during operation. These systems rely on collimators, which are specially designed movable jaws or absorbers positioned close to the beam envelope to intercept and localize beam losses. Their role is particularly critical in high-intensity hadron machines, where uncontrolled losses can lead to equipment damage or operational downtime. While the specific requirements vary across accelerator types, circular accelerators, especially present and future high-energy colliders, cannot operate safely without a well-optimized collimation system. This lecture offers an overview of the fundamental principles, design challenges and operational strategies of beam collimation, with emphasis on high-intensity hadron accelerators. The Large Hadron Collider, the most advanced example to date, will serve as the main reference for illustrating state-of-the-art collimation approaches and technologies.

We propose a monolithic, electrically driven source of photon pairs based on a non-linear AlGaAs Bragg reflection waveguide and a laser structure stacked on top. By introducing lateral tapers, the fundamental mode of the lasing waveguide is vertically coupled into a higher order mode of the Bragg reflection waveguide (Bragg mode) such that photon pairs can be generated through a type-II spontaneous parametric down conversion process. According to numerical simulations, a coupling efficiency of 28% is achieved between both modes. Phase matching the Bragg mode with two fundamental modes at 1550 nm results in a photon pair rate of 1.7*10^8 pairs/s for a 2 mm long device assuming 1 mW of power in the Bragg mode. Since the Bragg reflection waveguide does not require doping for this vertically coupled structure, free-carrier absorption losses and parasitic luminescence are avoided.

We introduce a taxonomy of interaction types and show that graphs are focal hypergraphs: every graph is canonically a focal hypergraph via its closed neighbourhood structure, and every graph dynamical model is a special case of the general hypergraph dynamical model. The central distinction is between \emph{focal} interactions, in which the interaction domain is defined relative to a designated reference node, and \emph{non-focal} interactions, in which all participants stand in equivalent structural relationship. Closed graph neighbourhoods are precisely focal hyperedges, so hyperedges generalise graph neighbourhoods by removing the focal constraint. This yields a strict three-level hierarchy: graph models $\subsetneq$ focal hypergraph models $\subsetneq$ general hypergraph models. Moreover, graph models do encode genuinely higher-order (many-body) interactions, in the sense that each node's update function may depend jointly on all members of its closed neighbourhood, but they remain a strict special case of the hypergraph dynamical model, not equivalent to it. We further show that universal encodings such as bipartite factor graphs are neutral with respect to this hierarchy, and that the symmetry condition of the hypergraph dynamical model -- often treated as an additional constraint relative to the graph model -- is in fact the dynamical definition of a non-focal interaction. The taxonomy is grounded in concrete phenomena from physics, biology, ecology, and social systems, and yields a principle of representational alignment: the choice between graph and hypergraph models should be governed by the type of interaction, not by a blanket preference for one formalism over the other.

Machine learned interatomic potentials (MLIPs) have enabled atomistic simulations with ab initio accuracy for a fraction of the computational cost. However, many widely used MLIPs are short-ranged and do not accurately capture long-ranged electrostatic interactions. At interfaces with polar solvents, such as water, this deficiency can drive unphysical long-distance dipolar alignment far away from the interface. Here we reveal that neglecting long-ranged physics leads to spurious metallization of the water layer due to artificially large fluctuations of the total solvent dipole, similar to the electron rearrangement observed to prevent polar catastrophes at polar interfaces. This metallization is eliminated in MLIPs that explicitly include long-ranged electrostatics. Our results showcase a fundamental flaw of short-ranged MLIPs, highlighting that long-ranged electrostatics are essential for studying systems with a polar-liquid component, especially if one is interested in electronic properties.

Conventional cardiovascular magnetic resonance (CMR) cine imaging relies on binning multiple heartbeats into a single cardiac cycle, which fails in arrhythmic patients where beat-to-beat variability causes motion artifacts and loss of functional information. Real-time 2D imaging captures individual beats but lacks volumetric coverage for mapping arrhythmic cardiac dynamics. We present a 3D real-time motion-field reconstruction method enabling continuous volumetric assessment in patients with premature ventricular contractions (PVCs) using a free-running CMR protocol. CMR-MOTUS was extended to jointly reconstruct real-time 3D motion fields and a motion-corrected reference image from continuous, ungated, non-breath-held data acquired with a variable-density Cartesian OPRA trajectory. Beat-to-beat ejection fraction (EF) was computed by propagating a single segmentation through all frames using the reconstructed motion fields. The method was validated on a cardiac motion phantom and tested in four healthy volunteers and four PVC patients. Phantom EF closely matched ground truth (22.1 +/- 0.6 percent vs. 21.9 percent). In healthy volunteers, EF values agreed with 2D references and showed narrow distributions reflecting physiological consistency. In PVC patients, EF distributions were bimodal, with the lower mode corresponding to PVC beats with markedly reduced EF. ECG confirmed alignment between EF irregularities and PVC episodes. These results show that 3D real-time motion-field reconstruction enables continuous beat-to-beat volumetric quantification in arrhythmia, revealing functional heterogeneity that conventional binning obscures. The bimodal EF distributions capture the true hemodynamic impact of PVCs and may provide clinically relevant metrics for monitoring and treatment evaluation.

Droplet microfluidics combined with optical detection has become a powerful approach for high-throughput single-cell assays, but these systems often face limited sensitivity and signal heterogeneity due to optical and geometrical constraints. We investigate how key operating parameters influence the performance of a droplet-based optomicrofluidic platform. Experiments examine optical interactions between guided light and aqueous droplets containing fluorescent (FL) particles flowing in oil. Geometrical optics simulations model light-droplet interactions, while FL simulations quantify signal variations caused by particle size and position. Two refracted signals are observed experimentally: a droplet-refracted signal (DRS) that scales with droplet diameter and a particle-refracted signal (PRS) produced by light interaction with encapsulated particles. Both experiments and simulations show that PRS becomes prominent when the particle-to-droplet size ratio $D^*_\text{p}$ lies between 0.23-0.33, enabling label-free detection. Particles near the droplet center ($r^*_\text{p} < 0.4$) display reduced angular dependence and more uniform FL signals. Simulations further show that FL intensity increases with $D^*_\text{p}$, rising sharply from 0.33 to 0.5 and more gradually up to 0.66. Additionally, reducing the oil layer thickness enhances fluorescence by minimizing optical losses at the droplet-channel interface. These results demonstrate that controlling $D^*_\text{p}$, particle position, and oil layer thickness improves FL strength and uniformity, providing a framework for optimizing droplet-based fluorescence detection in microflow cytometry and single-cell assays.

We study the occurrence of long-lived metastable states in the 4f$^{13}$5d6s electron configuration of Yb$^+$. By optical pumping of a single trapped ion on the $^2F^\text{o}_{7/2}\rightarrow (7/2,0)_{7/2}$ transition at 377.5 nm, we prepare a wide range of metastable electronic states. We use a co-trapped control ion to sympathetically cool the spectroscopy ion, allowing us to accurately time its subsequent decay. We record a strong decay signal corresponding to a lifetime of 0.92(8) s, a weaker decay signal with lifetime 9.8(+2.9, -2.0) s, and find evidence for a much longer lifetime, $>$ 30 s. We identify the metastable states with these lifetimes qualitatively, and corroborate our results with atomic structure calculations that support the observed lifetimes and decay paths. These long-lived states provide new opportunities in qubit and qudit state detection and optical clocks.

Two-dimensional particle-in-cell simulations with a realistic mass ratio reveal the generation mechanisms of the out-of-plane magnetic field in magnetic islands/flux ropes during magnetic reconnection. In the absence of an initial guide field, reconnection produces a large electron temperature anisotropy (around 4.5) inside magnetic islands that drives the Weibel instability. Strong out-of-plane magnetic fields (Bz/B0 around 0.4, greatly exceeding the Hall field) with a regular bipolar structure grow inside islands. A space-time analysis reveals a one-to-one correspondence between the temperature anisotropy and the development of the Weibel magnetic field. The instability relaxes the anisotropy, but island merging leads to anisotropy reemergence and re-excitation. In the presence of a strong ambient guide field (Bg/B0 = 0.5), the electron outflow from the X-point deflects along the separatrices and forms a circular current loop wrapping the flux ropes. This flux-rope separatrix current generates an out-ofplane magnetic field that reinforces the ambient guide field, reaching Bz/B0 around 1.4. The current can, in some cases, drive the electron Kelvin-Helmholtz instability, which produces electron vortices and strengthens the magnetic field. Mergers significantly broaden the islands and further strengthen the field. These self-generated out-of-plane magnetic fields scatter electrons and reduce their temperature anisotropy, which can potentially affect electron heating via Fermi reflection. The simulation results are supported by spacecraft observations suggesting that ambient guide fields can be enhanced within flux ropes in Earth's magnetotail.

The interfacial diffusion associated with finite volume method (FVM) discretizations of multiphase flows creates the need for an interface sharpening mechanism. Such solutions for structured quadrilateral grids are well documented, but various engineering applications require mesh designs specific to the irregular geometry of the physical system it is modeling. Therefore this study casts interface sharpening as an ant-idiffusive volumetric body force whose calculation procedure is generalizable to an arbitrarily constructed grid. The force magnitude is derived at cell centers as a function of the local compressible flow characteristics and the geometry of the cell neighborhood. The flow model uses an AUSM+up based method for flux evaluation and imposes a stiffened equation of state onto each of the fluids in order to close the linear system and extract auxiliary variables. Validation tests show good agreement with the Young-Laplace condition whereby the interface converges to the analytical solution corresponding to a balance between a pressure jump and interfacial forces. Further results show the recovery of a circle starting from a shape with highly variational curvature through the combined effects of surface tension and interface sharpening. Lastly shear-driven droplet pinchoff results show good agreement with droplet shapes provided by the surrounding literature at various Weber-Ohnesorge number combinations.

We point out that, in the usual way of specifying a sinusoidal waviness of the wall of a tube of circular cross section, in which the mean radius is kept constant, the interior volume of the tube increases with increasing wave amplitude. We compare this case with the case where the interior volume is kept constant by reducing the mean radius as the wave amplitude increases. We present and compare numerical results of these two cases for steady, pressure driven, laminar viscous flow in a tube with a stationary wavy wall, for both circular and annular tubes. The volume flow rate and the hydraulic resistance can differ in the two cases by as much as 10% for wave amplitudes as small as 20% of the mean radius and as much as 50% for larger wave amplitudes. For a circular tube, we derive a scaling law that relates the two cases based on dimensional analysis, allowing the behavior in the constant-volume case to be determined from that in the constant-mean-radius case. Additionally, we consider peristaltic pumping due to a moving sinusoidal wall wave and show that the volume-change effect is significant even at small wave amplitudes, and that the volume flow rates in the two cases can differ significantly, by as much as 50% as the wave amplitude approaches its maximum value.

Hyperuniform disorder is a type of correlated disorder characterized by vanishing spectral density at small wavevectors, making the configuration effectively homogeneous on long length scales. In photonics, hyperuniform disorder is promising for generating isotropic photonic pseudogaps and engineering photonic crystal waveguides. However, these studies are largely restricted to idealized lossless settings, although all photonic systems necessarily have loss. In this work, light propagation in photonic crystal slabs with imposed hyperuniform disorder is investigated theoretically and numerically. The system is intrinsically non-Hermitian due to radiative loss, with non-Hermiticity appearing as a complex effective mass of a quadratic photonic band. A theoretical framework for disorder scattering is analytically derived in Hermitian and non-Hermitian quadratic bands with real and complex effective mass, respectively. In contrast to the power law behavior $|\mathbf{k}|^\alpha$ observed in the Hermitian case (where $\alpha$ is the hyperuniformity exponent), the scattering loss in the non-Hermitian band is given by $C_0+C_{\beta_2}\cdot|\mathbf{k}|^{\beta_2}$, where $C_0$ is a finite constant and the exponent $\beta_2\leq 2$. Our theoretical predictions are verified with tight-binding and Finite-Difference Time-Domain simulations with realistic photonic crystal parameters, based on recent experiments.

Localisation and circularity in perishable food supply chains are essential for sustainability. Poor allocation of time-sensitive food leads to waste, higher transport emissions, and unnecessary long-distance sourcing. Algorithms used in digital trading platforms and allocation systems can help address these problems by improving how local supply is matched with demand under real operational constraints. This paper examines localisation and circularity in the UK apple supply chain. Apples are an informative case because they are perishable, consumed fresh as dessert fruit, used as inputs across multiple food industries, and generate valuable by-products. We present a weighted-sum mixed-integer linear programming formulation for supply-demand allocation. The model encodes a single global objective with explicit weights on four operational criteria: price matching, quantity alignment, freshness requirements, and geographic distance. These weights make priorities explicit and adjustable, enabling transparent balancing between economic and sustainability considerations. The framework also supports the circulation of unallocated supply across allocation cycles. Using a realistic apple supply-demand dataset, we evaluate allocation outcomes under different priority settings. Results indicate that allocation outcomes are strongly shaped by both priority settings and the structure of the underlying supply network characteristics.

In this work we analyze the emergence of phase transitions in a quantum brain model inspired by the Lipkin-Meshkov-Glick framework, where biologically motivated synaptic feedback modulates the collective interaction in a nonlinear and state-dependent manner. We demonstrate that incorporating this retroactive mechanism substantially reshapes the phase structure, yielding an expansion of the paramagnetic phase at the expense of the ferromagnetic phases relative to the feedback-free scenario. This effect is markedly enhanced in the presence of a longitudinal field, as the feedback couples directly to the longitudinal magnetization, leading to an appreciable displacement of the critical boundaries. We characterize the ensuing transitions from a phase-space perspective by means of the ground-state Husimi distribution and the Wehrl entropy, which provide a robust diagnosis of qualitative changes in localization and enable a quantitative assessment of feedback-induced deformations of the phase diagram. Additionally, we perform an explicit dynamical analysis based on mean-field equations for the collective-spin orientation self-consistently coupled to the synaptic dynamics, which reproduces with high fidelity the quantum time evolution of collective observables for the protocols considered. Overall, these findings substantiate the suitability of this quantum brain model as a controlled theoretical framework for elucidating how synaptic plasticity mechanisms can parametrically tune and reshape collective criticality.

Functionally graded materials (FGMs) are composites whose composition or microstructure varies continuously in space, producing position-dependent mechanical and functional properties. In recent years, FGMs have gained significant attention due to advances in additive manufacturing, which enable precise spatial control of composition and orientation. However, their graded, aperiodic structure breaks the assumptions of Bloch's theorem, making first-principles electronic and electromagnetic calculations challenging. Here we develop an ab initio quantum theoretical framework for the electromagnetic properties of FGMs. Using a non-interacting electron model, we formulate a theory of modulated Bloch states, derive effective field equations, and solve them by proposing a generalized WKB (GWKB) method, an effective mass approximation, the Boltzmann equation, and numerical approaches. Our GWKB solution is not semiclassical but remains valid in the fully quantum regime. We show that effective observables such as conductivity, magnetic permeability, and electric permittivity generally do not admit a tensorial description in graded media, and that engineered orientational gradients enable precise control of Landau quantization. As a device example, we further develop a theory of graded p-n junctions with enhanced electronic tunability. This framework lays the quantum foundation for predictive design of graded composite materials, enabling AI-accelerated discovery of next-generation functional architectures.

Hyperentanglement offers enhanced capacity for quantum information processing and communication protocols, especially in combination with robust high-dimensional degrees of freedom such as frequency-bin encoding. Here, we present a single-pass, unfiltered, down-conversion source of hyperentangled photon pairs in polarisation and frequency-bin degrees of freedom with dynamically tunable state dimension and composition at telecom wavelengths. We achieve this by optimal tailoring of the photons' joint spectral amplitude via pump and nonlinearity shaping. Using polarisation-resolved time-of-flight spectrometry and Hong-Ou-Mandel interference, we characterise the hyperentangled states and demonstrate for the polarisation component fidelities exceeding 99% averaged over frequency bins and concurrences above 98%. The degree of spectral entanglement, quantified by the Hong-Ou-Mandel visibility, is measured as 90%, well in line with numerical simulations. This approach provides a scalable route toward high-dimensional quantum states for quantum communication and computing applications.

The Integration Host Factor (IHF) is a nucleoid-associated protein critical for both DNA compaction and biofilm stability. While its role in DNA packaging within the cell is well understood, its structural role in scaffolding biofilms is more puzzling and difficult to reconcile with its known DNA bending activity. Here, we investigated how IHF-DNA interactions are modulated across a pH spectrum mimicking the acidic microenvironments of bacterial biofilms. By performing all-atom calculations we discovered that low pHs lead to a change in protonation of IHF residues, which in turn exposes positively charged patches. We then conjectured that these positively charged residues could lead to intermolecular DNA bridging and tested this hypothesis through single-molecule and bulk assays. We discovered that while at physiological pH IHF mostly bends DNA, at pH < 5 there is clear evidence of IHF-mediated intermolecular crosslinking. Our results demonstrate that pH significantly modulates IHF-DNA interactions and explains the structural role played by IHF in supporting biofilm mechanics through intermolecular crosslinking.

Low-thrust electric propulsion missions are often designed under simplifying assumptions such as constant thrust or fixed specific impulse, neglecting the strong coupling between trajectory dynamics, spacecraft power availability, and propulsion performance. In deep-space environments with reduced solar irradiance, these assumptions can lead to suboptimal or infeasible designs, underscoring the need to simultaneously optimize the trajectory and power subsystem. This paper presents a multidisciplinary design optimization (MDO) framework for the simultaneous design of low-thrust trajectories and spacecraft power systems, with explicit coupling to electric propulsion performance. The framework incorporates a high-fidelity variable-specific impulse model of the SPT-140 Hall thruster, in which thrust and efficiency are directly constrained by time-varying solar power availability and solar array degradation, rather than treated as fixed parameters. The coupled problem is posed as a time-optimal control problem and addressed using a framework built on top of OpenMDAO and Dymos toolchains, where Dymos employs a collocation-based direct-transcription approach for trajectory optimization. OpenMDAO provides accurate analytic partial derivatives, enabling efficient gradient-based optimization. A Fast Fourier Series shape-based method is used to generate dynamically feasible initial guess trajectories, and the resulting nonlinear programming problem is solved using IPOPT. The proposed framework is demonstrated through a low-thrust orbit insertion scenario around asteroid 16-Psyche, a regime in which reduced solar irradiance makes power-aware trajectory design particularly critical. Simulation results demonstrate the framework's ability to capture key power-propulsion-trajectory trade-offs, highlighting the importance of integrated power optimization for realistic electric propulsion mission design.

We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT), which can be efficiently represented as linear combination coefficients of atomic orbitals. In real-time TDDFT, the electronic wavefunctions of a molecule evolve over time in response to an external excitation, enabling first-principles predictions of physical properties such as optical absorption, electron dynamics, and high-order response. However, conventional real-time TDDFT relies on time-consuming propagation of all occupied states with fine time steps. In this work, we propose OrbEvo, which is based on an equivariant graph transformer architecture and learns to evolve the full electronic wavefunction coefficients across time steps. First, to account for external field, we design an equivariant conditioning to encode both strength and direction of external electric field and break the symmetry from SO(3) to SO(2). Furthermore, we design two OrbEvo models, OrbEvo-WF and OrbEvo-DM, using wavefunction pooling and density matrix as interaction method, respectively. Motivated by the central role of the density functional in TDDFT, OrbEvo-DM encodes the density matrix aggregated from all occupied electronic states into feature vectors via tensor contraction, providing a more intuitive approach to learn the time evolution operator. We adopt a training strategy specifically tailored to limit the error accumulation of time-dependent wavefunctions over autoregressive rollout. To evaluate our approach, we generate TDDFT datasets consisting of 5,000 different molecules in the QM9 dataset and 1,500 molecular configurations of the malonaldehyde molecule in the MD17 dataset. Results show that our OrbEvo model accurately captures quantum dynamics of excited states under external field, including time-dependent wavefunctions, time-dependent dipole moment, and optical absorption spectra.

We introduce a spectral approach to characterizing the three-dimensional Edwards-Anderson spin glass. By analyzing the eigenvalue statistics of overlap matrices constructed from two-dimensional cross-sections, we identify a crossover from the Wigner semicircle law at high temperatures towards a Gaussian distribution, which is consistently attained near the spin-glass critical point. Visible for different distributions of the random coupling, the Gaussian distribution can potentially serve as a robust spectral indicator of criticality. Remarkably, the spectral density is well-described by Tsallis statistics, with the entropic index $q$ evolving from $q = -1$ (semicircle, $T=\infty$) to $q = 1$ (Gaussian) at $T_c$, revealing a statistical structure inside the paramagnetic phase. We find $q\le 1$ within numerical precision. While the local level statistics remain consistent with GOE statistics, reflecting standard level repulsion, the temperature dependence appears mainly in the global spectral density. Our results present spectral statistics as a computationally efficient complement to multi-replica correlator methods and provide a new perspective on cooperative and critical phenomena in disordered systems.

The Hawking effect can be understood as a broad kinematic phenomenon associated with mode behavior near a horizon. While astrophysical black holes produce one specific realization of this radiation, this perspective inspires extensive theoretical and experimental efforts to create event horizons in diverse physical systems to observe the resulting analogue Hawking emission. One of the most successful realizations is the fiber-optical analogue, based on nonlinear quantum optics. In these notes, we introduce and motivate this system while outlining the theoretical concepts underlying the gravitational analogy. Finally, we review key experiments and discuss their impact on the field.

We present a potential improvement over the standard method developed to determine antineutrino directionality in inverse-beta-decay detectors. The previously developed method for quantifying directionality in monolithic and segmented detectors may be ambiguous in methodology. In this paper, we present a new directionality algorithm and include error analysis. We have developed a new algorithm based on a measure of ``distance'' between two matrices. We report findings for our research in reactor-antineutrino directionality, and emphasize that the algorithm has broad applications whenever one desires computationally efficient 2D pattern-matching. We treat data from detector segments in the form of a matrix. The validation of our algorithm boils down to comparing a Monte Carlo generated ``empirical'' data set to a simulated data set. The empirical data set is generated for a particular orientation of the neutrino beam. We identify an optimal segmentation scale in the low-count regime. We also discuss the shortcomings of the conventional method and how this knowledge can be applied to segmented detectors, hybrid designs, and generalized validation, agnostic to the physics of detector design.

Physical dynamical systems can be viewed as natural information processors: their systems preserve, transform, and disperse input information. This perspective motivates learning not only from data generated by such systems, but also how to measure them in a way that extracts the most useful information for a given task. We propose a general computing framework for adaptive information extraction from dynamical systems, in which a trainable attention module learns both where to probe the system state and how to combine these measurements to optimize prediction performance. As a concrete instantiation, we implement this idea using a spatiotemporal field governed by a partial differential equation as the underlying dynamics, though the framework applies equally to any system whose state can be sampled. Our results show that adaptive spatial sensing significantly improves prediction accuracy on canonical chaotic benchmarks. This work provides a perspective on attention-enhanced reservoir computing as a special case of a broader paradigm: neural networks as trainable measurement devices for extracting information from physical dynamical systems.

Coherent coupling between microwave cavity photons and magnon excitations enables quantum transduction, magnon-mediated entanglement, and magnon number-resolved detection. Micromagnetic simulation of photon-magnon coupling typically requires either modifying the core solver or implementing a full electromagnetic solver. Here we present a two-tier cavity magnonics extension for mumax+, a GPU-accelerated open-source micromagnetic framework. The first tier consists of CUDA kernels that integrate N cavity-mode ODEs simultaneously with the LLG equation inside the GPU-based RK45 adaptive time-stepper, eliminating per-step GPU-CPU transfers; spatially resolved mode profiles enter both the coupling and the feedback, enabling selective addressing of non-uniform spin-wave modes. The second tier is a lightweight Python co-simulation class that reproduces the same uniform-mode physics through operator-split RK4 integration without recompilation. We validate the implementation with eight benchmark simulations: (i) magnon-polariton anticrossing spectra, (ii) vacuum Rabi oscillations, (iii) the cooperativity phase diagram spanning weak-to-strong coupling regimes, (iv) cavity mode-profile-dependent coupling selection rules, (v) multi-mode polariton hybridization with magnon-mediated cavity-cavity energy transfer, (vi) mode-selective coupling via spatial overlap engineering, (vii) antiferromagnetic magnon-cavity coupling with Neel-vector spectroscopy, and (viii) abnormal anticrossing from dissipative photon-magnon coupling, demonstrating the transition from level repulsion to level attraction.

Parylene C thin films have significant applications in advanced packaging of microelectronics. Their thermal properties are critical for thermal management of electronic devices. However, a unified understanding of the tunable structure and the corresponding thermal conductivity is still missing. This study investigated parylene C thin films of varying thickness and post-annealing temperatures grown via thermal chemical vapor deposition. The ultralow thermal conductivity of as-deposited parylene C measured by time domain thermoreflectance (TDTR) is 0.10 W/m-K. The thermal conductivity can be tuned by post-annealing. Significant increase in thermal conductivity is observed in the annealed samples (0.18 W/m-K) which induces melting and recrystallization. The results of XRD and polarized Raman spectroscopy show that the enhanced thermal conductivity is due to improved crystalline quality and the change in chain orientations. The measured thermal conductivities of the as-deposited and annealed films are much lower than the values predicted by the Cahill minimum thermal conductivity model, which can be explained by the diffuson-mediated minimum thermal conductivity model. Parylene C is found to possess the lowest thermal conductivity among dense low-k materials. Our work provides guidance for the structural design of ultra-low thermal conductivity polymers and corresponding thermal design of electronics.

We ask whether the Hamiltonian, interaction geometry, and symmetries of a quantum many-body system can be inferred from a few low-lying eigenstates without knowing which sites interact with each other. Directly solving the eigenvalue equations imposes constraints that yield a highly degenerate subspace of candidate operators, where the local Hamiltonian is hidden among an extensive family of conserved quantities, obscuring the interaction geometry. Here we introduce O-Sensing, a protocol designed to extract the Hamiltonian and symmetries directly from these states. Specifically, O-Sensing employs parsimony-driven optimization to extract a maximally sparse operator basis from the degenerate subspace. The Hamiltonian is then selected from this basis by maximizing spectral entropy (effectively minimizing degeneracy) within the sampled subspace. We validate O-Sensing on Heisenberg models on connected Erdős--Rényi graphs, where it reconstructs the interaction geometry and uncovers additional long-range conserved operators. We establish a learnability phase diagram across graph densities, featuring a pronounced ``confusion'' regime where parsimony favors a dual description on the complement graph. These results show that sparsity optimization can reconstruct interaction geometry as an emergent output, enabling simultaneous recovery of the Hamiltonian and its symmetries from low-energy eigenstates.

We present a time-domain scheme for computing Casimir forces within the Maxwell stress tensor formalism, together with a specific realization using the finite-element-based discontinuous Galerkin time-domain method. The approach enables accurate evaluation of Casimir--Lifshitz interactions for a wide range of geometries and material properties at finite temperature. At the core of the method, the electromagnetic Green's tensor is expressed as the system's response to dipolar excitations, thereby recasting the Maxwell stress tensor into a set of classical scattering problems driven by electric and magnetic dipoles. We validate the approach against reference calculations of the Casimir interaction between parallel half-spaces at both zero and nonzero temperature. We further demonstrate its applicability to finite, cylindrically symmetric geometries for which closed-form solutions are unavailable, obtaining accurate agreement with asymptotic predictions based on physical considerations. These findings illustrate the method's potential for studying Casimir interactions in realistic micro- and nanoscale structures, relevant to nanodevice design and experimental settings.

Radiative-conductive systems are intrinsically nonlinear due to the quartic temperature dependence of thermal radiation. Under fixed total heating power, convexity arguments imply that nonuniform temperature distributions radiate more efficiently and therefore exhibit a lower mean temperature than their isothermal counterparts. However, this conclusion remains qualitative, and an explicit quantitative relation between temperature heterogeneity and mean temperature reduction has been lacking. Here we derive a variance-based analytical expression linking the area-averaged temperature to the corresponding isothermal equilibrium temperature in a nonuniformly heated radiative--conductive system. By integrating the governing equation and performing a systematic second-order expansion about the ambient temperature, we show that the decrease of the mean temperature relative to the isothermal equilibrium value is linearly proportional to the temperature variance, with a proportionality coefficient set solely by the ambient temperature. This result transforms the convexity-based inequality into a quantitative statistical relation within the perturbative regime and provides a physically transparent framework for describing nonlinear radiative averaging in thermally heterogeneous systems.

We present a novel gradient regularization to completely eliminate the magnetic divergence error in meshless magnetohydrodynamics (MHD), which offers a high spatial resolution and conservative advantage, due to its Lagrangian nature. Comparing with the counterpart of constrained-gradient (CG) technique, we reform $\nabla \cdot \mathbf{B}=0$ by an implicit projection method to modify the magnetic-field gradients. The accuracy of modified-gradient (MG) method is verified and it achieves exact divergence-free results with round-off precision, by using tests of shock tube, 2D and 3D vortex, magneto-rotational instability, and especially, advection experiment, compared with CG method and the GIZMO code. It leads to noticeable improvement in pattern, amplitude and numerical dissipation of divergence error of magnetic field.

Analog Ising machines have been proposed as heuristic hardware solvers for combinatorial optimization problems, with the potential to outperform conventional approaches, provided that their hyperparameters are carefully tuned. Their temporal evolution is often described using time-continuous dynamics. However, most experimental implementations rely on measurement-feedback architectures that operate in a time-discrete manner. We observe that in such setups, the range of effective hyperparameters is substantially smaller than in the envisioned time-continuous analog Ising machine. In this paper, we analyze this discrepancy and discuss its impact on the practical operation of Ising machines. Next, we propose and experimentally verify a method to reduce the sensitivity to hyperparameter selection of these measurement-feedback architectures.

New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design from atomistic simulations is limited by interatomic potentials missing key elements, being too inaccurate, or computationally too slow for large-scale simulations. Here we present development of a refractory alloy database and two computationally efficient and general-purpose machine-learned potentials (tabGAP and NEP). We also design a cross-sampling strategy for effective sampling of training data using predictions from two potentials with completely different underlying architecture. The potentials support arbitrary alloy compositions of elements in groups four to six in the periodic table (Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W). The database is diverse yet multitargeted to enable simulations of refractory metals and alloys across different pure-metal, solid-solution, intermetallic, and glassy phases. We demonstrate the usefulness of the potentials by reproducing known pressure-, temperature-, and solute-induced phase transitions, grain boundary segregation, and simulations of radiation damage in the WTaCrVHf metallic glass.

A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates good accuracy and robustness to a wide range of bond distances and environments, making it a useful tool for molecular dynamics simulations of MXenes subjected to mechanical load or irradiation. The ML potential is applied to simulations of light and heavy ion irradiation, gathering insight into the statistics and probabilities of sputtering, reflection, defect creation, and implantation into Ti$_{n+1}$C$_n$ MXene sheets. The results provide guidelines for defect engineering of MXenes through ion irradiation and implantation. Additionally, the ML potential development provides a landmark recipe for enabling machine-learning-driven atomistic simulations of other MXenes.

This work introduces a data-driven, non-intrusive reduced-order modeling (ROM) framework that leverages Optimal Transport (OT) for multi-fidelity and parametric problems. Building upon the success of displacement interpolation for data augmentation in handling nonlinear dynamics, we extend its application to more complex and practical scenarios. The framework is designed to correct a computationally inexpensive low-fidelity (LF) model to match an accurate high-fidelity (HF) one by capturing its temporal evolution via displacement interpolation while preserving the problem's physical consistency. The framework is further extended to address systems dependent on a physical parameter, for which we construct a surrogate model using a hierarchical, two-level interpolation strategy. First, it creates synthetic HF checkpoints via displacement interpolation in the parameter space. Second, the residual between these synthetic HF checkpoints and a true LF solution is interpolated in the time domain using the multi-fidelity OT-based methodology. This strategy provides a robust and efficient way to explore the parameter space and to obtain a refined description of the dynamical system. The potential of the method is discussed in the context of complex and computationally expensive diffuse interface methods for two-phase flow simulations, which are characterized by moving interfaces and nonlinear evolution, and challenging to be dealt with traditional ROM techniques.

We highlight recent progress in the study of artificial flat band systems with a threefold focus. First, we discuss single-particle flat band physics, which has advanced through the design of various flat band generators. These generators rely on the classification of flat bands in terms of compact localized states - their fundamental building blocks. A related development is the complete real-space description of flat band projectors. Next, we review studies on perturbations of flat bands, which provide new insights into the effects of disorder and, more importantly, the intricate interplay between many-body interactions and flat band physics. Finally, we survey the growing number of experimental realizations of flat bands across diverse physical platforms.

Since 2019, three scintillator detectors of the EEE collaboration have been continuously measuring cosmic muon rates at 78.9°N at the Ny-Ålesund Research Station (Svalbard). The resulting six-year time series reveals a pronounced annual modulation, driven primarily by seasonal atmospheric variations. Utilizing routine radiosonde profiles collected above the same site, we applied several established techniques --along with a tailored analysis approach-- to investigate the relationship between muon rate and atmospheric temperature. The temperature-corrected muon-rates are analysed using the Lomb-Scargle periodogram technique in order to investigate the presence of remaining periodic structures. Finally, the temperature corrections coefficients of our analysis are compared with measurements in other stations located at lower latitudes.

Precise and high efficiency concentration of mid-infrared (mid-IR) light into sub wavelength volumes is essential for probing low-energy excitations and achieving strong field enhancements, which can be hindered by absorption losses and coupling inefficiencies at long wavelengths. Here, we introduce an innovative diamond-based metal-insulator-metal campanile probe that adiabatically compresses free-space mid infrared light (10 \mum) into \approx 1 \mum domains. Integrated into a scanning photovoltage microscope, the probe enables sub-wavelength mapping of locally driven photocurrents in graphene, resolving polarization dependent and contact-sensitive responses at energies down to \approx 0.1 eV. Experiments reveal a photocurrent signal density enhancement of 10^3 and coupling efficiencies approaching 80%, in agreement with numerical simulations. Operation of the probe with quantum cascade and free electron lasers demonstrates a robust, spectrally tunable platform for high-resolution exploration of low-energy carrier dynamics in atomically thin materials, opening opportunities for mid-IR optoelectronics and quantum photonics.

Granular systems confined in a shallow box and driven by vertical vibration provide a simple geometry to study fluidized granular media. Grains gain kinetic energy vertically through collisions with the walls and redistribute it horizontally via interparticle collisions. The $\Delta$-model has been proposed as a simplified description of this setup. In this model, a fixed velocity increment $\Delta$ is added to the normal component of the relative velocity at collisions, effectively integrating out the vertical motion while preserving collisional energy injection. This compensates for inelastic losses and yields stable homogeneous steady states amenable to kinetic theory. An Enskog kinetic equation is formulated and analyzed to obtain the stationary temperature and equation of state. The Chapman--Enskog method is then applied to derive the Navier--Stokes transport coefficients and study inhomogeneous states. The theory is extended to granular mixtures with different masses, sizes, restitution coefficients, or $\Delta$ values, leading to nonequipartition of energy even in homogeneous states. The resulting hydrodynamic equations, with transport coefficients obtained in the low-density regime, show unconditional stability of the homogeneous state and violation of Onsager reciprocity. Theoretical predictions agree well with molecular dynamics and direct simulation Monte Carlo results.

Data assimilation (DA) combines model forecasts and observations to estimate the optimal state of the atmosphere with its uncertainty, providing initial conditions for weather prediction and reanalyses for climate research. Yet, existing traditional and machine-learning DA methods struggle to achieve accuracy, efficiency and uncertainty quantification simultaneously. Here, we propose HLOBA (Hybrid-Ensemble Latent Observation-Background Assimilation), a three-dimensional hybrid-ensemble DA method that operates in an atmospheric latent space learned via an autoencoder (AE). HLOBA maps both model forecasts and observations into a shared latent space via the AE encoder and an end-to-end Observation-to-Latent-space mapping network (O2Lnet), respectively, and fuses them through a Bayesian update with weights inferred from time-lagged ensemble forecasts. Both idealized and real-observation experiments demonstrate that HLOBA matches dynamically constrained four-dimensional DA methods in both analysis and forecast skill, while achieving end-to-end inference-level efficiency and theoretical flexibility applies to any forecasting model. Moreover, by exploiting the error decorrelation property of latent variables, HLOBA enables element-wise uncertainty estimates for its latent analysis and propagates them to model space via the decoder. Idealized experiments show that this uncertainty highlights large-error regions and captures their seasonal variability.

Molecular mechanisms that enable collective and upconverted energy transfer from multiple photoacceptors to a non-absorbing spectator reaction center are highly desirable for efficient light-energy utilization. Here, we show that intermolecular Coulombic decay (ICD), a nonlocal energy relaxation channel in photoexcited molecules, offers an avenue for such a novel energy transfer mechanism. On irradiation of pyridine-argon gas mixture at 266 nm and at low laser intensities, we observed a surprisingly dominant formation of argon cations. Measurements of the laser power dependence, together with systematic studies of Ar$^+$ yield versus laser intensity and molecular density, reveal that ICD mediates the collective funneling of excitation energy from multiple photoexcited pyridine molecules to a non-photoabsorbing argon atom, leading to its ionization. The density of the reaction center offers an efficient handle to optimize this collective energy transfer. This mechanism opens new avenues in light harvesting design and may help explain the remarkable resistance of biomolecules to photodamage.

Insufficient reference data is ubiquitous in data-driven computational fluid dynamics, as it is usually too expensive to compute or impossible to observe over long enough times needed for data-driven methods. The lack of data can significantly compromise the fidelity of results computed with data-driven methods or render them inapplicable. To challenge this problem, we propose a probabilistic reconstruction method that enhances the hyper-parameterisation (HP) approach with ideas underlying the probabilistic-evolutionary approach. We offer to use the HP method ``Advection of the image point'' on data sampled from the joint probability distribution of the reference dataset. The HP method has been tested regionally on the sea surface temperature and surface relative vorticity computed with the global 1/4-deg and 1/12-deg resolution NEMO model. Our results show that the HP solution (the solution computed with the HP method) in the probabilistically-reconstructed and reduced (in terms of dimensionality) phase space at 1/4-deg resolution is more accurate than the 1/4-deg-solution computed with NEMO. Additionally, the HP solution is several orders of magnitude faster to compute than the 1/4-deg NEMO solution. The proposed method shows encouraging results for the NEMO model and the potential for the use in other operational ocean and ocean-atmospheric models for both deterministic and probabilistic predictions. Furthermore, the method can be used as a fast reanalysis tool allowing the complex dynamics of a comprehensive ocean model to be approximated by the HP solution. It can also function as a dynamic interpolation method to fill gaps in observational data.

This study discusses the mechanism of the emergence of nearly constant mean angular momentum profiles, which are widely observed in curved turbulent flows including the bulk region of Taylor--Couette (TC) flows. For high-Reynolds-number TC flows where the inner and outer cylinders are weakly counter-rotating and co-rotating, both the bulk and boundary layers become turbulent without Taylor rolls, referred to as the featureless ultimate regime (UR). Thus, we utilize the Reynolds-averaged Navier--Stokes (RANS) equations to explain the mechanism of the nearly constant mean angular momentum. High-Reynolds-number experiments of TC turbulence are performed for reference, where the angular velocity ratio $a = -\omega_\mathrm{out}/\omega_\mathrm{in}$ is in the range $-0.5 \le a \le 0.1$. Verification of the RANS based on the conventional algebraic Reynolds stress model suggests that convection of the Reynolds stress is essential for predicting the angular momentum profile. This indicates that the physical origin of the nearly constant angular momentum is the history effect of the Reynolds stress. To rigorously incorporate the convection effect into the Reynolds stress, we employ the Jaumann derivative as a covariant time derivative. The model that takes into account the history effect involving the normal stress difference successfully predicts the nearly constant mean angular momentum in the co-rotating cases. This study suggests the significance of the history effects for understanding curved or rotating turbulent flows in terms of the statistical analysis.

The Reynolds equation from lubrication theory and the Stokes equations for zero Reynolds number flows are distinct models for an incompressible fluid with negligible inertia. Here we investigate the sensitivity of the Reynolds equation to large surface gradients, and explore flow recirculation in corner geometries in comparison to the Stokes equation. We compare the solutions for the Reynolds and Stokes equations in the backward facing step (BFS), the regularized BFS, and the lid-driven triangular cavity. For the BFS variations listed above, we compute the error in terms of the average pressure drop through the channel and show how the error increases with increasing expansion ratio and with increasing magnitude of surface gradients. We further investigate the phenomenology of corner flow recirculation that arises in the Stokes solutions. In particular, we observe that occluding the corner separated region in the Stokes solution to the BFS does not disrupt the bulk flow characteristics.

Physics-informed deep learning approaches have been developed to solve forward and inverse stochastic differential equation (SDE) problems with high-dimensional stochastic space. However, the existing deep learning models have difficulties solving SDEs with high-dimensional spatial space. In the present study, we propose a scalable physics-informed deep generative model (sPI-GeM), which is capable of solving SDE problems with both high-dimensional stochastic and spatial space. The sPI-GeM consists of two deep learning models, i.e., (1) physics-informed basis networks (PI-BasisNet), which are used to learn the basis functions as well as the coefficients given data on a certain stochastic process or random field, and (2) physics-informed deep generative model (PI-GeM), which learns the distribution over the coefficients obtained from the PI-BasisNet. The new samples for the learned stochastic process can then be obtained using the inner product between the output of the generator and the basis functions from the trained PI-BasisNet. The sPI-GeM addresses the scalability in the spatial space in a similar way as in the widely used dimensionality reduction technique, i.e., principal component analysis (PCA). A series of numerical experiments, including approximation of Gaussian and non-Gaussian stochastic processes, forward and inverse SDE problems, are performed to demonstrate the accuracy of the proposed model. Furthermore, we also show the scalability of the sPI-GeM in both the stochastic and spatial space using an example of a forward SDE problem with 38- and 20-dimension stochastic and spatial space, respectively.

Households play a crucial role in the propagation of infectious diseases due to the frequent and prolonged interactions that typically occur between their members. Recent studies have emphasized the need to include socioeconomic variables in epidemic models to account for the heterogeneity induced by human behavior. While sub-Saharan Africa suffers the highest burden of infectious disease diffusion, few studies have investigated the mixing patterns in the countries and their relation with social indicators. This work analyzes household contact matrices measured with wearable proximity sensors in a rural and an urban village in South Africa. Leveraging a rich data collection describing additional individual and household attributes, we investigate how the household contact matrix varies according to the household type (whether it is composed only of a familiar nucleus or by a larger group), the gender of its head (the primary decision-maker), the rural or urban context, and the season in which it was measured. We show the household type and the gender of its head induce differences in the interaction patterns between household members, particularly regarding child caregiving, suggesting they are relevant attributes to include in epidemic modeling.

Despite their apparent simplicity, coupled oscillators exhibit surprisingly complex phenomena. Two notable examples are Berry phase (a geometric or topological aspect of the oscillators' memory) and non-Hermiticity (the often counterintuitive impact of dissipation), both of which possess rich mathematical structures. Here, we demonstrate that combining Berry phase and non-Hermiticity leads to a fundamentally new form of amplification. Specifically, we show that this combination allows a lossy oscillator system to be converted into one with gain via slow modulation of its parameters. This is distinct from other amplification mechanisms, as it results specifically from the complex-valued Berry phase that is unique to non-Hermitian systems. We show that this mechanism produces continuous, useful gain in an optomechanical system, and that similar results can be realized in a very wide range of settings.

We report experimental evidence of an Eulerian-mean flow, $\overline{u}(z)$, created by the interaction of surface waves and tailored ambient sub-surface turbulence, which partly cancels the Stokes drift, $u_s(z)$, and present supporting theory. Water-side turbulent velocity fields and Eulerian-mean flows were measured with particle image velocimetry before vs after the passage of a wave group, and with vs without the presence of regular waves. We compare different wavelengths, steepnesses and turbulent intensities. In all cases, a significant change in the Eulerian-mean current is observed, strongly focused near the surface, where it opposes the Stokes drift. The observations support the picture that when waves encounter ambient sub-surface turbulence, the flow undergoes a transition during which Eulerian-mean momentum is redistributed vertically (without changing the depth-integrated mass transport) until a new equilibrium state is reached, wherein the near-surface ratio between $|\mathrm{d}\overline{u}/\mathrm{d}z|$ and $|\mathrm{d}u_s/\mathrm{d} z|$ approximately equals the ratio between the streamwise and vertical Reynolds normal stresses. This accords with a simple statistical theory derived here and holds regardless of the absolute turbulence level, whereas stronger turbulence means faster growth of the Eulerian-mean current. We present a model based on Rapid Distortion Theory which describes the generation of the Eulerian-mean flow as a consequence of the action of the Stokes drift on the background turbulence. Predictions are in qualitative, and reasonable quantitative, agreement with experiments on wave groups, where equilibrium has not yet been reached. Our results could have substantial consequences for predicting the transport of water-borne material in the oceans.

Machine learning (ML) models hold the promise of transforming atomic simulations by delivering quantum chemical accuracy at a fraction of the computational cost. Realization of this potential would enable high-throughout, high-accuracy molecular screening campaigns to explore vast regions of chemical space and facilitate ab initio simulations at sizes and time scales that were previously inaccessible. However, a fundamental challenge to creating ML models that perform well across molecular chemistry is the lack of comprehensive data for training. Despite substantial efforts in data generation, no large-scale molecular dataset exists that combines broad chemical diversity with a high level of accuracy. To address this gap, Meta FAIR introduces Open Molecules 2025 (OMol25), a large-scale dataset composed of more than 100 million density functional theory (DFT) calculations at the $\omega$B97M-V/def2-TZVPD level of theory, representing billions of CPU core-hours of compute. OMol25 uniquely blends elemental, chemical, and structural diversity including: 83 elements, a wide-range of intra- and intermolecular interactions, explicit solvation, variable charge/spin, conformers, and reactive structures. There are ~83M unique molecular systems in OMol25 covering small molecules, biomolecules, metal complexes, and electrolytes, including structures obtained from existing datasets. OMol25 also greatly expands on the size of systems typically included in DFT datasets, with systems of up to 350 atoms. In addition to the public release of the data, we provide baseline models and a comprehensive set of model evaluations to encourage community engagement in developing the next-generation ML models for molecular chemistry.

The Jiangmen Underground Neutrino Observatory (JUNO) employs a 20 kt liquid scintillator (LS) detector located 700 m underground. To meet its physics objectives, the LS must achieve an ultra-low $^{238}$U/$^{232}$Th content of 10$^{-17}$ g/g. Given that airborne dust exhibits radioactivity about 12 orders of magnitude higher, exceptional cleanliness is essential during on-site installation. The total permissible dust mass in the 20 kt LS is only about 8 mg. To attain this, the acrylic vessel interior must comply with class 1,000 cleanliness. Pre-filling water spray cleaning improves cleanliness by roughly two orders of magnitude, requiring the overall environment to be maintained between class 10,000 and 100,000. At JUNO, a cleanroom management system has been implemented across the 120,000 m$^3$ underground experimental hall. Since May 2022, continuous laser particle monitoring has consistently achieved an average cleanliness class of 74,000. Furthermore, we developed a method to directly measure $^{238}$U/$^{232}$Th deposition rates on detector surfaces. Using ICP-MS, sensitivity reaches sub-ppt levels ($<$10$^{-12}$ g/g), enabling effective cleanliness control and assessment of external contamination during detector construction.

The radiometric integral is the fundamental radiance--to--flux relation in imaging, whereas étendue is typically used as a compact system-level descriptor. For quantitative imaging and calibration, however, the operative mapping must be explicit at the level of individual detector pixels, including pixel acceptance and field-dependent pupil visibility. This work packages the pixel-restricted radiometric integral into a reusable geometric throughput factor by defining a per-pixel optogeometric (optical-throughput) factor $F_{\mathrm{opg},i}$ (units \si{m^this http URL}) such that, under weak radiance variation, $\Phi_i \approx L_i\,F_{\mathrm{opg},i}$. Making throughput explicit at the pixel scale yields an optics-delivered photon budget in which the incident photon count at the detector, $N_{\mathrm{inc},i}$ (before quantum efficiency), scales linearly with geometry: $N_{\mathrm{inc},i}\propto F_{\mathrm{opg},i}$ for a given scene radiance distribution and fixed acquisition settings (bandwidth, integration time, and optical transmission). The corresponding optics-delivered (pre-detection) shot-noise ceiling is set by the incident photon count $N_{\mathrm{inc},i}$, with $\mathrm{SNR}_{\mathrm{inc},i}\le \sqrt{N_{\mathrm{inc},i}}\propto \sqrt{F_{\mathrm{opg},i}}$, while in photoelectron units one has $\mathrm{SNR}_i \le \sqrt{N_{\mathrm{ph},i}}=\sqrt{\eta(\bar\nu)\,N_{\mathrm{inc},i}}\propto \sqrt{F_{\mathrm{opg},i}}$, where $N_{\mathrm{ph},i}$ is the detected photoelectron count and $\eta(\bar\nu)$ is the (narrowband) quantum efficiency; additional detector/electronics noise sources (e.g.\ dark current and read noise) can only reduce the achieved SNR below these shot-noise limits.

We propose an accurate, efficient, and low-memory sum-of-Gaussians tensor neural network (SOG-TNN) algorithm for solving the high-dimensional Schrödinger equation. The SOG-TNN utilizes a low-rank tensor product representation of the solution to overcome the curse of dimensionality associated with high-dimensional integration. To handle the Coulomb interaction, we introduce an SOG decomposition to approximate the interaction kernel such that it is dimensionally separable, leading to a tensor representation with rapid convergence. We further develop a range-splitting scheme that partitions the Gaussian terms into short-, long-, and mid-range components. They are treated with the asymptotic expansion, the low-rank Chebyshev expansion, and the model reduction with singular-value decomposition, respectively, significantly reducing the number of two-dimensional integrals in computing electron-electron interactions. The SOG decomposition well resolves the computational challenge due to the singularity of the Coulomb interaction, leading to an efficient algorithm for the high-dimensional problem under the TNN framework. Numerical results demonstrate the outstanding performance of the new method, revealing that the SOG-TNN is a promising way for accurately tackling quantum systems.

Active living matter continuously creates and annihilates topological defects in a process that remains poorly understood. Here, we investigate these dynamics in two distinct active living systems: swarming bacteria and human bronchial epithelial cells. Despite their entirely different evolutionary origins, biological functions, and physical scales, both systems exhibit half-integer defects, consistent with the nematic phase. However, in contrast to active nematic theory, we find that defect creation and annihilation undergoes spatial symmetry breaking. We propose that these results stem from a fundamental dualism between nematic structural organization and generated polar forces, which are intrinsic to living systems. Furthermore, estimation of entropy production reveals that creation and annihilation are not reversed processes. Our findings challenge conventional nematic models and emphasize the role of defect-mediated dynamics in non-equilibrium biological systems as a major source of entropy production.

Forward and backward scattering provide complementary volumetric and interfacial information, yet conventional three-dimensional (3D) imaging typically accesses only one. In this Letter, we present a substrate-enhanced diffraction tomography approach that simultaneously recovers both channels under multi-angle this http URL geometry captures one forward- and two backward-scattering bands in axially symmetric Fourier regions, where their complementary coverage enables phase-absorption separation in a non-Hermitian spectrum. Explicit 3D transfer functions are derived for both channels, and an axial Kramers-Kronig relation is established to incorporate substrate-induced boundary conditions in a unified framework. Our results establish a label-free, high-resolution 3D imaging modality that surpasses the limits of existing methods.

Lubrication theory makes use of the assumptions of a long and thin fluid domain and a small scaled Reynolds number to formulate a linearized approximation to the Navier-Stokes equations. Extended lubrication theory aims to improve the model accuracy by relaxing these assumptions and including additional terms in the formulation. However, such models are sensitive to large surface gradients which lead the assumptions of the model to break down. In this paper, we present a formulation of extended lubrication theory, and compare our model with several existing models, along with the numerical solution to the Stokes equations. The error in pressure and velocity is characterized for a variety of fluid domain geometries. Our results indicate that the new solution is suitable for a wide range of geometries. The magnitude of surface variation and the length scale ratio are both important factors influencing the accuracy of the extended lubrication theory models.

We propose a Hybrid High-Order (HHO) formulation of the incompressible Navier-Stokes equations with variable density that provides exact conservation of volume and, accordingly, pure advection of the density variable. The spatial discretization relies on hybrid velocity-density-pressure spaces and the temporal discretization is based on Explicit Singly Diagonal Implicit Runge-Kutta (ESDIRK) methods. The formulation possesses some attractive features that can be fruitfully exploited for the simulation of mixtures of immiscible incompressible fluids, namely: conservation of volume enforced cell-by-cell up to machine precision, pressure-robustness, ability to preserve density bounds at low-order, robustness in the convection dominated regime, weak imposition of boundary conditions, implicit high-order accurate time stepping, reduced memory footprint thanks to static condensation, possibility to exploit inherited $p$-multilevel solution strategies to improve the performance of iterative solvers. After addressing stability at the discrete level, numerical validation is performed showcasing spatial and temporal convergence rates. To conclude, we tackle the Rayleigh-Taylor instability at different Atwood and Reynolds numbers focusing on mesh independence capabilities.

Automatic differentiation provides an efficient means of computing derivatives of complex functions with machine precision, thereby enabling differentiable simulation. In this work, we propose the use of the norm of the tangent map, obtained from differentiable tracking of particle trajectories, as a computationally efficient indicator of chaotic behavior in phase space. In many cases, a one-turn or few-turn tangent map is sufficient for this purpose, significantly reducing the computational cost associated with dynamic aperture optimization. As an illustrative application, the proposed indicator is employed in the dynamic aperture optimization of an ALS-U lattice design.

We experimentally demonstrate a novel interferometric architecture for next-generation gravity missions, featuring a laser ranging interferometer (LRI) that enables monoaxial transmission and reception of laser beams between two optical benches with a heterodyne frequency of 7.3 MHz. Active beam steering loops, utilizing differential wavefront sensing (DWS) signals, ensure co-alignment between the receiving (RX) beam and the transmitting (TX) beam. With spacecraft attitude jitter simulated by hexapod-driven rotations, the interferometric link achieves a pointing stability below 10 urad/$\mathrm{\sqrt{Hz}}$ in the frequency range between 0.2 mHz and 0.5 Hz, and the fluctuation of the TX beam's polarization state results in a reduction of 0.14\% in the carrier-to-noise-density ratio over a 15-hour continuous measurement. Additionally, tilt-to-length (TTL) coupling is experimentally investigated using the periodic scanning of the hexapod. Experimental results show that the on-axis LRI enables the inter-spacecraft ranging measurements with nanometer accuracy, making it a potential candidate for future GRACE-like missions.

Fluid mixtures, such as the cellular cytoplasm and synthetic DNA nanostars, can spontaneously compartmentalize into many coexisting phases through liquid-liquid phase separation. Despite the diversity of fluid structures that emerge from interactions between different phases, the physical principles governing their spatiotemporal organization remain unclear. In this work, we show that the dynamics of multicomponent phase separation are intimately connected to mathematical coloring problems, including the four-color theorem. By confining the system to thin geometries, we demonstrate that the four-color theorem permits arrangements of fluid compartments that lead to suppressed coalescence. As a consequence, hydrodynamics is arrested, and the diffusion-dominated coarsening dynamics can be collapsed to a universal master curve. Varying the fluid interfacial tensions can change which arrangements are energetically permissible, resulting in highly complex coarsening dynamics that differ for each phase. In unconfined three-dimensional systems, the absence of an equivalent to the four-color theorem means that suppression of coalescence is only reached asymptotically for large numbers of phases, rather than at a sharp threshold. In general, the coloring approach employed here offers a topological framework for understanding the dynamic behaviour of phase separating fluid mixtures.

Observational data from buoys are of primary importance during the development, calibration, and evaluation of ocean wave models, and these data are also used to make real-time corrections to operational models via data assimilation. By association, systematic inaccuracies in any buoy data are equally important, and thus when two buoy types provide systematically inconsistent information, this is a concern for anyone using an ocean wave model. This report is concerned with the accuracy of the high frequency portion of the ocean wave spectrum commonly observable by buoys, roughly 0.2 to 0.6 Hz. We evaluate four buoy types (two moored, two drifting) using two quantitative measures. The first involves comparing each type with a co-located ocean wave model. The second method involves evaluation of high frequency energy level as a function of wind speed. Both evaluation methods suggest that the Datawell Waverider (DWR) buoys have a strong tendency to report higher energy levels than the other three buoy types. We evaluate high frequency energy level using three different metrics (mean square slope, energy in a band of high frequencies, and spectral density at a single, specific band, 0.4 Hz), and the conclusions are found to be insensitive to the parameter used.

Auto-differentiation is a powerful tool for computing derivatives of simulation results with respect to given parameters. In this letter, we have applied this tool to investigate the instability of a dynamics system that is governed by 21 ordinary differential equations. This second-order instability (named envelope instability) is driven by space-charge effects and has significant impact on the operational regimes of particle accelerators. Our study delves into the three-dimensional envelope instability, incorporating both transverse and longitudinal coupling. Conventionally, analyzing this complex system would necessitate solving 441 ordinary differential equations, which is computationally intractable. However, by employing auto-differentiation, we were able to track only 21 equations. This approach allowed us to uncover an additional instability stopband, which arises from space-charge-induced coupling and has not been reported in previous studies. This research highlights the significant advantages of auto-differentiation in analyzing complicated dynamical systems involving a large number of ordinary differential equations.

VOXES is a Von Hamos X-ray spectrometer developed at the INFN National Laboratories of Frascati for high-resolution laboratory X-ray spectroscopy in the 5--20~keV range. It uses curved mosaic crystals and motorized positioning stages to perform wavelength-dispersive X-ray fluorescence (WD-XRF) with sub-10~eV tunable resolution for extended and dilute samples. Recent developments include the integration of an energy-dispersive X-ray fluorescence (ED-XRF) line based on a silicon pin-diode detector, which enables flux monitoring and simultaneous ED and WD measurements. In addition, a dedicated liquid-sample holder has been introduced, and a Y-shaped support geometry, crucial for switching to a transmission layout, provides mechanical compatibility with laboratory XAS, now under implementation. These upgrades expand the versatility and automation of VOXES, strengthening its role as a table-top platform for laboratory X-ray spectroscopy.

Two-dimensional (2D) materials have been proposed, among many other applications, as a efficient tool for the separation of atomic and molecular species and their corresponding isotopes, given the confinement provided by their subnanometric dimensions. In this work we present three dimensional quantum wave packet calculations revealing an enhancement in the quantum transport in bilayer over monolayer graphdiyne membranes, one of the most popular 2D materials which is commonly employed for this purpose. Besides, resonances emerge superimposed over the typical monolayer profile for transmission probabilities, a feature that is general to other bilayer nanoporous 2D heterostructures and that shows a strong dependence on the interlayer separation.

The dynamics of a degenerate spherically symmetric wormhole in a vacuum is considered. An extension of the equivalence principle to matter free objects that are the source of a gravitational field is proposed. Using the Klinkhamer metric as an example, it is shown that a degenerate wormhole is precisely such an object. Application of the extended equivalence principle reduces the radial dynamics of the Klinkhamer wormhole to the dynamics of the radial fall of a test particle in a Schwarzschild gravitational field. It is proven that any bound state of the traversable Klinkhamer wormhole eventually collapses into a nontraversable Einstein-Rosen wormhole. An estimate is presented showing that the traversable Klinkhamer wormhole, although nonstationary, is a longlived state.

Polymers-macromolecular systems composed of repeating chemical units-constitute the molecular foundation of living organisms, while their synthetic counterparts drive transformative advances across medicine, consumer products, and energy technologies. While machine learning (ML) models have been trained on millions of quantum chemical atomistic simulations for materials and/or small molecular structures to enable efficient, accurate, and transferable predictions of chemical properties, polymers have largely not been included in prior datasets due to the computational expense of high quality electronic structure calculations on representative polymeric structures. Here, we address this shortcoming with the creation of the Open Polymers 2026 (OPoly26) dataset, which contains more than 6.57 million density functional theory (DFT) calculations on up to 360 atom clusters derived from polymeric systems, comprising over 1.2 billion total atoms. OPoly26 captures the chemical diversity that makes polymers intrinsically tunable and versatile materials, encompassing variations in monomer composition, degree of polymerization, chain architectures, and solvation environments. We show that augmenting ML model training with the OPoly26 dataset improves model performance for polymer prediction tasks. We also publicly release the OPoly26 dataset to help further the development of ML models for polymers, and more broadly, strive towards universal atomistic models.

Transcription factors (TFs) regulate gene expression by binding to specific genomic loci determined by DNA sequence. Their sequence specificity is commonly summarized by a consensus binding motif. However, eukaryotic genomes contain billions of low-affinity DNA sequences to which TFs associate with a sequence-dependent binding energy. We currently lack insight into how the genomic sequence defines this spectrum of binding energies and the resulting pattern of TF localization. Here, we set out to obtain a quantitative understanding of sequence-dependent TF binding to both motif and non-motif sequences. We achieve this by first pursuing accurate measurements of physical binding energies of the human TF Klf4 to a library of short DNA sequences in a fluorescence-anisotropy-based bulk competitive binding assay. Second, we show that the highly non-linear sequence dependence of Klf4 binding energies can be captured by combining a linear model of binding energies with an Ising model of the coupled recognition of nucleotides by a TF. We find that this statistical mechanics model parametrized by our in vitro measurements captures Klf4 binding patterns on individual long DNA molecules stretched in the optical tweezer, and is predictive for Klf4 occupancy across the entire human genome without additional fit parameters.

A size-extensive, converging, black-box, ab initio coupled-cluster ($\Delta$CC) ansatz is introduced that computes the energies and wave functions of stationary states from any degenerate or nondegenerate Slater-determinant references with any numbers of $\alpha$- and $\beta$-spin electrons, any patterns of orbital occupancy, any spin multiplicities, and any spatial symmetries. For a nondegenerate reference, it reduces to the single-reference coupled-cluster ansatz. For a degenerate multireference, it is a natural coupled-cluster extension of degenerate Rayleigh-Schrödinger perturbation ($\Delta$MP) theory. For ionized and electron-attached references, it can be viewed as a coupled-cluster Green's function, although the present theory is convergent toward the full-configuration-interaction (FCI) limits, while Feynman-Dyson many-body Green's function (MBGF) theory generally is not. Additionally, a new state-universal multireference coupled-cluster theory for general model spaces is developed by slightly modifying the $\Delta$CC ansatz. This quasidegenerate coupled-cluster (QCC) theory is size-extensive, converging, but not black-box, which is expected to be well suited for strong correlation. Determinant-based, general-order algorithms of $\Delta$CC and QCC theories are implemented, which are compared with configuration-interaction (CI) and equation-of-motion coupled-cluster (EOM-CC) theories through octuple excitations and with $\Delta$MP and MBGF theories up to the nineteenth order. For transition energies, the order of performance is: QCC $\approx$ $\Delta$CC $>$ EOM-CC $>$ CI at the same excitation order or QCC $\approx$ $\Delta$CC $>$ $\Delta$MP $>$ MBGF at the same cost scaling.

Technetium-99m ($^{99m}$Tc) is essential to more than 16 million diagnostic procedures performed annually in the United States. It is typically acquired on-site from generators containing $^{99}$Mo, in turn produced at nuclear reactor facilities. This supply chain involves multiple points of vulnerability, which can lead to shortages and delays with potentially negative patient outcomes. We report on the development of a new family of cyclotrons originally designed for the IsoDAR neutrino experiment, capable of operating at much higher current than typical cyclotrons. When operated with deuterons at 1.5 MeV/amu and an anticipated continuous beam current of 5 mA, simulations project that such a system would yield $\sim$10$^{13}$ neutrons per second using a thin beryllium target. This neutron yield is sufficient, in principle, to support $^{99}$Mo production without the use of highly enriched uranium or reliance on foreign reactors. Simulations and conceptual design studies suggest that the system's beam dynamics could make it a viable pathway toward decentralized, hospital-based isotope generation. The relatively low energy of the deuterons minimizes activation and safety concerns. This work presents the physics motivation, technical design considerations, and projected neutron yields, outlining a pathway from a neutrino-physics prototype to a biomedical isotope production platform.

Molecular polaritons have drawn great interest in recent years as a possible avenue for providing optical control over chemical dynamics. A central challenge in the field is to identify physical phenomena that require a quantum rather than a classical treatment of electrodynamics. In this work, we use our recently developed mean-field quantum (mfq) and full-quantum (fq) real-time nuclear--electronic orbital (RT-NEO) time-dependent density functional theory methods to simulate polaritonic dynamics for a molecule under vibrational strong coupling when a quantized cavity mode is initialized in a Fock state rather than a coherent state. Our previous work showed that a coherent state initial condition for the cavity mode leads to polariton formation for both the mfq-RT-NEO and fq-RT-NEO methods. Herein, we show that the mfq-RT-NEO method, which does not allow light--matter entanglement, does not predict polariton formation for a Fock state initial condition. Similar to the mfq-RT-NEO method, the fq-RT-NEO method does not predict oscillations of the cavity mode coordinate and molecular dipole operator expectation values for a Fock state initial condition. However, the fq-RT-NEO method does predict oscillations of the expectation values of even powers of these operators as well as light--matter entanglement, implicating polariton formation with a Fock state initial condition. All these observations can be explained with model systems. These results suggest that using a quantized cavity mode initial condition that does not have a direct analogy to an initial condition in classical electrodynamics can lead to physical phenomena that can only be described by a quantum treatment of the cavity mode.

While recent advances in AI have transformed protein structure prediction, protein function is also often strongly influenced by the thermodynamic and kinetic features encoded in its underlying free-energy surface. Here, we propose a framework to rationally modify these surfaces in order to control conformational transition rates, built on the Collective Variables for Free Energy Surface Tailoring (CV-FEST) framework, and validate it on point mutations of the miniprotein Chignolin. The framework relies on Harmonic Linear Discriminant Analysis (HLDA) based collective variables (CVs) constructed from short molecular dynamics trajectories restricted to the metastable states, requiring only limited sampling within each state. Notably, the HLDA CV derived solely from the wild-type system already provides residue-level scores that predict whether mutations at specific positions are likely to accelerate or slow conformational transitions. Furthermore, we find that the leading HLDA eigenvalue associated with the derived CV, a quantitative measure of the one-dimensional statistical separation between folded and unfolded ensembles, correlates strongly with conformational transition rates across mutations. Together, these results show that kinetic effects of point mutations can be inferred from minimal local sampling, providing a practical route to the rational engineering of conformational transition rates without exhaustive simulations or large training datasets.

Clustering techniques offer a powerful framework for analyzing complex flow dynamics and reducing computational costs in large-scale simulations. In this work, we propose a novel clustering-based approach using Vector Quantization Principal Component Analysis (VQPCA) to identify structural sensitivity zones, namely the regions where the fluid flow is more receptive to changes. To the authors knowledge, this is the first application of VQPCA to a fluid dynamics problem for the identification of flow patterns and dynamically relevant regions. As a fully data-driven technique, it does not rely on adjoint methods; therefore, this approach has the advantage of having low computational cost, since it depends exclusively on data from the direct problem. The VQPCA technique demonstrates its ability to extract dominant flow features by clustering the flow field into regions characterized by their intrinsic dynamics. To assess the validity of this method, it is used to investigate the wake behind a circular cylinder, revealing similarities to previously established structural sensitivity regions. The robustness of the approach is further assessed through validation and calibration in different operating conditions in this flow scenario. As an extension of the analysis, we address the complex dynamics of two planar synthetic jets, where the clustering insights can lead to develop flow control strategies. These results highlight the potential of clustering-based methods as practical and effective tools to analyze and optimize fluid flows.

This manuscript describes a radiation-hardened current-mode delta-sigma ADC fabricated in a standard 130 nm CMOS technology and qualified for total ionizing doses up to 100 Mrad. The operational signal range achieved with a 100 s integration window exceeds 200 dB. The converter is designed for beam loss monitoring applications in high-energy physics, where it must handle input currents spanning nine decades, from 1 mA down to 1 pA, while providing a fast 10 us response time for machine protection. To meet these conflicting requirements, the architecture exploits the inherent trade-off between resolution and acquisition time provided by delta-sigma conversion: a first-order architecture, sampling at 20 MHz, delivers 11-bit effective resolution within the critical 10 us window for critical currents around 1 mA. Integration times above 10 s enable the sub-picoampere resolution required for precise beam alignment and background monitoring. The chip integrates two independent channels, consumes 25 mW from a 1.2 V supply, and relies on radiation-hardening techniques such as triple-redundant digital logic, custom ESD protections, and manual enclosed layout for critical analog transistors. Post-irradiation measurements up to 100 Mrad show no significant performance degradation, and the uncalibrated integral nonlinearity remains within [+4, -5] LSBs over the 1 mA to 5 uA range. The converter's flexibility and radiation tolerance make it suitable not only for the HL-LHC beam loss monitoring upgrade but also for other precision current measurement applications in harsh environments.

Wall-bounded turbulent flows are chaotic and multiscale, rendering real-time prediction at high Reynolds numbers computationally prohibitive in applications such as wind farms. Classical data assimilation methods are based on repeated solution of the governing equations and thus inherit this cost. Generative models instead learn the probability distribution of flow states, enabling scalable probabilistic reconstruction. Using plane Couette flow as a canonical configuration, we develop a generative framework that couples a $\beta$-variational autoencoder with a transformer-based diffusion model to generate four-dimensional spatiotemporal samples. Bayesian conditioning enables data assimilation without retraining and allows statistical constraints to be imposed through sampling. The framework is applied to a subdomain of turbulent plane Couette flow at $Re_h=1300$, where the corresponding DNS resolution in this region requires $O(10^6)$ spatial degrees of freedom. The diffusion model reproduces two-point correlations, energy spectra, and single-point statistics up to fourth order using $O(10)$ latent spatial degrees of freedom, yielding a compression ratio of $O(10^5)$ - one to two orders of magnitude above prior reports. Two assimilation scenarios demonstrate that conditional diffusion models with the proposed sampling strategy can enforce complex statistical constraints. However, enforcing these constraints while preserving physical fidelity and sample diversity introduces an inherent trade-off. Excessive conditioning can distort the learned diffusion prior, paralleling limitations of classical ensemble-based data assimilation. These results highlight both the promise of diffusion models as probabilistic surrogates for turbulent wall-bounded flows and the challenges of conditioning such models, establishing a foundation for future real-time reconstruction from operational data.

The stringent demands of photonic quantum computing protocols motivate photonic integrated circuit (PIC) platforms with passive optical properties such as extremely low losses and correspondingly large circuit depths, as well as active optical properties such as high reconfiguration rates, low power dissipation, and minimal crosstalk. At the same time, many quantum photonic resource state generators, such as single-photon sources and quantum memories, require operation in the visible wavelength range. These requirements make the passive optical properties of CMOS-fabricated, ultralow-loss, low-confinement silicon nitride waveguides especially attractive. However, the conventional active properties of these systems based on thermo-optic modulation are plagued by high levels of crosstalk, slow modulation rates, and high power dissipation. Although there have been recent demonstrations of CMOS-fabricated, visible wavelength, piezo-optomechanical PICs that solve the above challenges associated with implementing active functionality, these have made use of high-confinement waveguides with currently demonstrated losses of order $0.3$-$1~\mathrm{dB/cm}$, precluding circuit depths required for scalable quantum algorithms. Here, we demonstrate that combining piezo-optomechanical actuation with a low-confinement, ultra-low loss silicon nitride platform addresses the scalability challenge while enabling high-performance active functionality at visible wavelengths. This platform achieves a propagation loss $0.026~\mathrm{dB/cm}$ at $780~\mathrm{nm}$, modulation bandwidths in the MHz range, and a phase shifter voltage-length product ($V_\pi L$) of approximately $2.8~\mathrm{\mathrm{V}\cdot\mathrm{m}}$ and negligible hysteresis. We further demonstrate reconfigurable Mach-Zehnder interferometers based on spiral phase shifters with 0.63 dB loss per phase shifter.

Relational Quantum Mechanics (RQM) treats quantum states as observer-dependent facts rather than absolute properties. While this relational stance is conceptually attractive, it raises concerns about empirical confirmation, particularly in multi-observer scenarios. Existing responses within RQM focus on securing agreement between observers by strengthening the status, stability, or accessibility of recorded outcomes. However, they leave open a more basic question: what grounds the persistence of an observer across time? Scientific observation presupposes stable records and the capacity to relate outcomes across successive measurements. We argue that the minimal definition of the observer in RQM as a merely interacting physical system is insufficient to support this requirement. We propose a complementary account of the observer that distinguishes physical interaction from informational coherence, and show how this distinction supports empirical confirmation in Wigner's friend-type scenarios.

For decades, mechanical resonators with high sensitivity have been realized using thin-film materials under high tensile loads. Although there have been remarkable strides in achieving low-dissipation mechanical sensors by utilizing high tensile stress, the performance of even the best strategy is limited by the tensile fracture strength of the resonator materials. In this study, a wafer-scale amorphous thin film is uncovered, which has the highest ultimate tensile strength ever measured for a nanostructured amorphous material. This silicon carbide (SiC) material exhibits an ultimate tensile strength of over 10 GPa, reaching the regime reserved for strong crystalline materials and approaching levels experimentally shown in graphene nanoribbons. Amorphous SiC strings with high aspect ratios are fabricated, with mechanical modes exceeding quality factors 10^8 at room temperature, the highest value achieved among SiC resonators. These performances are demonstrated faithfully after characterizing the mechanical properties of the thin film using the resonance behaviors of free-standing resonators. This robust thin-film material has significant potential for applications in nanomechanical sensors, solar cells, biological applications, space exploration and other areas requiring strength and stability in dynamic environments. The findings of this study open up new possibilities for the use of amorphous thin-film materials in high-performance applications.

Using open astronomical multifrequency databases, we constructed light curves and developed a comprehensive visualisation and sonification analysis for the blazars Mrk~501, Mrk~1501, Mrk~421, BL~Lacerta, AO~0235+164, 3C~66A, OJ~049, OJ~287, and PKS~J2134-0153. This study employed Musical Instrument Digital Interface (MIDI) and Parameter Mapping Sonification (PMSon) techniques to generate waveforms, spectrograms, and sonifications. These representations demonstrate that data visualisation and sonification are powerful tools for analysing astronomical objects like blazars, providing insights into their multifrequency variability. This work highlights how sonification and visualisation can aid in identifying potential patterns, power variations, regularities, and gaps in the data. This multimodal approach also underscores the importance of inclusivity in scientific communication, offering accessible methods for exploring the complex behaviour of blazars.

Surface release of radiatively active particles, with high infrared- (IR-)to-visible extinction ratios, has been proposed as a method of warming Mars. However, to warm Mars using aerosols, particles released locally must disperse globally. Here we provide an initial reference study in a plume tracking, dry Martian atmospheric model to address this question. The winds that transport aerosols respond to the aerosol's IR forcing, implying strong radiative-dynamical feedbacks (RDF). We investigate RDF from surface release of two particle compositions: carbon (graphene) and metal (Al). Self-lofting helps particles rise and spread locally and regionally, and the Hadley cell strengthens under warming, aiding latitudinal mixing. Within our model, Mars RDF enable engineered-aerosol warming. Warming is slightly greater for three-dimensional vs. 1D-models and also depends on spectral resolution of radiative transfer. We assess implications for Mars warming. Many open atmospheric science questions remain, including the role of agglomeration, dry-deposition rate uncertainty, and modeling water cycle feedbacks.

Genes are connected in complex networks of interactions where often the product of one gene is a transcription factor that alters the expression of another. Many of these networks are based on a few fundamental motifs leading to switches and oscillators of various kinds. And yet, there is more to the story than which transcription factors control these various circuits. These transcription factors are often themselves under the control of effector molecules that bind them and alter their level of activity. Traditionally, much beautiful work has shown how to think about the stability of the different states achieved by these fundamental regulatory architectures by examining how parameters such as transcription rates, degradation rates and dissociation constants tune the circuit, giving rise to behavior such as bistability. However, such studies explore dynamics without asking how these quantities are altered in real time in living cells as opposed to at the fingertips of the synthetic biologist's pipette or on the computational biologist's computer screen. In this paper, we make a departure from the conventional dynamical systems view of these regulatory motifs by using statistical mechanical models to focus on endogenous signaling knobs such as effector concentrations rather than on the convenient but more experimentally remote knobs such as dissociation constants, transcription rates and degradation rates that are often considered. We also contrast the traditional use of Hill functions to describe transcription factor binding with more detailed thermodynamic models. This approach provides insights into how biological parameters are tuned to control the stability of regulatory motifs in living cells, sometimes revealing quite a different picture than is found by using Hill functions and tuning circuit parameters by hand.

The pseudomode method for open quantum systems, also known as the mesoscopic leads approach, consists in replacing a structured environment by a set of auxiliary "pseudomodes" subject to local damping that approximate the environment's spectral density. Determining what parameters and geometry to use for the auxiliary modes, however, is non-trivial and involves many subtleties. In this paper we revisit this problem of pseudomode design and investigate some of these subtleties. In particular, we examine the scenario in which pseudomodes couple to each other, resulting in an effective spectral density that is no longer a sum of Lorentzians. We show that non-diagonalizability of the pseudomodes' effective single-particle non-Hermitian Hamiltonian can lead to terms in the effective spectral density which cannot be obtained by diagonalizable non-Hermitian Hamiltonians. We also present a method for constructing the pseudomode parameters to exactly match a fit to a spectral density, and in doing so illuminate the enormous freedom in this process. The case of many uncoupled pseudomodes evenly distributed in energy is explored, and we show how, contrary to conventional assumption, the effective spectral density does not necessarily converge in the limit of an infinite number of pseudomodes distributed this way. Finally, we discuss how the notion of effective spectral densities can also emerge in the context of scattering theory for non-interacting systems.

This work quantifies the effect of misfit and threading dislocations on the surface energies of PbTe-PbSe interfaces, with the defect structures of the interfaces being obtained from atomistic and multiscale simulations of their manufacturing processes. Simulation results show that direct bonding produces semi-coherent interfaces with two-dimensional misfit dislocation networks, while heteroepitaxial processes produce complex three-dimensional dislocation structures with both misfit and threading dislocations. Surface energies at these interfaces were determined by computing the interaction energies across these interfaces. Compared with coherent interfaces, directly bonded interfaces exhibit up to ~23% lower surface energy, while the surface energies of epitaxially grown interfaces can be nearly 50% lower. The results demonstrate the significant effects of dislocations on interfacial energy.

We extend the Harrow-Hassidim-Lloyd (HHL) algorithm, which is well-studied in the qubit framework, to its qutrit counterpart (which we call qutrit HHL, as opposed to qubit HHL, which is HHL using qubits), and develop a program for its implementation. We design Weyl-Heisenberg gadgets, the qutrit equivalents of Pauli gadgets, and come up with a practical implementation scheme for qutrit HHL. We test HHL with qutrits for simple matrices and verify the results against the expected outcomes. We apply the algorithm to quantum chemistry, and in particular, to the potential energy curve calculations of the model problem of the Hydrogen molecule in the split valence basis. We do so for two cases: 1-qutrit and 2-qutrit input states, where the latter makes use of our gadgets. We compare the number of qudits and the number of gates required between qubit and qutrit HHL implementations. In general, we find that for a fixed precision, the qutrit HHL circuit requires fewer number of qudits and comparable number of two-qudit gates than its qubit counterpart.

Scaling quantum networks to continental distances requires physical infrastructure capable of overcoming both exponential attenuation and severe phase decoherence. While the concept of vacuum beam guide (VBG) has recently emerged as a promising low-loss solution, we move beyond it by proposing a rigorous physical-layer architectural blueprint anchored by empirical data and methods from Advanced LIGO, particularly regarding its interferometric stability in dynamic environments. To evaluate its system-level viability, we benchmark this architecture against various protocols across quantum communication, metrology, and computation. Ultimately, we identify no fundamental technical blockers to scaling this infrastructure.

We couple projective limits of probability measures to direct limits of their symmetry groups. We show that the direct limit group is the group of symmetries of the projective limit probability measure. If projective systems of probability measures represent point processes in increasingly larger finite regions of the same infinite space, then we show that under some additional niceness and consistency assumptions, an extension of the direct limit group is the symmetry group of the projective limit point process in the whole infinite space. The application of these results to random graph limits provides ``shortest paths'' to graphons and graphexes as it recovers these random graph limits as trivial corollaries. Another application example encompasses a broad class of limits of random graphs with bounded average degrees. This class includes a representative collection of paradigmatic random graph models that have attracted significant research attention in diverse areas of science. Our approach thus provides a general unified framework to study limits of very different types of random graphs.

We present an experimental method to fabricate centimetric thin elastic capsules with highly uniform thickness and negligible bending stiffness using silicone elastomers. In our experiments, the capsules thickness is tunable at fabrication, while internal pressure and hoop (circumferential) stress are adjustable via hydrostatic inflation once the capsules are filled and immersed in water. Capsules mechanics are probed through hydro-elastic waves generated by weak mechanical perturbations at the capsule interface. By analyzing the surface wave dynamics in the Fourier domain, we extract the in-plane stress and demonstrate that the hydro-elastic waves are exclusively governed by hoop stress. This \reponse{provides a controllable macroscopic analogue of liquid drops} characterised by an effective surface tension, allowing the capsules to be modeled as large-scale ``elasto-drops'' with an inflation and thickness tunable effective surface tension. \reponse{In this limit, bending stiffness is negligible over the experimentally relevant wavelengths, so that the shell dynamics are governed primarily by in-plane tension.} Our work demonstrates that elasto-drops serve as a robust model system for parametric studies of large-scale \reponse{analogues of} liquid drops with experimentally adjustable surface tension.

Boundary-catalytic branching processes describe a broad class of natural phenomena where the population of diffusing particles grows due to their spontaneous binary branching (e.g., division, fission or splitting) on a catalytic boundary located in a complex environment. We investigate the possibility of the geometric control of the population growth by compensating the proliferation of particles due to catalytic branching events by their absorptions in the bulk or on absorbing regions of the boundary. We identify an appropriate Steklov spectral problem to obtain the phase diagram of this out-of-equilibrium stochastic process. The principal eigenvalue determines the critical line that separates an exponential growth of the population from its extinction in a bounded domain. In other words, we establish a powerful tool for calculating the growth-regulating absorption rate that equilibrates the opposite effects of branching and absorption events and thus results in steady-state behavior of this diffusion-reaction system. Moreover, we show the existence of a critical catalytic rate above which no compensation is possible, so that the population cannot be controlled and keeps growing exponentially. The proposed framework opens promising perspectives for better understanding, modeling and control of various boundary-catalytic branching processes, with applications in physics, chemistry, and life sciences.

The Reynolds equation is derived from the incompressible Navier Stokes equations under the lubrication assumptions of a long and thin domain geometry and a small scaled Reynolds number. The Reynolds equation is an elliptic differential equation and a dramatic simplification from the governing equations. When the fluid domain is piecewise linear, the Reynolds equation has an exact solution that we formulate by coupling the exact solutions of each piecewise component. We consider a formulation specifically for piecewise constant heights, and a more general formulation for piecewise linear heights; in both cases the linear system is inverted using the Schur complement. These methods can also be applied in the case of non-linear heights by approximating the height as piecewise constant or piecewise linear, in which case the methods achieve second order accuracy. We assess the time complexity of the two methods, and determine that the method for piecewise linear heights is linear time for the number of piecewise components. As an application of these methods, we explore the limits of validity for lubrication theory by comparing the solutions of the Reynolds and the Stokes equations for a variety of linear and non-linear textured slider geometries.

Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition and atomic counts in a unit cell, existing methods struggle with the NP-hard combinatorial challenge of rigorous symmetry enforcement or rely on retrieving known templates, which inherently limits both physical fidelity and the ability to discover genuinely new materials. To solve this, we propose a symmetry-driven generative framework. Our approach leverages large language models to encode chemical semantics and directly generate fine-grained Wyckoff patterns from atomic stoichiometry and counts, effectively circumventing the limitations inherent to database lookups. Crucially, to overcome the exponentially complex problem of combinatorial site assignments, we incorporate domain knowledge through an efficient, linear-complexity heuristic beam search algorithm that rigorously enforces algebraic consistency between site multiplicities and atomic stoichiometry and counts. By integrating this symmetry-consistent template into a diffusion backbone, our approach constrains the stochastic generative trajectory to a physically valid geometric manifold. This framework achieves state-of-the-art performance across stability, uniqueness, and novelty (SUN) benchmarks, alongside superior matching performance, thereby establishing a new paradigm for the rigorous exploration of targeted crystallographic space which can be previously uncharted, with no reliance on existing databases or a priori structural knowledge.