Understanding how vegetation loss alters rainfall remains a major challenge in climate and hydrological science, as deforestation modifies precipitation through heterogeneous, seasonal and nonlinear land-atmosphere feedbacks. Existing models struggle to capture these dynamics: convection is parameterised at coarse scales, tipping behaviour is poorly constrained, and rainfall-deforestation analyses are limited to multi-decadal timescales. Therefore, many approaches resolve correlations rather than causal effects, limiting our ability to anticipate hydrological disruption. Using a neural-network model for hourly rainfall prediction, combined with pathway diagnostics and sensitivity analyses, we examine how vegetation perturbations reorganise rainfall across space, intensity regimes, and timescales under deforestation. We assess whether the model captures physically consistent dependencies linking vegetation, atmospheric state, and precipitation, and whether sustained canopy loss induces threshold behaviour. The model accurately predicts rainfall occurrence and intensity (Spearman = 0.84, F1 = 0.93, ROC-AUC = 0.98) and learns temporally ordered dependencies aligned with ecohydrological theory. Sensitivity analyses reveal rapid, asymmetric responses to vegetation loss: heavy rainfall (20-50 mm/h) declines by up to 7% under sustained deforestation, while light rainfall (0.1-1 mm/h) increases by 4%. Rainfall entropy rises by 1.3%, and dry-season intensity increases by 0.3-0.5% per 0.5% forest-cover loss, with strongest impacts in the north-western Amazon and Andean foothills. Threshold analysis reveals a sharp decline in precipitating area fraction after 2-3 months of sustained vegetation change in sensitive regions. These results demonstrate that data-driven approaches uncover process-relevant land-atmosphere coupling and highlight growing hydrological vulnerability in the Amazon.

Pakistan experienced an unusually severe flood season between June and December 2025, with cascading impacts on population, infrastructure, and agriculture. Existing operational flood products (e.g., UNOSAT) provide valuable episode-level snapshots but rarely deliver spatially and temporally continuous inundation maps at near-real-time latency within the country. We present a multi-sensor, ensemble-based remote-sensing framework for continuous flood nowcasting in Pakistan that integrates Sentinel-1 SAR, Harmonized Landsat-Sentinel (HLS L30 and S30), MODIS, and VIIRS observations on a harmonized grid in Google Earth Engine. The framework employs a tiered nowcasting ensemble that prioritizes higher-resolution sensors (Sentinel-1 and HLS) and falls back to MODIS and VIIRS when necessary, preserving daily continuity of flood extent at each sensor's native resolution. Applied to the 2025 monsoon period, the system generates near-real-time, spatially consistent inundation maps across Pakistan. As a nowcasting case study, we track the super-flood of 26 August-7 September 2025 day by day, demonstrating the framework's ability to capture the evolving flood footprint in near real time and extend beyond the temporal limits of episodic mapping products. Validation against GloFAS discharge anomalies and precipitation datasets (CHIRPS v3.0, MSWEP) shows strong agreement with observed hydrometeorological conditions. By integrating nowcast outputs with exposure layers (WorldPop, ESA WorldCover, Giga-HOTOSM), the framework enables rapid estimation of affected populations, cropland, and critical infrastructure, supporting timely disaster response and resilience planning in South Asia.

The subpolar oceans are characterized by intense storm forcing and complex littoral topography. Submesoscale frontal instabilities are significant sources of turbulent kinetic energy (TKE) in these regions. However, criteria for identifying and parameterizing these instabilities in regional models have predominantly relied on a geostrophic framework that neglects generalized ageostrophic shear. We derive criteria for overturning instability that account for stabilizing and destabilizing effects of ageostrophic shear on mechanically forced boundaries, deviating from the geostrophically derived potential vorticity (PV) criterion, $qf < 0$. Ageostrophic forcing modifies stability from that implied by the vertical PV structure underlying bulk surface boundary layer diagnostics, which may limit the applicability of such bulk criteria in strongly forced regimes and motivate the need for layer-resolved measures. We demonstrate their application using a feature model of a wind-forced jet, as well as a 1-km Regional Ocean Modeling System (ROMS) hindcast of the high North Atlantic, and assess the importance of forced ageostrophic overturning instability (AOI) in intense frontal zones. In the feature model, ageostrophic shear increases overturning instability by up to 20%, compared to a strictly geostrophic framework.

The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dense geophone deployments, distributed acoustic sensing (DAS) arrays, and large-scale 2D and 3D surveys. This expansion makes complex noise suppression increasingly challenging, especially when signal fidelity must be preserved. Conventional supervised deep learning methods are often task-specific, require large paired datasets, and can suffer from domain shift under new acquisition conditions. Foundation models offer a promising alternative, but pre-training seismic foundation models from scratch requires massive domain-specific data and substantial computation. We propose an efficient framework that repurposes general-purpose Vision Foundation Models (VFMs) for geophysical tasks through Parameter-Efficient Fine-Tuning. The architecture uses a pre-trained VFM, a DINOv3 encoder, adapted with Low-Rank Adaptation (LoRA) to enable effective feature adaptation with few additional parameters. To improve robustness under unseen field conditions without ground truth, we introduce a kurtosis-guided unsupervised test-time adaptation module that updates only LoRA parameters during inference. This module self-calibrates the model to site-specific noise by identifying information-rich regions via kurtosis and performing self-training without labeled data. Experiments on public exploration seismic images and DAS vertical seismic profiling data from the Utah FORGE site show that the framework matches or outperforms domain-specific models. Tests on unseen cross-site data from a land survey in China and the Groß Schönebeck geothermal site in Germany further demonstrate strong generalization and effective signal-noise separation. These results highlight the potential of adapting pre-trained VFMs to data-intensive problems in exploration seismology.

Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or have sacrificed spatial resolution compared to the model they are trained to emulate. We developed a machine-learned solver that speeds up advection simulations without sacrificing spatial resolution through the use of temporal coarse-graining, where the model is trained to take larger integration steps than dictated by the Courant-Friedrich-Lewy (CFL) condition. Our solver framework includes a convolutional neural network that takes concentrations and CFL numbers as inputs and outputs mass flux. Our solvers emulate 10-day ground-level horizontal advection simulations with r$^2$ values against the baseline ranging from 0.60--0.98 with temporal coarsening factors of 4 to 32 times the baseline integration time step. Speed increases and accuracy decreases with increased coarsening, with $r^2 = 0.24$ in accuracy lost for every factor of 10 gained in speed, reaching a maximum 92$\times$ speedup while maintaining $r^2 = 0.60$. We deliberately trained our solvers only on January ground-level wind data to examine their ability to generalize across seasons and vertical heights. The 4$\times$-coarsened learned solver successfully reproduces simulations over 72 vertical levels. The 8$\times$--16$\times$ solvers (but not 32$\times$) emulate most vertical levels. The learned solvers also generalize well across seasons, except for instabilities in June and October. With additional fine-tuning, these learned solvers could be appropriate for operational use where trading accuracy for speed could be advantageous, such as in screening tools, in ensemble simulations, or with data assimilation.

Atmospheric correction is a critical preprocessing step in optical remote sensing, but repeated high-fidelity radiative transfer simulations remain computationally expensive for dense look-up-table generation, sensitivity analysis, retrieval support, and operational preprocessing. This study presents a physics-aware multi-fidelity surrogate framework for emulating atmospheric correction coefficients using paired 6S and libRadtran simulations. Atmospheric and geometric states are sampled using Latin Hypercube Sampling, and both radiative transfer models are evaluated under matched conditions for Sentinel-2 bands using spectral-response-function-aware coefficient generation. The high-fidelity targets are path reflectance, total transmittance, and spherical albedo. A physics-guided Kolmogorov-Arnold Network, termed pKANrtm, receives the atmospheric state and low-fidelity 6S coefficients, predicts the residual relative to libRadtran, and reconstructs the high-fidelity coefficients. The pKANrtm model uses an Efficient-KAN architecture and is trained with a physics-consistency penalty applied in the original coefficient space. The proposed model is evaluated against state-of-the-art regression-based RTM surrogates. Across both standard and out-of-distribution evaluation settings, pKANrtm achieves the strongest overall predictive performance among the compared models. Runtime benchmarking demonstrates substantial acceleration relative to libRadtran, with GPU inference providing approximately four orders of magnitude single-sample speedup and batched inference reaching tens of thousands of samples per second. These results indicate that physics-aware multi-fidelity pKANrtm emulation provides an accurate, physically structured, and computationally efficient strategy for atmospheric correction coefficient generation.

Folk meteorological proverbs encode centuries of empirical observation by agricultural communities. Two Hebrew proverbs link lunar calendar anchor days to monthly winter rainfall: (i) "If Rosh Chodesh is rainy, the whole month is rainy" and (ii) "If it rains on Shabbat Mevarechim, the whole month is rainy." Shabbat Mevarechim is the last Saturday before each new Hebrew month, preceding Rosh Chodesh by one to seven days. The first proverb is widely known; the second circulates in Hasidic oral tradition with no identified written source. Both have never been formally tested. We analyse 75 years (1950-2024) of daily precipitation data from seven Israeli cities across three climatic regions, comprising 191,758 station-days and 2,422 Hebrew-month observations during the winter rainy season (Marcheshvan-Adar). A rainy Rosh Chodesh increases the probability of a rainy month from 22.2% to 38.6% (lift +16.4 percentage points; chi-square = 57.8, p = 2.9e-14; Bayes factor 1.81). A rainy Shabbat Mevarechim produces a similar effect (lift +16.5 percentage points, p = 8.0e-13), despite preceding Rosh Chodesh by up to seven days. The effect decays with lag and mirrors daily rainfall autocorrelation (r = 0.35-0.44 at lag 1; ~0 at lag 7), consistent with Mediterranean cyclone persistence. A bootstrap permutation test (p < 1e-4) and a 15-year rolling analysis show declining predictive power (-0.20 percentage points per year, p < 0.001), consistent with shortening precipitation events under warming climate conditions. Both proverbs encode real but probabilistic meteorological signals whose reliability is decreasing over time.

We investigate a dark energy (DE) equation of state (EoS) parametrization in a curved spacetime using current observations. We constrain the model parameters by using observational Hubble data from Cosmic Chronometer (CC), Pantheon Plus SH0ES (PPS), and DESI BAO DR2, along with their reconstructed datasets using an Artificial Neural Network (ANN). The parameter $\alpha$ is constrained as $\alpha \approx 0.35 (\approx 0.56)$ from original (reconstructed) data. This means reconstruction pushes the model toward a significant deviation from the standard $\Lambda$CDM framework. We find that the curvature parameter $\Omega_{k0} = 0.068 \pm 0.029$ at 68\% CL with original data, suggests a slightly open universe, whereas with the reconstruction method, $\Omega_{k0} = -0.131 \pm 0.032$ at 68\% CL suggests a closed universe. This shift in the mean value indicates that the reconstruction method is highly sensitive to curvature. We perform statistical model comparison criteria, namely, AIC and BIC to assess the reliability of our framework.

This paper presents an interdisciplinary analysis of the "Star of Bethlehem" narrative described in the Gospel of Matthew (Mt 2:1-12), examining the hypothesis, originally proposed by Johannes Kepler, that the reported phenomenon may be associated with the Jupiter-Saturn conjunction of 7 BCE. The methodology is based on a systematic comparison between the textual account and independently verifiable astronomical data, including retro-calculated ephemerides, sky geometry from Judea, constraints of the Jerusalem-Bethlehem route, and the historical chronology of Herod the Great. The narrative elements are treated as distinct, partially independent constraints required to be jointly satisfied within an explicitly falsifiable framework, under restricted observational and kinematic conditions, avoiding arbitrary parameter choices. The analysis indicates that the 7 BCE Jupiter-Saturn conjunction-characterized by its triple occurrence and extended duration-exhibits an apparent motion consistent with key aspects of the reported behavior of the star, including its progression and apparent stopping. In particular, the stationary phase of Jupiter occurs within a few days of an independently identified sky-ground kinematic synchronization window, without ad hoc adjustments. A sensitivity analysis suggests that this compatibility remains stable under reasonable variations of assumptions. The Jupiter-Saturn conjunction thus emerges as a coherent candidate satisfying the constraints considered. This study does not aim to establish a definitive historical identification, but to propose a physical and testable framework for evaluating the compatibility of celestial configurations with the narrative. It highlights a convergence between astronomical data and textual constraints, indicating that the account cannot be dismissed as scientifically incompatible on the basis of rational analysis.

We discuss a formal and physical connection between the Bohm quantum potential and the scalar mode of the Aharonov-Bohm extension of electrodynamics. The analysis is motivated by the effective non-relativistic bosonic model recently proposed by Minotti and Modanese, in which the electromagnetic field is coupled to a conserved current while the field equations contain an additional source term. In the Madelung representation $\psi=R\exp(i\theta/\hbar)$, the Bohm quantum potential $ Q_B=-\frac{\hbar^2}{2m}\frac{\nabla^2 R}{R} $ is determined by the relative curvature $\nabla^2R/R$ of the amplitude profile $R$. In the same bosonic model, the scalar electromagnetic mode $S=\partial_\mu A^\mu$ is sourced by the extra-current $I=\partial_\mu j^\mu$, which contains the density-weighted electromagnetic combination $\nabla\cdot(R^2\mathbf A)$. Thus $Q_B$ does not act as a direct source of $S$; rather, the two quantities probe different differential aspects of the same amplitude profile: $Q_B$ is sensitive to the relative curvature of $R$, whereas the source of $S$ is sensitive to its density and gradient content through $R^2$ and $\nabla R$. We show that, once boundary and normalization data are fixed, this observation may be written as a mediated functional dependence of $S$ on $Q_B$ through $R$. We also clarify the physical status of $Q_B$: although it is state-dependent and should not be interpreted as an autonomous external potential, its density-weighted integral gives the amplitude-gradient energy, equivalently a Fisher-information contribution. This makes $Q_B$ a compact diagnostic of quantum pressure, rigidity, and inhomogeneity of a bosonic condensate. The resulting link with $S$ is therefore best understood as a structural relation between the order-parameter amplitude profile of the condensate and the scalar sector of the extended electromagnetic theory.

The Internal Conversion Electron SPectrometer In Coincidence Experiments (ICESPICE) demonstrator has been developed at Florida State University to enable particle/gamma-electron coincidence measurements in low-energy nuclear structure studies. ICESPICE is based on the mini-orange spectrometer concept and features a modular design using commercially available permanent magnets arranged in toroidal configurations to transport internal conversion electrons to room-temperature PIPS detectors while suppressing background from undesired particles. The system was optimized through SolidWorks modeling, COMSOL magnetic field simulations, and Geant4 particle tracking to maximize the magnetic transmission probability for electrons around 1 MeV. Commissioning tests using a calibrated 207Bi source demonstrated the performance of multiple spectrometer-detector configurations. Coincidence measurements between CeBr3 detectors from the CeBrA array and PIPS detectors revealed clear gamma-electron correlations. The first in-beam particle-electron measurements using ICESPICE were performed with the Super-Enge Split-Pole Spectrograph (SE-SPS) in the 208Pb(d,t)207Pb reaction. Prompt coincidences between tritons detected with the SE-SPS and electrons detected with ICESPICE were observed. The presented results show that ICESPICE is a promising ancillary detector system for in-beam internal conversion electron spectroscopy at the FSU SE-SPS.

The manuscript considers Scanning Transmission Electron Microscopy (STEM) images and derives transformations needed to correct various distortions occurring during scanning. These transformations form the basis for the correction algorithms implemented in the CEOS Panta Rhei and TEMDM software. The manuscript is intended as a technical reference and is meant to be published only on arXiv rather than in peer-reviewed journals.

TokaMind is a multi-modal transformer (MMT) foundation model pre-trained on tokamak plasma diagnostics data from MAST, where it was shown to outperform CNN-based approaches on fusion benchmarks. We investigate whether its learned representations generalize to physically distinct but structurally analogous domains. Through systematic experimentation across four domains-industrial bearing degradation, NASA CMAPSS turbofan degradation, and two independent power grid PMU datasets-we identify four transfer-favoring characteristics that help explain where TokaMind's pretrained representations are most effective. Power grid synchrophasor data matches this target-domain profile most directly, while industrial degradation datasets demonstrate that TokaMind can still yield useful performance under partial alignment, especially when task design and feature construction expose physically meaningful degradation structure. On the GESL/PNNL 500-event benchmark with provider-aware evaluation, TokaMind achieves test $\text{F1} = 0.837 \pm 0.040$ (3~seeds) for severe event classification. Our central finding, however, is not the aggregate score: classification difficulty is structurally determined by provider-level grid topology, not model capacity. In the single-window early-warning regime, TokaMind outperforms a CNN baseline (F1~0.889 vs.~0.878)--a reversal that disappears as more event windows are provided. Furthermore, Critical Slowing Down (CSD) indicators, used as a confidence gate rather than a classification label, improve F1 from 0.696 to 0.750 at 63% coverage-outperforming the CNN baseline (0.636) at any coverage level. These results establish the first cross-domain validation of TokaMind outside nuclear fusion and propose a transferability framework and revised evaluation protocol for multi-source PMU datasets.

We present results from the Big Mysteries Survey, a large-scale survey conducted through the American Physical Society's Physics Magazine on foundational and controversial topics in contemporary physics. The survey provides a snapshot of physicists' views on issues in cosmology, black-hole physics, quantum mechanics, quantum gravity, and anthropic coincidences. A central finding is that several positions often described publicly as field-wide ``consensus'' views are, in practice, supported by much narrower majorities or by pluralities rather than majorities.

Self-supervised learning (SSL) has emerged as a promising approach to seismic data denoising as it does not require clean reference data. In this work, the deployment of the Noisy-as-Clean (NaC) method was evaluated for real seismic data denoising under controlled conditions. Two independent seismic acquisitions, each comprising noisy and filtered data, were organized into four real datasets. The NaC SSL method was adapted to add real noise to the noisy input, controlled by a parameter. An experimental protocol with ten experiments was designed to compare different strategies for deploying the NaC SSL method with the supervised learning baseline, using identical network topology and hyperparameters. The models were evaluated in terms of denoising performance, computational cost, and generalization capability. The results show that the synthetic additive white Gaussian noise (AWGN) is inadequate for the denoising of seismic data within the NaC method, and performance strongly depends on the compatibility between the injected and actual noise characteristics. Furthermore, both the characteristics of the seismic data and the noise level influence the performance of the model. Self-supervised fine-tuning on test data has improved SSL performance, whereas no such gain was observed for fine-tuning of supervised models. Finally, NaC has shown to be a simple, effective, and model-independent method that offers a feasible solution for the denoising of real seismic data.

Electron-only reconnection (EREC) is a magnetic reconnection regime occurring within subion-scale current sheets (CSs), exhibiting only electron jets, without any ion outflows. EREC has been first observed in the Earth's magnetosheath, where its occurrence is linked to the small correlation length of magnetic fluctuations, limiting the growth of CSs to very large scales. On the other hand, the development of EREC in open systems with large magnetic correlation lengths, such as the solar wind (SW), remains an open question. To address this problem, we employ a large-scale 2D hybrid simulation with finite electron inertia, investigating the development of EREC driven by turbulence. By injecting energy at very large scales, we allow EREC to develop spontaneously due to the turbulent cascade, without any external small-scale forcing or imposed constraints on the turbulence correlation length. We find that EREC develops in our simulation via two distinct turbulence-driven mechanisms: (1) secondary EREC induced by the interaction of plasmoids in the outflows of large-scale ion-coupled reconnection; (2) EREC directly driven at subion scales by the electron Kelvin-Helmholtz instability in small-scale velocity shears. Furthermore, we perform a statistical analysis of CSs using the machine-learning clustering algorithm HDBSCAN, showing that subion-scale CSs capable of hosting EREC are dominant in our simulation. Our results suggest that EREC could occur even in large-scale space and astrophysical systems, like the SW, driven by secondary turbulent processes, potentially playing a key role in dissipating energy at kinetic scales.

Nonlinear optical processes in low dimensional materials are often weak or symmetry forbidden, limiting their use in nanoscale light sources and on chip frequency conversion. Here, we show that combining two weakly nonlinear systems, anisotropic borophene and excitonic zinc oxide, yields an enhanced and resonant nonlinear response. In borophene ZnO heterostructures, cathodoluminescence reveals a two orders of magnitude enhancement at 400 nm and 800 nm, due to an enhanced two photon absorption process. Under tunable near infrared excitation, a clear second harmonic signal emerges with quadratic power dependence and strong resonance near 800 nm. We attribute this to nonlinear plasmon exciton coupling, which reshapes the excitonic response and enables efficient hybrid pathways for frequency conversion. These results establish anisotropic plasmon exciton hybridization as a route to controlling nonlinear optical responses in low dimensional heterostructures.

Iron disproportionation reactions in mantle silicates can produce metallic iron that drives Earth's deep mantle toward metal saturation under reduced conditions. Subducting slabs transport hydrated silicates to these depths, where interactions with metallic iron can reduce structurally bound hydrogen in silicates to reduced hydrogen-bearing phases, such as molecular hydrogen or iron hydrides, leaving mantle rocks in effect dry. Using the thermodynamic code HeFESTo with its latest self-consistent treatment of iron-bearing mantle phases, we investigate the stability and distribution of metallic iron in Earth's pyrolitic mantle across a broad range of oxidation states, represented by whole-rock Fe3+/$\Sigma$Fe ratio from 1% to 10%. We find that metallic iron is present through much of the lower mantle across this range and, under very reduced compositions of whole-rock Fe3+/$\Sigma$Fe = 1-3%, extends into the upper mantle. Where subducted water meets metal-saturated regions, hydrous melts may form and migrate upward, rehydrating the overlying mantle or pooling near the transition zone. Metal saturation can thus redistribute hydrogen internally, creating a sharp contrast between a wet shallow mantle and a dry deep mantle. This redox-driven redistribution can decrease mantle silicate water storage capacity by 64-96% today, to only 0.1-0.8 modern ocean masses, and may explain the viscosity contrast near the upper-lower mantle boundary. Although quantitative estimates of metal abundance and distribution depend on thermodynamic assumptions and remain uncertain above 50 GPa, our results reveal the role of redox reactions between disproportionated iron and subducted water in governing the speciation and redistribution of hydrogen in Earth's mantle.

Two-body reduced density matrices (2RDMs) encode the essential two-electron physics of electronic states, but their quartic storage cost poses a major limitation in practical workflows. We investigate a simple protocol to compress both transition and non-transition 2RDMs into a lower-rank representation that preserves their wedge-product structure and physical symmetries under truncation. The resulting decomposition couples Coulomb and exchange channels through a common set of low-rank factors, yielding a more compact rank-sparse representation than single-channel factorizations. For correlated states, the effective rank scales linearly with system size, achieving a $\sim99$\% compression for the coupled-cluster 2RDM of octane while retaining chemical accuracy. We apply this to the recently introduced {\em ab initio} eigenvector continuation workflows, where many-body wave functions are interpolated across nuclear geometries with mean-field cost. Here, 2RDMs between training states act as projectors into a subspace but their memory scaling limits applications to larger systems. The compression scheme reduces the memory cost from quartic to quadratic for a fixed error per electron. Metrics to systematically control the decomposition are investigated, enabling statistically resolved structural, dynamical and spectroscopic observables from nonadiabatic molecular dynamics simulations of photoexcited H$_{28}$ chains, interpolating from compressed near-exact DMRG training data. This establishes these structure-preserving compressed intermediates for practical correlated electronic structure workflows.

In this work, we develop a size extensive Auxiliary-Field Quantum Monte Carlo (AFQMC) approach that scales as $O(N^5)$ for local energy evaluation by treating the Coupled Cluster Singles and Doubles (CCSD) trial wavefunctions perturbatively. Comprehensive numerical examinations, spanning from main-group molecules to $3d$ transition metal complexes, demonstrate that this perturbative treatment introduces negligible bias. For small systems, our method achieves an accuracy and level of noise comparable to AFQMC with configuration interaction singles and doubles (CISD) trial wavefunctions while outperforming CCSD(T). This size extensivity offers a decisive advantage for large systems, as suggested by the ground state energies of non-interacting monomers and one-dimensional atomic chains. Finally, the numerical simulations of the uniform electron gas (UEG) provide evidence that, unlike the CCSD(T) method, our new approach does not suffer from infrared divergence in the thermodynamic limit (TDL).

Reconstructing scalar fields from error-embedded gradient measurements is a fundamental linear inverse problem with broad applications in computational physics. Conventional approaches, such as Poisson-based solvers and the Green's Function Integration (GFI) method, require explicit boundary conditions extracted from the same error-embedded observations. In this study we assess the accuracy of a Gaussian Process Regression (GPR) framework for reconstructing pressure fields in turbulent flows from error-embedded pressure-gradient data derived from kinematic measurements. The probabilistic nature of GPR inherently provides tunable denoising, eliminates the need for boundary conditions, and produces a pointwise posterior-variance error estimate. A central theoretical result of the present work is that GFI is the noiseless limit of GPR, which on the unbounded plane reduces to the well-known logarithmic kernel and in three dimensions to the inverse-distance kernel. The framework is validated on two-dimensional slices and three-dimensional subdomains of a forced homogeneous isotropic turbulence from the Johns Hopkins Turbulence Database. With an empirical mixture-of-Gaussians (MoG-$3$) kernel fitted directly to the pressure correlation function, GPR performs at least as well as GFI. In situations with under-resolved data or high noise, GPR outperforms GFI, while delivering a calibrated pointwise posterior uncertainty whose standardized residuals satisfy $|z|<2$ over $95\%$ of grid points. The framework extends to three dimensions through a tensor-product Kronecker solver coupled to conjugate gradients with close to $\mathcal{O}(N^3\log N)$ cost. A closed-form error lower bound on a periodic cube is derived for the GPR operator, with the residual gap attributable to boundary contamination on non-periodic finite domains.

This article analyzes the feasibility of having a second synchrotron in Latin America, to be located, in principle, in a city within the Greater Caribbean region but open to all the continent. It is shown that an initiative of this sort is compatible with the economies of the region and would require a marginal increase of the current regional investment in science, which is broadly below that of other regions of the world, with peaks of low financing precisely in the Greater Caribbean. The project is not only feasible, but, beyond its purely scientific interest. it would have an impact for the development of cities in the region. The article is mainly focused to analyze this impact from the social, economic, and political point of view. It is shown that the return of the investment would have its break-even point long before the end of the expected lifetime of the infrastructure, and that through a system of smaller accelerators, that would be part of the same project, the benefit would not concentrate on the country hosting the facility. These smaller facilities could contribute to the national development as possible nuclei of cities of knowledge, project which belongs to the priority of some countries/cities of the region.

Dynamic alignment in magnetohydrodynamic (MHD) turbulence is usually interpreted as a cascade-wide tendency of Elsasser increments to become increasingly collinear at smaller scales. We argue instead that the standard measurements mainly detect a conditional survival effect of intense events. In high-resolution Johns Hopkins MHD simulations, the typical folded Elsasser-increment angle remains only modestly below the random-orientation baseline and shows no evidence for a rigid, monotone, volume-filling ordering of the cascade. Much smaller angles appear primarily in the strongest Elsasser-amplitude events, while conditioning on current density leaves the angle close to its unweighted behavior. Shuffled-null tests show that this reduction is caused by a genuine negative covariance between event amplitude and angular misalignment, not by weighting alone. Cross-scale angular correlations are measurable but decaying, indicating partial and non-rigid persistence of the local alignment field. A finite-time state-retention test directly supports the proposed mechanism: high-amplitude large-angle events leave their amplitude--angle sector faster than high-amplitude small-angle events, while incoming transitions continually replenish the large-angle sector. NASA Wind solar-wind observations show the same angle--amplitude hierarchy and negative covariance in Taylor-sampled Elsasser increments. These results indicate that dynamic alignment, as measured by conventional weighted diagnostics, is best understood as selective sampling of longer-lived intense small-angle events, not as a cascade-wide alignment of typical MHD fluctuations.

Conductivity models for warm dense matter inform simulations of planetary structure and fusion experiments. State-of-the-art conductivity calculations based on density functional theory approximate many-body physics and neglect electron-electron scattering lifetimes. We introduce a many-body framework for electrical conductivity using the GW approximation of the electronic self-energy. For beryllium, improved transition energies yield a surprisingly large reduction in low-temperature DC conductivity, while electron-electron scattering primarily reduces high-temperature DC conductivity.

The proposition that band geometry alone can protect optical states against disorder has proven not merely theoretically elegant but experimentally incontrovertible. A key attribute of photonic topological systems is their capacity to simultaneously possess high-intensity excitations at multiple distinct frequencies that are confined to the same topological interface. However, exploiting this freedom to protect the interaction between at least two topological states has remained an open experimental challenge. Here, we report an interaction between two topological states, with one being precisely frequency-doubled to the other, supported in a hybrid plasmonic and photonic topological insulator via nonlinear phase matching. We find that the phase matching inherits a unique spin-momentum locking unseen in conventional nonlinear systems. This ability to bring two topological states into phase-matched nonlinear interaction at a single interface sets the stage for a new class of doubly protected nonlinear photonic devices, potentially finding implications in generating entangled photon pairs with enhanced resilience and robustness for secure quantum information technology.

We have designed optical metasurfaces hosting high-quality factor quasi-bound state in the continuum (q-BIC) resonances for optical biosensing in flow. The unit cell of the metasurface contains two rectangular bars. An asymmetry factor is introduced by varying the gap width between the bars, to enable optical coupling to a q-BIC resonance confined to the air gap between neighboring nanoresonators. The location of the resonances makes them highly sensitive to changes in the local refractive index, leading to experimental bulk refractive index sensitivities exceeding 315 +/- 22 nm/RIU and a figure-of-merit of 66 +/- 5 RIU-1. Successful streptavidin-biotin binding was observed by measuring the metasurface transmission in real-time by exposing the metasurface to various concentrations of analytes via a commercial microfluidic flow cell apparatus. The experimental limit of detection, defined as 3{\sigma} above noise, was found to be 1.8x10-8 M. This platform represents a compact optical approach for point-of-care diagnostics with fast read-out.

Controlling optical chirality at the subwavelength scales is essential for many applications of nanophotonic structures in polarization optics, sensing, and nonlinear photonics. Achieving a strong chiroptical response in planar dielectric metasurfaces without intrinsically chiral building blocks (or "meta-atoms") remains challenging. The recent theoretical study [ACS Photonics 12, 6717 (2025)] predicted that bilayer metasurfaces with rotated C$_4$-symmetric apertures can exhibit pronounced chiral response originating from resonant chiral photonic modes realizing maximum chirality under the mode strong coupling. That observation uncovers a novel mechanism of metasurface chirality. Here, we confirm experimentally this novel concept and demonstrate resonantly enhanced circular dichroism in the near-infrared frequency range. We fabricate a free-standing silicon membrane metasurface that is nominally achiral. When out-of-plane symmetry is broken by a thin PMMA layer, it unlocks and activates a strong chiral response. The observed circular dichroism is explained by the properties of chiral photonic modes, and it is governed by interlayer coupling and symmetry breaking, in agreement with theoretical predictions. These results establish bilayer metasurfaces as a simple and versatile platform for engineering strong mode-induced chirality in compact planar photonic metadevices.

Ultrastrong magnetic fields, ranging from 100~T to 1,000~T, are generated exclusively by destructive pulsed magnets. While various generation methods exist, this review focuses on the Single-Turn Coil (STC) and Electromagnetic Flux Compression (EMFC) techniques, which provide optimal environments for high-precision measurements in materials science. First, we present recent technological breakthroughs in the EMFC method that have successfully achieved fields exceeding 1,000~T. We then describe specialized measurement infrastructures for magneto-optics, magnetization, and magneto-transport, highlighting the development of miniaturized all-plastic cryostats and custom sample holders designed for the dual extremes of cryogenic temperatures and megagauss fields. Representative physical phenomena revealed through these techniques are discussed, including quantum phase transitions in frustrated magnets, Aharonov--Bohm effects in carbon nanotubes, and semiconductor-to-metal transitions in strongly correlated systems. Furthermore, we address emerging measurement platforms such as magnetostriction, specific heat, and ultrasound velocity. Throughout this review, we emphasize the instrumentation and experimental refinements that ensure reliable data acquisition in the ultrastrong pulsed field regime.

Over the past two decades, the rapid surge in data-intensive computational techniques for statistical modeling may have had the effect of diminishing the use of applied mathematics in causal scientific inquiry. In this paper, co-authored by an astrophysicist, a mathematician, and philosophers, we assess the hazards of neglecting the branch of mathematics that constructs models to address causal questions in favor of statistical modeling alone. Causality is relevant in all branches of science and is often elucidated through applied mathematics. Here, we illuminate the idea with examples drawn from space physics and medical sciences. We examine causal questions to demonstrate how applied mathematical and statistical methods may differentiate between two fundamental facets of causality, i.e., mechanistic and difference-making. Understanding such foundational differences in causality may, in some cases, help explain discrepant or erroneous research results. Most importantly, understanding the relationship between causality and analytical approaches used in science has the potential to strengthen the rigor and reliability of scientific inquiry through optimal selection of mathematical and/or statistical methods.

The evaluation of thermoelectric conversion efficiency remains challenging owing to the lack of internationally standardized measurement protocols. Commonly used techniques -- including the heat flow, guarded heater, and AC Harman methods -- differ fundamentally in their operating principles and sensitivity to heat losses. In this study, we benchmark three module-level efficiency measurement techniques -- the heat-flow, guarded heater, and AC Harman methods -- using commercial Bi$_2$Te$_3$-based modules with different substrates materials. The conversion efficiencies obtained using the heat flow and guarded heater methods showed good agreement within experimental uncertainty for temperature differences up to 70 K. In contrast, the AC Harman method underestimated the conversion efficiency by approximately 30 %. Through systematic measurements on modules with different substrates and detailed finite element simulations, this underestimation was attributed to boundary-condition effects and radiative heat dissipation, which significantly reduce the effective temperature difference developed across the module in the Harman configuration. These results highlight the limitations of the AC Harman method for quantitative conversion-efficiency evaluation under non-ideal thermal environments and emphasize the necessity of accounting for radiative and substrate-related heat losses. Nevertheless, with appropriate modeling and correction, strategies, the AC Harman method remains a viable tool for rapid performance screening. Our results provide a quantitative benchmark of major measurement techniques and contribute to clarify best practices for module-level thermoelectric metrology and guide method selection, fully supporting future efforts toward methodological standardization.

An improved neural refractive-index-primitive method for background-oriented schlieren tomography is presented, enabling continuous three-dimensional reconstruction of refractive-index fields using a compact multilayer perceptron. The method adopts the refractive-index field as the sole neural primitive and integrates multiresolution hash encoding, automatic-discrete gradient losses, and a three-dimensional mask to enable fast convergence and high-resolution, spatially coherent reconstructions. Tests on numerical combustion phantoms and real flame data demonstrate accurate recovery of both large-scale structures and fine-scale turbulence, strong robustness to noise, and clear advantages over frequency-encoding-based and voxel-based reconstruction methods.

We present an efficient implementation of a one-step relativistic second-order multireference perturbation theory based on the multireference driven similarity renormalization group (MR-DSRG) using the exact two-component (X2C) Hamiltonian, which we denote X2C-DSRG-MRPT2. We show that the X2C-DSRG-MRPT2 method can accurately capture spin--orbit coupling (SOC) effects in the electronic structure of strongly correlated systems containing elements across the periodic table. We further demonstrate that the X2C-DSRG-MRPT2 method, through its variational treatment of SOC effects, can yield spin--orbit splittings with mean absolute percentage errors consistently below 7% with respect to experimental values for systems containing up to sixth row elements. With its modest computational scaling (fifth power in system size) and high accuracy, X2C-DSRG-MRPT2 provides a promising avenue for the routine treatment of relativistic effects in strongly correlated molecular systems.

The Ince-Gaussian modes form a complete set of solutions to the paraxial wave equation parametrized by an ellipticity parameter {\epsilon}, enabling a continuous transition between Laguerre-Gaussian and Hermite-Gaussian modes While each fixed {\epsilon} defines an orthogonal basis, modes associated with different ellipticities are not mutually orthogonal, and no explicit transformation between such bases has been reported. Here, we derive the first explicit finite analytical expression to transformation between Ince-Gaussian bases of arbitrary ellipticity, enabling direct and experimentally accessible mapping between non-orthogonal structured-light representations. We further demonstrate an experimental implementation using spatial light modulators to perform ellipticity-resolved modal decomposition. This framework introduces ellipticity as a controllable degree of freedom for structured light engineering, enabling new strategiesfor mode conversion, encoding, and high-dimensional optical information processing.

Physics-based climate projections using general circulation models are essential for assessing future risks, but their coarse resolution limits regional decision-making. Statistical downscaling can efficiently add detail, yet many methods treat variables independently, degrading inter-variable relationships that govern compound hazards such as heat stress, drought, and wildfire. Here we show that a diffusion-based multivariate generative framework, combined with bias correction, recovers degraded inter-variable correlations even under a 50$\times$ increase in linear resolution. When applied to five meteorological variables over Japan, the framework reduces inter-variable correlation errors by more than fourfold relative to existing baselines while improving both univariate and spatial accuracy, leading to more accurate detection of severe drought. These results demonstrate that multivariate generative downscaling improves the reliability of compound risk assessment under large resolution gaps.

Laser wakefield accelerators (LWFAs) are attractive compact drivers for free-electron lasers (FELs) because they can generate femtosecond electron beams with high peak current over centimeter-scale acceleration distances. However, their relatively large energy spread remains a major obstacle to high-gain FEL operation. Although bunch energy compression can reduce the slice energy spread to a level suitable for FEL amplification, it also introduces a strong energy chirp. The energy chirp detunes the FEL resonance along the planar undulator, causing phase slippage between the electrons and the radiation field, reduced bunching efficiency, and degraded radiation power and spectral quality. Here we investigate a longitudinally tapered undulator for compensating the chirp-induced resonance mismatch in a self-amplified spontaneous-emission (SASE) FEL driven by an energy-compressed LWFA beam. Using three-dimensional unaveraged simulations, we show that an optimized taper profile restores electron-radiation phase synchronization and significantly improves both the saturation power and the spectral properties relative to the untapered case. We also assess the sensitivity of the scheme to shot-to-shot beam-energy fluctuations characteristic of LWFA operation. Our results show that undulator tapering is an effective method for mitigating chirp-induced performance degradation in compact plasma-based FELs.

Perceptions of disciplinary recognition and identity can be shaped by various forms of feedback and experiences. Here we focus on the potential effects of course grades on the perceievd recognition and physics identity of students. We analyze patterns in changes in physics identity and perceived recognition from pre course to post course across three cohorts of university students enrolled in calculus-based Physics 1 (N=1,681). Students not receiving A grade, on average, showed declines in physics identity and perceived recognition. Even a B grade resulted in declines, and the declines were nonlinear across lower grades. Changes in perceived recognition fully mediated the changes in identity. Importantly, women showed significantly larger declines in identity and perceived recognition, compared to men, if they got less than A grade. The gender moderation was specifically localized to changes in perceived recognition, with no further gender effects on identity beyond the cascading effects on perceived recognition.

Hyper-Kamiokande (HK) is the world's largest water Cherenkov ring-imaging detector, planning to start data taking in 2028. The Outer Detector (OD) surrounds the Inner Detector and plays a critical role in rejecting background events entering from outside, particularly cosmic-ray muons. We report on the selection of $8\,\mathrm{cm}$ diameter photomultiplier tubes (PMTs) for the OD, comparing Hamamatsu R14374 and NNVT N2031 candidates, and present the evaluation of cosmic-ray muon background reduction performance using a full detector simulation. Hamamatsu PMTs were adopted for their superior in-water detection efficiency in deep-UV and stability. The cosmic-ray muon reduction inefficiency reaches $O(10^{-6})$ with OD-based cuts alone, and $O(10^{-9})$ is expected when combined with fiducial volume cuts, which is sufficiently negligible for nucleon decay and atmospheric neutrino analyses.

Developing reliable methods for certifying the dimension of a given quantum system or process is essential to ensure the validity of claimed realization of high-dimensional (HD) quantum advantages. The existing criteria for certifying genuine HD quantum teleportation (HDQT) mainly focus on demonstrating the successful transmission of genuine HD quantum states. However, a complete certification of HDQT must also identify the entanglement dimension of resource, which is critical for verifying whether the transmission capacity and noise resilience meet the necessary thresholds. Here we propose two universal criteria (based on fidelity and robustness, respectively) for certifying genuine HDQT behaviors that can close this gap by fully identifying the dimension of the entanglement. Both criteria require only the input and output teleportation data and remain feasible under partial Bell-state measurements. Furthermore, the robustness-based criterion has stronger noise resistance and it requires no prior assumptions about local operations, making it robust even in black-box scenario. Our results establish a universal and reliable theoretical framework for validating the core quantum advantage in HDQT, pivotal for ensuring the reliable links in HD quantum networks.

Cementitious materials are widely used for hazardous-waste encapsulation, yet the molecular mechanisms governing heavy-metal ion retention across different gel chemistries remain insufficiently resolved. Here, classical molecular dynamics simulations were employed to investigate the adsorption-controlled mobility of representative heavy-metal ions (Pb2+, Ba2+, and Cs+) within nanopores of C-S-H, C-(N)-A-S-H, and N-A-S-H gels. By combining pore-averaged diffusivity, spatially resolved diffusivity and residence-time analysis, ion-density profiles, two-dimensional adsorption maps, radial distribution functions, coordination analysis, and interfacial binding-strength descriptors, this study establishes a comparative atomistic framework linking gel surface chemistry to ion mobility suppression under nanoconfinement. Ion mobility is substantially reduced in all gel nanopores relative to bulk solutions, but the extent and mechanism of suppression vary strongly with gel chemistry. C-(N)-A-S-H with higher Al/Si ratios exhibits the strongest retention, driven by ion accumulation around Al-linked oxygen species via an ion-exchange-like mechanism with charge-balancing Na+. C-S-H immobilizes ions primarily through surface hydroxyl oxygens and Ca-mediated linkages, whereas N-A-S-H exhibits more distributed binding environments. Pb2+ and Ba2+ exhibit broadly similar immobilization mechanisms, whereas Cs+ shows more distinct, gel-dependent interactions with silicate and aluminosilicate oxygen sites. A relative total binding strength (rTBS) descriptor is introduced, showing a strong positive correlation with the extent of ion immobilization across gel types, ion species, and pore sizes examined. These results clarify gel-specific and ion-specific mechanisms controlling heavy-metal retention in idealized cementitious nanopores.

X-ray computed tomography (CT) reveals the materials' internal structures non-destructively from a tilt series of projected images. Filtered back projection (FBP) is a widely-adopted reconstruction algorithm in CT owing to its small computational cost. Under low-dose or sparse-view conditions, however, FBP often amplifies noise, severely degrading the reconstructed images. In this study, we evaluated the performance of a Bayesian CT reconstruction algorithm based on the Markov random field model under such adverse conditions. Through simulations, we demonstrated that the proposed algorithm shows higher reconstruction performance than FBP under both low-dose and sparse-view conditions. The hyperparameters are estimated by minimizing the Bayesian free energy, enabling adaptive reconstruction that reflects the noise characteristics of the observed projection data. These results suggest that the proposed algorithm can broaden the applicability of CT to dose-sensitive applications and time-constrained measurements, where only limited observed projection data are available.

Data assimilation (DA) integrates numerical model forecasts with observations to achieve the optimal state estimation. Ensemble-based methods, such as the ensemble Kalman filter (EnKF), are widely used for state estimation for high-dimensional and nonlinear dynamic systems. However, their performance strongly depends on the ensemble size, therefore causing a tradeoff problem between analysis accuracy and computational cost. To address this problem, this study presents a machine learning-based EnKF framework that maintains high accuracy with a relatively small ensemble size. Specifically, a multilayer perceptron (MLP) function is built to predict the difference between the forecast error covariances estimated from a limited ensemble and a sufficiently large ensemble, with the latter being assumed to be an accurate approximation of the underlying truth. This predicted covariance difference term is then incorporated into the EnKF algorithm via an element-wise scaling strategy, resulting in an amended forecast covariance matrix that better approximates the true uncertainty level and sequentially produces more accurate analysis results. To demonstrate the feasibility and robustness of the proposed algorithm, we perform a set of numerical experiments with the Lorenz-63 and Lorenz-96 systems under various configurations, and the results consistently indicate that the proposed algorithm can significantly outperform the standard EnKF with the same limited ensemble size, by achieving notably higher analysis accuracy while remaining computationally efficient. This approach provides a practical and feasible pathway to accurate and computationally efficient data assimilation for high-dimensional and nonlinear dynamic systems.

Silicon nanospheres are high-quality optical resonators and promising building blocks for Mie-tronic devices. While the Mie resonances of an isolated sphere are well understood, practical implementations require substrates that inevitably modify the measured optical response. Here, we investigate how substrates alter the observable spectrum of individual nanospheres, focusing on three fundamentally different cases: a thin silicon nitride membrane, that emulates a free-standing particle, bulk silicon, which is common in experiments, and gold, where mirror charges lead to hybrid optical modes. Cathodoluminescence and dark-field spectroscopy, combined with electrodynamic simulations, show that the measured resonances are not intrinsic to the particle but depend strongly on the environment and the excitation mechanism. We find that substrate-induced effects and probe-specific selection rules can suppress, enhance, or even invert the spectral signatures of electric and magnetic modes. These results provide practical guidelines for interpreting and designing substrate-supported dielectric resonators for Mie-tronic applications.

We study a triplet majority-rule opinion-dynamics model with collective reversal on quenched networks. Interactions occur on local triplets composed of one agent and two of its neighbors, while collective reversal acts only on unanimous triplets. This rule separates local conformity from external perturbations that disrupt local agreement. We show that quenched network topology shifts the order--disorder critical point away from the well-mixed value. For Barabási--Albert, Erdős--Rényi, and random regular networks, the critical point is shifted while the critical exponents remain close to the mean-field values. By contrast, Watts--Strogatz networks exhibit a much lower critical point and stronger deviations in the effective critical exponents, highlighting the role of clustering and local correlations. A rewiring analysis of Watts--Strogatz networks further shows that the ordered phase becomes more stable as the network becomes more random. These results indicate that quenched topology not only sets the transition point, but also leads to topology-dependent effective critical behavior in networks with strong clustering and local correlations.

Bound states in the continuum (BICs) arise from destructive interference suppressing radiation despite spectral overlap with the continuum. Here we show that Friedrich--Wintgen interference naturally emerges from a bright--dark supermode decomposition of resonances coupled through a shared radiation channel. In this basis, any finite leakage of a quasi-BIC induces a causality-driven reactive coupling enabling non-Hermitian pumping of the dark sector. We derive the optimal condition for this process and show that it corresponds to the supercritical coupling regime previously identified in [Nature 626, 765 (2024)], while naturally recovering universal quasi-BIC asymmetry scaling. Extending the theory to Dirac-like dispersions in photonic crystal slabs, we identify an open-Dirac singularity where the Dirac gap matches the supercritical regime. A four-wave Hamiltonian quantitatively reproduces rigorous coupled-wave analysis, revealing the breakdown of conventional critical coupling. Near this regime, absorptive cross-coupling induces coherent absorption interference and enables suppression of effective dissipative losses beyond conventional material limits. These results motivate the concept of a generalized bound state in the continuum (gBIC) as a limiting non-Hermitian state where radiative and effective gain compensate, producing a true divergence of the total quality factor. Overall, this work establishes a unified framework connecting BIC interference, Dirac topology, and non-Hermitian physics for ultra-high-Q enhancement and loss engineering in open photonic systems.

Existing models for droplet impact prescribe the spreading contact time and effective spreading velocity from asymptotic arguments, which prevents a self-consistent prediction of the maximum spreading ratio across regimes. Here, the total spreading time and characteristic spreading velocity are derived directly from the energy balance, with explicit capillary and viscous contributions. Multiplying this time and velocity to obtain the maximum spreading diameter yields a closed, unified scaling law for the maximum spreading ratio of wetting drops across inertio-capillary and inertio-viscous regimes. The resulting expression quantitatively collapses the present measurements and literature data over wide ranges of Weber and Ohnesorge numbers, droplet sizes, and surface wettabilities without prefactors that need to be adjusted to a certain regime.

Raman spectroscopy enables broadband, multi-species gas analysis by providing access to an entire vibrational spectrum in a single measurement. However, the sensitivity of gas-phase Raman sensing is often limited by weak signals and fluorescence background from various optical elements that constrain the achievable signal-to-noise ratio (SNR) through signal-dependent noise contributions (e.g. shot noise). Here, we present a cavity-enhanced Raman spectroscopy (CERS) gas sensor employing a 500 mW, 532 nm continuous wave (CW) laser and a simple, non-resonant two-mirror multi-pass cavity (MPC) operated at ambient pressure and near the concentric condition, providing up to 45 internal reflections. To quantitatively capture the impact of fluorescence on performance, a CCD-specific noise model was developed that links fluorescenceinduced baseline levels to measurement noise. Complementary optical simulations were employed to assess the signal collection efficiency in the MPC. Through a systematic analysis of fluorescence sources, the background was reduced substantially by step-wise elimination of fluorescent optics. The fluorescence-minimized setup resolves weak Raman signatures in ambient-air spectra, including CO2 peaks, O2 and N2 overtones, and ambient CH4 (2 ppm). Calibration measurements for O2 (diluted in N2), N2 (in O2) and H2 (in N2) demonstrate detection limits of 11 ppm, 5 ppm and 3 ppm, respectively, with a 180 s measurement time. The results highlight fluorescence mitigation as a key design lever for robust, field-oriented CERS instrumentation for trace gas sensing.

The accurate recovery of constituent-level optical properties from integrating sphere measurements is a central analytical challenge in pharmaceutical analysis, food science, and biomedical diagnostics. Neural network autoencoders can extract spectrally resolved absorption and scattering coefficients for each constituent without prior knowledge, but their fully connected encoders bind learned features to absolute wavelength indices, causing accuracy loss under spectrometer calibration drift or hardware exchange. This work introduces the Bin Latent Transformer (BiLT)-Autoencoder, in which the dense encoder is replaced by a cross-attention scanner: 16 learnable probe vectors query a convolutional feature map, aggregating morphological spectral information independently of absolute wavelength position. A physics-constrained linear decoder with enforced absorption/scattering separation and a three-phase curriculum augmentation strategy complete the architecture. On a liquid phantom benchmark (intralipid and two ink absorbers; 496 samples), the model achieves $R^2 = 0.979$ and $0.975$ for $\mu_a(\lambda)$ and $\mu_s'(\lambda)$, respectively, on held-out test spectra, maintaining $R^2 > 0.90$ for $\mu_a$ and $R^2 \approx 0.99$ for $\mu_s'$ across the full tested shift range of $\pm 10$ spectral bands. The model generalises to a simulated spectrometer with a broader instrument line shape (${\approx}24$nm FWHM) without retraining, retaining $R^2 \approx 0.96$ and $0.974$ for the two channels. Attention map analysis reveals a physically interpretable two-component probe strategy: sparse anchor probes at absorption-edge wavelengths combined with a diffuse, SNR-driven ensemble at the high-transmittance long-wavelength region, which recruits additional probes dynamically under noise to provide implicit spectral averaging.

Geometrical imperfections in quadrupole mass filters introduce higher-order field components that can significantly influence device performance, particularly under non-sinusoidal excitation. In this work, a comprehensive simulation study is carried out to investigate the effect of geometrical imperfections on the performance of a rectangular wave driven quadrupole mass filter operating in the first stability zone. Radial field distortions arising from controlled variations in rod geometry and position, including single rod radius variation, single rod displacement, diagonal rod radius variation, and diagonal rod displacement, are examined. These imperfections introduce octupole field components that distort the ideal quadrupolar field distribution. The influence of such distortions on key performance parameters, namely mass resolution and ion transmission efficiency, is systematically evaluated. The results show that the presence of radial asymmetry leads to a degradation of both resolution and transmission efficiency in all cases considered. Furthermore, the study reveals a strong dependence of mass filter performance on the initial state of the applied pulsed waveform, specifically whether the asymmetric rod pair is subjected to the high or low level of the RF pulse. These findings provide important insights into the tolerance limits of geometrical imperfections and their impact on the performance of pulsed wave driven quadrupole mass filters, which are relevant for the design and optimization of high-resolution digital mass filtering systems.

Large eddy simulation has been widely used to simulate turbulence at balanced computational cost and accuracy. Many Subgrid-Scale (SGS) models have been proposed over the years, where data-driven and machine learning-aided approaches set the recent trend. To address the problem of extrapolation in these models, we propose a new data-driven SGS model based on an information-theoretic picture of turbulence. To this end, we estimate the model parameters by maximizing mutual information, which correspond to the scale-by-scale local equilibrium hypothesis in developed turbulence or "information preservation." An a priori test confirmed that the estimated parameters are in good agreement with the previously reported empirical values. Furthermore, a posteriori tests on periodic box turbulence and channel turbulence exhibited accuracy comparable to the existing models. These results suggest the utility of the information-theoretic picture of turbulence for constructing more generic SGS models without the need for empirically prescribed model parameters, while enhancing physical interpretability beyond black-box approaches.

Qualification of components operating in future fusion power plants will be heavily reliant on simulations of component behaviour. The lack of representative test environments for many aspects of the expected operating environment will necessitate full or partial virtual qualification of components. The cornerstone of virtual qualification is credible validation of the simulation models on which it relies. In this work, we present a probabilistic model validation framework that forms the basis for implementation of virtual qualification in fusion. We demonstrate our framework on a representative component; a high heat flux heat sink subject to a tightly coupled multi-physics loading. We perform data-rich, optimised experiments, in which we implement high fidelity diagnostics and rigorously quantify aleatoric and epistemic uncertainty on all measurements. Our simulation approach efficiently samples input uncertainty distributions to predict probability boxes describing component response, using a statistical surrogate to replicate behaviour of the finite element model we wish to validate. We then used a novel implementation of the modified area validation metric to quantify the model form error of the finite element model, isolating it from the aleatoric and epistemic experimental uncertainty. We discuss the contribution of our validation approach towards virtual qualification, and the benefits of the risk-based decision-making it facilitates. The experimental, simulation, and validation datasets are published as a benchmark of a probabilistic validation approach for fusion, and for use in development of new model validation methodologies.

This work develops advanced numerical methods for free-surface simulations of polymer mixing processes, integrating a Volume of Fluid (VOF) interface-capturing approach with a non-conforming Immersed Boundary (IB) method to model two-phase flows of highly viscous polymer melts and air within partially filled rotating mixing devices, implemented within the Finite Volume OpenFOAM library. To overcome severe numerical instabilities arising from the strong viscosity contrast between polymer melts and air, a block-coupled scheme providing fully implicit viscous diffusion treatment is integrated into the VOF-IB framework, relaxing time-step stability constraints and substantially reducing computational cost with respect to standard segregated solvers. The resulting BC-VOF-IB solver is applied to industrially relevant geometries of single- and twin-screw extruders, yielding physically consistent predictions of velocity and pressure fields under partial filling conditions. While further developments, most notably the inclusion of thermal effects, remain necessary, the proposed framework represents a meaningful step toward bridging academic CFD research and the practical demands of industrial polymer processing.

Reconfiguring the spectral output of nonlinear metasurfaces after fabrication remains challenging. We address this by exploiting the nonlinear Pancharatnam--Berry phase of $C_{3v}$-symmetric plasmonic metasurfaces. By integrating two metasurfaces inside a multipass cell, we experimentally demonstrate continuous spectral tuning of second-harmonic generation (SHG) phase-matching peaks across a 900--970 nm pump range by rotating one metasurface relative to the other. The extracted geometric phase follows the $3\sigma\theta$ dependence, and a full $2\pi$ tuning cycle is completed with $120^{\circ}$ of physical rotation. This establishes geometric-phase metasurfaces as a reconfigurable nonlinear platform, where mechanical rotation enables post-fabrication and broadband tuning of nonlinear optical responses.

Air entrainment can occur when a water jet impacts a water/air interface, a process central in various real systems, ranging from dam spills to breaking waves. Despite its prevalence, a comprehensive description of the mechanism controlling bubble size distribution remains elusive. Here, we establish a link between the geometry and the dynamics of the cavity observed when an inclined impinging jet impacts a water interface and the resulting bubble cloud. We show that the bubbles result from the destabilization of the wavefield developing at the interface of the cavity. The origin of this wave field is the creation of a shear layer, due to the asymmetric detachment of the flow field from the interface.

The distortion of light's degrees of freedom when passing through complex random media is of great interest across a diversity of fields, e.g., scattering in biological studies. Emulating such media in a controlled laboratory setting conventionally relies on real-world physical samples (e.g., white paint), inhomogeneous mixtures with embedded scatterers, or biological tissue-mimicking phantoms. Such methods, while effective in certain contexts, are not without complexity and limitations: the exact medium properties are challenging to control and often require laborious preparation, external characterisation techniques, are not easily reproducible between studies and cannot be matched precisely by numerical simulations. Here, we propose a simple all-digital implementation of random scattering which can be readily implemented on any setup capable of producing digital holograms. Our approach employs binary random phase masks encoded onto a spatial light modulator which perturbs the input beam's phase and amplitude. We highlight two methods to precisely tune distortion strengths which show excellent agreement between simulated and measured results. We demonstrate distortion strengths comparable to real-world scattering samples and illustrate two example applications to emulate scattering of scalar and vectorial structured light. Finally we showcase the versatility of this toolkit for emulating various amplitude and phase profiles and suggest several easy to implement alternative modalities accessible with this method. This digital phantom circumvents many of the practical challenges of physical samples, making it ideally suited for applications at the intersection of structured light, biological imaging and optical communications.

Ammonia has strong poisoning effects on cathode catalyst layers of proton exchange membrane (PEM) fuel cells, but the poisoning mechanism is still unclear. In this study, all-atom molecular dynamics simulations are employed to investigate the poisoning mechanisms of ammonia. The results show that ammonium can replace the hydronium ions at the charged sites of sulfonic acid group of the ionomer side chain, and the adsorption of ammonium to sulfonic acid group can be attributed to van der Waals force and electrostatic interaction. Furthermore, other ammonia derivatives, amino and imino ions, can capture hydronium ions to form ion clusters. These ion clusters have strong capability to absorb hydronium ions, and their structures change with ammonia content and temperature. The main mechanism of formation of these clusters is due to the formation of relatively stable hydrogen bonds between ions within the clusters. These mechanisms significantly reduce the efficiency of proton transport, thereby decreasing the catalyst layer's performance in electrochemical reactions. We also discover that the increase in temperature leads to the dissociation of large ion clusters, the blockage in the ionomer layer can be alleviated, and the proton transport efficiency can be restored. The understanding of the poisoning mechanisms obtained in this study is helpful for subsequent research aimed at resolving ammonia poisoning and enhancing the anti-poisoning performance of catalyst layers.

The RD50-MPW4, a Depleted Monolithic Active Pixel Sensor (DMAPS) was analyzed using a Two Photon Absortion Transient Current Technique (TPA-TCT). This technique provides sensitivity maps with micrometer-scale spatial resolution, enabling the resolution of the boundaries of the detector's sensitive volume, even for small-area pixels (62x62 squared micrometers in this study). With a full 3D resolution, the depletion depth, the boundaries of the detector electric field, the 3D hit detection efficiency and the charge sharing between neighboring pixels were measured. The RD50-MPW4, a multi-project wafer chip developed by the HV-CMOS working group within the CERN RD50 collaboration, features a 64x64 DMAPS pixel matrix. Illuminating the chip from the backside, the TPA-TCT technique can characterize any pixel element in the matrix because silicon is transparent for near infrared laser light (1550 nm). Electron-hole pairs are generated only around the light focal point, deep in the silicon, so that any charge collected is precisely only from the focal point. With the TPA-TCT technique, the RD50-MPW4 was found to be have a 100\% charge collection efficiency and a depletion depth of 226 $\upmu$m. It was also found that part of the charge in the periphery of the pixel was collected in the neighboring pixel. A 3D map of the sensor clearly shows the in-pixel electronics and the limits of the depletion region.

Today's cutting-edge optical spectroscopic exploratory tools, such as Raman or infrared spectroscopy, rely on methods of signal enhancement as a route for their development. These methods are indispensable for substance identification and characterization in almost any scientific, regulatory, or industrial laboratory, therefore new and better methods of enhancement are always sought after. In this paper, the design of a new optical device for enhancement is presented, called nano-engineered microsphere (NMS). This device innovatively combines plasmons, a present flagship enhancement method, with a photonic nanojet, a new and emerging enhancement tool, to provide unprecedented properties in terms of affordability, stability, and performance. By using numerical simulations, a detailed design of the device is presented, and the optimization of device parameters for the strongest enhancement is investigated. The simulations show different influences of the parameters on the enhancement, from low to critical. The most influential parameter was found to be the radius of the nanoelement tip, which, at low values, showed a tremendous increase in the enhancement. The optimized device shows exceptionally promising abilities regarding the enhancement, while the estimated cost of production and use is low. Such properties paired with low price and ease of usage could enable the NMS to become one of the leading methods of enhancement in Raman and infrared spectroscopy with the spatial resolution towards nanometers.

Modern Earth System Models (ESMs) operate on horizontal scales far larger than typical cloud features, requiring stochastic subcolumn generators to represent subgrid horizontal and vertical cloud variability. Traditional physically-based generators often rely on analytical cloud overlap paradigms, such as exponential-random decorrelation, which can struggle to capture the complex, anti-correlated behavior of non-contiguous cloud layers. In this study, we introduce a novel two-stage machine learning subcolumn generator for the GEOS atmospheric model, utilizing a Conditional Variational Autoencoder combined with a Generative Adversarial Network (CVAE-GAN) and a U-Net architecture. Trained on a merged CloudSat-CALIPSO height-resolved cloud optical depth dataset, the ML generator creates 56 stochastic subcolumns representing cloud occurrence and optical depth profiles. Evaluated against the established Räisänen, the ML approach accurately reproduces bimodal cloud overlap distributions, significantly reduces biases in grid-mean statistics, and halves the root-mean-square error in ISCCP-style cloud-top pressure and optical thickness joint histograms. The improvements brought by our deep generative models translate into more accurate offline radiative transfer calculations, reducing the global-mean shortwave top-of-atmosphere cloud radiative effect bias by a factor of three. Provided that the generator can be accelerated on CPUs, this offers a practical pathway to reduce structural errors at the cloud-radiation interface.

Free-surface synthetic Schlieren (FS-SS) is a high-resolution, refraction-based optical technique for measuring the instantaneous elevation of a liquid interface. Under the assumptions of small amplitude, small slope, and small paraxial angle, the method yields a linear relationship between the gradient of the surface elevation and the apparent displacement field of a refracted pattern imaged through the surface. Here, we propose three new, nonlinear extensions of the FS-SS method that are specifically dedicated to telecentric imaging. Paraxial distortions are eliminated with a telecentric lens, thereby simplifying the optical model. This allows us to derive nonlinear surface reconstruction models that reach beyond the usual limits of small slope and small wave-magnitudes. We implement these nonlinear surface reconstruction algorithms and compare them to the original, linear reconstruction algorithm in three different experiments, using a solid glass lens, spreading oil drops and nonlinear Faraday waves. At the price of a few iterations, we can realise nonlinear surface reconstructions that are more precise, in particular when we reach high slopes or high amplitude regimes. We share a library that encodes these nonlinear surface reconstruction algorithms.

This study investigates active flow control (AFC) of a 30P30N high-lift wing at a Reynolds number Re$_c$ = 450,000 and angle of attack $\alpha$ = 23$^\circ$ using wallresolved large-eddy simulations (LES). Two optimization strategies are explored: open-loop Bayesian optimization (BO) and closed-loop deep reinforcement learning (DRL), both targeting the mitigation of stall and the improvement of aerodynamic efficiency via synthetic jets on the slat, main, and flap elements. The uncontrolled configuration was validated against literature data, confirming the reliability of the LES setup. The BO framework successfully identified steady jet velocities that increased efficiency by +10.9% through a -9.7% drag reduction while maintaining lift. In contrast, the DRL agent, despite leveraging instantaneous flow information from distributed sensors, achieved only minor improvements in lift and drag, with negligible efficiency gain. Training analysis indicated that the penalty-dominated reward constrained exploration. These results highlight the need for carefully designed rewards and computational acceleration strategies in DRL-based flow control at high Reynolds numbers.

We introduce two-quantum (2Q) fluorescence-detected pump-probe (F-PP) spectroscopy as a tool to probe ultrafast multiparticle interactions in many-body systems. We describe a pulse-shaper-based fully collinear setup utilizing phase cycling to capture the 2Q F-PP signal simultaneously with the one-quantum (1Q) F-PP signal. Thus, we investigate the dynamics of energy transfer and diffusion-limited annihilation. We apply a data post-processing strategy to isolate excited-state dynamics from spurious background. The technique is applied to a squaraine heterodimer and a squaraine copolymer to demonstrate the removal of so-called incoherent mixing that generally plagues action-detected nonlinear spectroscopy on multichromophoric systems. Specifically, we show that this approach is not only applicable to 1Q but also to 2Q F-PP signals, eliminating incoherent mixing contributions as well as other "parasitic" signals that result from pulse-overlap ambiguities. As a result, we retrieve background-free spectral and dynamical information of doubly excited electronic states.

Recent X-ray imaging experiments have revealed that multiphase flow through porous media involves transient fluctuations in local occupancy, even under fixed macroscopic steady-state conditions where capillary forces dominate at the pore scale. To examine how intermittency manifests at the pore scale we perform direct numerical finite volume simulations (DNS) of immiscible two-phase flow through a micro-CT-derived Bentheimer sandstone geometry at capillary numbers in the Darcy and intermittent flow regimes. We show that intermittent disconnection and reconnection are accompanied by strongly coupled local pressure redistribution and non-wetting phase flow. This behaviour contrasts with the Darcy flow regime, in which the phases remain predominantly in fixed pathways. Macroscopically the computed pressure-gradient-capillary-number relationship ($\nabla P$-Ca) recovers both the linear Darcy and the sub-linear intermittent scaling regimes consistent with previous experimental measurements. We show how an increase in intermittency leads to the transition from the linear to the sub-linear regime. Using topology-aware snap-off detection, we show that the spatial extent of intermittency increases with capillary number. Spectral, local-geometry, and network-connectivity analyses provide further evidence that the intermittent elements organise into connected conduits embedded within a stable backbone of fixed flow pathways: intermittency is a network-coupled rather than purely local process. This work characterises the pore-scale manifestation of intermittency as a periodic sequence of drainage and imbibition displacements triggered by local pressure fluctuations whose macroscopic consequence is to improve the overall mobility of the fluid phases.

The CN Van de Graaff accelerator of INFN--LNL provides forward-angle quasi-monoenergetic neutrons in the 0.7--4 MeV range via the 7Li(p,n)7Be reaction on thin metallic lithium targets. This work describes the development and experimental validation of this forward neutron capability, combining comparisons of commonly used transport tools with time-of-flight (ToF) measurements. Neutron yields calculated with EPEN, FLUKA, MCNPX, and PINO are compared over the CN energy range in order to assess model-dependent variations relevant for fluence estimates. For zero incident-energy spread, a mutually consistent set of transport calculations agrees within 5% and is used as a practical reference for normalisation. The effect of incident-energy convolution on the predicted yields is examined. Time-of-flight measurements performed using a sub-nanosecond secondary pulsing system verify the timing structure and forward-angle kinematics of the quasi-monoenergetic neutron component at the detector position, with neutron arrival times consistent with the expected forward kinematics within the experimental resolution. Using measured proton currents and transport calculations based on this reference set, forward neutron fluences at the device position are estimated with an overall uncertainty of approximately 9%, including contributions from current integration, target thickness, and geometry. A short device irradiation, carried out in parallel with the ToF campaign, demonstrates measurable response under CN beam conditions and confirms the practical usability of the beam for low-MeV neutron studies. Together, these results establish the current operational performance of the CN 0° forward quasi-monoenergetic neutron capability in the 0.7--4 MeV range and identify the steps required toward routine calibrated operation.

High-granularity calorimeters make ML-based fast shower simulation a high-dimensional generative modeling problem, where voxel-space generators must balance physics fidelity with training and inference cost. This work studies large-patch tokenization with x-prediction, enabling efficient raw voxel generation. We propose CaloArt, a modernized DiT-style backbone with 3D positional encoding and architectural refinements, trained via conditional flow matching with decoupled prediction and loss spaces. On CaloChallenge Dataset 2, where small patch size remains affordable, v-prediction performs well, and CaloArt achieves the best FPD, strongest high-level metrics, and strongest ResNet classifier metrics. On CaloChallenge Dataset 3, the 40500-voxel grid makes large patches necessary; x-prediction improves all reported metrics over v-prediction and places CaloArt on the quality-generation-time Pareto frontier. The final CCD2 and CCD3 models both retain O(10) ms single-GPU generation time, with 9.71 and 11.14 ms per shower. These results support large-patch voxel-space diffusion transformers with x-prediction as a compute-efficient route to high-granularity calorimeter shower synthesis, reducing training and inference cost without a pretrained latent tokenizer.

Preserving scalar boundedness is important for numerical schemes used in turbulent compressible multi-component flow simulations to prevent unphysical results and unstable simulations. However, ensuring scalar boundedness for high-order, low-dissipation numerical schemes poses challenges in highly under-resolved conditions due to inherent dispersion errors that generate spurious oscillations. Numerical dissipation is needed to mitigate these oscillations, but excessive dissipation negatively affects resolution. In this work, we propose formulations for high-order finite-difference schemes to preserve scalar boundedness without predefined bounds, while maintaining high accuracy and low numerical dissipation. The proposed formulations augment a non-dissipative numerical flux of a high-order central-difference scheme with an explicit dissipative numerical flux that adaptively switches between high-order and low-order formulations. Building on a deliberate choice of the non-dissipative flux, we construct two schemes using Jameson's artificial viscosity method and a monotonicity-preserving limiter as the dissipative flux. We examine the schemes in one-dimensional scalar advection problems and a three-dimensional temporal turbulent mixing-layer case involving sharp scalar gradients and under-resolved conditions, evaluating their accuracy, boundedness of species mass fractions, and numerical diffusivity. The scheme with the monotonicity-preserving limiter demonstrates superior performance.

It has recently been demonstrated that magnetically induced (MI) transitions, a class of transitions forbidden at zero magnetic field, of the Cs 6$^2$S$_{1/2} \rightarrow 6^2$P$_{3/2}$ (D$_2$) line, exhibit promising features for high-resolution physics applications in the near-infrared range. In this work, we study a group of seven MI transitions ($F_g = 3 \rightarrow F_e = 5$) of the Cs $6^2$S$_{1/2} \rightarrow 7^2$P$_{3/2}$ line at $\lambda = 456$ nm. The experimental measurements are in very good agreement with theoretical predictions based on the diagonalization of the Zeeman Hamiltonian. In magnetic fields ranging from $0.2-3$ kG, these transitions reach a maximum intensity above that of conventional transitions. Another noteworthy property is their large frequency shift, reaching approximately $17~\mathrm{GHz}$ with respect to the unperturbed hyperfine transitions in magnetic fields of about $3~\mathrm{kG}$. These interesting properties may prove useful for the realization of optical frequency references or magnetometers with sub-micron spatial resolution in the blue region of the spectrum.

Empirical confirmation of the environmental-dominance inequality Var(ln rho_eff) >> Var(ln k) from arXiv:2605.02985, computed directly from three public datasets (Opportunity Atlas, World Bank GDP per capita PPP, World Inequality Database) at four levels of aggregation: U.S. census tracts, between countries, within-country deciles, and the global pooled-individual distribution. The headline global value Var(ln rho_eff) = 4.33 yields a dominance ratio R in [27, 134] across plausible sigma_ln k in [0.18, 0.40]. The inequality holds with one-to-two orders of magnitude margin at the global and within-country-decile levels, with a single-digit but still dominant margin between countries, and collapses to R in [0.33, 1.61] within already-homogenized U.S. census tracts for income. A 1990-2022 time series shows the global aggregate stable while composition shifts from between-country dispersion (-34%) to within-country dispersion (+26%), consistent with international convergence plus Piketty r > g. Multi-outcome validation shows the inequality is robust for income, infant mortality and incarceration but shrinks toward parity for outcomes targeted by sustained global convergence (life expectancy). Partial-identification and selection-bias bounds (Chetty-style 40-50% selection share) leave R in [14, 80]. All inputs and outputs are SHA-256 hashed in an append-only manifest and fully reproducible from the accompanying notebooks.

The OH$^+$ ($X ^3\Sigma^-$) radical cation has been investigated by combining a 4 K 22-pole ion trap apparatus with high-resolution IR and THz radiation sources. Applying different types of action spectroscopic methods, the fundamental vibrational band in the 3 $\mu$m range and the spin manifold of the $N=1 \leftarrow 0$ rotational transition around 1 THz have been extended and refined. Additionally, the spin manifold of the $N=2 \leftarrow 1$ rotational transition, scattered around 2 THz, has been measured for the first time with microwave accuracy. Although all hyperfine components of the pure rotational transitions are affected by considerable Zeeman splittings, a simulation of their contours allowed us to extract the field-free center frequencies with high accuracy. A global fit combining rovibrational and pure rotational transitions from the literature with those newly obtained in this work was performed, leading to improvements in the spectroscopic constants of OH$^+$, particularly those in the ground vibrational state.

This is the six month progress report to Fusion Energy Science (FES) and the American Science Cloud (AmSC) on the MPEX AI Digtial Twins project that was started in October 2025. There are two milestones to demonstrate the Artificial Intelligence (AI) advantage for MPEX operations and scientific discovery, that will be completed by June 2026. The first is a Helicon AI Hot-Spot Controller (Sec. 3.1), which is the helicon heating component of the more comprehensive planned MPEX AI Hot Spot Digital Twin (Sec. 3). The second is an E-beam Damage Assessment Digital Twin (Sec. 4.1), which is a reduced electron beam damage modality prototype for the MPEX AI Damage Assessment Digital Twin (Sec. 4). These two phase I milestones are on track for the June demonstration. In addition to these two milestones, progress on configuring the Galaxy software interface for automation, validation and data analysis is reported (Sec. 5). This interface now connects a subset of the main physics simulation codes to DOE HPC resources and will connect to the MPEX data acquisition system so that analysis of data, validation and execution of simulations can be performed by the scientist or by AI-Agents. When AmSC is ready to accept connections and data, Galaxy will be the MPEX interface to AmSC

Recent developments in high peak-power table-top laser systems reaching highly relativistic light intensities have led to significant advances in laser-driven particle acceleration schemes (mainly the laser wakefield acceleration, LWFA) that heavily rely on particle-in-cell (PIC) simulations for the microscopic understanding of the acceleration process. Efficient algorithms have been developed by taking advantage of the cylindrical geometry of the laser-plasma acceleration interaction, which reduces the computational and memory costs of these simulations, but with the trade-off of reduced accuracy compared to the 3D simulations. The most successful solution solves the Maxwell equations on a Fourier-Bessel spectral basis in this geometry, as used by the well-known FBPIC code. In this work, we present a solution that is a real-space equivalent of the latter using the finite difference exponential time-domain method. Spatially, we represent the derivatives with high-order staggered finite differences locally and address issues of the near-axis particle representation. Additionally, we also develop an exponential solution to propagate the laser envelope potential with high accuracy in the cylindrically symmetric PIC model. We show that this method provides a very high accuracy without relying on a transformation to special basis functions. We verified the accuracy and the convergence of these methods in various benchmarks involving laser propagation in vacuum and in underdense plasma. Electron injection in the non-linear laser wakefield regime has also been simulated and the results are compared with 3D simulations, and to the cylindrical spectral solution of FBPIC. We found good agreement between these methods; however, the spectral solution resulted in less energetic electrons and a smoother spatial distribution near the cylindrical axis.

In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the machine learning model. Once the source distribution has been learned, the model is independent of the original particle list, allowing for further sampling in an efficient, rapid, and memory-costless manner. The performance of various generative models is evaluated, including a variational autoencoder, a normalizing flow, a generative adversarial network, and a denoising diffusion model. These approaches are then compared to existing source distribution estimations, and the advantages and disadvantages of each approach are discussed. The results demonstrate that source distributions can be modeled through the use of probabilistic generative models, which paves the way for further advancements in this field.

This work employs structured input-output analysis (SIOA) to study Waleffe flow. The SIOA framework employs structured uncertainty to include the componentwise structure of nonlinearity in Navier-Stokes equations, and SIOA quantifies the flow response using structured singular values. The structured input-output analysis identifies the wavelength and inclination angle of oblique turbulent bands observed in large-domain direct numerical simulations. The structured input-output response scales over Reynolds number as $\sim Re^{1.7}$.

The planar to Dewar valence isomerisation of 4a,8a-azaboranaphthalene (BN$_\text{Naph}$), a $\pi$ extended BN-doped analogue of azaborine, is investigated to evaluate how BN incorporation reshapes the minimum energy pathway on the ground state. This process is, for example, relevant in the context of molecular solar thermal (MOST) energy storage, where absorbed sunlight is converted into chemical energy through reversible photoisomerisation. Structures and vertical excitations were computed using DFT and TD-DFT, minimum energy pathways were mapped with nudged elastic band (NEB) calculations, and pathway energetics were refined with state averaged XMS-CASPT2. In addition, azaborine was examined as a comparison system, with particular emphasis on whether substituents at nitrogen and boron promote Dewar formation. The effect of BN doping on the system was analysed in detail. Compared with the carbon analogue, the conversion pathway becomes asymmetric with a metastable intermediate stabilized by a transient boron to carbon contact. The transition structure closely resembles an S$_0$/S$_1$ conical intersection, which is consistent with a vibrationally activated nonradiative funnel. For tuning MOST properties, screening of single substituents across the whole molecule reveals predominantly red shifted S$_1$ energies together with increased oscillator strengths and indicates that appropriate substitution can improve Dewar formation in azaborine derivatives.

We present an analytical emission model that accurately predicts the properties of effusive sources formed by long collimation tubes. By construction, it captures the full range of molecular flow, from the transparent flux regime, which occurs in highly rarefied gases, to the opaque regime, which arises as the flux increases and interparticle collisions become non-negligible. The model is based on a previously developed secondary-emission-surface approach, improved here to overcome its internal limitations and recover the well-established axial flux intensity. It provides accurate analytical predictions of the angular intensity distribution in the molecular flow regime, offering valuable guidance for the design of efficient primary sources across a broad range of experiments in atomic and molecular physics

A localised overpressure translating at a uniform speed greater than a critical value acts at the interface between two deep fluid layers with different densities. We analyse the resulting wave patterns using an initial-value problem formulation within the linearised, inviscid, potential flow framework. The steady-state interface exhibits short capillary waves ahead of the forcing and long gravity waves behind it, arising from an asymmetric cancellation of Fourier components in the far field. The time-dependent part of the solution, decaying algebraically with time, plays a crucial role in this mechanism. This contrasts with classical steady approaches, which require additional conditions to select a unique solution. We extend this approach to a two-fluid interface and validate the predictions against nonlinear simulations.

We present the first part of an efficient framework for nonlinear beam dynamics, termed Approximate Invariant Analysis (AIA). The framework is based on the construction of approximate invariants~[Y.~Li, D.~Xu, and Y.~Hao, Phys.\ Rev.\ Accel.\ Beams \textbf{28}, 074001 (2025)] and on the extraction of the betatron frequency with the geometric foundations of Poincaré rotation number~[S.~Nagaitsev and T.~Zolkin, Phys.\ Rev.\ Accel.\ Beams \textbf{23}, 054001 (2020)]. The method is demonstrated using the National Synchrotron Light Source~II (NSLS-II) storage ring as an illustrative example.

Computational fluid dynamics (CFD)-driven machine learning frameworks based on symbolic regression offer a promising pathway for turbulence model discovery, but are often hindered by numerical instability, residual stagnation, and non-physical model behavior during training. In particular, realizability, which is rarely enforced explicitly during model development, remains a critical yet overlooked requirement, especially for accurate wake prediction. In this work, a residual- and realizability-filtered CFD-driven framework is proposed to enhance both efficiency and robustness within a gene expression programming (GEP) paradigm. The method integrates two residual-based filtering criteria along with a barycentric-map-based realizability constraint directly into the CFD solution loop, enabling early identification and rejection of unstable and non-realizable candidate models. This reduces unnecessary computational effort while guiding the search toward physically admissible solutions. The proposed approach achieves a 42.3% reduction in computational cost relative to the baseline CFD-driven GEP framework and reduces non-realizable models at convergence from 58.4% to 1.7%. The framework is trained on a canonical cylinder wake. The resulting models enhance mean wake prediction and remain realizable across training and test cases, with robust generalization to diverse geometries and operating conditions, including a rectangular cylinder, an airfoil, and an axisymmetric body. The study further provides insights into realizable model statistics, coefficient trends, and conditions governing physically consistent wake behavior. These results demonstrate that incorporating realizability and stability constraints within CFD-driven learning enables efficient and physically consistent turbulence model discovery, offering a scalable pathway toward reliable data-driven closure development.

Fundamental inorganic chemistry shows that increasing concentrations of atmospheric CO2 will have no harmful effect on organisms that live in the natural waters of the Earths, and may well benefit them. Alkalinity and dissolved CO2 give high buffering capacity to most natural waters and minimize the change of pH from external influences. For example, doubling the atmospheric concentration of CO2 from 430 ppm to 860 ppm would reduce the pH of representative sea water at a temperature of 25 C from pH = 8.18 to pH = 7.93. This change is comparable to diurnal pH changes in biologically productive surface waters, due to photosynthetic fixation of dissolved inorganic carbon during the day and respiration at night. The change is also less than the variations of pH with latitude, longitude and depth in the oceans. This paper includes a quantitative review of the carbonate chemistry of seawater and freshwater, the buffering capacity, the Revelle factor, the transport of calcium carbonate in ground water, the formation of flowstone, and the classic use of limewater to detect gaseous CO2. The paper concludes with a brief review of those parts of chemical thermodynamics that are involved in ocean acidification.

The natural and Dyson orbitals are studied for small helium drops comprising 5 to 20 helium atoms interacting via a soft two-body gaussian potential. The wave functions of these drops have been obtained in the hyperspherical cluster model (HCM) which provides a correct description of the single-particle behaviour at large separations from the system. The natural orbitals are obtained from diagonalization of the nonlocal one-body density matrix, while Dyson orbitals are constructed by direct overlap of the wave functions of two drops differing by one boson. This overlap converges with increasing basis of the HCM. The shapes and occupancies of the natural orbitals as well as their link to Dyson overlaps and evolution with increasing number of atoms are discussed. Both natural and Dyson orbitals can be used to represent the density of the system. However, the natural orbitals representation is demonstrated to be superior. With increasing boson numbers the difference between Dyson and natural orbitals becomes less prominent and it is expected to disappear in infinitely large systems of identical bosons.

A numerical validation of the stress-jump coupling conditions for Stokes-Darcy flow in two dimensions is presented, addressing a gap that has remained since their introduction by Angot et al.. These conditions, formulated for arbitrary flow directions at the interface between a porous medium and an adjacent free-flow region, involve a friction tensor whose coefficients are not known a priori. We calibrate these parameters for a range of porous-medium configurations and flow regimes by matching the macroscopic model to reference solutions derived from processed pore-scale simulations. Several optimization strategies are assessed for this calibration task. The results show that, although three parameters are formally required, exploiting structural properties of the porous medium enables an effective reduction to a one-dimensional calibration with negligible loss in accuracy. A regional sensitivity analysis further indicates that even coarse parameter estimates can yield a well-performing model, highlighting the robustness and practical applicability of the stress-jump formulation.

Dyadic Green's function is an important tool of computational photonics, giving deeper insights into light-matter interaction. We present an operator approach to the derivation of the dyadic Green's function of a generic anisotropic planarly-layered medium for both electric and magnetic fields. The resulting Green's function is expressed through the evolution operators (a kind of transfer matrices) of the comprising layers and the surface impedance tensors, the singular term being naturally separated from other terms. The operator approach to the Green's function simplifies both the conceptual understanding of the problem and the subsequent practical applications, some of which are demonstrated here. The proposed approach can be easily generalized to the case of spherical and cylindrical layers. The obtained results can be applied in nanophotonics engineering problems.

To address the increasing demand for ultra-high-capacity wireless communication, terahertz (THz) frequencies near 300 GHz are attracting attention as a new spectral frontier. This work presents the first experimental demonstration of error-free (BER $< 1\times10^{-9}$) 10 Gbps transmission in the 300 GHz band using a soliton microcomb generated in an integrated silicon nitride (SiN) microring resonator. While many previous microcomb-based THz demonstrations have focused on coherent modulation formats and operation near the forward-error-correction (FEC) limit, this work investigates a simple intensity-modulation/direct-detection (IM-DD) on-off keying (OOK) architecture suitable for low-complexity THz links and fiber-wireless integrated systems. Although the experiment was conducted in a short back-to-back waveguide configuration, the generated THz wave enabled stable low-BER transmission without FEC or advanced offline signal processing. Analysis of the error-free threshold power indicates the feasibility of free-space transmission over several tens of meters with high-gain antennas and THz-band amplifiers. These results demonstrate the feasibility of robust low-complexity THz photonic links based on soliton microcombs for short-range fiber-wireless integrated systems.

Magnetic interatomic potentials need to account for coupled lattice and spin degrees of freedom, yet constructing reliable training sets remains costly because noncollinear first-principles labels are expensive. Active learning can mitigate this cost, provided that the uncertainty estimate is physically meaningful for the magnetic-response targets that drive spin reorientation. Here we extend the $\mathrm{e}^2\mathrm{IP}$ evidential framework to magnetic machine-learning interatomic potentials by formulating the projected spin-force likelihood and the corresponding epistemic uncertainty in the tangent plane orthogonal to the local spin direction. This construction prevents the uncertainty model from allocating probability mass to a radial spin component that is absent from the constrained-moment supervision. Using bulk BiFeO$_3$ and monolayer CrTe$_2$ as benchmark systems, we show that the resulting tangent-plane epistemic uncertainty indicator $U_{\mathrm{epi}}^{\mathrm{sf}}$ correlates strongly with prediction error and selects more informative configurations than random sampling, simultaneously improving energy, force, and projected spin-force accuracy. These results demonstrate a physically interpretable and data-efficient route for constructing uncertainty-aware magnetic machine-learning interatomic potentials.

High throughput screening was used to study of the biokinetics of F. venenatum A3/5 cultivation on alternative carbon substrates, including monosaccharides, disaccharides and mixtures relevant to food & beverage, dairy and agricultural waste streams. Expired functional drink from the beverage sector was also assessed as the primary carbon source for mycoprotein production. Growth data was analysed using modified single and multiphase Gompertz models for comparison of maximum specific growth rate and progression milestones across diverse growth regimes. Time-series substrate and byproduct data was analysed using comparative metrics, providing an explanatory basis for the different growth phenotypes observed. Substrate type strongly influenced the apparent carbon allocation strategies, with rapidly consumed sugars such as glucose and sucrose supporting high growth rates, low biomass yield and a high degree of fermentative byproduct formation. Fructose and xylose cultivations led to slower overall growth but higher biomass yield and lower byproduct formation. Galactose and lactose showed distinct dynamics that suggested co-existence of transport and metabolic induction limitations. In all dual-substrate systems, sequential utilisation was observed. However, metabolic inheritance and environmental shift effects were highlighted as potential kinetic limitations. These conditions exhibited stunted diauxic growth and low yield from secondary sugars, with glucose-dominated primary growth significantly reshaping secondary substrate efficiencies relative to their study in silo. The expired functional drink supported highly rapid growth and achieved the highest maximum specific growth rate and biomass titre of all conditions examined, alongside reduced fermentative overflow and enhanced ethanol reassimilation relative to a compositionally matched synthetic control.

Axions and axion-like particles are among the most promising candidates for dark matter and for manifestations of new physics beyond the Standard Model. In the present work, the contribution of axion exchange to the energy of lithium-like ions is investigated within the framework of relativistic bound-state quantum electrodynamics. A formalism for the interelectronic interaction mediated by axion exchange is developed in the Furry picture with finite nuclear size taken into account. Energy shifts are calculated for a wide range of nuclear charge numbers \(Z\) and axion masses. The magnitude of the axion-induced contribution is shown to increase with increasing \(Z\) for all states considered. Based on the analysis of lithium-like bismuth, constraints on the axion-electron interaction parameters are obtained in the high-mass region. The results indicate that precision spectroscopy of highly charged ions is a promising tool for searches for new physics associated with the exchange of pseudoscalar bosons.

Topography at the core-mantle boundary (CMB) couples the outer core to the mantle and likely generates observable variations in the length of day ($\Delta$LOD) and the geomagnetic field, though these effects remain poorly understood. We use direct numerical simulations of rotating shell convection with finite-amplitude CMB topography to investigate dynamical effects on the outer core. A range of topographic shapes is used, including individual spherical harmonics and a model representing seismically inferred heterogeneities in the deep mantle. As predicted by prior linear theory in the rotating annulus model, a new instability arises for Rayleigh numbers below the onset of convection; we confirm its existence in a global geometry, though the predicted scalings are quantitatively modified. The shape of the geostrophic contours -- lines of constant axial height -- plays a central role: deformed contours allow buoyancy to do work on the time-averaged flow, driving increases in Reynolds and Nusselt numbers of up to $\sim$100\% relative to a spherical boundary. Previous work showed that topographic torques scale linearly with topographic amplitude and quadratically with flow speeds; we confirm this scaling and extend it with new theory that estimates the torques for global, spectrally broad topography. When extrapolated to core conditions, the predicted torques are consistent with the magnitude required to drive observed decadal and subdecadal $\Delta$LOD variations.

Rod-like metamaterials are the structures that are obtained by periodically assembling its microstructural unit (network of rods) in just one direction. In this work, we present a scheme for obtaining the nonlinear constitutive response of such structures when homogenized macroscopically as a continuum rod. To capture accurately arbitrary and large deformation, the geometrically exact special Cosserat rod theory is used for modeling the rod at both micro and macro scales. By assuming the metamaterial structure to be strained uniformly (at macroscale) along its arc length, the full structure problem is reduced to just that of its microstructural unit but subjected to helically periodic boundary condition. The microscale problem, consisting of a network of rods and formulated in a variational setting, is solved in the presence of rod joint constraints and helically periodic boundary conditions. The expressions for the macroscale/homogenized rod's stress resultants (internal contact force and moment) and stiffnesses are then obtained. Finally, several numerical examples having different microstructural units/RVEs are presented to demonstrate our method. We start with simpler square and cross RVEs to validate our results with the existing literature. We then take up more complex RVEs such as square RVEs having helical constituent rods which have application as artificial muscle material and eventually we work on the homogenization of auxetic tubular metamaterials. We show how various design parameters of these RVEs can be tuned to obtain the desired macroscopic response.

We present a time-dependent pore-network model that couples transient mass transfer in the aqueous phase, capillary pressure heterogeneity, and realistic pore-throat geometries to capture the dynamic evolution of gas clusters during Ostwald ripening in porous media. The model is applied to Bentheimer sandstone to study Ostwald ripening after imbibition to residual gas saturation. Both imbibition (shrinkage) and drainage (growth) events occur as the local capillary pressure in trapped gas clusters approaches equilibrium. The model tracks event statistics, capillary pressure equilibration, cluster volume distributions, and spatial saturation profiles over 48 hours. While the volume-weighted average capillary pressure is constant, there is a rapid initial decline in average number-weighted cluster pressure and a shift in cluster size distributions toward fewer, larger ganglia, consistent with pore-scale imaging studies. Pore and throat occupancy analysis reveal persistent gas trapping in larger pore spaces. Since growth is by drainage, the pore-scale configuration of fluid is different from that predicted by an equilibrium percolation-without-trapping model that only allows imbibition events. The model reproduces displacement and ganglion rearrangement during time-limited laboratory experiments, and can then provide predictions of trapped saturation, relative permeability and capillary pressure under field-scale conditions with application to hydrogen, natural gas and carbon dioxide storage in the subsurface.

While magnetic fields and superconductors are both central to classical and quantum technologies, their combined use is often challenging, as magnetic fields significantly affect superconducting device performance. In superconducting nanowire single-photon detectors (SNSPDs), magnetic fields drastically reduce detection efficiencies, hampering their application in magnetically-active classical and quantum photonics. Here, we systematically characterize the performance of NbTiN SNSPDs under magnetic fields and show the enhancement of their intrinsic detection efficiency (IDE) at lower bias currents and its suppression at higher currents. This leads to SNSPD performance degradation through reduced or disappearing saturation plateaus. We show that the magnitude of this degradation is highly dependent on nanowire width and demonstrate width-optimized SNSPDs with saturating IDE for a wide range of photon energies under application-relevant magnetic fields. Minimizing degradation in superconducting devices under magnetic fields enables applications like detector-integrated spin-optic and atomic quantum processors, high-sensitivity magnetometry, and quantum transduction.

We present the Quantum Hamiltonian Analysis Toolkit (QHAT), a newly developed application that provides a user-friendly interface for studying Hamiltonians and performing Hamiltonian simulation on fault-tolerant quantum computers. QHAT enables the generation and analysis of Hamiltonians through a powerful and feature-rich application, driven by simple inputs designed to reflect user needs rather than algorithmic details, so that productive research on your application of interest can be done without needing a deep understanding of quantum computing algorithms. QHAT enables a streamlined workflow to analyze Hamiltonians and Hamiltonian simulation, supporting multiple choices of algorithms and analyses. It supports Hamiltonians from multiple sources but can also generate Hamiltonians based on a simple description of the system, saving intermediate data files for re-use when generating related Hamiltonians. Deriving the parameters for quantum computing algorithms can be a challenge, so QHAT is built around user-facing concepts such as maximum allowable error, rather than being built around algorithmic details such as steps counts or order parameters. An emphasis on user-friendly interfaces and efficient analysis means that the barrier to entry is low while rapidly providing results useful for a broad scope of studies.

Quadrupole insulators are a class of second-order topological insulators (SOTIs) that host zero-dimensional corner states within a two-dimensional bulk. Despite their unique properties, their realization in electronic systems on realistic material platforms remains rare. In this work, we present a general design principle to obtain quadrupole insulators based on two-dimensional graphitic structures. By engineering the positions and connections of zigzag edges, we identify four topological classes of graphitic structures. We show that topologically protected massless corner state emerge at the intersection of domains belonging to different topological classes. Crucially, by tuning the smoothness of the domain wall, we further demonstrate the appearance of additional massive localized states with non-zero angular momentum. Our results provide a practical framework for realizing experimentally accessible SOTIs and uncover the coexistence of both massless and massive bound states in two dimensions.

Regression to the Mean and Regression Dilution are often viewed as unrelated issues in the clinical and ecological literatures. In reality, they are different names for the same problem: measurement error in an independent variable that biases the perceived relationship between two factors. This study unifies these traditions by comparing specialized clinical tools, like the Berry correction, with standard structural estimators such as Major Axis and Reduced Major Axis regression. Using an analytical framework, we evaluate how these methods perform across various noise levels and sample sizes. Our results show that the Berry method is a specialized tool designed for clinical scenarios where a 1:1 relationship is expected. However, applying it to ecological trade-offs with negative slopes can lead to severe errors. We provide maps of optimality to identify which estimator most accurately recovers the true biological signal under different conditions. By reconciling these disparate methods, we offer a principled guide for researchers to choose the correct tool based on their data's noise profile rather than their disciplinary tradition.

Systems close to a phase transition turn weak perturbations into large responses. At equilibrium, this amplification is closely linked to criticality: fluctuations grow, dynamics slow, and a common soft mode controls the response. Whether this correspondence survives in driven-dissipative quantum systems, sustained by continuous pumping and loss away from thermal equilibrium, remains an open question. Here we show experimentally that it does. In a room-temperature semiconductor photon Bose-Einstein condensate, the critical slowing of spontaneous intensity fluctuations and the amplification of weak pump perturbations are measured independently. Both peak at the same condensate population, $\bar{n}_c = 1250$, where the dimensionless slowing factor and susceptibility reach the same value, $\bar{n}_c/2 = 625$. A single weakly damped collective photon-reservoir mode governs both effects. This fluctuation-response correspondence in a finite open quantum gas establishes critical susceptibility as a measurable dynamical signature of condensation, with peak gain set by system size.

We develop a theory that predicts the equilibrium states of a fluid contained in a capillary which has corners. Each section of the tube can take three states: completely wet state where the tube section is completely occupied by the fluid, partially wet state where only the corners are occupied by the fluid known as corner film or finger, and completely dry state. We calculate the phase diagram of these states for a square tube with rounded corners. It is shown that the partially wet state can exist only in a certain region in the parameter space spanned by the equilibrium contact angle and the corner curvature.

Scientific data processing often requires task-specific algorithms or AI models, creating a barrier for domain scientists who need to analyze their data but may not have extensive computing or image-processing expertise. This barrier is especially pronounced when data are noisy, have a high dynamic range, are sparsely labeled, or are only loosely specified. We introduce CVEvolve, an autonomous agentic harness with a zero-code interface for scientific data-processing algorithm discovery. CVEvolve combines a multi-round search strategy with tools for code execution, evaluation implementation, history management, holdout testing, and optional inspection of scientific data and visual outputs. The search alternates between discovery and improvement actions, and uses lineage-aware stochastic candidate sampling to balance exploration and exploitation. We demonstrate CVEvolve on x-ray fluorescence microscopy image registration, Bragg peak detection, and high-energy diffraction microscopy image segmentation. Across these tasks, CVEvolve discovers algorithms that improve over baseline methods, while holdout test tracking helps identify candidates that generalize better than later over-optimized alternatives. These results show that zero-code, autonomous LLM-powered algorithm development can help domain scientists turn unstructured scientific image data into practical algorithms and downstream scientific discoveries.

Identifying universal, topology-independent thresholds in the coronal magnetic fields at onset of solar eruptions is crucial for physics-based prediction of eruptions. To this end, we systematically analyze the evolution of magnetic energy and helicity in twelve high-fidelity 3D magnetohydrodynamic simulations where eruptions are triggered by magnetic reconnection. The simulations encompass a comprehensive parameter space, including bipolar and quadrupolar configurations, sheared arcades and pre-existing flux ropes, and various photospheric driving motions. We find that the ratio of current-carrying helicity to total relative helicity $(H_j/H_r)$ exhibits a remarkably consistent threshold of $0.38 \pm 0.04$ at eruption onset across all cases, with a coefficient of variation of only $\sim 10$\%. This threshold specifically characterizes the critical conditions at eruption onset and is largely independent of the subsequent temporal evolution, making it the most robust eruptivity indicator identified. In contrast, other normalized helicity and energy metrics show greater scatter. Crucially, we further find that $H_j/H_r$ does not necessarily achieve its peak at the eruption onset time and its post-eruption evolution diverges based on magnetic topology: it continues to increase in bipolar configurations due to tether-cutting reconnection, which transforms sheared arcade into the erupting current-carrying magnetic flux, but decreases in quadrupolar configurations as breakout reconnection peels off the erupting flux. These results highlight the helicity ratio as a promising and consistent eruptivity indicator and provide new insights into its dynamic evolution due to different reconnections.

The geometry of Bloch bands affects many physical properties of crystalline solids and other spatially periodic systems. Direct experimental determination of such geometry is an active area of research. In this work, we focus on the fundamental connection between optical excitations and the relative geometry of pairs of Bloch bands, as characterized by the interband Berry connection. We simulate the response of electrons in solids to optical excitation by the response of ultracold fermionic atoms in optical lattices to periodic modulation of the lattice position. The strength of resonant excitation between bands, measured at each quasimomentum and for various lattice-shaking polarizations, directly maps out the interband Berry connection. We apply this method to the optical honeycomb lattice, driving excitations between the ground $n=1$ band and the excited $n'=\{2,3,4\}$ bands. We observe transparency lines of quasimomenta at which the response to excitation of specific polarization is zero. Further, the interband Berry connection between bands 1 and 3 shows irreducible Dirac strings connecting the $K$ and $K'$ points in the Brillouin zone, lines along which the interband Berry connection abruptly changes orientation. Our work establishes optical response as a powerful tool for characterizing geometrical and topological properties of band structure.

The appearance of generative models has opened vast chemical spaces in the design of functional materials. Although machine learning interatomic potentials (MLIPs) have substantially accelerated phonon calculations, high-fidelity prediction of lattice thermal conductivity \k{appa}lat still requires accurate treatment of anharmonic interactions, which remains a key challenge for existing potentials across novel chemical spaces. To address this challenge, we present a comprehensive benchmark of 15 surrogate models for predicting \k{appa}lat using the Phonix database, which contains 6,966 entries with anharmonic phonon properties derived from first-principles calculations. Firstly, We categorize these surrogate models into three distinct groups: Physical-informed feature descriptors combined with ML models, end-to-end deep neural networks, and pre-trained MLIP-embeddings combined with ML models. By evaluating model performance across random, space-group disjoint (testing generalization to unseen crystal symmetries), and Out-Of-Distribution splits (OOD dataset that testing extrapolation to property regimes beyond the training range) based on \k{appa}lat, we probe both interpolation and exploration capabilities. Our results reveal that MLIP-embedded models excel in interpolation within well-sampled regions, deep neural network models especially ALiEGNN demonstrate superior robustness in OOD regimes critical for discovering novel low-\k{appa}lat. Additionally, we find a systematic degradation in performance when the structural representation is reduced. Although surrogate models exhibit lower accuracy than direct simulations using first-principles calculation, they reduce computational costs by orders of magnitude, enabling efficient high-throughput screening of thermoelectric materials with minimal loss in generative design workflows.

The rheology of phase-separated condensates plays a central role in applications spanning advanced materials design and cellular processes, yet quantitative characterization of their viscoelasticity remains challenging due to the limitations of existing microrheological methods that require tracer particles or mechanical contact. Here, we establish tracer-free and contactless acoustic microrheometry as a versatile platform for quantifying the frequency-dependent complex shear modulus of single microscale condensates over 0.01-10 Hz. Using spatiotemporally controlled acoustic radiation force generated within a micro-acoustic resonator, this method deforms condensates for creep-recovery and oscillatory viscoelastic measurements. Quantitative validation using dextran condensates in a polyethylene-glycol continuous phase successfully captures their size- and frequency-dependent mechanical responses, while application to nucleic-acid condensates reveals salt-dependent internal viscoelastic changes at single-condensate resolution. By enabling quantitative dissection of condensate mechanics without invasive probes, acoustic microrheometry provides a broadly applicable framework for investigating phase-separated condensates across materials science, soft matter physics, biology, and beyond.

The spacial heterogeneity of hydrogels composed of PEGDA and added polymer chains is expected to play a crucial role on their transport properties which can be exploited in filtration or tissue engineering. However little is known about the arrangement of the polymer chains in the matrix and the length scales of these heterogeneities. Here we combine solid-state NMR and Small Angle Neutron Scattering to unravel the structure and dynamics of PEGDA hydrogels containing added PEG chains of various concentrations. Our results show that the samples present heterogeneities in both the PEGDA and PEG concentrations and suggest that the PEG chains entangle with the PEGDA network. When plotting the sample permeability, K, as a function the specific surface of the PEGDA heterogeneities we obtain a master curve, showing that the heterogeneity of the PEGDA matrix controls the permeability of the sample. Moreover the scaling K ___ V/S suggests a structure composed of facetted PEGDA/PEG heterogeneities separated by a network of aqueous thin and flattened films in which the water can permeate.

Exact diagonalization (ED) is a workhorse technique in computational quantum many-body physics, but published ED results are rarely accompanied by machine-checkable evidence of their numerical correctness. The community typically relies on the implicit trust chain LAPACK $\to$ user code $\to$ result, with at most informal agreement against another package treated as confirmation. We argue that this practice is inadequate for a method whose output frequently underpins theoretical claims, and we present \textsc{certify-ed}, a verification framework designed to be used \emph{alongside} existing ED packages (QuSpin, XDiag, ALPS) rather than as a replacement for them. The framework consists of (i) a multi-oracle eigensolver that runs three independent LAPACK paths and reports their pairwise disagreement, (ii) thirteen logically independent validation layers covering algebraic invariants, analytic limits, alternative algorithms, arbitrary-precision reference computation, conservation laws, dynamical consistency, and finite-size scaling, and (iii) tamper-evident SHA-256 hashed certificates that downstream consumers can verify. The framework also ships an error-injection layer that confirms the entire pipeline detects six injected error classes. Running on sixteen physics models from one-dimensional spin chains to two-dimensional Kitaev honeycomb clusters, our reference implementation passes 53 of 53 unit tests and 81 of 81 individual validation tests in under thirty seconds, with maximum disagreement against QuSpin of $1.6\times 10^{-14}$ across 320 eigenvalue comparisons, and agreement with 50-digit \texttt{mpmath} reference values to $1.6\times 10^{-15}$. The package is released under the MIT license on Zenodo and Github

Alnico magnets have gained renewed interest in the search for rare-earth free permanent magnets due to their high thermal stability and magnetisation. However, the limited coercivity of these shape-anisotropy-based alloys constrains their performance. Starting from a reference Alnico sample, we realised a finite elements micromagnetic study of exchange-decoupled rods by varying their dimensions and interrod spacing across those observed experimentally. We computed the hysteresis properties by progressing from micromagnetic simulations of a small number of rods within the magnetostatic field of their neighbours to large systems treated statistically based on the distribution of orientations of the grains. We compared the coercivity of an isolated rod with that of the exchange-decoupled system to highlight the effect of magnetostatic interactions. We computed analytically the stray field acting on a single rod as a consequence of its surrounding rods in order to confirm the scaling of the coercivity with the packing fraction p. We explored how intrinsic material properties influence magnetic behaviour by examining materials with different magnetocrystalline anisotropy constants and saturation polarisation values. Results from several hundred simulations were used to train a multi-layer perceptron regressor and predict the magnetic properties as function of the dimensions of the rods, interrod spacing and orientation of the grains. With this approach, we highlight the underlying trends by which nanoscale structuring, intrinsic material properties and grain alignment can be tailored to improve the magnetic properties of Alnico alloys.

Forward propagation of input uncertainties in physics-based wildfire models is computationally prohibitive, limiting the use of high-fidelity simulators in risk assessment workflows. This work introduces a geometry-aligned bi-fidelity surrogate framework that addresses the convection-dominated nature of wildfire spread by mapping low- and high-fidelity solution snapshots onto a common reference domain prior to basis selection and reconstruction. Unlike conventional bi-fidelity schemes, which combine spatially shifted snapshots and thus suffer from oscillations and excess basis requirements near sharp fronts, the proposed mapping aligns the dominant front geometry through per-variable shift/stretch transforms in 1D and an activity indicator-based affine alignment in 2D, so that reduced bases compare physically corresponding structures rather than displaced ones. Building on the ADfiRe physics-based simulator, we demonstrate the method on 1D and 2D test cases in which low- and high-fidelity models differ in mesh resolution and physical completeness. Across both settings, the geometry-aligned surrogate reproduces full-field temperature and fuel composition with substantially lower error than its unmapped counterpart, eliminates Gibbs-type oscillations near steep gradients, and recovers high-fidelity probability density functions for key quantities of interest (e.g., maximum temperature, evaporated moisture, and burned area). After offline training, online predictions are roughly three orders of magnitude cheaper than direct high-fidelity evaluation, making the framework a practical building block for many-query uncertainty quantification once the offline cost is amortized over enough queries. We discuss the conditions under which the geometric alignment is most effective, its limitations for non-convex or topologically complex fronts, and the path toward validation against real data.

Developing tissues often maintain mechanical coherence while continuously remodeling through cellular processes such as cell divisions and rearrangements. In this way, they are an example of amorphous solids. In passive amorphous solids, local rearrangements can trigger one another through long-ranged elastic interactions, leading to system-spanning avalanches near yielding. Whether similar collective dynamics should be expected in living tissues is unclear, because cell divisions generate stress and remodeling events independently of local mechanical stability. Here, we address this question using a two-dimensional elastoplastic model in which cell divisions are treated as active plastic events. We find that while cell divisions fluidize the tissue below the passive yield stress, but preserve the marginal stability in the quasistatic limit. However, they also strongly suppress the system-spanning avalanches of cell rearrangements, in constrast with the expected behavior in passive amorphous solids. Finally, we show that the avalanche supression originates from the energy balance in the system. Namely, the energy injected by cell divisions allows for shear flow below the yield stress, but also provides a finite budget for rearrangements. These results suggest that proliferating tissues display the structural hallmarks of marginal amorphous solids while exhibiting much shorter-ranged correlations in dynamics, compared to passive amorphous solids.

A glass is conventionally obtained by cooling a bulk supercooled liquid through its glass transition temperature. The discovery of ultrastable glasses prepared using physical vapor deposition, together with the recent multiplication of numerical algorithms created to increase the stability of glasses, demonstrates the existence of a variety of strategies for designing glasses with different physical properties. This raises a broader question: which parameters most strongly govern the enhancement of glass stability? Existing computational strategies often produce highly stable glasses by optimizing certain physical properties through dynamical changes in particle diameters. We challenge the idea that these physical quantities are causally responsible for glass stability and suggest instead that diameter dynamics is the principal source of enhanced stability. To support our view, we introduce computational methods to optimize physical quantities without changing the particle diameters. Using the examples of enhanced hyperuniformity at large scale and local ordering at small scale, we design glass configurations with highly optimized values compared to bulk equilibrium states. However, these glasses do not show enhanced stability. The proposed physical quantities are correlated with glass stability, but are not causally responsible for ultrastability. These findings indicate that design rules for stable glasses should be reinterpreted in terms of the dynamical processes that generate stability, rather than the optimized physical quantities they target.

Low-dose computed tomography (LDCT) is the standard modality for lung cancer screening, known for its low radiation dose but high noise levels. While existing literature focuses on denoising LDCT images, comparative research on simulating LDCT characteristics to directly use these images for model development is lacking. This study shifts the focus from denoising images to degrading available standard-dose CT (SDCT) data, generating synthetic images for data augmentation to train classifiers for screening-detected nodules. We compare three degradation methods: (1) a sinogram domain statistical noise insertion; (2) replicate a validated physics-based simulation using Pix2Pix; and (3) unpaired CycleGAN. The generated images were utilized to simulate LDCT screening scenario replacing 695 SDCT cases from the LIDC-IDRI dataset, from which radiomic features were extracted to train machine learning models for lung nodule classification. Regarding image quality, CycleGAN achieved the best Fréchet inception distance (0.1734) and kernel inception distance (0.0813; 0.1002) scores, indicating distributional alignment with the target low-dose domain. In the nodule classification task, results confirmed the necessity of domain adaptation since a baseline model trained on non-degraded SDCT data failed to generalize to the real LDCT set (AUC 0.789) with a low sensitivity (0.571). Degraded images generated using CycleGAN approach led to the most balanced performance on the classification task using Adam Booster classifier, achieving an AUC of 0.861, sensitivity of 0.743 and specificity of 0.858 in the independent test. Our findings confirm that generating synthetic LDCT data from standard-dose scans is a viable strategy for training robust nodule classifiers for screening detected nodules.

We present novel, ultra-thin, large-diameter silicon nitride windows for various soft X-ray applications. Together with the company NORCADA, we developed windows with 200 nm and 300 nm thickness withstanding pressure differences above 1 bar. The windows have an open diameter of 14 mm. They were intensively vacuum- and overpressure-tested, showing very good results. At a measurement campaign at the synchrotron radiation source SOLEIL in France, the transparency of the windows was measured over a range from 50 eV to 15 keV, giving results comparable with the expected transparencies.

While nonclassical light sources are fundamental to quantum communication and computing, solid-state platforms like color centers and quantum dots require cryogenic temperatures to reach the performance levels necessary for practical applications. Over the past decade, low-temperature engineering has transitioned from manual handling of liquid cryogens to automated closed-cycle cryostats. This review details the principles behind modern cooling hardware ranging from flow cryostats to mechanical cryocoolers and dilution refrigerators, with a specific focus on the requirements of optical quantum devices. Aimed at the practicing scientist, this overview provides the technical insights and historical context needed to navigate the current cryogenic landscape and evaluate its role in the future of quantum technology deployment.

A Wiener-Hopf-type integral equation for the stationary PDF of a reflected random walk is derived rigorously based on modern probability theory, and an application to battery energy storage systems (BESS), specifically the sizing of the inverter, is discussed in depth. The methodological steps include the construction of a Markov kernel, the derivation of a Fredholm integral equation of the second kind for the PDF of the BESS power, and an analytical solution of the equation based on a Neumann series. The analytical results were compared against numerical solutions obtained with the Nystrom method, as well as against the results of an algorithmic simulation using simulated input time series. The use of truncated versions of the analytic solution allows for the construction of simplified design rules for the power systems practitioner. General insights into inverter sizing criteria of storage systems for ramp-rate control of variable renewable energy (VRE) sources such as wind and solar are provided.

Polarizable particle systems, including charged colloids, polarizable ions, biomolecular assemblies, and soft nanomaterials, can exhibit contact electrostatic interactions that depart strongly from Coulomb behavior when dielectric mismatch and geometric singularities amplify polarization effects. Here we use charged dielectric spheres as a model system and show that these polarization contributions can be canceled by jointly tuning size, charge, and dielectric asymmetries. By extending a recently developed image-charge formula to contacting dielectric spheres, we derive analytical conditions under which the contact interaction reduces to the bare Coulomb form. Accurate two-sphere calculations validate the resulting contact design rules with relative errors below $3\%$. Strikingly, many-body molecular dynamics simulations reveal that systems satisfying these two-body rules self-assemble into structures that closely match their pure Coulomb references. These results establish asymmetry as a route for turning electrostatic complexity into Coulombic simplicity at contact, with implications for controlled self-assembly and materials design.

X-ray imaging is a fast, precise and non-invasive method of imaging which, when combined with computed tomography, provides detailed 3D rendering of samples. Incorporating propagation-based phase contrast can vastly improve data quality for weakly attenuating samples via phase retrieval, allowing radiation exposure to be reduced. However, applying phase retrieval to multi-material samples commonly requires choice of which material boundary to tune the reconstruction. Selecting the boundary with strongest phase contrast increases noise suppression, but at the detriment of over-blurring other interfaces and potentially removing quantitative sample information. Additionally, conventional phase-retrieval algorithms cannot be used for regions bounded by more than one material, requiring alternative methods. Here we present a computationally-efficient, non-iterative nor AI-mediated method for applying strong phase retrieval, whilst preserving sharp boundaries for all materials within the sample. 3D phase retrieval is combined with morphological operations to prevent over-blurring artefacts from being introduced, while avoiding the potentially long convergence times required by iterative approaches. This technique, entitled 3DMPR, was tested on phase contrast images of a rabbit kitten brain encased by the surrounding dense skull. Using 24kVp synchrotron radiation with a 5m propagation distance, 3DMPR provided a 6.8-fold improvement in the signal-to-noise ratio (SNR) of brain tissue over the standard phase retrieval procedure, without over-smoothing the images.

Optical refrigeration of solids with anti-Stokes fluorescence has been widely explored as a vibration-free cryogenic cooling technology. A minimum temperature of 87 K has been demonstrated with rare-earth ion doped crystals using optical refrigeration. However, the depletion of the upper-lying energy levels in the ground state manifold hinders further cooling to below the liquid nitrogen (LN$_2$) temperatures, restricting its applications. In this work, we introduce a Purcell-enhanced optical refrigeration method to circumvent this limitation. This approach enhances the emission of high-energy photons by coupling the emitters to an optical cavity, blue shifting the mean emission wavelength. Such Purcell-enhanced emission facilitates cooling starting from a lower energy level in the ground state manifold, which exhibits a higher occupation below the LN$_2$ temperatures. Using experimentally measured optical coefficients, our theoretical analysis predicts a minimum achievable internal temperature of about 38 K for a Yb$^{3+}$:YLiF$_{4}$ nanocrystal near a cavity under realistic conditions. The proposed method is applicable to other rare-earth ion doped materials and semiconductors, and will have applications in creating superconducting and other quantum devices through solid-state cooling.

Biosensing based on optically trapped fluorescent nanodiamonds potentially allows to resolve biochemical processes inside living cells at a desired intracellular location. Towards this goal, we investigate near infrared (NIR) laser irradiation at 1064 nm on fluorescent nanodiamonds (FNDs) containing nitrogen-vacancy (NV) centers. The 1064 nm NIR wavelength is a popular choice for optical trapping because of its low absorption in bio-samples. By conducting comprehensive experiments, we aim to understand if and how NIR exposure influences the fluorescence and sensing capabilities of FNDs and to determine the potential implications for the use of FNDs in various sensing applications. Our experiments exposed FNDs to varying intensities of NIR laser light while carefully monitoring their optical and magnetic properties. Key measurements included all-optical fluorescence relaxation, optical spectroscopy, and optically detected magnetic resonance (ODMR) spectra. The findings reveal how increased NIR laser power correlates with alterations in ODMR central frequency but also that charge state dynamics under NIR irradiation of NV centers play a role. We demonstrate that FND biosensing works well with a protocol involving both NIR and green light, while mitigating the effect of NIR.

Accurate knowledge of power grid topology is a prerequisite for effective state estimation and grid stability. While data-driven methods for topology reconstruction exist, the minimum requirements for measurement quality, specifically regarding quantization, precision, and sampling frequency, remain under-explored. This study investigates the data fidelity required to reconstruct distribution grid topologies using voltage magnitude measurements. Adopting an information-theoretic approach, we utilize the Chow-Liu algorithm to generate maximum spanning trees based on mutual information. Rather than proposing a new reconstruction algorithm, our primary contribution is a comprehensive sensitivity analysis of the measurement data itself. We systematically evaluate the impact of data bit-depth, significant digit truncation, time-window length, and different mutual information estimators on reconstruction accuracy. We validate this approach using IEEE test cases (via MATPOWER) and time-series data from GridLAB-D. Our results demonstrate that grid topology can be successfully recovered even with highly quantized 8-bit data or millivolt-level precision. However, performance degrades significantly when downsampling intervals exceed 20 minutes or when data availability is limited to short durations. These findings establish an optimistic theoretical lower bound, suggesting that costly high-precision instrumentation may not be strictly necessary for structural inference under ideal conditions. This rigorous baseline provides a foundation for future evaluations of noisy real world smart meter data and hybrid approaches that incorporate existing engineering priors.

Cellular decision-making based on information received from the external environment is frequently initiated by transmembrane receptors. These receptors are known to propagate such information by triggering a series of irreversible, energy-consuming reactions. While this active mechanism ensures switch-like responses, here we show how spontaneous self-assembly of native 3D subunits on a two-dimensional substrate can similarly act as a tunable and robust switch for detecting receptors at physiological concentrations. This mechanism is much more sensitive than other passive mechanisms for receptor detection. We derive analytical expressions for the critical receptor density driving stable subunit assembly, in close agreement with stochastic reaction-diffusion simulations. The theory provides testable predictions for how lipids, subunits, and receptors each can control decision boundaries and magnitude of response.

Diseases caused by bacteria have been a threat to human civilisation for centuries. Despite the availability of numerous antibacterial drugs today, bacterial diseases continue to pose life-threatening challenges. The credit for this goes to Gram-Negative bacteria, which have developed multi-drug resistant properties towards \b{eta}-lactams, chloramphenicols, fluoroquinolones, tetracyclines, carbapenems, and macrolide antibiotics. V arious mechanisms of bacterial defence contribute to drug resistance, with Multi-Drug Efflux Pumps and Enzymatic degradation being the major ones. An effective approach to cope with this resistance is to target and inhibit the activity of efflux pumps and esterases. Even though various Efflux Pump Inhibitors and Esterase resistant macrolide drugs have been proposed in the literature, none of them has achieved FDA approval due to several side effects. This research has provided valuable insights into the mechanism of drug resistance by RND efflux pump and Erythromycin esterase. A handful of potential efflux pump inhibitors have been predicted through machine learning and molecular dynamics.

The prevailing assumption holds that on-orbit servicing (OOS) of mega-constellations is infeasible due to prohibitive fuel consumption incurred by multiple rendezvous maneuvers across vast and dispersed satellite populations. To address this challenge, a novel OOS architecture termed Non-Propulsive Payload Deployment (NPD) is proposed in this paper. Within this framework, a service spacecraft (SSc) ejects micro-payload spacecraft (PSc) into transfer orbits, after which the PSc autonomously rendezvous with target spacecraft (TSc). Since propulsion is required only for the minimal mass of the PSc, maneuvering fuel consumption is significantly reduced. This paper develops a phase-based approximation algorithm to resolve the scheduling problems arising from cumulative recoil-induced orbital perturbations. Numerical simulations for a constellation of over 100 satellites demonstrate that this algorithm reduces computation time by over 90% while maintaining ejection velocity errors below 1%. Further analysis yields an analytical formula for evaluating the deployment capability of the NPD system, providing planning estimates with less than 2% error within low Earth orbit (LEO) regimes. Finally, a case study of the Starlink Gen2 constellation confirms that the NPD system consumes less than 1/50 of the propellant required by conventional methods, enabling efficient multi-plane servicing via J2 perturbation.

Active particles exhibit complex transport dynamics in flows through confined geometries such as channels or pores. In this work, we employ a generalized Taylor dispersion (GTD) theory to study the long-time dispersion behavior of active Brownian particles (ABPs) in an oscillatory Poiseuille flow within a planar channel. We quantify the time-averaged longitudinal dispersion coefficient as a function of the flow speed, flow oscillation frequency, and particle activity. In the weak-activity limit, asymptotic analysis shows that activity can either enhance or hinder the dispersion compared to the passive case. For arbitrary activity levels, we numerically solve the GTD equations and validate the results with Brownian dynamics simulations. We show that the dispersion coefficient could vary non-monotonically with both the flow speed and particle activity. Furthermore, the dispersion coefficient shows an oscillatory behavior as a function of the flow oscillation frequency, exhibiting distinct minima and maxima at different frequencies. The observed oscillatory dispersion results from the interplay between self-propulsion and oscillatory flow advection -- a coupling absent in passive or steady systems. Our results show that time-dependent flows can be used to tune the dispersion of active particles in confinement.

This article presents an experiment that can be conducted today and that could provide a deeper understanding of the interaction between the wave and particle aspects of an atom. The wave-particle duality is often presented as mutually exclusive: one considers either the wave aspect or the particle aspect. Our proposed experiment involves both aspects simultaneously and raises new questions. It is a slightly modified version of Young's double-slit interference experiment (a grid of narrow slits is added between the two wide slits) and is carried out using Rydberg atoms. Young-type interference experiments typically involve only the de Broglie wave $\psi$, which depends solely on the mass and velocity of the atoms. However, with Rydberg atoms having a large principal quantum number, the ``size'' of the atom-particle also becomes significant. The two large slits are wide enough to allow the Rydberg atoms to pass through, whereas the grid of narrow slits prevents them from passing through. We numerically simulate the possible outcomes based on different hypotheses regarding wave-particle interaction. Conducting the experiment in practice would allow us to distinguish between these hypotheses and deepen our understanding of wave-particle interaction. The conceptual framework of Louis de Broglie's double solution theory is well-suited to this experiment because it distinguishes between two types of waves: an external or statistical wave (de Broglie's wave) and an internal or physical wave (corresponding to the physical particle). We will examine the relevance of this approach.

Lithium-Sulfur batteries (LSBs) are believed to have a high potential for aerospace applications due to their high gravimetric energy density. However, despite decades of research and advances, they still suffer from poor rate capability and low power output, eventually preventing their practical implementation. One particular aspect we want to shed light on is the influence of the porous cathode structure on the rate performance during discharge. Therefore, we present a scale-resolved simulation methodology involving high-performance computing (HPC), which aims to provide structural insights into the electrochemical cell behavior that are experimentally hardly accessible even for modern operando methods. Our \emph{numerical operando approach} employs scaling analysis for efficient model parametrization as well as rigorous parameter transfer between models of different dimensionality and is based on a coarse-grained continuum model. The latter is spatially discretized with a Discontinuous Galerkin (DG) method and advanced in time by an adaptive controller. The models and methods as well as HPC aspects of our toolbox will be critically discussed, finally showcasing the capabilities of our workflow to improve LSBs.

Controlling the magnetic properties of nanosystems by an electric field offers a number of advantages for spintronics applications. Using the noncollinear Alexander-Anderson model, we have shown that the interaction of localized magnetic moments formed by itinerant electrons strongly depends on the position of the d-level relative to the Fermi level, which determines the number of localized electrons. Depending on this parameter, the ground state of the magnetic dimer can be ferromagnetic, antiferromagnetic, or noncollinear without the effects of spin-orbit interaction. The magnetic state can be controlled by shifting the d-level with an electric field, even without current flow. For a sufficiently large value of the hopping parameter between localized states there can be several self-consistent solutions with different values of magnetic moments. This opens new possibilities for manipulation of the magnetic structure of nanosystems. The results obtained lead to a new interpretation of the mechanisms of magnetization reversal, recording, and deleting of magnetic structures in tunneling spectroscopy experiments.

Metamaterials benefit from unique architected patterns to achieve lightweight with exceptional mechanical properties inaccessible to conventional materials. Typical mechanical metamaterials are inspired by crystal-like lattice structures, whose closely packed frameworks often exhibit a rigid mechanical nature. Here, we present polymer-inspired metamaterials (PIMs) by programming deformation and strengthening mechanisms that mimic the mechanical roles of key constituent elements in polymer networks. By combining metamaterial programmability with polymer-inspired structures, we design crosslinking, proto-crystalline order, and entanglement in PIMs to enable macroscale strengthening mechanisms inspired by crosslink, molecular-density, and pre-stretch strengthening in polymers, expanding the metamaterial structure-property design space. This macroscale polymer-inspired programmability also suggests that PIMs could serve as a design platform incorporating the programmability strategies to achieve desired deformation and strengthening responses, holding a potential for applications in soft robotic joints and compliant connectors.

We report the characterisation of a FBK NUV-HD-cryo silicon photomultiplier (SiPM) sensor operated in a 9.4 $\pm$ 0.2 mK environment inside a dilution refrigerator, towards the development of a cryogenic cosmic ray muon veto system to be operated internal to a dilution refrigerator required for low background experiments such as the QUEST-DMC dark matter search experiment. We characterise the single photon response and the gain (the charge produced per detected photon), the dark count noise rate, and correlated noise contributions as a function of operating voltage. This paper also reports first proof-of-concept measurements of using a SiPM coupled to scintillator internal to a dilution refrigerator, towards detecting high-energy events consistent with candidate cosmic-ray muon signals.

The string tensions can be dynamical in the modified measure formalism and appear as an additional dynamical degrees of freedom . These tensions may not be universal, instead, each string generates its own tension. We then consider a new bulk field that can couple to the strings, the tension scalar which changes locally the tension along the world sheet. In the case with two string tensions there is a braneworld solution which gives rise to an induced de Sitter space in the brane, avoiding swampland constraints of the standard string theory. Strings with different tension to ours can appear also as Dark Matter and since they share the same space and compactifications as visible matter, they should lead to Dark copies of the standard model,

Relative Binding Free Energy (RBFE) calculations are a cornerstone of rational hit-to-lead and lead optimization in modern drug discovery. However, the high computational cost and limited reliability in tackling large or complex molecular transformations often prevent their routine, high-throughput use. Here we introduce dual-LAO, a novel, highly efficient method for calculating RBFE. Building on the Lambda-ABF-OPES framework, this method combines a dual-topology setup and suitable restraints to dramatically accelerate free energy convergence. We demonstrate that dual-LAO, in combination with the AMOEBA polarizable force field, achieves an unprecedented acceleration factor of 15 to 30 times compared to current state-of-the-art methods on standard drug targets. Crucially, the approach maintains high accuracy and successfully tackles previously prohibitive molecular changes, including scaffold-hopping, buried water displacement, charge changes, ring-opening, and binding pose perturbations. This significant leap in efficiency allows for the widespread, routine integration of predictive molecular simulations into the rapid optimization cycles of drug discovery, enabling chemists to confidently model historically challenging systems in timescales compatible with real-world project deadlines.

The simulation of fluid flow in real, multiscale porous media remains challenging due to the complexity of nanoscale phenomena and the difficulty of developing upscaling methodologies. In this study, we introduce a multiscale filtration framework based on quasi-static Pore Network Modelling, incorporating the effects of pore blockage resulting from capillary condensation of fluid in the nanoporous space. To accurately predict capillary condensation in nanoconfinement, we apply classical Density Functional Theory calculations considering capillary hysteresis. The pores blocked by condensate are excluded from the fluid flow, resulting in a decrease in permeability of the porous space. Our findings demonstrate that the resulting permeability is strongly dependent on the geometry of the porous space, including pore size distribution, throat size distribution, sample size, and the particular structure of the sample, as well as thermodynamic conditions and processes, specifically pressure growth or decrease. Overall, the presented research contributes valuable insights into multiscale transport phenomena and facilitates the advancement of upscaling techniques.

Accurate prediction of flow resistance in high-Froude-number aerated flows remains challenging due to air entrainment, which causes strong spatial variability in mixture density. Here, we introduce for the first time a density-weighted (Favre) averaging approach within a Shallow Water Equation framework specifically tailored to account for this strong mixture density variability. Within this framework, we present a novel Darcy-Weisbach friction factor formulation that decomposes contributions associated with uniform flow, spatially varying flow, and temporally evolving flow, and incorporates momentum and pressure correction factors reflecting the vertical structure of the mixture. Application to experimental data demonstrates that spatial flow development systematically reduces the effective friction factor relative to the uniform-flow estimate, and that momentum-based and energy-based formulations yield nearly identical results. The framework recovers classical uniform-flow predictions in the quasi-uniform downstream region and reduces to standard single-phase formulations in the absence of aeration. Overall, it provides a physically consistent tool for resistance prediction in high-Froude-number spillways, chutes, and open-channel systems, with a structure compatible with depth-averaged numerical solvers.

We report the first creation of a bulk sample of ultracold heteronuclear p-wave Feshbach molecules in an optically trapped Bose-Bose mixture of 23Na and 87Rb atoms. Using loss spectroscopy and binding energy measurements, we systematically characterize the interspecies p-wave Feshbach resonances near 284 G. Leveraging this understanding, we use magneto-association to form p-wave NaRb Feshbach molecules, producing both pure samples and mixtures of molecules in different p-wave orbitals. We further measure the molecular lifetime and identify atom-molecule and molecule-molecule collisions as the dominant loss mechanisms. This work establishes a previously unavailable ultracold molecule platform that combines orbital anisotropy with heteronuclear constituents, represents a significant step toward realizing tunable p-wave interactions in Bose-Bose mixtures, and provides a foundation for exploring non-zero angular momentum molecules.

Droplet impact and spreading on solid substrates are well understood for Newtonian fluids, yet how viscoelasticity alone modifies the maximal spreading remains unclear. To identify the mechanisms governing the spreading dynamics, we conducted impact experiments and measured the maximal spreading diameter to quantify how fluid elasticity modifies the maximal spreading of impacting droplets. Experiments were performed using fluids within a narrow range of viscosity and surface tension, but with varying relaxation times. For a wide range of conditions, viscoelastic droplets follow a similar behavior as Newtonian ones; however, their maximal spreading diameter is significantly reduced compared with the Newtonian behavior when the Deborah number is of order unity. These observations are rationalized by incorporating the viscoelastic effects into a classical energy balance model. The scaling argument obtained from this model explains the reported reduction in maximal spreading and identifies the range of fluid properties for which the strongest viscoelastic effects emerge.

Purpose: Following the clinical success of 177Lu-based therapies, accurate quantification of 177Lu using radionuclide calibrators (RNCs) and SPECT/CT is gaining importance as prerequisite for accurate treatment delivery and dosimetry. However, the lack of standardization can introduce inter-system variability, compromising multi-center clinical trials. This study aimed to assess the accuracy and variability of 177Lu measurements using RNCs and SPECT/CT across different systems and hospitals. Methods: A uniform cylindrical phantom and a NEMA phantom with hot spheres were prepared using traceable activities and imaged at 8 different hospitals using 13 SPECT/CT systems (9 conventional and 4 3D CZT). Acquisitions and reconstructions were performed using both site-specific and standardized protocols. The cylindrical phantom images were used to evaluate the system calibration and establish image calibration factors (ICFs), the NEMA images to evaluate effective resolution by calculating recovery coefficients (RCs). In parallel, two vials were measured to test RNC accuracy. Results: RNC measurements differed up to 11% between centers, while SPECT quantification of the cylindrical phantom differed up to 20%. While ICFs were consistent for systems of the same type, image quality varied strongly when using clinical protocols (36% difference in RCs in the largest sphere). Standardized reconstruction reduced variability in RCs for each system type (maximum 12% difference), regardless of acquisition protocol, but differences between system types persisted when standardizing acquisition and reconstruction (33% difference). Conclusion: Current 177Lu measurement practices yield significant variability in quantification and image quality. Harmonization efforts should prioritize standardized calibration and reconstruction protocols to improve multicenter reproducibility of quantitative 177Lu-SPECT/CT.

In thermally non-equilibrium plasmas, conventional harmonic models can significantly mispredict superelastic electron heating rates. When the vibrational temperature exceeds the gas temperature ($T_{\rm v}>T_{\rm g}$), these models underestimate energy transfer by several times; conversely, they overestimate heating when $T_{\rm g}>T_{\rm v}$. We show that this discrepancy arises from neglecting the exponential heating from overpopulated, high-lying states in anharmonic Treanor-Gordiets distributions, and their thermodynamic depopulation at high gas temperatures. To resolve this, we derive a closed-form, thermodynamically consistent macroscopic closure based on detailed balance and a second-order Dunham expansion. This unified framework introduces an analytic anharmonic correction factor that captures the kinetic competition between vibrational-vibrational (V-V) up-pumping and vibrational-translational (V-T) relaxation. By predicting the Treanor minimum, this formulation recovers the fidelity of full state-to-state kinetic benchmarks. Ultimately, this model provides a governing equation for heat exchange between electrons and excited states in non-equilibrium environments -- including plasma-assisted combustion and hypersonic flows -- enabling the development of accurate, rate-limited reduced-order models for macroscopic fluid solvers.

The Particle-Identification Silicon-Telescope Array (PISTA) is a new detection system designed for high-resolution studies of the fission process induced by multi-nucleon transfer in inverse kinematics. It is specifically optimized for experiments with the VAMOS++ magnetic spectrometer at GANIL (Grand Accélérateur National d'Ions Lourds). The array comprises eight trapezoidal $\Delta$E-E silicon telescopes arranged in a corolla configuration. Each telescope integrates two single-sided stripped silicon detectors, enabling target-like recoil identification, energy loss measurements, and trajectory reconstruction. Positioned in close proximity to the target, PISTA's compact geometry achieves high-efficiency tracking of target-like recoils produced in multi-nucleon transfer reactions at Coulomb barrier energies. The spatial segmentation of the array allows precise determination of the mass and charge of the target-like nucleus, and excitation energy of fissioning systems. This work presents the particle identification and excitation energy reconstruction performances for the interactions of $^{238}$U beam with $^{12}$C target. An excitation energy resolution of 800 keV (FWHM) was determined together with mass resolution of 1.1% (FWHM). The combination of PISTA and VAMOS++ magnetic spectrometer enables unprecedented investigations of the fission process as a function of the excitation energy of the fissioning nucleus, particularly for exotic systems produced in transfer-induced reactions.

Accurate assessment of anthropogenic climate change relies on historical instrumental data, yet observations from the early 20th century are sparse, fragmented, and uncertain. Conventional reconstructions rely on disparate statistical interpolation, which tends to smooth local features and create unphysical artifacts, often leading to an underestimation of intrinsic variability and extremes. While recent machine learning approaches have improved reconstruction accuracy, they remain confined to purely spatial inpainting of coarse-resolution fields. Here, we present a unified, probabilistic generative deep learning framework that overcomes these limitations and reveals previously unresolved historical climate variability back to 1850. Leveraging a learned generative prior of Earth system dynamics, our model performs probabilistic inference to estimate spatiotemporally consistent historical temperature and precipitation fields from sparse observations. Our approach preserves the higher-order statistics of climate dynamics, transforming reconstruction into a robust uncertainty-aware assessment. We demonstrate that our reconstruction mitigates the smoothing effects inherent in widely used historical reference products, including those underlying IPCC assessments, especially regarding extreme weather events. Notably, we uncover higher early 20th-century global warming levels compared to existing reconstructions, primarily driven by more pronounced polar warming, with mean Arctic warming trends exceeding established benchmarks by 0.15--0.29C per decade for 1900--1980. Conversely, for the modern era, our reconstruction indicates that the broad Arctic warming trend is likely overestimated in recent assessments, yet explicitly resolves previously unrecognized intense, localized hotspots in the Barents Sea and Northeastern Greenland.

All-sky OH airglow imaging provides two-dimensional observations of mesospheric gravity wave structure near ~87 km altitude. Ripple-scale instability signatures, characterized by 5-15 km horizontal wavelengths and short lifetimes, are particularly difficult to identify consistently using manual inspection. In this study, we develop a reproducible, automated detection framework based on a squeeze-and-excitation convolutional neural network (SE-CNN) trained on 41 x 41 pixel image patches, to identify ripple-scale structures in 512 x 512 pixel all-sky airglow images acquired at Yucca Ridge Field Station (40.7o N, 104.9o W). The time-differenced images are normalized using a robust median-absolute-deviation (MAD) scaling procedure to mitigate star contamination and background variability. The model is trained and validated on manually annotated ripple and non-ripple patches, then evaluated using independent test subsets. The automated detection is performed using a sliding-window approach with spatial and temporal clustering criteria for event definition. At the patch level, the classifier achieves 92\% F1-score with high precision and recall. At the event level, automated detections recover approximately 90\% of manually identified ripple events while identifying additional low-amplitude occurrences. Validated against previous manual identification study, the automated detection catalog enables objective quantification of ripple occurrence frequency, seasonal modulation, and lifetime distributions. By emphasizing methodological transparency, calibration considerations, and validation metrics, this framework establishes a scalable measurement technique for systematic detection of mesospheric instability signatures in long-term airglow image archives.

The parameterization of simulation-based models is a central yet laborious task in computational chemistry and physics, often driven by human intuition and manual iteration. Automating this task necessitates the definition of suitable objective functions, which tend to be expensive to evaluate, noisy, non-differentiable, or composed of heterogeneous contributions originating from separate sets of simulations. Gradient-free and black-box optimization algorithms are powerful tools which are particularly well-suited to minimizing such objective functions. Here, we introduce ChemFit, a flexible Python framework for the definition, composition, and massively concurrent evaluation of simulation-based objective functions, which is designed to operate in conjunction with these algorithms. We demonstrate the broad applicability of this approach by using ChemFit for three representative examples of increasing complexity and real-world relevance. First, we obtain the parameters of the Lennard-Jones potential for liquid argon from experimental measurements of the density. Second, we parameterize a polarizable and flexible potential energy function to reproduce the structure of small H$_2$O clusters obtained from density functional theory calculations. Finally, we tune a small subset of the parameters of a residue-level coarse-grained protein force-field, with the goal to reproduce the experimental critical solution temperature of the low complexity domain of the wild-type hnRNPA1 sequence and an arginine-enriched mutant of this protein. hnRNPA1 is an RNA-binding protein linked to amyotrophic lateral sclerosis. Together, these examples illustrate how ChemFit enables scalable, reproducible, and optimizer-agnostic parameter fitting for broadly applicable multiscale models.

PICOSEC is an ultrafast particle-detector concept, combining a photocathode-coated Cherenkov radiator coupled to a gas-avalanche multiplier. Particle-induced Cherenkov photons create photoelectrons emitted from an ultrathin semitransparent photocathode; they are multiplied and detected in fast gas-avalanche mode. In parallel to the constant progress made in the PICOSEC technique, we propose different detector configurations and operation modes with the aim of enhancing robustness and performance. They incorporate thick reflective photocathodes deposited on the readout electrodes of various types of avalanche multipliers. Some of these Reflective-PICOSEC detectors operate at mbar gas pressures.

We present a detailed investigation of the afterpulse effect in silicon photomultipliers (SiPMs), using a dedicated structure with single-cell readout. This enables direct measurement of intrinsic device properties and observation of individual pulses even after irradiation. Three independent analysis methods to quantify afterpulse induced events were developed and validated by Monte Carlo simulations. The first method is based on charge integration, while the other two methods use multiple linear regression to reconstruct transient waveforms and accurately identify individual pulse positions. These positions are then used either as direct event counts or to construct time interval distributions, enabling comprehensive characterization of the afterpulse probability and providing insights into the dynamics of trapping in silicon. Using this framework, we measured three SiPM samples: one fresh reference device and two irradiated devices exposed to reactor neutron fluences of 2e12 and 1e13 cm^-2. We report systematic measurements of the afterpulse probability and time constant as functions of bias voltage and irradiation fluence. For overvoltages in the range of 3 to 5 V above breakdown, the afterpulse time constant is found to be below 10 ns and the afterpulse probability below 6%. Both quantities show no significant dependence on irradiation fluence.

Ion temperature clamping -- the saturation of the ion temperature regardless of heating power -- is observed across stellarator experiments. We propose a minimal model based on Anderson localisation. Starting from a reduced fluid model for drift waves [Phys. Fluids 26, 880 (1983)], we show that aperiodic stellarator geometry leads to a quasiperiodic Hill equation for the ion-temperature-gradient (ITG) mode structure. In a tight-binding approximation this equation reduces to an Aubry--Andre--Harper difference equation, suggesting an Anderson-localisation mechanism for ITG eigenfunctions. We identify a three-threshold ordering: the linear instability threshold lies below the Anderson localisation threshold, which lies below the observed clamp. This is conjectured to create a low-transport second regime above the instability threshold, qualitatively analogous to the second stability regime of MHD ballooning theory, and provides a power-independent lower bound on the observed gradient.

We present the first measurement of 9p 2P1/2,3/2 and 10p 2P1/2,3/2 excited levels absolute wavenumbers and radiative lifetime in francium. We used the Collinear Resonance Ionization Spectroscopy (CRIS) technique, applied on a beam of 221Fr atoms. Prior to this work, no experimental data existed for francium p-states with n > 8. The results provide a precision experimental test of relativistic coupled-cluster theory for the heaviest alkali, showing good agreement for lifetimes and relative excitation energies, despite a residual global offset in absolute energies.

For many materials, macroscopic mechanical behavior is determined by an intricate microstructure. Understanding the relation between these two scales helps scientists and engineers design better materials. The relation which maps microstructure to bulk mechanical properties can be understood via the well-established theory of homogenization. However inverting the homogenization process, to recover microstructural information from measured macroscopic properties, is fraught with difficulties because of the averaging processes that underlie homogenization. Therefore, scientists and engineers usually need recourse to more invasive, often highly localized, investigations to learn about a microstructure. In this work, we develop a noninvasive methodology by which one can leverage large collections of measured bulk mechanical properties to learn information about the statistics of microstructure at a global level. We call this, distributional inverse homogenization. We study this problem in one and two dimensions, considering both periodic and stochastic homogenization. We demonstrate the methodology in the context of 2D Voronoi constructions and underpin the observed empirical success with theory in 1D. We also show how the natural spatial variability of microstructure can be exploited to gather data that enables distributional inversion. And we concurrently learn a surrogate model, approximating the homogenization map, that accelerates the resulting computations in this setting. The work formulates a new class of inverse problems, bridging ideas from probability and homogenization to facilitate the learning of microstructural material variability from macroscopic measurements.

Spatially fractionated radiation therapy (SFRT) planning requires three coordinated tasks: generation of high-dose sphere structures, position-aware optimization, and peak-valley dose ratio evaluation. We present MAAS-SFRThelper, a shared-source Eclipse Scripting Application Programming Interface (ESAPI) plugin that integrates structure generation, geometric-aware optimization, and peak-valley dose ratio evaluation for SFRT into a single workflow inside Varian's Eclipse treatment planning system. The plugin exposes five task-oriented tabs sharing common services for sphere extraction and objective creation. The SphereLattice tab generates sphere lattices using five placement patterns. The Optimization tab searches over candidate lattice positions using a four-metric geometric surrogate score and triggers VMAT optimization and dose calculation. The Evaluation tab implements four analysis modes; its three-dimensional peak-valley classification recovers sphere centers from the lattice structure through a geometric extraction pipeline rather than relying on dose thresholds. We validated all functionality on digital phantoms against analytic ground truth. The plugin is distributed as source code under the Varian Limited Use Software License Agreement. Source code and documentation are publicly available on GitHub.

Optical skyrmions have attracted significant attention across diverse physical systems for their promising scenarios in ultra-precise metrology, optical information processing, and quantum technologies. However, the lack of effective method for their on-chip directional transport and manipulation impedes their applications in photonic integrated devices. Here, we demonstrate a photonic platform that utilizes topologically protected valley edge state to achieve robust on-chip directional transport of optical spin skyrmions. These skyrmions originate from spin-orbit coupling within the evanescent field at the valley photonic crystal surface and exist as eigenstates of the topologically protected edge state, ensuring their robust unidirectional propagation. Leveraging the valley degree of freedom of topological edge states, we further achieve valley-locked spin skyrmions, enabling flexible control over the polarity of spin skyrmions. By endowing spin skyrmions with topological protection in momentum space, our work provides an approach for robust on-chip transport and manipulation of spin skyrmions, thereby paving the way for expanding their application potential in photonic systems.

A projective quantum logic in terms of relative states is developed, emphasizing the importance of information transfer between a system under study and its environment. The need for accounting for the historical evolution of system is highlighted and it is found that the conjunction of observations involving conjugate variables can be consistently defined but is found to be non-commutative. It is shown that the Birkhoff and von Neumann approach to quantum logic is unable to deal with such conjunctions. It is found that whilst the proposed scheme is still not distributive in general, the discrepancy is directly related to interference effects that may disappear when information is transferred from the system to its environment. It is argued that the probabilities associated with projections be mapped to an orthocomplemented ternary logic, in which it is shown that the law of the excluded middle still holds.

Massive objects in spatial superposition may provide insights into the interplay between quantum mechanics and gravity. Cold atomic interferometers offer a promising platform due to extended matter-wave coherence times and precise controllability. However, high-mass spatial superpositions beyond single atoms have yet to be generated in such setups. Here, we report the scalable realization of high-mass spatial entanglement via quantum tunneling of ultracold atoms in optical lattices. We observe coherent tunneling of bound clusters, forming a composite object with a mass of 608~amu. Full control of the model parameters allows us to mitigate the usual suppression of tunneling with increasing mass. Furthermore, we construct an interferometer to certify the entanglement and use spatially distributed Schrödinger cat states to perform quantum-enhanced measurements. These results establish an approach to generating and detecting massive superposition states relevant to studies of quantum gravity.

Population dynamics in fields such as molecular biology, epidemiology, and ecology exhibit highly stochastic and non-linear behaviour. In gene regulatory systems in particular, oscillations and multi-stability are especially common. Despite this, none of the currently available stochastic models for population dynamics are both accurate and computationally efficient for long-term predictions. A prominent model in this field, the Linear Noise Approximation (LNA), is computationally efficient for tasks such as simulation, sensitivity analysis, and parameter estimation; however, it is only accurate for linear systems and short-time predictions. Other models may achieve greater accuracy across a broader range of systems, but they sacrifice computational efficiency and analytical tractability. This paper demonstrates that, with specific modifications, the LNA can accurately capture non-linear dynamics in population processes. We introduce a new framework based on centre manifold theory, a classical concept from non-linear dynamical systems. This approach enables the identification of simple, system-specific modifications to the LNA, tailored to classes of qualitatively similar non-linear dynamical systems. With these modifications, the LNA can achieve accurate long-term simulations without compromising computational efficiency. We apply our methodology to classes of oscillatory and bi-stable systems, and present multiple examples from molecular population dynamics that demonstrate accurate long-term simulations alongside significant improvements in computational efficiency.

This paper reformulates complementarity-based time-stepping for frictionless nonsmooth contact between smooth rigid bodies as a recursively generated linear complementarity problem (ReLCP), involving a sequence of LCPs of increasing dimension. Starting from a classical single-constraint shared-normal signed-distance (SNSD) LCP, the method adds unilateral constraints only when the discrete-time update predicted by the current contact set would violate nonpenetration of the underlying smooth surfaces. The resulting procedure acts directly on smooth geometry, enforces nonpenetration to a prescribed tolerance, and avoids the oversampling inherent to proxy-surface contact models such as tessellations or multi-sphere decompositions, for which improved geometric fidelity can drive rapid growth in constraint count and cost. For strictly convex bodies, we prove that an initially overlap free configuration with sufficiently small timestep sizes, imply finite termination of the adaptive augmentation, and yield a unique discrete-time velocity update. In the small timestep limit and for any fixed overlap-free discrete state with a fixed geometric overlap tolerance, we prove that the recursion terminates after the initial solve, reducing the method to the classical single-constraint SNSD LCP and retaining the usual consistency of complementarity time-stepping with the underlying differential variational inequality. Numerical tests on colliding ellipsoids, compacting ellipsoid suspensions, growing bacterial colonies, and taut chainmail networks demonstrate stable large-timestep behavior, bounded interpenetration without discretization-induced surface roughness, and substantial reductions in both active constraint counts and runtime relative to representative discrete-surface complementarity formulations.

Neutral-atom quantum simulators offer a promising approach to the exploration of strongly interacting many-body systems, with applications spanning condensed matter, statistical mechanics, and high-energy physics. Through a combination of quantum experiments, numerical calculations, and analytical methods, we demonstrate a rich set of phenomena accessible on such quantum simulators by studying an array of Rydberg atoms placed on the Lieb lattice. First, we map out the ground states and phase diagram of the system, identifying a range of density-wave-ordered phases -- including a collinear phase stabilized purely by quantum fluctuations -- and find good agreement between theory and experiment. Allowing for local control of the detuning field thereafter, we discover a quantum analog of the classical liquid-vapor transition between two density-wave phases distinguished by sublattice occupation, and probe its underlying hysteretic dynamics. Furthermore, we study out-of-equilibrium quantum quenches and observe anomalously slow relaxation dynamics consistent with the kinetic constraints of an emergent string phase. These results highlight how geometric control offered by neutral-atom simulators can extend the frontiers of programmable quantum matter, enabling access to complex phases, metastability, and thermalization dynamics in many-body quantum systems.

Many headway-based car-following models describe longitudinal adaptation through linear relaxation laws, which can produce unrealistically large accelerations and limit the physical consistency of microscopic traffic dynamics. Motivated by this limitation, we develop a saturation-based extension of the classical Optimal Velocity Model (OVM) that preserves the headway-dependent desired-speed structure while introducing bounded nonlinear acceleration dynamics. Linear stability analysis shows that the proposed formulation preserves the classical long-wave instability mechanism associated with stop-and-go waves while modifying the stability threshold and enforcing bounded acceleration. Ring-road simulations support the analysis and illustrate how the model alters perturbation growth, wave amplitude, and relaxation behavior relative to the classical OVM. The resulting framework provides a compact and analytically tractable extension for studying nonlinear traffic-wave dynamics and physically constrained car-following behavior.

Physics-Informed Neural Networks (PINNs) embed the partial differential equations (PDEs) governing the system under study directly into the training of Neural Networks, ensuring solutions that respect physical laws. While effective for single-system problems, standard PINNs scale poorly to datasets containing many realizations of the same underlying physics with varying parameters. To address this limitation, we present a complementary approach by including auxiliary physically-redundant information in loss (APRIL), i.e. augment the standard supervised output-target loss with auxiliary terms which exploit exact physical redundancy relations among outputs. We mathematically demonstrate that these terms preserve the true physical minimum while reshaping the loss landscape, improving convergence toward physically consistent solutions. As a proof-of-concept, we benchmark APRIL on a fully-connected neural network for gravitational wave (GW) parameter estimation (PE). We use simulated, noise-free compact binary coalescence (CBC) signals, focusing on inspiral-frequency waveforms to recover the chirp mass $\mathcal{M}$, the total mass $M_\mathrm{tot}$, and symmetric mass ratio $\eta$ of the binary. In this controlled setting, we show that APRIL achieves up to an order-of-magnitude improvement in test accuracy, especially for parameters that are otherwise difficult to learn. This method provides physically consistent learning for large multi-system datasets and is well suited for future GW analyses involving realistic noise and broader parameter ranges.

Optical aberrations significantly degrade image quality in microscopy, particularly when imaging deeper into samples. These aberrations arise from distortions in the optical wavefront and can be mathematically represented using Zernike polynomials. Existing methods often address only mild aberrations on limited sample types and modalities, typically treating the problem as a black-box mapping without leveraging the underlying optical physics of wavefront distortions. We propose ZRNet, a physics-informed framework that jointly performs Zernike coefficient prediction and optical image Restoration. We contribute a Zernike Graph module that explicitly models physical relationships between Zernike polynomials based on their azimuthal degrees-ensuring that learned corrections align with fundamental optical principles. To further enforce physical consistency between image restoration and Zernike prediction, we introduce a Frequency-Aware Alignment (FAA) loss, which better aligns Zernike coefficient prediction and image features in the Fourier domain. Extensive experiments on CytoImageNet demonstrates that our approach achieves state-of-the-art performance in both image restoration and Zernike coefficient prediction across diverse microscopy modalities and biological samples with complex, large-amplitude aberrations. We further validate on experimental PSF data from a physical microscope and demonstrate robustness to realistic sensor noise, confirming generalisation beyond simulated conditions. Code is available at this https URL.

We propose an inverse-squeezing Kennedy receiver for discriminating binary phase-shift-keyed displaced squeezed vacuum states. The receiver combines a Kennedy-type nulling displacement, an orthogonally oriented inverse-squeezing operation and photon-number-resolving detection with a maximum-a-\emph{posteriori} threshold rule. Its key mechanism is that the inverse-squeezing stage converts transmitter-side squeezing into enhanced photon-number contrast, or equivalently an effective coherent-state energy gain, that can be directly exploited at the measurement stage. Under ideal equal-prior conditions, the receiver surpasses the standard quantum limit for squeezed-state binary phase-shift keying at approximately $N\approx 0.3$, outperforms the Helstrom bound of coherent-state binary phase-shift keying at approximately $N\approx 0.4$, and reaches the 1\% error level near $N\approx 0.6$. We further analyze its performance under realistic imperfections, including finite detector efficiency, dark counts, channel phase diffusion, receiver thermal noise and transmission loss. The results show that adaptive thresholding preserves robust performance against detector and noise imperfections over practical parameter ranges, whereas transmission loss progressively suppresses the squeezing-enabled advantage. These findings indicate that, for the fixed source parametrization adopted in this work, the proposed receiver is most advantageous in the low-loss regime, especially at low source energies.

Many protocols within quantum cryptography, communications, and computing require the ability to generate entangled states as well as spectral qudits. Nonlinear interferometry is a viable way to engineer these complex quantum states of light. However, it is difficult to achieve a high level of control over spectral correlations. Here, we present a protocol utilizing a nonlinear interferometer with linear spectral phases that can generate both high-dimensional spectral qudits and high-dimensional entangled states. We model the effect of loss and loss of overlap on interference visibility and thereby on the states generated.

We demonstrate a neutral atom networking node that combines high photon collection efficiency with high atom photon entanglement fidelity in a compact, fiber integrated platform. A parabolic mirror is used both to form the trap and to collect fluorescence from a single rubidium atom, intrinsically mode matching $\sigma$ polarized emitted photons to the fiber and rendering the system largely insensitive to small imperfections or drifts. The core optics consist of millimeter scale components that are pre aligned, rigidly bonded on a monolithic in-vacuum assembly, and interfaced entirely via optical fibers. With this design, we measure an overall photon collection and detection efficiency of $5\%$, from which we infer an overall collection efficiency of $9\%$ after the single--mode fiber coupling. We generate atom photon entangled states with a raw Bell state fidelity of 0.93 and an inferred fidelity of 0.98 after correcting for atom readout errors. The same node design has been realized in two independent setups with comparable performance and is compatible with adding high NA objective lenses to create and control atomic arrays at each node. Our results establish a robust, cavity free neutral atom interface that operates near the limit set by the collection optics numerical aperture and provides a practical building block for scalable quantum network nodes and repeaters.

In experimental High-Energy Physics, unfolding refers to the problem of estimating the underlying distribution of a physical observable from detector-level data, in the presence of statistical fluctuations and systematic uncertainties. Starting from its reformulation as a regularized quadratic optimization problem, we develop a framework to address unfolding using both classical and quantum-compatible methods. In particular, we derive a Quadratic Unconstrained Binary Optimization (QUBO) representation of the unfolding objective, allowing direct implementation on quantum annealing and hybrid quantum-classical solvers. The proposed approach is implemented in QUnfold, an open-source Python package integrating classical mixed-integer solvers and D-Wave's hybrid quantum solver. We benchmark the method against widely used unfolding techniques in RooUnfold, including response Matrix Inversion, Iterative Bayesian Unfolding, and Singular Value Decomposition unfolding, using synthetic dataset with controlled distortion effects. Our results demonstrate that the optimization-based approach achieves competitive reconstruction accuracy across multiple distributions while naturally accommodating regularization within the objective function. This work establishes a unified optimization perspective on unfolding and provides a practical pathway for exploring quantum-enhanced methods in experimental HEP data analysis.

We explore the nonlinear dynamics of classical field theories containing ghost degrees of freedom, focusing on two coupled scalar fields with opposite kinetic terms in (1+1) and (2+1) dimensional Minkowski spacetime. Using a spacetime finite element formulation, we perform a systematic numerical study across a broad class of initial data. We find that ghost-normal systems can exhibit long-lived, dynamically bounded evolution over extended time intervals, with stability strongly controlled by spectral content and amplitude. Ultraviolet-dominated and small-amplitude configurations remain stable significantly longer than infrared-dominated or large-amplitude data, indicating that instability is mediated by nonlinear spectral energy transfer rather than instantaneous runaway. Nonlinear self-interactions play a dual role: while they can accelerate energy exchange between sectors, certain potentials, including a lifted $\phi^6$ interaction supporting oscillon-like structures, generate transient metastable regimes that partially suppress ghost-induced growth. Our results demonstrate that the dynamical consequences of ghost modes in classical field theory depend sensitively on dispersion, nonlinearity, and phase structure, revealing a richer metastability landscape than commonly assumed.

We propose a time-domain boundary integral method to model linear wave propagation with refractive, focusing, and Doppler effects arising from medium heterogeneities and moving obstacles. In contrast to existing techniques, our method avoids volumetric discretization and yields a formulation posed only on the boundary of the obstacle. We combine two classical ingredients: a geometric--optics parametrix to capture leading-order behavior at propagating wavefronts, and a ray-based characterization of the distorted causal geometry. The former provides a framework for defining layer potentials and deriving the associated boundary integral equations, while the latter enables a pure boundary-only formulation. Taken together, these ingredients extend existing numerical techniques for the homogeneous, fixed-boundary case to the heterogeneous, moving-boundary setting, with appropriate modifications to capture the discrete causal structure arising from the intersection of distorted light cones with the worldsheet of the moving boundary. Numerical experiments demonstrate the ability of the method to resolve Doppler effects from moving obstacles, including a rotating turbine configuration, with stable performance up to Mach 0.9. In heterogeneous media, the method captures strong refractive effects from spherical inclusions: wave propagation wrapping around a gas bubble in water, and defocusing from a hot fireball rising through a stratified atmosphere.

Neural operators provide fast surrogate models for time-dependent partial differential equations, but their standard autoregressive use usually assumes that the instantaneous field $u(t,\cdot)$ is a complete state. This assumption fails for delay equations, distributed-memory systems, and other non-Markovian dynamics: two trajectories may agree at time $t$ and nevertheless have different futures because their histories differ. We introduce the History-Space Fourier Neural Operator (HS-FNO), a neural operator for delay and memory-driven PDEs formulated on the lifted state $u_t(\theta,x)=u(t+\theta,x)$, $\theta\in[-\tau,0]$. The key computational step is to decompose one history-state update into a learned predictor for the newly exposed future slice and an exact shift-append transport for the portion of the history window already known from the previous state. This avoids learning deterministic history coordinates, reduces the learned output dimension, and enforces the natural discrete history update. We test HS-FNO on five benchmark families covering delayed reaction--diffusion, spatial epidemiology, nonlocal neural-field dynamics, delayed waves, and distributed-memory closures. Across ten random seeds, HS-FNO attains the lowest aggregate one-step, history-space, and rollout errors among the principal baselines. The largest gain occurs in autoregressive prediction, where aggregate rollout error decreases from $0.241$, $0.188$, and $0.185$ for current-state, lag-stack, and unconstrained history-to-history operators, respectively, to $0.094$. The same model uses fewer parameters than unconstrained history prediction. These results indicate that enforcing the discrete shift structure of history-state evolution is an effective inductive bias for non-Markovian PDE surrogate modeling.

Ball lightning is one of the most mysterious atmospheric phenomena, whose long lifetime, penetrative ability, and stability are difficult to explain with traditional physical models. This paper proposes a novel theoretical framework, interpreting ball lightning as a projection of a high-dimensional topological soliton into three-dimensional space. Its essence is described by a nonlinear Schrödinger equation with attractive interaction, protected by a non-zero topological charge. Through numerical simulation of the three-dimensional Gross-Pitaevskii equation, we verify the core predictions of this model: in a Bose-Einstein condensate with attractive interactions, solitons carrying topological charge exhibit: (1)long-lived stability (topological charge conserved under perturbations); (2)low transmission probability (due to minimal overlap integral resulting from orthogonality with the ground state wavefunction); (3)energy and size scales consistent with actual observations. Theoretical analysis indicates that the soliton lifetime is governed by the system's decoherence rate, providing a natural explanation for the observed second-scale lifetimes. This work not only offers a self-consistent physical explanation for ball lightning but also provides a concrete scheme for the experimental preparation and observation of three-dimensional topological solitons.

We investigate the $N$-queens problem as a lattice gas -- a model in which $N$ queens are placed on an $N \times N$ chessboard with pairwise repulsive interactions along shared rows, columns, and diagonals -- from the perspective of statistical mechanics. The ground states are exactly the $Q(N)$ solutions of the classical $N$-queens problem, with entropy per queen $s_0 \approx \ln N - \gamma$ ($\gamma \approx 1.944$). This entropy reflects a characteristic constraint hierarchy: each successive geometric constraint -- columns, then diagonals -- reduces the entropy from the free-placement value $\ln N$ by a definite constant. We derive the exact high-temperature energy $E/N \to 5/3$ as $N \to \infty$. Extensive Monte Carlo simulations with $10^8$ sweeps per temperature point for $N = 8$--$1024$ reveal that the specific heat per queen $C_v/N$ converges to a universal function of $T$ as $N \to \infty$. The converged curve features a non-divergent peak $C_v^{\max}/N \approx 1.63$ at $T^* \approx 0.235\,J$, establishing the absence of a thermodynamic phase transition. Combined with the trivially exact high-temperature entropy $S(\infty)/N = (1/N) \ln \binom{N^2}{N}$, the convergence of $C_v/N$ enables a thermodynamic integration of $C_v/T$ from $T = \infty$ to $T = 0$ that recovers the ground-state entropy -- and hence the Simkin constant $\gamma$ -- purely from Monte Carlo data. This provides an independent thermodynamic route to a fundamental combinatorial constant. Thermodynamic integration yields $\gamma_{\rm MC} = 1.946 \pm 0.003$ at $N = 1024$, within $0.1\%$ of the precise combinatorial value $\gamma = 1.94400(1)$. We further present a transfer-matrix-based tensor network formulation that encodes the non-attacking constraints into a rank-9 site tensor with 17 nonzero elements, providing a complementary exact-enumeration route.