Papers

Advances in computational methods have made full-wave simulations in large disordered media increasingly feasible, but the resulting volume of field data, scaling with the cube of the ratio of system size to wavelength, creates a severe storage and post-processing bottleneck. Generic compression methods are sample-specific and preclude operations on compressed data. We introduce OSCAR, a physics-based lossy compression scheme for monochromatic wave fields in weakly scattering media. OSCAR exploits the universal confinement of the Fourier representation of wave fields to a thin dispersion shell, a direct consequence of wave propagation when the scattering mean free path significantly exceeds the wavelength, and a property of every disorder realization rather than of the ensemble average, so that one mask serves an entire ensemble. The resulting compression ratio reflects two distinct scale separations: on-shell confinement due to weak scattering, and the excess Fourier-space volume introduced by sub-wavelength discretization of the scatterers. Crucially, second-order quantities, demonstrated here on sensitivity maps, can be computed via convolution entirely in compressed space while preserving the interference effects. We validate the method in 2D scalar and 3D vector simulations of electromagnetic waves, spanning quasi-ballistic to diffusive transport. The reconstruction error is set by the discarded spectral weight. Demonstrated compression ratios reach ~810x in 2D and ~420x in 3D at a 3% error, with cell-averaged second-order observables accurate to an even smaller error. Tested from quasi-ballistic to diffusive transport, OSCAR stores the fields in up to 19x less space at a 2.5-4.2x lower runtime cost than SZ3. This lowers the storage barrier to ensemble studies at scales relevant to biomedical optics, seismology, and underwater acoustics.

Plasma acceleration promises to deliver high-energy particle beams by combining, or staging, several low- or medium-energy accelerator stages. However, chromatic aberrations from the combination of high divergence and energy spread make it nontrivial to transport beams between plasma-accelerator stages. This paper describes a compact and achromatic lattice optimized for staging, based on a new beam-optics element; a nonlinear plasma lens. The lattice preserves emittance for energy spreads up to several percent and has a tunable $R_{56}$ that enables bunch-length preservation or a longitudinal self-correction mechanism. The performance and limitations of the plasma-lens-based solution are modeled analytically and numerically, and compared to a more conventional yet novel solution based on quadrupole and sextupole magnets. While functional, the latter is double the length, has about twice the number of elements and a narrower energy bandwidth. Lastly, a solution for scaling to TeV energies is described, in which all lengths scale with the square root of the energy and the deleterious effects of coherent and incoherent synchrotron radiation are mitigated.

The AI race amplifies security risks and international tensions. While the US restricts mobility and knowledge flows, challenges regulatory efforts to protect its advantage, China leads initiatives of global governance. Both strategies depend on cross-country relationships in AI innovation; yet, how this system evolves is unclear. Here, we measure the processes of polarization and integration in the global AI research over three decades by using large-scale data of scientific publications. Comparing cross-country collaboration and citation links to their random realizations, we find that the US and China have long diverged in both dimensions, forming two poles around which global AI research increasingly revolves. While the United Kingdom and Germany have integrated exclusively with the US, many European countries have converged with both poles. Developing and further developed countries, however, only integrate with China, signaling its expanding influence over the international AI research landscape. Our results inform national science policies and efforts toward global AI regulations.

Many contemporary photodetectors operate beyond the readout of light intensity and enable the reconstruction of spectrum and polarization at the single-pixel level. However, the determination of light incidence direction with reconstructive detectors has not been realized so far. We show that photodetectors based on symmetric junctions of metals and 2d electron systems (2DES) enable (1) zero-bias photocurrent at oblique light incidence (2) reconstruction of incidence direction based on photocurrent measurements at variable carrier density. The former effect is based on peculiar electrodynamics of metal-contacted 2DES, where spatial variations of incident field phase translate into strong variations of local field amplitude. The local absorbances at two opposite metal-2DES junctions at oblique incidence are dissimilar, which results in finite photocurrent independent of microscopic rectification mechanism at these junctions. The direction of photocurrent uniquely determines the quadrant of light incidence. Quantitative determination of incidence angle becomes possible under conditions of 2d plasmon resonance at variable carrier density. In such a case, obliquely incident radiation excites the asymmetric plasmon modes, which amplitude carries unique information about angle of incidence.

This paper introduces two methods for analyzing periodic leaky-wave antennas (LWAs) within a new framework denoted as multi-modal network theory (MNT) with open periodic boundaries (OPBs). The approach is hybrid, combining analytical techniques with a commercial full-wave solver. The first method computes the dispersion diagram of periodic LWAs. It is iterative and relies on the full-wave simulation of a single unit cell of a LWA, coupled with the analytical solution of an eigenvalue problem. This method effectively captures both the phase and attenuation constants of periodic LWAs while using fewer modes than previous methods with commercial frequency-domain solvers. The method is validated by computing the dispersion of classic LWA unit cells and comparing them to those obtained through full-wave simulations of the full-length antenna and other state-of-the-art methods. The second, also based on OPB-MNT, focuses on LWA driven analysis. Specifically, it determines the response of a unit cell to an incident plane wave. To validate this method, we compute the response of LWAs with different unit cell designs. By comparing these results with the corresponding dispersion analysis, we highlight the differences between the driven case and eigenvalue problems and identify the scenarios in which the former is applicable.

Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed outputs. However, classical identification and forecasting approaches become markedly less reliable in turbulent-flow regimes, where the dynamics are high-dimensional, strongly nonlinear, and highly sensitive to compounding rollout errors. Diffusion-based models have recently shown improved robustness in this setting and offer probabilistic inference capabilities, but many current implementations inherit target parameterizations from image generation, most commonly noise or velocity prediction. In this work, we revisit this design choice in the context of nonlinear spatiotemporal system identification. We consider a simple, self-contained patch-based transformer that operates directly on physical fields and use turbulent flow simulation as a representative testbed. Our results show that clean-state prediction consistently improves rollout stability and reduces long-horizon error relative to velocity- and noise-based objectives, with the advantage becoming more pronounced as the per-token dimensionality increases. These findings identify target parameterization as a key modeling choice in diffusion-based identification of nonlinear systems with spatial outputs in turbulent regimes.

Recently arxiv:2604.12897 urged that the terminology "neutrinoless double beta decay" should be changed to "Majorana double beta decay" to properly give credit to Majorana, and to focus on the positive aspects of the phenomenon – supposed creation of matter in the laboratory – rather than the negative: absence of something, embarrassment over false claims of detection, and a "sociology of suspicion." I argue that the current terminology is more accurate and descriptive, and that the claimed reasons for its adoption are lacking in credibility.

The recent detections of thioformyl cyanide (HCSCN) and propynethial (HCSCCH) in TMC-1 provide critical insights into the interstellar sulfur inventory, yet their sequestration and survivability on dust grain mantles remain poorly constrained. Here, we present a computational study of the site-specific adsorption of HCSCN and HCSCCH on amorphous solid water (ASW), modelled via water clusters (H2O)n, n = 6-16, at the wB97X-D/def2-TZVP level of theory, corroborated by QTAIM topological analyses and TD-DFT vertical excitations. Our results reveal a highly heterogeneous binding environment, with desorption energies spanning 1500 to 4900 K. Strongly bound cavity sites induce significant Stark shifts in the C=S stretching modes. Crucially, while the ice matrix exerts a negligible solvatochromic shift on UV transition wavelengths, deeply bound CN-cavity configurations exhibit a pronounced hyperchromic enhancement of the oscillator strength. Implementing these site-specific parameters into the UCLCHEM gas-grain code demonstrates that these species undergo a gradual thermal desorption profile rather than a singular sublimation event. Furthermore, the hyperchromic effect establishes a Survival Paradox: while deeply trapped populations are thermodynamically shielded against thermal desorption, they simultaneously possess enhanced UV absorption cross-sections, rendering them vulnerable to photodissociation by the interstellar radiation field prior to sublimation.

This work aims to address the bottleneck issues of hardware resource limitation and decoherence error in the Hamiltonian simulation of quantum fluids, which are caused by the standard quantum Fourier transform and the evolution of momentum operators, resulting in excessively deep circuits and excessive two-qubit gates. We propose an approximate operator optimization scheme aimed at reducing the circuit depth in Hamiltonian evolution. The proposed scheme successfully reduces the depth of analog circuits from $O(n^2)$ to $O(nlogn)$ or even $O(n)$ by eliminating $O(n^2)$ redundant two-qubit entangling gates. In this work, the numerical experiments are implemented on a supercomputing-oriented quantum simulator, simulating two-dimensional unsteady divergent flow. Experimental results demonstrate that although the truncation of high-frequency qubit coupling terms introduces deterministic theoretical errors, scaling at $O(n)$ for AQFT and $O(n^2)$ for momentum truncation, the optimized simulations successfully preserve the inherent macroscopic temporal evolution characteristics of the fluid in a 10-qubit simulation, achieving high correlation coefficients of $r$=0.933, $r$=0.941, and $r$=0.977 for density, X-momentum, and Y-momentum distributions respectively. Furthermore, we also analyzed the relationship between the algorithm truncation error and the hardware cumulative noise when the qubit number is extended to a higher level. This study proves that rationally adjusting truncation thresholds can establish an equilibrium point, preventing the hardware cumulative error from rapidly approaching 100% at the 20-30 qubit scale, providing a feasible engineering pathway for simulating complex fluid systems on real quantum devices in the future.

We present velocity map imaging data on intramolecular ion-pair dissociation (IPD) of carbonyl sulfide (OCS) induced by electron impact over the 20 eV to 45 eV energy range. Two distinct IPD pathways were resolved: CO+ + S- (threshold 14.8 +- 0.7 eV) and CS+ + O- (threshold 16.8 +- 0.7 eV). The kinetic energy release spectra display a single peak for S- but split into two components for O-; in both channels the maximum kinetic energies level off once the beam energy exceeds roughly 30 eV, pointing to excitation through discrete superexcited states of quasi-resonant character. Partial wave decomposition of the fragment angular distributions reveals that the momentum-transfer parameter (beta) surpasses unity at every energy studied, invalidating the dipole-Born approximation, and that the dominant partial wave character shifts systematically with beam energy. These patterns are consistent with a mechanism in which the incident electron deposits energy through inelastic scattering, populating hybrid Rydberg-ion-pair superexcited configurations that subsequently undergo state-specific unimolecular dissociation along nonadiabatic pathways. From an applied standpoint, intramolecular ion-pair dissociation matters for astrochemistry and radiation biophysics because it generates reactive anions and cations without photon emission, redistributing excess molecular energy nonadiabatically in environments ranging from interstellar clouds to biological systems.

We study the hydrodynamic behaviour of a mesoscale numerical model for wetting dynamics based on the immersed boundary - lattice Boltzmann (IBLB) method. This IBLB model features a wetting potential to capture the interaction between a non-ideal droplet interface and a solid boundary; it is designed to prevent abrupt curvature changes near the contact line. As this approach prevents direct contact between the droplet and the solid, it forms a thin film beneath the droplet, which could compromise the hydrodynamic consistency in this region. This paper presents detailed comparisons against two other hydrodynamic solvers, respectively based on a boundary element method (BEM) and a volume of fluid (VoF) method, in order to examine the hydrodynamic behaviour of this IBLB scheme, elucidate its limits of validity in wetting applications, and explore the properties of its contact-line model.

A Single-Photon Detector (SPD) can detect extremely low intensity of electromagnetic wave - down to a single photon. Driven by the rapid developments in quantum information science and an increasing demand for ultra-low-light sensing across various domains, there is a need for transformative advancements in the design and development of SPDs. In this context, low-dimensional platforms, including quantum dots, superconducting nanowires and layered materials have emerged as crucial frontiers of research. This review explores the state-of-the-art of different low-dimensional SPD platforms, focusing on the engineering physics across their device architectures, performance parameters and application potential. By critically comparing the performance and addressing current challenges inherent to each low-dimensional platform, the review aims to outline future research directions to advance next-generation SPD technologies.

Metal-site density strongly influences oxygen reduction activity and selectivity in M-N-C electrocatalysts, but the descriptors that predict these trends remain under debate. Here, we compare spin state and the potential of zero charge (PZC) as predictors of density-dependent oxygen reduction behavior in Fe-N-C and Co-N-C catalysts. Using constrained-magnetization calculations combined with Landau analysis, we find that the ground-state magnetic moments vary only weakly across a broad range of metal-site densities, suggesting that magnetic descriptors alone cannot account for the pronounced performance changes. In contrast, explicit-solvent simulations reveal systematic density-dependent shifts in PZC, which alter the interfacial electric field and thereby modulate field-sensitive adsorption energetics of ORR intermediates. Incorporating these PZC shifts into a pH-field-coupled microkinetic model captures the density-dependent activity trends and reproduces the experimentally observed increase in two-electron selectivity at lower site densities under acidic conditions. Experimental PZC measurements further support the predicted trend. Within the investigated Co-N-C and Fe-N-C series, PZC-dependent electrostatics consistently rationalize the density-dependent ORR trends, whereas the examined magnetic descriptors show comparatively weak variation.

Meta-optics promises compact, high-performance imaging and color routing. However, designing high-performance structures is a high-dimensional optimization problem: mapping a desired optical output back to a physical 3D structure requires solving computationally expensive Maxwell's equations iteratively. Even with adjoint optimization, broadband design can require thousands of Maxwell solves, making industrial-scale optimization slow and costly. To overcome this challenge, we propose the Neural Adjoint Method, a solver-supervised surrogate that predicts 3D adjoint gradient fields from a voxelized permittivity volume using a Fourier Neural Operator (FNO). By learning the dense, per-voxel sensitivity field that drives gradient-based updates, our method can replace per-iteration adjoint solves with fast predictions, greatly reducing the computational cost of full-wave simulations required during iterative refinement. To better preserve sensitivity peaks, we introduce a stage-wise FNO that progressively refines residual errors with increasing emphasis on higher-frequency components. We curate a meta-optics dataset from paired forward/adjoint FDTD simulations and evaluate it across three tasks: spectral sorting (color routers), achromatic focusing (metalenses), and waveguide mode conversion. Our method reduces design time from hours to seconds. These results suggest a practical route toward fast, large-scale volumetric meta-optical design enabled by AI-accelerated scientific computing.

The hallmark feature of photonic time crystals (PTCs) is the momentum bandgap, yet opening such a gap is extremely challenging, as it demands strong and rapid temporal modulation of the material properties. Recent theoretical advances have shown that resonance effects can substantially expand the momentum bandgap, and even give rise to a full (infinite) momentum bandgap spanning the entire momentum space. Despite these predictions, a full momentum bandgap has yet to be observed experimentally. Here, we report the first experimental observation of full momentum bandgaps in a microwave PTC. By enhancing the resonant effect, we demonstrate that the momentum bandgap can be drastically widened in a dynamically modulated microwave surface plasmon transmission-line metamaterial, leading to tighter spatiotemporal field confinement and greater robustness against temporal disorder. Remarkably, using a dynamically modulated microwave coupled resonator metamaterial characterized by coupled-resonator optical waveguide dispersion, we achieve a full momentum bandgap spanning the entire momentum space, thereby enabling arbitrary spatial localization and temporal amplification of microwave fields. Our findings establish a unified experimental framework for expanding momentum bandgaps up to an infinite extent with minimal requirements on modulation strength and speed, thus paving a viable route toward the first experimental realization of PTCs at optical frequencies.

We report experimental results on Rydberg facilitation, whereby Rydberg levels can be excited off-resonantly in the presence of a nearby Rydberg atom because of Rydberg-Rydberg interactions, for high-lying $S$, $P$ and $D$ levels in rubidium. Facilitation is detected both through an enhancement of the number of excited atoms for off-resonant excitation (either blue or red detuning) and a positive Mandel $Q$ parameter indicating correlated excitation events (super-Poissonian counting statistics). We also calculate the pair-state potentials for the Rydberg states involved and find that our experimental results agree with the expected facilitation conditions for repulsive potentials (blue detuning) and attractive potentials (red detuning), with $P$ and $D$ states exhibiting facilitation on both sides of the resonance. Finally, we investigate inter-state facilitation between two different Rydberg levels (70 $S$ and 70 $P$).

Quartz Crystal Microbalance (QCM) sensors are widely used to study biological and soft-matter interfaces due to their exceptional sensitivity to mass loading and interfacial mechanical properties. While classical QCM theory assumes predominantly shear-wave coupling into a semi-infinite Newtonian liquid, finite liquid thickness and acoustic reflections give rise to pronounced compressional (longitudinal) wave effects that strongly modulate both resonance frequency and motional resistance. Such compressional acoustic-wave responses should be properly accounted for when sensing in the liquid phase, for instance when working with cell suspensions. In this work, we systematically investigate compressional-wave responses in cell culture media including DMEM and RPMI-1640 across varying droplet volumes using a 5 MHz AT-cut QCM. Time-resolved measurements are analyzed using four parameters: the time period of compressional acoustic waves (Tca), the time associated with a phase shift between resonance frequency and resistance oscillations (Tp), the peak-to-peak shifts in frequency ({\Delta}fpp) and resistance ({\Delta}Rpp). DMEM and RPMI-1640 both exhibit strong volume-dependent periodic oscillations. At lower volumes, they exhibit low-frequency oscillations with a time period of approximately 40 minutes. However, as volume increases, the oscillations gradually evolve into high-frequency oscillations with a time period Tca of approximately 5 minutes. The peak-to-peak shifts ({\Delta}fpp) and ({\Delta}Rpp) are approximately 100-150 Hz and 40-60 {\Omega}, respectively. The resonance frequency and resistance oscillations also exhibit a phase shift Tp of approximately 10 minutes. These results highlight that compressional-wave artifacts occur even in simple cell culture media, necessitating their explicit consideration when interpreting QCM data in the presence of cells.

Background: Three-dimensional dual-energy X-ray absorptiometry reconstructs three-dimensional maps of the proximal femur's density distribution from standard hip scans, enabling the estimation of trabecular and cortical bone parameters. The aim of this study was to assess the agreement of these three-dimensional cortical and trabecular femur parameters across different series and models of Hologic densitometers. Methodology: The study cohort was composed of 103 women and men recruited from four clinical centers in Spain and France. Subjects had duplicated hip scans using different Hologic scanners from the Horizon, Discovery, and QDR4500 series. Analyses were performed using 3D-Shaper software. Inter-scanner agreement was evaluated using Deming regression and Bland-Altman analysis. Results: The parameters demonstrated strong inter-device agreement across all clinical centers and scanner models, with coefficients of determination greater than 0.91. Absolute biases were less than 2.5 mg$/$cm$^3$ for integral volumetric bone mineral density, less than 2.9 mg$/$cm$^3$ for trabecular volumetric bone mineral density, and less than 1.7 mg$/$cm$^2$ for cortical surface bone mineral density. No statistically significant bias was found between parameters obtained from different scanners. Furthermore, the observed bias was lower than the expected least significant change, indicating that inter-scanner variability across these devices is not clinically significant. Conclusions: This study demonstrated excellent agreement for standard and three-dimensional derived bone parameters at the hip across Hologic densitometers. These findings support their suitability for clinical use.

Low-temperature hydrocarbon autooxidation involves radical intermediates whose reactivity depends not only on the stereochemistry of the intermediates themselves, but also on that of the transient species encountered along the reaction path. This study offers large-scale evidence for the importance of stereochemistry in low-temperature autooxidation by propagating stereochemical information from 498 C1-C7 hydrocarbons through radical formation, $\mathrm{O_2}$ addition, and the isomerization of alkylperoxy ROO radicals to hydroperoxyalkyl QOOH radicals. The resulting dataset comprises density-functional-theory-level data for 5,356 species, including 2,324 cyclic diastereomeric transition states associated with 1,162 unique ROO -> QOOH isomerization reactions, with transition-state connectivity confirmed by intrinsic reaction coordinate analysis. Explicit stereochemical treatment reveals that diastereomeric pathways may be either degenerate or separated by more than 60 kcal/mol, with the magnitude of these differences governed by steric strain at the carbon bearing the peroxyl group. These results show that constitutionally collapsed molecular representations can systematically miss kinetically relevant reactive channels and provide a foundation for stereochemistry-aware mechanism generation, rate estimation, and predictive combustion modeling.

Fan-array wind generators (FAWGs) provide controlled turbulent inflow conditions that cannot be reproduced in conventional wind tunnels. Despite their increasing use in experimental studies, numerical modeling of FAWG-generated flows remains largely unexplored. The present study assesses the capability of Reynolds-Averaged Navier-Stokes (RANS) modeling to predict jet interaction in a 10x10 fan-array wind generator. Numerical predictions are compared against experimental measurements of axial velocity and turbulence intensity from a reference configuration. Individual fan units are represented using a pressure-jump boundary condition based on a reconstructed performance curve derived from manufacturer data. Grid convergence is verified, and the influence of fan representation, operating point and inflow turbulence conditions is examined. The results show that RANS modeling captures the global jet interaction topology and downstream velocity decay with reasonable accuracy. However, systematic magnitude discrepancies are observed in the near-field injection region and peripheral shear layers. Turbulence intensity predictions exhibit larger deviations, reflecting limitations of the eddy-viscosity closure in highly mixing-dominated flows. A low-aspect-ratio flat plate is included as a demonstrative application to illustrate the aerodynamic impact of FAWG-generated inflow. Overall, the study shows that RANS modeling, combined with a pressure-jump fan representation, provides a computationally efficient framework for predicting the mean-flow structure of FAWG systems, while exhibiting clear limitations in resolving localized turbulence characteristics.

During cell division active flows occur in the cortex, a thin layer of gel like network of acto myosin filaments, beneath the cell surface. The cortical flow and the associated stresses bring about change in the cell shape, in particular a sharp invagination at the mid cell. Using 3D phase field simulation of an active deformable shell, which captures coupled dynamics of cortical velocity and nematic order, we show how a nematic like actomyosin ring spontaneously emerge at the equator and drive sharp invagination. We further demonstrate how different cortical flow patterns, including counter rotating flows emerge near the division furrow. We show that these flow patterns, often attributed to intrinsic chirality of actomyosin filaments can instead arise from bias in the initial nematic alignment, revealing a memory effect in the system. By analyzing a simpler model of activity gradient driven compressive flow on a flat interface we decipher the main ingredients for surface instability leading to invagination and counter moving flows.

Physical neural networks offer a transformative route to edge intelligence, providing superior inference speed and energy efficiency compared to conventional digital architectures. However, realizing scalable, end-to-end, fully analog recurrent neural networks for temporal information processing remains challenging due to the difficulty of faithfully mapping trained network models onto physical hardware. Here we present a fully analog resonant recurrent neural network (R$^2$NN) implemented via a metacircuit architecture composed of coupled electrical local resonators. A reformulated mechanical-electrical analogy establishes a direct mapping between the R$^2$NN model and metacircuit elements, enabling accurate physical implementation of trained neural network parameters. By integrating jointly trainable global resistive coupling and local resonances, which generate effective frequency-dependent negative resistances, the architecture shapes an impedance landscape that steers currents along frequency-selective pathways. This mechanism enables direct extraction of discriminative spectral features, facilitating real-time temporal classification of raw analog inputs while bypassing analog-to-digital conversion. We demonstrate the cross-domain versatility of this framework using integrated hardware for tactile perception, speech recognition, and condition monitoring. This work establishes a scalable, fully analog paradigm for intelligent temporal processing and paves the way for low-latency, resource-efficient physical neural hardware for edge intelligence.

Polycube structures provide parametric domains for all-hexahedral (all-hex) mesh generation and analysis-suitable volumetric spline construction in isogeometric analysis (IGA). Recent learning-based polycube pipelines have improved automation, yet several challenges remain when handling complex CAD geometries. These challenges include the limited diversity of primitive geometries, restricted grid configurations, and the increasing cost of genus-guided context search during inference as both the primitive set and the grid size grow. In this paper, we present {Scalable DDPM-Polycube}, an extended diffusion-based polycube construction method that addresses these limitations. First, we expand the primitive set from two primitive geometries to three by introducing a blind-hole cube primitive, thereby improving the representation of local hole-like features that do not change the global genus. Second, we extend the grid configuration from the previous $2\times 1$ setting to an enlarged three-dimensional grid configuration, which increases representational capacity and reduces mapping distortion for complex geometries. Third, we develop a genus-guided context generation strategy together with a hierarchical verification procedure, enabling robust context generation in both user-guided and automated modes. Once a valid polycube structure is generated, it is used for parametric mapping, all-hex control mesh generation, and volumetric spline construction. Experimental results demonstrate that scalable DDPM-Polycube improves the generality, scalability, and automation of diffusion-based polycube generation, and supports hex mesh generation and volumetric spline construction for IGA applications on complex geometries.

All-optical switching technology is a key solution to the future energy crisis in AI computing, where the performance of optical switches plays a critical role. Conventional integrated optical switches typically suffer from poor robustness to voltage fluctuations, fabrication variations, and temperature drifts. These limitations necessitate complex high-precision real-time calibration and control circuits, which greatly restrict their practical use. This paper presents a digital optical switch based on a thermally tuned multimode waveguide grating (MWG) filter. The switch maintains its on- and off-states across two voltage ranges: 0-0.7 V and 1.1-1.7 V, with a wide operating voltage margin of 0.6 V. It also exhibits excellent robustness to fabrication variations and temperature drifts. By introducing an innovative combination of positive dispersion and parabolic apodization design, the power consumption is reduced by two-thirds, reaching a maximum of only 6 mW. Owing to its low power consumption and wide voltage range, the device can be directly driven by digital signals, allowing for a simplified driver circuitry and a significant reduction in both energy use and overall cost. In addition, the switch offers low insertion loss (<0.5 dB), high extinction ratio (>20 dB), and fast switching (300 {\mu}s), demonstrating excellent overall performance and promising application prospects.

Seismic stratigraphic interpretation of shelf-edge clinothems is essential for revealing tectonic evolution, paleoclimate change, depositional dynamic conditions, and hydrocarbon generation and accumulation during basin filling. However, traditional interpretation methods remain labor-intensive, time-consuming, and highly subjective. Although AI-based method offer a potential solution for automated this task, its development has been limited by the scarcity of comprehensive and representative benchmark datasets for shelf-edge clinothems. This limitation primarily arises from limited field data availability, the scarcity of reliable geological labels, and the structural complexity and strong variability of clinothem-dominated systems. To address this gap, we develop a hybrid benchmark dataset through two complementary strategies of field data curation and geological and geophysical forward modeling, ultimately generating 3,000 unlabeled field and 4,000 labeled synthetic seismic data, respectively. We further evaluate several representative baseline deep learning models on these datasets, and the accurate results demonstrate that the curated dataset provides an effective and representative basis for model training, quantitative assessment, and practical application. Finally, we have publicly released this hybrid benchmark dataset (https://doi.org/10.5281/zenodo.18910271) to facilitate the development, validation, and assessment of deep learning methods for automated seismic stratigraphic interpretation.

The upcoming PandaX-xT experiment will deploy over 3,000 readout channels operating at a 500 MSa/s sampling rate, generating a sustained data bandwidth up to 1.6 GB/s. To meet this demanding requirement, we present AURORA, a high-performance, distributed data acquisition (DAQ) framework designed for scalability, low latency, and efficient resource utilization. Built on a modular architecture and leveraging modern I/O and networking technologies, including multi-level buffering, deferred and asynchronous processing, AURORA achieves a projected throughput of over 3 GB/s on the aggregation node in benchmark tests. While developed to support PandaX-xT, the framework is experiment-agnostic and readily adaptable to other large-scale particle and nuclear physics experiments.

Waste heat recovery improves energy efficiency and reduces greenhouse gas emissions; however, much industrial and environmental heat is wasted at low temperature. Thermomagnetic recovery of waste heat has a high potential for sustainable production of electric energy, especially for low-grade waste heat where conventional technology is inefficient or infeasible. Of particular interest are thermomagnetic generators (TMG) as they require almost no mechanically moving parts, which is beneficial for high reliability. However, all existing prototypes have two remaining challenges: low efficiency and low cycle frequency. In this work, we develop a digital twin of a recent TMG with genus 3 by using multi-physics simulations. We identify shortcomings of previous simulation approaches, and describe why simulations in three dimensions are necessary, which consider coupling between magnetic, thermal, fluid flow, and electrical physics domains. We validate our model, which only uses known geometry and material parameters, by experimental data of the TMG with highest power density today, and attain 96% accuracy in open-circuit voltage and 95% accuracy in power output. This high accuracy allows us to identify the origin of both challenges for TMGs, which are not accessible by experiments. First, we uncover inefficiencies by analyzing the energy flow within a Sankey diagram. Second, we trace the transient heat flow through the generator, which identifies the factors limiting frequency. This paves the way for more efficient and faster TMGs, and their development will be accelerated by our validated digital twin.

The discrete Boltzmann factor $B_E(\beta_n)=(1-bE)^n$, introduced by Chung, Hassanabadi, and Boumali, provides a lattice regularization of the canonical weight $e^{-\beta E}$ and imposes the compact-support condition $E<1/b$. In the present analysis we systematically separate results that follow directly from this bounded thermal weight from those that require additional phenomenological input. First, we study the discrete Bose–Einstein occupation factor relevant for Hawking radiation, derive the leading suppression of black-hole luminosity, and show that the thermal Hawking channel shuts off as the cutoff scale is approached. Second, we formulate a discrete work functional built from ratios of thermal weights and establish an exact Jarzynski-type identity for deterministic measure-preserving protocols; in contrast, the corresponding Crooks relation does not collapse to a function of work alone, and first-order approximations retain an explicit initial-energy dependence that cannot be reduced to a simple $W$-dependent correction without additional assumptions. Third, and purely as an ancillary kinematic extension rather than a derivation from the statistical framework itself, we examine a bounded modified-dispersion ansatz and estimate the associated time-of-flight constraints. Throughout, we include illustrative figures, clarify the non-universal status of the entropy correction, and emphasize that direct laboratory signatures are negligible whenever $b$ is universal and Planck suppressed. Finally, the standard continuum expressions are recovered smoothly in the limit $b\to 0$.

Gravitational spectroscopy tests the coupling of gravity to matter by measuring gravitationally induced frequency shifts of quantum transitions. While modern optical clocks probe the gravitational response of electronic transitions with extraordinary precision, tests in the nuclear sector have not progressed since the Mössbauer measurements of the gravitational redshift by Pound and Rebka. Here we introduce a new approach to nuclear gravitational spectroscopy based on phase-sensitive heterodyne interferometry of time-resolved nuclear resonant scattering of synchrotron radiation. In this scheme the gravitational redshift appears as a slowly accumulating phase drift of a delayed heterodyne beat signal, converting nuclear gravitational spectroscopy from energy-domain detection to time-domain interferometry. A Fisher-information analysis supported by Monte Carlo simulations and experimentally confirmed photon count rates shows that the nuclear gravitational redshift of $^{57}$Fe can be detected within hours on a few-meter-scale vertical baseline, with percent-level precision on deviations from general relativity becoming accessible on day-scale timescales. The method thus establishes an experimentally realistic and scalable platform for precision tests of gravitational coupling to nuclear structure.

Physics-informed neural networks (PINNs) have emerged as a promising framework for solving inverse problems governed by partial differential equations (PDEs), including the reconstruction of turbulent flow fields from sparse data. However, most existing PINN formulations are deterministic and do not provide reliable quantification of epistemic uncertainty, which is critical for ill-posed problems such as data-driven Reynolds-averaged Navier-Stokes (RANS) modeling. In this work, we develop and systematically evaluate a set of probabilistic extensions of PINNs for uncertainty quantification in turbulence modeling. The proposed framework combines (i) Bayesian PINNs with Hamiltonian Monte Carlo sampling and a tempered multi-component likelihood, (ii) Monte Carlo dropout, and (iii) repulsive deep ensembles that enforce diversity in function space. Particular emphasis is placed on the role of ensemble diversity and likelihood tempering in improving uncertainty calibration for PDE-constrained inverse problems. The methods are assessed on a hierarchy of test cases, including the Van der Pol oscillator and turbulent flow past a circular cylinder at Reynolds numbers Re=3,900 (direct numerical simulation data) and Re = 10,000 (experimental particle image velocimetry data). The results demonstrate that Bayesian PINNs provide the most consistent uncertainty estimates across all inferred quantities, while function-space repulsive ensembles offer a computationally efficient approximation with competitive accuracy for primary flow variables. These findings provide quantitative insight into the trade-offs between accuracy, computational cost, and uncertainty calibration in physics-informed learning, and offer practical guidance for uncertainty quantification in data-driven turbulence modeling.