Papers

Symmetry governs nonlinear interactions in condensed matter systems, particularly in high-harmonic generation (HHG), the interplay between the driving field and crystal symmetries dictate the properties of the emitted harmonics. A central open question is how quantum fluctuations of light modify these symmetry-imposed selection rules in solid state systems. Here, we address this by studying the nonlinear response of graphene and Molybdenum disulfide (MoS$_2$) to circular polarized quantum light, where both materials with distinct rotational symmetries and corresponding classical selection rules. We show that the quantum fluctuations break the dynamical symmetry of the driving field while preserving the crystal symmetry, which enables the generation of classically forbidden harmonics by breaking the corresponding selection rules. These results establish quantum states of light as a new degree of control over harmonic generation in solids, opening routes toward all-optical symmetry engineering of the quantum optical harmonic properties towards attosecond pulse generation.

The transition to a circular economy is pivotal for industrial decarbonization, particularly in the energy-intensive steel sector. While recycling scrap via electric arc furnaces offers a low-carbon alternative to primary production, the accumulation of contaminants in post-consumer steel threatens to render secondary material unusable for high-grade applications. This quality problem creates a market failure where traditional spot markets cannot guarantee the material provenance required for high quality (e.g. automotive-grade) steel. Here we show, using a massive longitudinal analysis of 1,163,561,858 news articles combined with global trade network data, that the industry addresses this partly through direct, vertically integrated alliances. We demonstrate that steelmakers are bypassing intermediaries to forge sovereign, closed-loop ties with manufacturers, effectively substituting market mechanisms with organizational integration. This reorganization coexists with a division between circular network cores in the Americas and Europe that pair their steel and scrap flows most tightly within their region, and economies whose steel and scrap flows are far less matched. At firm level, the quality of the returned scrap is a stated reason in twelve of thirty-six reciprocal loops closing after 2020 and in none of the eight loops detected before. Our results indicate that the circular economy is evolving into a geopolitical contest for material control, where competitive advantage is defined by the sovereign possession of closed material cycles rather than mere cost efficiency.

Adiabaticity is a key concept in physics, but its applications in mechanical and control engineering remain underexplored. Adiabatic invariants ensure robust dynamics under slow changes, but they impose impractical time limitations. Shortcuts to Adiabaticity (STA) overcome these limitations by enabling fast operations with minimal final excitations. In this work, we set a STA strategy based on dynamical invariants and inverse engineering to design the trajectory of a cart-pole, a system characterized by its instability and repulsive potential. The trajectories found guarantee a balanced transport of the cart-pole within the small oscillations regime. The results are compared to numerical simulations with the exact non-linear model to set the working domain of the designed protocol.

Accurate and timely weather forecasts are critical for high-impact decisions in modern society. Machine-learning-based weather prediction is emerging as an alternative for producing initial conditions, forecasts, and even both in end-to-end systems. These methods deliver predictions faster and often with higher skill than traditional numerical weather prediction (NWP). However, even end-to-end models typically rely on NWP-generated reanalyses for supervision, thereby inheriting the biases and resolution limitations of those NWPs, and limiting adaptation to settings where suitable reanalysis products are unavailable, infrequently updated, or expensive to produce. Here we introduce ObsCast, a regional system that generates both analysis and predictions, without using any NWP-derived data in either training or inference, while still achieving state-of-the-art performance in short-term high-resolution regional modeling. Over the contiguous United States and Europe, ObsCast outperforms operational NWP for near-surface variables through 18 h and produces skillful precipitation forecasts. It provides a simpler and more adaptable route to build and refine regional forecasting services directly from local observations, without the need to develop complex and costly traditional forecasting pipelines.

Fluid simulations, especially at high Reynolds numbers, are computationally expensive on classical computers, making them promising application targets for quantum computing. Recent studies have combined the lattice Boltzmann method (LBM) with Carleman linearization to design quantum algorithms for computational fluid dynamics (CFD). However, practical quantum-circuit implementations of these algorithms that incorporate non-periodic boundary conditions have not been fully explored. In this work, we implement a quantum algorithm for two-dimensional linearized fluid flow around an obstacle, using block-encoding of the linear-system matrix and quantum singular value transformation (QSVT) to solve it. Inflow, outflow, and no-slip boundary conditions are formulated as sparse matrix operations and efficiently embedded into quantum circuits using index-value encoding. We demonstrate logarithmic scaling of the required numbers of qubits and gates with respect to the number of lattice points, suggesting the potential feasibility of quantum-computational fluid dynamics simulations.

The Controlled Source Electromagnetic (CSEM) method aims to image electrical resistivity at intermediate depths (0-3 km) for geothermal, mineral, and groundwater exploration. It was developed both as a deeper extension of DC resistivity (ERT) and as an active alternative to magnetotellurics (MT), capable of overcoming MT's limitations in challenging environments (noisy areas or weak natural signals). As demonstrated in marine settings, CSEM can provide high resolution and complementary illumination compared to MT. However, due to the significant logistical effort required for land deployment, most land-based CSEM surveys have used only a few transmitter positions (often one or two), effectively resembling CSAMT configurations. This limited source coverage results in poor resolution and unreliable resistivity models, where the acquisition footprint can be difficult to distinguish from actual geology. In this work, we review ten years of progress in land CSEM and demonstrate that dense arrays-with dozens to hundreds of synchronized sources and receivers-are key to achieving the high-resolution imaging originally expected. Despite greater logistical demands, we show that such surveys can be conducted with reasonable effort and cost, making them accessible for both industrial and academic applications. Furthermore, CSEM can be combined with MT at minimal additional cost, improving model constraints and reducing exploration risk.

Primary hemostasis is initiated by platelet adhesion and aggregation at a site of vascular injury and is strongly regulated by local hydrodynamic conditions. At elevated shear rates, platelet capture is mediated by von Willebrand factor (vWF), a multimeric protein that undergoes shear-induced unfolding and becomes adhesive. We investigate early-stage clot formation under physiological high-shear-flow conditions by employing particle-based mesoscale hydrodynamics simulations with explicitly resolved red blood cells, platelets, and mechano-sensitive vWF in a microchannel geometry. The model incorporates vWF-mediated adhesion of platelets to a hemostatic surface, together with non-periodic inflow-outflow boundary conditions that allow continuous material supply and transport. We analyze the dynamics of platelet-vWF aggregation, clot growth dynamics, clot geometry and internal stresses, and thrombo-embolization across a range of elevated flow rates. Our results demonstrate that clot formation proceeds through the establishment of platelet-vWF aggregates at the hemostatic site, and that the clot reaches a finite size determined solely by hydrodynamic forces, without invoking biochemical stabilization mechanisms. Beyond a critical size, increased drag from fluid flow leads to recurrent embolization events that limit further growth. These findings highlight the central role of hydrodynamic stresses in regulating primary hemostasis and provide a mechanistic framework for understanding clot stability under physiological flow conditions.

Many prediction tasks in computational science and engineering become one-to-many after coarse graining and partial observation. In such settings, deterministic surrogates trained by squared loss may learn a well-defined mathematical object, the conditional mean, while still missing the task-relevant variability in the underlying conditional law. In this work, we formulate this limitation as the conditional-mean barrier and develop a diagnostic framework for identifying it in fitted scientific machine-learning surrogates. The framework combines residual-feature orthogonality and effect-size diagnostics to distinguish deterministic underfitting from irreducible conditional variability. We also make explicit a simple consequence of paired squared loss: stochastic outputs do not by themselves overcome the barrier, because the objective penalizes model variance and drives the predictor back to the conditional mean. The diagnosis therefore yields a modeling prescription: when residual variability matters, the loss must score richer features of the conditional law rather than a point prediction. Reproducible numerical studies on a controlled two-branch law and a two-scale Lorenz-96 closure problem show how the diagnostic identifies the barrier, how deterministic closures can suppress collective fluctuation statistics in rollout, and how a minimal likelihood-based stochastic-scale model can recover substantially more variability.

Designing compact and accurate circuits for the variational quantum eigensolver (VQE) is a central challenge in near-term quantum chemistry. Existing adaptive methods such as ADAPT-VQE design circuits by iteratively selecting operators from a predefined pool guided by gradient information and greedy heuristics. In this work, we adopt differentiable quantum architecture search (DQAS) as a circuit design framework based on the UCCSD operator pool, and introduce two complementary strategies: a global mode that simultaneously optimizes all operator selections, and a layerwise mode that constructs circuits incrementally while preserving previously learned structure. By relaxing discrete operator selection into a continuous differentiable optimization, DQAS enables gradient-based exploration over the combinatorial space of UCC circuit architectures. Benchmarks on BeH2, H4, LiH, H6, and H2O (8-14 qubits) show that both strategies achieve higher accuracy and fewer CNOT gates than ADAPT-VQE in the compact circuit regime, with up to 2.7-fold accuracy improvement for H2O and CNOT reductions of 13-17% at equivalent circuit depths. Benchmarks on the qubit-excitation-based (QEB) operator pool confirm that both advantages generalize beyond UCCSD. These results demonstrate that differentiable architecture search provides an effective and generalizable framework for designing accurate and compact VQE circuits in near-term quantum chemistry.

Subspace diagonalization techniques based on quantum sampling, such as quantum selected configuration interaction (QSCI) and sample-based quantum diagonalization (SQD), have recently emerged as promising quantum-centric approaches for approximating ground-state energies of many-body systems. However, their performance is fundamentally limited by an intrinsic trade-off between sampling efficiency and the sparsity of the ground-state wavefunction, which becomes particularly severe in strongly correlated systems. Here, we introduce a filter-assisted SQD protocol that engineers wavefunction sparsity via a quantum filter, i.e., a unitary transformation of the Hamiltonian designed to concentrate the ground-state weight onto a small number of computational basis states. Using the Gini coefficient as a robust sparsity measure, we establish a quantitative relationship between wavefunction sparsity and the resource requirements of SQD, providing theoretical bounds on the required subspace dimension and sampling cost. To realize the quantum filter, we employ a tensor-network-based circuit-encoding algorithm that maps target states to quantum circuits with controllable fidelity. We benchmark our approach on the quantum Ising model with transverse and longitudinal fields using both numerical simulations and quantum hardware experiments. Our results demonstrate that, compared with standard SQD, the proposed protocol significantly enhances wavefunction sparsity, reduces ground-state energy estimation errors by orders of magnitude, and substantially lowers sampling overhead. These findings establish filter-assisted subspace diagonalization as a powerful and scalable framework for quantum many-body calculations in the strongly correlated regime.

The Einstein-Bohr recoiling-slit gedankenexperiment, a cornerstone of quantum complementarity, has long been constrained by the zero-point fluctuations of the atomic slit – the spatial Standard Quantum Limit (SQL). Here we transcend this fundamental boundary through active quantum state engineering of a single-atom slit. By implementing a non-adiabatic quench-evolve-quench protocol, we prepare the atomic motion in a squeezed state, dynamically redistributing phase-space uncertainty to suppress which-path information and restore high-visibility interference beyond the static vacuum limit. We report an intrinsic visibility of $0.938_{-0.008}^{+0.004}$, violating the SQL ($0.819$) by over 10 standard deviations, corresponding to $7.6(2)$ dB of effective squeezing. Our work reveals Kerr-induced non-Gaussian dynamics and reinterprets the traditional interferometer as a powerful tool for continuous-variable Wigner tomography, bridging the gap between quantum foundations and advanced metrology.

Wave reflection by non-Hermitian antisymmetric parity-time (APT) photonics made of a balanced positive index material (PIM) and negative index material (NIM) is investigated. As one class of them satisfies a unitary relation, wave impedance matching (WIM) can be achieved not only for polarization independence but also for omnidirection. Remarkably, the result is independent of system sizes, material parameters, and polarization. As gain or loss is involved, any non-Hermitian systems are unable to support WIM; instead, there occurs a reflection dip, i.e., a minimum reflectance, at some system configurations, a certain incident angle, and polarization dependence. Our finding is in contrast to the parity time (PT) symmetric paradigms, in which the well-known exceptional point can be recognized as a WIM. Additionally, in the vicinity of a reflection dip, it is accompanied by a jump of reflection phase, offering an opportunity to observe a significant Goos-Hanchen (GH) beam shift in APT systems. We provide associated systems and numerically verify the GH shifts. Meanwhile, we derive a closed-form expression for a reflection dip in terms of incident angle, operating wavelengths, and material parameters. To imitate the electromagnetic responses of APT photonics for their angular dispersion, we discuss a dielectric heterostructure consisting of spatially symmetric multilayer slabs so as to have the same reflection and transmission for each APT component over a specific angular spread.

A mechanism of resonant excitation of surface magnetoplasmons (SMPs) is proposed in the terahertz (THz) frequency range by {beating of two p-polarized lasers}, obliquely incident at an angle $\theta$ on a graphene sheet deposited over a rippled surface of a magnetized n-type semiconductor. The resulting laser-beat-envelope induces a nonlinear velocity to free electrons, which couples with the modulated charge carrier density and generates a nonlinear current. This time-varying oscillating nonlinear current acts as the source of THz SMPs wave generation, as opposed to THz generation by a different process with {a single laser} in the earlier work [Phys. Rev. E 113, 015208 (2026)] where light dispersion characteristics as well as the required phase-matching conditions are markedly different. The resulting THz SMPs field amplitude is shown to be controlled in the frequency range of $2-5$~THz by varying the graphene's Fermi energy ($\textrm{E}_\textrm{F}=20-130$ meV), laser incident angle ($\theta = 0-90^{o}$), the semiconductor's temperature ($T = 320 - 380$~K) and external magnetic field ($\textrm{B}_{0} \approx 0 - 0.09 $~T). The amplitude of THz SMPs field now reaches on the order of $10^{-1}$ w.r.t. the incident field amplitude, and it is almost $10^1 - 10^2$ fold higher compared to previous works. Thus, the proposed mechanism may open new avenues for the development of actively tunable plasmonic device, with potential applications in future THz technologies and 6G wireless communication systems.

Ultra-low-field (ULF) MRI offers portable and low-cost imaging but suffers from poor image quality. To address this, we present our submission to the 2025 ULF Enhancement Challenge (ULF-EnC), where the goal is to synthesise high-field-like MRIs from 64 mT scans. Our pipeline enhances ULF MRI through a combination of anatomical conditioning and model ensembling. We first generate tissue segmentation priors using a Swin UNETR trained solely on challenge-provided data. These priors condition two independent enhancement networks - a CycleGAN and a transformer-based residual enhancement model (T-REX) - each trained to synthesise 3 T-like MRIs. Outputs from both models are combined using a weighted average. Our approach produces enhanced MRIs that were comparable to high-field scans both quantitatively and qualitatively.

Deploying Scientific Machine Learning surrogates in industrial CFD workflows requires adapting pretrained models to new vehicle families without large datasets; yet whether geometric representations learned by a geometry encoder transfer to topologically distinct shapes remains unvalidated. We address this through leave-one-family-out experiments on a 61.47M-parameter Transformer surrogate (AB-UPT) pretrained on four vehicle families (411 external aerodynamics cases) and adapted to the held-out fifth with only 20 samples. Three strategies are compared: Full Fine-Tuning (FFT), Lightweight Fine-Tuning (LFT), and Low-Rank Adaptation (LoRA). The central finding is that pretrained geometry encoders learn transferable representations, but the adaptation mechanism determines whether they can be exploited. FFT destabilizes as 61.47M unconstrained parameters overfit to 20 samples (R^2=0.40); LFT fails because the frozen encoder cannot represent unseen shapes (R^2<0). LoRA resolves both: rank-constrained adapters injected into all layers regularize the loss landscape while preserving pretrained features, achieving R^2=0.85+/-0.02 across all five families with 50% lower force RMSE than FFT and 28% lower pointwise field errors. LoRA also outperforms from-scratch training using 3x more target-family data, eliminating the need for large per-family datasets. These results recast LoRA from a memory-saving convenience into a convergence enabler for geometry transfer: a shared backbone paired with lightweight per-family adapters trainable in hours from minimal data.

Scintillation detectors with excellent timing resolution enable more precise localization of radiation sources in positron emission tomography, leading to substantial improvements in diagnostic capability for diseases such as cancer and dementia. At the extreme timing precision required for such applications at the picosecond scale, detector performance is governed by the microscopic dynamics of scintillation photons generated within the detector and their subsequent detection processes. However, detector signals have conventionally been treated only as collective responses of many photons due to structural constraints inherent to photodetectors. In this study, we overcome this fundamental limitation using deep learning, enabling direct access to the timing information of individual photons. The proposed method estimates photon-by-photon arrival times directly from detector waveforms without requiring any modification to the detector structure; the method operates on an event-by-event basis without ground-truth labels by integrating an unsupervised learning framework with a physically informed detector-response model. Through comprehensive validation combining Monte Carlo simulation and experimental measurements across various detector configurations, we experimentally demonstrate improved timing resolution, visualized depth-of-interaction-dependent photon transport, and classified Cherenkov and scintillation photons based on the estimated photon-level timing information using a unified deep learning-based framework. These results provide experimental access to photon dynamics, bridging the gap between theoretical modeling and experimental observation, and they open a new data-driven pathway for discovery in detector physics and optimization.

Virological measurements are often treated as reports of virion structure, mechanics, dielectric response, infectivity, or titer. In practice, an experiment observes a protocol-conditioned projection of a richer latent virion–environment ensemble. This paper defines this process as experimental collapse within protocol-resolved virophysics. Its central object is the null-inclusive observation operator $P_{\mathrm{obs},t}^{\varnothing}(\,\cdot\mid E\,) = \mathcal{M}_{E,t}^{\varnothing}P_{\mathrm{ref},t}$, which maps a reference latent ensemble to the observed ensemble generated by protocol $E$, including null outcomes. The formulation separates latent-state transformation, detection weighting, readout, and non-observation, making protocol effects explicit components rather than bias terms. The framework introduces protocol-conditioned latent ensembles, collapse functionals, protocol blindness, observation equivalence, Fisher-information observability, inverse inference, and multi-protocol consistency. It identifies collapse mechanisms including preparation, surface immobilization, mechanical loading, field steering, medium filtering, amplification, censoring, and detection thresholds. As a worked example, the plaque assay estimates an effective protocol-conditioned infectious concentration $\Lambda_{\mathrm{PFU}}=\int_{\Psi}\pi_{\mathrm{PFU}}(x;E_{\mathrm{PFU}})n_{\mathrm{ref}}(x),dx$, rather than total particle concentration. This recovers the Poisson plaque-count model and PFU titer formula in the dilute regime; extensions to overdispersion, zero inflation, plaque merging, endpoint dilution, neutralization, and morphology-augmented readouts recast deviations as protocol-conditioned information. Thus, virological data are outputs of explicit protocol kernels, clarifying what measurements report, miss, and how complementary assays can infer hidden latent virion structures.

Estimating a fractal dimension from a finite stochastic trajectory is a finite-size scaling problem: the apparent box-counting exponent is shaped by an occupancy crossover between the resolved range of scales and the finite number of sampled points, and need not equal the dimension of the limiting process. We model this crossover with a balls-in-boxes occupancy law, which predicts the box-count curve, the finite-size saturation scale, and a scaling function for the normalized local slope. Across random-walk traces, fractional Brownian graphs, and Levy flights, the normalized local slope collapses onto a single crossover curve, while the windowed box-counting bias collapses when the regression window is positioned relative to the saturation scale. Inverting the occupancy model gives a finite-size bias correction that reduces error on controlled stochastic trajectories and transfers across held-out model classes. Comparisons with correlation dimension, detrended fluctuation analysis, the variogram, and Higuchi's method show that the dominant bias is specific to point-sampled box-counting over finite scale windows, and that local-slope stability alone is not a reliable diagnostic. A DNA-walk example illustrates the workflow on measured data, and all figures, tables, and in-text numbers are regenerated from released single-seed code.

The optimization of energy group structures is integral to ensure the accuracy of multigroup neutron transport calculations. This works introduces the use of reinforcement learning (RL) with surrogate modeling to optimize the group structure for one-dimensional spherical k-criticality problems. The proximal policy optimization (PPO) RL algorithm is modified to be used with energy grid structures, rewarding accurate group structures while favoring fewer energy groups. This method starts from a high-fidelity energy grid and remove energy bounds until reaching a target energy structure. The RL agent identify which bounds are important for the final group structure, which prevent it being stuck in local minima without limiting the initial group structure. Neural network surrogate models that incorporate energy, material, and spatial information are used for evaluating energy grid structures without requiring full transport simulations. This alleviates the computational constraint commonly used in other group structure optimization problems in addition to accelerating the RL training process. Applied to Godiva and BeRP ball problems, the RL constructed group structures outperform commonly used group structures. The RL group structure optimization method is also shown to perform similar to the hierarchical agglomeration approach but offers more flexibility.

Sensitive mid-infrared (MIR) spectroscopy plays an indispensable role in various photon-starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi-pixel arrays in parallel spectral acquisition. Here, we devise and implement an ultra-sensitive MIR single-pixel spectrometer, which relies on high-fidelity spectral upconversion and wavelength-encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 $\mu$m is nonlinearly converted to the near-infrared band via synchronous chirped-pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single-element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 cm$^{-1}$ under an illumination flux down to 0.01 photons/nm/pulse. Moreover, we demonstrate faithful reconstructions at sub-Nyquist sampling rates by using the compressive sensing algorithm, which leads to a 95% reduction in data acquisition time. The presented single-pixel computational spectrometer features wavelength multiplexing, high throughput, and efficient sampling, which thus paves a new way for sensitive and fast spectroscopic analysis at the single-photon level.

The role of a transverse liquid n-heptane jet in initiating and stabilizing liquid n-heptane oblique detonation waves (ODWs) in a confined model combustor was computationally investigated in the present work. The jet-to-inflow momentum ratio, J, was identified as the primary control parameter. Under steady inflow pressures, a weak jet with a small J fails to initiate an ODW; a slightly stronger jet ignites only a local near-normal detonation between the OSW and the separation shock wave without forming a developed ODW branch; a moderate jet establishes a standing detonation wave system consisting of an ODW, a near-normal detonation branch, and a separation shock wave; a large but still admissible J produces a wall-coupled ODW-Mach-stem configuration; and an excessive jet momentum destabilizes the ODW by pushing it out of the combustor into the external compression region. Under oscillatory inlet pressure, the standing ODW remains dynamically stabilized within the combustor through bounded, phase-dependent transitions between distinct combustion modes. At sufficiently large J, the transverse jet ceases to act as an effective stabilization actuator. The resulting dynamic-stabilization map reveals a finite operating window governed jointly by jet momentum and inlet-pressure fluctuation.

Mid-infrared reconfigurable edge-enhanced imaging is highly demanded in sensing and vision fields. Here, we propose a novel scheme for mid-infrared upconversion imaging with high tunability between bright-field and edge-enhanced modalities. The involved engineering of the nonlinear process favors shaping the optical transfer function of the imaging system. Consequently, a nonlinear angle-selective filter can be configured to perform an all-optical Fourier processing of the image, which highly depends on phase-matching parameters. We numerically demonstrate the ability to switch modalities between the bright-field and edge-enhanced imaging by tuning the crystal temperature, and to simultaneously acquire both information by dichromatic illumination. Notably, the achieved reconfigurability is realized without changing the imaging settings, which contrasts to previous instantiations based on pump adaptation. Therefore, the proposed architecture of upconversion imagers would pave a novel way to implement layout-compact and all-optical processing for infrared images.

High-speed mid-infrared (MIR) videography constitutes an enabling tool to monitor and analyze various dynamics in scientific research and industrial applications, such as combustion diagnostics, explosion reactions, photosynthetic tracking, and thermal surveillance. However, the frame rate of conventional MIR imagers is typically limited by readout electronics and detection sensitivity, especially for large spatial formats with massive pixels. Here, we devise and implement a high-speed MIR upconversion imaging system based on time-multiplexed nonlinear structured pumping. Specifically, the dynamic infrared scene is optically gated by a sequence of spatially periodical pump patterns in a nonlinear crystal, which facilitates both rapid temporal encryption and sensitive upconversion detection. Then, the upconverted frames are superimposed onto a silicon camera within a single exposure, thus resulting in a multiplexed snapshot in the spatial-frequency domain. Finally, the sub-exposure images, corresponding to distinct transient events, can be computationally deciphered and reconstructed by the frequency recognition algorithm based on band-pass filtering and Fourier transform operations. The achieved frame rate is tenfold boosted to 10,000 frames per second without compromising the megapixel spatial format, which allows continuous real-time MIR videography at high speed and high definition. The presented approach could be readily extended to far-infrared or terahertz spectral regions, with an aim of performing high-throughput and high-sensitivity observation of transient phenomena with high temporal complexity.

Financial and economic research often relies on structured supply-chain disclosures and commercial databases. In China, supplier–customer disclosure is typically limited to major partners of listed firms, leaving unlisted firms and long-tail inter-firm links poorly captured in structured data. Public web evidence can partly complement this gap through corporate, government, and trade-media disclosures; however, full-text web mining at scale is costly because pages are often inaccessible or expensive to process with large language models (LLMs). We propose a snippet-driven method for constructing a supply chain knowledge graph (SCKG), with firms as nodes and inter-firm relationships as edges. Web search snippets are query-biased summaries returned with search results. We use them as a scalable first-pass evidence layer for LLM-based relationship extraction. We evaluate the pipeline in terms of extraction efficiency and coverage. For extraction efficiency, exhaustive full-text chunking discovers 19.8$\times$ more unique relationships than snippets, but requires 251.2$\times$ more input tokens and yields higher redundancy. For coverage, we use 130,685 Chinese firms as search seeds, covering Shanghai/Shenzhen-listed firms and large unlisted firms as of 2024. In the listed-firm subset, the resulting SCKG covers 7.2$\times$ more firms and 9.3$\times$ more relationships than the CSMAR disclosure-based benchmark, while revealing heavy-tailed degree patterns. Retained provenance metadata make the SCKG an auditable complement to disclosure-based databases.

Altermagnets are a recently identified class of collinear antiferromagnets that host large spin-split electronic bands, offering a promising platform for efficient spin-current generation. Among proposed candidates, the metallic oxide RuO2 is predicted to exhibit strong altermagnetic spin splitting; however, whether it sustains robust magnetic order beyond the ultrathin thickness limit remains unresolved. Here, we employ optical probes to investigate charge-to-spin conversion in a 12-nm-thick (101)-oriented RuO2 film grown on sapphire. Polarization-resolved second-harmonic generation reveals nonlinear optical responses consistent with the surface symmetry and Néel order of RuO2. Under an applied current, both second-harmonic generation and polar magneto-optical Kerr effect measurements detect a pronounced, directionally anisotropic spin polarization, exhibiting enhanced signals for current along [010] and strongly suppressed responses for current along [-101], in agreement with the symmetry of the altermagnetic spin-splitter effect. Non-magnetic or Rashba-type mechanisms cannot explain this symmetry-selective response. Scanning transmission electron microscopy further reveals that substantial strain persists even in relatively thick films, providing a possible explanation for the observed behavior. Therefore, these results establish RuO2 as an efficient spin source and demonstrate the potential of altermagnets for field-free spintronic devices.

High-repetition-rate, fully coherent extreme-ultraviolet (EUV) and X-ray free-electron lasers (FELs) are essential for advanced time-resolved ultrafast spectroscopies. While external seeding serves as the standard technique to achieve precise temporal coherence, conventional methods demand hundred-megawatt peak-power laser systems. Furthermore, advanced configurations like echo-enabled harmonic generation (EEHG) introduce the severe complexities of dual-laser synchronization. Together, these requirements fundamentally restrict operations to kilohertz repetition rates and compromise overall system stability. Here, we experimentally demonstrate a fully coherent EEHG-FEL driven by a single, sub-microjoule seed laser. By employing a direct-amplification enabled harmonic generation technique, we utilize an initial 0.4 microJ (2 MW peak power) ultraviolet seed to directly drive coherent lasing at nanometer wavelengths. By eliminating the need for extreme peak powers and multiple synchronized lasers, this approach significantly simplifies the seeding architecture and provides a practical and robust pathway toward megahertz-class, fully coherent EUV and X-ray light sources.

Single-photon avalanche diodes (SPADs) have emerged as a promising candidate for optical wireless communication (OWC) owing to their ultra-high sensitivity and singlephoton detection capability. However, under strong background radiation or high signal power, SPAD-based receivers suffer from photon-counting saturation, which severely degrades communication performance. To address this challenge, this paper introduces an automatic attenuation control (AAC) technique that dynamically optimizes the incident optical intensity to mitigate saturation effects. We develop a comprehensive analytical model for the SPAD-based OWC system, incorporating the influence of dead time and the lack of photon-number resolution. Based on this model, a convex optimization-based AAC algorithm is proposed to maximize the achievable rate in real time. Furthermore, a low-complexity AAC algorithm is devised using a closed-form trigger probability criterion, reducing computational complexity by two orders of magnitude. Numerical results demonstrate that the proposed AAC technique significantly improves both the achievable rate and symbol error rate across a wide range of background conditions, providing an efficient solution to enhance the dynamic range of photon-counting receivers.

Atmospheric gusts expose wall-bounded turbulence to severe unsteady forcing, triggering complex non-equilibrium dynamics and extreme aerodynamic loads. In this study, direct numerical simulations are performed to investigate the spatiotemporal modulation of turbulent structures and the triggering mechanisms of near-wall extreme events under Gaussian-type transient forcing. The results reveal that high-amplitude gusts inject energy primarily into the streamwise velocity component, inducing a pronounced non-equilibrium phase lag during turbulent energy redistribution. This process produces hysteresis in wall friction and extends the relaxation time. Spectral and continuous wavelet analyses demonstrate that intense gust forcing suppresses high-frequency random fluctuations and reorganizes turbulent kinetic energy into low-frequency coherent structures. The characteristic frequency of these energetic structures locks onto the gust driving frequency, with a relative deviation of only $2.4\%$. Furthermore, the occurrence probability of extreme near-wall events, including extreme positive (EP) wall-shear-stress events and rare backflow (BF) events, increases by up to an order of magnitude under severe forcing. Using a two-step conditional averaging technique, we demonstrate that BF events are actively driven by intense, localized adverse pressure gradients and energetic ejections, which promote spanwise vortex roll-up in the buffer layer. By contrast, EP events are governed by energetic sweeps of high-speed fluid that compress intense spanwise vorticity into the immediate vicinity of the wall. These findings provide physical insights into non-equilibrium energy transfer and offer theoretical guidance for load alleviation and robust flow control of unmanned aerial vehicles operating in unsteady atmospheric environments.

Solutions to well-posed problems in continuum mechanics are continuously dependent upon prescribed boundary conditions. Because of this, variations in the enforcement of boundary data can impact the reliability of inversion techniques that rely on efficient and accurate forward models. To this end, it is necessary to understand how specific boundary implementation techniques can affect the performance of a given forward model. Our work focuses on the impact that key modeling decisions have on physics-informed neural network (PINN) solutions for initial boundary value problems in continuum mechanics. By interpolating boundary data over implicit boundary representations, we measure the performance of a physics-informed neural network across different configurations of soft and hard boundary enforcement. We target the problem of elastodynamic plane-strain and present a method of hard-enforcement of traction conditions over arbitrary, implicitly-defined, domain boundaries considering both first and second order formulations of the governing equations. We show that PINNs achieve a higher relative accuracy when solving the first-order plane strain problem and we observe a tradeoff between the final relative error and the total run time to complete training. This tradeoff is characterized by the number of hard and soft boundaries where, in the extremes, all soft-enforcement results in greater accuracy with a longer run time, while all hard-enforcement leads to lesser accuracy and a shorter run time.

Despite Japan being one of the world's largest advanced democracies, the development of election forecasting models for its national elections remains limited. This study introduces nonlinear machine-learning forecasting models, based on decision tree and ensemble learning methods, for predicting the outcomes of Japanese lower-house elections. To assess the methodological benefits of our approach, we replicated the theoretical framework and dataset of Lewis-Beck and Tien's (LBT) foundational statistical forecasting model for Japanese elections. Our models demonstrated moderately but consistently improved predictive accuracy compared to LBT's model in both in-sample and out-of-sample evaluations, suggesting that nonlinear algorithms offer an alternative approach to classical linear methods in capturing complex electoral dynamics. This study represents one of the earlier applications of nonlinear machine-learning techniques to single-country election forecasting. It offers a replicable framework that, when combined with the country-specific electoral theories of other nations, may enhance the predictive performance of forecasting models in broader national contexts.