Papers

Navier-Stokes (NS) equations lose accuracy in the transition regime when the Knudsen number is large, while the Boltzmann equation provides an adequate kinetic description with a substantially higher computational cost. In this work, a data-driven generalized Navier-Stokes (GNS) model is proposed to extend the applicability of the NS equations to the transition regime. A new relationship between the non-equilibrium variables, such as the stress tensor and the heat flux, and the equilibrium variables, including the density, macroscopic velocity, and the temperature, together with the Knudsen number, is learned from the data formed by the numerical solution to the Boltzmann equation. The standard end-to-end learning is utilized to construct the loss function, based on which, a long-term end-to-end learning method is proposed for the loss function to reduce accumulated error and improve long-term predictive accuracy. Several numerical examples, including one-dimensional wave and Riemann problems as well as two-dimensional isentropic vortex and Taylor-Green vortex problems, are studied to validate the efficiency of this new GNS model.

Hyperspectral imaging (HSI) enables spatial mapping of haemoglobin oxygenation and cytochrome-c-oxidase (CCO) redox changes in exposed neural tissue. In this study, we present a technical upgrade of an existing HSI platform by the integration of laser speckle contrast imaging (LSCI), enabling contemporaneous assessment of oxygenation, oxidative metabolism, relative perfusion and a derived relative oxygen metabolic index in exposed spinal cord in a rat preclinical model. The upgraded system (hNIR+) uses a shared optical path and a single camera to acquire HSI and LSCI images from the same field of view using a sequential acquisition workflow. Hyperspectral images are acquired at 11 discrete wavelengths (600, 630, 665, 784, 800, 818, 835, 851, 868, 881, and 894 nm) to estimate changes in oxygenated haemoglobin (HbO2), deoxygenated haemoglobin (HHb) and oxidized CCO (oxCCO). LSCI is implemented using 632.8-nm coherent illumination to generate relative blood-flow index (rBFI) maps. The LSCI subsystem was validated using a controlled blood-flow phantom, where the LSCI-derived inverse speckle contrast index showed a strong positive linear relationship with pump-controlled flow. The upgraded platform was then evaluated in proof-of-concept observations of the rat spinal cord under normoxia, hypoxia and recovery conditions. Representative multimodal maps and ROI-based time-course analysis showed clear dynamic responses in HbO2, HHb, oxCCO and rBFI during oxygen challenges that were consistent with the expected physiological responses of the spinal cord during hypoxia, hyperoxia and subsequent recoveries. Overall, these results demonstrate that integrating LSCI into HSI extends the platform to multimodal and simultaneous monitoring of haemodynamic-metabolic coupling.

How a mobile impurity exchanges momentum and energy with a many-body environment is a central question in nonequilibrium quantum physics. This exchange becomes particularly complex when the impurity moves rapidly and is strongly coupled to its bath. Here, we realize an interaction-tunable cold-atom collider with fermionic $^{40}$K impurities immersed in a $^{23}$Na Bose–Einstein condensate. A species-selective Raman pulse simultaneously launches the condensate and quenches the interspecies scattering length, producing an initial relative speed 36 times the condensate speed of sound. We track the ensuing relative motion as the interspecies interaction is tuned from weak coupling to resonance. At weak and intermediate coupling, the impurity dynamics are well described by a finite-energy two-body collision model. Near resonance, however, the early-time impurity acceleration is markedly suppressed relative to the two-body prediction, corresponding to a strong enhancement of the apparent dynamical inertia. The enhancement is observed near two distinct Feshbach resonances. These observations provide evidence for a polaronic response in the strongly coupled, supersonic regime of a degenerate Bose–Fermi mixture.

Black holes are usually described either as classical geometries on which quantum fields propagate or as objects whose complete quantum description lies beyond semiclassical gravity. Hawking radiation already shows, however, that a black hole is not an exactly isolated system: the horizon carries a thermal scale and, after coarse graining, may be associated with fluctuations, dissipation and environmental correlations. Whether this horizon-scale openness can be encoded directly in the gravitational background, before any stochastic treatment of the metric, remains an open question. Here we answer it by introducing, for the first time, a conserved Hawking-scale anisotropic source representing the coarse-grained near-horizon atmosphere and solving the Einstein equations for a static black hole at fixed ADM mass. The dressed geometry has a renormalized mass function, a self-consistently shifted horizon and a modified surface gravity. Because the same lapse governs the horizon and timelike geodesics, the thermal response is accompanied by a correlated orbital restructuring: the effective potential is renormalized, the ISCO moves inward and the near-horizon residence time increases. The construction adds no Langevin force to a fixed Schwarzschild background; rather, it defines a deterministic dressed mean geometry, providing a controlled background on which stochastic metric fluctuations may subsequently be treated in the Einstein-Langevin framework. This establishes the deterministic mean-field sector as the necessary first step for future stochastic gravity programs addressing horizon-scale openness.

{A GEANT4-based simulation study of a silicon–tungsten (Si–W) sampling calorimeter is presented to investigate electromagnetic shower development and detector response in the energy range of 1–120 GeV. Two detector configurations are considered: a reduced sequential-sampling prototype (Configuration-I), where a single silicon detector layer is placed downstream of progressively increasing tungsten absorber depths to sample the longitudinal shower evolution, and a fully instrumented sampling calorimeter (Configuration-II) consisting of twenty silicon layers interleaved with tungsten absorbers. The detector response is studied in terms of longitudinal shower development, shower maximum position, energy deposition, detector linearity, and calorimetric performance. Configuration-II provides simultaneous longitudinal sampling and allows event-by-event reconstruction of the deposited energy, yielding the expected linear response and energy resolution. Configuration-I reproduces the average longitudinal shower profiles and exhibits a strong linear correlation with the integrated shower response of Configuration-II. Although the absence of event-by-event multi-layer sampling prevents a direct determination of calorimetric energy resolution in Configuration-I, the observed correlation between two configurations is used to relate the response of the reduced prototype to that of the fully instrumented calorimeter and to estimate the corresponding calorimetric performance. The results demonstrate that the reduced prototype successfully reproduces the main characteristics of electromagnetic shower development observed in the fully instrumented detector, establishing its usefulness for detector characterization and test-beam studies when the construction of a complete prototype is limited by available resources.

Metal-organic frameworks (MOFs) are widely explored for gas separation, catalysis or energy storage, yet their integration with other electronically functional materials remains largely unexplored. Here, we integrate the layered Hofmann-type spin-crossover (SCO) MOF {FeII(pyS2Et)2[PtII(CN)4]} into electrical nanodevices to control the properties of two-dimensional (2D) quantum materials. Our molecular approach exploits the stimuli-responsive nature of SCO layered materials as switchable building blocks. We demonstrate selective modulation of the electronic transport in van der Waals heterostructures interfacing SCO with 2D quantum materials (few-layer graphene, magnetic CrSBr, superconducting NbSe2) through the strain induced via thermal and light-induced spin transitions. In graphene, the conductivity is selectively switched by light. In spin-valves based on CrSBr bilayers, the MOF triggers magnetic hysteresis (absent in pristine CrSBr) and enables tunable non-volatile zero-field memory. In NbSe2, light modulates the superconducting critical current and transition temperature. These results establish layered stimuli-responsive SCO MOFs as active molecular control elements for 2D quantum materials, providing a route to strain-mediated control of electronic, magnetic, and superconducting functionalities and extending MOF-based architectures beyond traditional porous-matter applications towards multifunctional electronics and spintronics.

X-ray spectroscopy of kaonic atoms offers a unique experimental window on the interplay between low-energy strong interaction and precision atomic physics studies. This article reviews the scientific progress achieved with SIDDHARTA-2 at DAFNE and discusses how it shapes the next generation measurements. Particular emphasis is given to EXKALIBUR, a multifacility program designed to perform measurements of kaon-nucleus interactions across the periodic table and to extend kaonic atom spectroscopy toward precision tests of bound-state QED and the charged-kaon mass extraction. The first proposed strong interaction campaign at J-PARC aims to investigate kaonic atoms with solid Li, Be, and B targets using new 1-mm-thick Silicon Drift Detectors and high-resolution TES microcalorimeters, exploiting isotope pairs to isolate nuclear-structure and kaon-multinucleon effects. Measurements of Mg, Al, Si, and S with CdZnTe detectors will broaden this study toward heavier nuclei. EXKALIBUR will establish a systematic experimental framework for testing low-energy strong interaction with strangeness and bound-state QED predictions across a broad range of kaonic atoms.

We present a rapid, mask-free fabrication workflow for the Paul wheel trap chip based on selective laser etching (SLE) of fused silica. The complete process - femtosecond laser writing, wet-chemical etching in KOH ($\sim$230 $\mu$m/h, releasing the trap geometry from a 500 $\mu$m wafer in approximately 2 h), RF magnetron sputtering of silver electrodes, and direct femtosecond laser ablation for electrode patterning - is carried out in-house, bypassing photolithographic processing and custom shadow masks entirely. Nine chips per 4-inch wafer can be produced in a single working day, reducing the fabrication lead time by more than one order of magnitude relative to the outsourced CVD diamond design. The functionality of the fabricated wheel trap chip is confirmed by a proof-of-concept microparticle trapping demonstration, in which a single microparticle was confined continuously for 72 h at atmospheric pressure. This establishes SLE of fused silica as a rapid, economically accessible fabrication platform for Paul traps in levitated-optomechanics applications.

A physics-informed loss adds a discretised PDE residual to the data term, but is only strictly valid if the equations being enforced are closed on the fields being fitted. This fails on coarse-grained, stochastically forced data: nonlinear terms in the PDE generally do not commute with the filter, so the coarse-grained fields do not satisfy the original equations, and driving their residual to zero encodes an implicit closure into the learned operator. Under normalised, translation-invariant filtering on a periodic domain, constant-coefficient linear constraints remain valid on the filtered grid. For the 2D incompressible Navier-Stokes equations, two such constraints are continuity and global momentum balance. We enforce these exactly by applying a closed-form projection on the output of a Fourier neural operator (FNO) at $O(N \log N)$ cost. On two-dimensional isotropic turbulence, truncated so that its forcing lies entirely beyond the cutoff wavenumber, the projection achieves a continuity error of $5.0\times10^{-7}$ against 0.51 for a plain FNO and 0.17 for a physics-informed FNO, and a global momentum balance error of $8.6\times10^{-11}$ against $7.5\times10^{-4}$ and $8.3\times10^{-4}$. The projection only costs an extra 2% of training time, and takes the fraction of rollouts whose energy remains bounded at 448 steps from 0.18 to 0.87. On resolved Kolmogorov flow, test-time optimisation costs approximately 3,300$\times$ as much per trajectory as the projection, while leaving maximum divergence three orders of magnitude higher. Nonlinear constraints can also be enforced once their unclosed terms are modelled: closing the global energy balance with a constant subgrid flux, fitted from the coarse training data alone, removes the 20.9% energy deficit that enforcing the unclosed balance produces and keeps every free-running rollout bounded over 2,000 steps.

Direct numerical simulations of thermally stratified multi-layer Rayleigh-Taylor instability are performed to investigate the influence of interfacial Atwood number (At) on instability growth, interfacial coupling, turbulent mixing, and spectral characteristics. A fully compressible, three-dimensional, high-fidelity Navier-Stokes solver has been used to simulate ten cases spanning a broad range of interfacial $At$ to examine the evolution of coupled instability dynamics. The configuration consists of three superposed fluid layers with unstable density stratification across two interfaces generated through temperature variations. The temporal evolution of the flow is characterized using the maximum vorticity, mixing-layer growth, density fluctuations, turbulent kinetic energy, and energy partitioning between gravitational potential energy, TKE, and dissipation. The instability evolution is shown to proceed through distinct onset, quasi-exponential growth, and nonlinear saturation stages for all configurations. Increasing interfacial At leads to earlier instability onset, enhanced vorticity amplification, and accelerated development of turbulent mixing layers. The weakly stratified configurations exhibit dynamics resembling effectively single-interface RTI, whereas the strongly stratified cases develop two simultaneously active mixing layers that subsequently interact and merge into a coupled turbulent region. The wall-normal distributions of density fluctuations and TKE demonstrate progressive broadening of the mixing region and increasing redistribution of turbulent activity with increasing stratification strength. The late-stage spectra exhibit an approximate -11/5 scaling over an intermediate range of wavenumbers, consistent with buoyancy-dominated turbulent transport.

Liquid marbles (LMs), droplets encapsulated by hydrophobic particles, allow for non-wetting manipulation and containerless handling, making them well-suited for applications such as microreactors. When integrated with acoustic levitation, the LMs serve as contact-free reaction platforms. However, the complex behaviours associated with acoustic fields, including deformation, internal flow, and evaporation, remain insufficiently understood. This study investigates the evaporation dynamics of acoustically levitated LMs. Simultaneous visualisation of the droplet morphology and surrounding vapour concentration fields was achieved using backlighting and the Background-oriented Schlieren (BOS) method. At intermediate relative humidity (RH = 40%), the evaporation rates and vapour distributions of LMs closely resembled those of pure water droplets, indicating that the particle shell exerts minimal influence under these conditions. Conversely, significant differences were observed at high- and low-humidity, attributable to the evaporation resistance and interfacial properties introduced by the hydrophobic particle layers. Furthermore, although pure water droplets maintained a quasi-spherical shape, LMs demonstrated progressive flattening over time, suggesting mechanical constraints imposed by the particle shell. These findings provide new insights into the coupled evaporation and deformation behaviours of LMs in acoustic fields, informing the design of contamination-free microfluidic and microreactor systems.

Fragment molecular orbital (FMO) quantum chemistry offers a natural route to parallel quantum computing: large molecular calculations can be decomposed into smaller quantum subproblems that can, in principle, run concurrently. Their practical benefit on real quantum hardware, however, has remained unclear. We demonstrate parallel FMO calculations on three room-temperature diamond-based 'Quoll' quantum computers installed on-site at the Oak Ridge Leadership Computing Facility. Helium clusters (He$_{n}$, $n=2,4,8,10,14$) served as a testbed for total energy calculations using the Quantum Wave Function Sampler (QWFS), a selected configuration interaction (SCI) approach. We compare two execution modes: synchronous shot-parallel execution, in which quantum processors jointly sample QWFS circuits and combine their SPAM-corrected counts into a single probability distribution; and asynchronous fragment-parallel execution, in which processors independently evaluate FMO fragments using device-specific optimized circuits. Both modes increase quantum sampling throughput without systematic loss of accuracy. The three-QPU shot-parallel workflow reaches a parallel efficiency of $93.60\%$, while the fragment-parallel workflow achieves a measured efficiency of $75.01\%$, increasing to $95.17\%$ when excluding the non-useful idle time associated with non-interruptible submitted jobs. In both cases, the assembled FMO-QWFS energies remain chemically accurate compared to the classical ideal. These results provide a proof-of-concept demonstration that parallel quantum execution can improve the practical throughput of quantum chemistry workflows on prototype hardware, motivating the development of future HPC environments with many fault-tolerant quantum processors operating as a distributed resource for molecular and materials simulation.

Accurate prediction of continuous molecular properties supports chemical discovery and design. Many recent approaches combine multiple molecular representations within increasingly complex neural architectures, aiming to achieve higher accuracy. However, classical machine-learning models remain competitive when trained on chemically meaningful molecular representations. We therefore constructed a chemistry-guided classical branch from Morgan fingerprints, RDKit molecular descriptors, and conformer-derived geometric features selected to provide complementary views of molecular structure. We combined four tree learners trained on these representations with a CoMPT graph predictor and integrated their out-of-fold predictions using Ridge regression. Across ESOL, FreeSolv, and Lipophilicity datasets, the fused model achieved lower mean root-mean-square error (RMSE) than Chemprop and competitive graph and multimodal methods, including CoMPT, MoleSG, KFLM2, and MCMPP. After retraining on the separately curated AqSolDB dataset, the same approach, without redesign or retuning, reduced mean absolute error (MAE) relative to the classical ensemble and achieved lower mean MAE than Chemprop. These results show that accurate and transferable molecular property regression does not necessarily require a complex joint multimodal architecture: classical learning remains competitive when chemical insight guides the choice of molecular representations, and a graph model can add complementary information through simple prediction-level fusion.

We establish when coarse-grained observables uniquely determine an expectation-based biological response in boron neutron capture therapy (BNCT). Microscopic target populations are represented by probability distributions, while retained observables are finitely many population-averaged linear descriptors. For an admissible class containing all finitely supported microscopic distributions, exact distribution-free identification holds if and only if the single-target response kernel lies in the span of the constant function and retained descriptors. Thus mean dose, mixed-field component means, and finite moment sets are not generally sufficient under unrestricted microscopic heterogeneity. We also derive irreducible ambiguity and approximation bounds, sharp partial-identification intervals for bounded non-increasing convex survival kernels, a treatment-ranking criterion, and lower-tail and high-exposure bounds. A fixed-total-exposure temporal model distinguishes transient re-sampling from persistent target-specific underexposure, and a marked-Poisson model shows that separate frequency and event-quality marginals need not determine survival. The framework clarifies which microscopic information is lost through BNCT coarse-graining while leaving room for approximation, partial identification, and prediction under independently justified model restrictions.

A vectorial discrete unified gas kinetic scheme (V-DUGKS) is proposed for continuum compressible flows. In the vectorial kinetic framework, mass, momentum, and total energy are represented by coupled distribution functions with separate relaxation processes for momentum and energy transport, enabling an adjustable Prandtl number. All equilibrium distributions are truncated at the second-order Hermite level. Since only second-order velocity moments are required to recover the compressible Navier-Stokes equations, fourth-order Gauss-Hermite quadrature is sufficient for exact moment evaluation. Eliminating third-order Hermite terms allows compact discrete velocity sets to be used uniformly for all distribution functions, reducing sensitivity to the numerical reference temperature and improving stability and efficiency over the scalar DUGKS. The scheme employs a finite-volume formulation with characteristic-based flux evaluation and trapezoidal collision integration. Numerical tests, including shock-tube, Shu-Osher, two-dimensional Riemann, and three-dimensional Taylor-Green vortex problems, demonstrate its accuracy and robustness. For the three-dimensional Taylor-Green vortex, V-DUGKS achieves a speed-up of about 2.2-2.9 under the same CFL constraint due to a larger allowable time step and simplified equilibrium formulation. These results show that V-DUGKS provides a robust and efficient kinetic framework for continuum compressible flow simulations.

This work develops a pressure-based coupled (PBC) GSIS. Within each GSIS iteration, a conventional kinetic solver advances the velocity distribution function, from which non-equilibrium constitutive relations are extracted. A PBC macroscopic solver then solves the steady macroscopic synthetic equations: continuity and momentum equations are solved as a coupled pressure-velocity system under fixed temperature, followed by a segregated temperature solve. A boundary treatment is constructed to maintain strict consistency between kinetic boundary fluxes and macroscopic boundary conditions throughout the macroscopic iterations. The proposed method is validated against direct simulation Monte Carlo (DSMC) data across multiple three-dimensional test cases, including tube-to-vacuum flows, a dynamic gas lock configuration for EUV lithography, divertors in nuclear fusion, and sphere flow at Mach numbers up to 10. Numerical results demonstrate that our GSIS-PBC preserves asymptotic-preserving properties and yields satisfactory accuracy across continuum-to-transitional flow regimes. For low-speed near-continuum internal flows, GSIS-PBC delivers wall clock time speed-ups of up to two orders of magnitude compared with the conventional iterative scheme, and substantially outperforms the original density-based GSIS for challenging low-Mach internal flow problems. For nuclear-fusion divertor flows, GSIS-PBC is faster than the DSMC by four orders of magnitude.

Metasurface heat absorption is conventionally characterized by far-field infra-red spectroscopy under equilibrium conditions. The approach provides information on the surface emissivity but does not directly reveal heat transferred by electromagnetic absorption. To understand the resulting temperature change, however, the associated heat transfer must be considered. Here, we directly observe heat extracted by electromagnetic absorption using infrared thermography. By monitoring the cooling dynamics of a copper plate placed in contact with a metasurface-integrated thermoelectric device, we show that the copper plate cools faster than one placed on an unstructured planar surface (control). This result provides direct evidence of heat transfer from the adjacent solid to the metasurface; revealing a metasurface-mediated electromagnetic absorption heat transfer mechanism. These findings provide new insight into nonequilibrium thermal processes enabled by metasurface absorbers.

Scientific law discovery requires selecting measurements and converting evidence into a governing equation. We evaluate an explore-then-commit protocol in which a large language model proposes hypotheses, a programmatic planner gathers measurements, and a fresh prompt synthesizes the final law from fixed observations. The protocol combines structured probes, automatic numerical diagnostics, restricted measurement batches, and optional interpreter access. Across 576 NewtonBench trials, we compare eight configurations on 12 physics modules using GPT-4.1-mini and a medium-difficulty GPT-4.1 replication. On medium tasks, interpreter-enabled planners use 8.6 versus 22.5 measurements per trial for GPT-4.1-mini and 8.9 versus 43.0 for GPT-4.1. Their mean magnitude-based root-mean-squared logarithmic error falls from 2.514 to 0.202 and from 0.626 to 0.149, respectively. An additional audit retains incomplete and invalid submissions in a coverage-sensitive analysis. Observed symbolic-accuracy gains are less consistent across modules, and random acquisition is competitive with disagreement scoring. Measurement savings occur in every module, but unequal batch constraints prevent attributing them solely to acquisition quality. These results support the complete protocol as a promising measurement-efficient configuration, while leaving its causal components and generalization beyond noiseless direct-equation tasks unresolved.

In this study, the lean extinction behaviors of CH4 and a 16% cracked ammonia surrogate fuel, CR16, in counterflow premixed flames were compared. Experiments were conducted along with one-dimensional simulations using OPPDIF; the GRI-Mech 3.0 and UCSD mechanisms were applied to CH4 and CR16, respectively. CR16 exhibited higher extinction strain rates and resilience to strain-induced blow-out (RSIB) than those of CH4 under lean conditions. At Phi = 0.66, although both flames had the same extinction strain rate, CR16 showed a higher RSIB because of its longer flame time. Near its extinction, CR16 sustained H2 consumption, radical reactions, and heat release through the interflame region toward the stagnation plane. Hydrogen oxidation coupled with nitrogen-related pathways, including NH2 + NO and HNO reactions, supported this behavior. These results clarify the chemical basis for the superior extinction resistance of economically favorable low-cracking-ratio ammonia fuel.

A very-high-frequency (VHF) electron gun operating at a resonant frequency of 216.667 MHz has been developed at Tsinghua University for the Shenzhen Superconducting Soft X-ray Free-Electron Laser ($\rm S^3FEL$) facility. Building upon the SHINE gun design, the cavity profile was optimized to achieve a higher cathode electric-field gradient and a higher accelerating voltage at comparable input power. The revised cavity geometry also yields improved multipacting performance. In addition, thermal analysis was carried out to guide the water-cooling design, and enhanced cooling was implemented in the vicinity of the cathode. During high-power conditioning, 82 kW of continuous-wave radio-frequency power was successfully coupled into the gun, corresponding to a cathode gradient of 29.9 MV/m and an accelerating voltage of 856 kV. The gun voltage surpasses the previous world record for room-temperature VHF guns. The maximum dark current measured by the Faraday cup at the gun exit was only 6.8 nA.

Miniaturized magnetic components play an important role in achieving high efficiency and high power density in power converters. Accurate core loss data are essential when optimizing magnetic components, but making accurate core loss measurements remains challenging, especially at MHz frequencies. As a result, many research groups have developed their own testers in pursuit of improved accuracy, and the amount of published core loss data has increased. However, large variations in these data shed light on fundamental barriers to accurate MHz core loss measurement. This paper identifies these barriers and presents methods for overcoming them.

This work presents a comprehensive macroscopic electrodynamic framework isolating the magnetodipole (MD) and electrodipole (ED) contributions to the magneto-optical effects in bismuth-substituted iron garnet. By rigorously incorporating the dispersive magnetic permeability tensor, the dominance of the MD mechanism in the terahertz (THz) gap is demonstrated. In the mid-infrared (MIR) to near-infrared (NIR) domain, a fundamental spectral crossover is revealed where the magnitudes of the MD and ED contributions exactly equalize, invalidating the widespread assumption that magnetic contributions vanish uniformly at infrared frequencies. Furthermore, it is shown that interfacial phase shifts uniquely dictate how these competing mechanisms project onto observable rotation and ellipticity, yielding an inverted spectral hierarchy between transmission (Faraday) and reflection (polar and longitudinal Kerr) geometries. Most notably, the transverse Kerr effect for s-polarized light is established to be exclusively driven by the off-diagonal magnetic permeability. While strictly zero under the conventional ED approximation, the s-polarized transverse Kerr effect emerges as a robust, free of the gyroelectric contribution probe of MD activity in the THz and MIR–NIR regimes, yielding a THz response two orders of magnitude larger than its p-polarized counterpart.

Atomic oxygen (AO) erodes polymer surfaces in low Earth orbit, but durability data are limited. We evaluated a regression ensemble trained on 37 MISSE-2 polymers using eight selected bulk properties and five elemental-composition ratios. Its archived leave-one-out R^2(log) was 0.768; this estimate is conditional on feature selection using the complete training table. For nine post-MISSE-2 polymers, the ensemble gave R^2(log) = -0.037 and all predictions were within a factor of two. The training arithmetic-mean baseline also met that tolerance for all nine, highlighting the limited discrimination provided by their narrow target range. Ground-based comparisons included four PLAVIS grades. For an out-of-training mystery polymer with undisclosed composition, the predicted erosion yield was within a factor of 1.92 of the ground-based measurement. Conflicting PLAVIS measurement records prevent a definitive residue-erosion ordering or ground-validation score. Thermal residue, SEM contrast and surface chemistry provide distinct observational evidence; they do not establish a protective-layer mechanism. The results support further evaluation of orbital-data screening models with reconciled measurement records and prospectively fixed inputs..

The production of adipic acid generates nitrous oxide as a by-product, which, if emitted, very effectively warms the climate and depletes the stratospheric ozone layer. There exists cost-effective technology to mitigate these nitrous oxide emissions from adipic acid production, but the degree of implementation of this technology, most notably at sites in China, is uncertain. Using satellite observations from summer 2026, we quantify nitrous oxide emissions from three adipic acid production sites in China, finding emissions that total over half a million tonnes per year, a rate that implies limited emissions abatement. With respect to climate change, eliminating the annual emissions from just these three sites would be equivalent to eliminating the annual emissions from 49 coal-fired power plants or 43 million gas-powered vehicles in the United States.

The Distorted Wave Born Approximation (DWBA) is established for X-ray interface scattering, yet its domain of applicability has never been stated explicitly. The DWBA implicitly rests on the small refractive index deviation (SRID) condition - satisfied by X-rays ($|n-1| \sim 10^{-6}$) but violated by visible light ($|n-1| \sim 0.5$). The DWBA mixes microscopic and macroscopic Maxwell equations, whereas a rigorous optical theory must be built on the macroscopic Maxwell equations and constitutive relations. Rigorous alternatives for two specific inconsistencies in the standard DWBA are discussed. The Extended Transfer Matrix Approach (ETMA) for visible-light interface scattering is outlined.

Extending the Fokker–Planck equation to a coupled system of equations through non-Abelian symmetry groups offers a powerful theoretical framework for coupled multi-state stochastic systems. However, translating this abstract gauge field formulation into viable numerical predictions remains an unaddressed challenge. Motivated by our previous work on ice crystal habit evolution, in this work we bridge this gap by presenting the first numerical solver for an SU(2) gauge-coupled Fokker–Planck system in (1+1) dimensions. Using an operator-splitting scheme combined with a non-oscillatory advection solver, our algorithm strictly ensures probability conservation, non-negativity, and numerical stability. We demonstrate that non-Abelian gauge coupling acts as a cross-reactive feedback loop that fundamentally alters system dynamics, driving continuous probability exchange between states, suppressing localised density peaks, and forcing long-time spatial delocalisation. Validated across linear and quadratic coupling regimes up to strong interactions (g <= 1.0), this work establishes the foundational computational infrastructure needed to integrate complex multi-state physics. Overall, while our approach provides a key step toward improving ice cloud representation in atmospheric and climate models, the underlying physical framework and computational engine remain fundamentally general and multidisciplinary, offering a versatile paradigm for any domain requiring coupled multi-state stochastic equations.

We solve the near-tip coupled hydromechanical problem of a hydraulic fracture propagating in a permeable poroelastic medium, represented locally as a semi-infinite crack moving at constant velocity. In the moving tip frame, all fracture fields are steady. The porous solid obeys linear poroelasticity, fracture propagation follows LEFM, fluid flow within the tensile fracture is governed by lubrication theory, and the fracturing and pore fluids are treated as identical Newtonian liquids. The primary unknowns are the fracture opening and the fluid displacement function along the fracture, determined from the resulting non-linear system of equations. In a viscosity–toughness scaling, the problem depends on four dimensionless parameters: a dimensionless permeability $\varrho$ (associated with the intensity of fluid leak-in), a dimensionless effective confining stress $\Sigma_0'$ (measuring the relative importance of fluid leak-off and leak-in), the drained–undrained Poisson's ratio contrast $\beta$, and the poroelastic stress coefficient $\eta$. The solution evolves from a drained, toughness-dominated near-tip asymptote with uniform net pressure to an undrained, storage-viscosity-dominated far-field asymptote. Numerical solutions are obtained over parameter ranges representative of various rock types. The fully coupled model is compared with simplified models, namely uncoupled and one-way coupled formulations, over distances from the fracture front spanning laboratory-to-field scales. The resulting relative difference maps in the $(\varrho,\Sigma_0')$ parameter space identify four characteristic regimes: negligible poroelastic effects, dominance of the direct mechanical poroelastic response, importance of the poroelastic backstress, and a fully coupled regime. The latter occurs mainly at large $\varrho$ and small $\Sigma_0'$, corresponding to strong leak-in relative to leak-off.

Wave-based processors promise fast, energy-efficient Fourier layers for neural operators. They are usually validated on randomly sampled devices, but using them requires knowing how large their error can become under fabrication and alignment variation. In a stylised numerical case study, a hybrid Fourier neural operator runs its four spectral layers on simulated coherent 4f processors with 32 toleranced knobs, whose half-widths are representative rather than calibrated. For 120 models (four tasks, six training methods, five seeds), we compared the worst of N random in-spec devices with a searched one. On a deterministic simulator with one frozen draw of the random static errors, the searched device's held-out error was 1.08-3.10 times the maximum over 200 Monte Carlo devices and 1.06-2.71 times that over 1000. With 20 fresh static draws, it still exceeded the maximum over 200 random devices in 116 of 120 models. Under uniform sampling, the probability of drawing such a device is at most 0.37% per model (two-sided 95% Clopper-Pearson), which says nothing about how large its error is. The gap persisted with uniform or Sobol' sampling at the search's budget, shared knobs, a second crosstalk model, box scales of 0.25-2 and a pixel-level device model. Models trained only with random static errors reached 3.7-39.9 times their nominal error on searched devices, and fine-tuning on random and gradient-searched devices gave the lowest searched error of the six in all 20 task-seed pairs. For two heat-exchanger quantities, a search targeted at each exceeded the worst of 1000 random devices in all 39 models, and hence the Wilks 95/95 limit (worst of 59). For the mean pressure of 11 models, no random device exceeded a 1% error threshold, but the searched device did. Random testing estimates how often errors exceed a threshold; worst-device search gives a lower bound on how large they can be.

Major advances in chemical rate theory changed what was computed and what a barrier meant. I organize this conceptual history around those changes, while retaining the controlled corrections and limiting relations between theories. The calculated quantities include an empirical rate constant, a collision count, a thermodynamic activation free energy, a statistical flux across a dividing surface, a stochastic escape rate, an energy-resolved microcanonical rate, a quantum flux correlation function, and an ensemble of reactive paths computed with electronic-structure or machine-learned potentials. I compare the assumptions with the limits stated by the pioneering authors. A rate constant is inferred from measurements under a stated rate law; its temperature dependence defines an apparent activation energy $E_a$. Activation free energy $\Delta G^{\ddagger}$, activation enthalpy $\Delta H^{\ddagger}$, activation entropy $\Delta S^{\ddagger}$, the prefactor, transmission coefficient $\kappa$, and saddle height require a kinetic model and, where relevant, a standard state, dividing surface, ensemble, and dynamical correction for their interpretation in a rate calculation. In hydrogen transfer by soybean lipoxygenase-1, similar single-isotope activation parameters accompany a large change in isotope-sensitive dynamics. Five rate benchmarks illustrate the errors different comparisons test and motivate seven reporting items. These distinctions guide comparisons of rates and interpretations of activation parameters.

We present a method to obtain the proven exact 2-component (X2C) method, normally applied to Gaussian-type fixed Hilbert bases, in an adaptative non-uniform grid basis of the Hilbert space, namely Multiwavelets. The X2C method manages to block-diagonalise the Dirac Hamiltonian restricted to the subspace of the basis, in an energy-independent manner. The method takes inspiration from the success of the atomic mean-field X2C method on the Gaussian bases to represent the coupling operator as a projector onto the small components of the eigenstates of the constituent atoms of the system.