Papers

From the Higgs mechanism to magnetic ordering, spontaneous symmetry breaking organizes physics across scales. Whether an analogous transition governs waves scattered by periodically driven structures has remained an open experimental challenge. Here we demonstrate a universal symmetry-breaking transition in a periodically driven finite scattering system. By measuring the multispectral Floquet scattering matrix of a strongly modulated microwave resonator, we observe a transition from an unbroken symmetry regime with scattering eigenvalues of equal modulus to a broken symmetry regime where their moduli split apart, through an exceptional point. At parametric resonance, this transition culminates in coherent perfect absorption, where a tailored multispectral input state is completely absorbed without outgoing radiation, the singular limit of the underlying transition. Because it arises from the general algebraic structure of the Floquet scattering matrix rather than from implementation-specific details, this transition should occur broadly in such systems. Unlike instability phenomena in bulk driven media, it emerges in the eigenstructure of a finite scattering operator that couples waves across multiple frequency channels. Our results establish Floquet scattering as an experimentally accessible platform for the observation of symmetry breaking and for dynamic wave control in driven photonic systems.

In this paper, the 3D scheme of the fuel code ALCYONE is applied to a database consisting of more than 50 base irradiations and ramp tests performed on rods with UO 2 , MOX or Cr-doped UO 2 fuels and Zy4 or M5__ cladding tubes with burn-ups up to 70 GWd/tU. The ability of the 3D scheme to predict the behaviour of a single fuel pellet -cladding element situated at the maximum Linear Heat Rate (LHR) during ramp testing is demonstrated by comparing the following experimental and calculated data: residual clad diameter after base irradiation and ramp test, height of inter-pellet and mid-pellet ridges after base irradiation and ramp test, dish filling after ramp test. In the second part of the paper, 3D simulations that catch the stress -strain localization at the triple point are presented. It is shown that the stress-strain concentration occurs only at the end of the power transient and is quickly relaxed by pellet and clad creep.

This data article provides the datasets, symbolic derivations, and scripts used to reproduce master diagrams, stationary-phase spectra, windowed first-order coherence functions, and quantum-noise stability maps for strongly chirped dissipative solitons of the cubic-quintic complex Ginzburg-Landau equation in normal and anomalous group-delay dispersion regimes. The repository includes node-regularized normal-dispersion spectra and energies; small-parameter expansions of the branch roots; cavity-map gain-loss update relations; Airy uniformization at the normal-dispersion spectral edge; anomalous-dispersion spectra and coherence calculations; and processed tables for plotting and stability analysis. OriginLab projects are accompanied by open-format .csv/.txt numerical tables to support reuse without proprietary plotting software. Data and code repository: https://doi.org/10.5281/zenodo.22690899.

The fidelity of image reconstruction from projections in linear inverse problems, such as tomography, is critically dependent on the synergistic interaction between frequency-domain filtering and spatial-domain interpolation. However, a physical model that can quantitatively describe how these two components cascade interact in the frequency domain and ultimately determine image quality is still lacking to this day. Here, we introduce a unified frequency-domain model that conceptualizes the combined effect of filtering and interpolation in the filtered backprojection (FBP) algorithm as a cascaded modulation process. This model demonstrates that the effective reconstruction spectrum is determined by the original projection data being sequentially modulated by the frequency responses of the filter and the interpolation kernel. Comprehensive numerical simulations and synchrotron radiation CT experiments validate the model, confirming its power to explain the performance hierarchy of classical filter-interpolation pairs under both ideal and noisy conditions. The model successfully predicts key performance characteristics, including spatial resolution and structural fidelity, thereby elucidating the physical principles behind the efficacy of specific combinations. This work establishes a generalizable theoretical foundation for analyzing cascaded systems in linear inverse problems, moving the practice of algorithm selection in computational imaging from empiricism to a principled, physics-based paradigm.

Lean premixed hydrogen flames are highly susceptible to thermodiffusive instabilities, which generate complex cellular structures and enhance propagation speed. In practical combustors, incomplete premixing introduces spatial variations in local equivalence ratio upstream of the flame. This work investigates how the length scale of inlet fuel stratification affects the structure and propagation of lean laminar hydrogen flames using direct numerical simulations with detailed chemistry and transport. Controlled sinusoidal perturbations of fuel mass fraction are imposed at moderate amplitude, leading to local equivalence-ratio variations of $\phi \simeq 0.45-0.55$ in the flame region. Seven stratification wavelengths, from $\lambda = 15.4\delta_f$ to $133.3\delta_f$, are examined at constant density-weighted global equivalence ratio. Additional unity-Lewis-number simulations separate geometrical effects from differential transport. The results reveal a strongly scale-dependent response. In unity-Lewis-number flames, stratification organises the front at the imposed wavelength and monotonically increases flame surface area and propagation speed with wavelength. With differential diffusion, large wavelengths ($\lambda \gtrsim 40\delta_f$) produce a similar large-scale organisation, with rich channels forming forward bulges and lean channels recessed cusps, while thermodiffusive cells remain superimposed. This increases flame surface area and global propagation speed. At smaller wavelengths ($\lambda \lesssim 29\delta_f$), composition gradients interfere with intrinsic thermodiffusive cellular dynamics, reducing flame surface area and propagation speed relative to the perfectly premixed reference. These findings identify stratification length scale as a key parameter controlling the interaction between moderate mixture inhomogeneity and thermodiffusive instability in lean hydrogen flames.

Accurate digital maps are essential for Advanced Driver Assistance Systems (ADAS) or Autonomous Driving (AD), providing critical information such as road geometry, traffic signs and speed limits required by safety functions including Intelligent Speed Assistance (ISA). Maintaining these map layers using traditional surveying methods is costly and difficult to scale. Crowdsourced approaches based on fleets provide a promising alternative for continuously validating and updating map information. However, the relationship between the number of contributing vehicles and the quality of the resulting map remains poorly understood. To address this gap, this paper presents a simulation-based framework for evaluating crowdsourced traffic sign maintenance using a dissimilarity measure called GOSPAM (Generalized Optimal SubPattern Assignment for Maps), which combines localization errors with detection performance by accounting for False Positives (FP) and False Negatives (FN). The proposed system models multivehicle observations with representative sensor noise, detection errors, and semantic recognition uncertainties. Observations from multiple vehicles are aggregated using spatial clustering and semantic filtering to estimate traffic sign locations. Using simulated trajectories generated from data carried out by an experimental vehicle in an area containing ground-truth traffic signs, we assess the influence of fleet size on the performance of crowdsourced mapping. The number of vehicles ranges from 5 to 50, and performance is analyzed using standard evaluation metrics which are compared to the GOSPAM . The results show that GOSPAM can be used to effectively assess the quality of crowdsourced mapping, such as the contributions made by the first vehicles or the improvements made by numerous vehicles.

General-purpose machine-learning interatomic potentials (MLIPs) for organic reactions need to be accurate on both the minimum energy path (MEP) for static evaluation of basic properties and the broader configurational space for simulating reaction dynamics. Existing general datasets for gas-phase organic reactions rely on quasi-static relaxation that confines configurations to the MEP vicinity, so models trained on them could fail on direct molecular-dynamics trajectories; the gap is methodological, not a question of dataset size. We introduce a spatiotemporally resolved, system-independent collective variable (CV): Cartesian RMSD within randomly partitioned local domains against an expanding list of time-averaged reference geometries. The CV drives metadynamics as the main exploration engine, supplemented by structural relaxation towards transition state (TS) to augment the coverage around TS. Within a concurrent-learning workflow, this produces OpenRxn26, a dataset of 1.8~M DFT-labeled configurations covering neutral singlet unimolecular reactions in the H/C/N/O chemical space ($N_\mathrm{heavy} \leq 30$), containing reactive atomic environments underrepresented in community datasets. Trained on OpenRxn26, a DPA3 model (denoted DPA3_rxn) achieves transferable accuracy on barrier heights and reaction energies. On off-MEP reactive trajectories, DPA3_rxn is the only model in the benchmark suite to reach 1.0 kcal/mol energy accuracy compared with the labeling method, where the domain MLIP leading on static benchmarks degrades several-fold (e.g. MACE_OMol25), showing the insufficiency of quasi-static sampling and MEP-anchored benchmarks for guaranteeing dynamics reliability of MLIPs. OpenRxn26 thus provides MD-ready reactive training data for gas-phase neutral singlet organic reactions, verifying the generality and efficiency of the sampling strategy.

We show that the angular structure of the leading radiation associated with a bound state in the continuum (BIC) directly encodes topology, identifies the critical conditions for topological-charge transitions, and reveals the bifurcation of new BIC families under structural variations. For at-$\Gamma$ BICs, rotational and time-reversal symmetries determine the allowed angular harmonics of the leading radiation and hence the possible topological charges. As an example, we directly identify a BIC with $q=-5$ in a $C_{6v}$ dielectric slab with a triangular lattice of circular air holes, without computing nearby resonances or tracking the merging of BICs. This BIC is also a super-BIC, exhibiting an ultrahigh quality factor $Q\sim 1/\delta^{10}$ along all directions in momentum space, where $\delta$ is the wavevector detuning parameter. We also use a $C_{4v}$ structure to analyze a topological transition and the associated bifurcation of off-$\Gamma$ BICs. Our results may find applications in resonant, structured, and topological photonics.

This research establishes a connection between macroscopic urban commuting flows and thermal equilibrium. Using mobility data from around 30 million records across six Japanese cities over one year, we introduce the Gravity-based Home Swapping Model (GHSM). This model applies Metropolis dynamics to urban commuting by treating individuals as interacting particles. Within this framework, total commuting time dictates the system energy, and temperature controls how strongly mobility responds to cost savings. Consequently, urban commuting can be analyzed through an evaluable free energy and simulated similarly to physical systems of matter. The maximumentropy doubly constrained gravity model serves as the stationary state. We find that the residential dynamics exhibit glassy characteristics. Through calibration to observed data, we reveal that the system demonstrates signatures of kinetically constrained models, specifically ageing, hysteresis, and freezing near equilibrium. Simulations initialized from arbitrary states converge to empirical origin-destination matrices. This demonstrates that minimal physical mechanisms can reconstruct complex urban realities.

Atmospheric observations are sparse, heterogeneous, and unevenly distributed, whereas many generative atmospheric models learn distributions over regularly gridded multivariate states. Once pretrained, diffusion models can supply atmospheric priors that can be combined with observation-derived likelihood factors in a Bayesian formulation. However, these observation sources differ substantially in geometry and sampling density, complicating the consistent use of their observations within a common inference framework. Here, we formulate this reconstruction problem as generative atmospheric super-resolution and introduce composable observation interfaces for conditioning a single pretrained 13-variable atmospheric diffusion model. The interfaces convert sparse radiosonde (R), clustered aircraft (A), and dense irregular surface-station (S) observations into source-specific likelihood factors that specify where observations constrain the gridded state, how residuals are counted under uneven sampling, and how strongly each source guides posterior sampling. We developed the aircraft and surface observation interfaces using 2019 observations and evaluated the selected interfaces throughout 2020 without further tuning. Compared with reconstructions conditioned only on radiosonde observations, the composed R+A+S interface reduces RMSE evaluated against ERA5 by $9.24\%$ across all 13 state variables over the CONUS domain. The aircraft and surface factors provide complementary improvements in upper-air and surface variables. The R+A+S combination also lowers the Continuous Ranked Probability Score (CRPS), while evaluations at held-out aircraft and surface-station observations show reduced prediction errors. Together, these results demonstrate a modular route for conditioning a pretrained atmospheric generative prior on heterogeneous in situ observations without retraining the underlying model.

Natural load-bearing and transport networks are not assembled in a single step; they emerge through a temporally ordered process of growth, branching, reinforcement, and loop formation. Inspired by this developmental logic, this work introduces a morphogenetic graph-generation framework for mechanical lattices in which a discrete dot matrix provides potential nodes and the final architecture is created by sequential cross-layer and intra-layer growth. The same rule is visualized in two dimensions as a leaf-vein-like developmental sequence and implemented in three dimensions on a 3x3x3 nodal matrix containing 27 candidate nodes. A dataset of distinct three-dimensional lattices was evaluated by beam-based finite element analysis and represented directly as graphs. A graph convolutional neural network (GCNN) with three graph-convolution layers and dual global pooling learns the topology-property mapping and predicts effective compressive stiffness. Coupling the GCNN surrogate with rapid structural sampling enables inverse design: for a target stiffness of 1000 MPa, the selected design was predicted at 1042.43 MPa and validated by finite element analysis at 1027.49 MPa. Beyond straight members, the framework has also been extended to parameterized horseshoe-shaped curved beams made of nonlinear materials, enabling topology-geometry design toward prescribed deformation shapes. Our work provides a paradigm for augmenting the database of mechanical metamaterials, and the resulting perspective links biological morphogenesis, graph learning, and nonlinear shape programming in a unified generative design framework for architected materials.

Finite-domain normalized spatial measurements combine an incident field, geometric support, local response, and channel weighting. These operations can break an otherwise separable response, so accurate single-axis calibration alone does not establish separability of a multidimensional measurement. We represent normalized readouts as channel-weight expectations under a parameter-dependent effective measure. For fixed reception and channel operators, local sensitivity is the covariance between the channel weight and the source score. This representation distinguishes axis separability, parameter mixing, and local recoverability, and applies at the appropriate detector level to continuous-electrode position-sensitive detectors, Shack–Hartmann and pyramid wavefront sensors, back-focal-plane split detection, and finite pixel arrays. A quadrant photodetector (QPD) provides an analytically tractable case. Starting from the complete two-dimensional power integrals, we identify the spatial-moment origin of its lowest-order cross term. Axis calibration and system symmetry then constrain the inverse map, giving Axis-Anchored Cross-Residual Inversion (ACRI). Numerical tests of this QPD realization support the sensitivity relations and show that the constrained cross correction substantially reduces the off-axis bias retained by single-axis inversion. The results illustrate how the general measurement representation guides inverse construction, with performance bounded by the working domain, calibration state, and preserved symmetries.

Recurrent invariant solutions provide a dynamical framework for interpreting turbulence, but their computation becomes increasingly difficult in spatially extended flows, where close whole-domain recurrences are rare. We study two-dimensional Kolmogorov flow in a [Lx,Ly] = [2pi,12pi] domain with Re = 10 using a distributed reduced-order model composed of a patch-based autoencoder and a neural ordinary differential equation. Near- recurrence candidates are refined by latent multiple shooting, decoded to physical space, screened under the full Navier-Stokes dynamics and supplied to a full-state Newton- Krylov solver. Fourteen candidates converge to relative periodic orbits (RPOs), with periods 3.65 < T <16.45. The catalogue contains both domain-filling states and RPOs with recurrent activity localised to a restricted cross-stream region. The weakly modulated surroundings generally remain finite-amplitude and distinct from the laminar solution. Selected localised RPOs persist under continuation in Reynolds number, and recurrent states can be reconverged after truncating substantial portions of their surroundings. In addition, the model trained at Ly = 12pi generates convergent RPO seeds at Ly = 6pi and 8pi without retraining. These results demonstrate that distributed learned dynamics can provide useful initial conditions for exact coherent states searches in extended domains and reveal recurrent solutions with strongly localised temporal dynamics.

Identifying the minimal set of nodes whose removal breaks a complex network apart, also referred as the network dismantling problem, is a highly non-trivial task with applications in multiple domains. Whereas network dismantling has been extensively studied over the past decade, research has primarily focused on the formulations of the optimization problem for single-layer networks, neglecting that many, if not all, real networks display multiple layers of interdependent interactions. In such networks, the optimization problem is fundamentally different as the effect of removing nodes propagates within and across layers in a way that can not be predicted using a single-layer perspective. Here, we propose a dismantling algorithm named MultiDismantler, which leverages multiplex network representation and deep reinforcement learning to optimally dismantle multi-layer interdependent networks. MultiDismantler is trained on small synthetic multiplex graphs; when applied to large, real and synthetic networks, it displays exceptional dismantling performance, clearly outperforming all existing methods that rely on a single-layer approach to network dismantling. We show that MultiDismantler is effective in guiding strategies for the containment of diseases in social networks characterized by multiple layers of social interactions. Also, we show that MultiDismantler is useful in the design of protocols aimed at delaying the onset of cascading failures in interdependent critical infrastructures.

Vibrational spectral prediction can become inaccurate when localized stereoelectronic environments perturb intermediate response states and high-risk response units dominate characteristic spectral fingerprints, making prediction across external chemical space difficult. SO(3) Equivariant Neural Kalman Networks (SENK) form a response-state cascade that combines an equivariant transformer backbone for Hessian, dipole-derivative and polarizability-derivative learning, an Equivariant Neural Kalman bridge for state-dependent refinement and reliability sensing, and an NBO-informed electronic-prior pathway coupling consistency regularization with bounded, branch-specific guided spectral calibration. SENK outperforms DetaNet on QM9S and QMe14S while preserving full-spectrum IR and Raman fidelity from small molecules to drug-like systems. SENK remains stable and selectively improves spectrally sensitive features in biomolecular systems with complex stereoelectronic effects. It therefore integrates tensor prediction, reliability diagnosis and physics-informed calibration, supporting transferable vibrational spectroscopy from molecular systems to functional molecular materials.

Bound states in the continuum (BICs) exhibit rich momentum-space dynamics, including merging, annihilation, and reconnection across symmetry directions and bands. Yet these phenomena have largely been explained case by case, without a unified framework to classify or predict them. Here we develop a global symmetry-equivariant theory for the dynamics of nondegenerate and degenerate BICs. We show that BIC dynamics can be classified into $\alpha$-, $\beta$-, and $\gamma$-processes according to root motion, which not only encompass the reported dynamics in $C_{2v}$, $C_{4v}$, and $C_{6v}$ systems but also include previously unrecognized ones. More importantly, we reveal that distinct dynamics are bridged by the criticality of BICs through a merging of merging, in which selected direction–band branches acquire higher-order radiation zeros. Such a theory predicts which branches become critical and how to tune system parameters to realize them. Following this framework, we construct high-order BICs in full-wave calculations, realizing $Q\sim k^{-10}$ nondegenerate criticality and $Q\sim k^{-8}$ single-branch and band-paired degenerate criticalities. Our framework provides a systematic route to understanding, discovering, and selectively controlling BIC dynamics and high-$Q$ states.

The structure of a periodic multilayer X-ray mirror is obtained by fitting its X-ray reflectivity (XRR) curve, and the result depends on how the operator normalizes and trims the curve, frees parameters, and accepts a fit. The manual of the fitting program and the papers describing its engine leave these decisions to the operator, whose practice is tacit, so the fitting stays with the expert. To solve this problem, we proposed to develop a skill for a large language model (LLM) agent via elicitation: the expert's decisions were recorded during fitting and written as thirteen steps and a 22-item report template. The agent runs X-Ray Calc 3 through a Model Context Protocol (MCP) tool server. Fresh sessions, each given the skill, one curve, and a nominal design, were scored against fits the expert had withheld, under six tolerances fixed beforehand. The skill was developed on XRR curves of Co/C mirrors and of Ru/C mirrors from a public data deposit. The final version of the skill was tested on W/B4C multilayers. It was demonstrated that the skill recovered the mean period within 0.3 Å of the expert's fits and the period drift through the stack on both W/B4C specimens, and the W and B4C thicknesses within 1 Å on one of them.

Exploration seismic processing underpins subsurface imaging and resource exploration, but learning-based methods remain difficult to compare across studies. Our survey of 368 papers finds widespread reliance on private or difficult-to-reproduce datasets, with only 25 providing public code. This obscures whether reported gains arise from model design or experimental settings. We introduce the Seismic Processing Benchmark (SPBench), covering six tasks: random noise attenuation, trace interpolation, ground-roll suppression, multiple suppression, deblending, and first-arrival picking. We reproduce 24 supervised methods on 10 datasets under 43 standardized settings and release datasets, implementations, configurations, evaluation scripts, and results. To complement global scores and per-trace pick errors, we introduce signal-component-resolved evaluation (SCoRE) for reconstruction and a reference-free ridge-curvature score (RC_norm) for first-arrival picking. Our analyses show that synthetic rankings do not reliably predict field rankings, with task-dependent agreement when models train within each setting. As degradation strengthens, rankings reorder more under coherent ground roll than under random-like interference. The ridge score agrees with MAE-based model rankings in the evaluated settings, with a mean Kendall correlation of 0.881 across three field surveys, while SCoRE reveals frequency- and energy-dependent differences hidden by global scores. SPBench provides a reproducible basis for comparing learning-based seismic processing methods and characterizes how their relative advantages vary across data settings, degradation strengths, and evaluation criteria.

Superconducting strip single-photon detectors (SSPDs) have emerged as scalable, wide-strip variants of traditional nanowire counterparts. Despite practical advantages including improved optical fill factors and enhanced signal-to-noise ratios the fundamental detection physics governing these micro-scale geometries remains largely unexplored. Here, we investigate the underlying photoresponse of a 20 um-wide tungsten silicide SSPD, demonstrating a slew-rate-corrected timing jitter of 13.2 ps at 532 nm and 20.5 ps at 1550 nm, alongside saturated internal detection efficiency up to 1550 nm. Using focused free-space optical scanning, we reveal that detector timing jitter is strongly influenced by a spatially dependent slew rate between edge and center absorption events. To mitigate this impact, we utilize a parallel superconducting rail architecture to actively redistribute supercurrent. This in-situ tuning minimizes the latency mismatch and mitigates thermally activated intrinsic dark counts, extending the device's ability to operate at higher temperatures. Finally, comparing these dynamics with time-dependent Ginzburg-Landau (TDGL) modeling elucidates the physical origins of the position-dependent photoresponse, highlighting how superconducting rails or specialized readout electronics can mitigate negative impacts on timing jitter.

We analyze laminar-to-turbulent transition over a blunt capsule representative of Martian entry at Mach 26 using global modal and transient-growth analyses about a thermochemical base flow characteristic of high-altitude entry conditions. Across the Reynolds-number range considered ($Re_\infty = 10^4$–$10^6$), the axisymmetric global eigenspectrum remains stable, ruling out temporally unstable axisymmetric global modes as a viable route to transition. The flow nevertheless supports strong transient growth concentrated in the shear–entropy layer generated by bow-shock curvature. Energy-budget analysis reveals a two-stage process: Reynolds-stress production in the mean shear first amplifies kinetic disturbances, which then generate entropy fluctuations as they traverse the strong base-flow entropy gradient. The optimal gain scales linearly with the Reynolds number based on the capsule nose radius, whereas boundary-layer-localized disturbances become competitive at the highest Reynolds numbers examined. These results identify shear–entropy-layer transient growth as a plausible linear pathway for seeding transition in high-enthalpy blunt-entry vehicles.

Inertial propulsion in fluids generally arises from an unbalanced flux of momentum. For the case of wave-driven propulsion, momentum is transported away from an oscillating raft in the form of self-excited surface waves. While this mechanism has previously been analyzed for rigid rafts, the role of flexibility has yet to be investigated. In this work, we develop a fluid-structure interaction model for a periodically driven two-dimensional flexible raft resting at the free surface of a fluid. The raft is modeled as an Euler–Bernoulli beam and is coupled to a weakly dissipative quasi-potential model of the fluid beneath. Varying the flexural stiffness and forcing position reveals new features associated with the introduction of flexibility, including the possibilities of thrust enhancement and reversal. By projecting the raft response onto its free rigid-body and elastic modes, the fluid loading can be represented as a modal impedance, informing a computationally efficient reduced-order model. Analysis of the modal response and its symmetries facilitates physical interpretation of our key findings. For a uniform raft, excitation of any single mode in isolation is incapable of producing a net thrust, and thus efficient wave propulsion requires a blend of interfering modes with appropriately coordinated amplitudes and phases.

Raman preprocessing and enhancement methods are often assessed by comparing their output with a clean reference spectrum using measures such as mean squared error (MSE). Whether these spectral quality measures reflect downstream analytical performance is rarely tested. We propose a controlled-perturbation framework for this test. Five perturbation types (baseline distortion, independent noise, correlated noise, global wavenumber shift, and nonlinear axis warping) at eight strengths produce paired changes in a quality measure (metric harm) and in downstream performance (task harm). The alignment gap (AG) quantifies how much a single monotone metric-to-task relationship, fitted by isotonic regression, improves when each perturbation type receives its own relationship. Ordering concordance (OC), a clustered Kendall-type index, measures how often a measure ranks conditions of different perturbation types in the same order as their task harm. Cluster-bootstrap intervals and Holm-adjusted sign-flip tests compare twelve candidate measures with MSE. Three public datasets are used: bacterial classification by principal component analysis and logistic regression, sugar-mixture quantification by partial least squares regression, and mineral identification by cosine library matching. Models are fitted to unperturbed or to correspondingly perturbed training spectra. For bacterial classifiers fitted to unperturbed spectra, Wasserstein distance improved both statistics relative to MSE (Holm-adjusted p < 0.05), also after axis perturbations were removed or spectra were compared on a common grid; refitting to perturbed spectra reversed this advantage. Peak-based measures and a structure-to-noise ratio improved both statistics only for refitted sugar calibrations, and no candidate did so for mineral identification. Open code supports testing new measures.

Carbon credits, offsets and index insurance rest on counterfactuals: what the land would have done under the same weather without management, or what a bad season cost. Current baselines fail in two ways: some ignore rain, others condition on rain linearly and annually. But the true response lags the rain, depends on hidden state such as moisture from other sources, and responds differently from pixel to pixel. Focussing on grassland pasture as a proof of concept, we fit a temporal world model of MODIS greenness conditioned on CHIRPS rain over two 45 km^2 sites in Laikipia, Kenya: a transition density map for greenness given recent and current rainfall. We use it, conceptually, as Black-Scholes is used. Inverting the model on observed greenness it yields an implied residual, everything the land did that the rain does not explain. Two capabilities drop out of this solution: 1) attribution, what the land did beyond the rain; 2) sensitivities, derivatives of the rollout with respect to rain and state, close analogues of the Greeks. On Laikipia test sites, our learned model recovers three-month greenness with 23% less error than a history-only baseline and 13% less than the best hand-written model on unseen years. In the 2022 drought, the land ended 0.17 NDVI below normal; the rain that failed explains roughly half of that, and under normal rain with the same residual it would still have ended 0.10 below. Delta, the derivative of greenness with respect to rain, is computed at every pixel and averages 0.075 NDVI per extra 100 mm of rain. A Delta at every pixel is an exposure to rain that can be hedged, allowing efficient pricing of contracts and accurate attribution.

Edge-localized modes (ELMs) are transient magnetohydrodynamic instabilities commonly observed in tokamak plasmas. Although deep time-series models can learn complex dynamic patterns from multi-diagnostic signals, their predictions are mainly data-driven and lack explicit utilization of ELM physics, plasma operating states, and historical discharge experience. As a result, they may confuse categories with similar temporal patterns, particularly under severe class imbalance. To address this issue, we propose KGF-ELM, a Knowledge Graph Fusion Network for ELM Recognition, and introduce a Knowledge Graph Collaborative Fusion Module (KGCFM) that organizes physical evidence from the current time window, ELM prototypes, relational knowledge graph representations, and similar historical discharge cases into complementary multi-source knowledge representations. KGCFM collaboratively models these knowledge sources through physics prototype matching, graph-structured knowledge encoding, historical case retrieval, cross-evidence consistency analysis, and reliability estimation, thereby generating knowledge representations for enhancing temporal features and correcting classification results. Based on this module, KGF-ELM employs a dual-level reliability-aware fusion mechanism. At the feature level, physical residuals and graph-guided knowledge representations are adaptively incorporated into temporal features according to their reliability. At the decision level, reliability gating, minority-class protection, and bounded knowledge correction are used to safely refine the classification results and reduce the negative influence of unreliable knowledge on the base model.

Ion temperature Ti and electron temperature Te are fundamental kinetic parameters for evaluating tokamak plasma confinement, turbulent transport and magnetohydrodynamic stability. Reliable prediction of their spatiotemporal profiles is essential for long-pulse steady-state operation on the Experimental Advanced Superconducting Tokamak (EAST). However, existing EAST diagnostic systems have obvious measurement limitations, including narrow applicable conditions of charge exchange recombination spectroscopy and severe signal degradation of edge temperature diagnostics during long discharges. Classical physics-based transport codes suffer from high computational cost and error accumulation, while existing data-driven models only separately predict Ti or Te relying on spectral data, lacking joint Ti/Te modeling and failing under missing spectral measurements. In this work, we propose a multi-modal (i.e., global macroscopic diagnostic signals and spectral data) fusion attention neural network for simultaneous reconstruction of Ti/Te profiles on EAST. It leverages an adaptive attention module to weight global diagnostic features and suppress noise interference, employs convolutional layers to extract deep spectral representations, and utilizes multi-head self-attention to facilitate cross-modal feature interaction. Two separate prediction heads are constructed to output full radial distributions of Ti and Te. This work builds the first EAST-oriented joint Ti/Te prediction model compatible with operating scenarios without spectral diagnostics. Hierarchical attention mechanisms excavate nonlinear multi-modal correlations to realize high-performance dual-temperature profile prediction. The source code of this paper is available on https://github.com/Event-AHU/OpenFusion/tree/main/TiTe_Prediction

The Starterkit Lessons online and Starterkit Workshops held in Geneva each year have been the main method of onboarding newcomers to the LHCb experiment since its founding in 2015. The new software corresponding to Upgrade 1 of the LHCb and Run 3 of datataking at the LHC has necessitated a new version of this Starterkit to be written. This new version has made several improvements over the old one, with increased maintainability through testing of examples in CI pipelines, and has also allowed for translations of the Starterkit Lessons. In late 2025 the Run 3 Starterkit lessons were translated into Mandarin Chinese, to allow for a sibling Starterkit event to take place in China, with a dedicated liaison role set up to ensure synchronization between the two translations. To measure the success of the Chinese Starterkit Lessons, analytics have been added revealing that roughly 22% of all visits to the Starterkit website will access at least one Chinese-language page.

The Multi-Purpose Detector (MPD) experiment at NICA is designed to investigate strongly interacting matter at high net-baryon density through a broad program of heavy-ion measurements. Dielectrons constitute one of the key probes in this program, with contributions emitted throughout the entire space-time evolution of the collision. Their experimental measurement is, however, challenged by a large combinatorial background arising primarily from incompletely reconstructed photon conversions and Dalitz decays of light neutral mesons. In this work, we study the performance and capabilities of the MPD experiment for dielectron measurements using simulated minimum-bias $^{209}$Bi+$^{209}$Bi collisions at $\sqrt{s_{NN}}$ = 9.2 GeV. Electron identification is performed using the combined information from the TPC, the TOF detector and the ECal. A multilayer-perceptron classifier is used to optimize electron identification, leading to a substantial increase in electron detection efficiency relative to sequential one-dimensional selections while preserving an electron-sample purity close to unity over a broad momentum range. To address the dominant sources of combinatorial background, a pair-analysis strategy is developed that exploits partially reconstructed electron tracks. The method combines the pair opening angle, the TPC dE/dx signal, and an approximate reconstruction of the pair invariant mass to tag tracks from photon conversions and $\pi^0$ Dalitz decays at the pair level. In the mass interval 0.2 $< m_{ee}<0.7 $ GeV/$c^2$, the method improves the signal-to-background ratio by a factor of about 3.5 with the current track reconstruction algorithm. The study demonstrates the strong potential of the MPD experiment for dielectron measurements at NICA energies and highlights the importance of further improvements in low-$p_{\rm T}$ track reconstruction.

AI agents are advancing the automation of molecular dynamics (MD) research, with the potential to accelerate discovery and design of lithium-battery electrolytes. However, whether they can autonomously complete scientifically reliable electrolyte MD studies remains insufficiently evaluated. We introduce ElectrolyteMD-Bench, which unifies dilute, high-concentration, and localized high-concentration electrolytes in a comparative study across distinct solvation regimes. The benchmark connects local coordination, ionic association, single-particle dynamics, and collective transport within one continuous research process. Agents autonomously perform system construction, equilibration, production sampling, property analysis, and interpretation under a frozen scientific model, while deciding whether to correct errors, continue sampling, or stop. We audit each study along four dimensions: simulation validity, analysis validity, evidence integrity, and outcome calibration. Across eight model–harness configurations, all 24 system runs produced valid production trajectories, yet none of the eight studies satisfied all scientific requirements. Failures included missing analyses, incorrect physical definitions or numerical implementations, insufficient statistical support, and completion claims inconsistent with retained evidence. These results expose a gap between successful MD workflow execution and reliable scientific study completion. ElectrolyteMD-Bench therefore provides a foundational test and defines a key capability threshold that autonomous electrolyte simulation must cross to advance toward computation-first materials exploration.

Methane pyrolysis experiments were performed in a micro flow reactor to investigate the chemical mechanism sensitivities at elevated pressures and temperatures. The micro flow reactor used in this study provides independent control over reaction parameters. Experiments were performed at temperatures from 1400 to 1800 K and pressures from 1 to 10 bar at a fixed residence time of 60 ms. The yields of hydrogen, unreacted methane, and solid carbon were quantified for comparison to predictive models. The mass of heterogeneous depositions was also quantified. The presence of C2 and PAH species were measured by GC-MS. The results were compared to 1D simulations using a discrete sectional model for capturing the kinetics of particulate formation and surface deposition. The simulations replicated the pressure trend well at high temperatures (1800 K), but the sensitivity to temperature was in disagreement. Simulations generally found lower carbon and hydrogen yields and a stronger sensitivity to temperature than the experiment. An abundance of PAH intermediates was measured at lower temperatures and higher pressures. The simulated surface deposition rates were lower than the measured values but showed similar profiles. The results demonstrate the utility of micro flow reactors in recreating pyrolysis environments for hydrogen production from methane. The predictive capabilities of simulations could be improved by advancing the fuel decomposition and PAH formation and consumption kinetics at low temperatures and by detailed treatment of surface site density.

Societies worldwide are described as increasingly polarized, yet issue polarization – how divided a society is over political positions – has been measured almost entirely on a single left-right axis developed in Western democracies. Whether that axis describes political division elsewhere is largely untested, because no survey measures political attitudes on multiple dimensions across many countries. Here, we harmonize 1,299 items from nine cross-national survey programs and 23 national election studies to place 1.14 million people from 141 countries on four dimensions: social liberalism, economic left-right, democratic commitment, and anti-elite sentiment. A single left-right axis does not describe political division in most of the world. The median within-country correlation between a person's social and economic positions is -0.015. Societies differ instead in what they are divided about, and they do so by region. European countries divide most often over social liberalism, East Asian and Arab countries over democratic commitment, Latin American countries over economic left-right, and African countries over anti-elite sentiment. Of the 81 countries measured on all four dimensions, 39 are most divided on a dimension no left-right scale would capture. Most political disagreement lies within countries rather than between them, at 58 to 88 percent of the variance. Ideological constraint, long treated as a property of mass publics, holds in nineteen of the eighty-three we can test, most strongly in the United States at +0.70, and there it is concentrated among the politically attentive. Understanding political conflict therefore requires asking not how polarized a society is but what it is polarized about. Our released item bank and fitted parameters place respondents from any future survey on the same scale without re-estimation, once that survey's items are harmonized to the bank.