Papers

Precise manipulation of liquid droplets underpins lab-on-a-chip platforms for diagnostics, chemical synthesis, and biological assays. Yet autonomous droplet transport through confined geometries of varying complexity remains an open challenge. Droplets exhibit contact-angle hysteresis, deformability, and capillary pinning, which make their response to actuation nonlinear and history dependent, that classical controllers and pre-programmed trajectories cannot cope in multi-turn environments. Here we demonstrate autonomous navigation of a liquid droplet through geometries of increasing complexity on a gravity driven (Labyrinth) platform using model-based reinforcement learning. A thin silicone oil film reduces contact-line pinning while two-axis tilt supplies the gravitational driving force, and an overhead camera tracks the droplet in real time. An offline-trained policy discovers effective tilt strategies from limited physical interaction data, without simulation or analytical droplet models. The system operates under partial observability, as oil-film thickness, instantaneous contact angle, and droplet deformation state remain hidden from the controller. Despite these challenges, the learned policy achieves reliable navigation across straight, right-angle, and curved-arc paths, including outside-corner geometries. We further demonstrate that a policy trained on a simpler geometry transfers to complex ones, succeeding zero-shot on right-angle and staircase paths and reaching full success on a curved arc with a fifth of the training data. The findings suggest promising avenues for enabling droplet based microfluidic systems to serve as intelligent chemical laboratories.

Reaching compressible flow has usually forced lattice Boltzmann methods to abandon the compact stencil or the strict locality that make them efficient. We give up neither, solving the compressible Navier–Stokes–Fourier equations with a scheme that transports only the conserved mass, momentum and energy. The viscous stress and heat flux depend on gradients of the velocity and temperature. To capture them, existing compressible schemes go beyond a single lattice of the conserved fields. They enlarge the velocity set, carry the stress and heat flux as extra transported fields, place the energy on a separate grid, or give up exact streaming for an off-lattice advection. We instead recover the gradients from the non-equilibrium part of the distributions already present, inside the collision. No field beyond the conserved state is transported, no neighbour is read, streaming stays exact, and every computation runs in single precision. The method carries five fields where a transported-flux scheme carries fourteen, at a fraction of the memory and $4.9$ times the throughput. Verified against the exact Sod and Becker shock solutions and a supersonic Taylor–Green vortex, it tracks the direct-numerical-simulation reference on the kinetic energy and stays within the reference-solver spread on both dissipation rates.

Auger-electron spectroscopy has long accompanied x-ray photoelectron spectroscopy as a second modality to resolve chemical states with overlapping core-electron binding energies. However, analyzing the Auger spectrum is challenged by its complexity and the computational expense of its simulation. Here we demonstrate that the physical connection, and thus inter-task relationship, between the generation of a core-hole and its corresponding Auger-Meitner decay enables inductive knowledge transfer through the training of a multi-task graph neural network to predict both observables from a common graph embedding. Both task losses are combined with learned weights via the uncertainty weighting procedure. Overall, the single-task and multi-task models predict calculated and experimental Auger lineshapes with good accuracy in most cases. The performance between the two task regimes is similar, with the single-task models generally having better predictions of the finer peak structures in the spectrum. The present results demonstrate that multi-task training is a promising avenue for future developments of universal x-ray spectroscopy models with learned representations that map the molecular structure to a multitude of techniques.

This work presents the first steps for simulating kinetic plasmas with collisions using the characteristic mapping method (CMM). The CMM is a semi-Lagrangian method that explores a semi-group structure to store diffeomorphic flow maps efficiently. Using the semi-group structure, individual submaps can be composed to relate the flow backward in time to its initial food point. The novelty of the presented work is handling the source term by storing sub-integrals that correspond to the individual sub-maps and allow efficient integration. Furthermore, we use the Lagrangian structure to avoid implicit time integration in the hydrodynamic regime, which is known to be stiff. We benchmark our method on the Boltzmann-BGK and Vlasov-Poisson-BGK equations and consider different test cases, the Sod shock tube problem and nonlinear Landau damping for different Knudsen numbers. We show third-order spatial and temporal convergence and illustrate the fine-scale zoom property of CMM for the bump-on-tail instability.

Infrastructure networks essentially underlie human mobility and transport. Improving the quality of single links increases network performance locally. However, efficient transport requires high-quality connected corridors across multi-link paths that do not emerge from independent single-link upgrades. Here, we introduce a framework for evaluating the impact of jointly upgrading multiple links as inherently higher-order interactions, enabling us to quantify link synergies in complex transport networks. Two links are synergistic if an upgrade of one increases the benefit of upgrading the other, promoting upgrades of topologically complementary links along the same path while discouraging upgrades of redundant parallel links. By expressing these synergies as second-order derivatives of overall network performance, we develop an efficient computational framework to identify synergistic links that form a connected network backbone. We apply our theoretical framework by combining empirical street network and cycling demand data for Hamburg, Germany, with a perturbed utility route choice model for urban bicycle traffic. Our results reveal synergies from higher-order interactions, thereby enabling strategic infrastructure planning that goes beyond local link importance in complex transport and flow networks.

This study investigates the influence of frame rate, expressed in frames per second (FPS), on the experimental estimation of the acceleration due to gravity, g, using video analysis with Tracker. Two different dynamical systems were analysed: free fall and a simple pendulum. Starting from original recordings acquired at 120 FPS, equivalent 60, 30, and 10 FPS series were generated by uniform temporal decimation, preserving the same physical trajectory within each realisation. The analysis included ten independent realisations for free fall and eight for the pendulum. For each condition, the mean estimate of g, the standard deviation across realisations, the relative error with respect to g_ref = 9.81 m s^-2, and the RMSE were calculated. For free fall, the 120 FPS condition exhibited the lowest dispersion and RMSE, whereas the mean estimates obtained at 10 and 30 FPS were closer to the reference value. For the simple pendulum, by contrast, the mean value of g, dispersion, relative error, and RMSE remained essentially unchanged between 10 and 120 FPS. An additional analysis of the 12 possible subsampling phases at 10 FPS showed that this stability does not critically depend on a particular choice of decimation phase and that the variability associated with phase is small compared with the dispersion observed across realisations. The results show that a higher temporal sampling rate does not necessarily provide both greater precision and closer agreement with the reference value. The influence of FPS depends on the temporal characteristics of the system and on the procedure used to estimate the physical parameter. In particular, a higher temporal sampling density may reduce dispersion or increase robustness to sampling without necessarily yielding an estimate closer to the expected value.

Rotating detonation engines (RDEs) exhibit strongly nonlinear, multiscale wave dynamics that set the observed thermal field. High-fidelity simulations (DNS/LES) resolve these structures but remain computationally prohibitive, while low-order models such as the one-dimensional Koch-Kutz model capture circumferential wave motion yet lack the expressivity for high-frequency content. We use continuous data assimilation (nudging) to synchronize the Koch-Kutz solver with processed high-fidelity temperature data, introducing the prediction-observation mismatch as a relaxation source in the conserved energy equation; where observations are temporally sparse, interpolation supplies a target at every source update. As the nudging strength increases, the reduced model is progressively drawn onto the high-fidelity trajectory, and the forcing recorded along it provides an explicit, state-dependent estimate of the correction the model requires. We then train a Jacobian-regularized closure a priori on this recorded source. With the observation term removed, the corrected model advances autonomously, remains bounded, and recovers the temperature spectrum and the marginal statistics of the conserved variables relative to the baseline.

We propose a fully automated, structural error-aware, interpretable climate model parameter estimation method that leverages Perturbed Parameter Ensembles (PPEs). It is based on history matching and aligns with an increasingly-used iterative simulation-emulation-calibration methodology. The method is motivated by the negative impacts of structural error and emulator and observational uncertainties on climate model parameter estimation efforts, as well as the problems associated with sparsely-sampled PPEs. To address these challenges, the method explicitly builds simpler emulators that avoid overfitting, detect structural error, avoids compensating for structural error through inflated mismatch tolerances, and sequentially excludes structurally inconsistent variables for parameter estimation. The method decomposes the high-dimensional calibration problem into linked low-dimensional subproblems, and integrates their constraints to reconstruct the jointly plausible region of the full parameter space. The method is applied to a 100-member PPE with 34 perturbed parameters generated by a version of CAM6 with machine learning-based warm rain microphysics parameterization. Through iterative application, the method greatly reduces the ensemble spread and improves the matching between simulated and observed zonal climatologies. The method also finds ensemble members that outperform the default CAM6 configuration in root mean square error across multiple diagnostics. Controlled experiments demonstrate that overly-conservative emulator uncertainty could lead to neglect of informative observations, and tolerance of the structural error, in the context of this method, biases the estimated parameters toward compensating for structural error. Our work also emphasizes the value of interpretability for diagnosing structural error and informing parameter estimation in PPE-based calibration.

The Collision-induced Magnetic Reconnection (CMR) mechanism has recently been proposed as a filament formation model that emphasizes the active role of magnetic fields. To enable better observational tests with data from modern telescopes, we provide more specific observable predictions for filaments formed via CMR. Two types of CMR kinematics are revealed in position-velocity (PV) diagrams. First, due to the nature of CMR, the midplane contains a converging motion close to the filament and two diverging motions farther from the filament. They exhibit a blueshift, redshift, blueshift, redshift (BRBR) velocity pattern along the line-of-sight when the collision midplane is inclined, giving rise to a special pattern in the PV-diagram. Second, the longitudinal PV-diagram along the filament spine exhibits a velocity oscillating pattern, which is currently attributed to the magnetic transport of gas clumps in the converging motion. The first type of pattern involves emission primarily outside the filament, which can be confused with the environment. The second type, however, mainly emerges from gas inside the filament, thus more robust for observational tests. Both the integral-shaped filament and the Stick filament in the Orion A cloud show the velocity oscillation in PV-diagrams, although the spatial oscillation frequency and their velocity spread differ.

This article describes an open image dataset for developing and evaluating active-fire segmentation methods in satellite imagery. The dataset contains 2,148 image-mask pairs from 25 California wildfires, with acquisitions spanning July 2020 to August 2026. Each image is a 512x512-pixel, three-channel composite derived from Sentinel-2 Level-2A bands B12, B11 and B8A at 20 m spatial sampling. A fixed linear rendering is applied throughout the dataset. Corresponding masks distinguish background, SWIR-rule active fire and invalid observations. The masks were generated from shortwave-infrared brightness and near-infrared contrast, followed by constrained neighborhood growth. The release includes chip-level metadata and an incident-disjoint partition containing 18 training, three validation and four test fires. Among the image pairs, 841 contain active-fire labels; these labels occupy 0.0766% of all grid cells. A mask-blind analyst review covers 233 test chips and provides a separate assessment of the rule-generated labels at chip and connected-component levels. Reference training and evaluation code accompanies the data, including a ResNet-34 U-Net implementation with validation-based checkpoint and threshold selection. The archived images, masks, metadata and review annotations support research on rare-class segmentation, learning from algorithmic labels and transfer across fire incidents. The versioned dataset is deposited on Zenodo, with preparation and reuse software maintained in a public GitHub repository.

Manufacturability is one of several constraints on developing solid-state quantum processors, along with qubit performance, control, connectivity, cryogenic operation, and fault tolerance. Chemical residues, structural disorder, and non-uniform interfaces introduced during fabrication can compromise qubit coherence. Two-dimensional (2D) materials, with their atomically thin crystals and van der Waals interfaces, offer routes to address some of these challenges. Their concurrent development as next-generation CMOS channel materials motivates an examination of whether relevant processes can be transferred to quantum chips. This Perspective evaluates the opportunities and limitations of 2D materials for CMOS-compatible quantum hardware.

Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive assets, and parts of regional infrastructure systems. We examine vineyard wildfire resilience during the electrically initiated 2019 Kincade Fire in Sonoma County, California, using an open, event-anchored geospatial framework spanning 4,581 vineyard fields (8,813.2 ha), wildland vegetation, surveyed structures, roads, overhead smoke, and post-fire greenness. Sentinel-2, OpenET, gridMET, soils, terrain, NOAA smoke polygons, an ignition-date OpenStreetMap network, and three-dimensional data inventories were analyzed at native decision scales. Vineyard pixels showed substantially lower descriptive dNBR than wildland pixels inside the perimeter (means 0.130 and 0.337). This contrast did not identify a universal vineyard firebreak effect: a segment-clustered boundary model gave a small negative contrast at 100 m (tau = -0.0166) but changed across bandwidths, failed slope continuity, disappeared in a 100 m donut, and produced a wrong-signed placebo. A 250 m spatial GAM reversed the unconditional pattern: after conditioning on location, terrain, and water use, vineyard fraction was positively associated with dNBR, while residual Moran's I remained 0.519. Beyond spectral impact, all mapped vineyards intersected overhead smoke on at least one day (mean 7.78 potential smoke-days per field), 34.2% of road-network nodes were dead ends, and inside-perimeter vineyards showed a larger greenness deficit through 2021 (recovery ratios 0.815 inside and 0.854 outside). Lower immediate spectral impact therefore did not imply complete resilience. The study provides a reproducible urban-rural informatics template separating descriptive contrasts, conditional associations, exposure indicators, and recovery evidence for decisions in working landscapes.

Numerical simulations reveal how vortices stretch and transfer energy, but establishing smooth evolution requires bounds that remain valid beyond the simulated resolution. Here I develop a computer-assisted framework that establishes global regularity for continuous families of three-dimensional periodic Navier-Stokes flows. Its central construction combines finite reference trajectories with a common error bound that covers an interval of centre fields and infinitely many smooth perturbation modes. The method retains the complete nonlinear residual before spectral truncation and controls the evolution until viscous decay guarantees regularity for all subsequent times. Applications to cyclic-shear, Arnold-Beltrami-Childress and three-component Taylor-Green fields yield explicit perturbation radii and include initial conditions outside the direct Fourier-Wiener smallness criterion. A parameter-uniform extension covers a connected family of non-Beltrami Taylor-Green centres without repeating the proof for individual parameter values. An ensemble of 4,096 configurations, supplemented by 1,600 refinement trajectories and public turbulence data, connects the mathematical observables to spectral transfer and vortex geometry. Matched neural-operator experiments show that physics-informed training improves physical prediction, while also revealing that these gains do not necessarily improve the discovery of proof-limiting initial conditions. Together, these results provide a reusable method for establishing regularity across prescribed flow families and a quantitative setting for evaluating how learned predictions can assist rigorous computation.

The ENTSO-E Transparency Platform is the single mandated publication point for European electricity market data, and three of its data items carry a production-type label. Commission Regulation (EU) No 543/2013 Article 16(1)(a) requires actual generation output per generation unit but does not mention production type; the label on that item is ENTSO-E's own addition through the schema, and Article 5(d) delegates the classification of production types to a Manual of Procedures. This data descriptor documents two preregistered datasets that measure what that classification does in practice, along four axes: simultaneous multi-fuel reporting, type change between capacity-register vintages, persistent misclassification without type change, and cross-product consistency between the per-unit and the aggregated series. Criteria were locked and checksummed before measurement, thresholds are derived from measured noise rather than chosen, and what did not survive is recorded: six claims are withdrawn, three of them from published versions of the datasets themselves. Across 2191 documents covering 199 generation units in three bidding zones where biomass co-firing is documented in a dated primary source, no unit carries two production types in the same document. One practical warning applies to every user of the hourly series: all of it carries curveType A03, a step curve in which a missing position means the previous value holds rather than that data are absent. A parser that sums only the points listed under-reports energy unevenly, by 22.6 percent for one Finnish unit in 2021.

A decade ago, a landmark study on flow control via subsurface phonons transformed our understanding of fluid-structural interactions, compelling us to reimagine ways by which to suppress boundary layer instabilities. While subsequent investigations have steadily enriched this landscape, several questions remain largely unanswered. The notion of Tollmien-Schlicting (TS) wave stabilization relies on phase-engineered surface interactions, which destructively engage with the wave and impede its growth. Although the fundamental drivers of the phenomenon are established, its granular physics remain insufficiently resolved, particularly as it pertains to directional effects, competing energy production mechanisms, and the precise streamwise locations governing the process. Revisiting this initial vision, we confront these open questions, revealing fresh insights, and establishing broad foundational strokes to guide the next era of investigations. We begin by defining the amplitude and phase criteria of an elastic wall admittance driving perturbation energy changes relative to a rigid wall. The established framework isolates the roles of the work-rate, viscous energy, production, and dissipation in shaping the fluid's response and clarifies how each contributes to the collective outcome. Crucially, we demonstrate how streamwise translation of the interaction surface significantly alters these parameters, revealing critical thresholds at which the control result is fully reversed. Our model identifies specific pathways to sustained TS wave attenuation, and defines the limits of what can be accomplished passively. Finally, we conceptualize a two-dimensional phononic subsurface, whose tailored and disproportional response to the flow in the wall-normal and streamwise directions, brings about the elusive combination of local and downstream stabilization.

We present a three-dimensional visco-magnetoacoustic framework for analog gravity in magnetohydrodynamic (MHD) flows. While standard fluid dissipation typically breaks the Lorentzian signature of acoustic metrics, we demonstrate that evaluating wave perturbations in the eikonal limit alongside the Shakura-Sunyaev $\alpha$-viscosity prescription preserves a well-defined effective spacetime geometry for the fast magnetoacoustic mode. The slow-magnetoacoustic mode and Alfvén mode do not admit a non-degenerate metric. To investigate analog horizon thermodynamics, we utilize astrophysical accretion disks as background media, specifically modeling numerical magnetized advective accretion flows around rotating black holes and analytical advection-dominated inflow-outflow solutions (ADIOS). Standard self-similar ADIOS models strictly enforce a constant Mach number, precluding horizon formation. We therefore introduce a magnetic field perturbation that breaks self-similarity, generates a dynamic Mach number, and enables the formation of a visco-magnetoacoustic horizon. By evaluating the spontaneous phonon emission at these horizons, we reveal that the analog Hawking temperature is highly sensitive to the magnetic field topology; the spatial orientation of the background magnetic gradients dictates whether the Hawking radiation is amplified or suppressed. Furthermore, we find that increasing the viscosity parameter leads to a monotonic increase in the analog Hawking temperature.

Energy transition decision-making is pervaded by deep uncertainties. Computational models are helpful in addressing such uncertainties, as they can give insight into techno-economic complexity. However, models alone are insufficient, as part of the uncertainties involve justice dilemmas.

Many quantum sensors infer parameters from continuously emitted fields, yet in the presence of unmonitored loss, it is difficult to determine how much critical enhancement survives in the accessible output. In this work, we calculate the full frequency-resolved output quantum Fisher information (QFI) of a stationary vacuum-seeded optical parametric oscillator for cavity-detuning estimation. We show that, for any fixed nonzero unmonitored loss, the QFI of correlated sideband pairs grows quadratically with the mean intracavity photon number near threshold, but only within a spectral window that narrows inversely with that number; consequently, the spectrum-integrated output QFI rate scales asymptotically only linearly. We identify a far-detuned, near-threshold regime in which the monitored output alone asymptotically approaches the loss-imposed upper bound on the joint cavity-output QFI. We further determine the operating points that maximize the output QFI rate per intracavity photon and introduce a frequency-resolved homodyne strategy that maximizes the Fisher information obtainable from a single record. Our results establish an operational connection between critical enhancement and metrological information accessible in the output field, while providing concrete guidance for the design and readout of dissipative parametric sensors.

Laser-plasma accelerators have demonstrated the ability to produce ultrashort relativistic electron bunches with peak currents suitable for compact light sources, ultrafast diffraction, and strong-field studies. However, their performance critically depends on preserving the longitudinal phase-space density of the beam as it exits the plasma accelerator. Here, we report the first direct observation of a previously unresolved process in which a highly charged electron bunch undergoes significant longitudinal expansion and progressively loses electrons during extraction from a laser-driven wakefield accelerator, a phenomenon we refer to as electron shedding. Using femtosecond relativistic electron microscopy, we tracked the evolution of the beam far beyond the accelerator exit and observed the bunch stretching over many plasma wavelengths before shedding electrons during propagation. Particle-in-cell simulations reproduce the observed behavior and reveal that it originates from a combination of effects when a high-charge-density beam exits the accelerator. These dynamics redistribute energy from the beam head into a low-energy tail, thereby reducing the useful peak charge density and ultimately decreasing the efficiency of the laser-plasma accelerator. Our results provide new insight into beam extraction and phase-space evolution in laser plasma accelerators and highlight the importance of controlling these collective effects for future applications.

Upstream swimming drives bacterial spreading and surface colonization. Many pathogens encounter fluid flows as they infect the intestines, lungs, and urinary tract, so how bacteria use their flagella to counter these flows matters for disease and treatment. Yet how morphology and flagellar arrangement govern motility against flow remains unknown. Here, we investigate the biophysical determinants of rheotaxis by combining microfluidics, directed evolution, genetics, holography, and hydrodynamics simulations. Using upstream swimming competitions, we find that peritrichous E. coli and S. enterica rapidly outcompete monotrichous P. aeruginosa and V. cholerae, accumulating upstream at densities up to five orders of magnitude higher, even though Vibrio swims three times as fast. Motility selection experiments show that rheotaxis increases with flagellar number and length, confirmed by overexpressing the master regulator flhD/C. Three-dimensional holography and single-cell tracking reveal that multiflagellarity stabilizes surface residence and promotes the weathervane effect that reorients cells upstream, a mechanism further supported by simulations that fully resolve flagellar arrangement and fluid-structure interactions. These results establish multiflagellarity as a key facilitator of upstream navigation, governed by near-wall residence and shear-driven reorientation rather than by swimming speed.

Accurate prediction of transition-metal hydride (TM-H) bond dissociation energies (BDEs) remains challenging because of strong electron correlation, relativistic effects, and nuclear quantum contributions. Here, we assess the performance of neural-network variational Monte Carlo (NN-VMC) based on the Psiformer ansatz for systems with increasing complexity (LiH, OH, TiH and NiH), and compare it against CCSDT(Q)/CBS as well as available theoretical and experimental data. Throughout this study, both NN-VMC and ab initio calculations employ a common ccECP Hamiltonian to enable tractable and consistent comparisons. To reduce finite-training errors, we introduce complementary zero-variance and infinite-step extrapolation schemes. For LiH and OH systems, NN-VMC yields total energies that differ within sub-milli Hartree compared to CBS extrapolated ab initio results and the BDE differences remain under 2$\sigma$. In case of Ti and TiH, the variational NN-VMC energies at the end of training are already consistent with CCSD(T)/CBS, while post-training extrapolation systematically closes the gap towards the CCSDT(Q)/CBS results. In contrast, NiH provides a stringent test of wavefunction expressivity, where increasing the number of determinants in NN-wavefunction ansatz improves the recovered correlation energy. The comparison of NiH BDE reveals that, while the existing theoretical predictions cluster into distinct high and low BDE groups, the broad scatter in available experimental data prevents a definitive assessment of the most accurate theoretical approach. This work demonstrates that NN-VMC with ccECPs Hamiltonian provides a competitive framework for quantitative prediction of main-group and early TM-H energetics, while identifying late TM-H as an important benchmark for future developments in neural-network wavefunctions and electronic structure theory.

The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data. These advances have brought to the foreground fundamental connections between subdisciplines of physics, applied mathematics, and machine learning. In this review, we bring together some of these ideas, often expressed in different languages, to highlight a conceptual thread that links five distinct fields: control theory, optimal transport, probabilistic inference, non-equilibrium thermodynamics, and machine learning. A common theme is the optimization of free-energy-like functionals under dynamical or statistical constraints. We offer a guided tour through this thread and present selected applications in reinforcement learning, variational inference, and generative modeling. The review does not assume prior familiarity with these topics, and begins with principles originating from physics.

In $^{28}$Si/SiGe spin qubits with micromagnets, the longitudinal stray field gradient transduces charge noise into qubit frequency noise and limits coherence. We compare two neighboring qubits in the same device with 800 ppm residual $^{29}$Si, one near a decoherence sweet spot where the gradient is locally minimized and the other at a position with a larger gradient. At the sweet spot, $T_2^*$ reaches 67 $\mu\mathrm{s}$ and the Carr-Purcell-Meiboom-Gill coherence time reaches 4.6 ms, whereas $T_2^*$ is 5.2 $\mu\mathrm{s}$ at the neighboring qubit. Near 1 Hz, the frequency noise power spectral density at the sweet spot is nearly two orders of magnitude lower than at the neighboring qubit. The magnitude and low-frequency decay of the residual spectrum are compatible with the prediction for $^{29}$Si nuclear spin noise, and the weak interqubit correlation indicates that local noise becomes important for dephasing at the sweet spot. Despite a finite correlation with the charge sensor, sweet-spot operation strongly reduced the transduction of charge noise, bringing the residual frequency noise close to the level predicted for $^{29}$Si nuclear spin fluctuations.

Muography exploits the penetrating power of muons to image the interior of large dense objects, but cosmic-ray sources provide only ~1 {cm}^{-2} {min}^{-1} predominantly from above, limiting imaging speed and accessible geometries. Electron-driven muon production has recently been demonstrated with laser-wakefield accelerators, but shot-to-shot fluctuations hinder systematic studies required for quantitative accelerator muography. Using Geant4 Monte Carlo simulations, we model the full experimental setup at Shaft 2 of the Shanghai High repetition rate XFEL and Extreme light facility (SHINE), from a 3 GeV, 50 pC, 50 Hz commissioning electron beam interacting with a muon target through 25 m of beamline structures and a 3 m-thick concrete isolation wall. Approximately 0.28 effective reconstructed single-muon events per bunch, with residual kinetic energies below 1.2 GeV after traversing the wall, reach the downstream muography test area (~14 s^{-1}). The wall absorbs most charged background particles below ~1 GeV, while residual neutrons can be discriminated by their characteristic detector energy deposition. Scattering-tomography simulations show that $3\times 10^{5}$ effective muon events, accumulated in approximately 6 h at the commissioning rate, yield a Structural Similarity Index above 0.9, demonstrating the feasibility of quantitative accelerator-based muography using SHINE's electron-driven GeV-scale forward muon source. At the 8 GeV/50 kHz benchmark, the projected muon intensity exceeds $5 \times 10^{4}$ {\mu}/s, corresponding conservatively to approximately one muon per bunch at the detector. This exceeds the cosmic-ray flux by orders of magnitude, while SHINE's superconducting linac provides a stable, controlled platform for developing electron-on-target muography.

The Kohn-Sham (KS) formulation of density functional theory (DFT) is a map from the many-electron problem to an effective single-electron problem that is governed by a local multiplicative potential. The generalized-Kohn-Sham (GKS) formalism extends it to permit any single-electron operator—nonlocal, local non-multiplicative, local multiplicative, or any combination of them. Doing so expands the scope and ease of modeling the exchange-correlation (XC) functional in DFT, which encodes the complicated many-electron interactions into a mean-field of the electron density. However, unlike KS theory, development of XC functionals in GKS theory has been hindered by the absence of corresponding exact XC potentials and energies. We present the exact XC potentials and energies for atoms and molecules by solving the inverse GKS problem, using highly accurate correlated ab initio densities. Our approach is validated across weakly and strongly correlated systems. We further examine a common, yet untested, assumption that KS and GKS correlation potentials and energies are similar, finding instead that they differ substantially in strongly correlated systems. Overall, this work offers a powerful tool to model next-generation of XC functionals within the GKS formalism of DFT.

We report the absolute frequency measurement of the $4s$ $ ^{2}S_{1/2}\leftrightarrow 3d$ $^{2}D_{5/2}$ $^{40}$Ca$^{+}$ clock transition with respect to the SI second. To perform this measurement, a link between our laboratory in Innsbruck and the clocks realizing the Coordinated Universal Time at the Physikalisch-Technische Bundesanstalt (PTB) in Braunschweig was installed and characterized using the Global Navigation Satellite System GNSS. The comparison between our clock and the ones at PTB was done using the Precise Point Positioning technique. After the evaluation of the systematic shifts, the measured transition frequency is 411 042 129 776 401.2$\pm$0.6 Hz with a fractional uncertainty of 1.5 $\times$ 10$^{-15}$. The stability of the clock measurements was also corroborated by comparing two different calcium ion clock experiments, which share the clock laser source at our institute. Furthermore, after careful evaluation of the trap-drive induced ac magnetic fields, we estimate ac Zeeman shifts on the $D_{5/2}$ sublevels and reevaluate the Landé g-factor of the $3d$ $^{2}D_{5/2}$ level to be g$_{5/2}= 1.200329(1)$.

Physically interpretable power take-off models support wave-energy development at every stage. For the seawater-pump class, few have been identified on a real machine and validated at the system level. This paper presents a block-modular grey-box model of a short-stroke seawater pump, identified from bench data alone. The pump decomposes into chamber, relief valve, piston seal and tip check valve, each structured by component physics, and the few parameters are identified block by block from terminal pressure, displacement, force and discharged-mass measurements, with a closed-cycle mass balance that decouples the degenerate seal and valve losses and closes to within 1% of the inflow. Over the sinusoidal campaign, the pressure-energised piston seal carries 84% of the lost mass and 97% of the lost energy, and the pump delivers about four-fifths of its input as high-pressure flow. Generalisation is demonstrated rather than assumed. With parameters fixed on ramps and sinusoids, the model reproduces 360 s sea-state records in free run to about 10% of range, captures 80% of several thousand peaks within 5 bar, transfers to a re-sprung valve on cracking strokes once its crack pressure is identified, follows thousand-cycle endurance records that drift below 0.1 bar, and reproduces the force and cycle-mean absorbed power, 0.1 to 5.7 kW, to a 4% median error. A practical-identifiability and uncertainty analysis confirms the terminal data resolve the retained parameters; thirty-seven repeated records set the experimental floor. The result is a compact, physically labelled model for design, energy assessment and model-based control of seawater-pump power take-offs.

Likelihood-based network models are often fitted under links' independence and low-order constraints, while empirical networks frequently exhibit systematic higher-order structures such as triangles and wedges, characterizing the observed clustering patterns. Real-world core-periphery networks such as the interbank market or the air transportation system represent key examples, with cores displaying complex and nonlinear features. We formalize Penalized Likelihood with Structural Discrepancies (PLSD) as an inference-level correction that trades likelihood fit for agreement with targeted motifs. PLSD augments the negative log-likelihood with a penalty on standardized wedge and triangle discrepancies, yielding a controlled distortion of the likelihood surface that can be interpreted through a linear response analysis. A calibration approach for the unique tuning hyperparameter is introduced to turn off the penalization term when the model is correctly specified, thereby recovering maximum-likelihood estimation in that case. We then introduce a mixed-constraint maximum-entropy core-periphery Exponential Random Graph Model (ERGM), derive unconditional semi-closed motif formulas under Pareto-distributed core fitness, and interpret sparse-regime scaling through a big-jump mechanism for heavy-tailed distributions. We finally corroborate the PLSD inference methodology both with Monte Carlo simulations of a toy stochastic block model and by applying the novel core-periphery ERGM to weekly eMID interbank networks (2009-2015) and monthly US air traffic networks (1991-2000). We show that PLSD describes cores that are not only dense but also higher-order-rich and captures the observed heterogeneous degree distributions and the overexpression of wedges and triangles, at a link-level cost proportional to the model misspecification.

Bound states in the continuum (BICs) supported by periodic photonic structures are surrounded by high-$Q$ resonances and serve as centers of polarization vortices in momentum space, making them attractive for a wide range of applications. Recently, a perturbation theory has been formulated for resonant states near BICs, yielding a hierarchy of equations whose solutions directly approximate the resonant states and determine the asymptotic behavior of their $Q$ factors and polarizations. Efficiently solving these equations is therefore essential. Since many studies of BICs concern layered structures, we develop an approach based on the Fourier modal method. Numerical examples confirm the effectiveness of the proposed method and demonstrate its ability to capture higher-order asymptotic scaling. Our work provides a practical tool for the theoretical analysis of BICs and may also facilitate the design of BIC-based photonic devices.

The complicated physicochemical properties of metal complexes that exhibit thermal spin crossover make it difficult for routine electronic structure calculations to yield an accurate transition temperature prediction, $T_{1/2}$. The difficulty lies in the intricate connection between the spin-crossover energy, which is a molecular spectroscopic property, and $T_{1/2}$, a condensed phase property. Here we show how to obtain spin-crossover energies systematically by reverse engineering of experimental $T_{1/2}$ data. The protocol is based upon fitting the range separation parameter, $\omega$, in the hybrid LC-$\omega$PBE density functional to reproduce the experimental $T_{1/2}$ values for a series of metal complexes. We provide insights into the sources of variations of at least $\pm 15$ kJ mol$^{-1}$ found from common exchange and correlation functionals by comparing their performance against our reference data. By analysis of the sensitivity of transition temperatures to $\pm 1$ % shifts in the range separation parameter, we determined a typical uncertainty of $\pm 50$ K for them, and a $\pm 2$ kJ mol$^{-1}$ uncertainty in the extracted spin-crossover energies due to $\pm 1$ % variations of $T_{1/2}$. Lastly, we present results from the high-level, all-electron coupled cluster method for eight of the smaller molecules in the reference data set, and discuss the influence of the truncation of the excitation series upon the spin state energies.