Papers

The rapid advancement of Large Language Models has transformed scientific research workflows, including enabling the automated extraction of data directly from published literature. Most existing efforts, however, focus on extracting simple labeled key-value entities, whereas many scientific applications require more complex, hierarchically structured data. A representative example is Quantum Cascade Lasers, whose device architectures are defined by tens of interdependent parameters organized in nested layer sequences. In this work we propose a JSON-Schema Guided Information Extraction Pipeline (JSG-IE) that enables reliable extraction of deeply structured device data without model fine-tuning. By transforming extraction into a schema-constrained generation task, our approach significantly improves structural consistency and accuracy. Across 12 state-of-the-art LLMs, a properly designed JSON Schema improves performance by 5.7% over conventional prompting, with the highest $F_1$ score up to 83.4%, achieved by the reasoning-enabled Kimi-k2-thinking model. Importantly, this performance enhancement is most significant for mid-tier and open-source models, where $F_1$ gains reach as high as 24.1%, effectively enabling these widely accessible models to achieve extraction fidelity previously restricted to much larger architectures. This framework provides a scalable path toward automated construction of high-fidelity device databases, accelerating data-driven optoelectronic design.

Tokamak design is inherently challenging due to several cross-competing effects which require a careful and calibrated treatment to obtain an optimal operational envelope. Incorporating physics across varied fidelities is crucial in this exercise. Jenga is developed as a unified design and modeling framework for tokamaks, seamlessly coupling systems-level studies to high-fidelity models based on first principles. In this work, static Grad-Shafranov (GS) equilibrium for an entire pulse and the neutronics study of the Mega Ampere Spherical Tokamak Upgrade (MAST-U) tokamak are carried out in Jenga. Coil currents and plasma profiles from the EFIT++ reconstruction of MAST-U shots are used to reproduce the plasma poloidal flux and shape targets at different time slices. The results from Jenga are also in good agreement with FreeGSNKE and Fiesta codes. Neutronics analysis is performed for a hypothetical 50-50 mixture of deuterium-tritium (DT) fuel, using the same data structure as the systems and equilibrium studies. A distributed neutron source is initialized within the last closed flux surface (LCFS) of the plasma, with their strength being functions of the density and temperature of the ions. The distribution of the neutron flux across the energy spectrum is computed for the active coils and the first wall (limiter) independently over multiple scenarios. We demonstrate the capabilities of Jenga with a comprehensive analysis that takes inputs about the plasma geometry, tokamak design and plasma profiles and performs 0D, 2D and 3D numerics for the systems study, equilibrium and neutron transport respectively.

Sensing weak forces through observing a mechanical motion near or below its quantum zero-point fluctuation has been desired in diverse areas. While mechanical oscillators have played a crucial role in such studies, their application to free-fall-type sensing has been elusive, in particular in the quantum regime. Here, we demonstrate sensing a static force of the order of 10 zeptonewtons with a levitated nanomechanical oscillator below the zero-point fluctuation through the rapid modulation of its confining potential. We prepare a squeezed state with a reduced velocity uncertainty by abruptly decreasing the potential. Subsequently, we detect the exerted static force through time-of-flight measurements, where we release the nanoparticle from the potential and measure the displacement during a free fall. Furthermore, time-of-flight measurements allow us to perform quantum state tomography of the squeezed state, from which we reconstruct its Wigner quasiprobability distribution and evaluate the Fisher information for the position measurement to quantify the achievable force sensitivity of our protocol. Our results demonstrate that modulating the trap stiffness serves as a crucial technique for quantum-limited force sensing and paves the way to utilize a levitated nanoparticle as a promising sensing platform beyond the quantum limit with a capability of quantum state tomography.

Ball lightning remains one of the most enigmatic atmospheric phenomena, characterized by its long lifetime, ability to penetrate materials, and stable spherical structure. Here we propose a novel theoretical framework interpreting ball lightning as a three-dimensional projection of a high-dimensional topological soliton. The system is described by a nonlinear Schrödinger equation with attractive interactions, stabilized by a non-zero topological charge. Through comprehensive numerical simulations of the three-dimensional Gross-Pitaevskii equation, we verify the model's core predictions: (1) long-lived stability protected by topological invariants, (2) low transmission probability due to wavefunction orthogonality, and (3) energy and size scales consistent with observational data. The soliton lifetime $\tau \sim \hbar/\Gamma$ naturally explains the observed second-scale durations. Our work provides a self-consistent physical explanation for ball lightning while offering concrete pathways for experimental realization of three-dimensional topological solitons in Bose-Einstein condensates and nonlinear optical systems. This theoretical framework gains additional support from recent experimental breakthroughs in laboratory generation of ball-lightning-like structures.

We present a non-destructive, spatially resolved thickness characterization method for rhombohedral (3R) molybdenum disulfide (MoS$_2$) on polydimethylsiloxane (PDMS) substrates. Unlike broadband spectroscopic approaches, the proposed method reduces the measurement to a small number of discrete intensity images, enabling direct thickness mapping with a conventional microscope architecture and commercially available bandpass filters. Our approach combines a systematic framework for selecting optimal discrete wavelength samples of the material's reflectance with a robust thickness retrieval algorithm based on a multivariate Gaussian probability model. By sampling the reflectance with just five strategically chosen near-infrared bandpass filters, we demonstrate thickness characterization up to 691 nm with a mean 95% confidence-interval width of 8.3 nm. The method is adaptable to other van der Waals materials and conventional optical thin-film systems. It therefore provides a foundation for scalable, real-time thickness characterization in, e.g., dry-transfer fabrication workflows, where thickness screening remains a critical bottleneck for the production of van der Waals heterostructure devices.

We report a comprehensive measurement of the environmental $\gamma$-ray flux in Hall C of the Gran Sasso National Laboratory. A spatial mapping of the radiation was carried out using a high-purity germanium detector mounted on a movable cart and deployed at eight locations within the hall. The detector response function and full-energy-peak efficiencies were determined through Geant4 simulations validated with calibrated $\gamma$-ray sources, with particular attention devoted to the efficiency modeling and associated systematic uncertainties. In the energy range of 57-2800 keV, the average $\gamma$-ray flux is measured to be $(\mathrm{0.46} \pm \mathrm{0.06}_{stat} \pm \mathrm{0.03}_{syst})$ $\mathrm{cm}^{-2}$ $\mathrm{s}^{-1}$. The radon level was monitored for about a month using a radon detector mounted on the same cart, and a clear correlation is observed between the environmental $\gamma$-ray rate and the ambient radon concentration, consistent with the short-lived daughters of $^{222}\mathrm{Rn}$. This result represents the first high-precision and efficiency-corrected mapping of the $\gamma$-ray flux in Hall C, substantially improving its radiological characterization and providing key input for future rare-event experiments operating in this hall.

Imitating the highest earners is a common decision-making heuristic, but in finite populations it can generate persistent fluctuations between strategies. This paper studies whether such fluctuations persist as population size grows in heterogeneous two-strategy populations. We show that the Markov chains describing the discrete imitation dynamics form generalized stochastic approximation processes for a good upper semicontinuous differential inclusion, which defines the associated mean dynamics. We prove that these mean dynamics always converge to equilibria. Using stochastic approximation results, we then show that the amplitudes of fluctuations in the population proportions of the two strategies vanish almost surely as population size tends to infinity. Thus, in well-mixed large populations, highest-earning imitation is unlikely to produce large-scale perpetual fluctuations.

Numerous studies have shown that the machine-learning picker PhaseNet produces accurate P and S picks on local earthquake signals, but its performance can degrade sharply on teleseismic signals. To address this limitation, we present a reproducible MsPASS workflow that (i) enables scalable data preparation and management for large seismic archives and (ii) supports standardized PhaseNet training and inference. We assembled a control dataset of 1.6 million waveforms linked to teleseismic P-wave picks made by analysts at the USArray Array Network Facility (ANF). The control dataset confirms that the PhaseNet model trained on regional signals performs poorly on these data. We then trained PhaseNet from scratch on the training split of the ANF control dataset and evaluated it on a non-overlapping held-out test split, increasing P-pick recall by 741.5% and yielding 683.9% more picks within a 0.1s residual window. We also evaluated PhaseNet across different model sizes on both CPUs and GPUs. Increasing the model size by about 120 times improved precision and recall by 15.6% and 23.2%, respectively. However, the scaled model reduced inference throughput by 87.2% on an NVIDIA A100 GPU and by 97.3% on a 128-core high-performance CPU node. These results indicate that scaling PhaseNet is more practical on GPUs than on CPUs, and that simply enlarging the model is not an efficient way to achieve large accuracy gains.

Rapid earthquake magnitude estimation is crucial for effective early warning systems that can save lives and reduce economic damage. In this paper, we present a comprehensive study of magnitude classification using only the vertical component of the initial 7-second P-wave window from a single station. We compare six machine learning approaches that range from traditional models to state-of-the-art deep learning architectures. We also curated a novel dataset of 7,318 earthquake events in South Asia. The dataset was categorized into five Richter-scale classes: slight (3.0-3.9), light (4.0-4.9), moderate (5.0-5.9), strong (6.0-6.9) and severe (>= 7.0). Our experiments show that deep learning models substantially outperform traditional approaches. Our Transformer based architecture achieved 76.23% standard accuracy and 81.56% adaptive accuracy with 4.8 ms inference latency. The adaptive-accuracy metric is introduced for the inherent uncertainty in magnitude estimation of near class boundaries. These results indicate that the attention mechanisms in Transformers combined with adaptive classification effectively capture the temporal dynamics of seismic signals. The architectural advantage facilitates promising generalization to rare high-magnitude events, despite the inherent data scarcity characteristic of seismic catalogs. The adaptive accuracy provides a more realistic assessment of model performance, and the result suggests viability for real-time deployment.

Technologies that require surface wetting or evaporative cooling require the ability to efficiently spread fluids across large areas, as increased wetted surface area increases evaporative flux. However, the intrinsic surfacial and bulk properties of most engineered materials substantially limit the rate and magnitude of surface wetting and lack control of flow direction, preventing them from rapidly wetting large surfaces. Here, we introduce our approach for rapid and controlled wetting of surfaces by laser-engraving channel networks that provide pathways for rapid, long-distance (cm-dm scale) capillary fluid propagation across the area, while the intrinsic material properties enable slow, short-distance (mm-cm scale) surface wetting. We investigated this approach on hardened cement paste and showed that laser engraving is a fabrication-friendly, scalable, and reproducible solution for creating channels with properties conducive to capillary fluid propagation. We demonstrate that the rate and direction of surface wetting can be controlled by tuning the channel network density, channel network anisotropy, and supplied fluid flow rate. The integration of laser-engraved channel networks demonstrated significantly greater wetting performance (up to 10-fold greater wetted area and up to 180-fold greater wetting performance when wetted area is adjusted for fluid use efficiency) and greater evaporative cooling (up to 1.8 {\deg}C cooler surfaces) compared to control (hardened cement paste without laser-engraved channel networks).

Lightning, the most colossal discharge in nature, and flux avalanches in quantum superconductors–phenomena separated by twenty orders of magnitude in scale–display striking fractal similarity. We demonstrate that this is no mere analogy but reveals a deep physical unity. Here, we establish a universal theoretical framework that connects them. By mapping both onto the same three-dimensional reaction-diffusion-advection equation grounded in non-equilibrium statistical physics, we show they belong to the same critical universality class. We demonstrate that both systems belong to the three-dimensional Directed Percolation (3D-DP) universality class near their critical point, sharing a unified set of universal critical exponents (e.g., avalanche size distribution exponent $\tau \approx 1.41$ , fractal dimension $D_f \approx 2.5$). Furthermore, by incorporating the anisotropy and turbulence coupling intrinsic to real thunderstorm environments, we predict novel effects such as anisotropic fractality of lightning channels and the systematic shift of critical exponents by turbulence. The core theoretical breakthrough lies in proposing a geometric correspondence of quantum phase information: through a rigorous mapping, the microscopic quantum phase coherence of superconductors is translated into the curvature and torsion distributions of macroscopic lightning channels, revealing a quantum statistical fingerprint emergent in classical geometry. This framework not only provides a unified paradigm for understanding dissipative structures across scales but also, via seven testable predictions, opens avenues for simulating natural lightning with laboratory quantum systems and developing novel physical early-warning methods.

Discovering governing equations from data is crucial for understanding complex systems in many diverse fields from science to engineering. Yet, there still is a lack of versatile computational toolbox to deal with this long standing challenge due to the inherent non-autonomicity and unknowability of the underlying dynamics. Here, we introduce a data-driven approach for inferring non-autonomous dynamical equations by identifying an optimal set of basis functions within the model space, enabling the reconstruction of complex systems behavior under simplified prior specifications. Our method demonstrates effectiveness in equation discovery on canonical synthetic systems such as cusp bifurcation and coupled Kuramoto oscillators. Furthermore, we extend the application of this approach to leaf cellular energy, unmanned aerial vehicle navigation, chick-heart aggregates, and marine fish community under simple basis function libraries. Leveraging the inferred equations, we accurately predict the evolution of these empirical systems and further uncover their governing laws. Our approach offers a novel paradigm to reveal the underlying dynamics of a wide range of real-world systems.

TokaMind is a multi-modal transformer (MMT) foundation model pre-trained on tokamak plasma diagnostics data from MAST, where it was shown to outperform CNN-based approaches on fusion benchmarks. We investigate whether its learned representations generalize to physically distinct but structurally analogous domains. Through systematic experimentation across four domains-industrial bearing degradation, NASA CMAPSS turbofan degradation, and two independent power grid PMU datasets-we identify four transfer-favoring characteristics that help explain where TokaMind's pretrained representations are most effective. Power grid synchrophasor data matches this target-domain profile most directly, while industrial degradation datasets demonstrate that TokaMind can still yield useful performance under partial alignment, especially when task design and feature construction expose physically meaningful degradation structure. On the GESL/PNNL 500-event benchmark with provider-aware evaluation, TokaMind achieves test $\text{F1} = 0.837 \pm 0.040$ (3~seeds) for severe event classification. Our central finding, however, is not the aggregate score: classification difficulty is structurally determined by provider-level grid topology, not model capacity. In the single-window early-warning regime, TokaMind outperforms a CNN baseline (F1~0.889 vs.~0.878)–a reversal that disappears as more event windows are provided. Furthermore, Critical Slowing Down (CSD) indicators, used as a confidence gate rather than a classification label, improve F1 from 0.696 to 0.750 at 63% coverage-outperforming the CNN baseline (0.636) at any coverage level. These results establish the first cross-domain validation of TokaMind outside nuclear fusion and propose a transferability framework and revised evaluation protocol for multi-source PMU datasets.

The manuscript considers Scanning Transmission Electron Microscopy (STEM) images and derives transformations needed to correct various distortions occurring during scanning. These transformations form the basis for the correction algorithms implemented in the CEOS Panta Rhei and TEMDM software. The manuscript is intended as a technical reference and is meant to be published only on arXiv rather than in peer-reviewed journals.

High brightness and low coherence laser sources with wideband tunability are essential for many full-field imaging applications aiming for high contrast and speckle free performance. However, this combination of parameters is challenging to achieve. The current solutions focus on decreasing spatial coherence or generation of time-varying speckle patterns, while suppression of temporal coherence typically compromises brightness. Here we demonstrate a wideband pulsed laser source with low temporal coherence and the absence of phase correlation between pulses as an alternative approach with simultaneous time and frequency diversity. The full gain spectrum of a Tm doped fiber laser (1650 nm 2000 nm) is operated in a tunable noise like pulse regime, which by nature is composed of countless structured elementary events with uncorrelated phases randomly varying from bunch to bunch. The measured spectral widths range from 13.8 nm to 18.8 nm, while the average output power varies between 63.3 mW and 213 mW. Numerical simulations reveal that temporal coherence decreases significantly with increasing optical gain, dropping from near unity at low gain to approximately 0.2 at high gain. The startup dynamics of the noise like pulse laser are experimentally studied using the dispersive Fourier transformation (DFT) method. Based on single shot spectra and frequency resolved optical gating traces, the coherence properties of the laser are further analyzed by calculating the mutual coherence function and cross-spectral density. The noise like pulse laser exhibits a coherence time of approximately 100 fs and an average pulse burst duration of about 40 ps in the high-gain regime.

The Hotelling-Downs model considers parties changing policy to maximise their vote-share. Where policy position lies on a left-right axis, it describes a tendency for political parties to move towards centrist platforms. This is in contrast with widely observed political polarisation. We extend the model to two dimensions, with many parties and with single and multiple-peaked voter distribution. We find that a two party system reduces polarisation, even if voters are polarised with a bimodal distribution. By contrast, multiparty systems induce polarisation, even when most voters favour moderate position. We model the effect of turnout and activists as influences on the parties, showing that this results in more polarisation, even in a two-party system. This suggests that polarisation of parties can be driven by abstention, intra-party politics and turnout on the extremes. In the two-party case, the winning party's positions are more moderate than the views of their supporters but better representative of the electorate as a whole. With polarisation, individual voters are better able to find a party which represents their views, but the government (winning part or coalition) is less representative of the population, even when the population has a clear consensus on all issues.

We study a model for a dilute suspension of rod-like particles swimming at constant velocity in a Stokes flow. As the translational diffusivity of the particles decreases, a two-dimensional uniform concentration of randomly aligned particles undergoes either a codimension-2 pitchfork bifurcation or a codimension-4 Hopf bifurcation, depending on the particles' swimming speed. We use a weakly nonlinear expansion to reduce the system to a low-dimensional one for the amplitudes of the bifurcating eigenmodes. The originality of our calculations lies in incorporating spatio-temporal white noise forcing. The stochastic forcing terms in the amplitude equations are derived analytically from the noise acting on the original system. Past the onset of the bifurcations, the particles deterministically self-organize into steady or oscillating states of collective motion. For the Hopf bifurcation scenario, two stable periodic orbits are found to coexist, each corresponding to a distinct collective dynamics. The stochastic forcing induces rare transitions between them. Owing to the low dimensionality of amplitude equations, steady and dynamical statistics can be computed directly from the Fokker-Planck equation, or via the Adaptive Multilevel Splitting (AMS) rare-event algorithm. In particular, extremely long mean transition times and associated out-of-equilibrium paths between the periodic orbits are obtained. These paths can be understood in light of the invariant manifolds of the low-dimensional system, which brings insights into the mechanism behind the transitions. We also performed fully nonlinear stochastic simulations and used the AMS algorithm directly on the full system. The statistics are in good quantitative agreement with those computed on the reduced systems, the latter being obtained at a considerably lower numerical cost.

Optical fiber multi parameter sensing is fundamentally constrained by cross-sensitivity and the complexity of multi sensor integration. Here, we present a dual-dip heterogeneous long-period fiber grating (LPFG) sensing platform enabled by bending assisted annealing, which introduces anisotropic refractive index redistribution and mode dependent coupling enhancement. This process yields enhanced sensitivity, improved dip contrast, and opposite spectral responses between dual resonance dips, providing intrinsic spectral heterogeneity. To overcome temperature cross sensitivity, a polymer-encapsulated cascaded LPFG-FBG architecture is developed, where the LPFG serves as the microbending sensitive element and the FBG acts as a reference channel. PDMS encapsulation enhances stress transfer and suppresses interfacial slippage, improving linearity and repeatability. As a result, the bending sensitivity increases from -3.44 to -8.97 nm per cm, and the detection limit improves from 0.017 to 0.006 cm. Building on this, a multi parameter sensing paradigm is established by integrating dual dip heterogeneity with LPFGFBG spectral orthogonality. With PAAm functionalization, the platform enables simultaneous and decoupled sensing of temperature, bending, and humidity, demonstrating scalable and versatile multi parameter capability. Overall, this work establishes a minimalistic yet robust paradigm for multi-parameter fiber-optic sensing, offering a scalable strategy for high-performance sensing in structural health monitoring and harsh environments.

Symmetry strongly impacts wave transport in complex media. In this Letter, we demonstrate that the phenomenon of symmetry-induced through-barrier transmission enhancement enables quantitative sensing across barriers in complex media. We consider two mirror-symmetric chaotic cavities coupled through a narrow slit and containing point scatterers at mirror-symmetric positions. The characteristics of the scatterers in one cavity are unknown, whereas those of the scatterers in the other cavity are programmable. By tuning the programmable scatterers to maximize broadband total transmission, we recover the unknown scatterers' characteristics across the barrier. We show that reliable sensing requires a sufficiently large bandwidth, because otherwise a narrowband asymmetric resonant enhancement can dominate over the desired symmetry-induced enhancement. We further examine how absorption and barrier opacity influence the minimum required bandwidth. Our results establish a symmetry-empowered principle for through-barrier sensing in complex media, suggesting a route toward through-wall imaging in complex environments.

A general polarizable embedded (PE) quantum mechanics/molecular mechanics scheme for periodic systems is presented, describing mutual polarization of the two subsystems. The QM system, described with density functional theory (DFT), is coupled to a single center multipole expansion (SCME) model, characterising H$_2$O molecules in the MM region. In SCME the H$_2$O molecules are ascribed anisotropic dipole and quadrupole polarizabilities and permanent multipoles up to and including the hexadecapole. Our embedding scheme illustrates a smooth and efficient convergence pattern of the periodic interaction potential by introducing a single and clustered multipole expansion points in the far-field. By choosing the near- and far-field expansion of the potential carefully the PE-QM/MM calculation matches the level of accuracy of a the QM calculation. In the short range, the electrostatic interaction between the QM and MM subsystems is damped with a real-space and pair-wise isotropic damping functions - resulting in a screened interaction and preventing over-polarization. In molecular dynamics simulations the two subsystems are separated with the elastic scattering assisted flexible inner region [Kirchhoff et. al. JCTC, 2021, 17, 9, 5863] - ensuring a smooth transition in the radial distribution at the boundary between the two subsystems.

This paper expands on the TRANSP description given in [Computer Physics Communications 312 (2025) 109611] by describing recent progress in TRANSP's predictive functionality and emphasizing development of the PT_SOLVER module and integration of the high-fidelity T3D/GX framework for plasma profile prediction using a high-fidelity gyrokinetic model for turbulent transport. PT_SOLVER is a modular, multi-region, parallel solver for coupled transport equations of particle density, electron and ion energy, and toroidal angular momentum that uses an implicit Newton method to advance the solution of these equations. The numerical formulation includes source coupling, moving-geometry terms, and nonlinear stabilization based on modified Péclet numbers, thereby enabling the PT_SOLVER to handle the stiffness associated with gradient-dependent transport models. Stabilization occurs via a nonlinear function controlling discretization in zones of steep gradients or rapidly changing transport coefficients. Source terms that account for heating, current drive, alpha-particle effects, and collisional energy exchange are handled thoroughly, and both residual norms and profile-change measures are used to assess convergence. Verification is carried out using analytical benchmark solutions, manufactured solution benchmarks, convergence studies of stiff gradient-dependent diffusivities, and code-to-code comparisons of TGYRO using the TGLF/NEO models for anomalous and neoclassical transport. A multi-level parallel decomposition strategy in PT_SOLVER is described for both flux surface and TGLF wavenumber parallelism, along with representative scaling results. This paper also describes the TRANSP Interface to the modular T3D/GX workflow and presents verification examples related to the interface for coupled prediction simulations.

Quantum systems with more than two levels - so-called qudits - offer increased computational density and reduced circuit complexity compared to qubit-based architectures, but achieving universal and scalable control remains challenging. We propose an all-optical scheme for universal qudit control in trapped neutral atoms in moderate to high magnetic fields, focusing on the fermionic isotope 173Yb (I = 5/2). The strong hyperfine interaction in the 3P1 manifold enables fast and selective Raman transitions between nuclear-spin states in the 1S0 ground-state manifold using a single linearly polarized laser. For each neighboring transition in the qudit manifold, we identify a magic polarization angle that enables coherent, state-selective control while suppressing off-resonant excitations, with operation frequencies exceeding 100 kHz. Combined with phase-shift operations, this provides universal control of the full single-qudit space. We further discuss compatible two-qudit gates based on the Rydberg blockade mechanism, completing a universal gate set, and analyze state-selective readout schemes compatible with the proposed protocol. Our results identify 173Yb as a promising platform for high-fidelity, all-optical qudit-based quantum information processing.

Multi-nucleated cells exist in all domains of life, ranging from animals, plants and fungi to single-celled organisms such as the slime mold Physarum polycephalum. The large cell size, in the case of Physarum reaching centimeters and more, challenges the coordination of nuclei activity as signals need to cross large distances. In search for a mechanism for fast long-ranged communication among nuclei, we quantify nuclei dynamics and cytoplasmic flows in Physarum's tubular network. We observe nuclei in two interchangeable, dynamic states: mobile, flowing within the cytoplasmic shuttle flow, or trapped in the tube's porous cell cortex. As we find nuclei to accumulate at the tube's inner fluid-porous interface we theoretically explore and confirm, with physiological parameters, that slowing down of mobile nuclei during flow is sufficient for diffusible signal exchange between mobile and trapped nuclei. We analytically derive that communication akin to pigeon-post with mobile nuclei serving as pigeons shuttling between trapped nuclei acting as waypoints, gives rise to signaling velocities that account for the rapid intracellular reorganization observed in Physarum. Since signal transfer by flow-transported nuclei outcompetes the mere diffusion of signals encoded in cytosolic proteins, pigeon-post communication surpasses alternative signaling mechanisms, even diffusive relay signaling up to twenty-fold in velocity. The key ingredients of pigeon-post communication, namely alternating flows and waypoints, exist in other multi-nucleated cells and may also be generalized beyond intracellular signaling.

Experiments aimed at quantifying the interface between quantum and classical physics necessarily require a high degree of isolation from the environment: wavefunction collapse and quantum gravity effects at laboratory scales are predicted to be very subtle. Ideally, such tests would be performed in a closed system at extremely low temperatures in order to rule out any external influence and thermal fluctuations. Cryogenic levitated macroscopic bodies are excellent candidates for an accurate laboratory approximation of such systems, as a tether to another body would violate the requirement for the system to be fully closed. Here we report a significant milestone on the way to a practically suitable approximation of such closed system. We have built a milligram-mass superconducting oscillator operating at millikelvin temperatures showing extremely low dissipation rate, with the oscillator ring-down time exceeding 110 hours. This corresponds to the resonance linewidth of less than 0.8 $\mu$Hz. The experimental setup is highly tunable and is compatible with adiabatic nuclear demagnetisation, promising even lower temperatures and lower dissipation levels in the future. We demonstrate the capability of our device by measuring drag from $^3$He impurities in superfluid $^4$He at a level of $\sim10^{-8}$ with the drag force in the femtonewton range.

Intracardiac flow patterns are shaped by the coupled motion of the cardiac chambers and heart valves and provide important information about cardiac function. However, clinical flow imaging remains limited by exam times, noise, resolution, and incomplete details of the three-dimensional flow. Computational fluid dynamics (CFD) can potentially provide detailed flow quantification and predictive insight into treatment outcomes, but clinical translation requires frameworks that reproduce patient-specific measurements while balancing physiological realism, computational cost, and modeling effort. Herein, we present an image-based, patient-specific computational framework for simulating whole-heart intracardiac hemodynamics that balances physiological fidelity with computational efficiency. The framework first employs machine learning-based segmentation and mesh propagation to reconstruct moving cardiac anatomies from time-resolved images. CFD simulations are then performed to resolve blood flow in deforming domains, while resistive immersed surfaces (RIS) are used to model all four cardiac valves with physiologically realistic opening and closing dynamics. The framework was applied to model hemodynamics in a healthy adult and a pediatric patient with complex congenital heart disease (CHD). In the healthy case, the simulations reproduced physiologic pressure-volume behavior, valve timing, and ventricular vortex formation. In the CHD case, simulated chamber and vessel pressures showed agreement with cardiac catheterization measurements. Simulated flow fields were qualitatively consistent with 4D-Flow MRI, while providing higher-resolution visualization of flow structures that were partially obscured by imaging artifacts. Comparison between the healthy and CHD cases further revealed altered diastolic flow organization and elevated normalized viscous dissipation in the CHD heart.

Artificial intelligence (AI) is reshaping education, scientific training, and materials discovery. In materials science, AI models increasingly support property prediction, experiment prioritization, and hypothesis generation; however, the limiting factor is no longer only algorithmic capability but also whether students and educators can use AI with domain-specific scientific judgment. This workshop-informed white paper and curriculum-oriented position article argues that AI education for AI-powered materials discovery must move beyond tool access and surface-level interaction with generative AI systems toward a workflow-aligned model of AI literacy. We connect AI literacy to materials-informatics competencies: data provenance, domain-specific featurization, model validation, uncertainty quantification, physics informed reasoning, reproducibility, and experimental feedback. We also emphasize outcome-oriented equity: institutions should evaluate not only access, participation, and engagement, but also whether AI-enabled instruction produces comparable learning gains, transfer of learning, confidence calibration, defined as the alignment with students confidence and the quality or correctness of their work, persistence, and research readiness across student subgroups. The paper synthesizes relevant evidence, identifies risks for learners such as cognitive off-loading and cognitive surrender, and provides a dual-track curriculum model and implementation recommendations such as curriculum guides and an assessment plan for courses, bootcamps, workshops, and program-level reform. The central goal is to prepare students to become better scientists, not merely more efficient users of AI tools.

AI chatbots are increasingly used by students as study tools in physics, raising practical questions about their reliability on conceptual tasks. Existing evaluations of large language models (LLMs) on physics concept inventories rely almost exclusively on instruments that have been publicly available for years and likely appear in model training data, making it difficult to disentangle physics competence from familiarity with the test items themselves. We address this issue by evaluating three frontier LLMs (GPT-5.2, Gemini 3 Pro, Gemini 3 Flash) on the Classical Relativity Concept Inventory (CRCI), a recently developed and validated 21-item instrument on Galilean relativity that was not publicly available at the time of testing. Each item was administered 30 times per model, and all 1890 responses were qualitatively coded along three dimensions: visual interpretation, physics reasoning, and coordination. Mean accuracy was 97% for Gemini 3 Flash, 89% for Gemini 3 Pro, and 73% for GPT-5.2, compared to 62% for the student sample (N = 267). However, all three models fail completely on a small number of items. The qualitative analysis shows that these failures stem predominantly from misinterpretations of visual content rather than from deficits in physics knowledge, and that LLM errors differ structurally from those of students: when models err, they converge on a single distractor with high consistency, whereas student errors are more broadly distributed. These findings indicate that chatbot reliability on conceptual physics is item-dependent and unpredictable, with direct implications for how concept inventories are administered.

Finite nuclear size (FNS) effects on the magnetic-dipole hyperfine splitting in muonic hydrogenlike ions are investigated within a fully relativistic Dirac framework. The FNS contribution is quantified through the correction factor $\delta$, defined by $\Delta E_{\mathrm{ext}} = \Delta E_{\mathrm{point}}(1 - \delta)$, where $\Delta E_{\mathrm{ext}}$ is evaluated using Dirac wavefunctions computed for an extended nuclear charge distribution. Two nuclear models are considered: a homogeneously charged sphere and a two-parameter Fermi distribution. Bound-state energies and radial wavefunctions are obtained using a numerical iterative solver, while a semi-analytic matching scheme provides reference values and initial seeds. We present a systematic dataset of $\delta$ values for the $1s$, $2s$, and $2p_{1/2}$ states over a wide range of nuclear charge numbers $Z$. Nuclear-model dependence is quantified, including uncertainties induced by the nuclear radius in the uniform-sphere model. The results show that $\delta$ increases monotonically with $Z$ and exhibits clear state dependence, with reduced magnitude for the $2p_{1/2}$ state relative to $s$ states. A pronounced sensitivity to the nuclear charge distribution is observed, highlighting the importance of realistic nuclear modeling in precision hyperfine studies of muonic atoms.

Laser-plasma acceleration produces ultrashort, high-brightness ion beams reaching tens of MeV, yet their large divergence and broad energy spread require dedicated capture elements for beam transport. Using laser-accelerated protons from the GSI PHELIX laser to the LIGHT beamline as a reference, we developed a framework to optimize and assess such combined capture and transport systems, with emphasis on injection into conventional accelerators. In addition to our numerical analysis we derive scaling laws linking transmission and chromatic emittance growth to the initial half-opening angle, showing that the present performance is primarily divergence-limited. We also estimate and predict the longitudinal bunch quality and quantify the divergence reduction needed to approach injector-relevant intensities.

Full waveform inversion (FWI) can produce accurate subsurface velocity models. However, the lack of sufficiently low-frequency content in field data often causes cycle skipping and traps the inversion in local minima. The Hilbert-transform envelope (HTE) provides a low-frequency representation that helps mitigate cycle skipping, but it may be insufficient when the initial velocity model is highly inaccurate. To further enhance low-frequency information and reduce dependence on the initial model, we compute an approximate envelope using a sequence of 2D max-pooling operations. Compared with HTE, the resulting max-pooling-based approximate envelope (MPBAE) contains richer low-frequency components and better mitigates cycle skipping. We further combine the MPBAE loss with a shot patching strategy and exploit the inherent normalization property of the Euclidean loss to formulate the MPBAEP loss, in which each shot gather is divided into localized patches for misfit evaluation. This introduces local adjoint-source energy balancing, as the adjoint source associated with the Euclidean loss exhibits a normalization effect within each local region, thereby improving gradient balance and accelerating convergence. Numerical experiments on synthetic and field data demonstrate that MPBAE-FWI significantly outperforms HTE-FWI when the initial model is poor, while MPBAEP-FWI further improves inversion accuracy.