Papers

Burning neat hydrogen in aircraft turbines eliminates carbon emissions, yet rapid reaction rates trigger nozzle flashback hazards and high nitrogen oxide emissions across flight throttles. This study investigates aerothermal holding mechanisms, flashback safety margins, and emission pathways in a dual-swirl combustor across equivalence ratios from 0.55 to 1.00. Three-dimensional simulations combine curvature-corrected shear-stress transport turbulence closure, dual-rate finite-rate and eddy-dissipation chemical kinetics, and discrete ordinates radiation, validated against experimental laser benchmarks using ASME grid standards. Advancing engine throttle triggers a topological flame transition from a faceplate-attached M-flame at lean idle to a lifted V-flame above equivalence ratio 0.895, while wall flashback safety indices consistently exceed 3.42. Nitric oxide emissions transition from water-chaperoned nitrous oxide intermediate reactions at lean idle (28.01 ppm, EINOx = 1.85 g/kg), scaling to 319.21 ppm (EINOx = 35.40 g/kg) at takeoff under thermal Zeldovich dominance, governed by a power-law exponent of 4.92. These findings deliver validated operability limits and establish an accessible workstation-based screening methodology for practical zero-carbon aero-engine combustor development.

In the standard $\Lambda$CDM cosmology, dark matter is assumed to be a pressureless cold fluid with $w_{\rm dm}=0$. However, the microscopic nature of dark matter remains unknown, and whether its equation-of-state parameter strictly vanishes deserves observational scrutiny. In this work, we introduce a free dark matter equation-of-state parameter $w_{\rm dm}$ within the Modified Emergent Dark Energy (MEDE) framework, constructing the MEDE+$w_{\rm dm}$ model. We systematically derive its background evolution and linear perturbation equations, and constrain the model parameters using Planck 2018 cosmic microwave background (CMB), DESI DR2 baryon acoustic oscillation (BAO), and three independent Type Ia supernova datasets: Pantheon+, Union3, and DES5YR. Using the CMB + BAO + DES5YR combination, we find a preference for a positive dark matter equation of state, $w_{\rm dm}=0.00128\pm0.00044$, together with a 3$\sigma$ level preference for quintessence-like dark energy evolution, $\alpha=-0.66\pm0.22$. When the local $H_0$ prior is included, the constraint on $w_{\rm dm}$ remains essentially unchanged, whereas $\alpha$ shifts toward the $\Lambda$CDM limit, yielding $\alpha=-0.18\pm0.18$. Bayesian model comparison favors $\Lambda$CDM over MEDE+$w_{\rm dm}$, although the preference is reduced to the weak level after including the local $H_0$ prior. Overall, current observations exhibit a $2.6\sigma$–$3\sigma$ preference for a nonzero $w_{\rm dm}$ at the parameter-posterior level, but this indication does not yet constitute a robust detection of non-cold dark matter.

To enable flexible and rapid aerodynamic performance evaluation in turbomachinery design, this paper proposes a panoramic performance prediction framework. Unlike most previous prediction models that directly predict the objective functions of interest, our approach first predicts the basic parameters of the Navier-Stokes equations, such as temperature, pressure, and density. Utilizing these basic physical quantities, it subsequently predicts key performance parameters of the turbine stage meridian plane. By adopting this methodology, our proposed panoramic performance prediction framework functions similarly to a CFD simulator, capable of predicting various objective of interest to the designers. To enhance prediction accuracy, a transformer-enhanced neural operator (TNO) is introduced within this framework. Using the Rotor 37 blades as a reference, the proposed TNO is trained to predict the performance of a transonic compressor blade in the meridian plane. The TNO can accurately predict total quantities such as isentropic efficiency, mass flow, and distributions of total pressure ratio. Remarkably, the prediction error of TNO is observed to be smaller than that of state-of-the-art deep learning operators such as the FNO and DeepONet. Furthermore, the TNO is applied to downstream tasks, including sensitivity analysis and optimization of various objective functions. The results confirm that the TNO can operate almost like a CFD simulator, while reducing the computational cost of downstream tasks by four orders of magnitude. The effectiveness and reliability of the proposed TNO for solving different kinds of downstream tasks have been well demonstrated.

This paper uses the ERA5 weather dataset to assess the impact of the wind on an equatorial space elevator. An analytical formulation establishes that the effect of wind on tether inclination and payload capacity can be captured by a single scalar: RMS wind velocity, averaged over air pressure. This formulation clarifies what constitutes excessive wind for a particular elevator and provides a direct link between wind data and elevator design parameters. Analysis of equatorial ERA5 data shows that equatorial wind loading varies strongly with geography with lower values over continents and higher values over oceans, with the majority contribution coming from the lowest 20 km of atmosphere. The data indicate that realistic equatorial winds pose a significant constraint on thin-ribbon elevator designs if align to face the wind. Combining wind and lightning statistics highlights several promising equatorial regions for anchor sites. Finally, the implications of wind loading for elevator design are examined, and several mitigation strategies are discussed to improve survivability and operational payload capacity under realistic wind conditions.

Foundational machine-learning interatomic potentials (MLIPs) are transforming atomistic simulations by achieving near-ab initio accuracy across large chemical spaces at a fraction of the computational cost. A central challenge in using these tools for high-throughput property calculations is translating high-level scientific intent into adaptive simulation campaigns without compromising workflow rigour. We introduce El Agente Potente, an agentic system that combines typed execution graphs with a complementary coding mode for MLIPs-driven atomistic simulations. Typed execution graphs provide structured and provenance-aware execution for standardized workflows, with large language models (LLMs) restricted to planning and routing while deterministic Python components perform scientific computation and validation. Complementing this structured execution, a coding agent constructs customized workflows for tasks requiring greater procedural flexibility while invoking existing Potente functions for supported calculations. We demonstrate El Agente Potente across computational materials discovery, molecular energy-landscape exploration, adsorption, and catalytic reaction workflows, together with systematic benchmarks of reproducibility and LLM token cost. These results establish typed execution graphs and code-based workflow construction as complementary mechanisms for agentic scientific computing, combining controlled, auditable execution with the flexibility required for customized atomistic simulations

Continuous unitary transformations repeatedly evaluate the same operator algebra as the coefficients of a Hamiltonian and its observables evolve. We compile these algebraic relationships once into a numerical operation graph that can be saved and reused for new coefficient sets, additional operators, derivatives, and forward and reverse calculations on CPUs and GPUs. Reuse is demonstrated by applying the same compiled graph to hydrogen and deuterium parameterizations of a reduced molecular model without regenerating the operator equations. The main computational challenge is construction: generated many-body algebra can contain hundreds of millions of contributions before numerical propagation begins. We address this by determining the required storage in advance and writing contributions directly into the final disk-backed representation. In a deliberately large diagnostic-enabled stress test containing more than 350 million generated contributions, peak host memory remains only about 7% above the final stored size. Cross-checks show that CPU and GPU implementations, forward and reverse operations, and repeated loading of the compiled graph reproduce the tested operations to numerical precision. This compile-once representation separates expensive operator-algebra generation from the numerical transformations that reuse it.

This paper introduces an online evolve–filter–relax (EFR) strategy for long-term stability of Operator Inference (OpInf) reduced-order models (ROMs) of complex fluid flow simulations. The main novelty of the new EFR-OpInf strategy is the use of online (i.e., at the learned ROM online evaluation level) spatial filtering inspired from large eddy simulation to significantly improve long-term stability and predictive performance of standard OpInf. Furthermore, the EFR-OpInf strategy reduces, and in some cases even eliminates, the need for standard $L^2$ regularization, while providing physical interpretability for the OpInf hyperparameters. The EFR-OpInf framework is modular, readily integrated into existing OpInf workflows, and accommodates a user-selected ROM filtering strategy. We demonstrate the EFR-OpInf's effectiveness using a fully non-intrusive projection-based ROM filter, a ROM differential filter, and a hybrid projection-differential ROM filter. The new EFR-OpInf models are evaluated on a high-Péclet-number convection–diffusion–reaction problem that embeds the variation in one parameter and two unsteady Navier–Stokes problems that focus on predictions beyond a training horizon: a transitional two-dimensional flow past a cylinder and a three-dimensional turbulent minimal channel flow. Across these three scenarios, EFR-OpInf can reduce prediction errors by up to an order of magnitude relative to standard OpInf. Moreover, EFR-OpInf remains stable over long prediction horizons in cases where standard OpInf diverges. Depending on the ROM filter used, EFR-OpInf's computational cost is comparable to that of standard OpInf.

Scientific autoformalization turns verbal accounts into executable mathematics, but executable code does not settle which model has been constructed. We examine two sources of structural uncertainty: the formalizer that generates a response law, and the recurrence that turns that law into trajectories. In secondary analyses of an openly archived crossed experiment, we studied 320 response maps generated by two pinned language-model formalizers from five engineered cognitive accounts within one sparse quadratic grammar and 16 randomized blocks. With whole blocks held out, source-account identity was recovered at 78.8% accuracy (chance 20.0%) and formalizer identity at 96.3% (chance 50.0%; both p < 0.001). Program size was the stronger single feature family; a pre-specified exploratory comparison found no stable source-account predictive gain from local geometry beyond size. Holding every response map fixed, we then evaluated five recurrence families spanning 33 configurations and 1,013,760 finite-horizon trajectories. Added feedback, projection and leak produced sharply different outcome distributions. The consequential distinction was which comparisons survived: median cross-recurrence rank concordance was 0.73 for endpoint magnitude but 0.05 for settling, among the configuration pairs with defined rankings. Thus a common mathematical language did not erase translation provenance, and robust ordering under one observable did not transfer to another. Scientific autoformalization is usefully studied as model-space construction: the generated ensemble and its dynamical embedding are both part of the specification supporting a scientific claim.

We develop and implement a two-step location-scale analysis for repeated physical measurements when the underlying distribution is not known. For a chosen substitute distribution, stationarity of population Kullback-Leibler information loss at fixed scale motivates the location score, which we apply to the observed measurements through empirical cross-entropy. Steiner's curvature rule then defines a companion scale separately. We place Gaussian, Cauchy, and logistic substitutes in one common construction and implement them reproducibly on a physics benchmark. For the logistic substitute, we pair the established bounded location score with a scale defined by the curvature rule rather than by a fitted tuning constant or a scale-likelihood equation. For Gaussian measurements with different quoted uncertainties, an experiment-level curvature convention and a stated variance mapping give an exact algebraic connection to a standard residual-based variance estimate for a weighted fit. Applied to a published 21-measurement neutron-lifetime compilation, the inverse-variance weighted, most frequent value, and logistic locations are 878.689, 881.164, and 882.835 s, respectively. These values are benchmark results of the compared constructions and are not proposed as a new recommended neutron lifetime.

Droplet-laden cloud dynamics emerge from coupled phase transitions and turbulent mixing. Combining experiments and mean-field theory, we establish scaling laws for steam-injected clouds in unsaturated air seeded with condensation nuclei. Depending on whether the cloud is confined to the initial momentum-dominated zone of the jet or extends into the buoyancy-dominated plume, two asymptotic regimes arise. In the momentum regime, cloud dimensions scale with the injector radius. In the buoyancy regime, size follows a \(\dot{m}^{2/5}\) scaling, where \(\dot{m}\) is the injected steam mass flow rate. Assuming inter-droplet air remains humidity-saturated, our mean-field analysis predicts cloud persistence governed by enthalpy and humidity mixing. These findings bridge microphysical processes and macroscale dynamics, offering a quantitative framework for atmospheric cloud evolution.

Tunable low-noise lasers are crucial components employed in modern metrology. Advances in photonic integrated circuit technology have provided a new platform for compact integrated lasers with large output powers, frequency-agile tuning, and narrow linewidths. For instance, lithium niobate extended-DBR lasers can yield output powers in the tens of mW, GHz-level tuning ranges, and tuning rates in the hundreds of MHz. However, noise levels achievable in other integrated laser designs remain challenging to reach. Here, we address this challenge by implementing extended-DBR lasers in lithium tantalate, thereby achieving intrinsic linewidths as low as 24 Hz, output powers of 10 mW, with tuning features on par with those of lithium niobate. The extended-DBR laser is assembled with modern packaging solutions to improve its robustness and demonstrate long-term stability with a free-running frequency drift within a range of 232 MHz over 19 hours. These benefits, in conjunction with lithium tantalate's suitability for volume manufacturing, promise to considerably expand accessibility to a new generation of widely tunable and low-noise integrated lasers.

Sociology emerged from Comte's ambition to study society as a form of "social physics" and was later refined by Durkheim's concept of the social fact a collective regularity that cannot be reduced to any single individual's behavior. This essay argues that a structurally similar problem is now emerging in a new domain: interactions among artificial intelligence agents. Drawing on recent empirical work showing that populations of large language model agents can spontaneously develop shared conventions and collective biases through repeated interaction, the essay proposes a new field of inquiry the Sociology of Artificial Agents dedicated to studying the relational, normative, cultural, and organizational patterns that emerge among AI agents themselves, independent of direct human involvement. It introduces the concept of the "artificial social fact" as an analytical bridge between classical sociology and this new research terrain, while cautioning against anthropomorphizing AI systems. The claim is not that artificial agents form societies in the human sense, but that their interactions already produce measurable collective patterns worthy of systematic sociological study.

Hamiltonian and Lagrangian mechanics are powerful frameworks for analyzing physical systems. Previous work has extended these formalisms to ecological systems, such as the predator-prey Lotka-Volterra (LV) system. In this Article, we derive a canonical Lagrangian for the two-dimensional LV model directly from its Hamiltonian representation. We find that the two-dimensional LV system admits a standard canonical Lagrangian formulation with one degree of freedom and a non-quadratic kinetic structure. This formulation admits a mechanical interpretation in terms of a particle moving in a potential well, where the non-standard kinetic structure produces a position-dependent damping term that can instead act as "revving." The derivation provides a direct connection between predator-prey dynamics and a canonical formulation of mechanical dynamics. As a verification of the construction, we apply Noether's procedure to the explicitly time-independent derived Lagrangian and reveal that the well-known Hamiltonian of the LV system is the corresponding conserved quantity. We also uncover a subtle redundancy associated with the choice of canonical momentum and its identification with the original population variables.

Magnetic hyperthermia integrated with magnetic resonance imaging (MRI) requires nanoparticles that combine strong heating, T2 contrast, and hemocompatibility after systemic administration. Here, we prepared citrate-stabilized ferrite nanoparticles (Fe3O4, Co0.6Fe2.4O4, Zn0.3Fe2.7O4, and Zn0.35Mn0.25Fe2.4O4) in spherical (8-10.5 nm) and quasi-cubic (9-12.5 nm) forms, along with 35 nm Fe3O4 cubes. By coupling morphology engineering with spinel-lattice cation substitution, we tuned saturation magnetization, coercivity, T2-weighted signal attenuation, and heat dissipation in ovarian tumor-mimicking phantoms under clinically compatible alternating magnetic fields. The 35 nm Fe3O4 cube and quasi-cubic Zn0.35Mn0.25Fe2.4O4 generated the highest phantom temperature rises, whereas smaller spherical particles produced softer heating. All formulations were cytocompatible in SKOV3 cells, with >96% viability at 24 h. However, hemocompatibility distinguished the leading candidates: 35 nm Fe3O4 cubes induced red blood cell deformation despite minimal hemolysis, while 9 nm quasi-cubic Zn0.35Mn0.25Fe2.4O4 showed no change versus PBS up to 1,000 {\mu}g mL-1. Perfusable GelMA models under physiological flow further confirmed biocompatibility. MRI phantoms showed that Fe3O4 cubes produced extended signal voids that may obscure boundaries. Overall, 9.5 nm quasi-cubic Zn0.35Mn0.25Fe2.4O4 is the leading intravenous candidate, combining strong heating, T2 contrast, and hemocompatibility, whereas large Fe3O4 cubes are better suited for localized intratumoral hyperthermia.

Purpose: To compare four multimodal large language models (MLLMs) with radiologists of varying expertise in breast density assessment, BI-RADS assessment, biopsy candidacy determination, and continuous malignancy probability estimation using digital mammography (DM) and contrast-enhanced mammography (CEM). Methods: This study included 179 women with paired DM/CEM examinations and reference standards. Four MLLMs (ChatGPT-5.2, Gemini-3.1 Pro, Sonnet-4.6, Muse Spark) interpreted images with and without masks; three radiologists interpreted non-masked images. Results: For binary density classification on DM, radiologist accuracies ranged from 55.81% to 78.60%, exceeding most MLLM values (62.33%-71.63%), while masks added limited benefit. Five-category BI-RADS accuracies were higher for radiologists on DM (56.74-67.44%) and CEM (62.33-82.79%) compared with MLLMs (DM 31.16-45.12%; CEM 40.00-55.81%). Binary biopsy-candidacy accuracies were likewise higher for radiologists (DM 85.12-89.77%; CEM 86.98-92.09%) than for MLLMs (DM 61.39-75.35%; CEM 69.30-82.79%), although CEM improved performance across all readers. Lesion masks substantially improved MLLM continuous malignancy-probability accuracies from 64.65%-71.63% to 72.56-78.60% on DM and from 67.91%-77.21% to 72.56%-81.86% on CEM, approaching radiologist ranges (DM 63.72-82.79%; CEM 81.86-88.84%). The corresponding AUCs for the top masked models overlapped those of the human readers. Overall, Muse Spark, followed by Sonnet-4.6, demonstrated the strongest performance among the MLLMs across domains. Conclusion: Radiologists generally outperformed MLLMs in categorical tasks, while selected masked models approached human performance for continuous malignancy probability estimation, suggesting a potential adjunctive role.

A recent study of various turbulent flows showed that intermittency and dissipation exhibit consistent scaling behavior beyond homogeneous isotropic turbulence (HIT). For turbulent wakes, grid turbulence, and a turbulent jet, the dissipation parameter $C_\varepsilon$ and intermittency parameter $\mu$ vary while their product remains constant, independently of the Reynolds number $Re_\lambda$. Here, we extend this result from centerline hot-wire measurements to non-centerline data, including regions with significant shear. We identify two additional relations: $(\gamma-1)C_\varepsilon$ remains constant, where $\gamma$ is the absolute inertial-range spectral slope, and $\gamma$ varies linearly with the Kolmogorov parameter $C_k$. The main constraint is that the large-scale increment probability density function is Gaussian, indicating random large-scale driving. These relations reduce the varying quantities $C_\varepsilon$, $\mu$, $\gamma$, and $C_k$ to three constants and define a turbulence state that can be characterized by any one of these parameters, independently of shear, flow type, or Reynolds number. We further show that, for a given flow configuration, $C_\varepsilon$ depends on $Re_\lambda$ and turbulence intensity $TI$. Because the four parameters vary over broad ranges around their nominal HIT values, we interpret this framework as a generalization of HIT to inhomogeneous turbulence. Extensive robustness tests show that the results are insensitive to the specific post-processing method. We also analyze their streamwise and spatial evolution and show that a measured $C_\varepsilon$ can be used to predict $\mu$, $\gamma$, and $C_k$ within quantified uncertainty. The framework thus links key leading-order features of turbulence while recovering established HIT trends.

Compact high-temperature superconducting quantum interference device (HTS-SQUID) magnetometers require a liquid-nitrogen Dewar that balances operating duration against instrument-envelope and mass constraints. Here we introduced a constraint-traceable design workflow in which body radius and neck-length fraction were scanned under fixed outer-envelope volume and geometry-based mass limits. The longest-duration feasible grid point lay adjacent to the 3.85 kg and 550 mm constraint intersection, predicting 224 h hold time from a body-only initial fill to a residual liquid depth of 10 mm. A reference Dewar was experimentally monitored for 137.3 h, consuming 1.22 L liquid-nitrogen of its initial inventory. A thermal model calibrated over the first 40 h reproduced the remaining 97.3 h. Device measurements at liquid-nitrogen temperature showed a superconducting transition and Fraunhofer-like junction response, a maximum voltage-modulation depth of 36.2 $\mu$V, and a median noise level of 41.5 fT Hz$^{-1/2}$ from 100 to 1000 Hz. These results establish a reproducible method for selecting Dewar geometry under coupled constraints and demonstrate compatibility with long-endurance and low-noise HTS-SQUID magnetometer systems.

During the autumn of 1923, Niels Bohr stayed for about three months in Canada and the United States where he gave a series of lectures on atomic and quantum theory. This paper provides details about his many meetings and lectures. It deals in particular with his stay at Amherst College, where he had a long conversation with the celebrated poet Robert Frost. Based on new archival sources, the paper includes a more fine-grained and better documented version of the episode than the one offered by literary critics and scholars. It concludes, perhaps somewhat disappointingly, that the Bohr-Frost meeting was of no great importance to either the physicist or the poet.

This text explores the fundamental divide between empirical laboratory data and continuous theoretical physics models, framing the "quantum measurement paradox" as an ontological error, specifically E.T. Jaynes' “mind projection fallacy” where abstract mathematical tools like wavefunctions are mistaken for physical reality. While quantum theory successfully predicts ensemble averages, it remains inherently silent on individual, event-by-event detection statistics. To resolve these conceptual mysteries without resorting to ad-hoc explanations like wavefunction collapse, the text introduces Event-By-Event Simulation (EBES). As a modular, discrete-event simulation framework utilizing causally-driven Deterministic Learning Machines (DLMs), EBES bypasses continuous time and pre-specified probability distributions. Instead, it generates discrete data from the bottom up in a strictly localized, cause-and-effect manner. Ultimately, EBES provides a computationally efficient methodology that mirrors actual laboratory observations, modeling phenomena that lie entirely beyond the reach of standard quantum theory.

We derive exact analytical expressions for higher-order correlations of Gaussian level-set interfaces, establishing a direct link between bulk field statistics and interface geometry. This framework enables efficient reconstruction of disordered media, detailed modeling of {Gaussian foams}—structured, double-thresholded interfaces with tunable morphology—and analysis of {memory-enhanced anisotropy} reflecting directional persistence in surface structure. These results open new possibilities for characterizing complex transport, guiding stochastic reconstructions, designing materials with desired properties, quantifying the information content of correlation functions and modelling directional processes on irregular boundaries.

We determine statistics of allele-frequency trajectories across replicate populations. Defining $R = 2N_e|s|$ where $N_e$ is the effective population size and $s$ is the additive selection coefficient of a focal allele, we focus on strong selection which corresponds to $R \gg 1$. Strong selection does not require large selection coefficients: in populations of realistic size, it can readily arise from small selection coefficients. Our analysis concerns a biallelic locus over timescales on which mutation can be neglected. Every allele-frequency trajectory belongs to one of two absorbing classes: fixation or loss. When $R$ is large, individual allele-frequency trajectories can appear nearly deterministic. This does not imply that an approximation of allele-frequency statistics can be based on a single deterministic trajectory. For a beneficial allele, the deterministic trajectory approaches fixation, yet a non-negligible fraction of stochastic trajectories may be lost. Ignoring trajectories that achieve loss can produce severely erroneous statistics. We address this by conditioning separately on the eventual loss and eventual fixation of the focal allele. This yields an outcome-conditioned representation of a statistic. In the strong-selection regime, we apply a small-noise expansion that reasonably captures transient behaviours of statistics while recovering their exact long-time limits. We illustrate results with numerical examples for the mean allele frequency and its variance. The biological interpretation of this work is that, even under strong selection, both trajectory types are of importance. We provide a simple framework for calculating time-dependent allele-frequency statistics relevant to experimental evolution, longitudinal population-genomic data, and the dynamics of alleles driving selective sweeps.

Pulsar wind electron–positron plasma lacks the heavy inertial species required for the conventional ion-acoustic-like compressive modes. However, if ions are mixed into the reconnecting striped-wind current sheet, a low-frequency compressive branch can appear. We develop a local, comoving-frame theory for such ion-acoustic-like modes in an ion-loaded, pressure-balanced pulsar-wind current sheet. The background model connects directly to pulsar observables through the light-cylinder magnetic field and Goldreich–Julian density, while the sheet structure is represented by a Harris-like field reversal and species-dependent compression factors. The pair species are treated as inertialess thermodynamic shielding populations, whereas the ions are described by a warm, nonrelativistic, magnetized fluid. Pressure balance fixes the pair and ion temperatures rather than prescribing them freely. This gives a closed expression for the pair-shielded ion-acoustic speed in terms of pulsar spin parameters. We derive the warm-ion electrostatic dispersion relation and discuss the conditions under which the ion-acoustic-like branch can exist in such a current sheet. We find that the ion-acoustic-like mode does not occur for all sheet parameters but is restricted to specific regions of parameter space. Thus, ion loading alone is not sufficient to sustain the mode; the local current-sheet conditions determine where an admissible ion-acoustic-like mode can exist. Consequently, any wave-driven anomalous dissipation or particle heating mediated by this mode must be highly localized rather than distributed uniformly across the striped wind. This framework provide the physical domain where the mode can exist, providing the necessary foundation for future studies of kinetic excitation and damping.

Achieving universal health coverage, as set out in Sustainable Development Goal 3.8, requires closing persistent geographic and socioeconomic gaps in healthcare access, especially in under-resourced settings across Africa. Healthcare-access inequality is shaped not only by poor local access, but also by limited connectivity to city- and regional-level services and opportunities. Conventional accessibility analysis can identify where poor access occurs, but not whether poorly served places form structurally disconnected pockets across scales. This paper therefore builds on and extends the percolation divergence tree framework to develop a connectivity-based multiscale approach for examining healthcare-access inequality in Ghana. It combines street-level accessibility mapping with the hierarchical structure of the road network to identify the scales at which inequality coincides with connectivity breaks. The results first show substantial inequality: around one quarter of the population lives more than 5 km from the nearest healthcare facility. Multiscale analysis further reveals distinct structural forms of poor access. In relatively well-connected, monocentric regions, local poor-access pockets emerge around metropolitan fringes despite overall regional advantage. In less well-connected, polycentric regions, poor access extends across larger subsystems, with local pockets nested within broader poorly served areas. These findings show that healthcare-access inequality reflects both local conditions and the hierarchical connectivity of the wider spatial system, which may also constrain marginalised communities' access to other key resources and services. The framework can inform targeted local interventions and policy coordination across local and regional scales.

Geophysical data collected from active field sites are often contaminated by complex and heterogeneous noise, obscuring weak seismic events, and complicating automated interpretation. Although deep learning offers promising solutions for seismic processing, its performance is highly sensitive to the nature of training noise, especially under out-of-distribution (OOD) conditions. This study investigates the influence of noise parameters, such as type, scale, and complexity on the performance, generalization and robustness of neural networks in two geophysical tasks: first break picking and denoising. We simulate seismic-while-drilling data and apply controlled input source noise augmentation using stochastic generators to vary the noise characteristics. Different neural networks are trained on fixed noise types and scales, then evaluated across both seen and unseen noise scenarios. We incrementally increase the complexity of the noise by introducing compound noise mixtures and assess the performance of the model under increasingly challenging OOD conditions. This yields a robustness matrix that captures the generalizability of each model relative to its training configuration. Results indicate that larger noise scales boost generalization, and that effective alignment between noise type, task complexity, and architecture is key for maximizing generalization gains. In addition, training with compound noises mitigate weaknesses associated with single-noise training, acting as an additional implicit regularizer to improving robustness. These findings highlight key factors influencing model resilience in noisy geophysical environments and offer guidance for developing deep learning models that generalize effectively across diverse and unpredictable noise conditions.

The recently proposed concept of generalized superradiance (GS) allows for the generation of coherent radiation without the need for complex compression or prebunching of relativistic electrons. However, the quasiparticles in current GS schemes are formed through local electron accumulation and their sizes exceed 100 nm, restricting the achievable radiation wavelength. In this study, we demonstrate a realization of narrow quasiparticles with <10 nm widths through sheet-crossing. When an energy chirped electron beam collides with an intense laser pulse, lower energy electrons at the front slip back, forming an accelerating quasiparticle. These quasiparticles undergo transverse oscillations within the laser field, thereby extending GS emission from the Cherenkov regime into the synchrotron regime. Three-dimensional particle-in-cell simulations indicate the production of gigawatt-class chirped radiation in the 10-100 nm range. The proposed scheme is compatible with existing Thomson scattering facilities, paving the way for the generation of coherent ultrafast radiation.

Quantum confinement restructures the electronic phase space of ultrathin metals, but the resulting density-of-states (DOS) change is not generically large. We show that, within a three-dimensional confinement theory based on the suppression of long-wavelength electronic states, the Fermi-level DOS enhancement on the weak-confinement branch is bounded by $(4/3)^{1/3}-1\simeq10.1\%$. Instead, confinement-induced redistribution of electronic phase space can be strongly amplified by resonant denominators in the second-order susceptibility. A minimal double-resonance closure predicts giant amplification when the film thickness approaches $L_c=(2\pi/n)^{1/3}$, where $n$ is the carrier density, and a dominant optical pathway lies within a dephasing linewidth of resonance. Applied to recent Ag(111) measurements, the theory gives an effective phase-space carrier density parameter $n_{\rm eff}\simeq3.6\times10^{20}\,\mathrm{cm}^{-3}$, a tenfold enhancement of $|\chi^{(2)}|$, and hence the observed $\sim100$-fold increase of thickness-normalized second-harmonic-generation efficiency, together with the observed non-oscillatory thickness dependence.

Slowly sheared granular materials generally store mechanical energy and dilate between abrupt failures that release energy and compact the material. Here, simultaneous measurements of torque, layer thickness, acoustic emission, and photoelastic force networks reveal all four combinations of energy release or storage with contraction or dilation in a compressed granular fault. Most strikingly, some avalanches both dilate the layer and increase the elastic energy transmitted to the resisting boundary. These events reorganize force chains beyond the shear band and produce a distinct acoustic response. The results show that an avalanche need not relax a driven disordered material; it can instead redistribute stress into a more highly loaded configuration, a mechanism with potential relevance to the physics of both laboratory and natural faults.

Passive photonic structures offer a potential route toward reservoir computing by exploiting intrinsic optical propagation, delay, interference, and memory effects for temporal information processing. This work investigates whether a chirped Bragg grating (CBG) can exhibit fundamental physical properties relevant to a passive photonic reservoir. Rather than presenting the CBG as a complete reservoir computer, the study provides an early proof-of-concept assessment based on experimentally measured device characteristics. Wavelength-dependent group delay and reflection coefficient measurements obtained from a silicon photonic CBG are incorporated into a distributed delay model in which multiple delayed contributions form the reservoir states. The resulting system is examined with respect to distributed delay, fading memory, temporal mixing, and memory capacity using a linear readout based on the standard memory capacity formulation. The results demonstrate that the measured CBG response provides a distributed temporal memory over the experimentally observed delay range and produces temporal mixing through the superposition of multiple delayed contributions. The memory capacity analysis further indicates that past input symbols can be reconstructed from the distributed delay states, providing quantitative evidence of temporal information retention in the measured CBG response. These results suggest that a passive CBG possesses several physical characteristics relevant to reservoir computing and may therefore provide a simple photonic substrate for reservoir-like temporal processing. The study is intended as an initial proof-of-concept, providing a foundation for future experimental and task specific investigations of passive CBG-based photonic reservoirs.

Many engineering building blocks behave as multi-port linear time-invariant systems. RF cavities, photonic devices, and superconducting quantum chips, despite their different underlying physics, all share a common mathematical structure for their port-level response. Each entry of the response matrix is a sum of contributions from a small number of intrinsic resonant modes, the pole-residue form. A model capable of predicting such responses for arbitrary geometries and arbitrary port configurations, while simultaneously extracting the underlying eigenmode structure, would therefore establish a foundational design principle spanning all these domains. We propose a neural framework that learns this modal decomposition end-to-end, supervised only by system-level observables and without supervising the modal parameters themselves. The architecture decomposes into a port-independent pole predictor and two port-dependent coupling predictors whose outputs are combined entry-wise, separating intrinsic from port-dependent features. This factorization yields a single trained model that generalizes to port counts unseen during training, dissolving the $\mathcal{O}(N^2)$ scaling barrier of direct regression. Despite no modal supervision, the freely-parameterized poles converge to physically meaningful eigenmodes, verified by cross-validation against the AAA rational approximation algorithm. We instantiate the framework in radio-frequency electromagnetic surrogate modeling. A model trained only on 2-port data accurately predicts $N$-port responses unseen during training.

Thin-film silicon solar cells exhibit weak absorption at longer wavelengths of visible light due to their limited optical thickness. To overcome this inherent limitation, a hybrid nanocone-nanohole light-management architecture is proposed by integrating front-surface silicon nanocones with embedded air-filled nanoholes in the absorber layer. The nanocones provide a gradual refractive-index transition that suppresses front-surface reflection, while the nanoholes enhance optical confinement through multiple scattering and internal reflection. The structural parameters are systematically optimized using finite-difference time-domain (FDTD) simulations coupled with electrical device modeling. Under optimized doping conditions and isothermal steady-state operation, the optimized design yields a short-circuit current density of 34.64 mA cm-2, an open-circuit voltage of 0.82 V, a fill factor of 86.27%, and a power conversion efficiency of 24.42%, representing a 17.74% improvement over a comparable nanohole-based design. Furthermore, coupled opto-electro-thermal simulations show that the proposed hybrid nanocone-nanohole structure retains 95.21% of its efficiency under non-isothermal operation up to45 degree celsius, demonstrating excellent thermal stability. These results highlight the potential of the proposed hybrid nanocone-nanohole design as an effective light-management strategy for high-efficiency and thermally robust thin-film silicon solar cells.