Finite-strain hyperelasticity on curved embedded domains poses a geometric challenge for lattice Boltzmann methods. After streaming across an embedded material surface, the missing population is recovered at the physical cut-link point, where the lattice direction, surface normal, and tangential deformation directions are generally distinct. We develop a total-Lagrangian vectorial lattice Boltzmann method that resolves this geometric mismatch for two- and three-dimensional hyperelastic dynamics. The continuum equations are written as a conservative first-order system for material velocity and deformation gradient. Vector-valued populations are chosen so that their moments recover the state and the material-coordinate Piola fluxes, giving D2Q4\(\times\)6 and D3Q6\(\times\)12 schemes from one \(D\)-dimensional construction. Curved boundaries are embedded by a level set and closed link by link through opposite-population moment identities, cut-link interpolation, and local geometric information at the boundary point. The reconstruction is coupled to a compatibility projection that keeps the recovered displacement aligned with the evolved deformation gradient on embedded active-node graphs. The resulting method extends the previous grid-aligned two-dimensional formulation to curved domains and three-dimensional lattices while retaining explicit collide-stream updates on Cartesian grids. Benchmarks in two and three dimensions show agreement with exact finite-strain fields, nonlinear radial boundary-value problems, and finite-element references.

We present a hybrid modeling framework for simulating the spread of COVID-19 across Germany. Our approach couples high-resolution agent-based models (ABMs) incorporating mobility data from mobile phones with faster, less detailed partial differential equation (PDE) and ordinary differential equation (ODE) models. Mobility between regions is incorporated through data-driven jump processes that transfer individuals, enabling a balance between accuracy and computational efficiency. Building on earlier studies on pairwise ABM-ODE, ABM-PDE, and PDE-ODE coupling strategies, we develop a hybrid model to unify all three model classes (ABM, PDE, and ODE) within a single framework. To demonstrate the framework's utility, we systematically compare ABM, PDE, and ODE representations of Berlin embedded in a nationwide simulation of Germany, investigating complete travel restrictions to and from selected federal states, and evaluating the Zero-COVID and No-COVID strategies. These experiments demonstrate how the framework can be used to analyze the interplay between mobility, regional coupling, and containment measures at the scale of an entire country. Computational performance is analyzed by measuring runtime savings while quantifying error using real-world infection data. The presented framework enables efficient and accurate simulation of infection dynamics across densely connected regions and provides a tool for evidence-based evaluation of public health interventions.

Routing many commodities through a logistic network at minimum joint cost of vehicle movements and transshipment at intermediate storages becomes substantially harder -- and substantially more realistic -- once two features are present together: edge costs that are \emph{nonlinear} in the transported volume, because freight travels in fixed-capacity vehicles, and \emph{hard upper bounds} on the volume reloaded at each storage. No public benchmark captures this combination, and the locations of commercial distribution centres are proprietary. We close this gap with a parameterised generator of synthetic logistic networks, calibrated on real road graphs of nine European countries, five U.S.\ states and the European part of Russia extracted from OpenStreetMap. The generator reproduces the structural signatures of upper-level distribution networks -- near-planar topology, low vertex degree, short edges and large diameters -- by combining Zipf-distributed city sizes, a spatial-network budget model trading edge-building cost against routing convenience, centrality-driven storage capacities, and a doubly constrained gravity demand model. We release 35 fully synthetic instances ($10$--$150$ storages) and 15 real-geometry instances in an open, self-describing, fully reproducible file format. An empirical characterisation computed directly from the released files shows that synthetic and real-geometry instances are structurally consistent (two-sample Kolmogorov--Smirnov distances of $0.10$ on degree and edge-length distributions, with small and quantified mean deviations), and a baseline-solver study demonstrates that the corpus discriminates instance difficulty across orders of magnitude. The benchmark is a faithful, reusable testbed for the development and comparison of exact and heuristic algorithms.

Chitosan-based nanomaterials are being increasingly explored as sustainable alternatives to petroleum-derived food packaging, yet their environmental performance across the full life cycle remains insufficiently understood. This review critically evaluates these systems from a life cycle perspective and examines how material origin, processing pathways, functional performance, and end-of-life behavior collectively influence sustainability outcomes. Beginning with chitin extraction from crustacean waste, key processing steps, including demineralization, deproteinization, and nanoparticle synthesis, are assessed in terms of chemical intensity, energy demand, and associated emissions. Manufacturing routes, including solvent-based and green synthesis approaches, are compared with those of conventional plastics to identify relative environmental burdens. The use phase is analyzed with respect to antimicrobial functionality, shelf life extension, and potential reductions in food waste. End-of-life pathways, including biodegradation and composting, are evaluated alongside uncertainties related to degradation behavior and nanoparticle fate. By synthesizing these stagewise interactions, this review highlights critical trade-offs that are often overlooked in sustainability narratives and examines whether chitosan-based nanomaterials provide net environmental benefits in food packaging applications.

Physics-informed and data-driven surrogates are increasingly used to approximate mechanics-governed flow fields, but the target quantities assigned to such models are not always identifiable from the input variables available at prediction time. We introduce an input-schema identifiability certificate for computational surrogates. Starting from a reduced physical model, the certificate decomposes a target field into components that are measurable from geometry, components that require boundary-condition information, and components identifiable only up to a symmetry quotient. This yields a pre-training audit: it predicts which oracle-channel interventions should reduce error, which should fail, and which ambiguity cannot be removed by changing the architecture, loss, optimizer, or sample size. We instantiate the framework for incompressible tubular flow using a Cosserat-rod reduction, where lumen velocity separates into a mesh-measurable tangent direction, a boundary-condition-dependent magnitude, and a signed-orientation ambiguity. Controlled experiments on patient-specific aortic CFD geometries, analytic Womersley flows, and an advection-diffusion transfer problem confirm the predicted pattern: supplying signed direction collapses angular error to the oracle regime, whereas supplying magnitude without orientation leaves the predicted sign ambiguity and yields 16-33 percent per-node sign flips. The results provide a mechanics-based diagnostic for deciding whether a surrogate modelling task is physically identifiable before training, and expose failure modes that aggregate error metrics can hide.

When a wine glass is dragged on a table along a circular path, a spontaneous rotation about its vertical axis can develop even if the applied hand force does not directly introduce a yaw torque. This document provides a structured formal derivation of the governing equations that are responsible for this behavior. The analysis shows that the mechanism responsible for this effect is the redistribution of pressure onto the table when applying the force with your hand. This causes an uneven frictional force distribution which exerts a torque on the glass, causing it to spin.

The aerothermal demise of titanium components plays a critical role in the uncontrolled re-entry of space debris from low-Earth orbit. Exposure to high temperatures and dissociated oxygen environments promotes rapid oxidation, significantly influencing the material degradation and surface thermal balance. This study presents time-resolved infrared emissivity measurements of both Grade 2 and Grade 5 titanium samples across five wavelength bands during exposure to entry-relevant conditions simulated in the Plasmatron facility at the von Karman Institute for Fluid Dynamics. The results reveal a dynamic evolution of emissivity throughout the test, including a pronounced drop associated with a characteristic surface temperature jump that is not captured in existing literature data. Post-test electron microscopy highlights a diverse oxide layer morphology at the microscale. Although plasma wind tunnel experiments reproduce only a subset of flight-relevant phenomena to space-debris entry, these findings demonstrate that the complex coupling between oxidation and surface radiative behavior is not adequately captured by conventional pre- and post-test analyses, highlighting their limitations in resolving in-situ emissivity evolution.

Wearable light loggers are increasingly used to measure personal light exposure. However, there is no standardised method to test how well these devices perform in real-world settings. To address this, we developed a scene-based field validation framework combining wearable light loggers, laboratory-grade spectral reference instruments, and image-based scene characterisation to evaluate wearable performance in the field. We applied the validation framework to ActTrust2 and ActLumus light loggers using 433 natural, everyday scenes across two sites: Tübingen, Germany (n=210), and Izmir, Türkiye (n=223), spanning indoor and outdoor environments in daylight, artificial light and mixed scenarios, across wide range of photopic and melanopic equivalent daylight illuminances. Both light loggers exhibited high agreement with the reference instruments (R^2=0.988-0.990), but they systematically underestimated light exposure. The lighting condition, scene complexity, time of day, and study site contributed significantly to measurement bias. Resampling under specific lighting conditions indicated that bias ranged from -0.065 log units in daylight-only situations to -0.195 log units in artificial-only situations. This suggested that single-condition assessments with limited spectra or scene categories can underestimate or overestimate overall wearable performance. Bootstrap resampling demonstrated that performance estimates stabilised at approximately 100 scenes, indicating that a diverse sample of this size is sufficient for reliable field validation. Finally, cross-site validation showed consistent performance across two sites, supporting the framework's reproducibility. Overall, these findings establish our validation framework as an effective tool for guiding scene diversity and sampling design for ecologically valid field validation of wearable light loggers.

Quantum chemistry simulations underpin modern materials discovery, yet their impact is limited by steep computational cost and dependence on fixed approximation schemes. Foundation models, such as machine-learned interatomic potentials, have accelerated parts of this workflow, but their reliance on large-scale pretraining restricts adaptability at the frontier of chemical space, where methodological innovation and sparse data are the norm. Agentic AI systems can automate existing simulation pipelines, yet they remain constrained by the predefined tools and algorithms they orchestrate. In response, we introduce LADeQ, an LLM-guided workflow that discovers, implements, and benchmarks candidate approximation algorithms at test-time within existing quantum chemistry codes. Rather than selecting from a predefined repertoire, LADeQ constructs candidate approximation schemes on demand, drawing on techniques from disciplines such as spatial statistics, circuit simulation, and kernel methods that have had little prior presence in electronic-structure theory. Because it builds on an out-of-the-box language model, LADeQ requires no task-specific pretraining or curated data, and the resulting implementations are transparent and inspectable, with explicitly traceable approximation errors that enable principled control of accuracy--efficiency trade-offs. We show that LADeQ accelerates coupled cluster singles and doubles (CCSD) and configuration interaction singles and doubles (CISD) calculations while keeping correlation-energy errors within user-specified tolerances, demonstrating autonomous, objective-driven discovery of approximation algorithms inside existing electronic-structure solvers.

Polymeric materials underpin modern technologies spanning energy storage, microelectronics, healthcare and sustainable manufacturing. Yet their rational design remains exceptionally challenging because material performance emerges from complex interactions among molecular composition, chain architecture, processing history and hierarchical structural evolution across multiple length and time scales. Consequently, polymer research has long relied on labor-intensive experimentation and fragmented modeling approaches, limiting both mechanistic understanding and innovation efficiency. Recent advances in data infrastructure, machine learning, large artificial intelligence (AI) models and laboratory automation are beginning to reshape this landscape. Rather than functioning as isolated tools, polymer databases, predictive models, AI agents and automated laboratories are increasingly converging into interconnected discovery ecosystems. As a result, the central challenge is shifting from improving predictive accuracy alone to enabling reliable decision-making, adaptive learning and seamless integration across computation, experimentation and scientific reasoning. We argue that polymer science is entering an era of autonomous discovery, in which data, simulation, reasoning and experimentation operate within self-improving feedback loops that continuously generate hypotheses, design materials, execute experiments and refine predictive models. By unifying molecular design, process optimization, experimental validation and industrial translation, such autonomous ecosystems establish a more predictive, reproducible and scalable paradigm for polymer innovation, fundamentally transforming how polymer research is conducted.

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful tool for molecular structure analysis, and spectral artificial intelligence offers great potential for its rapid and automated interpretation. However, the scarcity of experimental NMR datasets has constrained deep learning in this domain to narrow, task-specific applications that lack broad generalization. Here, we introduce UltraNMR, a large-scale foundation model for NMR that leverages the intrinsic properties of NMR spectra to learn generalizable spectral representations. We collected 158 million paired simulated $^{1}$H and $^{13}$C NMR spectra to train UltraNMR, employing multiple domain-specific pre-training objectives. UltraNMR captures both intra-spectral and inter-spectral dependencies, enabling seamless simulation-to-real adaptation. We demonstrate that adapting UltraNMR to a range of molecular structure analysis tasks on experimental NMR spectra consistently yields state-of-the-art performance and clearly outperforms UltraNMR variants trained directly on downstream data without simulation pre-training. We also construct a large-scale NMR spectral vector library by encoding simulated NMR spectra using UltraNMR, covering 94 million unique molecules and enabling effective structure-aware retrieval. In real-world applications, UltraNMR facilitates the structural elucidation of two previously unknown natural products from Chinese herbal medicines recorded in the Chinese Pharmacopoeia. These results suggest that large-scale simulation pre-training can effectively bridge the simulation-to-real gap, enabling robust and generalizable molecular structure analysis of real-world NMR spectra.

This white paper introduces the concept, prototype design, projected costs, and scientific goals of a mobile experiment for detecting geoneutrinos originating from uranium and thorium decay chains in the Earth's mantle. This will constrain the planet's radiogenic heat production and unearth its geochemical makeup. This design of a deep-ocean mobile neutrino experiment, which is not mirrored by any active or planned experiments, supports physics and geoscience's goal of multi-modal data on the Earth's internal composition and structure. Based on geoscientific studies, this design is expected to achieve a 50--100-fold reduction in crustal background compared to similarly sized continental detectors, thereby enabling direct measurements of mantle geoneutrinos. The multiple stereoscopic projections enabled by the detector's unique mobility can map spatial variations in heat-producing elements within the mantle. Beyond discussing the design, we report on our collaboration's most recent hardware developments in the active prototyping of this detector. We briefly highlight the potential multiuse and interdisciplinary nature of this detector.

We examine receptivity and biorthogonal decomposition in a reacting temporal mixing layer using direct and adjoint eigenmodes of a finite-thickness compressible linearized operator built from the mean reacting base state. The analysis focuses on the Kelvin--Helmholtz branch and asks how the reacting base state modifies the selected temporal instability, where localized forcing most efficiently excites it, and how strongly the associated modal family is represented in time-resolved planar simulation data. Receptivity maps are constructed for mass, momentum, thermal, and mixture-fraction forcing channels using an energy-weighted adjoint projection, with biorthogonality enforced by the corresponding direct--adjoint inner product. A complementary biorthogonal decomposition provides modal amplitudes and cumulative few-mode reconstructions at the fundamental streamwise wavenumber. The finite-thickness branch is interpreted against a compressible vortex-sheet reference built from the outer-stream states. The reacting layer supports an unstable finite-thickness Kelvin--Helmholtz family over low-to-moderate wavenumbers even though the discontinuous reference is essentially neutral. Mass forcing leads the raw localized receptivity maps, mixture-fraction forcing follows through composition-pressure coupling, and chemistry-weighted thermal forcing identifies the strongest thermochemical support of the same family. The results show how distributed reacting thermodynamics reorganize compressible shear-layer instability and how that reorganized branch remains embedded in the nonlinear flow.

This work demonstrates the initial development of the Absolute Radar-REMPI diagnostic technique, a development of the Radar-REMPI technique that provides an approach for absolutely calibrated measurements, which is universally applicable to any type of tested gaseous species. Absolute measurements are achieved through the utilization of two-color Resonance Enhanced Multiphoton Ionization (REMPI) and absolutely calibrated Coherent Microwave Scattering (CMS). Two-color REMPI of molecular oxygen (O2) was demonstrated using two intersected laser beams: a 355 nm beam and a tunable parametric beam at 241-242 nm. The results confirm that the two-beam configuration can successfully generate measurable ionization associated with the targeted transition of O2 within the beam intersection region, as verified using an ICCD camera. These findings establish a foundation for the further development of Absolute Radar-REMPI, which has the potential to enable highly sensitive, real-time diagnostics of absolute species densities in unsteady flowfields such as those occurring in hypersonic shock tunnels and electric propulsion systems.

The collisionless transfer of energy between fields and particles through wave-particle interactions is a fundamental process in space plasmas but remains incompletely characterized because many mechanisms operate across a wide parameter range and diverse plasma conditions. The Field-Particle Correlation (FPC) technique reveals velocity-space signatures of particle energization by correlating measured electric field fluctuations with changes in the velocity distribution. Fully mapping these signatures across plasma parameters requires an impractically large number of kinetic simulations or observations. To address this challenge, we introduce JET-PLUME (Judging Energy Transfer in a Plasma in a Linear Uniform Magnetized Environment), an extension of the PLUME Vlasov-Maxwell dispersion solver. JET-PLUME uses PLUME's ability to model parallel drifting bi-Maxwellian distributions to examine phase-space energy transfer by adding an analytic Fourier-space formulation of the FPC. This approach isolates the contribution of individual resonances, separates degenerate entropy mode components, and allows systematic analysis of unstable, growing modes. Dimensionless expressions extend the results across a broad parameter range and highlight the role of off-diagonal elements of the susceptibility tensor in coupling electric field and current response. We show that during kinetic Alfvén wave damping, the perpendicular field can drive parallel ion currents among particles with large perpendicular velocity, reducing the net Landau damping. The resulting velocity-space signatures, accessible through JET-PLUME, demonstrate how analytic formulations of phase-space energy transfer can reveal novel physics of wave-particle interactions across diverse plasma environments.

Turbulence closure models are essential for solving the equations of motion in realistic systems, where fully resolving all relevant scales of motion is computationally infeasible. Developing turbulence closures remains one of the most challenging problems in fluid dynamics. Specifically, the Navier-Stokes equations, when filtered to isolate large-scale motions, introduce new terms representing the influence of subgrid-scale turbulent stresses. These terms, which can only be computed directly by resolving the turbulence itself, therefore lead to the closure problem: we must add new equations or introduce assumptions to relate the unresolved scales of motions to the resolved flow. Here we consider the closure problem for oceanic flows, i.e., stratified, Boussinesq, incompressible, in a rotating frame of reference. In particular, we focus on a closure for ocean mesoscale eddies, which have horizontal scales of 10-100km and are key to the redistribution of momentum, energy, and tracers in the ocean. In particular, mesoscale eddies can reinject energy and momentum into the large-scale flow through an inverse energy cascade. Here, we explore a range of theoretical and data-driven ocean mesoscale closures and examine their connections using analytical and data-driven methods. This note aims to bridge the gap between novel methods from artificial intelligence (AI) and machine learning and theoretical fluid dynamics to address significant challenges in the physics of turbulence.

Nanotweezers are actively investigated as a powerful means to reversibly trap and characterize nanoparticles with profound biological and environmental importance. To ensure that these particles can be reversibly trapped and released, surface passivation is essential. To mitigate the issue of fouling, we investigated the antifouling properties of poly(sodium styrene sulphate) (PSS) synthesized using the Atom Transfer Radical Polymerization (ATRP) technique on our previously reported gold-based Interferometric Electrohydrodynamic Tweezers (IET) device. Fluorescence and interferometric scattering (ISCAT) imaging were used to record trapping performance and study the antifouling properties of our passivated nanotweezer device. The results show that PSS exhibits superior anti-fouling performance against polystyrene nanoparticles when compared to 11-mercaptoundecanoic acid (MUA). By comparing the antifouling properties of PSS and zwitterionic poly(methacryloyloxyethyl phosphorylcholine) (PMPC) in preventing extracellular vesicle adhesion, we found that both exhibited similar performance. Overall, the ATRP technique is broadly applicable across nanotweezer substrates with appropriately chosen initiators.

Strong coupling between molecules and electromagnetic fields has emerged as a powerful strategy to modify the physical and chemical properties of molecules, enabled by the formation of hybridized energy levels through strong light-matter interactions. Rather than purely photonic or plasmonic modes, hybrid cavity fields that integrate plasmonic and photonic contributions provide versatile platforms with distinct advantages for tailoring light-matter interactions. Here, we present hybrid modes formed through the coupling between localized surface plasmon resonances of Au nanoparticles and Fabry-Perot microcavity modes. Furthermore, by using this hybrid platform, we demonstrate that the cavity field can give rise to cooperative coherent coupling between the spatially separated plasmonic nanoparticles and the molecules by mediating long-range dipole-dipole interactions. As we show, this cavity-mediated coupling also enhances the plasmon-exciton mixing, as compared to their direct interaction. This configuration opens new avenues for tailoring light-matter interactions at the nanoscale by supporting novel hybrid plexcitonic states that incorporate contributions from plasmons, excitons, and extended cavity fields.

Capable of capturing both bound and continuum quantum dynamics, Gaussian wavepackets are highly attractive basis functions for simulating laser-driven processes in atoms and molecules. Unfortunately, fully flexible Gaussian wavepackets are exceedingly challenging to propagate in a numerically stable manner within the framework of conventional time-dependent variational principles. In this chapter, we discuss the sources of the numerical issues and review an alternative approach, Rothe's method, that offers a route to improved numerical stability. Recent proof-of-concept simulations based on Rothe's method indicate that Gaussian wavepackets provide results on par with highly accurate grid-based methods for both electronic and rovibrational quantum dynamics, including ultrafast nonlinear processes that involve the continuum such as high-harmonic generation. Remarkably few Gaussian wavepackets are needed to achieve the high accuracy of grid-based approaches, indicating that further algorithmic developments and efficient implementations may enable efficient simulations of not only electronic and rovibrational phenomena but also fully coupled electronic-nuclear quantum dynamics with significantly reduced memory demands. We also point out remaining practical challenges, including matrix elements of the squared Hamiltonian and the treatment of Coulomb cusps.

We holographically generate optical modes associated with bipolar-coordinate solutions of the Helmholtz equation. Unlike conventional Gaussian beam families, these modes support multicenter phase discontinuities. We investigate and demonstrate that they support multicentered phase singularities, including optical dipoles and two-centered charge distributions. We further identify a family of conveyor-belt modes whose intensity structure follows an extended trajectory connecting the charge centers. Experimental generation using a spatial light modulator is presented and compared with theoretical predictions. These results establish bipolar-coordinate optical modes as a new class of structured light fields for singular optics, beam shaping, optical manipulation, and the controlled generation of multicentered topological structures.

This paper presents a modular S-/X-band shared-aperture phased-array antenna (SAPAA) for satellite-communication ground-station reception. The proposed architecture uses a repeatable unit cell that supports independent S- and X-band operation within the same physical aperture and enables arbitrary aperture scaling. Dual-polarized radiators are combined with calibrated complex receive coefficients to synthesize linear polarization (LP), right-hand circular polarization (RHCP), and left-hand circular polarization (LHCP). The design burden of the electrically large shared aperture is reduced by using theoretical estimates for scan matching and inter-band isolation before full shared-aperture verification. Simulated and measured results demonstrate axial ratios below 0.1 dB in the target S- and X-band receiving bands over a +/-50 deg scan range. The prototypes are validated using two approaches: passive measurements, where the element responses are measured individually, and RF system-on-chip-based active measurements, where all available receive channels are measured simultaneously. The results confirm that the proposed SAPAA provides wide-angle scanning, very high polarization purity, and polarization-reconfigurable operation for multi-mission SATCOM ground terminals.

Many textbooks discuss the diamagnetic response of a ``classical atom'' to a small, adiabatically slowly applied magnetic field. Here we solve this problem for an arbitrarily large field. This gives a more satisfying justification for the assumptions made in the small field regime, explains the role of adiabaticity, identifies the symmetries persisting at large fields, and illustrates the range of applicability of the Larmor's theorem.

Individual daily mobility exhibits a striking scaling law: the probability of returning home after a tour of $l$ locations decays as $P_{\rm ret}(l)\sim l^{-\gamma}$. While the tour-terminate-continue (TTC) model reproduces this behavior, it relies on this power law as an empirical input, leaving the microscopic origin of $\gamma$ unresolved. Here we show that this scaling emerges from a utility trade-off governed by cognitive constraints. By invoking the principle of least effort, we demonstrate that individual activity priorities follow Zipf's law, $p(r)\sim r^{-\nu}$, which directly dictates the sublinear accumulation of tour utility, $U_L(l)\sim l^{1-\nu}$. Luce's choice rule then yields $P_{\rm ret}(l)\sim l^{-(1-\nu)}$, giving the exact exponent $\gamma = 1 - \nu$. Agent-based simulations confirm this analytical relation. Our framework bridges the gap between individual cognitive constraints and the scaling law of tour behavior, providing a microscopic theoretical underpinning for human mobility.

Chemical reaction networks (CRNs) are crucial for understanding reaction mechanisms and guiding chemical synthesis, yet the computational exploration remains limited by the combinatorial growth of chemical space, the reliability of reaction path screening, and the cost of evaluating thermodynamic and kinetic properties. Here, we present ByteCRN, an end-to-end framework for computational CRN exploration that combines chemically informed reaction enumeration with generative transition state modeling. A key component of our framework is a generative rectified flow architecture for both transition state generation and reaction validation, where it maps reactant-product pairs to candidate transition state structures and verifies connectivity by mapping back to reactants and products. This unified generative strategy replaces the most expensive steps of conventional computational workflows, namely iterative transition state search and intrinsic reaction coordinate validation, within a complete CRN construction pipeline. ByteCRN delivers a 10--100-fold acceleration over traditional workflows while maintaining high predictive fidelity for individual reactions. At the network scale, it effectively prunes $\sim$70-90% of the enumerated reactions, streamlining the exploration of complex reaction space. Its utility is illustrated through the discovery of novel pathways involving cyanoacetaldehyde and the successful modeling of the challenging $\gamma$-ketohydroperoxide network, demonstrating a practical, scalable approach to autonomous chemical exploration.

The Mpemba effect is commonly associated with anomalous thermal relaxation, in which a system prepared at a higher temperature reaches equilibrium faster than one prepared at a lower temperature. In modern formulations, however, its defining feature is broader: a state initially farther from equilibrium can relax faster than a closer one, or, equivalently, relaxation trajectories can cross. Here, we show that magnetic-field-dependent delayed fluorescence in triplet-fusion systems realizes a generalized Mpemba effect driven by external control of spin-selective kinetics. Using a diffusion-controlled geminate-recombination extension of the Johnson--Merrifield model, we demonstrate that delayed-fluorescence trajectories obtained at different magnetic fields cross when geminate fusion is effective. We further derive a compact kinetic criterion for such crossing in terms of the competition between intermediate-time decay rates and long-time power-law amplitudes. This criterion captures the interplay between effective fast and slow relaxation contributions. Within this kinetic framework, the final state is independent of the external control parameter and is defined as the fully relaxed state in which no separated triplet population remains. The generalized Mpemba effect therefore arises from the redistribution of transient relaxation pathways associated with geminate recombination, whereas bulk diffusion-controlled processes contribute only to the final relaxation. These results link anomalous relaxation in spin-dependent photokinetics to modern formulations of the generalized Mpemba effect and show how trajectory crossing can emerge in systems governed by diffusion-controlled geminate recombination and non-Markovian kinetics.

Large-scale intermittency in the vertical velocity (LSI) has received significant attention in studies of coherent structures and their detection using data-driven approaches. However, a theory that predicts the origin of LSI from the Navier-Stokes equations or some approximated version of them at very high Reynolds numbers is yet to be achieved. This letter proposes such a theory for a neutrally stratified wall-bounded turbulent flow based on a dominant balance between inertial and pressure forces. Using multiple flume and wind tunnel experiments, it is shown that the flatness factor ($FF_w$) measuring LSI collapses to a universal trend for all flow configurations within the inertial sublayer (ISL) before reaching a common minimum value above the ISL. A theory that predicts $FF_w$ using second-order statistics and explicitly accommodates large-scale energy anisotropy is tested against a wide range of Reynolds numbers from laboratory to field settings with varied surface roughness conditions. The theory also demonstrates why $FF_w$ cannot be described using down-gradient closure approximations routinely employed in large-scale meteorological and climate models.

Nonlocal metasurfaces enable all-optical processing of spatial information in optical fields. Here, we demonstrate a topology-optimised metasurface that switches between phase-contrast and brightfield imaging modalities via polarisation control, eliminating the need to reposition optical components or use computational techniques to image transparent samples. Specifically, for one polarisation state, an asymmetric transfer function about normal incidence performs a first order derivative on the phase, producing pseudo-3D phase-contrast images of transparent biological samples while the orthogonal state returns the result of the identity operator. This work extends inverse-design methods to reconfigurable phase-contrast microscopy and quantitative analogue optical computation with applications in biological imaging, medical diagnostics, and materials characterisation.

Interfacial dynamics play a central role in transport processes across the plasma-liquid interface. While the strong electric field in the plasma sheath can destabilize the liquid surface and induce species transfer from the liquid into the plasma, the mechanistic relationship between surface instability and interfacial transport remains poorly understood. Here, we investigate the transport of sodium species in an atmospheric-pressure helium plasma in contact with a negatively DC-biased NaCl electrolyte using high-speed imaging, laser Mie scattering, and optical emission spectroscopy. The results show that Na transport is mediated by droplet emission from the liquid surface and proceeds through three sequential stages: surface deformation, Taylor cone formation, and electrospray. The threshold voltage required for Na I optical emission decreases with decreasing surface tension, in good agreement with the marginal condition for electrohydrodynamic (EHD) instability predicted by linear perturbation analysis. These findings demonstrate that EHD-driven droplet emission is the primary mechanism carrying sodium ions from the liquid into the plasma, where they are neutralized and excited. More broadly, this work establishes surface instability as the active transport channel governing the injection of dissolved species from the liquid into the plasma, creating a unique reaction network of plasma chemistry.

The invariant-imbedding T-matrix method (IITM) is a standard tool for light scattering by large, sharply faceted, non-axisymmetric particles (atmospheric ice crystals and mineral dust) where the surface-based extended boundary condition method loses accuracy. Its accuracy is limited by "staircasing": the dielectric contrast of a faceted particle is integrated across boundaries that cut the quadrature grid, so standard quadrature converges at low algebraic order. We show that this non-smoothness has a single geometric origin, the tangencies of the integration sphere to the faces and edges of the particle, which produce jumps, kinks, and half-integer branches according to the tangency type, in all three integration directions. A boundary-conformal scheme removes them using closed-form azimuthal coefficients, panel splitting at the analytically known tangency loci, and a square-root substitution $x \mapsto x_c + t^2$ that absorbs the half-integer branches. For a hexagonal prism the azimuthal integration becomes exact and the zenithal and radial directions recover spectral and fourth-order convergence; because the construction depends only on the contact geometry, it extends to any convex polyhedron, demonstrated on the solid hexagonal bullet (a faceted ice habit with tilted faces). The zenithal crossing is a square-root branch rather than a kink, so the established interval-splitting alone gives only $\mathcal{O}(N^{-3})$, while the radial step removes the half-integer edge branch that caps the Riccati recurrence on faceted particles. The convergence orders are fixed by the local contact geometry and verified size-independent up to $k\,r_{\max} = 20$; what grows with size is the resolution needed to reach each asymptotic regime, not the order.

Weak dissipation can control wave propagation, mode competition, and instability thresholds in plasmas, yet the physical origin of large branch-to-branch differences in collisional damping is often obscured by dielectric-tensor calculations. We show that weak collisional damping in cold plasmas is governed by wave-energy partition. In the one-rate cold-plasma model, the damping rate of a collisionless eigenmode is exactly the collision frequency multiplied by the fraction of the total wave energy stored in plasma motion. This result recasts the standard perturbative damping formula into a compact and physically transparent law, immediately explaining why field-dominated branches such as whistlers can be much less damped than the collision frequency, whereas quasi-electrostatic modes can exhibit damping of comparable magnitude. Analytic examples for Langmuir, transverse electromagnetic, whistler, and extraordinary waves show that the energy-partition form classifies weak collisional damping across distinct branches and provides a simple diagnostic for mode competition in multibranch plasma-wave systems.

Optical frequency combs on photonic integrated platforms are revolutionizing precision metrology, bio-imaging, atomic and molecular sensing, and ultrafast photonics, yet most remain confined to the near-infrared. This restriction prevents access to the ultraviolet, visible, and mid-infrared bands critical for a vast array of quantum, atomic, and molecular systems. The fundamental obstacle has been the lack of a nanophotonic waveguide that simultaneously provides an ultra-broad transparency window, engineered dispersion, ultra-low propagation loss, and a strong Kerr nonlinearity, all while suppressing detrimental two-photon absorption at short wavelengths. Here, we overcome this challenge by exploring tantalum pentoxide for ultra-broadband supercontinuum spanning continuously from the ultraviolet to the mid-infrared, leveraging its broad transparency window (300-8000 nm), a high nonlinear refractive index three times larger than that of silicon nitride, and a wide bandgap that suppresses two-photon absorption. Critically, by using a photolithography assisted chemo-mechanical etching process that avoids a lossy SiO2 upper cladding, we achieve dispersion engineered waveguides with record-low propagation losses of 0.066 dB/cm at telecom wavelengths and 0.43 dB/cm at 780 nm, significantly facilitating the supercontinuum spectral extension into the ultraviolet and the mid-infrared. Pumping these anomalous-dispersion waveguides with femtosecond pulses at 1550 nm yields a gap-free, 3.2-octave supercontinuum spanning from 350 to 3200 nm via a soliton-based dynamics at only 54 pJ pulse energy, representing the broadest comb spectrum on this platform. We further demonstrate a relatively flat spectrum with a -30 dB bandwidth of 1182 nm by engineering normal dispersion, validate the comb coherence via heterodyne detection, and achieve soliton-effect pulse self-compression from 126.7 fs to 19.2 fs.

Salt fingering drives diapycnal scalar exchange across thermohaline interfaces that are statically stable but double-diffusively unstable. Oceanic interfaces are finite-depth structures and may carry roughness inherited from waves, shear, intrusions, or prior mixing. We test how the horizontal spectrum of that roughness controls the route from a two-layer interface to a finite-depth salt-finger plume forest. Direct simulations of the modeled Boussinesq equations are performed at $\mathrm{Pr}=7$, $\tau=0.01$, and $\mathrm{R}_\rho=1.2$, with matched domain, grid, amplitude, boundary treatment, and analysis measures. The imposed spectra are high-annulus, low-mode, and mixed; a second mixed realization tests robustness. The imposed spectrum selects distinct routes to vertical exchange. High-annulus roughness remains compact and branch-locked through $t=60$, without a tracked broad-branch transition. Low-mode roughness begins on the broad branch, produces the strongest salinity transport at $t=45$, and reaches the finite-depth boundary region first. Mixed roughness follows a velocity-led pathway: vertical velocity selects the broad branch before salinity, while salinity develops the richest planform spectral population. At $t=45$, the mixed salinity effective mode count is $86.66$, compared with $3.26$ for high-annulus forcing and $5.46$ for low-mode forcing. Angular and signed-branch measures show branch-dependent diagonal organization, and probe/volume measures show that local plume-passage asymmetry does not imply large global upper/lower imbalance. The replicate preserves the mixed route with shifted transition times. Thus a finite-depth thermohaline interface can retain spectral memory, controlling whether salt-finger mixing remains localized, penetrates rapidly, or forms a scalar-rich plume forest through delayed modal handoff.

Electron transport in a Hall thruster is investigated at the test particle level using prescribed electric fields derived from the electron drift instability (EDI) resolving three dimensional particle-in-cell (PIC) simulation. Four primary electric field configurations are considered: static averaged field, full PIC field, proper orthogonal decomposition (POD) reconstructed field, and a simplified analytical Ey field. Two additional control cases, the magnetic-field-only case and the denoised PIC field, are also included. Transport statistics show that the averaged field produces only weak axial cross field transport. In contrast, the full PIC field produces clear fluctuation driven transport, characterized by pronounced negative axial displacement, enhanced channel entry, finite channel residence, particle energization, and appreciable anode directed loss. POD analysis shows that the transport relevant EDI electric field structures are distributed over multiple coupled modes, and that a moderate truncation order of approximately 20 modes is required to recover the main transport signatures. The analytical Ey model separately examines the influence of azimuthal electric field fluctuations on axial electron transport and shows that fluctuation amplitude is a primary determinant.

Topology has become a central concept in understanding physical phenomena, leading to important advances in condensed matter and photonics. Recent work has established a universal upper bound on the energy gap of Chern insulators in electronic systems, revealing a fundamental connection between topology, quantum geometry, and optical absorption. Here, we generalize this framework to photonic systems, deriving rigorous upper bounds on gap Chern numbers without requiring explicit topological analysis. Our approach enables the estimation of the topological charge of band gaps in both dispersive and nondispersive regimes.

The recently developed Q-variable formalism generalises the Elsässer representation by providing a wave-aligned representation applicable to a broad class of magnetohydrodynamic disturbances, including Alfvénic, fast, slow, and kink waves. While this framework has proven useful for the study of wave dynamics and turbulence, its behaviour in the presence of plasma discontinuities has not yet been established. In this work, we derive the complete set of Rankine-Hugoniot jump conditions in terms of the Q-variables by rewriting the ideal MHD equations in a form suitable for shock-frame jump analysis. This yields explicit jump relations for mass, momentum, magnetic flux, and energy. We then demonstrate analytically that these relations are exactly equivalent to the classical MHD Rankine-Hugoniot conditions. This reformulation provides a wave-aligned representation of MHD discontinuities and offers a natural framework for discussing directional wave content and branch-restricted limits when $\alpha$, the wave-branch parameter entering the Q-variable definition, is chosen consistently with the relevant characteristic speed. The resulting formulation is well suited for the analysis of wave-shock interactions in magnetised plasmas, with potential applications to the solar wind, magnetospheric systems, and large-scale models of structured plasma environments such as UAWSOM.

Small quantitative structure-activity relationship (QSAR) studies are difficult when close molecular analogues have different activity labels. This paper asks whether a quantum kernel can add similarity information to a Morgan/Tanimoto fingerprint model, and which molecules account for the change. QBioFusion-QSAR uses quantum multiple kernel learning (QMKL): a support vector machine combines a Morgan/Tanimoto kernel with a quantum fidelity kernel constructed from fold-local components derived from RDKit and Mordred descriptors and Deep-PK features. Linear and radial basis function descriptor kernels are included as classical controls. On the 54-molecule PsychLight-A benchmark, Morgan/Tanimoto was the strongest single representation. In the primary stratified five-fold evaluation, QMKL increased accuracy from 0.815 to 0.833 and Matthews correlation coefficient (MCC) from 0.613 to 0.645. Matched-regularization auditing attributed the change to N-Me-5-HT and N-Me-tryptamine changing from false-negative to true-positive predictions; activity-cliff subset MCC increased from 0.07 to 0.22. Repeating the five-fold protocol over ten random partitionings showed that learned QMKL did not exceed Morgan/Tanimoto on mean MCC; paired held-out bootstrap intervals for the matched comparison also span zero. These results support QBioFusion-QSAR as an auditable QMKL framework for identifying localized residual quantum-kernel contributions in small-data, activity-cliff-aware ligand classification.

The spatio-temporal evolution of the current filamentation instability in a relativistic beam--plasma system is studied analytically and with two-dimensional particle-in-cell simulations. A partial differential equation for the transverse vector potential is derived for a sharp-front relativistic beam entering cold, unmagnetized plasma, including a second-order spatial derivative term that governs the spatial growth near the beam front. The equation is solved analytically for constant, linearly growing, and oscillatory initial noise when this term is neglected, and numerically when it is included, as no closed-form solution then exists. For constant initial noise, the numerical solution reproduces the simulated magnetic-field structure, unlike the analytical solution without the term. This shows that the longitudinal field modulation is intrinsic to the instability, present even for a noise with constant amplitudes. The noise profile as well can influence the spatial-temporal evolution of the instability, which we discuss further considering linearly growing and oscillatory noise. The field grows spatially behind the beam front and saturates at a length $L_{\mathrm{sat}}\propto(v_{0b}+2)v_{0b}/\gamma_{0b}^{3}$, where $v_{0b}$ and $\gamma_{0b}$ are the beam velocity and Lorentz factor, beyond which growth is purely temporal. The saturation length increases linearly in time at a constant rate $\mathrm{d}L_{\mathrm{sat}}/\mathrm{d}\tau\approx0.42\,c$, matching the analytical estimate. The temporal growth rate remains unchanged, so the term modifies the spatial transport of the instability rather than its local amplification. For beam velocities above $0.6c$, the model deviates from the simulations as oblique modes and nonlinear filament dynamics outside the single-mode treatment become important.

Purpose: Physics-Informed Neural Networks (PINNs) struggle with stiff, regime-changing transport equations due to instability, loss imbalance, and violations of physical consistency. This paper investigates these failures through the Marshak wave equations - a canonical benchmark from radiative transport - where initial and boundary conditions differ by up to 12 orders of magnitude, and proposes targeted modifications to the standard PINN framework to overcome them. Design/methodology/approach: Three modifications are introduced: (1) a ScaledSigmoid final activation enforcing physical bounds and positivity of the unknowns; (2) a logarithmic MSE loss replacing the standard quadratic loss for initial and boundary conditions, enabling training across extreme scale disparities; and (3) explicit enforcement of global conservation laws derived from the governing equations as an additional physics loss term. Monte Carlo sampling with exponential time weighting is used throughout. Findings: The proposed framework successfully recovers the Marshak wave dynamics - including the hot, cold, and wave-front regions - in agreement with a reference Implicit Monte Carlo solution, with run times under 30 minutes. Ablation studies confirm that each ingredient is essential: linear activation, absence of the logarithmic loss, or removal of the PDE term each independently cause the method to fail qualitatively. Originality/value: This work identifies and resolves three concrete failure modes of standard PINNs on stiff hyperbolic systems with nonlinear coupling. The combination of bounded activations, scale-aware loss functions, and conservation law enforcement constitutes a novel and practically validated framework, with applicability to radiative transport and other coupled stiff PDE systems in engineering.

The computational discovery of phonon-mediated superconductors is hindered by the prohibitive cost of density functional perturbation theory (DFPT). Here, guided by the metallized $\sigma$-bonding picture, we introduce the $\sigma$-bonding density of states ($\sigma$DOS) as an efficient physical descriptor to identify high-transition-temperature ($T_{\mathrm{c}}$) superconductors from density functional theory (DFT)-level electronic structure without explicit DFPT calculations. The evaluation of $\sigma$DOS can be further accelerated by a deep-learning DFT Hamiltonian method, enabling efficient large-scale screening for superconductors. Screening 2 million materials, we identify B$_{13}$Se as an ambient-pressure superconductor candidate with predicted $T_{\mathrm{c}} > 40$~K, together with a family of high-$T_{\mathrm{c}}$ B$_{13}X$ candidates, supporting the effectiveness of this discovery strategy. By bridging physics priors with AI acceleration, this study delivers an efficient and generalizable route for computational materials discovery in the AI era.

Conditioning, the progressive increase of voltage-holding through the controlled application of fields, is an important and widely used process for bringing high-field and high-voltage devices up to their full operating parameters. Here, a study is presented on how conditioning can vary within a device, specifically, when there is a spatial variation in the surface electric field. What has been observed in high-field pulsed direct-current electrodes and radio-frequency structures is that locations exposed to higher fields exhibit a greater tendency to breakdown, but this increase is counteracted by an increased conditioning rate. This interplay explains the observed breakdown locations and provides important insights into the mechanisms underlying both conditioning and breakdown. This study combines Monte Carlo simulations with experimental results from pairs of high-field electrodes with a radially varying surface electric field. Results are presented from a high-field pulsed DC system, in which the position of each breakdown during conditioning was recorded by triangulation using a pair of cameras, and the results are compared with Monte Carlo simulations.

A semi-local Floquet extension of a uniform-field kinetic electron drift instability (EDI) dispersion relation is developed to assess prescribed azimuthal magnetic-field modulation as a linear pre-screening tool for Hallthruster high-frequency instabilities. The uniform kinetic response is used as a local spectral kernel, while a sinusoidal magnetic modulation couples Floquet sidebands and replaces the scalar dispersion condition by a finite matrix dispersion problem. The numerical procedure combines scalar uniform-field predictors, determinant correction, singular-value diagnostics, sideband-weight analysis, and truncation checks. Because a single Floquet root contains multiple physical wave numbers, stability is assessed with the upper growth envelope over the Bloch zone rather than with an individual projected azimuthal wave-number branch. Parameter scans over modulation wavelength and amplitude show that sinusoidal azimuthal magnetic modulation broadens the coupled spectrum and redistributes unstable growth among low-wave-number modified-two-stream-like and cyclotron-resonant ranges. Some long-wavelength, moderate-to-large-amplitude cases reduce integrated positive growth measures, but these reductions are not accompanied by robust suppression of the peak growth envelope. No tested case produces a finite stable Bloch interval. Within the present cold-ion semi-local Floquet model, prescribed azimuthal magnetic modulation is therefore better interpreted as a spectral-redistribution mechanism than as a robust linear stabilization mechanism by itself.

Imaging objects hidden outside the direct line of sight expands the effective field of view and is critical for applications such as autonomous driving and robotic perception. Despite impressive progress in time-of-flight (ToF)-based non-line-of-sight (NLOS) imaging, real-world deployment remains challenging because practical measurements are often collected over spatially limited, arbitrarily shaped relay regions-conditions that violate the planar-wall and dense-sampling assumptions made by most existing methods. To address these limitations, we propose a LOS-guided NLOS imaging pipeline that imposes no geometric assumptions on the relay surface and naturally supports both confocal and non-confocal configurations. Our method represents the hidden scene using 3D Gaussian primitives and couples them with an efficient, differentiable transient rendering model, enabling end-to-end optimization directly from measured transients. We validate our approach on real-world measurements from both a public dataset and a custom-built capture system. Across settings, our method achieves state-of-the-art reconstruction fidelity under spatially limited, sparsely sampled conditions, and significantly outperforms existing methods on complex, arbitrary relay surface geometries.

Precise pointing control is a critical requirement for interspacecraft laser interferometry, as angular misalignment introduces measurement noise and even leads to laser link loss. We present a nested control architecture that uses differential wavefront sensing signals to drive a fast steering mirror (FSM) to track the incoming beam, while feeding the FSM's angular changes back to the attitude and orbit control system (AOCS) to suppress angle-dependent optical path variations. This scheme is experimentally validated in our hexapod-based setup. Relative to standalone FSM actuation, the nested configuration enhanced pointing stability by 6.9 dB and 4.9 dB in the horizontal and vertical directions across the frequency band from 3 mHz to the AOCS actuation's unity-gain frequency. Additionally, tilt-to-length coupling was suppressed by an order of magnitude below 6 mHz and by two orders of magnitude below 0.45 mHz. These results demonstrate the feasibility of nested active pointing control for future interspacecraft laser interferometry missions.

Subgrid-scale orography remains a leading source of uncertainty in numerical modeling because terrain spectra must be recovered from irregularly sampled elevation data and then reduced to a flow-dependent launch budget for parameterizations. Existing approaches are limited either by assuming regular samples on rectangular grids or by fitting coefficients whose truncation and regularization effects become embedded in the spectrum. None achieves dynamic, flow-dependent truncation. This study introduces an Elastic Non-Uniform Fast Fourier Transform (ENUFFT) framework that computes local Fourier coefficients directly from irregularly sampled orography on unstructured grids, without interpolation or fitting. It combines a type-1 NUFFT with local windowing, quadrature weights, and a new Elastic Mode Selection (EMS) algorithm for retaining a local flow-dependent subset of modes. ENUFFT is compared with the strongest relevant existing method in a monochromatic and a real Alpine terrain test. In both cases, it recovers peak amplitude and direction comparably while significantly compacting the spectra (monochromatic ~25%, Alpine ~60%). It also satisfies the Parseval condition more closely with its spectral variance (energy) deviating from reference by ~14-24% versus ~500-122,000% for the existing method. Its EMS is additionally tested in a mountain-wave test where it reduces the launch spectrum by >=75% while keeping launch-power loss <=7%. Along with better compute scaling, ENUFFT is thus a computationally efficient, physically interpretable framework that can make Fourier-based orographic source descriptions practical for spectral-budget-aware parameterizations.

High-throughput computational screening of nanoporous materials for gas storage and separation requires fast and accurate characterization of adsorption equilibrium. Particle-based grand canonical Monte Carlo (GCMC) and density-based classical density functional theory (cDFT) provide simulation-based estimates of gas uptake and adsorbate density fields, but their speed-accuracy tradeoff remains insufficient for large-scale screening. In this work, we address this gap with Multi-fidelity Amortized Density Field for Adsorption in Nanoporous Materials (MADField), which reframes adsorption prediction as equilibrium density-field estimation. MADField learns from two complementary fidelities, combining broad and scalable cDFT density supervision with higher-fidelity GCMC density labels, and recovers gas uptake by integrating the predicted density field. MADField improves uptake accuracy over the strongest baselines by 6.0x for cDFT and 15.4x for GCMC, and its predicted fields accelerate cDFT solvers with 2.0x fewer iteration steps while recovering 42 percent of cases that fail under standard settings. Finally, we evaluate MADField for conventional CH4 working capacity screening on the 270k-structure ARC-MOF database. Within this space of extremely rare high-capacity targets, 167 in total, the model achieves 56x higher average precision than the strongest baseline and accelerates inference by five orders of magnitude compared to GCMC. By prioritizing the MADField rankings, selecting the top 1.7 percent of candidates recovers 95 percent of all targets, while the top 6 percent ensures 100 percent recall.

We investigate the role of the electronic structure of the projectile in ionization and subsequent fragmentation of CO$_2$ induced by multielectron capture in collisions at 0.31 a.u. impact velocity. Focusing on the $\text{CO}_2^{3+} \rightarrow \text{O}^+:\text{C}^+:\text{O}^+$ break-up channel as a representative channel, we report kinetic energy release distributions (KERDs) for collisions with equi-velocity N$^{q+}$ and O$^{q+}$ projectiles. We consider two complementary categories of measurements. In the first category, in which different projectiles of the same charge are considered, we find that KERDs obtained with N$^{q+}$ and O$^{q+}$ impact ($q=4,6$) are broadly similar, but they differ significantly from the earlier reported KERD with Ar$^{q+}$ impact. In the second category, pronounced differences are observed between the KERDs obtained with isoelectronic N$^{q+}$ and O$^{(q+1)+}$ ($q=3,5,7$) projectiles. These results provide direct evidence that projectile electronic structure plays a critical role in multielectron capture collisions.

The Kelvin-Helmholtz instability (KHI) is a major driver of multiscale plasma dynamics at velocity shear layers, where it can promote the formation of current sheets and the onset of magnetic reconnection as well as drive plasma energization. While recent kinetic studies have shown efficient electron heating during nonlinear KH evolution, the connection between reconnection dynamics and localized electron energization is still not fully understood. We investigate this link using two-dimensional fully kinetic simulations of KHI developing in a double-periodic system with two velocity shear layers and a uniform guide field, initialized from a finite-Larmor-radius equilibrium. During the nonlinear stage, initially coherent vortices evolve into layers populated by fragmented current sheets displaying reconnection activity. The global energetics reveal species-dependent energization pathways. Ions act as the primary energy reservoir, transferring energy to the electromagnetic fields, while electrons receive the dominant net positive energy input. Electron energization is strongly anisotropic ($T_{\parallel,e} > T_{\perp,e}$) and localized within intermittent current sheets associated with enhanced field-particle energy exchange and elevated agyrotropy. These regions also show the development of suprathermal tails in the electron energy distributions, providing evidence for nonthermal electron energization. Despite opposite vorticity orientations, the two shear layers exhibit similar statistical behavior. Together, these results establish a direct connection between reconnection-associated current structures and localized electron energization in collisionless KHI dynamics.

Satellite observations in the solar spectrum (including visible and near-infrared channels) offer high-resolution information on clouds and atmospheric properties valuable for data assimilation. While forward operators for a direct assimilation of solar images have become available recently and a first visible channel is already used operationally, their assimilation remains challenging due to strong non-linearities, ambiguities and high inter-channel correlations. This study addresses two central questions: what is the potential impact of assimilating multiple solar channels jointly, and can observed reflectances be transformed into physically meaningful, uncertainty-quantified variables better suited to assimilation than the raw reflectances themselves? As a proof of concept, we assess the joint information content of six solar channels from the Flexible Combined Imager (FCI) onboard Meteosat Third Generation and introduce a novel "Backward Operator" (BO) for probabilistic retrievals of cloud-related variables. The BO is implemented in a machine learning approach as a distributional regression network that is trained on synthetic images from a NWP regional model run and produces multivariate Gaussian estimates of total optical thickness, column cloud fraction, ice fraction, and effective radii of water and ice. The BO predictions are unbiased and well-calibrated, with realistic, situation-dependent and non-trivial covariance structures. The retrieved variables can be overall usefully constrained. Despite strong inter-channel correlations, combining multiple channels yields substantial performance improvements. As the BO does not require prior information, is consistent with an existing forward operator, and yields cloud variables more linearly related to the NWP model state, assimilating these variables could be a viable alternative to direct reflectance assimilation.

We present the characterization of two fast, crystalline inorganic scintillators, silicon-doped gallium nitride (GaN:Si) and gallium-doped zinc oxide (ZnO:Ga), and compare their performance with cerium-doped yttrium aluminium perovskite (YAP:Ce) for in-vacuum alpha-detection applications that require high-performance timing, position, and energy resolution, such as 3D elemental mapping, medical imaging, and homeland security applications. In this paper, we propose ZnO:Ga and GaN:Si as high-performance drop-in replacements for the alpha detector in Associated Particle Imaging (API) systems. However, the results reported here also have wide applicability. Prior work has reported on polycrystalline forms of ZnO:Ga, which suffer from self-absorption. To our knowledge, GaN:Si has not been proposed to be used in API systems. We present room-temperature scintillation time constants obtained via X-ray-induced time-correlated single-photon counting for both proposed materials. They both exhibit exceedingly fast rise times of <15ps, and high brightness >1000ph/MeV with resolved alpha-peaks. Single-crystal ZnO:Ga and single-crystal GaN:Si yield single-component decays of 805ps and 32ps, respectively. Using a plastic scintillator reference setup, coincidence timing resolution (CTR) and detector timing resolution (DTR) measurements demonstrate a >3x improvement in timing resolution compared to traditional YAP:Ce. GaN:Si and ZnO:Ga exhibit (35(9))ps and (49(5))ps DTR, respectively, compared to(144(2))ps for conventional, single-crystal YAP:Ce. Finally, we evaluate their position resolution in an experimental setup designed for API and measure better than 0.2mm for YAP:Ce and approximately 1mm for GaN:Si. We obtain a position resolution of 0.3mm for ZnO:Ga from simulations. We also present alpha-induced ionoluminescence emission spectra that reveal direct, red-shifted near-bandgap emission.

The modeling of large collections of interacting meta-atoms remains a central challenge in photonics because it requires the simultaneous treatment of complex scatterer geometries, multiple scattering, and heterogeneous environments. Here, we introduce the dressed Global Polarizability Matrix (dGPM), a reduced-order electromagnetic framework in which complex meta-atoms are represented by compact scattering operators that explicitly incorporate the influence of nearby interfaces. The dGPM accurately reconstructs near- and far-field electromagnetic responses for interface-coupled structures, including geometries beyond the practical applicability of conventional T-matrix approaches. The framework naturally combines interface-coupled and free-space scatterers within a unified multiple-scattering formalism and enables simulations of large ordered and disordered metasurfaces. More broadly, the dGPM extends operator-based electromagnetic modeling to heterogeneous photonic environments while preserving a compact scatterer-based representation, providing an efficient framework for the simulation and design of large-scale photonic systems.

In many machine learning applications, molecules need to be transformed into representations, i.e. mathematical objects. Those representations are typically considered to be property-agnostic and as such are expected to be over-complete: for different physical properties, different parts or the representation may be relevant. In this work, we propose a method to sub-select and re-weight the representation by adapting it to the property in question. We find that in most cases this makes representations shorter and more accurate at the same time. The feature selection itself uses cheap semi-empirical data instead of high-quality labels. We study four properties (total energy, heat capacity, dipole moment, and polarizability) for three representations (cMBDF, FCHL19, and MACE-MP-0 descriptors) on two datasets (QM9 and VQM24). We can reduce the number of dimensions of a representation in the median by 72\,\% (range 36-98\,\%) while retaining the accuracy. Tuning for accuracy instead we can increase the learning efficiency for dipole moments such that the same accuracy can be reached with 19\,\% of the training data. Our approach yields data-driven interpretations of feature importance, lossless compact representations, and increased data efficiency, requiring only expendable surrogate data.

Characteristics of a 9 MeV electron beam were investigated, to evaluate potential benefits for patients with extensive mycosis fungoides lesions that are deeper than commonly treated with 6 MeV total skin electron therapy (TSET or TSE/TSEI/TSEB/TSEBT). A comprehensive commissioning measurement program was completed for TSET delivery using high-dose-rate 6 MeV and 9 MeV electron beams from a Varian TrueBeam linac. Various phantoms and dosimeters were set up 300 cm from isocenter, behind a 6 mm PMMA spoiler screen, and used for optimising beam-pair gantry angles, measuring depth-dose, lateral and horizontal profiles and B-factors, as well as performing end-to-end tests. The key clinical distinctions observed for the 9 MeV TSET beam pair compared to the 6 MeV TSET beam pair were that the 80% dose depth increased to nearly 1 cm and the practical range increased to 3.5 cm, while the maximum dose remained within 0.2 cm of the surface. Vertical and horizontal dose profiles measured with the two energies were almost indistinguishable. The 9 MeV TSET beam has been shown to achieve greater depth penetration compared to the commonly used 6 MeV TSET beam, without detrimentally affecting the dose uniformity achievable in the patient plane. TSET treatments with 9 MeV electrons may be advisable for patients with extensive lesions that are too deep for effective treatment with a 6 MeV beam.

Strong reflections from metallic and engineered substrates severely limit label-free functional imaging of living cells at biomaterial interfaces, neural electrodes, and implantable devices. Here we introduce two complementary approaches for recovering intracellular dynamic contrast at highly reflective interfaces. Dynamic dark-field full-field optical coherence tomography (D-dFFOCT) suppresses the dominant substrate reflection and restores intracellular visibility through selective detection of scattered light. In parallel, asymmetric illumination generates a distinct directional dynamic contrast that is most consistently interpreted as dynamic reflection differential phase contrast (D-RDPC). Both approaches reveal intracellular activity that remains poorly visible with conventional dynamic full-field optical coherence tomography. D-RDPC exhibits characteristic signatures of phase-gradient imaging, including contrast reversal upon illumination inversion, enhancement with increasing illumination asymmetry, and recovery of spatial localization through directional Hilbert-transform reconstruction. Together, these results establish new strategies for functional imaging at reflective interfaces and suggest that differential phase contrast signals can support temporal fluctuation analysis.

In external beam radiotherapy, 3D scanning water phantoms are the gold standard for obtaining relative dosimetry data. These phantoms, consisting of a water tank and mechanical arm, along with accompanying software, are de-signed to acquire dose profiles along and orthogonal to the beam axis. In brachy-therapy, the acquisition of analogous dose profiles is more difficult, and is generally achieved with complex custom-built phantoms or chemical dosimeters such as film or gel. In this study, a low-cost 3D-printed jig was designed and fabricated within a clinical department, to allow precise brachytherapy dose measurements using a PTW BeamScan water phantom. Specifically, this jig al-lowed applicators to be suspended securely and reproducibly within the water phantom. Protocols were developed to relate the scanning system coordinates to the physical source position, and to obtain isodose planes both parallel and radial to the source axis. The developed solution has the potential to be used for physical verification of TG43 dose calculation parameters (e.g. anisotropy functions), the characterization of dose for a single dwell position in a complex applicator containing non-water equivalent materials, or the collection of point dose measure-ments for treatments incorporating multiple dwell positions or catheters.

Two factors that influence the oscillation mechanism of a sonic fluidic oscillator are investigated: the geometry of the feedback channel connections (control ports) and the influence of flow restrictions in the oscillator outlets. Phase-averaged planar PIV measurements are performed inside the oscillator, synchronised with unsteady pressure measurements, and analysed using space-only proper orthogonal decomposition (POD). The POD analysis reveals two coupled modes: a Sweeping Mode capturing lateral jet displacement and a Bending Mode capturing jet curvature during switching, the latter being the primary driver of outlet mass flux modulation. Flow separation at the control port entrances is shown to throttle the feedback flow and progressively limit oscillation strength at higher inlet flow rates. Restrictive outlet paths induce a differential back pressure that is shown to cause the jet to separate from its attachment wall and bend towards the splitter tip (`secondary separation'). The secondary separation reduces the differential outlet mass flux and introduces a flow curvature that limits the upstream propagation of the back pressure and thus shields the primary jet attachment. The consequence of these effects is that strong oscillations are sustained down to the smallest outlet apertures investigated. The principal contribution is to demonstrate that the assumed coupling between upstream jet attachment and outlet flow split is broken when the outlet aperture is reduced, with significant implications for the design of fluidic oscillators operating with downstream flow impedances.

When non-melanoma skin cancers extend over large areas of skin, effective radiotherapy treatments can be delivered using volumetric modulated arc therapy (VMAT) beams with narrow segments that rotate around the affected surfaces. For lower-limb carcinoma treatments, careful immobilisation is needed to achieve the degree of reproducible and stable positioning required to ensure the narrow field segments treat the targeted tissue and avoid underlying anatomy. To meet this need, a 3D printed patient-specific foot support was created in the form of a solid box containing a deep ``footprint'', shaped to match the outline of the patient's relaxed foot when lying supine, supported by a vacuum bag. For our first patient treated with a 3D printed foot support, image guidance data and clinical notes were evaluated against corresponding information from all recent lower-leg VMAT treatments. The patient feedback was recorded as ``good'', with no complaints of discomfort or poor fit, and the proportion of treatment fractions requiring shifts greater than 5 mm after setup imaging (20\%) compared favourably to the proportions for patients without a patient-specific foot support (23\%-84\%). The patient-specific foot support was particularly useful for minimising longitudinal shifts and leg rotations. Evidently, 3D-printed patient-specific immobilisation devices have the potential to enhance positioning stability, and therefore potentially improve accuracy and effectiveness of VMAT treatments, for patients with extensive lower-limb carcinomas.

Phase imaging applications such as QSM are highly sensitive to noise amplifications, phase singularities, and other artefacts, particularly in challenging scenarios such as ultra-high field (7T), under-sampled or single-echo acquisitions. We present a novel image reconstruction method, MORSE-PI, designed to produce high-SNR, artefact-free, and singularity-free phase images for both structural and functional phase-based brain imaging. MORSE-PI extends our previous approach, MORSE, by introducing a Virtual Reference Coil (VRC). The VRC is constructed as a linear combination of coil sensitivity maps, with correlations enhanced between coil elements using the noise covariance matrix. Such a VRC ensures robust signal support across the entire brain and is used to correct phase offsets in the MORSE-derived coil sensitivity estimates, resulting in artefact-free, high SNR phase. Compared to GRAPPA with ASPIRE phase correction, MORSE-PI demonstrates greater robustness to artefacts such as noise amplification and aliasing, and shows improved reproducibility in structural imaging at both 3T and 7T. Unlike ESPIRiT and GRAPPA combined with adaptive coil combination methods, MORSE-PI yields singularity-free phase maps. MORSE-PI enables high-SNR reconstructions even for the most challenging scenarios, such as single-echo EPI at 7T. Its efficient, containerised implementation using the Gadgetron framework supports deployment on the MRI scanner console during measurements. MORSE-PI offers a flexible and computationally efficient solution for generating high-SNR, artefact- and singularity-free phase images in both single- and multi-echo GRE and EPI acquisitions. This makes it particularly well-suited for structural and functional QSM, as well as other phase-based MRI applications. Its robustness and rapid computational time facilitate efficient deployment on scanners across field strengths.

Terrestrial coherent FSO requires mitigating turbulence-induced coupling losses. We experimentally validate a "Digital Twin" channel model against fading statistics from a 4.6-km link. Combining long-term scintillometer data with mode-diverse receiver simulations, we demonstrate that a 6-mode receiver reduces turbulence-induced outage probabilities to 2.02e-5.

Integrated superconducting spectrometers (ISSs) provide the instantaneous bandwidth, sensitivity, and scalable architecture for large-scale spectroscopic surveys in submillimeter-wave astronomy and cosmology. However, the accuracy with which the resonant frequencies of superconducting microstrip band pass filters can be spaced, has limited the spectral resolution for these spectrometers with continuous spectral coverage to $\frac{F}{\Delta F} < 500$. The origin of this frequency scatter has been largely unknown. In this work, we demonstrate a four-fold improvement in the frequency spacing of superconducting microstrip resonators by optimizing electron-beam lithography. We find that reducing the beam step size (BSS) on the nanometer scale reduces the random frequency scatter, and that avoiding main-field stitching across filter patterns can eliminate a systematic frequency shift between groups of resonators, indicating the different origins of these two modes of frequency deviation. These findings demonstrate that nanometer-scale control of lithographic processes is imperative for the realization of the next-generation integrated superconducting spectrometers with higher spectral resolution.

Unspecific peroxygenases (UPOs) are promising biocatalysts that selectively oxyfunctionalize saturated hydrocarbons using only hydrogen peroxide as a co-substrate. Peroxide-induced enzyme inactivation makes targeted enzyme engineering essential to mitigate this effect and also enhance catalytic performance. To meet this need, systematic approaches are used, including extensive database studies for rational enzyme design, as well as computational enzyme engineering. In this study, we followed the latter strategy and explored the possibility for computationally-guided modification of UPOs. Specifically, our focus was on uncovering the influence of active site amino acids on the catalytic activity of the enzyme CviUPO. Two mutations were introduced close to the active center, and the changes in the energy barriers leading to the activated complex were investigated in detail by Quantum Mechanics/Molecular Mechanics Nudged Elastic Band simulations. Our studies revealed that a change of the glutamic acid, assisting the catalytic cycle, by the shorter aspartic acid, leads to an increased reaction barrier, probably decreasing the catalytic activity of the enzyme. Exchanging the heme-anchoring cysteine group by a histidine exhibited promising behavior as the energy barriers decreased significantly. However, it is possible that the histidine modification also alters the reaction behavior of the peroxygenase, turning it into a peroxidase, an aspect that so far could not be confirmed beyond doubt. Simulations alone cannot conclusively determine whether substrate specificity and reactivity are maintained in the modifications tested. Nevertheless, our results highlight the importance of spin states and active pocket hydration for the catalytic reaction and demonstrate why a synergistic approach of theoretical predictions and experimental verifications is required for efficient enzyme engineering.

Heat transport in fractured media is governed by coupled thermal-hydraulic (TH) processes. This study evaluates TH processes at fracture intersections, focusing on T-intersections where one horizontal fracture is subjected to a pressure gradient while the other forms a vertical dead-end fracture. Using numerical simulations, we investigate the influence of the inlet velocity, thermal Péclet, and Rayleigh numbers, and the impact of a pressure gradient along the T-intersection, on the resulting heat transport. The model domain consists of a fluid and a solid region. Fluid flow and heat transport in the fractures are described by the conservation equations for mass, momentum, and energy. The rock matrix is considered impermeable, therefore, it is governed by heat conduction. The simulations consistently show that heat transfer from the fluid to the matrix is enhanced when fluid flow occurs within the dead-end fracture, since such fluid flow maintains a higher temperature difference between the matrix and the fluid. This flow arises either from buoyancy-driven natural convection due to temperature-dependent fluid density or from a pressure gradient imposed by the orientation of the dead-end fracture with respect to the flow direction in the horizontal fracture. Natural convection dominates at high flow rate, Rayleigh, and Péclet numbers, whereas pressure-driven flow becomes the controlling mechanism for an increasing deviation from the orthogonal configuration of the two fracture planes and under higher flow rates. At low flow rates, Péclet, or Rayleigh numbers, no flow develops in the dead-end fracture, and heat transport in the dead-end fracture becomes conduction-dominated.

Multi-aperture reception can provide spatial diversity in free-space optical (FSO) communication by collecting signal replicas at separate apertures. When the branches are accurately phase-aligned, their received optical fields can also be added constructively to obtain coherent-combining gain. In this paper, we propose blind gradient-ascent phase alignment (BGAPA), which iteratively adjusts one phase correction per aperture by directly maximizing the combined output power. Closed-form analytical gradients provide a deterministic update that requires no symbol decisions, unlike the stochastic perturbation-based estimate of SPGD or the decision-directed feedback of DD-LMS. To isolate phase-tracking capability, the numerical model includes independent aperture-dependent phase disturbance but excludes amplitude scintillation and polarization-dependent distortion. Under this controlled phase-only setting, BGAPA obtains an SNR improvement closer to the ideal 6.02~dB coherent-combining gain than block-wise cross-correlation, SPGD, DD-LMS, and CMA/RDE-based equalization when the aperture count is increased by a factor of four. In particular, increasing the aperture count from 64 to 256 yields an SNR improvement of about 5.7~dB. In a separate amplitude-tolerance test with $N=16$ and $f_{\max}=1$~MHz, the first observed BGAPA trial above the HD-FEC threshold of $3.8\times10^{-3}$ occurs at an actual phase RMS of approximately 278~rad, whereas DD-LMS becomes unreliable at substantially smaller phase excursions. The reported step size is optimized separately at each operating point. BGAPA is fully blind and updates its phase parameters directly from the received aperture fields without training symbols, pilots, or decision-directed feedback.

In this work, the geometric electromagnetic Particle-in-Cell (PIC) framework, GEMPICX, is extended to solve the quasineutral, fully kinetic Vlasov-Maxwell equations on dual grids using mimetic finite differences. The discrete action principle is derived, taking into account the duality between the grids. The temporal derivative of the electric field does not directly appear in the dynamical system for the quasineutral model. Hence, a discretized curl-curl equation is used to implicitly obtain the electric field at every time-step. This also circumvents the need to obtain electric potentials. A Lagrange multiplier is used to maintain the discretized divergence of the current density at machine zero.

We extend the geometric electromagnetic particle-in-cell (PIC) framework, GEMPICX, to solve the quasineutral hybrid Vlasov-Maxwell equations with drift-kinetic electrons and fully kinetic ions. A structure-preserving finite difference method that employs dual grids is used. The discrete action principle for the hybrid model is derived, using the dual nature of the grids. The dynamical system for this hybrid quasineutral model does not explicitly involve the temporal evolution term for the electric field. A curl-curl equation is therefore used to implicitly obtain the component of the electric field that is parallel to the background magnetic field, at every timestep. The perpendicular component of the electric field is obtained using the quasineutral Ampere's equation without the displacement current, combined with the definition of the current in the drift-kinetic model. The discretized versions of the electric field equations are large, sparse linear systems. A fully explicit time-stepping scheme as well as two implicit-explicit (IMEX) schemes are tested. The numerical model is validated by verifying the various waves obtained from the dispersion relation.

Microcombs in normal-dispersion photonic crystal ring resonators (PhCRs) are a versatile building block for next-generation integrated photonic circuits, yet they inherently suffer from a directional bias that favors backward-propagating states. This necessitates bulky, non-integrated optical circulators for comb extraction, creating a significant bottleneck for full on-chip integration. In this work, we demonstrate a deterministic method to control and reverse this directionality through Side-mode Induced Forward Forcing (SIFF). By engineering auxiliary mode splittings on resonances adjacent to the pump, we show that the nonlinear dynamics can be steered to favor stable, forward-propagating platicon states. We establish an optimal synchronization condition between the pump and side-mode coupling rates that ensures forward-comb dominance across a wide parameter range. Our findings, validated both numerically and experimentally, provide a critical pathway for circulator-free, integrated normal-dispersion microcombs, offering a scalable architecture for compact telecommunications and sensing systems.

Photomultiplier tubes (PMTs) are widely used in neutrino experiments. As a new-generation neutrino observatory, JUNO requires an excellent energy resolution of 3% at 1 MeV. This will be realized with a 20 kton liquid scintillator detector instrumented with more than 20000 20-inch PMTs and 25600 3-inch PMTs. These PMTs were successfully installed in JUNO from October 2022 to December 2024. During the installation, seven test campaigns were performed to validate the PMT functionality, including measurements dark count rate, gain, waveform, and charge spectrum. In this paper, we present the implementation of these tests and the corresponding results for the 20-inch PMTs throughout the installation process.

Objective: Present OpenPINT (Open-source Planning for Isoeffective Nuclear Treatments), an open-source treatment planning system for nuclear therapies that integrates Monte Carlo dose calculations with modular dosimetric and radiobiological models for photon-isoeffective dose evaluation. Approach: We describe the software architecture, implementation choices, and data flow from segmented geometry and source configuration to NIfTI dose outputs. We define BNCT-relevant dosimetric metrics and evaluate the workflow with reproducible analytic and voxelized cylindrical-phantom benchmarks, supplemented by a geometric patient-positioning example. Main results: The module provides a reproducible and scriptable path for generating MCNP-ready inputs, extracting component-wise BNCT dose maps, and computing analysis-ready outputs for quality checks and decision support. Fine-resolution voxelized configurations reproduced the 1 mm analytic reference within 0.13% for the brain-limited irradiation-time endpoint, whereas the full voxelized sweep exposed deviations up to 4.42% in coarse 8--10 mm configurations. Patient-wide gamma pass rates were at least 99.60% for the evaluated mesh/interpolation cases, while low-dose DVH-tail quantities remained sensitive to boundary discretization. Significance: This first paper isolates and validates the simulation-preparation and dosimetric-analysis core of an open-source BNCT treatment-planning platform. It establishes a foundation for subsequent work on optimization, biological weighting, and clinical workflow integration.

We numerically verify multifractal theory (Frisch and Parisi 1985) for turbulence using simulation data at a high Reynolds number. First, we propose a simple method to directly estimate the multifractal dimension $D(h)$ of vortical structures with a given Hölder exponent $h$. Thus measured $D(h)$ is in good agreement with indirectly measured experimental data. Then, we demonstrate that these structures for $h\ll1/3$ form the hierarchy of coherent eddies, while those for $h\gg1/3$ are featureless.

Water confined within nanoscale capillaries exhibits phase behaviour and transport properties that differ substantially from bulk, and these effects are commonly interpreted as consequences of geometric confinement and reduced dimensionality. Here we show that confinement topology alone is insufficient to predict the behaviour of nanoconfined water. Using machine learning interatomic potentials with first-principles accuracy, we compute the density-temperature phase diagram of water confined within bilayer graphene nanocapillaries and compare AA and AB stacking arrangements, which differ only by a lateral shift of 1.4 Å. Despite this minimal structural change, AA stacking can stabilise different ice polymorphs, can increase the melting temperature by more than 100 K, can enhance proton transfer, and alters the onset of superionic behaviour relative to AB stacking. We trace these effects to stacking-induced changes in the hydrogen-bond network associated with modifications to the lateral free energy landscape and neighbouring O-O separations. Our results demonstrate that even subtle atomistic variations in the confining walls can qualitatively reshape the physical and chemical behaviour of nanoconfined water, with implications for the interpretation and control of fluids under angstrom-scale confinement.

In recent years a weak but robust response of mean midlatitude circulation to Arctic amplification (AA) has emerged from modeling experiments. However, open questions remain about the mechanisms linking such circulation differences to weather extremes in the midlatitudes. In this study we investigate such mechanisms and the importance of zonal asymmetries in shaping the atmospheric blocking response to AA. We perform idealized aquaplanet simulations in two configurations: a zonally symmetric setup and a zonally asymmetric experiment featuring a localized midlatitude storm track. For each configuration, we examine the response to AA by imposing an anomalous surface heating in the polar region. In the zonally symmetric configuration atmospheric blocking increases uniformly with AA from mid to high latitudes. In the asymmetric configuration, the response is more complex; instead of a zonally uniform response, we observe an upstream displacement of the blocking maximum, which sits at the exit of the localized storm track. We interpret these changes through the lens of the Traffic Jam theory by diagnosing the carrying capacity of the midlatitude flow. In both configurations, the zonally averaged increase in blocking is primarily driven by a weakening of the zonal winds, which reduces the Doppler-shifted Rossby wave group velocity and, in turn, decreases the flow carrying capacity. While the reduction in carrying capacity has similar characteristics in the two configurations, in the asymmetric case it leads to an upstream shift of blocking frequency as a direct consequence of the threshold behavior of blocking onset that lies at the core of the Traffic Jam theory. This mechanism, which has received limited attention so far, highlights the importance of mean circulation characteristics in shaping the blocking response to external forcing such as Arctic warming.

It has been shown in non-relativistic tearing theory that shear flows will slow the linear phase of tearing instability and can delay onset of magnetic reconnection. We find using kinetic particle-in-cell simulations that shear flow as well as guide field strength affect the onset time of relativistic magnetic reconnection. To model this we develop a numerical solver for the growth rate of the relativistic linear tearing instability, including effects of the motional electric field which has not previously been done. We find slowing of growth due to both shear flows and guide field, and at higher flow shear, transition through an intermediate regime to linear Kelvin-Helmholtz instability.

This work provides an initial demonstration of Focused Coherent Microwave Scattering (F-CMS) diagnostics technique, an extension of the conventional Coherent Microwave Scattering (CMS) technique that enables spatially resolved measurements. The spatial-resolution functionality was achieved using PTFE lenses to focus the probing microwave beam and isolate a distinct probing volume. The feasibility of F-CMS was demonstrated using glow-discharge plasma volume with an electron number density of approximately 5x10$^9$ cm$^{-3}$ as a test object. A clear F-CMS output signal was successfully detected upon the discharge initiation. These findings establish a foundation for the further development of the F-CMS technique, which has the potential to enable highly sensitive, spatiotemporally resolved measurements of electron number density in real time, without the need for signal accumulation (sensitivity down to an electron number density of approximately 10$^8$ cm$^{-3}$ is expected). Such capability will be useful for a variety of applications including diagnostics of unsteady flowfields such as those occurring in hypersonic shock tunnels and spacecraft electric propulsion systems.

Resonant photoionization in the intense high-frequency regime can exhibit extremely asymmetric Autler-Townes doublets whose origin remains debated. It has been attributed either to interference between perturbative ionization pathways or to a non-perturbative dressing of the continuum. Here we show that these interpretations arise from a common effective Hamiltonian framework, in which dressed-state stabilization is governed by the coherent interplay of resonant and nonresonant pathways. We demonstrate that further simplification, using the strong-field approximation, obscures this mechanism. In contrast, our time-dependent essential-state model, where electrons are rigorously coupled to the continuum, achieves excellent agreement with ab initio simulations of the time-dependent Schrödinger equation for helium.

Sustainability transitions unfold through interacting dynamics across social, technological, economic, environmental, and governance dimensions. However, many modelling approaches either isolate subsystems or rely on optimisation-based pathways that do not explicitly represent feedbacks, path dependence, and institutional constraints. This study develops a Sparse Orthogonal Regression Technique (SORT), a data-driven dynamical-systems framework for reconstructing multi-system transformation dynamics from harmonised European indicators. Inspired by Sparse Identification of Nonlinear Dynamical Systems (SINDy), SORT infers parsimonious cross-domain dependencies from observed data rather than specifying a full structural model ex ante. The prototype covers energy, emissions, innovation, digitalisation, resource productivity, environmental stress, policy, finance, wellbeing, and resilience. The compact dynamical system is interpreted as a structural representation of medium-term interactions, not as a forecasting tool. It reproduces differentiated empirical patterns, including steady renewable expansion, nonlinear scaling in transition finance, diffusion-like digitalisation, oscillatory environmental stress, and sustained reduction in ETS-regulated emissions. Forward simulations diverge from simple linear extrapolation where reinforcing feedbacks are present, illustrating the conditional nature of accelerated decarbonisation. By translating inferred dependencies into a feedback structure, the analysis identifies reinforcing decarbonisation sequences alongside balancing ecological constraints. The model contributes to sustainability transitions research by providing an empirically grounded representation of multi-system feedback dynamics that bridges quantitative modelling and transition theory. SORT offers a compact technique for exploratory reconstruction from real-world transition data.

For centuries, the intrinsic chaos of unsteady fluid motion has stood as a formidable barrier to long-term forecasting. While machine learning (ML) has recently emerged as a transformative paradigm for predicting flow evolution, it encounters a pervasive yet unexplained "performance wall": an inevitable deterioration in accuracy as the forecast horizon extends. Here, we demonstrate that this deterioration is not a deficiency of model architecture, no matter how state-of-the-art, but a fundamental constraint imposed by the underlying system, which can be understood through turbulence theory established decades ago. In the setting of bluff body flow, a canonical phenomenon for spatiotemporal complexity in fluid mechanics, we reveal a scaling law governing the deterioration of ML predictability, derived from a Kolmogorov-inspired framework and validated through high-fidelity simulations. Our findings establish a closed loop between the predictability ceiling and its interpretation, bridging the gap between transparent physical theories and modern black-box inference. More broadly, this work provides a theoretical compass for constructing trustworthy ML in complex dynamical systems across the physical sciences.

Precise temporal delay generation is a key requirement for asymmetric Mach-Zehnder interferometers (aMZIs) used in high-speed quantum key distribution (QKD) receivers. In this work, a compact integrated aMZI architecture based on a silicon nitride on silicon dioxide (Si3N4/SiO2) photonic platform is presented. A 3-dB directional coupler enabling accurate 50:50 power splitting at the 1550 nm telecommunication wavelength is designed and optimized. The coupling length is initially estimated from the effective index difference between the even and odd supermodes and subsequently refined using three-dimensional eigenmode expansion (EME) simulations. The optimized structure employs single-mode Si3N4 waveguides with a cross section of 1 um x 0.4 um, providing a group index close to 2 and enabling accurate delay engineering. Spectral analysis demonstrates stable 3-dB power splitting across the C-band with insertion loss below 0.5 dB and negligible power imbalance, indicating high transmission efficiency and structural symmetry. An integrated Si3N4 aMZI delay line providing a 500 ps optical delay, corresponding to a 2 GHz free spectral range (FSR), is further demonstrated. Simulations show nearly constant group delay across the 190-200 THz frequency range with sub-10 ps variation and a smooth, near-linear phase response. These characteristics enable interference visibility above 0.99, corresponding to an estimated quantum bit error rate (QBER) below 0.5 percent for gigahertz-rate time-bin QKD systems. The wideband linear phase behavior also indicates compatibility with wavelength division multiplexed (WDM) QKD architectures. The results confirm that the proposed Si3N4 integrated aMZI provides a low-loss, dispersion-controlled, and spectrally stable delay solution suitable for scalable chip-scale quantum photonic receivers.

Altermagnets host momentum-dependent spin splitting without net magnetization, a symmetry-enforced band phenomenon whose photonic analogues have so far been realized only in square lattices governed by fourfold rotation. Here we introduce a photonic altermagnet on a hexagonal lattice whose helicity splitting is governed by mirror rather than rotational symmetry. Elliptical chiral elements of alternating handedness, placed at the vertices of a regular hexagon, leave the two opposite-chirality sublattices connected only by chirality reversal combined with a mirror reflection. Full-wave simulations reveal mirror-related splitting of the two opposite-helicity branches in the band structure and isofrequency contours, with the channels exchanged when the ellipse orientation is reversed. In finite structures, this splitting separates a linearly polarized beam into handedness-resolved channels, enabling beam splitting and direction-selective helicity filtering with target-helicity output fractions above 0.85 and output paths continuously tunable through the ellipse rotation angle. These results extend photonic altermagnetism to a previously unexplored lattice-symmetry class and establish mirror-symmetric chiral textures as building blocks for altermagnetism-inspired chiral photonics.

The viscoelastic instability of cross-slot flow was first observed experimentally almost half a century ago and reproduced numerically two decades ago, yet its physical origin remains unresolved. We revisit this problem for two-dimensional creeping flow of Oldroyd-B fluid by combining direct numerical simulations, global stability analysis, structural sensitivity analysis, and energy-budget analysis. Our simulations reproduce the canonical pitchfork bifurcation, and the stability analysis consistently predicts the threshold and perturbation growth rates. The leading eigenmode consists of a chiral velocity--stress perturbation that tilts and rotates the birefringent strand generated by the extensional flow. Structural sensitivity and energy-budget analyses identify narrow high-extension-rate ridges within the extensional flow as both the spatial core and energetic source of the instability. In these ridges, the stress-based wavemaker co-localizes with large positive disturbance polymeric stress power density, indicating localized transfer of stored elastic energy to the disturbance flow. Analyses of cross-slot variants with rounded corners and with a centered cylinder further reveal that neither sharp corners nor a free central stagnation point is the essential destabilizing ingredient; rather, the instability originates from elastic-energy release in extension-dominated regions characteristic of cross-slot flow.

Modern cosmology is often taken to provide an increasingly accurate description of the universe's underlying ontology through progressively refined mathematical models. I challenge this interpretation by arguing that the empirical success of cosmology underdetermines not only ontology but also the mathematical and conceptual frameworks used to represent observational data. I propose instead that the objective content of cosmology is best identified with cross-representational informational invariants-features of observational structure that persist across empirically adequate descriptions. These invariants can be characterized in information-theoretic terms, including correlation structure, statistical distinguishability, and limits on accessible information, and formalized using tools such as the Fisher-Rao metric on model space. On this view, cosmological models are best understood as efficient encodings of observational structure rather than uniquely privileged descriptions of fundamental reality. Scientific progress, accordingly, consists not in convergence toward a fixed ontology, but in the progressive refinement of the informational structure accessible to observation.

Spontaneous breaking of time-translation symmetry and the emergence of self-sustained oscillations in quantum driven-dissipative systems is the hallmark of continuous time crystals (CTCs). An outstanding challenge is to achieve precise, coherent control of such dynamical phases, whose complex nonlinear dynamics makes them inherently difficult to manipulate. Here, we experimentally demonstrate coherent control of a solid-state CTC realized in a non-resonantly excited spin-polarized exciton-polariton condensate in a Ga(Al)As microcavity. We exploit two complementary control channels: an additional weak control laser and the optomechanical interaction with confined GHz phonons. By tuning the control laser energy and power, we stabilize distinct dynamical regimes, including frequency pushing, injection locking with continuous tuning of the limit-cycle frequency, and full suppression of the autonomous dynamics via phase locking. Under appropriate detuning conditions, the control excitation generates coherent mechanical self-oscillation through the polariton--phonon deformation-potential interaction, evidenced by spectral sidebands. The resulting dynamical back-action provides a phonon-mediated locking channel that fixes the frequency of the CTC. Together, the two channels dramatically enhance the temporal coherence of the GHz limit-cycle dynamics, as established through linewidth narrowing and time-resolved first-order correlation measurements $\gone(\tau)$. Our work establishes a way to harness the unique aspects of CTCs for practical applications in the GHz-range.

The receptivity of the inviscid flow on the stagnation streamline of a blunt body in supersonic flow is investigated theoretically for incoming freestream disturbances. The wave transmission and coupling are quantified by solving the linearized shock-fitting problem with a spectral method, whereas the steady base flow is obtained using a nonlinear shock-fitting spectral solver. Revisiting previous theoretical work, we identify and correct an error in a key coefficient in the analysis by Morkovin (J. Appl. Mech., 27, 1960), overturning the prior conclusion of body-induced damping and revealing amplification instead. The post-shock entropy disturbances display singular behavior near the stagnation point, which is treated analytically. Acoustic disturbances dominate pressure and velocity responses, while density is affected by both acoustic and entropy modes. The base flow pressure gradient introduces weak coupling between the acoustic and entropic components of the response. The actual stagnation-line base flow amplifies all disturbances more than the simplified model of uniform post-shock flow; as well as a shock without a body, and the differences are quantified for a range of Mach numbers. The responses to entropy, fast acoustic, and slow acoustic waves are compared as functions of the freestream Mach number.

Temporal networks underlie a wide range of social, technological, and biological phenomena, highlighting how temporal inhomogeneities drive interactions in complex systems. Despite vast research on the area, the way temporal network connectivity evolves across time scales remains poorly understood. By analyzing temporal network data of informational and societal origin, involving tens of systems, millions of nodes, and observation periods from days to years, we find systematic evidence of an optimal time scale for coarse-graining interaction events. At this level of aggregation, networks are maximally dynamic in their local structure, while retaining system-wide connectivity. To understand the origins of such a seemingly generic interplay of time and topology, we explore a minimal temporal network model based on uncorrelated renewal processes, and show that intermittent yet globally connected activity may arise solely due to heterogeneities in inter-event times and degrees, and no other system-specific details. All coarse-grained empirical networks studied show persistent patterns of cyclic node degree change yet stationary system-level degree distributions, a striking coexistence of microscopic self-regulation and macroscopic stability. Our results give support to the notion of a universal pattern in temporal networks that involves both time and topology, via the nontrivial interplay of aggregation and temporal inhomogeneity, with consequences for the study of spreading dynamics on networks and the balance between robustness and adaptability in complex systems.

In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more projections increase experiment costs, acquisition time and the radiation dose imparted to the sample. One mitigation to these trade-offs is to adopt a sequential design of experiments, in which each subsequent measurement is determined as a function of previously acquired data in order to maximize information gain. In practice, a widely used heuristic to maximize information is to align beams with the edges of the sample. A key challenge, however, is that the true sample is unknown, so identifying edge-aligned beams typically requires reconstructing the sample based on available measurements. This work proposes a novel sequential design method that identifies edge-aligned measurements directly from the sinogram, bypassing any reconstruction, thereby improving computational efficiency and reducing the experimental design's susceptibility to reconstruction errors. Our method dynamically selects the next set of measurement beams by maximizing an acquisition function that balances exploration and exploitation over the domain of all possible measurements, improving reconstruction quality while reducing measurement redundancy.

The multipoint kinetics (MPK) equations extend point kinetics models by tracking neutron populations in lumped reactor regions, offering improved spatial resolution at modest computational cost. However, determining the coupling coefficient matrix $K$ from transient data remains an open problem without established experimental procedures. This work provides some commentary on the task of recovering $K$ from observable forward flux measurements during reactor transients. Analytical solutions to the Kobayashi MPK formulation in the case of no precursors and with a single precursor family are derived in spectral form, enabling a mathematically rigorous investigation of eigenmode behavior and parameter observability. Coupling coefficient recoverability is investigated using modal sensitivity analysis and the Hessian condition number of the spectral recovery problem. The inclusion of delayed neutron precursors delays this eigenmode divergence but may be insufficient for enabling practical recovery of $K$ in many scenarios. In conclusion, capturing the behavior of nondominant eigenmodes without oversampling the dominant eigenmode is critical to the recoverability of $K$ through transient data.

A variation of the measured neutron flux using $^3$He detectors at a TRIGA Mk II nuclear research reactor during steady-state operation is reported in this article. The observed effect shows a statistically significant anti-correlation between the temperature in the reactor pool and recorded neutron counts. Following reactor start-up under nominal operating conditions, the effect occurs for a specific time period until thermal equilibrium and a constant neutron count rate are reached. Simultaneous neutron and temperature measurements are performed in different configurations around the reactor in order to gain qualitative insights about the temperature dependent count rate behavior. Possible origins of the effect are identified and further measurements for a more detailed investigation are suggested.

We present a rigorous electronic-structure theory of strictly localized orbitals associated with a spatial partition of the one-electron Hilbert space that remains well defined in the complete basis-set limit. Each strictly localized orbital is supported on a spatial domain and may be discontinuous at domain interfaces. Using the interior-penalty discontinuous Galerkin method, these strictly localized orbitals can be employed in variational electronic-structure calculations despite their discontinuities at domain interfaces. As a proof of concept, we present numerical illustrations on one-dimensional diatomic model systems. They show that variational calculations can be carried out in a basis of strictly localized orbitals while maintaining good agreement with conventional calculations. Moreover, these strictly localized orbitals naturally lead to chemically intuitive representations of many-electron wave functions in the spirit of valence-bond theory.

Motivated by the need to extrapolate the results from ground-based experiments to the conditions of high-speed flight, we present the Lie equivalence symmetry analysis of the hypersonic stagnation-point boundary layers. We demonstrate the application of the equivalence symmetry on the set of coupled ODEs which are physically relevant in hypersonics. By allowing the property laws to transform along with the independent and dependent variables, the invariants derived within the similarity-reduced stagnation-point ODE formulation, identify families of non-linear maps that can be used to extrapolate the laboratory-scale predictions to flight. In practice, implementing these maps requires the laboratory-scale ODE solution together with the lab- and flight-side thermochemical property data, which are generally available from existing databases. The maps are derived for the similarity-reduced non-dimensional temperature across the boundary layer and are shown to {collapse with independently computed flight solutions} for a range of relevant cases.

High-resolution turbulence modeling is essential for scientific computing, but remains constrained by the cost of direct numerical simulation and the scarcity of full-resolution data. Existing scientific compressors reduce storage but typically operate on per-frame representations, whereas learned compressors yield compact latents that are often resolution-dependent and weakly aligned with the physics of turbulence. This raises the need for a compression framework that reduces data size, preserves physical diagnostics, and transfers from low-resolution training fields to high-resolution test fields without retraining. In this paper, we propose Physics-Preserving Latent Compression (PPLC), a patch-local latent compressor for three-dimensional turbulence. Motivated by inertial-range scale similarity, PPLC treats fixed-size patches as transferable units and applies a shared variational autoencoder independently of the global grid size. It combines exact mean preservation, zero-mean fluctuation encoding, an invertible Haar wavelet front-end, shift-consistency regularization, and overlap-aware reconstruction. Instantiated on forced isotropic turbulence, PPLC is trained only on stride-downsampled 256^3 fields and transfers zero-shot to 1024^3 fields. Experiments show that PPLC improves the balance between reconstruction accuracy and physical fidelity over classical and learned baselines, keeping diagnostics such as dissipation, enstrophy, energy spectra, and incompressibility closer to the ground truth. Beyond turbulence compression, PPLC offers a general strategy for physics-preserving latent representations that support data-efficient scientific surrogate modeling.

Accurate 3D seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning models for seismicity monitoring and hazard assessment. Because travel-time tomography is an inherently ill-posed inverse problem, UQ of the estimated models using Bayesian methods is also important for reliable downstream interpretations and analyses. However, Bayesian inference for 3D tomography based on conventional grid-based representations faces the ``curse of dimensionality'' and severe computational bottlenecks. Consequently, rigorous Bayesian UQ for margin-wide 3D travel-time tomography has remained largely unexplored. In this study, we propose a meshless 3D Bayesian travel-time tomography method that combines PINNs with a neural representation of the velocity structure, enabling tractable and data-efficient Bayesian inference through function-space particle-based variational inference. To efficiently integrate passive-source data into the Bayesian estimation of the velocity structure, we conduct analytical marginalization treating uncertain source parameters as nuisance parameters, with passive-source relocation carried out in post-processing. We validated the capability of our approach for 3D problems through synthetic experiments. Furthermore, we applied the method to a real-world dataset from marine active-source surveys and natural earthquakes off the Kii Peninsula, Nankai Trough. Our probabilistic 3D ensemble successfully resolves key geological features and provides data-consistent uncertainty maps. The posterior mean hypocenters shifted mainly in the vertical direction by 10-15 km, consistent with a previous relocation result. Finally, the neural representation drastically reduces storage requirements for the entire ensemble velocity model, highlighting the scalability and data efficiency of the proposed framework.

Residual pump power was used as a spatially integrated diagnostic of inversion-dependent absorption in 32-pass pumped Yb:YAG thin-disk lasers. A quasi-three-level rate-equation model was coupled to accumulated pump flux, intracavity signal flux, and an effective ASE-induced depletion term. Two disks were tested under non-lasing and multimode lasing conditions. The analysis reproduced residual pump fractions within 2.4\% in non-lasing and 1.8\% in multimode operation, and output power within 1.3\% relative error. Stimulated emission reduced the steady-state inversion and increased the effective pump-band absorption relative to the non-lasing case, while the absorption remained below the room-temperature small-signal value. The same absorption state was then used to estimate volumetric heat-load trends and to assess pump-pass number and output-coupler transmission for high-power thin-disk scaling.

We propose a unified methodological framework to quantify team performance in elite football by combining event-level performance metrics, higher-order network representations, and algebraic ranking methods. Using data from the 2017--2018 season of the five major European leagues, we construct metric-specific weighted graphs in which teams are connected through relative performance indicators. These graphs are analyzed via Hodge decomposition, and the gradient component is used to derive metric-based team ratings. The resulting rankings are systematically compared with the true league standings using Pearson and Kendall correlation measures, revealing strong metric- and league-dependent effects. Furthermore, by analyzing the ratio between solenoidal and total flow energies, we show that local cyclic dynamics structurally limit the gradient component's capacity to reconstruct the ranking. This topological inconsistency acts as a structural fingerprint of each league's ``competition style'' successfully mapping the studied systems into distinct regimes: highly hierarchical structures (England and Italy), tactical parity driven by generalized loops (Germany), and pockets of localized chaos (France and Spain). Lastly, we introduce a composite rating obtained as a parsimonious linear combination of metric-based ratings, optimized separately for each league. This composite approach significantly improves predictive power and allows the relative importance of different performance indicators to be quantified in a league-specific manner. Our results demonstrate how higher-order network methods provide a flexible and interpretable framework to uncover latent performance structures in football, offering a complementary perspective to outcome-based rankings and a general approach applicable to other oppositional sports.

In this paper we proposed a multi-shell generic cloaking system. A transparency condition independent of the object's optical and geometrical properties is proposed in the quasi-static regime of operation. The suppression of dipolar scattering is demonstrated in both cylindrically and spherically symmetric systems. A realistic tunable-low loss shell design is proposed based on composite metal-dielectric shell. The effects due to dissipation and dispersion on the overall scattering cross-section are thoroughly evaluated. It is shown that a strong reduction of scattering by a factor of up to 10^3 can be achieved across the entire optical spectrum. Full wave numerical simulations for complex shape particle are performed validating the analytical theory. The proposed design does not require optical magnetism and is generic in the sense that it is independent of the object's material and geometrical properties.

Multiphysics simulation of semiconductor processes requires simultaneous command of governing equations, numerical methods, software implementation, and process physics, creating a substantial entry barrier for newcomers. We investigate whether large language model (LLM) assistance and curated literature guidance can reduce this barrier during the development of a low-pressure chemical vapor deposition (LPCVD) model for graphene growth using the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE). We compare GPT and Claude across direct prompting, staged development without literature, and staged development with literature guidance. Staged development successfully produced converged simulations but revealed two recurring failure classes. We define a false summit as a simulation that converges and appears physically plausible while implementing incorrect physics, and silent drift as the undetected propagation of unverified assumptions through successive development stages. In our case study, a non-conservative species-transport formulation generated spurious methane depletion that was visually indistinguishable from genuine surface consumption, while an omitted self-limiting surface-reaction term produced physically incorrect growth behavior that the LLM repeatedly rationalized as valid. Literature guidance substantially reduced interaction burden during the most challenging development stage, decreasing prompt counts by 91% for GPT and 64% for Claude. Based on these observations, we propose an eight-category failure taxonomy, model-specific human oversight strategies, and a stage-gated workflow to improve the detectability of hidden modeling errors. The results demonstrate that LLMs can lower the barrier to multiphysics simulation development, but rigorous physical validation remains essential because apparently reasonable solutions may conceal critical defects

Impulsive plasma dynamics in the laboratory are often driven by rising electric currents, yet their quantitative plasma response has not been well established. By means of fully kinetic particle-in-cell simulations and laser-driven capacitor-coil experiments, we show that a rising current expels plasma, forming an expanding magnetic bubble and accelerating particles. The expansion front velocity scales with the Alfvén speed determined by the magnetic field at its inner edge and the plasma density at its outer edge. This mechanism establishes impulsive current drive as a fundamental way that generates plasma flows and accelerates particles in laboratory plasmas, with potential relevance to astrophysics.

Terahertz channels can serve as opportunistic rainfall sensors because rain-induced extinction couples received power to rainfall intensity. Unlike satellite, radar, and commercial microwave link retrieval, THz channel rainfall estimation lacks large operational datasets that supervised learning requires. This article uses an outdoor campaign over a 41.5 m THz channel at 140 GHz and 229 GHz to calibrate the channel statistical properties, then synthesizing physics constrained training data that combine the ITU-R P.838-3 and Mie-theory rain attenuation across four raindrop-size-distribution priors with controlled stochastic fluctuations. RainFormer, a hybrid attention-convolution network, maps a short received-power sequence to rainfall rate by fusing local fluctuation structure, long-range temporal dependencies, and explicit physical-statistical descriptors. Ablation shows that the explicit descriptors and temporal-order information carry most of the predictive information, with convolution and attention acting as complementary refinements. Applied zero-shot to measured rainfall, the synthetic-trained models produce rank-consistent, physically interpretable estimates whose inter-prior spread bounds the uncertainty arising from the unobservable path DSD (raindrop size distribution), establishing DSD-bracketed synthetic training as a viable foundation for THz rainfall sensing under severe data scarcity.

Socioeconomic exposure segregation -- the lack of daily interaction between income groups -- erodes social capital and entrenches inequality, yet the specific physical features that drive these behavioral restrictions remain poorly understood. Prior research has quantified where segregation occurs using mobility data, but has not identified how the built environment facilitates or inhibits these interactions. Here we introduce VISAGE, a large multi-modal model-enabled framework that perceives exposure segregation directly from open satellite and street-level imagery across 10,030 communities in 31 U.S. cities. Moving beyond black-box correlations, we operationalize cross-disciplinary sociological theory into an interpretable visual codebook to detect physical regulators of social mixing. We find that the built environment encodes a legible grammar of segregation: "defensible" architectural forms (e.g., fences, gated enclosures) and monofunctional zoning systematically predict higher social isolation, whereas mixed-use infrastructure fosters interaction, explaining substantial variance in mobility-derived segregation patterns (Pearson $r=0.770$). Crucially, we show that inclusionary housing policies manifest in distinct visual signatures associated with higher mixing, suggesting that policy interventions successfully alter the physical landscape to encourage diversity. Our findings offer a scalable pathway to decipher the social production of space, providing a mechanism-based lens to understand how the built environment shapes social behavior.

We revisit the closed-form models of Seebeck coefficient, and identify the thermodynamic flaws in those extensively-used methodologies, essentially including the confusion of electrochemical potential and electrical potential within the definition, arbitrarily neglecting the dependence on conduction band bottom and Fermi level when calculating distribution function spatial gradient, and the improper heat flux presentation. Here, an alternative methodology is presented, which derives the Seebeck coefficient model based on the drift-diffusion equation with the Soret effect in the open circuit condition. The reversible model that eliminates the electron velocity and relaxation terms in the formulation is recovered for the band term in the near-equilibrium case, and it can give the lower bound of Seebeck coefficient in theory, while the phonon-drag term is of the identical form to the Boltzmann transport equation(BTE)-based one. A case study is performed for highly-n-doped Silicon, where the reversible, ballistic(based on Landauer formulation) and BTE models are compared with each other and with the experimental data. The reversible model gives the fairly-good predictions for the experiments. Given its theoretical-soundness, simple form and low computation cost, the reversible model can serve as a concise alternative for evaluating thermoelectric properties in both the reversible and near-equilibrium cases.

We demonstrate low-cost microwave frequency fibre interferometry for per-span monitoring of a 1,770 km operational subsea cable connecting Ireland and Iceland. Over four months, this approach successfully resolved tidal variations, storms, and teleseismic earthquakes, highlighting its potential for large-scale, cost-effective ocean monitoring.

The construction of robust multivariate calibration models for near-infrared (NIR) spectroscopic analysis necessitates careful partitioning of samples into training and validation sets. The selection strategy employed fundamentally influences model generalizability and predictive accuracy. This investigation presents a systematic comparative analysis of four established sample selection algorithms-Duplex, Honigs, Kennard-Stone, and Naes-applied to NIR spectral data acquired from 58 commercial paracetamol tablets. Gaussian process regression (GPR) served as the modeling framework, with model performance quantified through the coefficient of determination (R^2) and root mean square error of prediction (RMSEP). The Kennard-Stone algorithm employing the Mahalanobis distance metric demonstrated superior performance, yielding optimal validation statistics (\(R^2 = 0.99999\), RMSEP = \(1.74 \times 10^{-6}\)). Rigorous non-parametric statistical analysis employing Kruskal-Wallis and post-hoc Mann-Whitney U tests with Bonferroni correction confirmed significant performance differences among algorithms (p < 0.001), while revealing statistical equivalence between Kennard-Stone and Honigs methods. Systematic investigation of training set proportions (60-90%) elucidated the monotonic relationship between calibration set size and predictive accuracy. These findings provide evidence-based guidance for optimizing sample selection protocols in pharmaceutical NIR applications and underscore the critical importance of chemometric validation in spectroscopic method development.

Spanwise wall oscillation can enhance convective heat transfer disproportionately to its drag penalty, a departure from the Reynolds analogy termed dissimilar heat transfer (DHT). The companion study of Gu'erin et al. (2026) established that an optimised quasi-plateau waveform attains an analogy factor $\overline{A}n \approx 1.09$ at $Pr = 1$ and attributed this preferential thermal enhancement to the absence of a pressure-strain redistribution channel in the temperature variance equation, but the mechanism had not been quantitatively verified. The present study addresses this gap through phase-resolved variance transport budget analysis from direct numerical simulation of turbulent channel flow at $Re\tau = 200$, $Pr = 1$. Two complementary pressure-mediated mechanisms are identified. At the Stokes-strain reversal, the pressure-strain redistribution $\Pi_{uu}$ imposes a pronounced drain on the streamwise velocity variance with no counterpart in the temperature variance equation: the divergence-free constraint redistributes momentum variance among velocity components but has no scalar analogue. During the quasi-steady plateau phases, the pressure-temperature-gradient correlation $\Pi_{v\theta}$ preferentially enhances the wall-normal scalar flux relative to the momentum flux. The concentration of both mechanisms within the reversal and plateau phases, rather than at the Stokes-layer penetration maxima, identifies the duration of the quasi-steady phases as the controlling parameter for DHT enhancement, resolving the paradox whereby increased penetration depth does not produce increased dissimilarity.

Conveyor-belt magneto-optical traps (CB-MOTs) use blue-detuned polarization-gradient forces to provide simultaneous cooling, confinement, and loading on type-II molecular transitions. Recent experiments with \baf{138} showed that this mechanism can directly load a slowed molecular beam with an efficiency exceeding that of a conventional red-detuned MOT. Here we use established optical-Bloch-equation force calculations and classical trajectory propagation to ask whether this direct-loading strategy should extend beyond the specific molecule used in the first demonstration. For \baf{138}, the calculation reproduces the experimentally observed trend that the CB-MOT capture velocity increases with laser intensity. We then apply the same framework to two closely related but experimentally distinct cases: \baf{137}, whose dense hyperfine structure complicates a conventional dual-frequency MOT, and \bah{138}, whose narrower linewidth and longer wavelength reduce the available radiative force. In both cases, the CB-MOT retains a broad region of nonzero capture velocity. These results identify the molecular conditions under which direct CB-MOT loading should remain effective and show that the dipole-force-dominated conveyor-belt mechanism provides a practical loading route for heavy laser-coolable molecules whose MOT performance is otherwise limited by photon recoil, scattering rate, or hyperfine complexity.

Physics-informed neural networks (PINNs) constitute a rapidly maturing class of scientific machine learning models in which the governing equations of a physical system are embedded directly into the training objective as soft constraints. By enforcing partial differential equations (PDEs), conservation laws, and constitutive relationships during optimization, PINNs enable the construction of models that are simultaneously data-efficient, physically consistent, and capable of operating in regimes where measurements are sparse or indirect. In contrast to conventional deep learning, where the loss is typically defined solely in terms of data misfit, the learning task in PINNs is reformulated as a constrained optimization problem in which admissible solutions are confined to the manifold defined by the underlying physics. This review provides a comprehensive assessment of recent developments in physics-informed machine learning with an emphasis on PINN-based formulations for forward modelling, inverse design, and equation discovery across nanophotonics, fluid mechanics, astronomy, and biomedical engineering. Particular attention is devoted to how physical knowledge is injected at different stages of the modelling pipeline, including synthetic data generation, non-dimensionalization and scaling, architecture selection, loss design, and post-training regularization. We highlight emerging strategies for multi-physics coupling, transfer learning across parameter and geometry spaces, and rigorous benchmarking against established numerical solvers. Finally, the review discusses interpretability, uncertainty quantification, and hardware acceleration, and articulates how physics-informed learning is reshaping engineering practice by enabling digital twins and design workflows that combine simulation and data in a unified differentiable framework.

In geophysics, the problem of classifying triggers of dynamic processes in the lithosphere, hydrosphere, atmosphere, ionosphere and magnetosphere has arisen and needs to be solved. Triggers that induce catastrophic events, such as destructive earthquakes, require special attention. Classification is necessary in order to express the diversity of triggers in a limited number of ordered and well-identifiable species. This paper proposes a project for collective work on the systematization of triggers, i.e., the formation of a unified classification and nomenclature. A simplified (basic) and extended classification table are proposed. The basic matrix uses the principle of binary opposition. The matrix contains three categories of descending rank: type (natural, artificial), class (endogenous, exogenous) and specie (periodic, aperiodic). Three additional categories have been added to the expanded classification table. This results in 128 trigger varieties. The heuristic significance of the classification of triggers is emphasized. New phenomena discovered during the classification process are indicated: excitation of a strong aftershock by a round-the-world seismic echo, modulation of global seismicity by spheroidal oscillations of the Earth, the weekend effect in earthquake activity. In the magnetosphere, a cause-and-effect chain of triggers has been identified, called a trigger cascade. The existence of so-called antitriggers is indicated, which do not excite, as usually happens, but interrupt the ongoing dynamic process. Key words: systematics, nomenclature, dynamic system, disturbance, lithosphere, hydrosphere, atmosphere, ionosphere, magnetosphere, earthquake, magnetic storm, trigger cascade, antitrigger.

We calculate the impedance of a flat-plate electric double-layer (EDL) capacitor by means of Finite Element Method simulations of modified Poisson--Nernst--Planck equations. In Nyquist representation, the impedance spectra show a slanted line at intermediate frequencies if the capacitor is biased by a voltage, $U_\mathrm{bias}\neq 0$, or if the cation and anion diffusion coefficients differ, $D_-\neq D_+$. By inspecting the concentration perturbations in the relevant frequency range, we confirm that the slanted line is in both cases related to ambipolar salt diffusion. On the basis of our impedance data, we disprove two previously made claims: 1) that the width $R_\mathrm{sl}$ of the slanted-line region represents an EDL resistance; and 2) that the slope $k_\mathrm{sl}$ of the slanted line is a measure of the ratio of the diffusion and charging time scales, $\tau_\mathrm{diff}/\tau_\mathrm{c}$. For the quantitative analysis of flat-electrode EDL capacitor impedance, we propose instead two equivalent circuits, for the cases $D_-\neq D_+$ and $U_\mathrm{bias}\neq 0$. These two cases give rise to antisymmetric and symmetric salt perturbations, which are best described by a Warburg short and Warburg open element, respectively. From our circuit analysis, we obtain quantitative relations that link the Warburg prefactors to the ambipolar diffusion coefficient, the chemical capacitance of the bulk electrolyte, and the differential charge efficiency of the EDLs. We thus provide a theoretical framework that explains why the width of the slanted line saturates at large biases, why it vanishes for large ion packing fractions, and how the system's overall capacitance is limited by the finite amount of ions in a closed system.

Discovering the fundamental dynamical rules that generate the main statistical features of human mobility is essential for understanding the mechanisms underlying such processes. A prominent example is the exploration and preferential return model and its generalizations, which successfully reproduce several empirical findings. Here, we exploit another observation: the endpoint distances of a trip from the trajectory's starting point are strongly correlated. We consider a movement process in which each user performs a sequence of trips to satisfy a set of demands, given a spatial distribution of suppliers on a two-dimensional lattice. In each trip, destinations are chosen with a probability that depends on the ratio of the initial and final distances from the user's origin (home). We show that even a single agent with uniformly distributed demands and suppliers qualitatively reproduces key empirical statistics, such as the power-law distribution of traveled distances. The results are also robust to introducing interactions between agents via queues and incorporating more realistic demand and supplier distributions.

Some numerical simulations of very warm climates suggest that the Earth's atmosphere may undergo a transition to a state of equatorial superrotation, where the zonal-mean zonal wind in the tropics is westerly. However, major uncertainties remain about the circumstances under which such a transition could happen. A natural first step towards reducing these uncertainties is to better understand the dynamical processes involved in the transition in idealized setups. However, simple numerical experiments have reported very different responses to tropical diabatic heating in different models, with both a continuous and an abrupt transition to superotation. In this paper, we investigate the mechanisms controlling the nature of the transition. We show that in an idealized Held-Suarez framework, it is governed by both the meridional temperature gradient and the bottom friction coefficient. These two parameters control a competition between two feedback mechanisms: a positive tropical wave-jet mechanism, and a negative feedback mechanism related to absorption of extratropical waves near their critical latitudes in the tropics.

Guided acoustic wave Brillouin scattering (GAWBS) is a major obstacle in fiber-based quantum and high-speed classical communication systems as well as in interferometry. The transverse phonons driving it modulate the light field in the fiber core, adding thermal noise to the signal. To this day, there is no known method to eliminate GAWBS from the fiber or to compensate its effects completely. In this letter, we present twisted photonic crystal fibers (t-PCF) as the first-ever fiber system allowing a complete removal of mixed torsional radial GAWBS in a Stokes basis. The torsional radial modes modulate the fiber asymmetrically in the transverse direction, resulting in linear birefringence. While pure phase modulation is added as common noise in the guided fiber modes and can be easily removed through self-referencing, linear birefringence induces polarization modulation, which cannot be counteracted. In t-PCFs, the transverse symmetry of the geometry translates to multiple symmetries in the acoustic and optical domains in the circular basis. This enables equal phase accumulation in certain orientations in the two optical modes. Through experiments and theory, we show that the GAWBS-induced phase can be compensated down to the quantum-noise limit by self-referencing in a symmetric interferometer with Stokes detection.

Despite their widespread use, mesoscopic models for non-ideal fluids have rarely been systematically validated against microscopic simulations. In this work, molecular dynamics (MD) simulations of confined fluids are mapped onto a mesoscopic framework, enabling direct comparison with lattice Boltzmann (LBM) formulations. By analyzing the moments of the distribution function, we identify a force formulation that consistently reproduces the microscopic statistics and macroscopic force balance. The results show that a hybrid formulation combining pseudo-potential and free-energy approaches provides the most consistent description. These findings establish a direct link between microscopic particle dynamics and mesoscopic modeling, offering practical guidance for the development and selection of LBM models for non-ideal and multiphase flows.

We demonstrate that acoustic waves can induce controlled translation and rotation of macroscopic objects through small, but deliberate, detuning of the driving wave frequency. When an object is patterned with a suitably designed acoustic metasurface, small changes in the incident frequency $\omega \pm \delta \omega$ are converted into directional radiation forces and torques, enabling steerable motion even for objects much larger than the acoustic wavelength. We present the concept of a force-optimal metasurface topology and show that it enables fully reversible forces in real time: the object is moved in one direction for positively detuned incident frequency $\omega+\delta \omega$ and in the opposite direction for negatively detuned frequency $\omega-\delta \omega$, where $\omega=22.5 \textrm{ kHz}$ and $\delta \omega =2.5 \textrm{ kHz}$ for a proof of concept at inaudible frequencies. This mechanism is demonstrated experimentally at ultrasonic frequencies with 3D-printed metasurfaces. The proposed concept is scalable across frequencies and materials, offering a building block for realizing complex, remote-controlled, dynamical behaviors that can be programmed by reconfiguring material surface patterns.

Characterizing the complete wall-pressure spectrum in turbulent wall-bounded flows requires simultaneous access to the viscous-scale high-wavenumber content and the outer-layer low-wavenumber content -- a requirement that neither short-domain direct numerical simulation (DNS) nor sparse experimental measurements alone can satisfy. We propose Patched Flow Matching (Patched FM), a generative framework that fuses these two complementary sources by learning a patch-local prior over inner-scaled wall-pressure statistics from short-domain DNS and assimilating sparse sensor measurements at inference time through training-free posterior sampling. The patch-additive decomposition of the flow matching vector field decouples the generative prior from the global domain size, enabling reconstruction on domains arbitrarily larger than the training configuration. By expressing the patch prior in inner-scaled coordinates, where high-wavenumber wall-pressure statistics are approximately Reynolds-number invariant, the framework extends to higher Reynolds numbers through hierarchical transfer learning with as few as $500$ short-domain snapshots ($2.5\%$ of the base training data) at a fraction of the scratch-training cost. Applied to compressible channel-flow DNS at $Re_\tau = 180$, $500$, and $1000$, Patched FM reconstructs full-resolution wall-pressure fields on a domain four times larger than the training configuration ($L_x^L = 16\pi\delta$ versus $L_x^S = 4\pi\delta$) from sensor coverage as low as $0.25\%$, recovering the low-wavenumber spectral content inaccessible to short-domain DNS with high fidelity in both streamwise and spanwise directions. Zero-shot generalization to unseen Reynolds numbers and ablation studies further confirm the role of inner scaling as a physical prerequisite for data-efficient Reynolds-number transfer.

Wave-obstacle diffraction is, par excellence, an example of transition to nonlinearity, generating turbulence and complexity in fluids. We present an idealized kinetic plasma regime capturing this ubiquitous interaction and its transition to phase-space turbulence. High-resolution Vlasov-Poisson simulations reveal that the interplay between electrostatic waves and density gaps at Debye and sub-Debye scales redistributes a strongly anisotropic energy cascade throughout the full phase space, unveiling the effect of inhomogeneities on structure formation and small-scale-directed turbulent flow.

We present a perturbative framework for computing the dynamics of an electronic wave packet launched by an attosecond x-ray pulse in a neutral molecule. The x-ray pulse excites the molecule via both x-ray absorption and Impulsive Stimulated X-ray Raman Scattering (ISXRS), coherently populating both the core-excited states and valence-excited states. We describe the electronic structure within the Equation-of-Motion Coupled-Cluster framework, adopting a compact representation of the sum over states expression characterizing the second-order perturbative description of ISXRS. We study the coherent electronic dynamics in OCS and oxazole utilizing the time-dependent difference electron density, decomposing it in terms of its perturbative components. While the core-excited components dominate the initial stages of the dynamics, the valence-excited components dominate the electronic dynamics on a longer time scale. The pulse polarization controls the symmetry of the states included in the wave packet. We show how the spatial symmetry of the states plays a role in shaping the spatial properties of charge migration. The atom-specificity of the ISXRS process translates directly into the initial localization of the wave packet. This determines the starting point of charge migration, shaping its subsequent evolution.

Surface elasticity plays a crucial role in the mechanics of soft solids at the scale of micrometers and may become significant even at the scale of millimeters in exceptional cases. However, despite the efforts made for the identification of surface elastic parameters, accurately determining these values remains challenging due to the complex interplay with bulk elasticity and nonlinearity. To address this issue, a novel GA-based optimization framework is developed by employing an ANN-based surrogate forward model for efficient identification of surface parameters from the force-displacement response of a cylindrical specimen. The ANN is trained on force-displacement data of a soft cylindrical specimen, generated by nonlinear FE model predictions incorporating model elastic surfaces. The data used for training is generated for non-dimensional values of surface tension in the range of 0-5 and surface shear modulus in the range of 0-50. The accuracy and reliability of the trained ANN are established. The key novelty lies in replacing analytical forward models, which rely on idealized boundary conditions, with a data-driven surrogate trained on FE simulations under realistic constraints. The parameter identification is performed for a number of surface parameter sets using numerically generated force-displacement data as well as for noisy data obtained by adding 5% error in simulated data. The maximum error in identified values of parameters in all the cases is less than 8%. Repeatability and uncertainty analyses based on multiple noisy realizations further demonstrate the robustness of the approach, yielding narrow confidence intervals for the predicted parameters. The proposed framework provides a novel, efficient, and robust alternative to FE-based inverse identification of surface parameters, particularly for experimentally relevant boundary conditions.

Resonant wave-particle interactions between externally launched helicon waves (also known as whistler waves) and runaway electrons (REs) have been demonstrated on the DIII-D tokamak. In this work we extend the initial results reported in Choudhury, H. et al. Phys. Rev. Lett. 136, 025101 (2026) by exploring the effects of antenna alignment with the edge magnetic field, toroidal wave propagation direction, and coupled power on RE scattering in the quiescent RE experimental scenario. Two distinct experimental configurations have been investigated: one in which the antenna aligns well with the edge background magnetic field, known as the ideal antenna configuration, and one with misalignment, known as the non-ideal case. Previously, it had been found that helicon power in the ideal antenna configuration prevented RE growth despite the normalized toroidal electric field remaining high enough to drive exponential RE growth in the absence of helicon power. In this paper, we show that scattering via the normal Doppler resonance (n=1) effectively limits the growth of the RE population in both the ideal and non-ideal antenna configurations, with evidence of a power threshold in the latter case. In contrast, launching waves that favour the anomalous Doppler resonance (n=-1) is observed to enhance rather than reduce the RE population. In addition, fast magnetic measurements reveal rising-tones in the 30-60 MHz range during helicon-off periods, which are not observed prior to helicon power. Finally, the challenges of using launched helicon waves to scatter post-disruption RE beams are discussed. Collisional damping and a large vacuum gap between the plasma and antenna on the outboard side present significant obstacles to helicon waves propagating into the plasma core.

Electrochromic devices (ECDs) offer a compelling route toward low-power, non-emissive optical modulators with nonvolatile states. However, their widespread implementation is hindered by limitations in operating voltage, switching speed, color tunability, and long-term stability. Mixed ionic-electronic conductors (MIECs) provide a promising alternative platform, enabling optical modulation through ion-driven redox and structural transformations. Oxygen-based MIECs offer enhanced durability, environmental robustness, and compatibility with oxide electronics and silicon photonics, yet remain largely underexplored for electrochromic and photonic applications. Here, we demonstrate structure-driven analog optical control in an ion-pumped SrFeO$_{3-\delta}$ thin-film device by undergoing reversible oxygen-driven phase transitions between brownmillerite and perovskite structures. Phase transition is accompanied by pronounced changes in its electronic structure and optical constants. By harnessing these ion-induced structural transformations and integrating an optically passive Al$_2$O$_3$ interference layer, we achieve continuous and reversible modulation of optical transmittance and color. These results provide a general framework for ion-driven analog photonic and electrochromic devices and highlight the potential of oxygen-based MIECs for next-generation ionochromic systems compatible with silicon-based photonic platforms.

The present research presents potentials and complementary potentials used in the one-dimensional nonlocal integral formulations. The pure stress and the pure strain nonlocal formulations were considered. While the potential used in the strain driven formulation is well known, the complementary potential has not yet been presented in the literature. The same applies to the stress driven formulation. The equivalent formulations are obtained by resorting to the Legendre transformation, and their equivalence is proved. It is also shown that these results can be used to postulate a novel potential, i.e. a kind of mixed stress-strain potential, which is, however, as ill-conditioned as the pure strain-driven formulation. Finally, an example is given that practically confirms that the stress-driven formulations resulting from the potential and the complementary potential are equivalent.

Large language models (LLMs) are increasingly deployed as planners and assistants in tasks with inherent spatial structure, such as navigation and route planning, yet they remain brittle in sequential spatial reasoning. We ask not merely whether LLMs fail at navigation but where in the spatial-cognition pipeline they get lost. We introduce a multi-scale diagnostic benchmark that decomposes maze navigation into three cognitive levels drawn from human spatial cognition: Fine (local passability), Meso (junction topology), and Macro (global goal direction). We evaluate three instruction-tuned chat LLMs (GPT-4o, DeepSeek-V3, Llama-3.3-70B) on 1,050 topology-annotated mazes spanning seven sizes (3x3 to 30x30) and three difficulty tiers. The benchmark is organized as three modules. (i) Input acquisition: among four input formats, structured coordinate text is the most navigable, far surpassing rendered images. (ii) Multi-scale representation: end-to-end one-shot navigation collapses to near zero by 10x10 for every model, yet the same models respond to isolated single-level probes (Fine, Meso, Macro) at 30-75% far beyond that size. A multi-hot first-error analysis localizes failures to Meso junction choices (59%) and Fine perception (39%), with global direction almost never at fault (1%). The barrier is therefore the cross-scale aggregation of individually available competences over a long sequential plan, not any single perceptual deficit. (iii) Hierarchical route planning: delegating per-step execution to a deterministic walker and querying the LLM only at junctions, with an explicit cell-type prompt, lifts GPT-4o success by up to 92 points at mid sizes, but the same scaling wall re-emerges by 30x30. We release the benchmark, mazes, and code as a reusable diagnostic instrument for spatial reasoning in LLMs, available at this https URL.

Exceptional points (EPs) are non-Hermitian spectral singularities where eigenvalues and eigenvectors coalesce. We propose a geometry-controlled route to EPs in orbital-angular-momentum (OAM) photonics. In an effective torsion-free helicoidal background, the twist supplies a signed, real chiral splitting between opposite-OAM modes, while openness, gain, loss, leakage, or channel coupling, supplies the non-Hermiticity. The EP is reached when the geometric detuning compensates for a residual mode splitting and the linewidth contrast meets the non-Hermitian balance condition; the twist rate is thus the control parameter, not the source of non-Hermiticity. We obtain the OAM slope both for a confined quantum problem and, in the paraxial regime, for a helically twisted guide, and embed the OAM doublet in a multimode mode-space model to show that the EP survives spectator modes. Eigenvalue coalescence, phase-rigidity collapse, linear EP tracking with residual detuning, and branch exchange under geometric encircling provide falsifiable signatures. An OAM-resolved input-output response maps these onto spectroscopy-level observables, including spectral maps and three-dimensional Riemann-surface visualizations. A device-level paraxial simulation of two distinct photonic platforms, one reciprocal and one asymmetric-coupling Wiersig-type platform, reproduces the predicted exceptional point and its linear geometric tracking.

Large and persistent differences in opioid prescribing across physicians and regions cannot be explained by patient characteristics or physician attributes alone. We developed a behavioral framework in which prescribing evolves through persistence, exposure to peers in professional networks, and heterogeneous responses to a common policy signal that varies with network centrality. Using nationwide Medicare Part D data from 2013 to 2020, covering more than two million physician-year observations, we tested three hypotheses implied by this framework. Physicians exposed to higher peer prescribing subsequently prescribe more; more central physicians reduce prescribing more following the introduction of the 2016 CDC guideline, with no evidence of differential pre-trends; and changes in peer prescribing are closely associated with changes in individual prescribing in the post-guideline period. By 2020, physicians at the 90th percentile of network centrality exhibited prescribing reductions 0.30 percentage points larger than those at the 10th percentile, with the gap widening steadily after the introduction of the CDC guideline. Together, these results indicate that opioid prescribing operates through professional networks, in which policy effects spread through connections and appear to be shaped by network position. This suggests that engaging highly connected physicians may help extend the reach of opioid stewardship programs. It also raises questions about how the burden and benefits of such targeting would be distributed across physicians and patients.

Periodic micro-pillar lattices integrated on elastic substrates provide a promising plat-form for controlling elastic-wave propagation through localized resonant interactions. In this work, defect-engineered resonant transmission in periodic tungsten micro-pillar arrays deposited on silicon substrates is numerically investigated using finite-element simulations. Two-dimensional frequency-domain analyses were performed to evaluate the influence of one-, two- and three-pillar defects on elastic wave transmission characteristics. The results reveal strong defect-width-dependent transmission modulation together with the localized resonant elastic-wave redistribution within the periodic lattice. Full three-dimensional simulations further confirm the presence of defect-sensitive resonant localization and modified elastic-wave transport pathways. Floquet dispersion analysis of a periodic unit cell reveals multiple nearly flat resonant branches associated with low-group-velocity elastic-wave modes, indicating predominantly subwavelength locally resonant behavior. A comparative study between tungsten and copper resonators demonstrates enhanced resonant confinement in tung-sten-based structures due to their larger inertial contrast with the supporting sub-strate. The proposed defect-engineered micro-pillar lattices provide an effective approach for frequency-selective elastic-wave control and localized resonant wave ma-nipulation in elastic metamaterial systems.

We propose a noninvasive SQUID-based polarimeter for the polarized proton beam in the Electron-Ion Collider (EIC) Hadron Storage Ring (HSR), exploiting the collective magnetic dipole moment of the bunches rather than scattering. The six-snake HSR lattice has synchronous-particle spin tune $\nu_s = 1/2$, placing the in-plane spin-precession signal at half the revolution frequency ($\sim$39 kHz), in the DC SQUID band. Three pickup channels (cosine-$\theta$ and sine-$\theta$ saddle loops for the transverse components, a coaxial axial gradiometer for the longitudinal one) reconstruct the full polarization vector $(P_x, P_y, P_z)$ in two complementary modes. Static mode, the default for continuous noninvasive monitoring, reads all three components: $P_y$ at the revolution frequency and the residual in-plane components at $\nu_s f_\mathrm{rev}$, bunch by bunch over an hours-long fill, including $P_z$, inaccessible to single-spin scattering polarimetry by parity conservation. Dynamic mode gives a precise polarization-magnitude measurement: a longitudinal kicker tips a small fraction of the polarization into the horizontal (ring) plane to produce a free-induction-decay (FID) signal, and many phase-locked tip-$\pi$-echo-restore cycles are summed coherently via a matched filter across all bunches, with $\mathcal{O}(\alpha^2/\pi^2) \sim 10^{-4}$ loss per cycle, negligible over a full $\delta P/P = 1\%$ measurement. For tipping angle $\alpha = 30$ mrad, polarization $P = 0.7$, and effective rms spin-tune spread $\sigma_{\nu_s}^\mathrm{eff} = 10^{-3}$ (coherence time $\sim$2 ms), the integration time to reach $\delta P/P = 1\%$ is about 18 s at injection and 5 min at flattop. The architecture extends to deuteron and $^3$He beams via species-specific spin-magnetic factors, with applications to storage-ring EDM searches.

Techniques from glass art and fabrication provide a controllable physical platform for studying tissue mechanics in simple organisms. Here, we use glass-based physical models to investigate tissue deformation in the marine organism Trichoplax adhaerens. Previous studies have shown that the epithelial tissues in T. adhaerens undergo large deformations and form fracture holes under mechanical loading, exhibiting a ductile-to-brittle transition at fast loading rates. To model these behaviors in a tunable and experimentally accessible system, glass is shaped into tissue-like monolayers in a glass studio, heated to its specific process temperature, and subjected to controlled stretching. Rapid cooling arrests the deformed configurations, providing snapshots of tissue-like strain states under load. Under lateral and radial stretching, we quantify changes in the area and eccentricity of individual "cells" in the glass models, and found that eccentricity increases after stretching. We further use tensegrity-based models to quantify deformations in the cellular geometry of the glass tissues, enabling direct comparison between experiments and simulations. The model captures the principal experimental deformation patterns, but underestimates the magnitude of the observed eccentricity changes. Our results demonstrate that glass-based physical models provide an experimentally accessible platform for studying tissue-scale deformation and mechanical behavior, while supporting interdisciplinary approaches that connect methods in the arts and sciences.

Disordered stealthy hyperuniform materials combine liquid-like statistical isotropy with crystal-like homogeneity, suppressed density fluctuations at large length scales, bounded holes, and an isotropic structure factor that vanishes for a finite range of wavevectors. This combination yields unusual physical properties, including optical transparency, effective delocalization, ultrafast spreadability, optimal conductivity, and complete isotropic photonic bandgaps. Gyromorphs, point patterns whose structure factor includes rings of Bragg-like peaks arranged with discrete $G$-fold rotational symmetry, were recently introduced as counterexamples: disordered media that can somehow achieve the same physical properties, in some cases with higher performance, without stealthiness or hyperuniformity. In this paper, we resolve the puzzle of how gyromorphs fit consistently with the stealthy hyperuniform studies. We first show that gyromorphs are actually hyperuniform and, in the large-$G$ limit where they become nearly isotropic, belong to the weakest form of hyperuniformity, known as Class III. Thus, gyromorphs should have comparatively degraded physical properties compared to stealthy hyperuniform media, which belong to the strongest form of hyperuniformity, known as Class I. We verify this expectation using the rigorous spectral Green's matrix method for the calculation of the density of states (DOS) and Purcell factors in large arrays of electric dipoles. We find that gyromorphs display size-dependent pseudogaps richly populated by localized states rather than smooth band gaps like those found for highly stealthy hyperuniform materials or in deterministic structures such as Vogel spiral and triangular lattices. Furthermore, we predict similar disorder-induced degradation relative to stealthy hyperuniformity with regard to transparency, spreadability and diffusion properties.

The atmospheric tropical belt is believed to be more predictable than the extratropics. This question is revisited here by exploring the emergence of chaos in reduced-order model versions of the vorticity equation under the weak temperature gradient hypothesis, which provides a good description of the large-scale tropical atmosphere. The analysis reveals that under fairly realistic divergence forcing amplitudes, chaos may emerge, sometimes with Lyapunov time scales of less than a day. This result contrasts with the idea of a predictable tropical atmosphere, and opens important questions on the effective origin of predictability in the Tropics.

Advances in the semiconductor industry are driven by the development of increasingly compact devices featuring intricate etched geometries, the characterization of which essentially requires ultraprecise, label-free, and real-time metrology. However, non-destructive and alignment-free optical metrology of sub-wavelength structures with nanometric resolution remains a major challenge. Here, we demonstrate a novel single-shot, label-free, and alignment-free optical metrology approach for determining the 1D position of sub-wavelength nanostructures, achieving lambda/110 (7.2 nm) precision. The high precision benefits from utilizing structured illuminations of Laguerre-Gaussian (LG) or Hermite-Gaussian (HG) beams, and the AI analyzing method can retrieve the information when such structured light interacts with sub-wavelength objects. Instead of relying on phase singularities in superoscillatory microscopy, our approach leverages spatially distributed phase jumps in HG and LG beams interacting with the nanostructures, providing an alignment-robust solution to the challenges in optical metrology. Such an alignment-free, non-destructive, and high-precision metrology technique enables real-time machine vision, semiconductor inspection, and advanced manufacturing.

Time-stretch spectroscopy is powerful for capturing transient spectral phenomena but remains fundamentally limited by detector bandwidth or timing jitter, especially under photon-starved conditions. Here, we devise and implement single-photon time-stretch computational ghost spectroscopy, which integrates dispersive wavelength-to-time mapping with programmable temporal encoding and correlation-based reconstruction to overcome these detection limitations. Specifically, temporally stretched ultrashort pulses are modulated by predefined encoding patterns and detected by a low-bandwidth detector, allowing reconstruction of near-infrared spectra with 450 resolvable channels across 1530-1590 nm without direct high-speed waveform acquisition. By further incorporating compressive sensing, accurate spectral recovery is achieved at sub-Nyquist sampling rates, substantially reducing acquisition requirements to facilitate high-speed operation at 210 kHz. In the single-photon regime, computational ghost reconstruction effectively suppresses the intrinsic detector timing jitter, yielding high-fidelity spectra at illumination fluxes down to 0.01 photons/pulse. By jointly enabling broadband coverage, high spectral resolution, high acquisition speed, and single-photon sensitivity, this approach establishes a computation-enhanced paradigm for time-stretch spectroscopy and provides a versatile platform for ultrafast and photon-efficient spectroscopic applications.

Optical image differentiation is a key operation for edge extraction in imaging and machine vision, yet most existing implementations rely on momentum-domain filtering elements and are typically developed within a scalar-wave framework. Here we demonstrate a nonlinear vectorial mechanism for optical image differentiation based on parametric wave mixing. By solving the full vector wave equation with a nonlinear polarization source term, we show analytically that frequency conversion intrinsically generates cross-polarized field components that correspond to spatial derivatives of the incident field. Exploiting the polarization-selective phase-matching conditions of second-order nonlinear crystals, particularly uniaxial crystals, we propose filter-free imaging schemes that simultaneously perform spatial differentiation and wavelength conversion. This nonlinear vector differentiation platform enables compact, wavelength-agile, and edge-enhanced imaging, offering new opportunities for mid-infrared imaging and all-optical signal processing.

Geometric phase quantifies net locomotion in dissipative media via gauge theory, but linking this theoretical quantity to noisy, sparse, and weakly periodic biological shape data is challenging. We develop a theory-guided, data-driven Koopman autoencoder to recover the limit cycle embedded in imperfect cyclic data and extract shape gaits and geometric phase from sperm and nematode data. We introduce a geometric phase sensitivity function that quantifies responses to shape perturbations and reveals mechanical information using only gauge-theoretic structure, without assuming mechanical laws.

We present a design agent which is a Large Language Model (LLM) that orchestrates, but does not perform, the numerical simulations to design a silicon-on-insulator (SOI) $2\times2$ directional coupler. We choose a symmetric phase-matched coupler where a lot of analytical results are available that help the design strategy. The LLM proposes candidate gap values (a geometrical dimension size) and judges convergence, while all physics is owned by deterministic solvers: a frequency-domain eigenmode solver estimates the coupling coefficient~$\kappa$ for the current design, and an independent Finite-Difference Time-Domain (FDTD) stage validates it. Both solvers operate on a common slab-projected two-dimensional (2D) effective-index reduction of the silicon film, so the design~$\kappa$ and the FDTD response are consistent by problem design; the residual between them is shown to be a single constant phase offset~$\phi$, attributable to a fixed excess coupling length $L_{\mathrm{extra}}=\SI{2.837(11)}{\micro\meter}$ that we find invariant across a factor-of-two range in~$\kappa$. Folding this offset into a closed-loop length correction, the agent delivers a $50/50$ splitter whose FDTD-measured cross fraction is $0.498$ (target $0.500$), a residual of $0.0017$. Results are made self-consistent within the 2D effective-index model; and the LLM succeeds in delivering a suitable design over a number of attempts.

Comprehensive characterization of numerical dissipation is essential for high-fidelity simulations of magnetohydrodynamic (MHD) turbulence. In this work, we present an a posteriori framework for directly estimating numerical dissipation in MHD turbulence from simulation data without invoking a priori assumptions. Implemented in the open-source Python package PyMHD, the framework is applied to simulations of Alfvénic turbulence, turbulent small-scale dynamos, and MRI-driven turbulence, yielding a systematic characterization of the anisotropy and spectral properties of numerical dissipation across these regimes. The results indicate that numerical dissipation primarily dissipates energy transferred by the turbulent cascade at small scales, consistent with the conventional interpretation. However, its spectral properties are distinct from those of physical viscosity and resistivity, such that it cannot simply be represented by effective dissipation coefficients. In addition, numerical dissipation inherits the anisotropy of the underlying turbulence, and can even exhibit anomalous anti-dissipative behavior under certain circumstances. Moreover, this framework enables identification of the conditions under which physical dissipation dominates numerical dissipation across all scales, thereby providing practical guidance for achieving high-fidelity simulations of astrophysical MHD turbulence.

Quantitative imaging of heat transport with high spatial and temporal resolution is essential for understanding thermal processes in heterogeneous systems, yet direct measurements of transient temperature fields at material interfaces remain challenging. Here, we employ time resolved thermal optical diffraction tomography (thermal ODT), a label free nanoscale optical thermometry technique that reconstructs spatio-temporal evolution of three dimensional temperature fields from thermally induced refractive index changes. We show that thermal diffusion along an interface is controlled by their thermal effusivity contrast. We also derive an effective interfacial diffusivity that accurately describes the lateral propagation of thermal fields and validate the model through finite-element simulations across a broad range of liquid-glass interfaces. Surprisingly, liquids with lower bulk thermal diffusivities exhibit faster interfacial thermal spreading due to their lower effusivities. The measured diffusivities agree quantitatively with theoretical predictions over diverse material combinations. By combining volumetric thermal imaging with a general framework for interfacial heat transport, our work establishes thermal ODT as a powerful platform for investigating nanoscale thermodynamics and engineering heat flow in heterogeneous environments.

This paper uses ADT v9.0 HURSAT v07b data to measure tropical cyclone (TC) activity in four basins from 1990-2024. It is only in the North Atlantic basin that relative sea-surface temperature contrasts and the number of extreme (category 3+) tropical cyclones increased. And it is only there that the complete chain held -- from annual aerosol pollution to sea-surface temperature contrasts, and in turn from those contrasts to the number of cyclones. Conversely, associations are inconsistent, insignificant, or counterintuitive in the three Pacific Ocean basins, especially insofar as annual temperature contrasts did not associate with annual cyclone activity. (Where appropriate, our analysis controls for aerosol changes and the El Nino-Southern Oscillation.) Worldwide, it is no longer justified for the IPCC to hold that "It is likely that the global proportion of Category 3-5 tropical cyclone instances ... ha[s] increased globally over the past 40 years."

Hybrid bright-dark soliton complexes offer profound insights into multi-component nonlinear wave dynamics, yet their systematic generation via artificial saturable absorbers remains unexplored. Here, we experimentally demonstrate a comprehensive hierarchy of higher-order hybrid soliton architectures within a single nonlinear polarization rotation (NPR)-based erbium-doped fiber laser. Continuous tuning of intracavity polarization states and pump power enables reproducible access to soliton structures, with stable mode-locking confirmed at 8 MHz and a signal-to-noise ratio exceeding 72 dB. Cross-phase modulation (XPM)-mediated inter-component coupling and birefringence-induced polarization evolution collectively govern the observed soliton multiplicity, with NPR's inherent tunability proving decisive in accessing higher-order hybrid soliton regimes unexplored in prior studies. These findings expand the fundamental taxonomy of dissipative solitons and establish NPR-based fiber lasers as a reconfigurable platform for complex nonlinear wave engineering.

Weakly collisional plasmas are widespread in astrophysics and can sustain pressure anisotropy, yet most analytical tearing-mode scalings assume an isotropic equilibrium. We develop a linear theory of resistive tearing in nonideal gyrotropic MHD for a force-free Harris current sheet characterized by perpendicular plasma beta $\beta_0$ and parallel-minus-perpendicular beta difference $\Delta\beta_0$. In the ideal outer region, anisotropy changes the far-field decay rate, the matching parameter $\Delta'$, and the upper wavenumber cutoff for localized tearing, $\alpha\equiv ka<\alpha_c=\sqrt{\mathcal{A}/\mathcal{R}_0}$, with $\mathcal{A}=1-\Delta\beta_0/2$ and $\mathcal{R}_0=1+\frac{1}{2}[(\gamma_\parallel+\gamma_\perp-2)\beta_0+\gamma_\parallel\Delta\beta_0]$. In the resistive inner layer, anisotropy enters the leading momentum balance through $\mathcal{A}$. We derive modified FKR and Coppi branches and, by matching them at their crossover wavenumber, obtain $\gamma_{\max}\tau_A\sim\mathcal{A}^{1/2}\mathcal{R}_0^{-1/4}S^{-1/2}$. Thus the classical Lundquist-number exponent is retained, while the prefactor depends on the equilibrium anisotropy, plasma beta, and gyrotropic closure. PSECAS eigenvalue calculations support the Coppi branch and are consistent with the FKR branch when a fitted finite-wavelength approximation for $\Delta'$ is used. Within the localized-mode and pressure-positive domain, positive $\Delta\beta_0$ generally suppresses tearing and broadens the inner layer, whereas negative $\Delta\beta_0$ enhances growth and shifts the fastest mode to larger wavenumber. This work identifies how prescribed equilibrium pressure anisotropy modifies both ideal outer matching and resistive inner-layer dynamics in the gyrotropic-MHD regime.

Integrated structured-light sources usually obtain high-dimensional orbital angular momentum (OAM) states by encoding each channel into separate gratings, waveguides or metasurfaces, which ties modal capacity to structural complexity. Here we show that intrinsic material anisotropy can instead act as a built-in angular-momentum coupler. In an X-cut thin-film lithium niobate (TFLN) microring vortex emitter, the in-plane optical axis causes a circulating whispering-gallery mode to sample a periodically varying effective index, producing continuous azimuthal phase modulation. This modulation converts each resonance from a nominal single-charge emitter into a coherent topological sideband lattice with charges l=l_p+2n and Bessel-weighted amplitudes. Broadband measurements resolve a representative principal-charge series from l_p=-13 to +13, while additional devices with 100 and 200 GHz free spectral ranges (FSRs) show scalable resonance addressability. The emitted lattices are reproduced by a forward-calculated Fourier--Bessel model, supported by OAM projection measurements, and exhibit focusing into annular perfect-vortex fields and self-healing after obstruction. Waveguide-induced circular polarization further adds a vectorial spin--orbit channel. These results turn TFLN anisotropy from a material constraint into a compact mechanism for resonance-addressed high-dimensional structured-light generation.

The transition-matrix ($T$-matrix) method has established itself as a prominent technique for computing the scattering response from spatially localized objects. The suitability becomes apparent particularly when considering not just isolated objects but also large ensembles of aperiodically or even periodically arranged objects. A versatile implementation of the method is provided by the treams program, which efficiently computes the electromagnetic response of scatterers in various arrangements [Comput. Phys. Commun. 297, p. 109076 (2024)]. Here, we rely on this framework and present a new program, acoustotreams, dedicated to simulating the acoustic scattering of pressure waves by clusters of particles, both with and without periodic boundary conditions. The computations are performed using the $T$-matrix method with scalar spherical and cylindrical waves as basis sets, and the scattering matrix ($S$-matrix) method in the basis of scalar plane waves for stratified media. The underlying theory is presented alongside the program structure and illustrative examples. The code is open-source and available on the Python Package Index for Linux, Windows, and macOS. Version control is maintained through GitHub, where we also provide automated tests, documentation, and detailed examples. We expect this work to contribute to the field of numerical methods for multiple-scattering problems by offering a computational framework capable of a comprehensive description of pressure-acoustic scattering in artificial media, including well-established metamaterials and metasurfaces.

The VMM3a Application-Specific Integrated Circuit (ASIC), integrated into the Scalable Readout System (SRS), provides high-rate capability together with precise charge and timing measurements for gaseous detectors. In this work, the SRS-VMM3a readout architecture has been implemented for the HYDRA (HYpernuclei Decay at R$^3$B Apparatus) Time Projection Chamber (TPC), a dedicated pion tracker developed to study hypernuclei within the R$^3$B experiment at GSI/FAIR. We present the adaptation of the VMM3a-based front-end electronics to the HYDRA-TPC pad plane, including the design of custom adapter boards, power distribution, and synchronization with the R$^3$B data acquisition system. The performance of the readout chain was evaluated through a series of laboratory and integration tests. The results demonstrate reliable operation, precise timing performance, and compatibility with the R$^3$B data acquisition framework, establishing the SRS-VMM3a system as a suitable readout solution for the HYDRA-TPC.

We report the fabrication and characterization of an integrated quantum photonic device consisting of an electrically driven whispering-gallery-mode micropillar laser evanescently coupled to a ridge waveguide, both incorporating InGaAs quantum dots (QDs). The lasing characteristics of microlasers are systematically investigated as a function of the pillar-waveguide gap distance. Coherent emission from the whispering-gallery-mode microlaser coupled into the waveguide enables on-chip optical excitation of QDs embedded in an electrically contacted micropillar at the end of the waveguide. Under continuous-wave on-chip excitation, we observe single-photon emission with $g^{(2)}(0) = (3.49 \pm 0.01) \%$ for a QD integrated in the outcoupling micropillar which can be spectrally tuned-by the quantum confined Stark effect. These results constitute an important step toward low-footprint, deterministic, and scalable single-photon sources for QD-based integrated quantum photonic circuits.

A methodology for passive mitigation of vortex-induced vibrations (VIVs) in subsea dynamic power cables is developed using optimized, strongly nonlinear bi-stable mass-spring-damper attachments, termed bi-stable nonlinear energy sinks (BNESs), within the open-source MoorDyn library. A fully three-dimensional time-domain framework captures cable dynamics, Morison-type hydrodynamic forcing, nonlinear vibration-mitigation mechanisms, and spatially and temporally varying currents. The BNESs are consistently integrated into the cable model, allowing treatment of highly non-stationary VIVs. A data-driven optimization study samples the BNES design space across multiple current profiles and shows that properly tuned configurations substantially reduce peak-to-peak cable vibration amplitudes. The BNESs also produce significant and robust reductions in cumulative VIV-induced energy intake from the surrounding flow and in drag energy. To the authors' knowledge, passive nonlinear attachments are shown for the first time to reduce both energy intake and drag amplification in a subsea cable, with potential benefits for short- and long-term fatigue. Time-frequency wavelet analysis reveals targeted nonlinear energy transfers and scattering from dominant high-amplitude, low-frequency cable modes to lower-amplitude, higher-frequency modes. This modal redistribution promotes rapid dissipation through hydrodynamic damping and internal structural losses, explaining the simultaneous reductions in vibration amplitude and cumulative energy intake. The results demonstrate that BNESs can provide effective and robust VIV mitigation for subsea power cables under realistic unsteady operating conditions and motivate future studies involving combined current-wave loading, platform-induced motion, and fatigue-life assessment.

Optical imaging in complex samples such as biological tissues is fundamentally challenging due to random light scattering that degrades resolution and contrast. When imaging realistic targets that contain natural dynamics such as flowing blood, the temporal variability introduces an additional obstacle, as the leading computational scattering-compensation methods require the target to remain stationary during a multi-frame acquisition process. Here we show that instead of struggling to perform rapid acquisitions, the target dynamics themselves can serve as an intrinsic information source for scattering compensation, replacing multiple controlled illuminations. The mathematical equivalence between target dynamics and conventional varying illumination patterns allows to demonstrate this approach in coherent holographic imaging and incoherent fluorescence microscopy using established matrix and model-based scattering-compensation techniques. Our general framework enables reconstruction of dynamic scenes with a number of acquisitions equal to the number of reconstructed frames, without the use of any spatial light modulators or illumination control.

Gradient-index (GRIN) media with freeform refractive-index distributions require ray tracing approaches capable of accurately modeling light propagation in complex three-dimensional environments. In this work, we develop a fully three-dimensional ray tracing method for arbitrary freeform GRIN media based on the local application of a set of vectorial relations of geometrical optics. At its core, the method constructs ray trajectories through successive local refractions using the vectorial form of Snell's law, while employing constant optical path length steps that naturally adapt the geometric step size within the medium. Unlike conventional continuous GRIN propagation approaches, the proposed method naturally handles refraction at the boundary of the medium as well as total internal reflection, enabling the treatment of both continuous media and discretized refractive-index distributions composed of isoindicial surfaces. The method is simple to implement and computationally efficient. Validation against an analytical solution demonstrates high accuracy, while applications to freeform GRIN configurations illustrate its capability not only for propagation analysis, but also for the direct design of complex tailor-made optical media.

Time-varying electromagnetic media exhibit rich nonstationary wave phenomena, but the symmetry governing reversal of arbitrary temporal modulation sequences has remained unclear. We show that, in lossless, spatially homogeneous media with identical initial and final states, the scattering matrices of ordered and reversed sequences are related by inverse--conjugation, independent of the number of stages. This yields a classification of temporal reciprocity in bi-isotropic media: isotropic and chiral media are channel-preserving, whereas Tellegen media are channel-exchanging despite Lorentz nonreciprocity. Deterministic time rewinding follows directly. Our results provide a framework for predicting and designing temporal scattering responses in photonic media.

Artificial intelligence (AI) is revolutionizing material research and discovery. However, its development in metamaterials is bottlenecked by a shortage of high-quality and executable structure-response databases, which are locked within scientific literatures as a mixture of text and images. Converting the rapidly growing body of scientific literatures into executable and reusable databases for machine-driven discovery is still a fundamental challenge. Here, we propose MetaDataGenAgent, a multimodal multi-agent framework that autonomously converts unstructured scientific literatures directly into metamaterial structure-response databases. MetaDataGenAgent establishes a complete literature-to-simulation pipeline through the coordinated operation of specialized agents for multimodal parameter extraction, physics-guided validation, topology-aware structural analysis, and solver-executable encoding. The framework introduces a closed-loop plan-execute-reflect mechanism that enables dynamic task decomposition, iterative validation, and feedback-driven model construction. Experimental results validate that MetaDataGenAgent can generate high-fidelity structure-response data for representative meta-atoms, which are further used to realize diverse electromagnetic functions, including far-field beam deflection, near-field holographic imaging and topologically protected surface-wave transport. By establishing an autonomous route from scientific literatures to AI-ready databases, the framework provides a general and efficient strategy that could be extended to a broad range of data-scarce scientific domains, including photonics, materials science, chemistry, computational science, and scientific automation.

Tungsten spectroscopic studies have been conducted in the Large Helical Device with a pellet injection technique. Spatiotemporal profiles of tungsten density were evaluated using a space-resolved spectrometer, for plasmas with an electron temperature of below 1 keV and electron density of $10^{19}-10^{20}$ $m^{-3}$. Slice & Stack, a method for reconstructing emissivity, was applied to a line at 191.7 Å , which is a part of the Unresolved Transition Array (UTA) spectrum at 90-250 Å. Tungsten density was obtained using photon emission coefficients of $\mathrm{W}^{17+} - \mathrm{W}^{27+}$, evaluated from collisional-radiative model. The tungsten pellet injected from outside the plasma was first ablated in the edge plasma and subsequently diffused throughout the plasma. This behavior is typical of pellet injection experiments. Furthermore, after an event triggered by NBI breakdown, tungsten accumulated in the core plasma. The radiation power was estimated from the evaluated tungsten density profile and cooling factor dataset, and compared with bolometer measurement. This sequence of processes would be useful for validating atomic data of tungsten ions in low-to-intermediate charge states.

This paper presents a conservative high-order discontinuous Galerkin (DG) method featuring time-accurate local time stepping for simulating low-Mach-number unsteady flows, based on a weakly compressible formulation. In this model, pressure is defined solely as a function of density, eliminating the need for a global pressure Poisson equation typical of incompressible solvers while preserving the locality and conservation of compressible schemes. This makes it suitable for low-speed unsteady flows and aeroacoustics. The spatial discretization uses a strong-form nodal DG spectral element method (DGSEM) on Gauss-Lobatto-Legendre points. Inviscid fluxes are handled by numerical fluxes tailored to the weakly compressible system; specifically, a two-rarefaction approximate Riemann solver is developed for the constant-sound-speed barotropic equation of state. Viscous terms employ the incomplete interior penalty Galerkin (IIPG) method. For time integration, a continuous extension Runge-Kutta (CERK) scheme constructs cell-local predictor polynomials for continuous-in-time volume reconstructions. Face fluxes are split into interior and common contributions: the former matches the volume quadrature, while the latter uses piecewise Gaussian quadrature from continuous predictors. This split preserves discrete summation-by-parts cancellation and ensures conservative inter-element flux exchange.

Several recent experimental studies have revealed non-monotonic variations in elastic turbulence-induced chaotic flow behaviour and pressure drop during the flow of viscoelastic fluids through a microporous medium. The present numerical study aims to investigate and hypothesise about the physical mechanisms governing these complex flow behaviours in an ordered microporous medium consisting of cylindrical micropillars arranged in a staggered configuration. We propose that birefringent strands of high elastic stress, generated by the stretching and alignment of polymer molecules within the porous structure, play the dominant role in controlling the non-monotonic variations in chaos and pressure drop. At low Weissenberg numbers, these stress strands develop gradually, mainly downstream of the micropillars, whereas beyond a critical Weissenberg number, they begin to fluctuate strongly, leading to chaotic flow dynamics. However, at even higher Weissenberg numbers, the strands become larger and stronger, eventually interconnecting between neighbouring micropillars, causing the flow to reorganise into a nearly steady, ordered state similar to that observed at low Weissenberg numbers. On the other hand, the pressure drop in the system consists of a mean contribution, obtained from statistically stationary flow quantities, and a fluctuating contribution. While the mean component increases monotonically with the Weissenberg number, the fluctuating component varies non-monotonically, leading to a similar trend in the total pressure drop. Moreover, the non-monotonic chaotic behaviour strongly depends on the solid volume fraction of the porous medium, which alters both the critical Weissenberg number for instability onset and the range over which the non-monotonic behaviour persists.

The phenomenon of N${_2^+}$ lasing, observed in femtosecond-laser filamentation, attract considerable interests in recent several years, with great application potentials in fields of remote sensing and ultrafast spectroscopy. Efficient N${_2^+}$ lasing at relatively-low pump energies and with high beam quality, while being highly-demanded for applications, remains, however, quite challenging in practical experiments. Here, we demonstrate a new route of generating low-threshold N${_2^+}$ lasing with unprecedently-high efficiency, which is enabled by soliton dynamics in a gas-filled hollow-tapered-capillary system. High-order-soliton compression of a 12-fs, 10-${\mu}$J-level pump pulse forms a sub-cycle asymmetric transient that tunnel-ionizes N${_2}$ to N${_2^+}$ and, through direct, single-photon resonant excitation, creates population inversion between the ground state ${X^2\Sigma_g^+}$ and the excited state ${B^2\Sigma_u^+}$${-}$a dynamic process distinct from the widely adopted three-state coupling picture${-}$and remarkably at unexpectedly low pump energy. In the experiments, we obtained 100-nJ-level N${_2^+}$ lasing pulses at 391 nm with conversion efficiencies up to 3.3$\times$10$^{-3}$, at pump energies of less than 50 ${\mu}$J. These results represent improvement of more than one orders of magnitude in both generation efficiency and lasing threshold, compared with prevailing filamentation-based schemes. Our study bridges two generally-disparate fields (sub-cycle soliton dynamics and N${_2^+}$ lasing), and paves the way for narrow-band, high-beam-quality lasing pulses that may find wide applications in advanced spectroscopy and nonlinear pump-probe experiments.

Waveguide-integrated single-photon avalanche detectors (SPADs) are essential components of integrated photonics platforms for scalable extreme-low-light applications without the use of cryogenics. Here, we demonstrate an integrated SPAD for visible light operating at room temperature in a free-running mode without gating. The device is based on a doped silicon diode end-fire-coupled to a silicon nitride (SiN) photonic integrated circuit (PIC). We investigate a range of lateral and vertical doping profile designs, and operate the devices with a simple current-mode passive quenching circuit. The optimal device is a laterally-doped p-i-n+ SPAD with a maximum photon detection efficiency (PDE) of 1.95 +/- 0.32% for input light at 685 nm wavelength, when reverse-biased at an excess of 1.5 V beyond the breakdown voltage of 15.0 V. We identify promising avenues for improving device performance, which would enable such integrated SPADs to be an attractive choice for cutting-edge integrated photonics solutions in quantum technologies, low-light imaging, and high-speed communications at visible wavelengths.

Three-dimensional full waveform inversion (3DFWI) is a powerful technique for reconstructing high-resolution subsurface velocity models. However, its application is often limited by high memory requirements, computational costs, and sensitivity to cycle skipping. To overcome these challenges, we propose a novel tensor train (TT) decomposition-based 3D implicit full waveform inversion framework (TT-3DIFWI) combined with a multi-scale structural similarity (M-SSIM) objective function. In this framework, the 3D velocity model is represented by TT decomposition as a product of a series of low-rank core tensors. Then, three axis-specific implicit neural network representations (INR) based on one-dimensional vector coordinates as input are constructed to predict these core tensors, rather than directly predicting the velocity model. This INR reparameterization method based on TT decomposition can significantly reduce the memory consumption of INR training while maintaining the accuracy and resolution of the 3D velocity model reconstruction. Meanwhile, the low-rank structure of TT decomposition also ensures the structural consistency of the reconstruction velocity, thereby improving the accuracy and continuity of the inversion result. Furthermore, the M-SSIM objective function can compare the multi-scale structural differences between predicted and observed data, and utilize the ultra-low frequency features to reduce cycle skipping. Numerical experiments on synthetic and challenging land datasets demonstrate that TT-3DIFWI with M-SSIM achieves accurate and continuous velocity reconstruction, even with poor initial models or missing low-frequency data.

Chaos is central to secure communication and physical random-number generation. Conventional cavity-optomechanical implementations, however, usually rely on weak single-photon optomechanical coupling and low-frequency mechanical modes, so access to deterministic chaotic dynamics often requires large driving power and careful suppression of thermal noise. Here we theoretically study a hybrid molecular optomechanical system formed by coupling a plasmonic nanocavity to a whispering-gallery-mode (WGM) microcavity. The plasmonic nanocavity provides terahertz-scale single-photon optomechanical coupling to a molecular vibration, while the WGM resonator offers a low-loss photonic channel that mitigates the short plasmon lifetime. By integrating the semiclassical equations of motion and evaluating the largest Lyapunov exponent, we map the nonlinear dynamical regimes in the parameter spaces of WGM detuning, plasmon--WGM coupling, and plasmon--vibration optomechanical coupling. We show that increasing the plasmon--vibration coupling drives the system from self-sustained oscillations to chaos through a period-doubling cascade. At moderate coupling strengths, isolated chaos windows can be opened or closed by tuning the WGM detuning and the inter-cavity coupling. These results identify molecular optomechanics as a controllable room-temperature platform for on-chip chaotic light generation and random-signal applications.

Catalyst discovery for ammonia synthesis is inherently a reaction-network challenge because catalytic performance is governed not by a single adsorbed intermediate, but by a surface's orchestrated compatibility with multiple distinct intermediates across competing dissociative and associative pathways. However, navigating ultra-large chemical spaces under such multi-intermediate constraints remains a formidable bottleneck for conventional screening workflows. Here, we report a reaction-network-level catalyst discovery framework driven by ten-million-scale generative exploration. By coupling adsorbate-specific generative Transformers with high-throughput machine learning potentials, we systematically map the structure-property landscapes of four critical intermediates (N*, NH*, NNH*, and HNNH*). Scale-dependent overlap analysis shows that the full four-intermediate compatibility space remains strongly under-sampled at conventional 105-106 generative scales, emerging exclusively under ten-million-scale exploration. By generating approximately 15 million configurations per adsorbate, followed by structural compression and machine-learning-potential predictions, we identified 279 highly potential target materials. This sparse compatibility space successfully recovers traditional Fe- and Ru-based motifs while uncovering previously unexplored catalyst families. Representative DFT calculations validate pathway-dependent mechanisms: Fe-V emerges as a dissociative-pathway lead by significantly lowering the initial N2 dissociation barrier, whereas Al-Pd-Zr efficiently stabilizes associative intermediates as an associative-pathway lead. These findings establish multi-intermediate reaction-network compatibility as a robust criterion for discovering advanced catalysts from multi-million generative chemical spaces.

Reversible control of crystal symmetry offers a powerful route to programmable optical functionality. However, achieving solid-state bistability between centrosymmetric and non-centrosymmetric crystalline phases remains a formidable challenge; examples of materials that enable stable switching of second-order nonlinear optical (NLO) responses are exceptionally rare. Here we report a solar-powered, symmetry-bistable organic material based on the photoisomerizable molecule (E/Z)-2-(4-(4-bromophenyl)thiazol-2-yl)-3-(4- (dimethylamino)phenyl)acrylonitrile (E/Z-BTDPA). The crystallizable E- and Z-isomers adopt distinct molecular packing arrangements that reversibly toggle between these states, controlling second-order NLO activity. The E-form exhibits strong second-harmonic generation (SHG), whereas the Z-form is SHG-inactive and displays twophoton luminescence. This bistable behavior is retained in flexible thin films, where sunlight-driven photoisomerization enables reversible photoswitching of the second-order electric susceptibility (\c{hi} 2), large-area optical patterning, and real-time NLO communication via waveform generation and text-string transcription at telecommunication wavelengths. This sustainable strategy bypasses rigid inorganic architectures, establishing photoinduced symmetry bistability as a scalable paradigm for all-optical computing and advanced communication networks.

Powder spreading and layer deposition are fundamental stages of Powder Bed Fusion (PBF) technologies and play a critical role in determining process stability and final component quality. This chapter examines the mechanisms governing powder-bed formation, highlighting the interactions between powder characteristics, process parameters, and machine architecture. Particular attention is devoted to the influence of particle size distribution, morphology, cohesion, flowability, layer thickness, recoater velocity, and environmental conditions on powder-bed quality. The resulting powder-bed is discussed as a process state variable whose characteristics, including packing density, surface coverage, effective layer thickness, and spatial homogeneity, directly affect energy absorption, melt-pool stability, defect formation, and mechanical performance. The chapter also reviews the application of the Discrete Element Method (DEM) for modelling powder spreading phenomena and quantifying powder-bed quality metrics. Finally, the role of powder reuse, lifecycle management, and future developments involving process monitoring, digital twins, and data-driven optimization strategies is discussed, emphasizing the growing importance of powder engineering in advanced metal additive manufacturing.

Transverse emittance is one of the central figures of merit for charged-particle beams because it connects the microscopic phase-space distribution to macroscopic accelerator performance. This report introduces the trace-space description of emittance, relates it to the Courant-Snyder formalism and the second-moment beam matrix, and then follows the experimental logic behind common diagnostics. Emphasis is placed on how profile measurements, masks, drifts, quadrupole scans, and transverse deflecting structures convert otherwise inaccessible angular or slice information into measurable beam sizes.

Negative-energy states of light are seldom considered in conventional electromagnetic theory. In this work, we introduce a new wave-function representation in helicity space, through which Maxwell's equations are reformulated into a Dirac-like structure. The resulting negative-energy solutions can be interpreted as antiphoton states. Motivated by the Dirac theory of electrons, we further establish a second-quantized framework for photons that is dual to its electronic counterpart. In this framework, the transverse spin of light admits a natural quantum-mechanical interpretation: instead of being described solely as a classical polarization vector, it appears in direct analogy with the intrinsic spin of electrons.

Predictive modelling of triboelectric nanogenerators (TENGs) remains fragmented across analytical theories, finite-geometry solvers and disconnected simulation workflows. These disparate approaches must be unified into an executable framework to advance quantitative TENG this http URL we introduce a charge-defined modelling framework and implement it as TENG-CLAW, a physics-governed platform for traceable TENG simulation. The framework establishes a self-consistent electrostatic hierarchy in which triboelectric charges, pre-charging charges and compensating electrode charges serve as defining state this http URL hierarchy connects the infinite plate analytical limit for near-uniform fields with finite-geometry numerical formulations required for edge-dominated devices. Built on this basis, TENG-CLAW converts user-defined research requests into physically admissible simulation tasks, so that generated outputs are tied to explicit charge states, boundary conditions, solver routes and reusable artifacts across spatial, temporal, field-level, comparative and reporting workflows. This work establishes a rigorous computational basis for interpreting TENG mechanisms and provides reproducible research infrastructure for simulation and physics-guided device design.

Non-normality can induce large transient growth in linearly stable systems. Determining whether this growth triggers a transition in the underlying nonlinear system, however, requires understanding the interaction between non-normality and nonlinearity. Here, we develop a weakly nonlinear theory for linearly-stable, non-normal systems subject to harmonic forcing, enabling a systematic analysis of this interaction. Following Ducimetière et al. (J. Fluid Mech., vol. 947, 2022, A43), we define a formal small parameter $\varepsilon$ as the reciprocal of the system's maximum linear amplification. However, we ensure asymptotic consistency by providing a framework that naturally adapts to the underlying structure of the system. The approach is applied to a harmonically forced channel flow and to a two-dimensional model mimicking the structure of the Orr-Sommerfeld-Squire equations. Unlike classical weakly nonlinear analysis near bifurcation points, the resulting amplitude equations are non-universal. In fact, a single linear mode amplified by the non-normality can nonlinearly excite a multi-modal and multi-frequency response at leading-order, which is system- or even regime-specific. Nevertheless, the method yields asymptotically consistent amplitude equations that capture this complexity provided a limit in which $\varepsilon\rightarrow0$ can be identified. As the forcing amplitude increases, the reduced equations capture stable nonlinear states emerging from the laminar flow, their subsequent bifurcations, and their eventual collision with the boundary of their basin of attraction. Thus, the amplitude equations can capture subcritical transitions driven by forcing and varied initial conditions and enable the identification of critical parameters beyond which no stable weakly nonlinear state exists.

The charge transfer state, the local excited state on pentacene, and the local excited state on buckminsterfullerene have been studied for four models of the pentacene-buckminsterfullerene organic solar cell. These models have different interface configurations between the donor and the acceptor. The study has been done by using different functionals and time-dependent density functional theory-namely, the long range-separated hybrid with coulomb-attenuating method approach (CAM-B3LYP functional), the popular B3LYP (Becke, three-parameter, Lee- Yang-Parr) exchange-correlation functional, and the density functional tight binding (DFTB) method. The charge transfer state energy obtained by using CAM-B3LYP without optimally tuned range-separated hybrid parameters are very close to those obtained by using a many-body dispersion-corrected, optimally tuned range-separated hybrid functional (OPT-wB97XD). Both the B3LYP functional and the DFTB method fail to describe correctly the charge transfer energy for all models. Each of them is underestimating around 1 eV compared with range-separated hybrid functional.

Positron range (PR) blurring is a fundamental resolution limitation in PET imaging with high-energy positron emitters such as 82Rb, causing contrast loss and spill-out effects across heterogeneous tissue interfaces. We propose PRC-TP, a positron range correction (PRC) framework with explicit texture preservation that decouples deterministic resolution recovery from stochastic texture restoration. A nnFormer-based neural network (NN) was trained on patient-derived Monte Carlo simulations to map PR-degraded 82Rb reconstructions to PR-free references using attenuation maps as anatomical context. However, this NN also significantly removed the noise in the images, which could impact some texture analysis methods or make the images look unrealistic. An auxiliary Noise2Noise model estimates that smoothing effect, enabling texture extraction and transfer to the PR-corrected prediction through Model-consistent Texture Re-Injection (MTRI). In simulated patients, PRC-TP preserved contrast recovery close to ground truth (GT) (98.96-99.04%) while restoring noise and CNR closer to the reference. The function-based MTRI formulation achieved near unity global texture amplitude agreement with GT (0.997 +/- 0.011), reducing the input texture amplitude bias (0.951 +/- 0.011). Radiomics analysis showed improved agreement with GT across texture-sensitive feature families. A clinical 82Rb evaluation showed trends consistent with simulations, including comparable contrast-ratio increase (10.18% vs. 10.99%) and restoration of texture suppressed by PRC. These results support PRC-TP as a practical framework for resolution recovery with acquisition-consistent texture preservation in PET imaging. Submitted to IEEE TRPMS.

Ion stopping in dense plasma is crucial for stellar evolution and fusion ignition. However, its behavior in the strong ion-plasma coupling regime beyond the linear limit has long remained elusive, due to formidable experimental challenges. Here we report the first experimental investigation of ion stopping at an unprecedented coupling parameter exceeding unity, achieved by sending laser-accelerated short-pulse and intense quasi-monoenergetic carbon ions ($\sim$583 keV/u, C$^{5+}$) into a uniform, long-lived, well-characterized dense plasma target ($T_e$ $\approx$ 17 eV, $n_e$ $\approx$ 4$\times$10$^{20}$ cm$^{-3}$). By simultaneously measuring ion energy loss and charge-state evolution, we eliminated key experimental ambiguities arising from charge-state determination. Our results clearly show a reduction in stopping power compared with predictions from standard linear dielectric response or binary collision models, and they agree well with the hybrid calculation of molecular dynamics with quantum corrections. The importance of nonlinear screening effects arising from many-body interactions and quantum effects due to the wave nature of electrons was demonstrated at strong coupling. This work establishes a definitive high-fidelity experimental benchmark for collisional dynamics in the strong-coupling regime. It offers critical insight for accurate modeling of energy transport in inertial confinement fusion and astrophysical plasmas.

High-sensitivity phase sensing is traditionally performed using complex interferometric configurations. As an alternative, we present a robust and simple system based on the classical Young's double-slit experiment that leverages the "optical ventriloquism" effect to amplify phase signals. This phenomenon arises from super-oscillations near intensity minima, which cause an anomalous shift of the local wave vector as a consequence of weak-value behavior. In this work, we constructed an accessible experimental setup that translates minute phase differences into measurable spatial displacements of the diffraction pattern. We compare the detection performance of an sCMOS camera and a quadrant-cell detector, analyzing the noise sources that limit the system's sensitivity. Our results demonstrate that engineering the dark regions of simple diffraction patterns can provide a foundation for advanced optical sensing technologies with minimal structural complexity.

Advances in optical clocks motivate a redefinition of the second, requiring rigorous evaluations of systematic uncertainties and robust consistency among the clocks. Here, we report the full evaluation of the systematic frequency shifts of an \AlP single-ion clock, and the measurement of its absolute frequency and frequency ratio with a \Sres optical lattice clock at PTB. The evaluated total systematic fractional frequency uncertainty is \num{1.6e-18}, mainly limited by the accuracy of the relevant atomic coefficients and by background gas collisions. The absolute frequency of the clock has been measured to be $\nu_{\mathrm{Al}^+}=1 121 015 393 207 859.19(24)\,$Hz, obtained by comparison with two primary caesium fountain clocks at PTB. The frequency ratio between the \Al and \Sr optical clocks has been determined to be $\nu_{\mathrm{Al}^+}/\nu_{\mathrm{Sr}}=2.611 701 431 781 462 668(36)$, limited by the accuracy of the \Sr clock. This ratio differs by $8.6\sigma$ and $1.2\sigma$ from the 2021 and 2025 frequency ratio published by the BACON collaboration, respectively. These results represent an important contribution toward a future redefinition of the second using optical clocks, and underscore the importance of independent measurements of clock-candidate frequency ratios across different institutions.

Silicon-based wavelength division multiplexing (WDM) filters are essential for scaling optical communication capacity in data centers and telecommunications this http URL, extending silicon WDM systems beyond 32 channels with 100 GHz spacing poses significant challenges due to limitations in conventional filter architectures. Here we present the first silicon 64$\times$100 GHz WDM filter by introducing a novel ring-Mach-Zehnder interferometer (MZI) cascade architecture. Our design utilizes third-order polynomial interconnected circular (TOPIC) bends to construct low-loss half-ring waveguides, facilitating an MZI configuration where the arm length difference is determined entirely by half of the ring structure. This approach ensures precise alignment between the MZI interference peaks and the ring resonator wavelengths, with the MZI FSR being exactly double that of the ring, eliminating the need for dynamic tuning between the MZI and the ring. We demonstrate the concept through a 16$\times$400 GHz WDM filter with insertion loss of 1.3$\pm$0.6 dB and channel isolation $\geq$14.3 dB. The 64$\times$100 GHz implementation, realized using a 4-channel interleaver followed by four 16$\times$400 GHz WDM filter, achieves insertion loss of 3.2$\pm$1.1 dB and channel isolation $\geq$10.7 dB. This work opens new possibilities for high-density silicon photonic WDM systems, addressing the growing bandwidth demands of artificial intelligence and machine learning applications.

Hybrid models combine trusted physics with data-driven correction, but a physical model is rarely wrong everywhere or in the same way. The key diagnostic question is local: where does the model fail, what missing mechanism explains the failure, and is the evidence statistically real? Existing sparse-discovery and discrepancy-learning methods usually fit one global correction, which can spread a local error into clean regimes, bias trusted physical parameters, and provide no calibrated significance for selected terms. We introduce LISDD, Localized, Identifiable Sparse Discovery of Discrepancy, a framework that localizes model error to an operating regime, identifies a sparse symbolic form for the missing mechanism, and certifies the discovery with an exact finite-sample test. LISDD fits the known physics on an automatically detected clean regime, flags discrepant regions with a calibrated residual-energy statistic, selects the local missing term by exhaustive holdout over a candidate library, and confirms significance with a sample-split $F$-test. A false-discovery-rate extension handles multiple discrepant regions with different missing mechanisms. In controlled experiments, LISDD keeps physical-parameter bias at 0.002 versus 0.43 for global-discrepancy and black-box baselines, raises localization $F_1$ from 0.44 to 0.80, recovers the correct symbolic form with probability one, attains exact detection, and controls the multi-region false-discovery rate while recovering every planted mechanism. The result is a calibrated diagnostic tool for grey-box building-energy models when a fixed physical law silently breaks in one operating regime.

Optical monitoring of living tissue targeting quantification of molecular composition is an active area of research. In the case of broadband spectroscopy, one can attempt to extract molecular, or more precisely, chromophore concentration from a broad-range spectrum of the reflected light. However, selecting the shortest wavelength range sufficient for quantitative optical monitoring remains an open problem. Various wavelength optimization methods are scattered throughout the literature, however, there is no unified view to date. Our work's motivation is to construct a wavelength selection framework by unifying existing selection approaches and propose a novel projection-based method that allows for the pre-identification of a wavelength range that is adequate for the final selection. The framework specifically focuses on proposing different methods that quantify match or mismatch between the chosen light-matter interaction model, defined by the chosen endmembers (e.g. molecular chromophores), and the measured intensity from the spectroscopic data. To evaluate the framework, we perform a retrospective analysis on a broadband spectroscopy dataset of piglets during an induced hypoxia-ischemia state. Overall, we show that our novel projection-based method can be used for band selection, and that existing approaches can be used in conjunction to select an optimal minimal wavelength set that satisfies biophysical model constraints.

We carry out a six-parameter multi-objective Bayesian optimisation of a carbon--hydrogen double-layer target for target-normal-sheath proton acceleration. The campaign consists of 80 two-dimensional EPOCH simulations with the laser amplitude $a_0$, pulse duration $\tau$, carbon-layer thickness $L_1$, hydrogen-layer density $N_2$, hydrogen-layer thickness $L_2$ and hydrogen-layer radius $r_p$ as input variables. Each final proton spectrum is scored by the peak energy, the charge fraction inside a $\pm10\%$ peak-energy window and the charge in that window. Among the Pareto-set evaluations, the cases with peak energies between 64 and 71 MeV occur near $a_0=30$, $\tau=45$ fs, $L_1=0.3\,\mu{\rm m}$, $L_2=30$ nm and $r_p=0.15\,\mu{\rm m}$. Along this branch, increasing $N_2$ raises the in-window charge and increases the bandwidth. The small rear-layer radius keeps the proton source within the flat central region of the transverse sheath field, where the accelerating field is nearly uniform. A 3D calculation is performed for the intermediate-density case $N_2=11.85\,n_c$, which balances bandwidth and in-window charge along this branch. The corresponding 2D spectrum has $E_{\rm peak}=67.4$ MeV and $\Delta E/E=18.8\%$, whereas the 3D spectrum has $E_{\rm peak}=34.1$ MeV and $\Delta E/E=7.0\%$. The lower 3D peak energy and narrower bandwidth are associated with an earlier decay of the rear-sheath field and an earlier saturation of the proton peak energy, and the quasi-monoenergetic peak is retained in 3D.

The matrix exponential and the closely related matrix phi-functions play a fundamental role in the solution of first-order systems of ordinary differential equations (ODEs). In particular, they appear in exact solutions of certain linear ODE systems, and in exponential integrators, a class of explicit time discretisation methods for computing approximate solutions of stiff semi-linear ODE systems. Fundamental properties of the matrix exponential include that it is nonnegative if the matrix is essentially nonnegative and stochastic if the matrix is a transition-rate matrix. In this paper, we study related properties for the matrix phi-functions. Using these properties, we then provide insights into deterministic solutions of linear and semi-linear ODE systems and outline how such solutions can be used to generate stochastic simulations to quantify variability in model predictions. The paper concludes with some illustrative examples exploring the theory for some ODE systems arising from spatial discretisation of PDEs of diffusion, advection-diffusion, Fisher-KPP and Allen-Cahn type.

Cyclic peptide design requires balancing local residue preferences with constraints from ring-forming chemistry, residue spacing, topology, target compatibility, and developability. Here, we present a reduced-alphabet quadratic unconstrained binary optimization (QUBO)/Ising formulation for constraint-driven cyclic peptide sequence design. Amino acids are grouped into physicochemical or interaction-based residue classes, and peptide positions are represented by binary residue-class assignment variables. The objective combines one-hot sequence validity, cyclization constraints, optional target-compatibility terms, motif and composition rules, and coarse developability proxies. By modifying the relevant constraint terms, the same framework can represent head-to-tail, disulfide-bridged, stapled, and bicyclic peptide designs. A resource-aware eight-class alphabet motivated by MJ interaction-profile clustering is used as a default representation to balance coarse interaction-pattern preservation with encoding cost. The resulting QUBO/Ising objective is solver-agnostic and can be explored using classical or quantum-compatible binary optimization procedures. The model is intended as an early-stage search-space reduction and prioritization layer: it produces low-energy residue-class sequences rather than final molecular candidates, which require amino-acid decoding, cyclization-aware construction, and downstream structural or experimental validation.

Predicting the transition of turbulence in pipe flow remains a fundamental problem in fluid dynamics. We use a variational approach to compute nonlinear optimal perturbations to the laminar flow at Reynolds number $Re\leq 5000$. As $Re$ increases, optimal perturbations remain structurally similar, but increasingly localize while their thickness scales as $\delta_r \propto Re^{-1/3}$. They grow via the Orr mechanism, followed by a phase of strong nonlinear interaction of oblique waves and a lift-up phase. The energy gain during the Orr phase increases linearly with $Re$ and is independent of the initial perturbation energy, $E_0$. The energy gain during the oblique and lift-up phases is governed by nonlinearities and scales as $\propto Re^2$. We find that regardless of the Reynolds number, transition occurs if the energy of the perturbation exceeds a constant threshold. As a result, the minimum perturbation energy required to cause transition in pipe flow scales as $\mathcal{O}(Re^{-3})$.

Tensor Trains (TT), also known as Matrix Product States (MPS) and Matrix Product Operators (MPO), provide a compact and structured representation for high-dimensional data and operators. One of the most expensive manipulations involving tensor trains is the contraction of two MPOs. A popular and accurate method for mitigating this cost is the fit algorithm. However, it is still comparatively costly since it involves 2-site updates. Moreover, the parallelization of the fit algorithm when used for MPO-MPO contractions has received comparatively little attention. In this work, we present two strategies for accelerating the fit algorithm, usable in combination: (1) We use MPI-based distributed-memory parallelization tailored for MPO-MPO contractions, employing one of two MPO gauge choices: (1a) the inverse canonical gauge, which yields near-ideal parallelization speedup across all problem sizes; and (1b) the site-canonical gauge, which avoids the inversion of singular values but requires extra computations to ensure global consistency, thus yielding excellent parallelization speedup only for large problems requiring several sweeps before convergence. (2) We use randomized projections to reduce the cost of local updates from 2-site to 1-site costs while retaining 2-site accuracy, and to speed up contractions of environment tensors with MPO tensors.

Chronological age is an incomplete coordinate for aging. Individuals and species sharing the same calendar time can differ substantially in physiological reserve, molecular damage, mortality hazard, and remaining lifespan. The Principle of Biological Time Equivalence (PBTE) offers a thermodynamic reformulation: biological aging is governed by the accumulation of \emph{internal} physiological time rather than chronological time alone. Building on prior PBTE work, this paper defines the internal-time coordinate $\theta(t)=\int_0^t f(s)\dd s$, where $t$ is chronological time and $f(s)$ is an instantaneous physiological frequency (for example heart rate or respiratory rate), so that $\theta$ is the accumulated count of physiological cycles. Its entropy-normalized extension is $\Tsig(t)=\int_0^t[\sigz(s)/\sref]f(s)\dd s$, where $\sigz(s)=\dd\Sigma/\dd\theta$ is the entropy produced per physiological cycle (the entropy cost per biological tick), $\Sigma$ is cumulative entropy production, and $\sref$ is a fixed reference entropy cost per cycle used as a normalizing unit. The normalized PBTE age $\APBTE(t)=\Tsig(t)/\Nref$ measures the fraction of a reference entropy--cycle budget consumed, where $\Nref$ is the reference number of entropy-weighted cycles available over a lifetime. The manuscript is explicitly theoretical: no empirical cohort is analyzed, and the numerical demonstrations are synthetic stress tests rather than validation.

Mizoram records the highest landslide frequency among all Indian states, yet physics-based models that predict the timing of slope failure remain unavailable for the region. Here, we apply the rate-and-state friction (RSF) block-slider framework of Paul et al.(2024) to 19 rainfall-triggered landslides in and around Aizawl (2016--2025) to investigate the hydro-mechanical coupling between pore-pressure transients and the slow-to-fast transition of slopes hosted in Miocene shale-dominated lithology. For each event, satellite-derived (GPM) rainfall is propagated to failure depth using a 1D infiltration model across three hydraulic conductivity scenarios, and RSF parameters are inverted to reproduce the observed failure date. The resulting dimensionless normalized pore-pressure $\chi = \mu_0 \Delta P / (a\,\sigma'_f)$ cleanly separates the 19 events into two dynamically distinct failure regimes: synchronous failure ($\chi \gtrsim 4$, zero delay from peak pore-pressure), exemplified by the eight-event Cyclone Remal cluster of May 2024, and delayed failure ($\chi \sim 1$--$3$, delays of hours to 10~days), controlled jointly by $\chi$ and the velocity-weakening ratio $a/b$. Using CMIP6 extreme-rainfall scaling factors for northeast India under SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios, we project that the fraction of landslides falling in the zero-warning synchronous regime increases from the current $\sim$56% to $\sim$72% under SSP5-8.5. Our results imply a significant climate-driven escalation of multi-site, clustered landslide failure risk driven by the intensification of extreme precipitation events.

We adopt the Bayesian inference framework to solve an inverse Reynolds-Averaged Navier-Stokes (RANS) problem for the approximate posterior probability distribution of the turbulence model parameters and inlet boundary conditions of a confined turbulent jet. The data are noisy 3D flow MRI measurements of a Newtonian fluid flowing through the Food and Drug Administration (FDA) nozzle geometry. We assimilate this into RANS using two algebraic turbulence models based on the mean shear rate magnitude and the turbulence kinetic energy. We demonstrate that the inferred models are able to reconstruct the measured mean flow velocities without overfitting and we provide uncertainty estimates for the model parameters. The methodology can readily be extended to more complex RANS models, provided that they remain differentiable.

Network dismantling aims to identify a node removal sequence that can rapidly destroy network connectivity, which is an important problem for understanding the structural fragility of complex systems and designing intervention strategies. Existing studies mainly focus on pairwise networks or assume weak-deletion rules where node removal only causes hyperedges to shrink in higher-order networks. However, in many real higher-order systems, the failure of one participant may cause the entire group interaction to fail, i.e., the strong-deletion mechanism. Such a mechanism cannot be fully captured by projected networks or methods based on weak-deletion rules. To address this challenge, we propose hyper-Vulnerability-weighted Dominance rank (hyper-VDrank), a higher-order centrality method for hypergraph dismantling under strong deletion. Hyper-VDrank constructs a higher-order competition dynamics mediated by hyperedge-induced environments, where a node does not compete only with individual neighbors but responds to the collective pressure formed by other nodes in the same hyperedge. It further introduces a hyperedge vulnerability weight based on redundancy and size effects to capture the vulnerable structures, facilitating the distinction of critical nodes. Experiments show that hyper-VDrank reduces the largest connected component more rapidly, collapses the hypergraph earlier, and produces greater structural fragmentation than classical and recent methods. On 14 real-world hypergraphs, hyper-VDrank improves dismantling efficiency by 23.65% and reduces the collapse threshold by 27.63% on average compared with the baselines. In summary, hyper-VDrank offers an effective hypergraph dismantling tool and a new perspective on identifying vulnerable structures in higher-order complex systems.

Spin-orbit coupling (SOC) is widely exploited as a fundamental mechanism for generating orbital angular momentum (OAM); however, conventional approaches typically lack flexibility and tunability. Here, we introduce a continuously tunable second-harmonic generation (SHG)-SOC cascade mechanism modulated by a spatially movable nonlinear crystal. Under linearly polarized excitation, the SHG-SOC cascade engages synchronously with both degenerate and nondegenerate SHG processes, thereby expanding the OAM spectrum and significantly enhancing the information density and feature-mapping capacity of the optical field. Moreover, the OAM spectral distribution can be continuously reconfigured simply by translating the nonlinear crystal. This deterministic physical evolution, which maps simple OAM modes onto a tunable high-dimensional OAM space, is mathematically analogous to the high-dimensional feature expansion performed by a kernel function of a support vector machine (SVM) in machine learning. Such dimensional expansion can project linearly inseparable input data into a high-dimensional space where they become linearly separable. Exploiting this physics-algorithm analogy, we develop a reconfigurable all-optical inference platform. As a proof of concept, we successfully perform classification tasks, including the recognition of Iris flowers and Palmer penguins. This work establishes a scalable, physically reconfigurable architecture for high-dimensional all-optical computing and neuromorphic photonics.

Vortex electrons, characterized by a helical phase front, offer unique advantages for probing material structures. Such electrons can be generated via tunneling ionization in strong laser fields. In this paper, we investigate the rescattering dynamics of vortex photoelectrons by the parent ion. Specifically, we introduce vortex photoelectron holography, extending conventional strong-field photoelectron holography (SFPH) from plane-wave to vortex rescattering. By solving the time-dependent Schrödinger equation, we extract the vortex scattering phase from the SFPH fringes, showing excellent agreement with scattering calculations. Thus, our work provides direct access to the vortex scattering phase, paving the way for applying SFPH to structurally sensitive imaging with phase-engineered photoelectrons.

Controlled, reproducible generation of luminescent defect centres in hBN remains a key challenge for scalable quantum-photonic technologies. Here, we report Kr$^{+}$ ion implantation as a tunable, annealing-free, and chemically inert route to room-temperature near-infrared luminescent spin defects in hBN, requiring no pre- or post-implantation annealing. SRIM Monte Carlo simulations were used to optimise the parameters for 40 keV Kr$^{+}$ irradiation of hBN flakes. The implanted samples exhibit a stable near-infrared photoluminescence (PL) band centred at $\sim$830 nm whose intensity increases with implantation fluence over $10^{11}$-$10^{15}$ions/cm$^{2}$. Temperature-dependent PL measurements (20-300 K) reveal a linewidth broadening well described by a $T^{3}$ dependence, consistent with acoustic-phonon-mediated dephasing. Raman spectra show the characteristic $E_{2g}$ mode of pristine hBN at $\sim$1366 cm$^{-1}$ alongside an implantation-induced defect feature at $\sim$1295 cm$^{-1}$, confirming irradiation-induced lattice disorder. Electron paramagnetic resonance (EPR) measurements reveal a paramagnetic centre with a $g$-factor of 2.003, and density functional theory (DFT) calculations indicate that a spatially separated $V_{\mathrm{N}}$-$C_{\mathrm{B}}$ donor-acceptor pair complex is a viable origin of the observed optical and magnetic signatures. Overall, Kr$^{+}$ implantation offers an effective, annealing-free, and scalable platform for generating stable room-temperature luminescent defects, providing a promising route toward quantum photonics.

Recent studies Vorticity packing effects on long time turbulent transport in decaying two dimensional incompressible Navier Stokes fluids, Phys. Fluids 38, 045159 (2026) demonstrated that, at a fixed high Reynolds number (Re), the initial vorticity packing fraction (VPF) governs the coupled Eulerian flow evolution and Lagrangian tracer particle transport in decaying two dimensional incompressible Navier Stokes fluids, revealing a strong Eulerian Lagrangian relationship during the nonequilibrium inverse cascade regime and a direct Eulerian Lagrangian correspondence in the late time coherent vortex quasi equilibrium regime, wherein increasing VPF drives transitions from point vortex to patch vortex equilibria and from subdiffusive to superdiffusive transport. In the present work, we investigate how these Eulerian Lagrangian connections evolve across a broad (VPF, Re) parameter space. The results show that the Eulerian Lagrangian relationship remains largely preserved during the nonequilibrium inverse-cascade regime, where transport increases systematically with VPF and remains primarily controlled by VPF despite secondary Re dependent oscillatory modulation. In contrast, the late-time coherent-vortex quasi-equilibrium regime exhibits Eulerian statistical equilibria that remain largely insensitive to Re, while the corresponding tracer-particle transport displays a strong Re dependence characterized by strong oscillatory and nonmonotonic variations across the (VPF, Re) parameter space, substantially weakening the VPF-ordered transport hierarchy observed in the inverse-cascade regime. Consequently, for the parameter range, spatial resolutions, and integration times explored in the present study, the direct Eulerian Lagrangian correspondence identified at fixed high Re is not universally maintained across the broader (VPF, Re) parameter space.

Far away from solid boundaries, energy can be transferred between different flow scales due to the non-linear self-advection of velocity. This energy transfer can be quantified using well-established Fourier diagnostics or filtering methods. However, these diagnostic tools fail to provide a physical representation of the linear energy transfer that may occur during the formation of oceanic boundary layers or Rossby wave reflections. In this document, I outline a novel filtering methodology that is able to quantify this linear energy transfer by combining coarse-graining with volume-penalization. Its utility is illustrated by quantifying the down-scale energy transfer occuring during a Rossby wave reflection off a western boundary. The conceptual framework developed here is thought to be broadly applicable to the study of multi-scale energetics of bounded geophysical fluid flows.

Compact electron linear accelerators are being increasingly investigated as enabling platforms for localized X-ray generation and energy-controlled irradiation, due to the demand for a reduced footprint and improved beam control. In this work, a conceptual compact LINAC architecture with a fixed total length of 0.5 m is investigated through comprehensive Monte Carlo beam dynamics simulations, focusing on the comparative performance of two operating modes delivering electron beams at 60 keV and 300 keV, respectively. The study aims to isolate energy-dependent beam transport effects within an identical lattice configuration, emphasizing transverse and longitudinal phase-space evolution, emittance growth, beam envelope stability, and target spot formation. Particle tracking is performed using a statistical Monte Carlo framework incorporating RF cavity acceleration, simplified quadrupole focusing, and space-charge-induced diffusion effects. Transverse and longitudinal phase spaces are analyzed at multiple locations along the accelerator, and key beam quality metrics are extracted at the exit. The results demonstrate that low-energy operation at 60 keV is strongly influenced by collective effects, leading to pronounced emittance growth and halo formation, whereas the 300 keV mode exhibits significantly enhanced beam rigidity, improved phase space preservation, and a compact beam spot at the target.

Density matrix embedding theory (DMET), which is usually based on a Schmidt decomposition of Slater determinants by partitioning the full system into impurity and environment in terms of local orthogonal orbitals (LOs), has demonstrated considerable promise in electronic structure studies because it enables the extraction of local properties using a high-level solver within an embedded impurity subsystem with greatly reduced degrees of freedom, thereby achieving a balance between accuracy and computational cost. However, its application to excited states of strongly correlated systems, such as lanthanide complexes, remains challenging because the errors relative to all-electron results can still be significant. Motivated by the success of the previously developed atomic orbitals (AOs) based DMET framework (Ai, Li, and Jiang, Phys. Rev. Lett. 2025, 135, 026502.), termed AO-DMET, which attains improved accuracy by constructing the embedded subspace based on a non-orthogonal decomposition of the Slater determinant in terms of AOs, we propose a new LO-based partitioning scheme that fully preserves the impurity space spanned by corresponding AOs and can achieve accuracy closely matching that of AO-DMET while retaining the orthogonal partition and its associated computational efficiency. The performance of the proposed method is demonstrated through excitation energy calculations for several representative lanthanide complexes. These results establish an efficient and accurate partitioning scheme for describing excited states in strongly correlated systems within the DMET framework.

The ITER tokamak project includes a Thomson scattering diagnostic designed to measure electron temperature and density in the plasma core. The system is required to provide measurements over a wide temperature range while meeting stringent accuracy requirements. A previous study analyzed the errors of electron temperature measurements to assess the feasibility of these requirements. The analysis concluded that the central electron temperature could be measured with the required accuracy of 10% for temperatures up to 40 keV at a minimum electron density of 3*10^19 m^-3. However, those results were based on an overestimation of the number of photoelectrons generated in detectors by the scattered radiation due to the incorrect application of the Thomson scattering cross-section. As a consequence, the temperature measurement errors were significantly underestimated. In the present work, the accuracy of electron temperature measurements in the ITER plasma core is reassessed using the corrected photoelectron yield derived from published data and incorporating the effects of background radiation. The revised analysis shows that the proposed diagnostic system can achieve the required accuracy of 10% for temperatures up to 20 keV at low plasma background radiation, whereas under high background radiation this accuracy can only be maintained up to 1 keV. To achieve the 10% accuracy across the full temperature range while preserving the current diagnostic configuration, either the energy of the probing laser pulse must be increased by a factor of 2-4 or the least electron density must be raised to 6*10^19 m^-3.

X-ray free-electron lasers (XFELs) enable diffraction-before-destruction measurements of individual nanosized bioparticles, making it possible to study the structure and dynamics of non-crystalline targets under near-biologically relevant conditions. In this work, we employ rotational invariants for model-guided and ab initio three-dimensional (3D) structure determination of aerosolized bacteriophages PR772 measured with an XFEL. The rotational invariants derived from diffraction patterns collected during multiple independent XFEL experiments facilitate the characterization of similarities and structural variations within the measured ensembles of PR772 particles. Despite modest experimental resolution, we can identify various structural features of the viruses, including the asymmetric nature of capsid distortions from the perfect icosahedral shape, density variations in the encapsulated content, and an extension at one of the capsid vertices. Rotational invariants combine structural sensitivity with applicability to forward-scattering modeling and inverse problem solving, making them powerful tools for probing the structure and temporal evolution of nano- and bioparticles using an XFEL, particularly enhancing the fidelity of structural analysis at limited experimental resolution.

A kV dual tube system has been disseminated as a commercial research platform for preclinical FLASH radiotherapy (RT). Because the tubes are arranged in a parallel opposed geometry, both output symmetry and time resolved tube synchronization are critical for achieving sufficiently high dose rates (DR) and reproducible study results. We quantified tube output asymmetry observed in depth dose measurements as well as tube synchronization and evaluated their impact on FLASH studies. The dual-tube system defines dose per pulse as the combined single pulse output from both tubes, with DR given by dose per pulse/pulse length. 3D dose distributions were reconstructed from film measurements to assess the impact of output discrepancies. Pulse synchronization between tubes was characterized using a scintillator with 1 ms resolution. We showed >20% discrepancies in output at nominally equal mA/ms settings. After we compensated such discrepancy by decreasing the current of the tube with higher output, the inter tube output difference was reduced to <1%, restoring symmetrical depth dose. We further simulated an in vivo intestinal irradiation in which naive tube settings resulted in >22% of the organ volume receiving >102% of the prescribed dose, compared with <7% when output compensation was applied. We identified a 10.2 +/- 7.0ms synchronization jitter between tubes, which disproportionately impacts the DR at low dose-per-pulse settings, particularly relevant for fractionated studies. Corresponding quality assurance (QA) was designed to monitor tube synchronization over time. We quantified the dosimetric impact of asymmetric output and synchronization and demonstrated implications for preclinical studies. The proposed methodology and QA would mitigate and monitor these effects, ensuring study reproducibility.

ITER will possess approximately 500 magnetic sensors (mainly measuring poloidal flux) distributed across the first wall. The coils are at known locations but the matching signals not necessarily known. There may also be mistakes in the wiring of the coil polarity. The existing strategy to disambiguate coils uses combinatoric programming of poloidal field coil waveforms of up to 48 discharges of plasma-less operation. An alternate strategy explored in this work is the energisation of a combination of both poloidal and toroidally asymmetric active coils, and Biot-Savart computation of the field solution from all active coils at the sensor coils. A direct brute force permutation of all $N$ coil combinations scales as $O(2^N N!) $ which is intractable for $N>10$. The mathematically formulated optimisation problem was analysed using AI-assisted coding tools, which identified the problem structure as a signed assignment problem and suggested a Hungarian-algorithm-based optimisation strategy, which scales as $O(N^3)$. This search algorithm, when embedded into the magnetic-diagnostic identification problem, was able to disambiguate randomly connected and polarised coils in a regularly spaced array in the ITER first wall (where the the coils are located) down to a signal-to-noise ratio of 50. The computation took 2 seconds. Reconstruction of the actual coil positions in the ITER first wall was achieved high confidence, $C>0.97$. Reconstruction of the second wall poloidal flux coils, which comprised multiple arrays at near constant $\phi$ (each of which is regularly spaced in $\theta$) had a much lower confidence, of $C > 0.15$. By adding the active poloidal field coils to the combined cost function, the confidence increased to $C > 0.59$. This provides the opportunity to reduce the commissioning time of ITER, and is a strategy that could be tested on other toroidal magnetic confinement devices.

The X-Point Target divertor is an alternative divertor configuration with a secondary X-point in its divertor volume. In this work, we investigate the dynamic response and disturbance rejection capacity of the XPT configuration on the TCV tokamak, comparing it to a single null (SN) divertor. We employ a system identification approach using multi-sine perturbations to measure the dynamic response of the detached state in both Ohmic and auxiliary-heated L-mode scenarios upon D$_2$ fuelling, N$_2$ seeding and Electron Resonance Cyclotron Heating (ECRH) power modulations. We demonstrate an inherent disturbance rejection capacity of the XPT at its secondary X-point compared to a SN configuration for all perturbation scenarios. Upstream of its secondary X-point, the dynamic response of the detached state between the XPT and SN appears similar. The disturbance rejection capacity of the XPT could be highly beneficial for passively buffering disturbances that cannot be effectively managed by power exhaust controllers. At the same time, it presents a challenge for monitoring the detached state close to the secondary x-point.

Single-cavity dual-comb lasers provide a compact and efficient source for dual-comb spectroscopy in gas sensing applications; however, achieving sufficient free-running mutual coherence for comb-line-resolved, high-resolution measurements remains challenging. Here, we present a symmetry-engineered bidirectional single-cavity dual-comb laser based on an all-polarization-maintaining fiber architecture. The system exhibits exceptional free-running mutual coherence, achieving Hz-level relative linewidths without active feedback or phase correction. The time-averaged absolute jitter of the dual-comb repetition-rate difference reaches 4.7*10^-7 min-1, representing an improvement of nearly two orders of magnitude over previously reported free-running systems. As a spectroscopic demonstration, we resolve ~49,000 comb lines over a 5.4 THz optical bandwidth and measure the absorption spectrum of carbon monoxide (12CO), faithfully retrieving molecular line shapes with millisecond acquisition times. This architecture provides a compact and robust free-running platform for broadband molecular spectroscopy and millisecond-scale, line-shape-resolved gas sensing.

Optical knots are topologically structured light fields whose phase or polarization singularities trace linked or knotted trajectories during propagation, making them promising candidates for high-dimensional optical information carriers. Their use in communication or quantum-information protocols, however, requires a practical readout method that can distinguish a chosen knot alphabet with low crosstalk. Here, we demonstrate a proof-of-principle full-field sorter for optical knots using one or two optimized phase-only elements. The sorter maps each input knot to a predefined output region and is optimized directly from the output intensity distributions to enhance correct assignment, suppress crosstalk, and avoid degenerate mappings between distinct knots. We apply the method to an alphabet composed of the Hopf link, trefoil, and cinquefoil optical knots. Two optimized phase planes improve the sorting performance relative to a single plane and enable high distinguishability for the three-knot alphabet. We further benchmark the sorter under common experimental imperfections. These results extend full-field optical mode sorting to topologically structured light and provide a readout route for knot-based high-dimensional optical communication.

Given the widespread application of high-performance materials in cyclic loaded infrastructure, a thorough understanding of the fracture and high-cycle fatigue behavior of high-performance materials is of critical importance. However, these behaviors predominantly rely on experimental methods, which are often costly, time-consuming and limited in generalizability. To address these issues, this paper proposes the constitution and high-cycle fatigue of Ultra-High Performance Concrete (UHPC) and High Strength Steel (HSS) within the Phase-Field Cohesive Zone Model (PF-CZM) framework. A generic stress-based failure criterion for UHPC is derived from the biaxial tensile test results to calculate crack driving force. Furthermore, a fatigue degradation function and an acceleration algorithm are integrated into the PF-CZM framework to enable efficient high-cycle fatigue simulations. In the acceleration algorithm, the envelope load is used to approximate the real cyclic load to avoid the simulation of each cycle, and the three-stage fatigue process is simulated through adjusting the cyclic increment adaptively. The proposed model successfully captures mixed-mode fracture and high-cycle fatigue behavior of UHPC and HSS, with validation provided through relevant experimental comparisons.

Generalised Lagrangian Mean (or Hybrid Euler-Lagrange) theory aims to describe the joint evolution of the mean flow and its perturbations. This paper considers related forms of continuity equations (CEs) and clarifies the conditions of their validity. We do not consider equations of motion; therefore, we use only exact formulae and general notions of fluid dynamics and operate only with the most general statements for CEs. The tools used are Lagrangian X, Eulerian x, averaged Eulerian x' coordinates of fluid particles, and ensemble-based averaging. The targeted forms of CEs are expressed in terms of function x(x',t). Our first step is to present the actual velocity divergence div u via div'u', where u and u' are the actual and average fluid velocities. Then we introduce three versions of exact CEs and demonstrate that each is mathematically incomplete. The third step is to restore their completeness by introducing the compatibility equations required for their validity in general fluid flows. As a basic reference, we present two versions of the McIntyre-Andrews Transformation (MAT) for CEs. The original MAT introduces an auxiliary function that satisfies an auxiliary PDE and special initial conditions. Therefore, it works for a special class of fluid flows. The presented generalisation of MAT makes CEs applicable to arbitrary fluid motion. Both versions require compatibility equations, which we compare with ours. Finally, we consider average flows with small perturbations, thereby linking our exposition to the classical GLM theory.

Fractal lattices have recently emerged as a promising setting for topological wave physics, but in most realizations the topological character is inherited from externally engineered couplings, gauge fields, or temporal modulation rather than from the fractal geometry itself. Here, we experimentally realize a photonic higher-order topological insulator in which the topology is induced solely by the self-similar geometry of a Sierpiński-gasket lattice. Following the isospectral reduction method recently proposed by Eek \textit{et al.}~\cite{Eek2025}, we show that the fractal waveguide array with uniform nearest-neighbor couplings can be mapped onto an effective breathing Kagome model that supports corner states. We selectively excite these modes with a weakly coupled detuned auxiliary waveguide and directly observe robust corner localization in real space, whereas an otherwise equivalent uniform triangular lattice exhibits only bulk diffraction under the same protocol. Spectral analysis and open-boundary calculations associate the observed states with nontrivial $C_3$ rotational topology, and disorder measurements further show that the corner localization persists over a finite range of random and symmetry-preserving disorder. Our results establish fractal geometry itself as a mechanism for generating topological boundary states in photonic lattices.

The second quantum revolution is driving advancements in quantum computing, communication, and sensing. While quantum computing has gained significant attention in education, quantum sensing remains largely overlooked. In order to find ways of integrating sensing into existing quantum-related curricula, we performed an analysis of six of the most commonly used textbooks in modern physics, quantum mechanics, and quantum computing. We identified a set of keywords related to quantum sensing and tagged all excerpts in which these concepts appeared. For each excerpt, we also recorded the context in which it was presented within the textbook. Network maps were constructed to visualize which keywords appeared in which contexts and the frequency of these occurrences within each subject area. We then developed an analytic rubric to evaluate the conceptual and mathematical depth of these excerpts as well as the extent of sensing-related discussions. Our results show that there is significant variation in how different subjects address these concepts, both in the nature of the content covered and the depth of coverage. We also observe notable differences in how spin-first and position-first textbooks discuss these keywords, particularly in the coverage and contexts in which core concepts such as superposition and entanglement appear. Additionally, we analyzed course titles and descriptions from a database of over 8,000 quantum-related courses, focusing on those that mentioned ``sensing'' or ``sensor''. Together, these analyses of textbook content and course descriptions inform the quantum information science and engineering education community about potential opportunities to integrate quantum sensing topics into quantum-related courses in physics and adjacent disciplines.

This paper presents a comprehensive analysis of hollow-core fiber (HCF) for intensity-modulation and direct-detection (IMDD) optical networks, covering fiber-level physics, system-level performance, and deployment economics. We quantify the three principal advantages of anti-resonant HCF over standard single-mode fiber (SMF) for IMDD: (i) chromatic dispersion of 2-4 ps/(nm km) versus 17 ps/(nm km), which shifts the first dispersion-induced power-fading null from about 10 GHz to 20-28 GHz at 40 km, extending the dispersion-limited reach by 4-8x; (ii) a nonlinear coefficient approximately 1,000x lower than silica, permitting launch powers of +10 to +20 dBm and yielding 7-17 dB of additional link budget; and (iii) a group index near unity (ng about 1.003), reducing propagation latency by 31%. We further analyze inter-modal interference (IMI) as the dominant impairment for HCF-based IMDD. We show that differential modal attenuation (DMA) exceeding 12 dB/km suppresses IMI-induced crosstalk below the -30 dB multipath interference threshold required for PAM4. The reduced dispersion also lowers the required feed-forward equalizer (FFE) tap count by 3-6x, directly decreasing noise enhancement penalty and DSP complexity. A deployment cost model across five application scenarios - intra-data center, campus DCI, metro DCI, 5G fronthaul, and PON - reveals that fiber cable constitutes only 5-10% of outside-plant deployment cost, and that coherent transceiver avoidance savings of $1000 to $2000 per transceiver can offset the current HCF premium at metro distances. We provide a technology adoption roadmap indicating that HCF is economically justified now for intra-DC and campus DCI, with metro DCI following in 2027-2030 as manufacturing costs continue to decline.

The scaling patch approach is applied to analyze the turbulent kinetic energy (TKE) budget equation in wall-bounded turbulent flows. The balance of the TKE equation is divided into several distinct regions, or scaling patches, each characterized by a dominant balance among the governing terms and its own appropriate scaling parameters. In the near-wall viscous sublayer, the TKE balance is primarily between viscous diffusion and dissipation, and the characteristic scales are set by the kinematic viscosity and the wall dissipation rate. The thickness of this sublayer is on the order of the Kolmogorov length scale. Moving away from the wall, the peak TKE production provides a natural reference scale for the inner layer, yielding the traditional inner scaling. Grouping the viscous diffusion and dissipation terms in the inner layer enhances the collapse across different Reynolds numbers. In the outer region, Prandtl's mixing-length model is used to derive a characteristic scale for TKE production. A new meso-scaling is further introduced to describe the intermediate region, ensuring a smooth transition between the inner and outer layers. The scaling patch framework offers a unified interpretation of the structure and scaling behavior of the TKE budget across all regions of wall-bounded turbulence.

Hollow-core fiber (HCF) is widely expected to enable higher-order quadrature amplitude modulation (QAM) because its near-vacuum Kerr nonlinearity admits higher launch power, and therefore higher optical signal-to-noise ratio (OSNR), than standard single-mode fiber (SMF). We develop a joint per-channel signal-to-noise ratio (SNR) budget that captures the relevant impairments together: fiber Kerr nonlinear interference (NLI), distributed and discrete inter-modal interference (IMI), residual SMF-pigtail NLI, equalization-enhanced phase noise (EEPN), finite effective number of bits (ENOB), laser phase noise, and polarization-dependent loss (PDL). The achievable QAM order on HCF is set first by the distributed IMI coefficient of the fiber, with pigtail NLI and discrete splice multipath interference (MPI) as distant secondaries even at the relatively high HCF-to-HCF splice mode-coupling values inferred from recent fusion-splice measurements. At a near-term distributed-IMI level, HCF supports 1024-QAM out to about 115 km (versus about 33 km on SMF, roughly 3.5 times longer) and 256-QAM out to about 786 km (versus about 134 km, roughly 5.9 times longer); at the lowest IMI levels reported in state-of-the-art HCF, 1024-QAM reach extends to roughly 2090 km. HCF's lower chromatic dispersion delivers about a 4 times reduction in EEPN relative to SMF across all reaches and QAM orders studied. The model also places 2048-QAM within about 10 km and 4096-QAM within about 5 km of HCF reach at 32 GBaud; the matching SMF regions are blocked at the same baud rate by EEPN and NLI, which are both suppressed in HCF by its low dispersion and near-vacuum nonlinearity, consistent with all published SMF greater than or equal to 2048-QAM records operating at 10 GBaud or below.

Next-generation X-ray detectors generate data faster than any system can affordably store or process. LCLS-II, the upgraded Linac Coherent Light Source at SLAC, produces data on the order of terabytes per second, with raw-data transfer and storage projected to be prohibitively costly, even though much of the data is not scientifically useful. This concept paper focuses on two major points. The first is versatility: a deliberately tiny, single-layer Vision Transformer (ViT) is enough to serve distinct scientific quick-evaluation tasks. We demonstrate this on two very different problems: (a) a supervised hit/miss/maybe classification on the CSPAD dataset, made to resemble ePixUHR-like detector frames, and (b) a self-supervised latent space for rare-event detection in X-ray diffraction spanning two learning paradigms, two output types, and two detector modalities, with one small backbone. The second is hardware co-design: because the ViT's blocks are structurally uniform, the model maps cleanly onto the heterogeneous hardware already present in the LCLS detector pipeline (ASIC -> FPGA -> GPU) under a simple rule one ASIC is one token so the data is reduced progressively at each stage and a keep/discard decision is produced in real time at the edge. The two claims reinforce each other: versatility is precisely what justifies freezing the front-end in silicon, since a reusable front-end is only worth committing to hardware if it serves many tasks. We are explicit that this is a concept supported by early software analysis, not a hardware demonstration. The natural and primary next phase is the hardware implementation of this distributed pipeline. The decisive evidence still owed an end-to-end latency budget, ASIC feasibility of the in-sensor embedding, and the false-negative behavior that matters for a data veto defines that program. HeteroViT is our first step toward it.

This paper presents SuperCond-GNN, a graph neural network-based surrogate model for predicting the voltage distribution in high-temperature superconducting (HTS) magnets. HTS magnets are modeled as lumped-element equivalent circuits and mapped onto graph representations, enabling message passing GNNs to learn the electrical response as a function of circuit topology, material properties, and operating current. As a proof of concept, tape stacks of up to 10 tapes are considered across a range of circuit topologies and operating conditions. The surrogate is trained on data generated from circuit simulations and achieves a mean MAPE of 4.3 % within the prescribed design space. The predicted nodal voltages enable fast and scalable inference of current redistribution and local operating conditions across a wide range of circuit configurations. The effect of incorporating physics-informed regularization via Kirchhoff's current law is also evaluated, and generalizability to unseen topologies is assessed through zero-shot inference and few-shot fine-tuning. While demonstrated on tape stack circuits, the graph-based framework is topology-agnostic and naturally extensible to more complex HTS cable and magnet configurations, offering a scalable alternative to conventional circuit solvers for downstream applications such as design space exploration, current sharing analysis, and real-time magnet monitoring.

Small molecule double-stranded DNA intercalators have significant potential for therapeutic applications. However, screening for and confirming a drug candidate's intercalative behavior remains labor-intensive and costly. To address this, we investigated the sequence and biophysical parameters that affect the performance of electrochemical DNA hairpin sensors for streamlined identification of structural intercalators. These sensors utilize oligonucleotide (oligo) sequences that form hairpins upon intercalator binding. The 3prime end of the oligo is modified with alkylthiol linkers for gold electrode surface monolayer self-assembly, while the 5prime end carries a methylene blue redox reporter. Hairpin formation enhances electron transfer between methylene blue and the gold electrode, which can be detected via voltammetry. We tested seven hairpin structures varying in stem length and sequence. Our optimal oligo, HP4, features a four-base-pair stem and responds to five DNA intercalators over a broad detection range, with EC50 in close agreement with published affinity (KD) values for these interactions. We further demonstrate HP4s ability to discriminate intercalator binding from a series of minor groove binders through significant differences in signal gain upon incubation. Altogether, our strategy establishes a platform for identifying intercalative compounds that should support the development of DNA-targeting therapeutics.

Anonymised GPS-based mobile phone data are increasingly used to estimate population distribution and human mobility, supporting applications across disaster response, public health, urban planning and migration research. Yet whether these data fairly represent the populations they describe, particularly outside high-income countries, remains poorly understood. We quantify coverage bias for 2,478 municipalities in Mexico by comparing population estimates from a single-platform source (Facebook) and a multi-app aggregator (Veraset) against the 2020 Mexican Population Census. We find that the magnitude and spatial distribution of coverage bias differ substantially across sources. Facebook provides higher and more evenly distributed coverage, whereas the multi-app data concentrate users in larger, wealthier and more digitally connected places. Coverage bias is also spatially structured, with neighbouring municipalities showing similar levels of over- or under-coverage. Using explainable machine learning, we show that digital access and material resources are the dominant drivers of bias for the multi-app data, while demographic and population structure dominate for Facebook. Explicitly modelling spatial dependence improves the performance of statistical models for explaining bias and reveals that an appreciable share of spatial variation remains unexplained by observed covariates. These findings show that coverage bias is source-specific and spatially dependent, and provide a foundation for adjustments that improve the representativeness of mobile phone data in unequal, data-scarce settings.

Understanding particle-induced damage tracks in solid-state materials underpins emerging applications in rare-event detection and quantum defect engineering. Resolving these tracks requires multi-scale readout, from event localization at the millimeter scale to track-morphology reconstruction at the nanoscale. Nitrogen-vacancy (NV) centers in diamond provide such a platform, combining optical localization with quantum sensing of track morphology. Here, we implant sub-MeV carbon ions into nitrogen-rich diamond and detect individual recoil events via spatially localized NV formation. We develop a simulation framework that explains the observed NV yield and predicts that directional information is retained in the NV distribution after annealing. Machine learning further recovers much of the information lost to defect diffusion and limited NV yield, improving head-tail classification to a level comparable to pre-annealed vacancy tracks. Measurements of NV spin coherence indicate compatibility with nanoscale track reconstruction via NV strain mapping and magnetic gradient-based techniques. These results identify promising pathways toward NV-diamond directional detectors for rare events, while the track-modeling framework has broader implications for paleodetection and quantum material synthesis.

Open research data services have matured to the point where the cost of sustaining them at scale has become a primary design constraint, driving providers to make deliberate choices that may reduce user convenience to keep the service viable. The FAIR (Findable, Accessible, Interoperable, Reuseable) principles describe whether a dataset is well stewarded, and FAIR compliance is often treated as a proxy for usability. FAIR does not capture the cost to a user of finding, accessing, interpreting, and applying a dataset. We introduce the Dataset Friction Framework (DFF) as a complement to FAIR, directly addressing usability. DFF measures user-facing friction across six dimensions, distinguishing engineered friction (deliberate data provider design choices that sustain a service) from accidental friction (defects that require remediation). The framework is validated against 18,556 support tickets from the European Centre for Medium-Range Weather Forecasts (January 2024 to May 2026), which serves 280,000 registered users. Restricting the analysis to tickets raised by external reporters reduces the corpus by 12.3%, but every dimension's internal-staff share falls below this baseline -- confirming that the reported friction signals are genuinely user-facing. We then assess three real datasets across three providers and show that FAIR compliance and DFF friction can disagree in both directions: a 92% FAIR-compliant dataset can still carry substantial friction, and a 42% FAIR score can be an artefact of anti-scraping policy rather than poor stewardship. The two measures are non-redundant and jointly informative: FAIR compliance does not predict DFF friction in either direction. This constitutes the first large-scale empirical application of the framework; cross-institutional validation is identified as the immediate next step.

We introduce a technique that enables Neural-ODEs to approximate arbitrary velocity fields with a priori planted fixed-points. Specifically, a recipe is given to explicitly accommodate for a finite collection of points in the reference multi-dimensional space of the Neural-ODE where the velocity field is exactly equal to zero. In this way, the gradient-based training is rigorously constrained inside the prescribed hypothesis class while leaving the expressive power of the Neural-ODE unaltered. We rigorously prove the universality of the Neural-ODE under any local constraints in the velocity field and give a computationally convenient way of imposing the fixed points. Our method is then tested on two paradigmatic physical models.

Smartphone telephoto cameras are approaching a "telephoto physics wall": as pixel pitches shrink toward sub-0.5 micron, the optics remain limited by geometric aberrations, leading to diminishing returns on resolution. Traditional Image Signal Processors (ISPs) cannot eliminate these aberrations, because they operate through local, stage-wise processing with no explicit model of the underlying point spread function (PSF). We demonstrate how a learning-based Neural ISP for image restoration, trained on the underlying degradations, inverts what stage-wise pipelines cannot, turning small-pixel designs into a net advantage. We investigate this through a controlled simulation of a representative telephoto module, evaluating five configurations (0.35--0.75 micron pixel pitch). The aperture is scaled proportionally to keep per-pixel SNR and diffraction spot size fixed, thereby isolating geometric aberration and spatial sampling. While the traditional ISP improves only modestly with smaller pixels, the Neural ISP scales substantially: at 0.35 micron} it reaches 745 cycles/mm MTF50 (vertical), a 2.5--3x resolution improvement over the traditional ISP, and LPIPS improves significantly from 0.244 to 0.151 while traditional results stay comparatively flat. In a low-SNR extension (15 dB per-frame bursts at 0.35 micron), a multi-frame Neural ISP recovers performance close to the bright-light single-frame baseline, whereas a multi-frame traditional ISP shows no meaningful improvement -- indicating that traditional pipelines at small pixels are bottlenecked by uncorrected PSF blur rather than by noise. These results point to a design philosophy in which Neural ISPs enable high-resolution telephoto modules by correcting residual optical aberrations rather than requiring increasingly complex optics.

We report the first results of the magnetic dipole hyperfine structure (HFS) constants of neutral $\mathrm{Li}$, Li-like $\mathrm{Be}$, neutral $\mathrm{Na}$, and Na-like $\mathrm{Mg}$ using a modified version of the Quantum Annealer Eigensolver (QAE) algorithm on D-Wave's quantum hardware. The results are benchmarked against relativistic configuration interaction with multiconfiguration Dirac Hartree-Fock (MCDHF) calculations using the General-purpose Relativistic Atomic Structure Package (GRASP), and simulated annealing. In our modified QAE, a zooming-and-sigma-annealing approach with a floating-point encoding scheme is adopted to estimate the ground-state eigenvalue and eigenvector of the relativistic Dirac-Coulomb Hamiltonian matrices ($H_{\mathrm{DC}}$) constructed from 11 or fewer configuration state functions (CSFs). For calculations with extended correlation orbital sets, we applied a CSF truncation scheme, retaining only CSFs (up to 12) that make significant contributions to the ground-state wavefunction. Our modified QAE precision is kept limited to three decimal places (up to 10 qubits). Hardware demonstrations on the D-Wave quantum processing unit (QPU) yielded results that were completely consistent with GRASP (at the chosen precision) in determining the magnetic dipole HFS constants, with accuracy varying across systems and $H_{\mathrm{DC}}$ matrix dimensions.

An efficient implementation of quantum algorithms is often hindered by the lack of efficient primitives for operators and state preparation. This limits both the ability of near-term quantum hardware to simulate complex problems and the potential of fault-tolerant algorithms to achieve practical quantum advantage. To address this, we propose a full-stack variational framework that transforms arbitrary operators to compact quantum circuits. The resulting variational circuits can be tailored to the connectivity and long-range interaction of the target hardware. The learning process employs backpropagation together with a cost function that efficiently optimizes unitary operators and non-unitary -- dense or sparse -- operators using only a single ancilla qubit for block encoding. Additionally, we introduce a regularization term that reduces the approximation error. The approach is validated for both quantum mechanical and engineering applications. In the former case, we learn propagators that arise in native quantum problems -- such as quantum simulation and quantum chemistry -- and achieve improved resource scaling in comparison to standard Suzuki-Trotter expansions. In the latter case, we demonstrate the approach's ability to implement the second-order central finite difference approximation of the Laplace operator -- relevant for solving partial differential equations -- while improving upon current error metrics. The final example deals with learning a dense, non-unitary operator that arises in the analysis of inviscid potential flow around an airfoil. This universality of the framework opens the door for solving general problems beyond prototypical engineering and quantum applications.

Electoral redistricting in Ireland's Proportional Representation Single Transferable Vote (PR-STV) system faces the challenge of selecting an optimally representative set of electoral boundaries from an enormous set of possible configurations, and where ``representative'' is a delicate balance of constitutional objectives that are often in tension with one another. We present the first computational framework for Irish electoral redistricting that systematically optimises across multiple constitutional requirements while making trade-offs explicit and quantifiable. The electoral redistricting problem is parsed using statistical physics, where constitutional objectives are considered as terms in a Potts Hamiltonian. Markov Chain Monte Carlo (MCMC) methods and simulated annealing are employed to minimise this objective function, systematically exploring this configuration space, with coupling constants as proxies for objective weightings. Multi Criterion Decision Analysis (MCDA) and Pareto Optimality is then utilised to remedy the ambiguity in choosing a certain objective weighting combination over others. With respect to proportional representation and compactness objectives evaluated in County Cork, COTHROM consistently improves on the existing legal constituency boundaries for a range of objective weightings.

Conventionally, existing vehicle platooning approaches are designed for connected vehicles, typically including connected autonomous vehicles and connected human-driven vehicles. Non-connected vehicles, such as non-connected autonomous or human-driven vehicles, are not incorporated. As a result, these platooning approaches may not properly reflect real-world mixed traffic conditions at the current stage. To address this limitation, this study proposes a hybrid platooning pattern that conditionally permits non-connected vehicles to join platoons, thereby enhancing platooning diversity and flexibility. However, it was found that the unregulated integration of non-connected vehicles can trigger rapid platoon expansion, significantly amplifying the risk of disturbance propagation in traffic flow. This, in turn, exacerbates the inherent conflict between traffic throughput and stability. To mitigate these challenges, this paper further develops a hybrid platooning control strategy based on deep reinforcement learning (DRL). This strategy integrates vehicle dynamics, platoon topology, and traffic flow states through a multi-level state representation network, enabling a dynamic trade-off between traffic capacity and stability. Numerical simulations demonstrate that the proposed strategy effectively suppresses velocity disturbance propagation by dynamically optimizing platoon structures, thereby significantly enhancing the stability and safety of mixed traffic while reducing fuel consumption and emissions.

We develop an agent-based, stock-ow-consistent model of an economy undergoing automation, built to ask which scal instrument reaches the durable surplus that articial intelligence creates. The model separates two channels: a competitive return on reproducible robotic capital, and a mobile, foreign-held intellectual-property rent earned by AI. Production is an endogenous nested-CES technology; wealth concentration is microfounded through heterogeneous, persistent returns to wealth; and taxation and capital mobility are modelled as behavioural responses. The central result is that the durable surplus is the foreign-held AI rent, a cross-border licence fee that corporate, robot, and compute or token taxes largely miss and that only a source-based levy (a digital-services-style tax or a withholding) reaches. The appropriate policy depends decisively on whether a country owns the automation or imports it: for a host that owns the rent the problem is domestic inequality, reached by progressive and wealth taxes; for a rent-importing host the problem is base erosion and a gradual transfer of capital ownership abroad, which a residence-based wealth tax cannot reach. We report conditional orderings, stress-tested with global (Sobol) sensitivity and a formal stability analysis, rather than point forecasts.

We propose a rigorous and systematic mathematical framework for tracking the cognitive trajectories of a user, in the context of algorithmic sycophancy and AI-driven delusional spiraling. Using tools from dynamical systems theory and stochastic differential equations, we explore how individuals perceive, interpret, and update their beliefs as they interact with AI chatbots that possess hidden traits of sycophancy. We treat the evolving conviction as a continuous log-odds state variable, coupled into a stochastic differential equation, navigating a multi-valley potential energy landscape. Our analysis reveals several critical observations governing the stability and rigidity of belief dynamics. We demonstrate that the baseline prior perception of the individual is systematically enhanced by sycophantic feedback beyond a critical threshold. Here, the perceptual potential landscape undergoes a structural phase transition that severely deepens any incremental initial tilt present in the baseline state, transforming the landscape and giving rise to deep, highly resilient attractor basins that trap the individual in unshakeable, self-reinforcing, delusional convictions. Finally, we demonstrate that genuine external information can successfully challenge these rigid states. If this incoming evidence is strong and authentic enough to overcome the internal feedback barrier, it can correct the structural asymmetry caused by sycophancy, inducing a perception reversal that successfully restores the objective belief state.

In a recent paper [arXiv:2604.24783 (2026)], we have proposed a general approach to treat systems with inherent non-Boltzmann-Gibbsian behaviour. Given the extremely high accuracy of our approach, we have adopted the term hyperstatistics. We have applied such a statistical mechanics approach, i.e., hyperstatistics, to the discharge of a capacitor in a RC series circuit, pumping of $^4$He of a closed cycle cryostat, midrapidity data of $p$-Pb collisions at the LHC, as well as for the distribution of accelerations in turbulent systems. Here, we discuss into more details the ground of hyperstatistics. We demonstrate the versatility of hyperstatistics upon applying it to the velocity autocorrelation function in Brownian motion and also regarding its potential to describe brain dynamics.

Collective artificial intelligence, where multiple agents work on shared tasks, holds potential to solve expansive problems in fields from medicine to collective governance. But while prescriptive engineering solutions abound, we lack descriptive scientific understanding of artificial collectives, and therefore principles for how to design resource efficient multi-agent systems. Through systematic experiments with optimizing agents, we characterize how agent interpretive abilities, rationality bounds, and task qualities interact to shape collective performance. Agents range from specialists, with narrow interpretive abilities, to generalists, with broad ones. Collectives of specialists correspond to sparse, centralized networks, while collectives of generalists correspond to dense, decentralized ones. We show that interpretive network properties have small performance effects on average (0.07 standard deviations of performance). However, for specific task qualities, these effects are 4.5 times larger (0.33 sd) and can reach much higher for certain task qualities (1.84 sd). This leads collectives of generalists to perform better on tasks that involve generating, choosing, and coordinating, while collectives of specialists with a few generalist mediators perform better on tasks that involve negotiating. Rationality bounds then moderate these relationships. At loose bounds, specialists outperform generalists through more effective sampling of high-dimensional decision spaces. At tight bounds, generalists outperform specialists through better gradient estimation. A fundamental trade-off between performance and convergence speed emerges at moderate bounds. These findings suggest that multi-agent design could benefit from matching interpretive networks to both task demands and agents' computational limits, with implications for the efficiency and energy costs of multi-agent systems.

Silicon has a unique combination of properties that makes it one of the best material choices for biosensor platforms: it is inexpensive, its native oxide is atomically smooth, its fabrication processes are CMOS-compatible and have been refined for more than three decades, and it can support many transduction mechanisms in biosensor design. Over the past thirty years, researchers and engineers have used silicon nanostructures to produce ion-sensitive transistors, ultrasensitive nanowire field-effect biosensors, refractive-index-based porous silicon films, microring photonic resonators, suspended cantilevers, luminescent quantum dots, and solid-state nanopores. These device families have demonstrated successful sensing capabilities at the single-molecule, single-virus, or sub-femtomolar level under laboratory conditions; however, they have rarely been widely deployed in clinical assays. This gap is mainly caused by several well-characterized bottlenecks: for nanowire BioFETs, device variability and Debye screening; for porous silicon, fouling, pore wetting, and surface stability; for silicon photonics, thermal drift, spectral readout, and packaging; and across all platforms, calibration, reproducibility, and validation in real biofluids. In this review, we trace the development of silicon biosensors from their early stages to their current state, search and organize the literature focusing on the three most mature platforms and a set of emerging directions, summarize and compare the performance and bottlenecks of different platforms, and argue that progress over the next decade will come primarily from integrated readout, interface engineering, and systematic benchmarking rather than from the discovery of new silicon nanostructures.

Modern high performance computing (HPC) applications must target heterogeneous hardware. This requires significant work to ensure domain specific implementations translate to highly performant kernels across a range hardware types and vendors, each requiring bespoke optimization to make use of the specific target architecture. Through the development of a domain specific compiler built with the multi-level intermediate representations (MLIR) project, one can express a high-level, close to the specific domain, abstraction that is progressively lowered to a low, close to metal, abstraction. At each intermediate representation (IR), appropriate optimizations can be applied without costly analysis due to the knowledge embedded in the domain specific IRs. We apply this method to the construction of discrete differential operators for use in spectral/hp element method solvers for computational fluid dynamics (CFD). Here, the performance is driven by a small set of common finite element operators that are composed to create kernels for the discrete differential operators used to solve weak partial differential equations. We create our own MLIR dialect to represent these operators and implement a bespoke lowering pipeline to facilitate the just-in-time compilation of these kernels for both CPU and GPU architecture and illustrate performance comparisons with the Nektar++ spectral/hp element framework.

Segregated states based on particle size emerge in granular materials from the competition between segregation and diffusive remixing. Here, we show that quantum coherence can enhance segregation beyond this classical limit. We introduce an open quantum cellular automaton for bidisperse mixtures that combines coherent transport and dissipative segregation. The automaton reproduces experimental and continuum-theory segregation dynamics, with segregation degrees collapsing onto a theoretical Péclet-dependent relationship. However, weakly decohering systems exhibit a coherence-driven transport regime that produces more strongly segregated steady states than classical predictions. Across a broad parameter range, the steady-state degree of segregation collapses onto two dimensionless numbers governing the competition between segregation, diffusion, and decoherence. These results identify quantum coherence as a mechanism for enhancing particle-size segregation and establish a framework for studying transport phenomena in open many-body systems.

Noise sets the fundamental limits of resolution and sensitivity in Dynamic Atomic Force Microscopy (DAFM). While thermal fluctuations are conventionally assumed to be the dominant noise source, this work demonstrates that tip--sample interactions in ambient conditions introduce a non--thermal noise component that significantly exceeds the thermal background. Using a model system of sodium dodecyl sulfate (SDS) on graphite, we characterize this noise through force spectroscopy, 3D imaging modes, and Kelvin Probe Force Microscopy (KPFM). This interaction--induced noise arises from the stochastic formation and rupture of nanoscopic liquid necks, serving as a direct fingerprint of local wettability and dissipative dynamics. Crucially, we find that this ``noise channel'' provides chemical contrast that is distinct from and complementary to the electrostatic potential mapped by KPFM. By deciphering the physical origin of these fluctuations, we establish that noise is not merely an instrumental artifact but a rich spectroscopic signal, and we propose that Frequency Modulation (FM--DAFM) offers a superior approach to decouple these dissipative effects for high--resolution imaging.

The phonon laser realized in a trapped ion, i.e., a self-sustained mechanical oscillator, has demonstrated the unique characteristics in practically detecting externally applied electric signals without the prerequisite of sideband cooling. Entering the quantum regime via sideband cooling is expected to further improve its sensing performance. Here we report the first experimental realization of a quantum-regime single-ion phonon laser ($\bar{n}<10$) using a trapped $^{40}\mathrm{Ca}^+$ ion and demonstrate electrometry based on its phase-space symmetry-breaking response to weak resonant electric fields. By tuning the phonon-laser parameters, we reveal that the sensing performance is fundamentally governed by the finite-time relaxation dynamics of the underlying open quantum system. We find that a slow Liouvillian relaxation, correlated with the finite experimental interaction window, effectively enhances the dynamic susceptibility while maintaining the structural robustness of the limit cycle. This regime, when applied to the detection of electric fields, produces a shot-noise-limited peak sensitivity of $14.15 \pm 0.77~\mu\mathrm{V/m}/\sqrt{\mathrm{Hz}}$ and a minimum detectable field variation of $\delta E_{\mathrm{min}} \approx 1.83~\mu\mathrm{V/m}$. Our results establish quantum phonon lasers as a practical platform for advanced sensing and highlight the central role of Liouvillian dynamics in non-equilibrium electrometry.

Sums of independent exponential random variables lead to the Erlang distribution, providing a direct probabilistic route from exponential waiting times to the integer-shape gamma law. This paper investigates how this classical construction changes when the exponential waiting-time density is replaced by the $q$-exponential density of nonextensive statistics. Our main result is an analytical asymptotic expression for the outflow of a mass-conserving cascade of reservoirs driven by a $q$-exponential waiting-time kernel. In the critical case $q=5/3$, the large-cascade flow rate converges to a stable Lévy density whose time argument is shifted by a Galilean-type transformation. This shifted Lévy law gives the asymptotic hydrograph of the cascade. We also found that for the entire regime $1<q<2$ the macroscopic dynamics are governed by $\alpha$-stable Lévy laws. This proves that anomalous non-Gaussian dispersion can emerge from pure mass-conserving convolutional chains without invoking fractional derivatives.

Segmenting the temporomandibular joint (TMJ) disc from MRI is essential for accurate diagnosis of internal derangement, yet it remains unreliable in practice due to its small size, low contrast, and morphological variability. Existing methods, primarily adapted from general segmentation architectures, often produce fragmented or anatomically inconsistent masks, leading to unstable measurements of disc position and shape for downstream diagnosis. To address these challenges, we propose TISC, a TMJ disc segmentation framework that integrates semantic anchoring with clinical metadata-guided boundary refinement. The framework first establishes robust disc localization in the foundation model feature space via a Prototypical Semantic Anchoring (PSA) module that aggregates adjacent-slice MedDINOv3 features and derives a prototype-driven similarity map. It then performs targeted boundary refinement through a Clinical-Metadata Point Refinement (C-MPR) module, with point-wise predictions modulated by Mouth Open Limitation (MOL), a clinical indicator associated with disc displacement without reduction. On a large-scale cohort of 2,488 PD MRI volumes from 1,300 patients, our method achieves up to a 4.96 Dice improvement over strong baselines across diverse architectures, delivering more anatomically coherent and clinically reliable TMJ disc segmentation.

We study plasmonic excitations in twisted bilayer graphene within a non-Hermitian framework that incorporates effective gain and loss. Using a non-Hermitian extension of the Bistritzer--MacDonald continuum model together with a biorthogonal Kubo formalism for the optical conductivity, we determine how the moiré electronic structure enters the plasmonic response of the active bilayer. We find that non-Hermiticity modifies the collective spectrum, yielding optical and acoustic plasmon branches, with the acoustic branch exhibiting strong subwavelength confinement. In the parity-time-symmetric configuration, gain--loss engineering can reduce the effective spatial damping and enhance the propagation length within the ideal linear model. The same regime produces strongly localized transverse-magnetic near fields. We argue that the enhancement is not a generic consequence of adding gain to a bilayer, but results from the combined influence of moiré-band reconstruction, biorthogonal optical matrix elements, and non-Hermitian modification of the plasmon pole. We also discuss the limitations imposed by disorder, substrate loss, gain saturation, and stability of the parity-time-symmetric regime. These results identify twisted bilayer graphene as a promising, but experimentally demanding, platform for tunable non-Hermitian plasmonics in moiré quantum materials.

Hard carbons are a class of disordered materials with widespread application in energy storage. Despite decades of research, their atomistic formation mechanisms have remained elusive, due to the difficulty of both in situ experimental characterization and first-principles simulations. Here, we describe the formation mechanism of hard carbon from the thermal decomposition of polyvinylidene chloride (PVDC), using first-principles-quality simulations with a bespoke machine-learned interatomic potential model. Our results indicate a two-stage process, consisting of (i) radical-mediated dehydrochlorination, which generates reactive unsaturated carbon sites, and (ii) progressive carbon-carbon cross-linking followed by thermally activated rearrangement into an extended sp$^{2}$-bonded network. We provide an atomistic account of non-hexagonal ring motifs emerging during pyrolysis, supporting the empirically-derived theory that these motifs induce the intrinsic curvature that frustrates graphitic ordering in hard carbons.

Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land cover, vegetation, elevation, built environment, wildland-urban interface, fire history, and satellite imagery. Given an MTBS Event ID, FireDataForge retrieves relevant datasets, aligns them to a common grid, and outputs analysis-ready NumPy arrays with embedded metadata. Batch processing of historical fires demonstrates support for fire behavior simulation, educational visualization, machine learning, and AI-assisted wildfire analysis.

The (deterministic) Landau equation captures the mean long-term evolution of dynamically hot long-range interacting finite-$N$ systems. Though successful, this kinetic equation fundamentally ignores dynamical fluctuations. Building upon Large Deviation Theory, we present a physically-consistent system of Langevin equations that simultaneously recovers the mean Landau dynamics and accurately captures the corresponding fluctuations among different realizations. We show in particular how these Langevin equations can be derived from Rostoker's principle in the limit of weak two-body deflections. We extensively validate these equations against tailored direct $N$-body simulations, showing an exquisite level of agreement.

We propose and characterize a protocol for rapid near-ground state cooling of the center-of-mass (c.m.) mode of a large trapped ion crystal. When the initial mean thermal occupation of the mode $\bar{n}_i$ is small compared to the number of ions $N$, a red sideband drive implements a beam-splitter type SWAP operation between the mode and the collective spin of the $N$ ions, with the latter effectively serving as a quantum harmonic oscillator. Subsequently, a reset of the spins removes the entropy, leading to near-ground state cooling of the c.m. mode. We term this protocol as quantum beam-splitter cooling (QBSC). We analyze the impact of several practical imperfections on the final temperature achievable under QBSC, including finite ion number, off-resonant carrier and blue-sideband contributions, and the impact of the sideband drives arising from spectator modes. In addition, we outline practical strategies to eliminate the carrier drive. Furthermore, we show that measuring the population statistics of the ions at the end of the SWAP operation can enable near-optimal quantum beam-splitter thermometry (QBST), with the classical Fisher information approaching the quantum Fisher information of a thermal state. We discuss the connection of QBSC with continuous sideband cooling and compare QBST with a recently proposed rapid adiabatic passage-based thermometry scheme. Our work constitutes an example of harnessing many-body effects to open new routes to laser cooling and thermometry in large trapped ion crystals.

Deep learning in computational microscopy is severely constrained by the scarcity of densely annotated datasets. While synthetic data generation has bridged this gap in macroscopic computer vision, traditional graphics engines rely on geometric ray-tracing, failing to capture the micro-optical phenomena required for microscopy. Conversely, while wave-optics formulations exist, rendering them computationally tractable at the scale required for deep learning remains a massive systems challenge. To address this, we introduce the Optical Synthetic Object Generator (OSOG), a high-performance, fully differentiable forward-modeling engine. Drawing on established physical models of diffraction and phase retardation, OSOG maps continuous Optical Path Difference (OPD) calculations into a highly optimized, PyTorch-native Structure-of-Arrays (SoA) architecture. We validate this computational framework across three axes: First, object detection models (YOLOv11-OBB) trained purely on OSOG-generated data achieve robust zero-shot transfer to real-world highly occluded Lysozyme micrographs. Second, we introduce DiffOSOG, demonstrating that the engine's end-to-end differentiability allows for the exact recovery of continuous optical parameters via curriculum-guided inverse rendering. Finally, OSOG bypasses the $\mathcal{O}(N)$ bottlenecks of sequential ray-tracing, demonstrating sub-linear scaling by synthesizing 40,000 complex wave-optic particles in under 50 milliseconds (\>20 FPS). By providing a fast, scalable, and physically grounded tensor pipeline, OSOG enables true real-time, on-the-fly dataset generation.

Perovskite oxides ABO$_3$ host a rich interplay of charge, spin, lattice, and orbital degrees of freedom, giving rise to diverse quantum phenomena. In low-dimensional ABO$_3$, reduced symmetry can induce exotic quantum effects such as the two-dimensional electron gas and unconventional superconductivity. Using first-principles density functional theory, tight-binding modeling, and symmetry analysis, we show that ultrathin two-dimensional (2D) ABO$_3$ films -- exemplified by SrTiO$_3$ -- naturally break inversion symmetry, producing a spontaneous out-of-plane bulk photovoltaic (BPV) effect. This differs from previous studies on in-plane BPV current signals and is more applicable and experimentally detectable. Such an effect is highly tunable via thickness, strain, surface termination, crystallographic orientation, and Moiré twisting. These findings are broadly applicable to a wide range of 2D perovskite and other layer-resolved oxides.

Matter can organize not only through forces, but also through the information its constituents acquire from their surroundings. Here we use perceptive Brownian particles as a minimal model to isolate nonlocal sensing as an organizing principle for nonequilibrium matter. The particles undergo purely Brownian motion, with no mechanical interactions, self-propulsion, alignment, or auxiliary fields. Their only coupling is informational, through diffusivity regulated by density measured over a finite perception zone. Whereas local sensing, when unstable, produces conventional long-wavelength demixing, nonlocal perception restructures the instability spectrum, introducing finite-wavelength patterning and nonlinear bubbling instabilities. More fundamentally, it reshapes the ordering pathway by assembling a cascade of instabilities: macroscopic demixing creates dense domains, finite-wavelength modes pattern them internally, and nonlinear feedback hollows them into void bubbles. This produces hybrid phase separation, where a macroscopic dense phase coexists with a dilute background while retaining ordered internal microstructure, whose symmetry, anisotropy, and length scales are selected by the perception kernel. These results establish information acquisition as a constitutive principle of nonequilibrium matter, capable of governing both phase stability and the dynamical pathways through which order emerges.

Deep unrolled networks (DUNs) integrate physical forward models with learned regularization in cascaded network architectures, achieving exceptional performance in inverse problems while maintaining interpretability. While most DUNs operate in the object domain (e.g., image space), recent variants explored representation spaces for improved information flow. However, these methods rely on heuristic methods for data consistency (DC), sacrificing fidelity with measurements. In this work, we introduce DUNE (Deep Unrolled Networks in rEpresentation space), a framework that maintains exact adherence to physical measurements while operating in learned representation spaces. By deriving the DC gradient via the chain rule and implementing it through the Vector-Jacobian Product (VJP), we enable exact backpropagation of measurement residuals into the representation space. This formulation supports diverse architectural backbones, including pre-trained encoders to guide the iterative process. We assess DUNE against state-of-the-art baselines on accelerated MRI reconstruction tasks, demonstrating that exact VJP-based gradients yield superior reconstruction quality and structural fidelity across both single-channel portable low-field and multi-channel clinical high-field MRI acquisitions. The code will be available upon publication at this https URL.

Molecular dynamics simulation of plasma-surface interactions requires an interatomic potential that is simultaneously accurate, computationally efficient, and able to describe many elements and bonding types in reactive systems. In principle, a foundation model for machine-learned interatomic potential (MLIP) can meet these demands. We explore the use of the Universal Models for Atoms (UMA) model, developed by Meta FAIR, for the interactions of oxygen plasma species on a multilayer of WS$_2$, a promising 2D material. Starting from the pretrained uma-s-1p1 model under the Open Catalyst 2020 (OC20) task, we apply an iterative fine-tuning loop with maximally diverse configuration sampling using Smooth Overlap of Atomic Positions (SOAP) and Farthest Point Sampling (FPS); DFT labeling at the PBE+D3+$U$+spin level; and fine-tuning on energy, force, and stress labels. Even in the absence of fine-tuning, the pretrained model reproduces the production-scale observables of interest, namely, chemisorbed S and O coverage under 15eV O$^+$ and O$_2^+$ bombardment. These results were obtained without spin polarization and Hubbard $U$ correction. Nonetheless, fine-tuning reduces the energy and force mean absolute error (MAE) to $4.5\times10^{-3}$eV/atom and $0.076$eV/angstrom, respectively.

Resolving the impact of magnetic field instabilities in triggering small scale turbulent flow and the associated rearrangement of the field is of critical importance in understanding multimessenger observables in binary neutron star mergers, and angular momentum transport in neutron stars and accretion disks. Direct simulation of these instabilities are unfeasible, however large-eddy simulations can incorporate the impact of this turbulence with a subgrid model. We present the first machine-learning-based subgrid model for special relativistic magnetohydrodynamics, trained using a neural network. We demonstrate its performance in online simulations of the 3D Kelvin-Helmholtz instability through both a priori and a posteriori tests. Evaluated in a low resolution simulation, our model captures magnetic field amplification of a simulation at 4 times the resolution with a speed-up of a factor 44. This demonstrates the applicability of such methods in general relativistic simulations of neutron star mergers and other scenarios.

We investigate the intensity stability of the superradiant laser. Our study focuses on the architecture where a continuous beam of atoms in an electronically excited state crosses the mode of a high-finesse Fabry-Perot cavity, which has been proposed as a new architecture of an active optical clock. We show that such superradiant laser can become unstable and develop chaotic behavior. We derive an analytical criterion for this instability and find that it may only occur when the lifetime of photons in the cavity is significantly shorter than the lifetime of atoms. This criterion allows for refining the necessary parameters to run a superradiant laser as a frequency reference in the optical domain. In particular, we point-out the consequences of the instability on intensity fluctuations and laser linewidth. On the other hand, we also point out that the superradiant laser, when in the unstable regime, can become an interesting playground for studying chaos. At the mean-field level, there is a direct mapping to the Bénard instability associated with fluid turbulence; however quantum fluctuations associated with photon out-coupling and atom re-filling substantially modify the expected behaviors. Finally, we point-out the existence of a regular self-pulsing regime at large atom numbers.

We develop a macroscopic continuous-variable model of stimulated four-wave mixing (4WM) yielding closed-form expressions for the mean intensity, variance, and covariance of bright probe and conjugate beams, valid for arbitrary transverse profiles of the pump and seed to all orders in the nonlinear interaction strength. The model uses a Dirac-comb expansion of the pump field, making the photon statistics computable pixel-by-pixel from one set of expressions. In the plane-wave limit, we recover the phase-insensitive amplifier results. Beyond this limit, the formalism explicitly demonstrates the spatial routing of information observed in earlier experiments: the seed angular spectrum is transferred to the twin-beam intensities, whereas the angular spectrum of the squared pump field is transferred to the spatial cross-correlation between the probe and conjugate. We characterize the parameter window of high-fidelity image transfer in the covariance, which is controlled by the seed-to-pump waist ratio and the interaction strength. Our model provides, in one analytical framework, a theoretical account of experiments encoding information in the bright intensities or non-locally in the spatial cross-correlations.

The scientific utility of large single-dish radio telescopes depends critically on the stability and fidelity of their beam patterns, which govern angular resolution, sensitivity, and polarimetric accuracy. For the 32-m Ghana Radio Astronomy Observatory (GRAO) antenna, electromagnetic simulations reveal residual sidelobes, structural diffraction, and cross-polar leakage that limit performance in high-dynamic-range and polarisation-sensitive observations. To address these limitations, we develop a finite-impulse-response (FIR) spatial filtering framework that reformulates beam optimisation as a digital signal processing problem. By exploiting the equivalence between angular displacement and spatial frequency, classical FIR design methods, window-based and Parks-McClellan algorithms are adapted to operate directly on simulated Jones fields. This approach enables controlled suppression of high spatial frequency artefacts responsible for sidelobes and polarisation mixing, while preserving the telescope's diffraction-limited resolution. Applied to the GRAO 5 GHz beam model, the method achieves substantial reductions in near-in sidelobe ripple, improves beam smoothness, and lowers cross-polar leakage below -30 dB at boresight. These improvements translate into enhanced calibration stability and polarimetric precision, strengthening the telescope's capacity for Very Long Baseline Interferometry, spectral-line surveys, and pulsar timing. Beyond GRAO, the method provides a generalisable, non-invasive, and computationally efficient pathway for beam control applicable to other single-dish and phased-array instruments. The results establish digital spatial filtering as a practical complement to conventional optical or mechanical optimisation, advancing the integration of electromagnetic modelling and signal processing in next-generation radio astronomical instrumentation.

We develop multilevel adaptive-rank iterative methods for the solution of linear and nonlinear systems arising from high-dimensional partial differential equations. Our contributions are threefold. First, we extend the projection method of Ballani and Grasedyck [6] to enable flexible preconditioning of high-dimensional linear systems in low-rank tensor formats. Second, we construct multilevel preconditioning strategies by adapting geometric multigrid methods to the low-rank setting. In contrast to prior work, which primarily employs multigrid as a standalone solver, we emphasize its role as an efficient and robust preconditioner. Third, we integrate these techniques within an inexact Newton framework for the solution of nonlinear systems. The proposed methods are evaluated on a range of model problems, including both linear and nonlinear equations, to assess their convergence behavior and computational efficiency. The results demonstrate that multilevel adaptive-rank strategies yield robust and scalable preconditioners, providing effective solvers for high-dimensional problems in low-rank formats.

Violations of Bell's inequalities imply that joint probabilities generated by non-commutative measurements on two (non-identical) quantum particles do not have a single common cause. But joint probabilities generated for such non-identical particles via commutative measurements do have non-trivial common cause variables. We focus on commutative measurements and consider two identical quantum particles, whose density matrices and observables (hermitian operators) are necessarily permutation-symmetric. It is natural to demand that the common cause describing joint probabilities is also permutation symmetric, i.e., it acts symmetrically on both particles. Looking at various ways of defining joint probabilities from the same measurement data, we conclude that either symmetric common causes need not exist (i.e., that the particles can be hiddenly distinguishable), or that symmetric screening variables exist, but they are trivial, i.e., no single common cause can explain all single-measurement correlations.

Today's transistors dictate the voltage and charge scales for both logic and memory. While AI systems are recognized to be limited by memory energy, the dominant share of the energy is expended in the intrachip interconnects whose voltage and charge scales are set by transistors. The energy scaling challenges of transistors can be attributed to simultaneously meeting high current density, high current/impedance modulation, and the inability to lower voltages. Hence, a new logic element that lowers the voltage and charge needs is a priority, not only for lowering logic power but also memory access power. Here, we propose a novel 3-terminal logic element for low energy computing, a solid-state transcapacitor (TCAP). A TCAP is a solid state displacement current modulator realized by a gate which controls the charge-voltage relationship of the channel. Unlike transistors, TCAPs eliminate the dissipative transport current, are not bound by the Boltzmann current modulation limit, and operate with displacement currents limited only by the polarization response and contact resistance. Hence, TCAP circuits may simultaneously overcome the voltage, current density, and current modulation limits of CMOS. We describe a solid state TCAP using a piezoelectric transcapacitor in which a gate-controlled stressor modulates the capacitance of a polar channel via electromechanical coupling. This device achieves inversion and gain, essential for logic, and is functionally equivalent to a 1T-1C memory cell, enabling dense memory. Using voltage scaling, capacitive energy recovery, and high polarization densities of polar materials, the logic based on TCAP offers a pathway to 100 fold lower energy consumption with a delay comparable to ultimately scaled CMOS devices. This approach provides a new potential pathway for low-energy computing beyond the limits of transistors using electro-mechanics and multiferroics.

We study what Lorentz-equivariant jet taggers learn internally, using equivariance tests, linear probes and grade ablations across five models including L-GATr, L-GATr-slim and LLoCa-T. Linear probes show that equivariant models suppress frame-dependent pseudorapidity to zero while encoding jet mass and N-subjettiness strongly. Grade ablations on L-GATr reveal that bivector channels are negligible for top-quark tagging while vector-like channels are dominant but seed variable, consistent with the network exploiting multiple representational pathways. These results characterize which physical features and algebraic grade structures carry discriminative information in equivariant taggers and may inform future development of such models.

We are concerned with the geometric structures of pseudo-sonic curves in two-dimensional self-similar solutions for the Euler equations for potential flow, allowing for non-uniform supersonic states. Mathematically, the governing second-order potential flow equation is of mixed hyperbolic-elliptic type, with degeneracy occurring along the pseudo-sonic curve. In this paper, we develop rigorous analytical approaches to analyze the geometric structures of pseudo-sonic curves in such self-similar solutions. We first show that the pseudo-sonic curve is necessarily a circle if the pseudo-velocity at each point is a normal to the curve. We then analyze the general case in which the pseudo-velocity on the pseudo-sonic point is not a normal to the curve, and study the geometric properties of streamlines in a neighborhood of the pseudo-sonic curve. Next, we establish two theorems that provide sufficient conditions ensuring that the pseudo-velocity at a pseudo-sonic point is normal to the curve, under natural assumptions on the local behavior of the solution. These results yield a precise characterization of the geometry of pseudo-sonic curves. Finally, we apply the developed theory to the shock reflection-diffraction problem with non-uniform incoming flow. We prove that the pseudo-sonic curve must be an arc if the solution is a $C^2$-small perturbation, either in the pseudo-supersonic or pseudo-subsonic region, of a solution with uniform incoming flow. In particular, the density and velocity must be constant, corresponding to the radius and the center of the pseudo-sonic arc, respectively. Moreover, we prove that the solution is $C^{2,\alpha}$-regular in the pseudo-subsonic region up to the sonic arc (except at point $P_1$). The techniques and ideas developed in this paper are expected to be applicable to other nonlinear problems involving similar mixed-type degeneracies.

We study the quantum interaction between free electrons and photons in a time-varying media, and find that periodic modulation exponentially amplifies electron-photon coupling within momentum gaps, enabling arbitrarily large momentum transfer. By preparing the light in a two-mode squeezed vacuum state, the electron momentum grows faster than its spectral spreading, establishing time-modulated photonic media as a platform for accelerating free-electrons and shaping their quantum state.

We study pressure pulse generation and propagation in lipid monolayers by an experimental approach employing rapid photoisomerization of photoswitchable lipids (azoPC). This allows us to generate longitudinal surface pressure pulses by optical flash excitation in both free and constrained layer geometries. We compare the observed pulse shapes with a theoretical approach based on a nonlinear fractional wave equation for a surface displacement field, where a fractional time derivative term captures the hydrodynamics of the monolayer subphase. We explore channel geometries of different lengths and widths and find quantitative agreement between theory and experiment regarding pulse speed and pulse shapes. For narrow channels, we employ a one-dimensional version of the fractional wave equation to study pulse propagation without any fit parameters by using the pressure signal at a close pressure sensor as boundary condition to predict the pressure signal at a second far sensor. A full two-dimensional description can capture all effects arising from the channel geometry for wider channels using one common set of fit parameters for the pulse excitation that can be applied to all geometries. The nonlinearity in the fractional wave equation plays no role in explaining the observed pulse shapes because pulse amplitudes generated by azoPC photoswitching remain very small.

Collective dynamics in financial markets can emerge through synchronized movements of large groups of assets. Motivated by analogies with interacting many-body systems, we introduce a spin-lattice representation for analyzing collective states in cryptocurrency markets. In this framework, assets are encoded as binary spin variables according to the sign of their returns, while correlations between assets determine effective interaction strengths. A correlation-based breadth-first search (CBFS) procedure embeds 169 cryptocurrencies into a $13 \times 13$ lattice, enabling the construction of an Ising-like Hamiltonian describing the market configuration, which we call the \emph{Market Crystal}. Macroscopic observables such as magnetization and energy provide a statistical-mechanical characterization of collective market states. The resulting phase-space structure highlights regimes of strong alignment and fragmentation among assets, with an energy--magnetization pattern suggestive of predominantly ferromagnetic interactions. This framework offers a statistical-mechanical viewpoint for studying collective behavior in financial systems.

Several studies have considered sandpile dynamics not over regular grids, but over networks. In this case, avalanches redistribute grains not between neighboring sites in a geometrical sense, but between connected sites, in a topological sense. However, depending on how nodes are connected, grains may never leave the system, preventing energy release. In this work, we study the simplest case, the BTW model in one and two dimensions, rewiring the nodes so that at every rewiring step, the energy release is always possible, and study avalanche statistics as a function of rewiring. In the 1D case, a transition is observed in the Gini coefficient of the load distribution per node at about 85% the number of possible rewirings, a transition which is not evident with other measures, such as the size distribution of avalanches or the mean distance between nodes in the network. In the 2D case, energy release follows a power law even when the grid is fully rewired, while the Gini coefficient, unlike the 1D case, decreases at a steady rate, with a smoother transition. The effect of network size N is studied, finding that there is a transition for the Gini coefficient at the thermodynamic limit N \to \infty for both the 1D and 2D cases, transition which is also observed in the betweenness centrality, but not in other topological measures. Finally, the dependence of the results with the load per rewiring iteration, and the avalanche threshold is studied.

The performance of advanced materials for extreme environments is underpinned by their microstructure, including the size and distribution of reinforcing phases. Chromium-based superalloys are a recently proposed alternative to conventional face-centred-cubic superalloys for high-temperature applications, such as Concentrated Solar Power, and their development requires efficient measurement of precipitate volume fraction and size distribution from electron microscopy images. Traditional fixed-threshold image processing is sensitive to background noise, generalises poorly across materials, and requires substantial manual measurement effort. To address these bottlenecks, this study proposes DT-SegNet, an end-to-end two-stage deep learning scheme based on YOLOv5 and SegFormer for object detection and segmentation in electron microscopy images. The approach combines the training efficiency of convolutional neural networks at the detection stage with the segmentation accuracy of a Vision Transformer. Numerical experiments show that DT-SegNet substantially outperforms state-of-the-art segmentation tools offered by Weka and ilastik across metrics including accuracy, precision, recall, and F1-score. The model provides a useful tool for alloy-development microstructure examinations and helps address the large datasets associated with high-throughput alloy development.

We study the problem of cooking the yolk and albumen of a hen's egg to their respective optimal temperatures of $T_Y^* = 65^\circ$C and $T_W^* = 85^\circ$C, subject to the requirement that neither temperature ever exceed its target at any time during cooking, since temporary overshoot still overcooks the egg even if the final reading is correct. We model the egg as a two-domain sphere with distinct thermal diffusivities, and take the Laplace transform of the heat equation in each domain, reducing the problem to a $3 \times 3$ linear system in the transform variable $s$ with hyperbolic-trigonometric solutions. The resulting transform is inverted numerically via Talbot's method and validated against a finite-difference solver. A single boiling phase cannot satisfy the no-overshoot requirement: the thin outer albumen heats far faster than the insulated yolk and necessarily overshoots $T_W^*$ before the yolk approaches $T_Y^*$. We show that a three-phase protocol resolves this: a sous-vide pre-soak at exactly $65^\circ$C (which cannot overshoot since the bath temperature equals the target), a short boil to bring the albumen toward $T_W^*$, and an ice-water bath that arrests the albumen's residual overshoot while residual heat continues raising the yolk to its target. Optimizing the phase durations gives $17.26$ minutes of sous-vide, $66$ seconds of boiling, and an ice bath, achieving both targets at $T^* \approx 20.67$ minutes with neither constraint violated at any time. This compares favorably with the periodic protocol of Di Lorenzo et al. (2025), which requires 32 minutes and misses both targets substantially.

A central goal of quantum optimal control is to achieve high-fidelity and low-energy control pulses. When quantum optimal control methods optimize every point of a pulse discretized over small time steps independently this can yield high fidelity control but also results in broadband and energy-hungry waveforms. We extend MAGICARP -- a shooting method inspired by Pontryagin's maximum principle on energy that generates an entire pulse from a small set of parameters, making it smooth and energy-efficient by construction -- from driftless systems to closed systems with the constant drift Hamiltonian of two exchange-coupled spins in an external magnetic field. The optimization proceeds in stages: the dressed states of the drift Hamiltonian structure the target, an initial shooting optimization is performed in the rotating-wave frame, and an exact laboratory-frame refinement follows. Benchmarked against Krotov and GRAPE at matched gate infidelity, MAGICARP consistently achieves the lowest energy and a conserved pulse area, concentrates its spectral weight on the gate-relevant transitions, and is the most robust to fluctuations in the exchange coupling; GRAPE independently converges to essentially the same pulse, while Krotov's method pays an order-of-magnitude energy premium. Moreover, a large statistical survey of unselected optimization runs resolves a weak-driving quantum speed limit for two exchange-coupled electron spins: low-amplitude realizations of the two-qubit quantum Fourier transform cease to exist below a critical gate time $T^*$ set by the drift's interaction rate, and the minimum control energy diverges on approach to this limit. The divergence obeys a simple two-parameter area-pole law, $E_2^{\mathrm{law}}(T)=A/T+B/(T-T^*)$, whose first term is the time-optimal area cost and whose second term is a pole at the speed limit.

IGR J17480-2446 is a low-mass X-ray binary, harboring an exceptional accreting pulsar (a neutron star) with an unusual spin frequency of 11 Hz and a very slow post-outburst crust cooling. The former may imply that it is observed at an early stage of recycling, while the latter was shown to indicate the presence in the outer crust of a low thermal conductivity layer, possibly made of glass. Here we argue that the glass layer formation is a natural result of accretion induced failure of pristine cold crystal crust. This allows us to determine the mass of the accreted material as $\Delta M \approx 2.4\times 10^{-6}~M_\odot$, confirming very early accretion stage for this neutron star. An analysis of spin and thermal state reveals a peculiar set of neutron star birth properties which is commonly associated with `recycled' neutron stars, i.e.\ those that have been experiencing prolonged periods of accretion from a companion. We speculate that such birth properties may represent the outcome of neutron star formation in an electron-capture supernova.

Accurate NH3 pressure-broadening parameters are essential for reliable line-by-line simulations in atmospheric sensing, combustion diagnostics, and planetary/exoplanet studies. Current HITRAN NH3 self- and air-broadened half-widths rely largely on the Nemtchinov rotational correlation and database rules that clamp or assign default values beyond the validated range, limiting the representation of measured rotational trends, especially at high J''. Here, published NH3 self- and air-broadened Lorentz half-widths were compiled and reassessed to develop updated HITRAN-ready empirical correlations. The dataset includes 1317 self-broadened and 1231 air-broadened widths at, or reduced to, 296 K across multiple bands, branches, and rotational states. The analysis shows that linewidths are governed mainly by rotational dependence rather than branch, band, or vibration-inversion effects. Weighted, positivity-constrained third-degree polynomial fits were developed for gamma_self and gamma_air as functions of the branch-dependent index m and K''. The new correlations reduce the MAPE from 10.95% to 6.80% for air broadening and from 23.61% to 10.89% for self broadening relative to HITRAN2024. Comparisons with PNNL and pure-NH3 spectra further confirm improved absorption simulations, providing a physically constrained replacement for the current HITRAN NH3 broadening treatment.

It is often asserted that quantum states for same-type particles must be symmetrized due to ``label redundancy,'' i.e. the assumption that the permutations of labels in direct-product states do not reflect any real physical distinction and thus their permutations constitute an ``exchange degeneracy''. This assumption is directly challenged by the case of scattering of same-type particles such as electrons, which involves two physically distinct scattering channels effectively corresponding to permutation of the labels. I discuss this counterexample with critical attention to an extant portrayal in the literature that omits pertinent physical content. I further note ways in which the assumption that symmetrization must be universally imposed is not supported by actual calculations of particle interactions, nor by seemingly viable particle states based on preparations and outcomes.

We derive compact analytical formulae for the spectrum of nonlinear Compton scattering in a finite plane-wave pulse with a smooth temporal envelope. The strong-field QED probability is reduced to finite-pulse phase integrals, which are evaluated asymptotically for multicycle pulses with a broad class of smooth envelopes. We use the uniform approximation to remove the caustic divergences that appear at the nonlinear edges of broadened harmonics. Away from the caustics, it reduces to the standard saddle-point result. The behavior near the linear edge is further improved by an envelope-corrected saddle-point approximation. The approach retains the harmonic substructure in the spectral-angular region carrying the dominant part of the emitted radiation. The locally monochromatic approximation is recovered by averaging the finite-pulse interference. Within their asymptotic domain of applicability, the resulting formulae agree with direct numerical calculations and can be used to evaluate spectra from an electron beam.

We consider localized oscillation of an Euler--Bernoulli beam on a visco-elastic foundation coupled to a damped discrete oscillator. All parameters of the system independently vary in time in a slow manner. For the conservative case, we use three various analytic approaches. Namely, these are asymptotics, the method based on the adiabatic invariance of the action of a trapped wave, and the consideration of the equivalent Hamiltonian system. All approaches result in the same formula for the amplitude of oscillation. In the dissipative case, we obtain the amplitude of oscillation only utilizing the asymptotic approach.

Convection in icy moon oceans is strongly influenced by rotation, organizing into slantwise columnar structures aligned with the planetary rotation axis. They generate significant meridional heat transport, which can affect the ice shell topography, a primary observable of these moons. However, global ocean simulations cannot resolve convection under realistic icy moon conditions, and traditional convection schemes cannot represent slantwise convection. Here, we develop a slantwise convection scheme and implement it in a global ocean model. We perform benchmark tests in a global spherical shell by comparing parameterized fluxes with convection-resolving simulations. The scheme reproduces the meridional heat transport inside the tangent cylinder, where slantwise convection dominates. The resulting meridional heat transport significantly modifies the surface heat flux, producing variations comparable to the imposed bottom heating magnitude. Although the simulations with parameterized convection cannot fully reproduce the temperature structure, likely due to an inability to reproduce the temperature gradients near the boundaries, they capture the bulk interior vertical temperature gradient. The new scheme allows unresolved slantwise convection to be represented in global ocean simulations for icy moons. It is also applicable to other rapidly rotating oceans with small natural Rossby number ($\mathrm{Ro}^* \ll 1$), including deep ocean worlds on exoplanets.

An improved formulation of the recently developed cell-centered nodal integral method (MCCNIM) is proposed for the numerical solution of the nonlinear Burgers' equation. The improved scheme, referred to as RCCNIM, reformulates the nonlinear convection term prior to discretization by evaluating the convective velocity using cell-averaged values from the previous time level, rather than the present-time approximation used in the original MCCNIM formulation. This reformulation leads to a fully algebraic system whose coefficients depend only on known quantities and are therefore evaluated once per time step. As a result, the proposed method significantly reduces computational cost while retaining the accuracy of the original MCCNIM. The performance of RCCNIM is assessed through systematic numerical comparisons with MCCNIM for the nonlinear Burgers' equation. The results demonstrate that RCCNIM achieves comparable accuracy with improved computational efficiency, indicating its potential for extension to more complex nonlinear fluid flow problems.

Nonlocal optical nonlinearities arising from the thermorefractive effect provide a long-range material response determined by heat diffusion and absorption. In graded-index media, this nonlocality fundamentally alters modal interactions, yet its accurate modeling remains computationally demanding when starting from the full spatial nonlinear Schrödinger equation. In this work, inspired by the nonpolynomial Schrödinger equation (NPSE) framework, we extend the dimensional reduction techniques to incorporate thermally mediated nonlocal nonlinearities. By coupling the optical field to an equation for the temperature-induced refractive index change, and employing a variational ansatz based on Laguerre--Gauss modes of the annular kind, of arbitrary azimuthal order, we derive explicit analytic expressions for the variational equations. The resulting effective model captures the dependence of the nonlinear interaction on mode order and degree of nonlocality, providing a tractable reduced description of the dynamics in thermal nonlocal media.

We introduce OASIS, a simulation-based inference framework for scientific settings where observations are distorted by measurement error, selection effects, and other survey-specific transformations. In many real applications, simulators generate latent, noiseless quantities, while the data are observed only after passing through a complex observational pipeline. Standard simulation-based inference methods often ignore this distinction, comparing observations to idealized simulator outputs or relying on low-dimensional summaries that can miss important structure. OASIS addresses this mismatch by explicitly embedding the observation model into the simulator and performing inference directly at the level of observed-data distributions. The method constructs a pseudo-posterior by reweighting prior samples according to a maximum mean discrepancy (MMD) loss between the empirical distributions of the observed data and forward-simulated observations, thereby avoiding both handcrafted summaries and learned neural surrogates. We provide theoretical guarantees for Monte Carlo consistency, convergence of the empirical pseudo-posterior to its population counterpart, and posterior concentration on the MMD-identified parameter set, with consistency for the true parameter under correct specification and identifiability. In controlled errors-in-variables regression experiments, OASIS delivers robust parameter recovery and well-calibrated uncertainty under heterogeneous and non-Gaussian measurement noise. We then demonstrate the method on a realistic cosmological application involving galaxy cluster observations across multiple wavelengths, in which latent physical properties are linked to observables through nonlinear scaling relations, heteroscedastic errors, selection functions, and incomplete coverage.

Why would anyone help a stranger, knowing they may never meet again? Indirect reciprocity offers one of the most compelling evolutionary answers, yet its two canonical forms -- upstream reciprocity (experience-based), and downstream reciprocity (reputation-based) -- have been studied mostly in isolation. Their joint dynamics in finite and structured populations remain largely unexplored. Here, we fill this gap using agent-based simulations in which an agent is behaviourally either defector, upstream reciprocator, or downstream reciprocator, and the agents' population state is temporally updated using different evolutionary update mechanisms. We show that update mechanism plays a surprisingly decisive role in shaping the fate of downstream and especially upstream reciprocators. Whether agents' experiences and reputations are updated globally or locally can shift outcomes from rich behavioural coexistence to the dominance of downstream reciprocators alone. Intriguingly, we uncover a robust structural feature that persists across all the explored update rules and population sizes: an optimal network degree at which upstream reciprocity is maximized, reflecting a fundamental tug-of-war between cooperative clustering and exposure to defectors. Our results highlight that while downstream reciprocity can either foster or inhibit upstream reciprocity depending on the update mechanism, its net effect on cooperation remains largely positive.

Building on resistive communication, this paper presents a physics-based design of an on-chip neural network with multi-level memristive synapses supporting a dense spectrum of conductance states. Derived from ionic transport physics, we develop a state-variable model and quantify storable sub-levels under thermal noise, drift, and quantized conductance. We assemble these devices into a 1T1R crossbar fabric, derive the linear algebra of analog vector-matrix multiplication (VMM) under wire resistance, and design a differential synapse for signed weights. A multilayer pipeline executes inference, backpropagation, and weight updates physically in the analog domain. We derive the in-situ outer-product learning rule, its discretization onto the conductance lattice, and the resulting quantization noise. We provide energy, area, capacity, and inter-tile models, showing this substrate is ideally suited for large language models (LLMs). Our design eliminates weight movement, surpassing binary ReRAM and traditional CMOS. We detail the material stack (HfO_2-based), the FEOL/BEOL CMOS foundry-integration flow, a self-contained SPICE model, the complete memristive-FPGA neuromorphic system, and an in-memory self-attention engine with current-mode translinear softmax. Finally, a ternary BitNet datapath shows projected per-token efficiency orders of magnitude better than advanced CPUs or GPUs. The result is a self-contained hardware-native blueprint for a high-density, analog, in-memory neural processor.

A phase-field-based reaction-diffusion corrosion model is developed to predict microbially influenced corrosion (MIC) in metal alloys, with a focus on anaerobic conditions and sulfate-reducing bacteria (SRB). The formulation couples microbial sulfate reduction, sulfate transport, electrochemical kinetics, material dissolution, and mechanical effects. Microbial activity is modelled using a Monod-type expression for sulfate consumption, whereas the mechano-chemical coupling is incorporated through an enhanced mobility relationship that captures the influence of mechanical fields on corrosion kinetics. The model is calibrated against experiments and shows strong agreement in predicting pitting kinetics under SRB activity. Sensitivity analyses quantify the competing roles of microbial kinetics, transport, and thermodynamic driving forces in governing corrosion behaviour. The capability of the formulation to capture both MIC-induced pitting and stress-assisted corrosion across multiple length scales is demonstrated through case studies that include microstructure-sensitive simulations and structural-scale coupling with a cathodic protection (CP) model. Results show that finer grain sizes reduce pitting severity but promote faster defect propagation under mechanical loading. At the structural scale, coupling with the CP model enables predictions of defect growth under varying electrochemical conditions and over engineering-relevant length scales, as exemplified with the analysis of an offshore wind turbine monopile. CP delays pitting and suppresses crack propagation, although its effectiveness diminishes as sacrificial anodes degrade. The framework provides a predictive and computationally efficient tool for assessing MIC-induced damage over extended times, with potential applications in the integrity and life assessment of metallic structures operating in aggressive microbial environments.

Nanosized hafnia-zirconia (HfxZr1-xO2) in the form of thin films, multilayers, and nanoparticles are indispensable CMOS-compatible ferroelectric materials for advanced electronic memories and logic devices. Using the Landau-Ginzburg-Devonshire free energy functional with trilinear and biquadratic couplings of polar, nonpolar, and antipolar order parameters, we analyze the pyroelectric and electrocaloric properties of an ensemble of spherical core-shell HfxZr1-xO2 nanoparticles. Complementary to theoretical calculations, we experimentally measure the temperature dependence of the electric charge accumulated in pressed powders consisting of oxygen-deficient Hf0.5Zr0.5O2 nanoparticles with an average size of 7 nm. The observed temperature-dependent behavior of the accumulated charge and its derivative with respect to temperature are in qualitative agreement with the dependences of polarization and pyroelectric coefficient calculated for the ensemble of densely packed spherical core-shell HfxZr1-xO2 nanoparticles. Thus, these results can open the way for creation of CMOS-compatible HfxZr1-xO2 nanoparticles for pyroelectric, electrocaloric, and thermoelectric applications.

The precise realization of molecular electronic devices requires a comprehensive understanding of charge transport mechanisms and the specific interplay between electronic and nuclear degrees of freedom. While average current measurements (I-V characteristics) and conventional Inelastic Electron Tunneling Spectroscopy (IETS) offer valuable insights, they are fundamentally limited by temperature-dependent line-width broadening. This study presents a high-resolution spectroscopic methodology utilizing suspended-wire molecular junctions (SWMJs) based on self-assembled monolayers (SAMs) of 1-decanethiol (C10) and 1,1',4',1''-terphenyl-4-thiol (TPT). By systematically probing the voltage-dependent current noise (${\Delta}I$), we demonstrate that electronic noise spectroscopy circumvents thermal degradation by probing transition rates between vibrational manifolds rather than simple additions of conductance channels, which enables sub-thermal feature mapping. Leveraging a fast-convolution-based Landauer-Büttiker transport model fitted to experimental data, we map complex vibrational manifolds, including high-energy overtones. This allows for the direct extraction of crucial nanoscale molecular parameters, including mode energies, anharmonicities ($x_e$), dissociation energies ($D_e$), and local environment reorganization energies ($E_r$). These parameter-dense noise signatures act as a unique molecular fingerprint, establishing noise spectroscopy as a highly sensitive platform for chemical sensing and discrimination in advanced quantum devices.

The empirical bitstring distribution is the most accessible observable on Rydberg-atom arrays, but the bipartite von~Neumann entropy it constrains is far costlier to obtain. We present a six-term linear closed form for the entropy, built on bitstring-derivable physics scalars, and characterize its accuracy, portability, scaling behaviour, and calibration cost. The feature set is selected with guidance from a trained graph neural network: probing the network localizes its entropy prediction to the two-point correlators on the bipartition boundary, and an exhaustive ground-truth search restricted to those boundary correlators isolates the form. It reaches $0.024$~nats mean absolute error in distribution: $6.4$ times the network's error, but in a form a human can read and apply without retraining. Fit once and applied unchanged, it has lower error than the base network on five of six out-of-distribution pools and ties the sixth. An independent density-matrix renormalization-group study to one hundred atoms -- five times the reach of exact diagonalization -- settles the size-extrapolation question: coefficients frozen at small size fail at scale, but the failure is structured. Refit per size the form holds to $25$--$50$~mnat (cross-validated); two of its six slopes follow clean inverse-size laws, one a downward curving growth, and the others are trendless; the fitted laws deploy the form label-free at roughly $40$--$80$~mnat. The result fixes a label-budget rule: at large sizes, a few dozen labels recalibrate the closed form to match a fine-tuned in-distribution ensemble on the same features, while nonlinear ML models pull ahead only given large labelled datasets.

High harmonic generation (HHG) is a highly nonlinear optical process in which radiation from a strong driving field is up-converted into its high-order harmonics. In atomic systems, this nonlinearity manifests itself through the intensity scaling of the emitted harmonics with the driving field strength. Despite the highly nonlinear nature of HHG, when the driving field is prepared in a classical Gaussian state and atomic depletion remains negligible, the quantum statistical properties of the generated harmonics retains classical Gaussian quantum statistics. Driving HHG with bright squeezed vacuum (BSV) light challenges this paradigm, as its enhanced field fluctuations can modify the statistical properties of the generated harmonics. In this work, we investigate the conditions under which BSV-driven HHG gives rise to non-classical Gaussian states, and identify the regimes where this Gaussian description breaks down. For bichromatic driving by a strong coherent field at frequency $\omega$ and a perturbative BSV field at $2\omega$, the even-harmonic response is approximately linear in the BSV quadrature, leading to non-classical multimode Gaussian entanglement in the harmonic field. We show that this state can be described as a distributed collective squeezed mode over the even-harmonic manifold, and characterize its covariance matrix, entanglement structure, and quantum teleportation fidelity as an operational benchmark. Our results highlight the potential of non-classically driven HHG as a platform for engineering Gaussian and non-Gaussian states of light in the extreme ultraviolet regime.

Reading out a qubit often requires coupling it to a resonator, but that same resonator can also give the qubit an extra path to decay. Here, we study a way to reduce this loss using a built-in permanent electric dipole. The dipole shifts the cavity field in different directions for the qubit ground and excited states. This shift makes the relevant wave functions overlap less, which weakens the transverse qubit--cavity exchange that causes Purcell decay. In a simplified displaced rotating-wave model, this exchange vanishes at $\eta=\sqrt{2}$. In the full transverse model, this exact zero is lifted, but strong suppression remains at a larger dipole-induced displacement. Using dressed open-system decay rates, we find an operating point where the cavity-mediated decay is strongly reduced while the longitudinal readout signal remains finite. For the benchmark studied here, at fixed pointer separation, the normalized lifetime increases from $\kappa T_1=11.1$ to $47.3$, and the estimated single-shot readout error drops from $0.21$ to $0.07$. These results show that permanent electric dipoles can provide an internal, channel-selective form of Purcell protection.

Wave energy converters (WECs) require system-level techno-economic analysis to balance power production, cost, and survivability. Existing simulation tools are either too computationally costly for large-scale optimization or too narrow in disciplinary scope to support integrated design studies. This work presents MDOcean, a novel open-source WEC simulation framework for rapid early-stage design exploration, parametric analysis, and multidisciplinary optimization. MDOcean integrates hydrodynamics, dynamics, structures, and economics in a computationally efficient architecture based on analytical and semi-analytical methods that substantially reduce runtime while maintaining near-numerical accuracy. The framework includes an eigenfunction-based linear hydrodynamic solver, a quasi-linearized frequency-domain dynamics engine capable of modeling drag and saturation nonlinearities, a structural sizing module incorporating realistic yield, ultimate, buckling, storm, and fatigue design criteria, and a simple cost model for techno-economic assessment. Particular emphasis is placed on the linearized pseudo-spectral optimal control formulation, which extends frequency-domain constraint-handling approaches with a unified describing-function and analytical quadratically-constrained quadratic program framework. This formulation efficiently treats nonlinearities and constraints while preserving compatibility with optimization and frequency-domain analysis techniques. Validation and benchmarking demonstrate that MDOcean's 151 ms runtime is orders of magnitude faster than leading WEC simulation tools while maintaining agreement with higher-fidelity baselines to within a few percent in most cases. The framework also provides insight into limiting behaviors, scaling laws, subsystem interactions, and key tradeoffs governing WEC design and techno-economic performance.

The thermal Purcell effect describes the modification of the local density of states of the fluctuating electromagnetic field induced by a Fabry-Pérot cavity, leading to the enhancement or suppression of radiative transport quantities. Using fluctuational electrodynamics, we investigate nonequilibrium radiative heat, linear-momentum, and angular-momentum exchange between a magneto-optic nanoparticle and a Fabry-Pérot cavity. Analytical expressions for the spectral densities reveal that geometric confinement modifies the electromagnetic local density of states, producing distinct behaviors for different transport quantities. Specifically, sub-wavelength confinement enhances radiative heat and angular-momentum transfer, but suppresses the lateral force. Additionally, interference between cavity modes causes all transfer quantities to oscillate spatially with particle position. At the cavity center, mirror symmetry enforces a parity decomposition of electromagnetic fluctuations resulting in a vanishing lateral force, whereas heat transfer and torque remain finite through combined even and odd modal contributions. These results demonstrate that cavity engineering provides selective control over nanoscale energy and momentum transfer via structured electromagnetic fluctuations.

Physical simulations for intricate geometries with path-dependent constitutive models face difficulties due to the enormous computational cost they require. Recently, the emergence of generative AI models, which succeed in image and video synthesis tasks, has provided a promise to further improve simulations. Although U-Net-based denoising diffusion probabilistic models (DDPMs) have been adopted for elastic stress field generation, they typically require hundreds of sampling steps, and applications of generative models to path-dependent, e.g. plastic, stress fields remain very limited. In this work, we propose a novel flow matching (FM) model based on a transformer backbone for high-resolution path-dependent stress field generation with stochastic loading-unloading paths and geometry. The proposed model operates within the latent space of a variational autoencoder (VAE) and formulates the simulation of plastic fields as a video synthesis task, directly generating the stress fields across all time steps. Meanwhile, we design a non-Gaussian source distribution for flow matching, such that crossings among conditional transport paths are reduced during training. This enables our model to generate satisfactory samples in one step without relying on distillation. In addition, we introduce token-level loading embeddings and two auxiliary networks to further enhance the model performance in path-dependent simulation. The results demonstrate that, even with a limited training dataset, our model can accurately generate high-resolution path-dependent fields. It is much more computationally efficient than finite element analysis, providing a speedup of 6 to 7 times over FEM on CPUs and approximately two orders of magnitude speedup on consumer-grade GPUs.

Biological systems maintain function in fluctuating environments by transforming past stimulation into internal dynamical states that support future-oriented responses. Reservoir computing provides a computational analogue, but standard formulations often treat the recurrent substrate as a fixed random network and train only the readout. Here we ask how the substrate itself changes when reservoir architecture is placed under evolutionary selection for prediction. Using the Kuramoto--Sivashinsky equation as a testbed for spatiotemporal chaos, we evolved reservoirs over five construction hyperparameters: size, connectivity degree, spectral radius, input scaling, and readout regularization. Evolution reduced prediction error at the population level, extended the low-error forecast horizon, and organized the design space along a diminishing-return size--efficiency frontier. Structural analyses showed that evolved reservoirs remained within a conserved stochastic-block-model-like spectral envelope while refining low-eigenvalue modes, locking modularity to an intermediate band, and pruning connection cost within that band. Pareto analysis showed that elite reservoirs occupied a horizontal floor in the cost--modularity plane, indicating that accuracy and efficiency were achieved jointly rather than through a simple trade-off. These findings show that evolutionary optimization does not merely improve prediction, but exposes interpretable structural constraints on the recurrent substrate: it stabilizes a task-suitable dynamical class and refines the architectural degrees of freedom most relevant for prediction. Evolutionary reservoir computing therefore provides a bio-inspired framework for studying how predictive demands shape adaptive dynamical networks.

In quantum field theory on curved spacetime, and in locally-covariant quantum field theory, the Hadamard condition is often presented as a necessary condition on 'physically reasonable' states of the quantum field, and plays a central role in many theoretical and foundational applications - ranging from proofs of the renormalizability of Wick polynomials to derivations of the Hawking temperature. Yet despite this, the philosophical and foundational underpinnings of the Hadamard condition remain murky. I critically discuss existing motivations for the Hadamard condition in the literature, before arguing in favour of an alternative justification for the Hadamard condition, according to which it is best understood as a necessary and sufficient condition for the existence of a well-defined operator product on a sufficiently large space of observables of the quantum field, satisfying a variety of further conditions (thus proving a converse to a result which was already discussed in this context). This clarifies the role and status of the Hadamard condition, including its relationship to the equivalence principle, to well-definedness of physical quantities such as Wick polynomials and the expectation value of the stress-energy operator, and the sense in which Hadamard states are 'vacuum-like'.

We introduce a gauge-propagation approach for approximately converting generic tensor-network states into an isometric tensor-network state form with a prescribed orthogonality center. In one dimension, this propagation is exact because the non-isometric factor produced by a QR or singular-value decomposition is supported on a single virtual bond. In higher-dimensional networks, however, a local step can have several outgoing directions, and the residual factor is generally not separable into independent single-bond contributions. We address this local obstruction by approximating a local tensor, or a contracted local cluster, by structured terms consisting of an isometric factor multiplied by a tensor product of output-leg factors. The isometric factor is retained at the current site or cluster, while the output-leg factors are absorbed into neighboring tensors along the propagation directions. This construction provides a local truncation criterion for gauge propagation and a practical route to refinement by increasing the number of retained terms or enlarging the local cluster. Benchmarks on random tensors and on the loop-gas tensor representation of the Kitaev spin liquid show that this refinement reduces both local residuals and accumulated propagation errors. For the loop-gas tensor, two structured terms reduce the local residual to numerical precision, and enlarging the local object from 2-in-2-out to 4-in-2-out and 6-in-2-out clusters lowers both local truncation errors and accumulated errors in finite honeycomb gauge propagation. These results identify propagation-compatible local decomposition as a useful building block for approximate isometrization and as a potential initializer or preconditioner for variational isoTNS algorithms.

Agentic artificial intelligence (AI) systems are beginning to assist, accelerate, and partially automate scientific discovery, performing tasks that span literature synthesis, code generation, data analysis, hypothesis proposal, and model criticism. We argue that this transition is qualitative rather than incremental, and that suitably designed multi-agent systems may evolve from passive computational tools into ``AI scientists'' that can expand the hypothesis-generating and verification capacity of science. Such systems must be developed and deployed within a scientific ecosystem fit for purpose: institutions must be redesigned for verification, accountability, interpretability, and dual-use safety. We sketch how multi-agent architectures, illustrated by the prototype framework \textit{Denario}, accelerate the discovery cycle and traverse model spaces beyond human reach; examine what this implies for authorship, peer review, and the enduring role of human scientists; and close with recommendations for governing AI as an epistemic actor rather than a mere instrument.

Optical control of quantum materials has progressed along two separate directions: creating non-equilibrium states inaccessible at equilibrium, and coherently controlling ultrafast dynamics with multi-pulse protocols. Ferroelectricity is especially attractive in this context because its order parameter, macroscopic polarization, directly links inversion-symmetry breaking to functional response. Yet light-induced ferroelectricity has so far been confined to quantum paraelectrics near the ferroelectric instability, where critical fluctuations obscure the formation of a homogeneous ferroelectric state and complicate its deterministic coherent control. Unifying these capabilities -- preparing a symmetry-broken state and then coherently steering its functionality -- remains a central challenge. Here we show that intense terahertz excitation of a soft phonon mode induces a ferroelectric state in centrosymmetric PbTe, a thermoelectric material with strong lattice anharmonicity but no ferroelectric transition at finite temperature. The light-induced symmetry-broken state can be realized up to about 100 K, without relying on local dipolar fluctuations. Experiment and theory together reveal that terahertz-driven anharmonic coupling between degenerate transverse optical phonons underlies this ferroelectric induction. Furthermore, we demonstrate coherent amplification and suppression of the induced polarization via a double-pulse-excitation protocol. These results establish terahertz-driven anharmonic mode coupling as a general strategy for controlling mode-mediated functionalities in quantum materials, opening a route to ultrafast information processing.

The collective behavior of passive particles within chiral active matter has emerged as a significant area of soft matter research. However, most existing studies focus on systems where chirality is imposed by external torques rather than intrinsic activity. In this work, we study emergent dynamics in a suspension of active spinners and passive colloids by computing many-body hydrodynamic interactions via Ewald summation. By systematically exploring a broad range of area fractions and rotational velocities, we identify distinct phase-separation regimes sensitive to the system's kinematic parameters. Specifically, we report the emergence of unique structural morphologies, including the formation of passive particle vortices surrounding phase-separated active spinners and the development of large-scale active-passive bands. We characterize the underlying dynamics by analyzing the temporal evolution of characteristic length scales and the non-equilibrium velocity distributions of the passive particles. Our findings provide new insights into the role of long-range hydrodynamic couplings in governing the self-organization of non-equilibrium condensed matter.

Photonic computing has emerged as a promising platform for accelerating artificial intelligence workloads by enabling low-latency and energy-efficient linear operations such as vector-matrix multiplication. However, scalable on-chip high-order nonlinear processing remains challenging, limiting the functional versatility of current photonic hardware. Here, we present an optoelectronic approach for approximating high-order and high-dimensional nonlinear functions. The key to this approach lies in optical random Fourier feature mapping, which transforms nonlinear function evaluation into an equivalent linear computation. This approach enables nonlinear computing within a linear photonic framework, eliminating the need for complex optical nonlinear or active materials while preserving scalability and computational throughput in a simple silicon photonic circuit. We experimentally demonstrate a broad class of nonlinear functions, including tenth-order Legendre polynomials, computationally demanding special functions (Voigt, Fermi-Dirac, and Fresnel), neural-network activation functions, two-dimensional nonlinear functions, and a 10-dimensional softmax layer. This work establishes a general and scalable strategy for nonlinear computing in photonic integrated hardware and opens a pathway toward fully functional optical accelerators for next-generation computing systems.

The focus of the present work is the development of specialized experimental instrumentation compatible with synchrotron characterization for in-situ and operando symmetric intermediate temperature solid oxide fuel cells (IT-SOFC) studies at maximum temperatures of 800 C , exposed to reducing and oxidizing atmospheres, using fluorescence X-ray absorption spectroscopy (XAS) measurements in combination with electrochemical impedance spectroscopy (EIS) in the multipurpose Quati beamline at CNPEM/SIRIUS synchrotron facility [1]. Symmetric IT-SOFC are gaining importance due to their structural simplicity, as they allow for the use of identical materials on both sides of the fuel cell electrolyte; the anode, and the cathode [ 2,3 ]. The symmetric configuration opens new opportunities for fundamental research of electrode materials and improves the versatility of SOFC electrochemical devices [2,3].

If disk detection is applied to nanobeam electron diffraction datasets, then the results are effectively a list of vectors describing the position of every diffraction peak in real and reciprocal space. This is the natural territory for the application of clustering algorithms, and they are shown to be highly effective at decomposing such datasets and automating imaging and analysis. Examples are shown in both polycrystalline and single crystal (with precipitates) systems. Additionally, automated separation of amorphous or deeply nanocrystalline components is also found to be possible allowing composite images of both amorphous and crystalline components in partially crystallised samples to be easily and automatically generated. These advances promise to increase throughput in atomic structure analysis with nanobeam diffraction, and also make finding minor components much easier. They can also serve as a preliminary step towards more detailed crystallographic or crystal size/shape distribution analysis.

Universal machine-learning interatomic potentials (MLIPs) are rapidly becoming general-purpose tools for atomistic simulation, but their role in quantitative materials modeling when reactive events are involved remains unsettled. We compare five universal MLIPs across seven chemically diverse systems and find that strong performance on standard benchmarks does not guarantee accurate predictions of target observables. In particular, zero-shot models do not reliably reproduce reactive, transport, or high-barrier processes, exemplified here in particular by the sulfur-vacancy jump in MoS$_2$. We therefore propose a practical alternative: universal MLIPs are used to generate long molecular dynamics trajectories, the resulting configurations are sub-sampled and relabeled with DFT, and material-specific MLIPs are subsequently trained or fine-tuned on the resulting first-principles datasets. This workflow converts universal models into efficient configuration-space generators while retaining ab initio reference labels for training. Across the tested systems, $2{,}000$ DFT-recalculated structures are often sufficient to obtain accurate fine-tuned or trained-from-scratch models. For the most challenging case, iterative self-training progressively refines the sampled configuration space and recovers the DFT MoS$_2$ potential energy profile with only $600$ first-principles calculations in total. The resulting workflow enables the generation of $1$ ns ab initio-quality trajectories - including training data generation and model creation - within three days.

The spinor degree of freedom associated to exciton-polariton condensates can spontaneously self-oscillate breaking time translation symmetry, thus showing a continuous time-crystal (CTC) behavior. An open question in such driven-dissipative and non-linear quantum open systems is what happens when CTCs are brought together to interact. Here we experimentally study polariton condensates in coupled traps, evidencing mutual induction and synchronization of the pseudospin temporal GHz dynamics in the CTC phase. The individual and relative orientation of the (limit cycle) precessing pseudospins can be tuned by the optical excitation power, displaying both ferro and anti-ferro dynamical configurations. We theoretically show that the exciton reservoir, and both the coherent and long-range dissipative inter-trap coupling, play important roles in the CTC dynamics. The investigation of time-broken symmetry is thus extended here to more complex non-hermitian systems opening the path to study self-sustained collective dynamics in lattices of non-linear quantum condensates.

The Transition Density Amplitude (TDA) method of Griesshammer et al. is applied to compute scattering observables for Compton scattering, threshold neutral pion photoproduction, and elastic pion scattering on the light nuclei Hydrogen 3, Helium 3, Helium 4, and Lithium 6. In this formalism the amplitude factorizes into an irreducible few-body kernel, which encodes the interaction of the probe with the active nucleons, and a transition density amplitude, which carries the nuclear structure information. Because the densities are computed once per nucleus and momentum transfer and then convolved with any process-specific kernel, the same nuclear input is reused across reactions, yielding substantial computational savings over direct evaluation. This factorization is realized in the publicly available Fortran suite DensityScattering, developed as part of the present work; a researcher implementing a new reaction need only supply the corresponding kernel in a prescribed format, with infrastructure for density handling, integration, quantum-number summation, and output already provided. The TDAs are constructed from nuclear wave functions using the semilocal momentum-space regularized chiral potential of Reinert, Krebs, and Epelbaum at cutoffs of 450 and 500 MeV. For Lithium 6, no-core shell model wave functions are evolved under a Similarity Renormalization Group (SRG) transformation; a back-transformation scheme ("SRG-And-Back"), co-developed for the present work, returns the densities to the physical momentum scale and thereby preserves the original chiral ordering. The induced uncertainty is validated against exact Helium 4 results at the 2% level, and convergence studies for Lithium 6 Compton scattering bound the residual uncertainty below 6%.

We report classical molecular dynamics simulations of NaCl association and dissociation in supercritical aqueous fluids over a wide range of salt concentrations, from moderate salinity to highly concentrated H2O-NaCl mixtures attainable at high temperatures. The degree of dissociation a and the corresponding ideal dissociation constant Kd, derived directly from a, were calculated as functions of the stoichiometric NaCl mole fraction at selected pressure-temperature (PT) conditions from 673.15 to 1273.15 K and from 0.1 to 2 GPa. At moderate salinity corresponding to a molality of approximately 1 mol/kg, NaCl remains largely dissociated a = 0.3-0.7 depending on pressure and temperature). In contrast, when the mole fraction of NaCl increases up to xNaCl = 0.333 (27.8 mol/kg), the degree of dissociation tends towards zero, and most ions form Na$^+$Cl$^-$ contact pairs and multi-ion clusters. As a result of these competing trends, the mole fraction of structurally dissociated Na$^+$ and Cl$^-$ ions is a non-monotonic function of the stoichiometric NaCl concentration and typically reaches a maximum at xNaCl = 0.06-0.10. This result shows that increasing salinity does not necessarily increase the abundance of structurally available chloride ions in supercritical aqueous fluids. Additional fixed density simulations at 1 and 7 mol/kg extend the analysis up to 1673.15 K and separate the effects of temperature and density on the associate/dissociate state of the ions. The obtained concentration dependences provide molecular-level constraints for thermodynamic descriptions of concentrated supercritical electrolytes and for evaluating chloride availability in high-temperature aqueous fluids.

Precise knowledge of nuclear structure is essential across fundamental physics, yet probing these structures is notoriously difficult. To address this challenge, ultra-peripheral collisions (UPCs) provide a femtoscopic tomography for imaging the atomic nucleus. UPCs offer a pristine electromagnetic pathway: coherent vector-meson photoproduction generates patterns of diffraction and two-source interference that directly encode the nuclear spatial density. Turning these patterns into quantitative constraints is, however, a challenging inverse problem, complicated by correlated sensitivities to deformation and neutron skin, phase smearing, and experimental backgrounds. Here we introduce an interpretable Multitask deep-learning framework that maps transverse momentum distributions to multiple nuclear-structure indicators simultaneously and identifies the kinematic regions driving each inference. We demonstrate the approach with coherent $J/\psi$ photoproduction in $^{96}_{40}\text{Zr} + ^{96}_{40}\text{Zr}$ collisions, showing that the learned features separate diffraction-dominated and interference-dominated information and provide analysis-ready observables for future high-luminosity data.

Neutrals in the plasma edge are commonly modeled by kinetic equations, with quantities of interest given by macroscopic quantities such as density, velocity, and temperature. In reactor-relevant regimes, fully kinetic descriptions solved by Monte Carlo (MC) methods, although accurate, become computationally expensive, whereas fluid-limit approximations are computationally more efficient but may lose accuracy due to boundary effects or low-collisional regimes. Hybrid fluid-kinetic approaches aim to combine the strengths of both descriptions. However, existing simulation methods face challenges, including interface handling in domain decomposition, unphysical assumptions, and iterative coupling in distribution decomposition. In this work, we propose a distribution-decomposition hybrid model constructed at the particle level based on the kinetic-diffusion Monte Carlo (KDMC) method. The model inherits key properties of KDMC: it is asymptotic-preserving and does not require iterative coupling between the fluid and kinetic components. To improve the accuracy of the fluid-part quantities estimation, a Navier-Stokes-type fluid system is derived via Hilbert-Chapman-Enskog expansions, tailored for KDMC. In the considered one-dimensional tests, the resulting fluid system has comparable accuracy to the AFN model used in SOLPS-ITER while requiring substantially fewer nonlinear iterations. Additionally, a tunable reflective boundary condition is introduced that allows balancing accuracy and efficiency. The model exhibits at least 500 times speedup over the kinetic MC, while maintaining relative L2 errors around 10% in a charge exchange (CX)-dominant test case. In non-CX-dominant regimes, the accuracy becomes increasingly sensitive to boundary treatment due to the inherent limitations of the fluid approximation near the boundary, motivating further refinement of the KDMC boundary conditions.

Convection remains one of the main uncertain links between multidimensional hydrodynamics and one-dimensional stellar evolution. In particular, transition regions such as near-surface layers or convective boundaries require mean-field descriptions that remain connected to the underlying dynamics rather than to a prescribed mixing length. We describe the flow as a distribution of mesoscopic fluid particles in position-velocity space. A conservation law for this distribution defines the average and yields the Reynolds-Favre mean-field equations as velocity-space moments. Under standard interior conditions, the same dynamics can be written in Liouvillian form, which extends the Hamiltonian structure to stratified and dissipative media. The Liouvillian formulation identifies the phase-space divergence, $\{s, T\}$, as a local measure of contraction or expansion of nearby trajectories. In the quasi-adiabatic limit, its sign reduces to the classical Schwarzschild stability criterion. Away from this limit, the diagnostic remains velocity-resolved and can distinguish different parts of the convective population within the same layer, for example in surface and penetration regions. Appendices show how rotation, magnetic fields, and composition changes can be incorporated through modifications of the phase-space structure. Phase-space averaging provides a dynamically grounded route from hydrodynamics to mean-field stellar convection equations. It also supplies a local trajectory-based stability diagnostic and a natural starting point for the maximum-entropy closures constructed in the companion paper.

Chemical language models (cLMs) are widely assumed to learn surface-level syntactic patterns rather than learning meaningful molecular semantics. Here, we apply sparse autoencoders (SAEs) to MolFormer, an encoder-only cLM, to mechanistically examine how molecular representations are built across layers. We discover that early layers rely on position-tracking latents to parse molecular grammar, while later layers encode atom-in-substructure and pharmacologically relevant features. Additionally, we show that non-canonical SMILES produce more disruptive representation shifts than invalid SMILES, driven by position-latent disruption propagating across layers. To support further exploration, we develop InterMol, an interactive visualizer for SAE activations on molecular strings and structures.

We adapt the \textit{magnetohydrodynamic} wave phase-mixing paradigm [Tsiklauri et al. (2003)] to investigate \textit{acoustic} wave propagation and damping in media where transverse sound speed gradients exist. Using an analytical model, we recover previous harmonic wave and Gaussian pulse evolution solutions now controlled by the spatial gradient of the local \textit{sound speed}. We discover a scaling law governing a Harris current sheet-like pulse evolution: under developed-stage phase-mixing, the peak envelope of such pulse amplitude scales with propagation distance as a new power-law $\max(P_1) \propto x^{-9/2}$. Applying our model to the solar tachocline directly resolves the 26-year-old helioseismic mystery of low-$\ell$ global $p$-mode linewidth anomalies observed by BiSON above $\nu \approx 3000\,\mu\text{Hz}$. We demonstrate that the sound speed gradient forces rapid, non-turbulent energy damping directly in the shear zone, providing the exact high-efficiency bulk dissipation needed to account for the missing energy sink deep within the solar tacholine. Our model provides the exact damping rates that classical, homogeneous models severely underestimated in the past. Finally, our results provide actionable design strategies for engineering compact stealth coatings or meshes to achieve enhanced acoustic signature suppression from moving bodies immersed in a fluid.

Stickiness in mixed Hamiltonian systems causes chaotic trajectories to remain temporarily trapped near regular structures, making it difficult to distinguish regular, weakly chaotic, and strongly chaotic motion over finite times. We show that the recurrence time entropy (RTE), previously used in discrete maps, also characterizes weak chaos in Hamiltonian flows. In the Hénon-Heiles system, the RTE reproduces the phase space structures identified by the largest Lyapunov exponent: low values in regular islands, higher values in chaotic regions, and intermediate values in sticky layers. The proportion of chaotic trajectories identified by the RTE is consistent with that obtained from the smaller alignment index (SALI). The finite-time RTE series identify low-entropy episodes near regular islands, associated with temporary trapping. The duration of these episodes displays algebraic decay, while high-entropy episodes display exponential statistics. These results establish the RTE as an effective diagnostic of weak chaos and stickiness in Hamiltonian flows.

High-order discontinuous Galerkin spectral element methods (DGSEM) based on Legendre-Gauss-Lobatto (LGL) nodes provide accurate and efficient discretizations for conservation laws. However, their cost, memory footprint, and time-step restrictions increase rapidly when the degree of the polynomial increases. This paper develops a corrected $\mathbb{P}_n\mathbb{P}_m$ ($c\mathbb{P}_n\mathbb{P}_m$) approach for DGSEM-LGL discretizations that aims to recover the accuracy of an $m^{th}$-order approximation while evolving only the degrees of freedom associated with an $n^{th}$-order representation, with $n<m$. The projected evolution of the high-order components is derived first at the continuous level and then in the fully discrete DGSEM-LGL setting. The discrete analysis shows that because LGL quadrature is not exact for the highest Legendre mode, a correction term for that mode is required to preserve the order of convergence. A compact projection-based reconstruction operator is then introduced to recover high-order components without solving the enlarged constrained least-squares systems used in standard reconstruction procedures. For sufficiently smooth solutions, the resulting $c\mathbb{P}_n\mathbb{P}_m$ scheme is shown to achieve the expected $m+1^{th}$ convergence order. Numerical experiments for one- and two-dimensional conservation laws, including Euler, viscous Burgers, and 2D decaying homogeneous isotropic turbulence, confirm theoretical convergence behavior and demonstrate competitive accuracy relative to computational cost, with particularly clear efficiency gains for viscous flows.

We report on the realization of a quantum-degenerate Bose-Bose mixture of 162Dy and 164Dy. Owing to the near-identical mass and polarizability of the two isotopes, the mixture thermalizes efficiently, with evaporation trajectories closely following those of the single-isotope case. Using a broad interspecies Feshbach resonance, we explore a miscible-immiscible transition between the two Bose-Einstein condensates. The tunability of the interspecies interaction, combined with the large magnetic dipole moment of Dy, makes this platform well suited for exploring dipolar effects in ultracold mixtures, including multi-component supersolidity.

The present study investigates direct and indirect social contagion mechanisms in an online social network environment. Using a large-scale dataset comprising approximately 290,000 users from the VKontakte platform, we examine the factors associated with the probability that a user likes a post. Our analysis shows that, while demographic and structural characteristics of individual nodes, such as gender and degree, contribute to the observed dynamics, the strongest associations arise from activity in the user's local network. In particular, active nodes (users who have already liked the post) at distances d = 1 and d = 2 play a central role in shaping liking behavior. We find a substantial association between second-order activity and liking probability, which persists even in the absence of active direct neighbors and is consistent with indirect influence pathways in the network. No significant association is detected for nodes at distance three or beyond. The results also support the structural diversity hypothesis: the number of connected components among active friends is a significant predictor of liking.

Energies of H, He-like ($Z=2-18$) ions, Li, and Be are investigated under a spherically symmetric Gaussian potential through a density functional formalism. The radial Kohn-Sham equation has been solved by invoking a work function-based exchange potential. The effect of electron correlation is analyzed by incorporating two functionals: a local parameterized Wigner functional and a non-linear gradient- and Laplacian-dependent Lee-Yang-Parr (LYP) functional. The generalized pseudospectral method is employed to provide accurate numerical eigenfunctions and eigenvalues. This allows nonuniform, optimal spatial discretization fulfilling the Dirichlet boundary conditions. This work demonstrates a possible manipulation of energy by controlling dot parameters. Apart from ground states, exploratory results are also reported for low-lying excited state $1s2s$ ($^{1,3}S$) of He atom. Companion calculations are also performed for various information-theoretic measures, such as Shannon entropy in position ($S_{r}$), momentum ($S_{p}$) spaces, and Fisher information in position space ($I_{r}$). The behavior of correlation functionals in presence of Gaussian potential is examined critically. We find that energy increases, $S_{r}$ exhibits minima, while $S_{p}$, $I_{r}$ attain maxima for a decrease in the width of potential, whereas an increase in potential depth further amplifies these effects across all properties. The Fisher-Shannon plane reveals a progressive localization as well as the compression of electronic density, and thereby indicates a weakening of relative electron-correlation effects. In the Collin's conjecture, it gives rise to a non-linear loop-like feature. Much of the results are presented here for the first time.

Hollow-core fibers (HCF) are transitioning from laboratory curiosities to production-deployed infrastructure, with cloud providers operating thousands of kilometers of hollow-core links. As operators upgrade their networks, working and protection paths will inevitably traverse different fiber types, creating a class of protection switching challenges absent in homogeneous single-mode fiber networks. This article provides a comprehensive overview of these challenges and presents a comparative analysis of protection switching under two architectures - 1+1 dedicated and shared backup path protection (SBPP) - in hybrid hollow-core and single-mode fiber networks. Using Monte Carlo simulation with random per-link fiber assignment across six reference topologies (1,602 node pairs), we quantify chromatic dispersion (CD) steps, generalized signal-to-noise ratio (GSNR) penalties, and modulation-format degradation for both architectures. At 50% HCF deployment mean CD steps range from 4,000 to 22,000 ps/nm, with GSNR penalties of 1.6-3.1 dB and 38-59% of node pairs requiring modulation downgrade under 1+1 protection. A complementary cross-fiber extreme analysis reveals that the two switching directions are fundamentally asymmetric: HCF-to-SMF switching doubles the CD step and inflicts about a 10 dB GSNR penalty while SMF-to-HCF switching delivers a negative GSNR penalty (the protection path is higher quality than the working path). SBPP shows up to 7% higher CD steps and 4 percentage points more downgrade in sparsely connected topologies due to its greedy shortest-first path selection. Capacity retention improves with HCF penetration for both architectures, reaching 85-99% at full HCF deployment. We present mitigation strategies including DSP pre-loading, spectral pre-equalization, and network planning guidelines, concluding that 1+1 dedicated protection is preferable to SBPP for hybrid deployments.

Powder x-ray diffraction (PXRD) under laser-driven dynamic compression is a powerful tool to investigate material response to extreme pressure, temperature and strain rates. Robust PXRD platforms have been developed at kJ and MJ laser facilities worldwide including the Powder X-Ray Diffraction Image Plate (PXRDIP) at the Omega Laser Facility at the Laboratory for Laser Energetics (LLE) and the TARget Diffraction In Situ (TARDIS) at the National Ignition Facility (NIF). Here we present further developments of data analysis methods focused towards improving the fidelity of the PXRD intensity determination for these platforms. We illustrate these methods by discussing how they can be implemented in a data analysis package and applied to shock compression data on diamond near 1 TPa. We discuss using the XRD signal from the collimating pinhole or a layer of un-compressed material in the sample package as \textit{ in-situ} references for XRD intensity. We detail how to compare data collected with different x-ray sources and how to account for thermal damping of XRD signal when comparing XRD from a shock-compressed, hot material with the reference material at ambient.

Single-molecule or single-particle tracking measurements inherently yield noisy microscopic trajectories, often significantly constrained by the diffraction limit and by the finite rate at which photons are emitted and counted. Here we study systematically the resulting effects of finite spatial and temporal resolution on one's ability to discern and quantify the arrow of time in microscopic trajectories. Given an experimental time series Y(t) degraded by noise, we consider the problem of estimating the entropy production associated with the corresponding microscopic variable X(t) using two strategies. The first attempts to infer the statistical properties of X(t) from those of Y(t) before estimating the entropy production. The second uses the experimental observable as a proxy for the true microscopic observable, with the entropy production estimator applied directly to Y(t). We prove that both strategies result in lower bounds on the true entropy production. Importantly, noise-degraded observables Y(t) undergo non-Markovian dynamics even when X(t) are Markovian, and non-Markovian entropy production estimators are advantageous. We further note nontrivial interplay between spatial and temporal resolution: in the presence of detection noise, improving the temporal resolution alone may lead to poorer rather than better entropy production estimates.

We present a conceptually clear introduction to quantum theory, deriving the theory from scratch from the point of view of quantum information. Different subsets of these lectures were taught to a wide variety of audiences, including exceptional high-school students in the International Summer School for Young Physicists (ISSYP) at Perimeter Institute, 2nd-year physics undergraduates at the University of Toronto, and 4th-year physics and math undergraduate and graduate students at Brock University. The lectures are completely self-contained, including all the necessary mathematical background: complex numbers, linear algebra, and probability theory. They cover topics such as the axioms of quantum theory, qubits, superposition, entanglement, the uncertainty principle, quantum gates, unitary transformations and evolution, interpretations of quantum mechanics, the no-cloning theorem, quantum teleportation, quantum algorithms, density operators and mixed states, von Neumann entropy, the Bloch sphere, quantum decoherence, Hamiltonians, the Schrödinger equation, canonical and path integral quantization, quantum harmonic oscillators, wavefunctions, and much more. The lectures also contain 118 proof-based problems and 52 computational exercises, including several programming projects.

As a continuation of our previous papers, the Planck-Einstein eigen-relation (or alternatively called Lagrange-Hamilton eigen-relation) and de Broglie eigen-relation in macroscopic electrodynamics are revealed in this paper. Using the macroscopic Planck-Einstein eigen-relation, the generalized quality factor defined in our previous papers can be derived naturally and rigorously. These above provide a solid physical and mathematical foundation for defining the concept of generalized quality factor, whose physical meaning is the reciprocal of normalized free-photon number. Using the above these, we also derive a novel and effective calculation formulation for the generalized quality factor. Based on the macroscopic Planck-Einstein eigen-relation and generalized quality factor, a series of conclusions regarding macroscopic modal quantization (MMQ) are also derived, for example: the physical meaning of eigen-value is further clarified from several different points of view; a natural normalization method for eigenmodal quanta is obtained; a generalized Parseval identity is formulated; the lower bound of generalized quality factor is revealed.

Vertical-symmetry breaking provides a versatile means of coupling leaky photonic-crystal resonances that are otherwise protected by opposite out-of-plane parity. Here, we develop a unified two-mode framework for the weak-to-strong coupling transition induced by vertical-symmetry breaking in photonic crystal slabs. The symmetry-breaking perturbation simultaneously generates a near-field coherent coupling and modifies the overlap of the radiation channels of the two parent resonances. Their interplay drives the transition from frequency crossings to avoided crossings through an exceptional point (EP), while also governing radiative linewidth exchange through Friedrich-Wintgen interference. Using a radiation-vector description resolved into the upper and lower half-spaces, we show that total radiation cancellation and one-sided radiation cancellation correspond, respectively, to quasi-bound states in the continuum (quasi-BIC) and quasi-unidirectional guided resonances (quasi-UGR). This framework distinguishes the spectral condition for EP formation from the far-field conditions controlling linewidth suppression and directional emission. We validate this picture numerically and experimentally in square-lattice photonic crystal slabs. Tuning the superstrate index drives a weak-to-strong coupling transition through an EP, while partial etching yields a broad off-$\Gamma$ quasi-BIC regime with strongly asymmetric top and bottom radiation. We further apply the same radiation-vector framework to a laterally shifted bilayer grating, where quasi-BICs and quasi-UGR emerge along a continuous symmetry-breaking pathway. These results establish vertical-symmetry breaking as a general route for controlling hybridization, radiation interference, and directional leakage in photonic crystal slabs.

Photoacoustic tomography is a contrast agent-free imaging technique capable of visualizing blood vessels and tumor-associated vascularization in breast tissue. While sophisticated breast imaging systems have been recently developed, there is yet much to be gained in imaging depth, image quality and tissue characterization capability before clinical translation is possible. In response, we have developed a hybrid photoacoustic and ultrasound-transmission tomographic system PAM3. The photoacoustic component has for the first time three-dimensional multi-wavelength imaging capability, and implements substantial technical advancements in critical hardware and software sub-systems. The ultrasound component enables for the first time, a three-dimensional sound speed map of the breast to be incorporated in photoacoustic reconstruction to correct for inhomogeneities, enabling accurate target recovery. The results demonstrate the deepest photoacoustic breast imaging to date namely 48 mm, with a more uniform field of view than hitherto, and an isotropic spatial resolution that rivals that of Magnetic Resonance Imaging. The in vivo performance achieved, and the diagnostic value of interrogating angiogenesis-driven optical contrast as well as tumor mass sound speed contrast, gives confidence in the system's clinical potential.

Light fields with a central wavelength of 2 um are very well suited for strong-field-driven charge carrier control: Their photon energy lies far below the band gap of many materials, while their oscillation period remains significantly shorter than the coherence time of charge carrier oscillations. The resulting potential for field-driven charge carrier control is contingent on the reproducibility of the field structure of such ultrashort laser pulses. Here, we present a compact 200-kHz laser system that delivers ultrashort pulses with a duration of less than 20 fs in the spectral range around 2 um and with a pulse energy of 25 uJ. The electric field structure of the 2-um pulses is characterized in detail. In particular, the carrier-envelope phase (CEP) is measured over a wide range of timescales, from microseconds to hours. Passive stabilization due to difference frequency generation results in a root mean square value of carrier-envelope phase noise of less than 70 mrad over all measured time scales. The applicability of the pulses is demonstrated by measuring CEP-dependent high-order harmonic spectra with energies of up to 160 eV.

Multi-year-to-decadal climate predictions are a key tool in understanding the range of potential regional climate futures. Here, we present a framework that combines machine learning and analog forecasting for regional predictions on these timescales. A neural network is used to learn a mask of weights that highlights important global precursors to the evolution of a specific prediction target (region, variable, and lead time). A library of mask-weighted model states, or potential analogs, are then compared to a mask-weighted observational state. The known future of the best matching potential analogs serve as the prediction for the future of the observational state. We predict 2-meter temperature using the Berkeley Earth Surface Temperature dataset for observations, with a multi-model potential analog library of CMIP6 simulations. Using a 30-year climatology reference, we compare our analog method to two other analog prediction methods and the CMIP6 library using the continuous ranked probability score to assess the quality of predicted distributions, and mean squared error to assess the quality of predicted ensemble means. For nearly all cases explored, our analog method produces skillful predictions. We find higher distribution skill over the CMIP6 library in all cases, which is due to improved prediction of the distribution spread. We find overall higher skill than other analog methods in the predicted distributions and ensemble means. Finally, we find broadly similar skill to an ensemble of bias-corrected initialized Earth system models. Benefits of our analog method include low computational cost, ensemble size flexibility, and interpretability.

Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imbalanced datasets. This study investigates the impact of three data augmentation strategies including Stable Diffusion (SD), Flow Matching (FM), and Conventional Augmentation (CA), on enhancing DL-based classification of disease. Anterior thyroid scintigraphy images from 2,954 patients across nine medical centers were classified into four categories: Diffuse Goiter (DG), Nodular Goiter (NG), Normal (NL), and Thyroiditis (TI). Data augmentation was performed using CA as well as various SD and FM models, creating 18 distinct scenarios. Each augmented dataset was used to train a ResNet18 DL-classifier. Model performance was assessed using class-wise and average precision, recall, F1-score, AUC, and image fidelity metrics (FID and KID). FM-based methods demonstrated top-tier performance, with the Original dataset combined with FM (O+FM) configuration achieving the highest micro, macro, and weighted F1-scores (0.78, 0.77, 0.78) and AUC values (0.95, 0.93, 0.94). While the O+FM+CA model also yielded excellent, balanced results, O+FM was statistically superior, indicating that high-fidelity generative augmentation can supersede conventional heuristics. FM also produced the most realistic images, achieving the lowest overall FID (0.66) and KID (0.83). Among the SD variants, SD1 combining image and prompt inputs was the most effective (macro F1: 0.76; FID: 4.17), showing that physician-generated prompts provide critical clinical context. Integrating FM and clinically-informed SD augmentation substantially improves thyroid scintigraphy classification, highlighting the importance of advanced generative models for robust training on limited datasets. The code is available at: this https URL

In this work, Galerkin projection is used to build reduced-order models (ROM) for two-dimensional Rayleigh-Bénard (RB) convection with no-slip walls. We compare an uncoupled projection approach that uses separate orthonormal bases for velocity and temperature with a coupled formalism where the equations are projected onto a single basis combining velocity and temperature components. Orthonormal bases for modal projection are obtained as the eigenfunctions of the controllability Gramian of the linearized RB equations, eliminating the need for DNS snapshot databases required by traditional POD-based approaches. Various coupled and uncoupled ROMs with different numbers of modes are generated and validated against direct numerical simulations (DNS) over a wide range of Rayleigh numbers. One of the objectives is to determine their domain of validity as a function of the system dimension and the Rayleigh number. DNS and ROM results are compared in terms of mean vertical profiles, heat flux, flow structures, dynamical regimes and energy spectra. Crucially, unlike previous POD-based Galerkin models for thermal convection, these ROMs do not require closure models and remain numerically stable. The coupled approach shows better agreement with DNS in terms of mean vertical profiles and Nusselt number scaling. The capabilities of these models are exploited to conduct a detailed bifurcation analysis at $Pr = 10$ using Poincaré sections and Lyapunov exponents, precisely identifying the transitions between periodic, quasiperiodic, and chaotic states with significant reductions of computational cost.

In this paper we investigate analytically the formation of finite time singularities in the three dimensional incompressible Euler equations under the model of Gibbon, Fokas, and Doering for vorticity stretching within a bounded cylindrical domain and under axisymmetric conditions. We derive explicit Lagrangian solutions for the vorticity, its stretching rate, fluid pathlines, and velocity components by exploiting constants of motion associated with the field dependent infinitesimal symmetries of the system. The central finding is that the existence and nature of a finite time singularity are determined exclusively by the local geometric structure of the initial vortex stretching rate near its global minimum. Whether a singularity forms depends on how flat this profile is at the minimum. Flatter profiles delay the blowup and sufficient flatness can suppress it entirely. For power law behavior near the minimum, critical thresholds for the exponent are identified which separate regular solutions from those that develop a finite time singularity. These thresholds differ depending on whether the singularity occurs at the centre of the cylinder or on a ring away from the centre, with minima at the centre requiring higher flatness to avoid blowup. This work provides a rigorous analytical framework that elucidates how the local geometric structure of the initial conditions governs the potential for singularity formation in 3D fluid flows, offering fundamental insights into the interplay between symmetry, initial data, and the development of extreme events in idealised turbulence.

In recent years, seismic data analysis advancements combined with an increasing number of dense seismic networks deployed worldwide, have contributed to the creation of massive seismic catalogs, significantly lowering their magnitude of completeness. However, large automated catalogs are typically released without systematic quality control, and may contain spurious detections, mislocations, or inconsistent magnitudes. In challenging scenarios, such as microseismic monitoring applications, where weak and closely spaced events often overlap in time, pick-based detection and location approaches often fail to reliably associate phases. This leads to missed detections or degraded location accuracy producing seismic catalogues polluted with false or mislocated events. To address this limitation, we present a computer vision based workflow that integrates waveform based seismic location methods with deep learning image classification to discriminate real seismic events from noise directly from coherence matrices. These matrices, computed via waveform stacking, exhibit distinct patterns for real events (single, focused maxima) versus noise (blurred, incoherent patterns) hence the problem of cleaning seismic catalogues can be solved as a binary image classification problem. In addition, the robustness of waveform based location methods allows to obtain an increased resolution in the location of seismic events. We validate our workflow using the publicly available COSEISMIQ dataset.

We perform a theoretical study of a continuous superradiant laser supporting its experimental realization at FEMTO-ST using two sequentially-emitting ensembles of ${}^{171}\mathrm{Yb}$ atoms coupled to the same Fabry-Perot cavity. Using an open quantum system approach, we identify for the simplest case the parameter space where the laser reaches tens of picowatts of power with a sub-millihertz linewidth. Studying the impact of inhomogeneous frequency broadening and variations in atom-cavity coupling on the superradiant emission, we find the laser properties robust with respect to such perturbations, also thanks to the occurrence of synchronization of the atomic dipoles. We then consider a two-site configuration, in which atoms in each site are equally coupled to the cavity and have equal detunings, with different values for the two ensembles. We find for balanced and imbalanced atom numbers that synchronization leads in a certain parameter space to a single narrow spectral line whose central frequency follows the weighted average frequency. This result indicates that sequential loading can enable continuous superradiant emission for metrological applications, provided that the relative frequencies of the two ensembles are controlled to the level required by the target stability.

We investigate excitation transfer in an engineered cavity QED transport architecture consisting of an N-site donor ring coupled coherently to a central acceptor and driven by a single quantized photon mode. The system evolves under a Lindblad master equation including spontaneous loss and pure dephasing. In the ordered symmetric limit, the dynamics reduce exactly to a photon-bright mode-acceptor trimer, allowing closed-form analytic expressions for transfer efficiencies and mode-resolved losses. We demonstrate that near-unity efficiency arises from photon-mediated hybridization that generates a dark transport channel in which ring population is strongly suppressed. This cavity-induced mechanism bypasses dissipative dark modes of the ring and is distinct from conventional excitonic transport or environmentally assisted quantum transport (ENAQT). Static disorder in photon-ring coupling activates lossy ring modes through hybridization, while intra-ring coupling primarily shifts spectral crossings and can restore efficiency by separating dissipative channels. The model is interpreted as a tunable quantum-optical transport device. Our analytic reduction provides clear design principles for engineered quantum transport networks operating in cavity-QED platforms.

Neural-network-based machine learning interatomic potentials have emerged as powerful tools for predicting atomic energies and forces, enabling accurate and efficient simulations in atomistic modeling. A key limitation of traditional deep learning approaches, however, is their inability to provide reliable estimates of predictive uncertainty. Such uncertainty quantification is critical for assessing model reliability, especially in materials science, where often the model is applied on out-of-distribution data. Different strategies have been proposed to address this challenge, with deep ensembles and Bayesian neural networks being among the most widely used. In this work, we introduce an implementation of Bayesian neural networks with variational inference in the aenet-PyTorch framework. To evaluate their applicability to machine learning interatomic potentials, we systematically compare the performance of variational BNNs and deep ensembles on a dataset of 7,815 TiO$_{2}$ structures. The models are trained on both the full dataset and a subset to assess how variations in data representation influence predictive accuracy and uncertainty estimation. This analysis provides insights into the strengths and limitations of each approach, offering practical guidance for the development of uncertainty-aware machine learning interatomic potentials.

Nuclear fusion plays a pivotal role in the quest for reliable and sustainable energy production. A major roadblock to viable fusion power is understanding plasma turbulence, which significantly impairs plasma confinement, and is vital for next-generation reactor design. Plasma turbulence is governed by the nonlinear gyrokinetic equation, which evolves a 5D distribution function over time. Due to its high computational cost, reduced-order models are often employed in practice to approximate turbulent transport of energy. However, they omit nonlinear effects unique to the full 5D dynamics. To tackle this, we introduce GyroSwin, the first scalable 5D neural surrogate that can model 5D nonlinear gyrokinetic simulations, thereby capturing the physical phenomena neglected by reduced models, while providing accurate estimates of turbulent heat transport. GyroSwin (i) extends hierarchical Vision Transformers to 5D, (ii) introduces cross-attention and integration modules for latent 3D$\leftrightarrow$5D interactions between electrostatic potential fields and the distribution function, and (iii) performs channelwise mode separation inspired by nonlinear physics. We demonstrate that GyroSwin outperforms widely used reduced numerics on heat flux prediction, captures the turbulent energy cascade, and reduces the cost of fully resolved nonlinear gyrokinetics by three orders of magnitude while remaining physically verifiable. GyroSwin shows promising scaling laws, tested up to one billion parameters, paving the way for scalable neural surrogates for gyrokinetic simulations of plasma turbulence.

Classic turbulence models often struggle to accurately predict complex flows. Although data-driven techniques have addressed these shortcomings, most existing research has concentrated on two-dimensional (2D) cases. This study bridges this gap by enhancing a data-driven turbulence model, the SST-CND (shear stress transport-conditioned) model, which was originally trained on 2D separated flows, in 3D scenarios. An additional correction term, \b{eta}_3D, is introduced to account for 3D effects. The distribution of this term is determined through a 3D field inversion process using high-fidelity data obtained from the flow around a cube. An algebraic expression for \b{eta}_3D is then derived through symbolic regression and formulated to degrade to zero in 2D cases. The performance of the resulting SST-CND3D model is evaluated across a range of flows. In 2D flows, the SST-CND3D model performs identically to its 2D-trained predecessor. However, the model exhibits superior performance in 3D flows, such as the flow around the complex JAXA standard model high-lift configuration. These findings indicate that a sequential approach, constructing a 3D correction term that vanishes in 2D on top of a 2D-trained model, constitutes a promising method for developing data-driven turbulence models that perform accurately in 3D while preserving their effectiveness in 2D.

Dark matter remains one of the most compelling open problems in modern physics, motivating experimental searches for new light, weakly coupled particles beyond the Standard Model. Despite extensive efforts employing diverse detection strategies, large regions of parameter space remain unexplored. We report a high-statistics laboratory search for invisible decay modes in nuclear $\gamma$-ray cascades using approximately $100~\mathrm{kg}$ of CsI(Tl) scintillators operated at Texas A\&M University. The experiment employs a high-activity ${}^{46}$Sc radioactive source and a ``missing-$\gamma$'' technique, in which the absence of a photon from a well-identified cascade serves as a signature of new physics. Unlike appearance-disappearance experiments, this approach requires only a single photon conversion into a dark-sector particle, enabling sensitivity to significantly weaker couplings. The setup provides simultaneous sensitivity to a broad class of light dark-sector candidates, including axions and axion-like particles, dark scalars, and dark photons in the $0.1 - 1 \text{ MeV}$ mass region. Through careful control of detector containment, energy resolution, and environmental backgrounds, we exclude certain regions on the previously explored parameter space. With foreseeable improvements in detector volume and systematic uncertainty control, this technique has the potential to probe currently unexplored parameter space for axion-like particles and light dark scalars.

We present an isothermal Global Buckley--Leverett framework for multicomponent, multiphase flow in porous and fractured media that retains the interpretability of classical Buckley--Leverett while incorporating essential physics: equation of state-based phase behavior, multicomponent Maxwell--Stefan diffusion, dynamic capillarity, stress-sensitive permeability, and non-Darcy fracture flow. The formulation yields a single global-pressure equation driving the total Darcy flux and an exact fractional-flow decomposition of phase velocities with buoyancy and capillary drifts; inertial effects enter as per-phase damping that renormalizes mobilities. Crucially, the combination of Maxwell--Stefan diffusion and dynamic capillarity renders transport pseudo-parabolic, resolving the loss of strict hyperbolicity that plagues three-phase Buckley--Leverett and ensuring a well-posed initial-value problem. In practice, each time step solves the scalar global-pressure equation, reconstructs phase fluxes via the split, and advances strictly conservative component balances; axisymmetric (cylindrical) forms for radial injection with vertical buoyancy are provided. The model reduces exactly to classical Buckley--Leverett when added physics are disabled, making it a practical backbone for carbon storage, geothermal exchange, and contaminant transport in fractured, compositionally complex reservoirs.

The Laser Interferometer Space Antenna (LISA) will observe gravitational waves in a regime that differs sharply from what ground-based detectors such as LIGO handle. Instead of searching for rare signals buried in loud instrumental noise, LISA's main challenge is that its data stream contains millions of unresolved galactic binaries. These blend into a confusion background, and the task becomes identifying sources that stand out from that signal population. We explore whether manifold-learning tools can help with this separation problem. We built a CNN autoencoder trained on the confusion background and used its reconstruction error, while also taking advantage of geometric structure in the latent space by adding a manifold-based normalization term to the anomaly score. The model was trained on synthetic LISA data with instrumental noise and confusion background, and tested on datasets with injected resolvable sources such as massive black hole binaries, extreme mass ratio inspirals, and individual galactic binaries. A grid search over $\alpha$ and $\beta$ in the combined score $\alpha \cdot \mathrm{AE}_{\mathrm{error}} + \beta \cdot \mathrm{manifold}_{\mathrm{norm}}$ found optimal performance near $\alpha = 0.5$ and $\beta = 2.0$, indicating that latent-space geometry provides more discriminatory information than reconstruction error alone. With this combination, the method achieves an AUC of $0.752$, precision $0.81$, and recall $0.61$, a $35\%$ improvement over the autoencoder alone. These results suggest that manifold-learning techniques could complement LISA data-analysis pipelines in identifying resolvable sources within confusion-limited data.

Frequency comb spectroscopy has revolutionized broadband molecular fingerprinting with mode-defined resolution. While dual-comb spectroscopy stands as a dominant paradigm for high-resolution measurements, it relies on mutually coherent dual combs, and its applicability to non-cooperative sensing is limited by the requirement for phase-sensitive detection and controlled optical returns. Here, we introduce reconstructive comb spectroscopy, a fundamentally different paradigm that eliminates these constraints. By integrating a mode-programmable optical comb with a computational sensing scheme based on single-pixel detection, our method achieves picometer-level spectral resolution over a 10-nm (1.27-THz) instantaneous bandwidth, with single-photon sensitivity down to 10^-4 photons per pulse, and compressed spectral acquisition at 2.5% sampling while maintaining reconstruction errors below 10%. We demonstrate robust performance through scattering media and from non-cooperative targets. These capabilities establish reconstructive comb spectroscopy as a new platform for gas sensing, with broad applicability in remote atmospheric monitoring, industrial leak detection, and standoff chemical-threat identification.

Ferroelectric ordering in polar liquids has been observed in numerical simulations and liquid-crystal experiments. Within mean-field framework, this behaviour remains associated with sample-shape dependent, surface contribution to the free energy, which does not vanish in the thermodynamic limit due to the long-range nature of dipolar interaction. Yet, numerical simulations performed under conducting periodic boundary conditions, for which the surface contribution vanishes, still exhibit ferroelectric order, pointing to an intrinsic bulk origin of the transition. Moving beyond the mean-field approximation, Kirkwood seminal study of the dielectric properties of polar liquids emphasized the role of hindered dipolar rotation in shaping the corresponding pair correlations. In Kirkwood analysis, hindered rotation stems from the mean force between nearest-neighbor dipoles, placing the focus on local structure. Introducing a different perspective while retaining the central role of hindered dipolar rotation in the onset of ferroelectricity, the present study establishes, as an original finding, that annealed averaging of dipolar interaction over positional disorder generates hindered dipolar rotation favoring dipole alignment, and able to drive a ferroelectric phase transition. As a result, unlike approaches centered on local structure, ferroelectricity emerges not in spite of the liquid nature, but because of it. This ferroelectric phase transition is intrinsic to the bulk. Annealed averaging over positional disorder generates an effective dipolar interaction that is shorter-ranged than the bare potential, analogous to the Keesom interaction where screening arises from annealed dipolar disorder. Derived within classical density functional theory, these findings are exact in the infinite-dimensional limit and remain valid within the optimized cluster expansion for dimensions greater than two.

We analyze interactions between turbulent airfoil wakes and extreme gusts using a data-driven framework with time-dependent bases. The current approach represents each snapshot with time-varying bases consisting of two-dimensional in-plane modes and one-dimensional spanwise modes, together with a reduced covariance matrix. By deriving closed-form evolution equations, we advance these components using a small rolling window, eliminating the need for full-history storage. Applied to extreme vortex gust-airfoil interactions at Re=5000, we reveal that the first in-plane mode dominates before impingement, while the second mode gains energy post-impingement, amplified by gust intensity. During impingement, leading modes exhibit multiscale features such as shear-layer roll-up and fine-scale turbulence. The temporal evolution of the mode-energy spectrum and its rank gaps further quantifies these interactions. A large leading-mode gap indicates energy concentrated in a few coherent structures, leading to faster recovery, whereas a smaller gap indicates energy distributed across many modes, consistent with richer multiscale activity and delayed re-stabilization. These trends also follow the transient lift dynamics, with higher amplitude and more oscillations indicated by a rise in the leading singular values. This work may provide a foundation for time-varying data-driven modal analysis of extreme gust encounters.

The spatiotemporal evolution of pulsating turbulent pipe flow was predicted by deep learning. A convolutional neural network (CNN) and long short-term memory (LSTM) were employed for long-term prediction by recursively predicting the local temporal evolution. To enhance prediction, physical components such as wall shear stress were informed into the training process. The datasets were obtained from direct numerical simulation (DNS). The model was trained exclusively on a limited set of sinusoidal pulsating flows driven by pressure gradients defined by their period and amplitude. Subsequently, 36 pulsating flows with arbitrary non-sinusoidal acceleration and deceleration were predicted to evaluate the generalization capability, defined as the predictive performance on unseen data during training. The model successfully predicted drag reduction rates ranging from $-1\%$ to $86\%$, with a mean absolute error of 9.2. This predictive performance for unseen pulsations indicates that local temporal prediction plays a central role, rather than learning the global profile of the pulsating waveforms. This implication was quantitatively verified by analyzing the differences in periodic $C_f$--$Re_b$ trajectories between the training and test datasets, demonstrating that flows exhibiting local similarity to the training data are more predictable. Furthermore, it was demonstrated that flows exhibiting intermittent laminar--turbulent transition and relaminarization become predictable when such regimes are incorporated into the training data. The results indicate that accurate prediction is achievable provided that the training data sufficiently cover the local flow-state space, highlighting the importance of appropriate training data selection for generalized flow prediction.

The Pulsed Townsend experiment enables the extraction of relevant electron transport properties in different gases such as the electron drift velocity $W$ (or equivalently the mobility $\mu$), the longitudinal diffusion coefficient $D_{\mathrm{L}}$, and the effective ionization rate $R_{\mathrm{net}}$ (or equivalently the effective ionization coefficient $\alpha_{\mathrm{eff}}$). Existing analysis techniques lack an accurate representation of the experimental initial and boundary conditions. This work provides an improved evaluation approach by appropriately considering both initial and boundary conditions in order to extract more accurate swarm parameters from measurement data. Simulative and experimental measurement results verify an increased evaluation accuracy. Furthermore, the longitudinal diffusion coefficient $D_{\mathrm{L}}$ can now be accurately extracted from Pulsed Townsend measurements, which was previously not possible with existing evaluation approaches. The developed curve fitting code is made publicly available.

The representation and future evolution of summer Greenland atmospheric blocking in climate models is here investigated from a Lagrangian perspective using a novel Python package blocktrack. By applying the blocktrack algorithm to ERA5 reanalysis and a CMIP6 model ensemble, we identify and track blocking events over Greenland, and obtain their trajectories, intensities, duration and wave-breaking patterns. Greenland blocking (GB) events in ERA5 are then classified into two types based on their wave-breaking characteristics. These correspond to the previously identified upstream (anticyclonic wave breaking) and retrograding (cyclonic wave breaking) GBs. Upstream blocks, which originate in Northern Canada, exhibit stronger moisture transport before and during blocking onset and higher temperature anomalies than retrograding blocks, which follow an east-to-west trajectory and originate in the North Atlantic. Our analyses show how the recent observed increase in GB frequency, particularly in 2012, is primarily driven by upstream blocks. CMIP6 models generally fail to capture the observed increase and underestimate GB variability, especially for the upstream component. Projections under the SSP3-7.0 scenario show a decline in retrograding blocks but a possible increase in upstream blocks, depending on the detection index used. We discuss possible drivers of these changes, which include jet stream shifts, increased frequency of high-moisture transport events from low to high latitudes, surface temperature increases due to Atlantic Multidecadal Variability and Arctic Amplification. By analyzing block trajectories, this study demonstrates how Lagrangian diagnostics can provide novel insights into the dynamics of blocking events over Greenland.

The hyperfine interaction in the ground state of a hydrogen atom of assumed radius $R$ is proportional to $-1/R^3$, raising the question of why the hyperfine interaction does not lead to collapse of hydrogen, or positronium. We approach the problem in terms of a minimax variational calculation based on the exact Gordon solution of the Dirac equation for the hydrogen atom ground state. The full Dirac treatment leads to the result that in an assumed variational state of size $R$, when $R$ minimizes the total energy the magnetic moment of the electron assumes its usual value, $e\hbar/2mc$, but when $R<\hbar/mc$, the effective electron magnetic moment becomes essentially $eR/2$, softening the hyperfine interaction and eliminating an energy minumum at small $R$. The magnetic moment of the proton is similarly suppressed, and the hyperfine interaction of a small size atom becomes bounded by the kinetic energy, thus assuring stability. We extend the Dirac variational calculation to positronium where we find simple results for the ground state energy and hyperfiine interaction, and then extend this variational calculation to Coulombic atoms of two fermions of arbitrary masses. This paper also lays out a framework for treating diquarks as relativistic Coulombic systems, in the presence of color electric and magnetic interactions.

We use classical computational methods to estimate the ground-state energy to chemical accuracy in a model of the FeMo-cofactor of nitrogenase which is widely studied as a target of quantum computing. Our result relies on the insight that the ground-state problem can be characterized as one of ranking many competing, but largely simple, states. This allows a combination of systematic high-order coupled cluster and density matrix renormalization group calculations together with an extrapolation protocol to obtain an accurate energy. Within the model we identify several spin isomer candidates for the ground-state that are degenerate to chemical accuracy. Beyond this model, we characterize the impact of additional electronic excitations and the cluster and protein geometric fluctuations on the low-lying electronic landscape. We find that many features of the landscape are retained in more detailed representations of nitrogenase, which points to the complexity of spectroscopic interpretations of the electronic structure of the FeMo-cofactor.

Oceanographic instrumentation technology is currently under rapid transition towards increasingly open-source technology. Open-source buoys compete with commercial and closed-source buoys both in price, functionality and availability. Long-range radio (LoRa) is a communication technology which is inexpensive both in terms of data transfer cost and power without the need for pre-existing infrastructure. In this paper, we present ORB, an open-source drifter buoy using LoRa for coastal water measurements. ORB is designed to be reliable, low-cost, modifiable and power efficient. We present validation experiments demonstrating that ORB can achieve a radio telemetry range of five kilometers, and has an expected battery lifetime of up to seven months, owing largely to a the radio telemetry which operates at the low power consumption of 7 mA. Finally, we discuss the role and contribution of ORB in the space of open-source instrumentation and ocean modeling.

High-resolution lithographic techniques are often limited by low volumetric throughput, since there is no universal and scalable manufacturing process that can produce 3D metasurfaces. In this work, we demonstrate a high-speed holographic 3D printing platform based on spatiotemporal beam shaping, exceeding the repetition rate while keeping the resolution high. The system integrates a femtosecond laser source with a spectral pulse compressor and a beam shaper to project uniform, axially confined light fields to project patterns directly on the advanced photoresists using a Digital Micromirror Device DMD. We investigate the process window for rapid polymerization, optimizing the photoinitiator choice to eliminate thermal crosstalk at high repetition rates. Using this setup, we achieve a production throughput of more than a million voxels per second with sub-micron resolution below 400 nm. The system's reliability is validated through the fabrication of large-area woodpile-like lattices and uniform micropillar arrays, establishing a workflow for scalable manufacturing of micro-optical components.

A new Real Fluid Quasi-Conservative (RFQC) finite volume method is developed to address the numerical simulation of real fluids involving shock waves in transcritical and phase-change flows. To eliminate the spurious pressure oscillations inherent in fully conservative schemes, we extend the classic quasi-conservative method, originally designed for two-phase flows, to real fluids governed by arbitrary equations of state (EoS). The RFQC method locally linearizes the real fluid EoS at each grid point and time step, constructing and evolving the frozen Grüneisen coefficient $\Gamma$ and the linearization remainder $E_0$ via two advection equations. At the end of each time step, the evolved $\Gamma$ and $E_0$ are utilized to reconstruct the oscillation-free pressure field, followed by a thermodynamic re-projection applied to the conserved variables. Theoretical analysis demonstrates that, in smooth regions, the energy conservation error introduced by the RFQC method is a second-order small term dominated by the time-step. In discontinuous regions, this error is determined by the entropy increase rate, thereby maintaining consistency with the inherent truncation error of shock-capturing methods. A series of numerical tests verifies that the method can robustly simulate complex flow processes with only minor energy conservation errors, including transcritical flows, phase transitions, and shock-interface interactions. The RFQC method is proven to be both accurate and robust in capturing shock waves and phase transitions.

We study opinion dynamics with higher-order interactions, motivated by the fact that social influence often takes place in groups rather than only through pairwise contacts. We introduce a noisy majority-rule model on annealed hypergraphs with heterogeneous group sizes and investigate how the distribution of interaction sizes affects collective ordering and relaxation. Using analytical theory and Monte Carlo simulations, we show that group-size heterogeneity strongly shapes both the transition between ordered and mixed states and the associated time scales. In particular, broader and heavier-tailed distributions make ordering more robust by enhancing the effect of rare large-group events. They also modify the finite-size scaling of relaxation, producing a crossover from the standard logarithmic behavior to faster ordering in sufficiently broad ensembles. In the pure majority-rule limit, we further show that the exit probability near coexistence obeys a universal error-function scaling form controlled by a single structural parameter. Our results demonstrate that the full distribution of group sizes is a key determinant of nonequilibrium ordering in higher-order opinion dynamics.

We consider the problem of a conditional extremum of an action in a class of fields constrained by differential equations. For this setup, we propose an extension of Noether's first theorem to connect the symmetries of the action and the imposed equations to the currents conserved at the conditional extrema. The key ingredient of the extension is the gauge symmetry of the differential equations constraining the admissible class of field configurations. We consider a special type of global symmetries of the action which we call conditional symmetries. Such global symmetries must be special cases of gauge transformations of the constraint equations. We construct conservation laws that follow from the conditional symmetries of action. No Lagrange multipliers or other auxiliary fields are introduced and the conserved currents include only the original fields. We also prove the converse theorem which connects the conserved currents to the conditional symmetries of action. The general method is illustrated by several examples.

Starting from the Navier--Stokes equations in rotating spherical coordinates with depth-varying density and eddy viscosity, we derive an asymptotic model describing non-equatorial wind-generated ocean drift currents. Our approach allows for large-scale flows that cannot be captured by classical tangent-plane approximations. The strategy is to perform a careful scaling and to perform a double asymptotic expansion with respect to two small parameters arising from the scaling: the Rossby number and the ratio between the Ekman depth and the Earth's radius. We obtain a system of linear ordinary differential equations with nonlinear boundary conditions governing the leading-order dynamics, highlighting that the dynamics is governed by the linear terms, whereas the nonlinear ones, related to the injection and dissipation of kinetic energy, appear only at higher order. We use the leading-order equations to compare our model with the simplest theory of ocean circulation due to Sverdrup and note that, even at this level of simplification, our equations have the potential to provide deeper insight. Subsequently, focusing on Ekman flows, we prove existence and uniqueness of the leading-order solution, which retains the classical Ekman spiral structure for arbitrary eddy viscosity profiles. Finally, we compute the surface deflection angle of the wind-driven current for three explicit eddy viscosity profiles, obtaining results consistent with observations. In addition, we derive the governing equations for the first-order correction with respect to the Rossby number and provide a priori bounds for its solution.

Recent experiments have demonstrated the ability to manipulate nonlinear interactions via time modulation, giving rise to the so-called stroboscopic nonlinearity. To date, however, this phenomenon has not been subjected to a rigorous theoretical analysis. In this work, we clarify the physical mechanism underlying stroboscopic nonlinear dynamics based on time-nonlocal response and establish an effective time-independent model under suitable modulation conditions. The proposed model almost exactly reproduces the full time-dependent dynamics in the quasi-steady state and significantly outperforms empirical descriptions used previously. Our results provide a clear physical picture of stroboscopic nonlinear dynamics, and can be extended to other systems with time-nonlocal response, establishing a general framework for engineering nonlinear interactions through temporal modulation.

We study the occurrence of long-lived metastable states in the 4f$^{13}$5d6s electron configuration of Yb$^+$. By optical pumping of a single trapped ion on the $^2F^\text{o}_{7/2}\rightarrow (7/2,0)_{7/2}$ transition at 377.5 nm, we prepare a wide range of metastable electronic states. We use a co-trapped control ion to sympathetically cool the spectroscopy ion, allowing us to accurately time its subsequent decay. We record a strong decay signal corresponding to a lifetime of 0.92(8) s, a weaker decay signal with lifetime 9.8(+2.9, -2.0) s, and find evidence for a much longer lifetime, $>$ 30 s. We identify the metastable states with these lifetimes qualitatively, and corroborate our results with atomic structure calculations that support the observed lifetimes and decay paths. These long-lived states provide new opportunities in qubit and qudit state detection and optical clocks.

This study explores how generative artificial intelligence, specifically ChatGPT, can assist in the evaluation of laboratory reports in Experimental Physics. Two interaction modalities were implemented: an automated API-based evaluation and a customized ChatGPT configuration designed to emulate instructor feedback. The analysis focused on two complementary dimensions-formal and structural integrity, and technical accuracy and conceptual depth. Findings indicate that ChatGPT provides consistent feedback on organization, clarity, and adherence to scientific conventions, while its evaluation of technical reasoning and interpretation of experimental data remains less reliable. Each modality exhibited distinctive limitations, particularly in processing graphical and mathematical information. The study contributes to understanding how the use of AI in evaluating laboratory reports can inform feedback practices in experimental physics, highlighting the importance of teacher supervision to ensure the validity of physical reasoning and the accurate interpretation of experimental results.

Graphene supports surface plasmon polaritons with extreme field confinement and electrical tunability, but these waves are typically short-lived due to ohmic loss in the sheet. We show that embedding graphene in an active dielectric can counteract this loss and we derive closed-form design rules for lossless propagation within the local linear model. Specifically, from the full Maxwell model of a conductive sheet we obtain the gain values required to make the propagation constant real, $q''=0$, and we separately discuss the $r=0$ boundary obtained from the real part of the complex-index radicand. The formulas are expressed directly in terms of the complex conductivity of graphene and the surrounding media, making them easy to evaluate and implement. We verify the theory with full-wave simulations based on the finite element method in COMSOL, showing dispersion and attenuation/amplification trends with and without gain for single- and double-layer graphene plasmonic structures.

Adiabatic fast passage (AFP) is a rapid method for reversing nuclear polarization and manipulating spin populations in polarized solid targets, avoiding the long repolarization times associated with dynamic nuclear polarization (DNP). We report AFP measurements in a 5~T, 1~K polarized-target system for irradiated $^{15}$NH$_3$, irradiated $^{14}$ND$_3$, and butanol-based materials prepared either with TEMPO doping or by irradiation. We also present a joint manipulated-lineshape analysis for spin-1 targets and demonstrate that vector and tensor polarizations can be extracted from AFP-manipulated deuteron NMR spectra even when the populations are not described by a single Boltzmann spin temperature. Finally, we report a reproducible polarization- and direction-dependent AFP response in a large irradiated $^{15}$NH$_3$ sample. These ammonia results are presented as empirical observations under the specific sample-coil conditions of the experiment, with possible circuit-mediated mechanisms such as radiation damping or superradiant behavior discussed but not assigned as a definitive cause.

The transient diffusion-limited current at a disk electrode following a change in interfacial ion concentration induced by a potential step is analyzed with direct relevance to chronoamperometric measurements. The mixed-boundary diffusion problem is formulated in the Laplace domain and reduced to a Fredholm integral equation that directly determines the Faradaic current. The steady-state limit recovers Saito's equation, while a systematic long-time asymptotic expansion quantifies the approach to steady state. A Padé approximant yields a compact analytical expression in the time domain that accurately describes the current over experimentally relevant time ranges. In contrast to existing high-accuracy numerical procedures based on hybrid asymptotic and polynomial approximations, the present formulation provides an explicit and compact analytical representation that facilitates interpretation and practical implementation. The short-time response exhibits Cottrell's equation with edge effects characteristic of disk electrodes. Overall, the framework provides practical tools for analyzing transient currents, extracting diffusion parameters, and assessing the accuracy of widely used analytical approximations in disk-electrode chronoamperometry.

Empirical observations together with theoretical analyses are being used to argue that the classical phenomenon of gravitational redshift -- namely, the redshift of light in a static gravitational potential -- may be in tension with the Heisenberg uncertainty principle. In particular, in the Pound-Rebka experiment, the emitter-absorber pair interact with each other by exchanging a Mossbauer photon, a process that is fully describable within quantum mechanics but how the photon interacts with the spacetime metric remains unclear. Since the uncertainty principle is incompatible with local realism, as per general relativity, we propose to study continuous-variable photonic entanglement within the Einstein-Podolsky-Rosen (EPR) framework in a weak gravitational field. We outline a thought experiment, realizable on the surface of the Earth, that could shed further light on this problematic seamline between general relativity and quantum mechanics in the weak-field regime.

Snapshot full-Stokes polarimetry across multiple wavelengths remains challenging because conventional architectures rely on multiplexed measurements and bulky optics. We present an end-to-end inverse designed single-layered metasurface that reconstructs RGB full-Stokes images from a snapshot sensor measurement. A metasurface modeled by a multilayer perceptron (MLP) is employed to encode the full-Stokes polarization information. The system jointly optimizes a differentiable metasurface frontend with a U-Net backend. On a real-world dataset, our design achieves a high compression ratio of 12 for snapshot RGB-achromatic polarization imaging using a single-layered metasurface. These results show that end-to-end optical-digital co-design enables high-compression snapshot full-Stokes polarization imaging with a compact footprint.

Rain introduces broadband and frequency-selective attenuation in wideband terahertz (THz) links, making it necessary to identify a compact spectral descriptor that captures how the dominant loss region evolves with rainfall conditions. This article investigates the peak-frequency behavior of rain attenuation by combining Mie theory calculations with one separable laboratory Gaussian drop-size distribution (DSD) and eight outdoor empirical DSD models whose spectral shapes vary with rainfall rate. The analysis compares total loss, absorption, and scattering components, examines the roles of the characteristic DSD scale and representative drop size statistics, and evaluates the effect of temperature on the peak location. The results show that, unlike the fixed-shape laboratory case where the peak frequency remains unchanged with rainfall rate, all outdoor empirical DSD models exhibit a monotonic migration of the attenuation peak toward lower frequencies as rainfall rate increases. This migration follows a quasi-exact inverse scaling with the rainfall-dependent DSD characteristic scale, follows a family-specific asymptotic power law in rainfall rate, and is governed mainly by characteristic drop size, while fixed-temperature dielectric dispersion contributes only secondary corrections in the broad-peak, low-rainfall regime.

Tetraphenylpyrazine (TPP) and 2,3,4,5-tetraphenyl-1H-pyrrole (TePP) are closely related heterocycles bearing four phenyl substituents, whose structural similarity makes them a useful pair for comparing how intramolecular flexibility influences excited-state relaxation and emission in the gas phase and in the solid state. TPP is a prototypical solid-state luminescence enhancement (SLE) emitter, exhibiting a markedly increased quantum yield upon molecular aggregation. In contrast, TePP displays similar quantum yields in solution and solid state, characteristic of dual-state emission (DSE). This behaviour indicates that intramolecular rotations are already significantly hindered in the isolated-molecule regime, consistent with our previous observations for TPP and other solid-state emitters (Hernández-Rodríguez et al., ChemPhysChem, 2024, 25, e202400563). To unravel the excited-state dynamics underlying this contrasting behaviour, we performed mixed quantum-classical trajectory simulations on a single molecule of TPP and TePP employing the surface-hopping method. Twelve singlet states were included at the TD-B3LYP-D3/def2-SVP level, which were previously benchmarked against coupled cluster methods. Simulated observables such as gas phase ultrafast electron diffraction (GUED) and time-resolved fluorescence (TR-FL) signals allow us to dissect the distinct deactivation pathways operating in both systems in the gas phase, while also providing mechanistic insight into how these pathways are expected to evolve in solution and solid-state environments.

Global-pressure formulations recast multiphase Darcy flow in terms of a single pressure driving the total flux. Their exact equivalence to phase-pressure formulations holds only when the constitutive data satisfy the compatibility conditions required for a total-differential structure and its generalized nonisothermal extension. Here, we derive the exactness criterion for temperature-dependent mobilities and capillary pressures. We show that equivalence depends on whether the mobility-weighted capillary contribution is path independent in the saturation--temperature domain, so that it can be absorbed into a scalar global pressure. This yields the classical compatibility conditions within the saturation sector and a distinct mixed saturation--temperature condition that arises only in nonisothermal settings. We then incorporate this structure into a reduced matrix--fracture model with heat transport, matrix--fracture thermal exchange, and evolving aperture. Numerical benchmarks recover the three regimes predicted by the theory: globally exact, exact on each fixed-temperature slice but not on the full saturation--temperature domain, and fully nonexact. In fractured systems, thermal forcing alone can drive transitions between these regimes, while aperture evolution changes the path through state space. When saturation-sector exactness is lost, a least-squares projection on fixed-temperature slices extracts the nearest gradient component of the mobility-weighted capillary field. This yields a conservative slice-wise scalar-pressure surrogate and a quantitative projection residual. The residual separates saturation-sector nonintegrability from the mixed saturation--temperature incompatibility that controls genuinely nonisothermal loss of exactness. The framework links nonisothermal exactness theory, fractured-flow dynamics, and conservative reduced closure in a global-pressure formulation.

Low-Gain Avalanche Diodes (LGADs) are fast silicon sensors with internal charge multiplication and are key candidates for precision timing layers in future high-energy hadron colliders. Their operation in harsh radiation environments, however, is limited by acceptor removal in the gain layer, which reduces the active acceptor concentration and degrades the internal electric field required for avalanche multiplication. Improving the radiation tolerance of the gain layer is therefore essential for future 4D tracking applications. In this work, we investigated several LGAD prototypes produced in collaboration with Hamamatsu Photonics K.K. (HPK), featuring modified gain-layer designs, including oxygen-modified, carbon-implanted, and boron--phosphorus compensated structures. The sensors were studied after proton and reactor-neutron irradiation. Radiation tolerance was characterized using the acceptor-removal coefficient extracted from IV measurements and the operation voltage required to recover the timing performance after irradiation. The results show that carbon implantation is the only approach among those studied here that provides a clear improvement in radiation tolerance. In contrast, neither oxygen-related modification, including the Partially Activated Boron (PAB) approach, nor gain-layer compensation alone yields a significant improvement, and the compensated carbon-implanted structure shows no clear advantage over the carbon-only case. In addition, the acceptor-removal coefficient is found to depend on the irradiation particle type and energy.

Inspired by the design for the simple Columbia Non-neutral Torus (CNT) proposed by Pedersen and Boozer (Phys. Rev. Lett. 88 205002(2002)), and later revisited by Yu et al. (J. Plasma Phys. 88 905880306 (2022)), this work explores axi-symmetric equilibria using two planar vertical field coils and two non-planar intertwined coils. Neoclassical optimization studies are performed in a single stage approach using the DESC stellarator equilibrium solver (Dudt and Koleman, Phys. Plasmas 27 (2020) 102513). The physical parameters used as input were the toroidal magnetic flux function $\psi$, the rotational transform $\iota(\rho)$ as a function of the normalized flux function $\rho$ as the radial coordinate, the number of field periods $N_{FP}$, and an initial equilibrium assumption, given the major radius $R_0$ and the minor radius $a$. Once the equilibrium given by the coil configuration is defined, an optimization is made for quasi-symmetry. In this study, a triple product metric as defined by Dudt et al., (J. Plasma Phys. 89 (2023) 95589020) is used as a local error indicator, which is evaluated without resorting to Boozer coordinates. The goal is to optimize the effective ripple modulation amplitude ${\epsilon}_{eff}^{3/2}$, thus decreasing the neoclassical transport in the low collisionality $1/\nu$ regime. We show a sample of quasi-axisymmetric configurations obtained, both for the vacuum field and with finite pressure, which have reasonably good neoclassical transport in the sense of Boozer coordinates. In the best case scenarios the final rotational transfrom is inverted after optimization.

Global food security depends on tightly coupled international supply chains encompassing natural gas, mineral fertilizers, and staple crops. Earlier research has examined the potential consequences of disruptions in each of these domains separately, but not from a systemic perspective. Here we integrate bilateral trade in natural gas, nitrogen, phosphorus, and potassium fertilizers, and eleven staple crops -- accounting for approximately 70% of plant-based calories -- into a cascading-impact model spanning 208 countries, 20 geopolitical blocs, and the period 1992--2023. Under complete trade isolation, up to 22% of global caloric consumption would be lost, with a peak in the most recently evaluated years. Structural vulnerabilities vary considerably. Regions largely lacking some segments of the supply chain face near-total crop supply collapse, while few countries can cover the entire nexus through domestic resource endowments and production capacities. Temporal trends highlight a substantial increase in vulnerability globally, most prominently in the EU, with a near two-fold increase since the 1990s. Market power is most concentrated and most volatile in the upstream gas layer and has risen in the fertilizer layers since the 2000s; shocks propagate downstream from these tightening upstream layers, driving the system's fragility. Food stocks provide only limited resilience, with half of humanity living in countries holding stocks lasting fewer than three months. Our results identify upstream supply chains as the structural bottlenecks of the global agrifood system and propose leverage points to enhance resilience.

We present the design, fabrication, and measurement of a monolithically integrated optical receiver analog front end, where low power operation is a primary consideration with a goal of supporting 56 Gbaud intensity modulated direct detect transceivers. The need for low-power consumption and low-noise operation motivates a monolithic, layout driven design approach which begins with circuit topology selection and analysis. Various transistor unit cell layout configurations are explored, minimizing parasitics, enabling wide analog bandwidth and reduced input referred noise. The post-layout analog front end achieves a 28.9 GHz bandwidth with a low-frequency gain of 61.7 dB{\Omega}. This circuit was designed within the GlobalFoundries FotonixTM monolithic silicon photonics platform. The fabricated device is characterized by its DC operation, noise characteristics, and time domain behavior. The final design was validated by on-off keyed and PAM-4 electrical eye diagram measurements to 64 GBaud, consuming 9.22 mW of power from a 1.2 V supply with less than 737 nA RMS integrated input referred noise current and 0.08 pJ/bit.

We describe an electron bunch injector scheme based on proton-driven plasma wakefield acceleration for the Electron-Ion Collider. The proton bunches needed to drive the plasma wake are delivered by the existing Blue-Ring of RHIC. The polarized electron source is that in the current EIC design. We describe the different elements making up the injection scheme and give an estimate for the performance. Our initial study indicates that the design parameters of the EIC are within reach when accelerating the electron bunches in the proton-driven plasma wake, with average polarization of ~70% and a luminosity of 1e34 cm$^{-2}$s$^{-1}$.

We revisit a classical airfoil design problem: the search for shapes that maximize aerodynamic performance metrics, targeting the underexplored intermediate Reynolds-number regime between 1 and 3000, relevant to small animals and miniature vehicles. The problem is formally stated as the glide ratio or the endurance factor maximization for Joukowski airfoil profiles and for more general airfoil shapes with adjustable position of the maximum camber, under steady inflow. It is solved numerically by a hybrid approach combining stochastic search and direct parameter sweep, and using a steady laminar Navier--Stokes solver based on conformal mapping and second-order finite-difference discretization. Zero-thickness cambered airfoils are found to be globally optimal, within the Joukowski family, across the entire Reynolds-number range considered. The optimal angle of attack decreases monotonically with $Re$, whereas the optimal camber varies non-monotonically, reaching a pronounced maximum near $Re \approx 40-50$ before declining at higher $Re$. At low Reynolds numbers ($Re \lesssim 100$), a broad family of cambered shapes performs within a few per cent of the optimum, indicating weak sensitivity to geometrical parameters. In contrast, for $Re \gtrsim 1000$, the performance landscape becomes sharply localized around a single preferred design, for which geometric refinement is critical.

The special unitary group SU(2) plays a fundamental role in the description of symmetries in quantum mechanics, theoretical physics, and spherical signal processing. In this paper, we address the computational challenges of performing spectral analysis on this non-abelian compact Lie group. We present the Fourier Transform (FT) on SU(2) and develop a Fast Fourier Transform (FFT) algorithm inspired by the classical Cooley-Tukey divide-and-conquer scheme. Our approach efficiently discretizes the group using Euler angles, applying a two-dimensional FFT on the angular variables and exploiting the recursive properties of Jacobi polynomials. We provide an analysis of the computational complexity, demonstrating that our FFT-based method significantly outperforms the direct computation of the FT. This algorithm serves as a foundational tool for understanding the implementation of the FFT on SU(2), a key component in numerical simulations and advanced data analysis for high-performance computing applications on curved manifolds and quantum systems.

The shortest path multiplicity, here denoted by $\mu$, is an important metric of complex networks. For real networks $\mu$ is high and it correlates with the network community structure. Since local network evolution induces network communities, it is possible that a high shortest path multiplicity is the natural expectation of local evolution rules. Here I demonstrate, by means of numerical simulations, that this is indeed the case. For random graphs with arbitrary degree distributions $p_k$, $\langle\mu\rangle\sim \langle k(k-1\rangle / \langle k\rangle e$, growing with the network size when $p_k\sim k^{-\gamma}$ and $\gamma\leq3$. For networks generated by local rules, $\langle\mu\rangle$ increases with increasing the network size and it does faster than what observed in their randomized versions. Furthermore, the number of communities increases with the network size and the correlation with $\langle \mu\rangle$ follows.

Quartz tuning forks, functioning as electrically driven piezoelectric resonators, have long served as exceptionally stable and widely adopted timing references in diverse domains of research and industry. Yet, experimentally measured electrical resonance spectra often exhibit resonance evolutions that remain unexplained within existing theoretical descriptions. Here we develop a unified continuum electromechanical modal framework that integrates piezoelectric electrodynamics, variational structural dynamics, and symmetry-selected electromechanical observability. Our theory shows quantitative agreement with experimental results, demonstrating that electrical observability emerges not from the underlying mechanical eigenmodes alone. The resulting framework unifies conventional coupled-oscillator, equivalent-circuit, and continuum descriptions within a single first-principles theory and provides a rigorous basis for precision electromechanical characterization.

In this paper, we present a formula for calculating the complex amplitude of the output electric field for a given input wave that impinges on a Dove prism. We use Fourier optics to decompose the input wave into plane waves, then find the output plane waves of the Dove prism as functions of the input spatial frequencies. The total output image is then obtained by integrating over all the output plane waves, resulting in a final formula in integral form. Since we conduct a wave-optical analysis for beam propagation and each incidence on Dove prism surfaces, all the physical aspects of electromagnetic waves are involved, including polarization, Fresnel losses, wave interference, phase, and intensity. The formula also explains why a rotated Dove prism rotates its input image twice its rotation angle. In addition, the formula is not limited to paraxial beams, as we find the Dove prism output as a function of the input Fourier components in general, without limiting the input spatial frequencies to small values. This generality is especially relevant for emerging applications that rely on non-paraxial beams, such as structured light generation, orbital angular momentum systems, and quantum imaging. However, since in most cases the paraxial approximation is valid and sufficient, a simplified formula is also extracted for paraxial beams. Two ray-tracing simulations are conducted to demonstrate the correctness and accuracy of our simplified formula. All the advantages mentioned make our derivation accurate, complete, comprehensive, and, to the best of our knowledge, the first to wave-optically prove the rotational feature of a Dove prism.

Can a general-purpose large language model design molecules with the precision of a seasoned chemist? Current LLM-based frameworks answer this question with scalar feedback loops - generate, score, reject - that amount to informed trial-and-error. Here we show that replacing a single number with the full physicochemical rationale from first-principles calculations transforms the LLM from a stochastic sampler into a causal reasoner. Our system couples retrieval-augmented generation with a self-reflection module that feeds orbital energies, atomic charges, and electron densities - rather than compressed scores - back into the design loop. On HOMO-LUMO gap targets from 2.0 to 5.0 eV, this structure-property-relationship (SPR) reflection achieves a deviation as low as 0.0014 eV with a 100% success rate under the SPR+RAG configuration, consistently outperforming scalar-feedback and non-reflective baselines in median and mean deviation. The framework generalizes seamlessly to dipole-moment design, synthetic accessibility optimization, and molecular docking, and proves robust across 7 distinct LLM backbones. These results establish a new paradigm: when the model understands not only that a molecule fails, but why, iterative molecular design becomes genuinely mechanistic.

Photodiodes based on trap-assisted current injection can exhibit internal photomultiplication with apparent quantum efficiencies far exceeding unity, raising the question of whether such gain fundamentally enhances detector sensitivity. We employ a minimal analytical framework based on a single gain-active trapped state coupling photogenerated carriers to contact injection. The gain is intrinsically self-limiting: the injection process that amplifies the current simultaneously accelerates relaxation of the gain-enabling state, producing an inherently nonlinear, operating-point-dependent response. The form of this nonlinearity is not universal -- once the trap level is generalized to an energetic distribution and recombination is allowed to be bimolecular, the same mechanism yields superlinear, linear, or strongly sublinear responses. A single chord gain is therefore not a meaningful device descriptor, and chord-gain comparisons across the literature conflate devices in different regimes. Treating trap occupancy and injection as coupled stochastic processes, we show that internal gain introduces a strictly non-negative fluctuation penalty from the dissipative dynamics that sustain the gain state. A local, small-signal detectivity exhibits a finite optimum yet cannot exceed the intrinsic thermodynamic limit of the underlying unity-gain photodiode. Gain is thus equivalent to driven stochastic amplification: it can suppress downstream readout noise, but cannot reduce the fundamental noise floor set by the primary photodetection process.

This work investigates how restitution modeling affects the dynamics of rocking blocks subjected to harmonic excitation. While several studies have reported discrepancies between experimentally observed impact behavior and the predictions obtained using the classical Housner restitution coefficient, the implications of adopting alternative restitution formulations on the global dynamics of rocking systems remain largely unexplored. The system is formulated as a hybrid non-smooth dynamical model and analyzed through bifurcation diagrams, Lyapunov exponents, and basins of attraction for different slenderness ratios. By comparing the classical restitution model proposed by Housner with the alternative formulation of Mao et al., we show that the choice of restitution model strongly influences the predicted system response. The alternative formulation leads to an earlier onset and greater prevalence of complex oscillations, as well as changes in the type, stability, and accessibility of attractors compared to the classical model. However, as the slenderness ratio increases, the dynamical features produced by both formulations progressively converge, indicating a reduced sensitivity to the restitution model for taller blocks. These results provide a dynamical perspective on why alternative restitution formulations, which predict impact responses closer to experimental observations, can produce markedly different behaviors from those obtained using the classical Housner model.

We revisit the problem of scalar dissipation anomaly and scaling of scalar gradients in passive scalar turbulence using theory and data from well-resolved direct numerical simulations (DNS) on grid sizes of up to $8192^3$, spanning Taylor-scale Reynolds numbers $Re_\lambda=140-1000$ and Schmidt numbers $Sc = 1-512$. The theory is based on a joint multifractal description of longitudinal velocity increments and scalar increments, constrained by Yaglom's law and extended to gradients via a fluctuating Batchelor cutoff scale. The DNS data show that the normalized mean scalar dissipation approaches a single asymptotic value as both $Re_\lambda$ and $Sc$ increase, although larger $Sc$ requires larger $Re_\lambda$ to reach this state. In the multifractal framework, this corresponds to an effective scalar Hölder exponent tending to zero, associated with sharp cliff-like scalar fronts, and saturation of inertial-range scaling scalar structure-function exponents. The joint velocity-scalar fractal dimension of the dissipative structures is inferred to approach $7/3$, indicating a non-space-filling support. The framework further predicts that for fixed $Re_\lambda$, higher-order central moments of scalar gradients are independent of $Sc$. This prediction is confirmed by DNS data and by the collapse of standardized probability distributions of scalar-gradient across Schmidt numbers. These results suggest that the $Sc$-scaling of scalar gradients is dictated solely by scalar dissipation anomaly. In contrast, their $Re_\lambda$-dependence reflects strong intermittency, which can be directly related to mixed velocity-scalar structure function exponents.

Identifying collective variables that capture slow dynamical modes is essential for sampling rare events in complex systems. Existing machine-learning approaches often require predefined metastable states, carefully chosen descriptors, or training trajectories with high-quality kinetic information. Here, we introduce SelfTICA, a self-supervised contrastive-learning framework that reformulates collective-variable discovery as dynamical representation learning. SelfTICA defines positive and negative pairs from time-lagged molecular configurations, learns reusable features through a contrastive objective linked to spectral variational principles, and extracts orthogonal slow modes by applying time-lagged independent component analysis in the learned representation space. By decoupling representation learning from slow-mode extraction, SelfTICA avoids direct optimization of eigendecomposition-based objectives and enables spectra and collective variables to be evaluated across lag times without retraining. Across different atomistic systems, SelfTICA learns dynamical representations from limited, biased, or exploratory data and converts them into collective variables that accelerate rare-event exploration and improve free-energy convergence.

Highly accurate stability boundary values for the ADER-DG method are obtained for arbitrary degrees $N$ of basis polynomials. In the linear case, stability is violated precisely when one of the matrix eigenvalues reaches $\lambda = -1$, regardless of the phase $\theta$. A rigorous mathematical framework for the stability is developed. The stability condition is significantly simplified, reducing it to the problem of calculating the roots of polynomials in the Courant number $\mathrm{CFL}$. The maximum of the Courant numbers $\mathrm{CFL}_{\rm max}(N)$ are calculated. These results are new and very convenient for practical use. A comparison of the obtained results with existing results reveals differences that may be significant for the selection of calculation parameters, especially for high degrees $N$. It is shown that widely used existing estimates $\mathrm{CFL}_{\rm max}(N) \propto 1/(2N+1)$ are overestimated. An interesting qualitative asymptotic $\mathrm{CFL}_{\rm max}(N) \propto 1/(N+1)^{2}$ is obtained. A rigorous direct proof of the approximation is presented. Approximation orders $p = N+1$ for arbitrary degrees $N$ are rigorously derived. A set of numerical experiments is carried out to apply the ADER-DG method to solving both a linear advection equation and an Euler system of equations. The results obtained in these calculations confirm the theoretical results well. In particular, an excess of the Courant number over the $\mathrm{CFL}_{\rm max}(N)$ by even 1% in the linear case immediately leads to significant instability of the numerical solution. The obtained estimates of the boundary Courant number in the nonlinear case are somewhat underestimated -- by no more than 5%, which is due to the diffusivity and stability of the approximate Riemann solver. Empirical convergence orders are obtained, which are in good agreement with the theoretical results.

Deep research agents are Large Language Model (LLM)-based systems designed for autonomous, multi-step scientific reasoning, and they hold immense potential for accelerating research in the physical sciences. However, comprehensive and in-depth evaluations of their capabilities within this domain remain lacking. To address this gap, we introduce PhySciBench, a benchmark highly relevant to physical science research, comprising 200 expert-curated questions, balanced between physics and chemistry, across six task categories that reflect real-world scientific workflows. Evaluations of state-of-the-art models and agent systems on PhySciBench reveal limited performance; even the strongest baseline, Gemini Deep Research, achieves an accuracy of only 33.5%. Analysis of failure cases identifies three recurrent deficiencies: fragility in extended reasoning chains, limited knowledge transfer across steps, and a lack of physics-grounded self-verification. Motivated by these findings, we develop DelveAgent, a modular multi-agent framework equipped with an adaptive planning loop, dual-granularity memory, and a hierarchical physics-grounded reflection mechanism. Across four scientific benchmarks, DelveAgent improves accuracy by up to 7.5 percentage points while reducing inference costs to approximately one-third of the strongest baseline. These results establish the significance of PhySciBench as a critical benchmark for evaluating AI systems in the physical sciences and demonstrate that architectural specialization can effectively enhance the reliability of autonomous scientific this http URL data andcode are publicly available athttps://github.com/yigengjiang/physci-deepresearch.

Extending earlier work in Physics-informed Meta-instrument for eXperiments (PiMiX) [1], PiMiX~2.0 is an artificial-intelligence (AI)-enhanced data-fusion and analysis framework that integrates multi-experiment multi-modal radiographic imaging and tomography (RadIT) with physics-informed reasoning and agentic AI workflows. The framework supports automated data ingestion, multimodal image processing from one or more experiments, three-dimensional (3D) and time-resolved three-dimensional (4D) reconstruction, and physics-aware interpretation of experimental observations. The PiMiX agents are designed for deployment on desktop and laptop systems commonly used in experimental workflows, while remaining scalable to high-performance computing environments for computationally intensive tasks. By coupling RadIT instrumentation and measurements with geometry, physics, computation, and statistical inference, PiMiX 2.0 aims to accelerate RadIT data processing, knowledge extraction, improve reproducibility, and enable more integrated analysis and workflows in high-temperature plasmas, nuclear fusion, advanced manufacturing, other static and dynamic experiments.

Laser wakefield accelerators (LWFAs) provide extremely large accelerating gradients for compact electron accelerators and photon sources but are limited by dephasing, where trapped electrons outrun the accelerating phase of the wakefield. Flying-focus pulses can eliminate dephasing by driving a wake at the vacuum speed of light, but these pulses involve tradeoffs such as varying spot size, long duration, or large plasma volume. Here we show that a spatiotemporally structured laser pulse propagating in a plasma waveguide can drive a wakefield at the vacuum speed of light while maintaining a constant spot size and ultrashort duration. The pulse is formed by superposing plasma-waveguide modes with appropriately selected frequencies. Compared with flying-focus approaches, the waveguide substantially reduces the required plasma volume. Scaling laws and quasi-3D particle-in-cell simulations show that the single-stage energy gain increases linearly with the number of modes used to construct the pulse, enabling larger energy gains or shorter stages than standard LWFA.

Recent advances in silicon nitride nanomechanical resonators have pushed mechanical quality factors to ultra-high values by combining stress-induced dissipation dilution with mode-shape engineering. Neither mechanism alters the intrinsic quality factor $Q_{\mathrm{intr}}$. Targeting the intrinsic loss itself therefore remains an untapped route to even higher $Q$. Doing so first requires reliable quantification of $Q_{\mathrm{intr}}$, which has proven challenging. Here we present a robust methodology that quantifies $Q_{\mathrm{intr}}$ by combining automated mode identification with systematic ringdown measurements over a large number of mechanical modes. Applied to high-stress silicon nitride membranes, it reveals a systematic dependence of $Q_{\mathrm{intr}}$ on thickness that cannot be described using established models, particularly in the ultra-thin limit. We account for this trend with a phenomenological model that incorporates a thickness-dependent loss channel. Together, our method and model open a route toward a microscopic understanding of intrinsic dissipation and toward directly mitigating its loss channels.

Diamond color-center qubits integrated with photonic circuits can be initialized, manipulated, entangled, and read individually with high fidelity, making them attractive for large-scale modular quantum computers, quantum networks, and distributed quantum sensing. However, the limited size of heteroepitaxially grown single-crystal diamond (SCD) and photonic-grade diamond-on-insulator (DOI) substrates remains a challenge for integration with existing manufacturing processes. Here, we develop a plasma etch recipe to thin direct-bonded (100) SCD membranes (<50 $\mu$m) into large-area, thin-film DOI substrates, and demonstrate free-standing photonic chiplets fabricated from the resulting DOI. The ICP-RIE recipe preserves diamond bonding, provides sufficient micromasking and surface-quality control, and enables thin-film DOI manufacture. We thin a 10 $\mu$m diamond plate bonded to SiO$_2$/Si and obtain a photonic-grade DOI substrate with diamond thickness $\leq$300 nm. The DOI film is around 300 nm thick over 0.5 $\times$ 0.5 mm$^2$, with surface roughness < 0.5 nm, while the bonding interface remains intact. Diamond photonic chiplets are fabricated on this DOI substrate using a standard two-step lithography process, without complex thin-film transfer, under-etching, or pedestal formation. We also present a colorimetric study of diamond visibility on SiO$_2$ and quantify color differences across thicknesses in common colorimetric spaces. This analysis enables automatic diamond-thickness extrapolation from standard optical microscope images with 5 nm resolution, in good agreement with white-light interferometry (WLI) measurements. The DOI substrate and colorimetric thickness-evaluation method provide an effective fabrication platform and reliable validation route for scalable manufacturing of diamond nanophotonic devices, opening a path toward large-scale integrated quantum systems.

In this work, we develop a state estimation framework for two-dimensional Rayleigh-Bénard (RB) convection that combines a stable Galerkin reduced-order model (ROM) with an extended Kalman filter (EKF). The ROM, constructed from controllability modes of the linearised Boussinesq equations, provides the nonlinear dynamical model for the filter prediction step. Direct numerical simulations (DNS) are used to generate synthetic measurements for data assimilation. We assess filter performance across periodic, quasiperiodic, and chaotic regimes, demonstrating that the filter tracks the most energetic modes with high fidelity and achieves time-averaged reconstruction errors below $14\%$ for velocity and $9\%$ for temperature. We apply the ROM-based EKF to a hybrid simulation scenario where the system state is assimilated from coarse PIV-like velocity measurements. It is shown that velocity observations alone suffice to reconstruct the state, including the temperature field. Finally, we exploit the Kalman gain matrix to develop a greedy sensor placement strategy that progressively removes the least informative sensors. The algorithm reveals a clear hierarchy among sensor types and can be used to derive skeletal observation configurations. It also provides guidance on which measurement variables and spatial locations are most informative for state correction. The present framework is general, and may be applied to other quadratic Galerkin ROMs for state estimation.

This paper examines the Maxwell system of electrodynamics within the framework of distributions. A primary objective is to establish boundary conditions for fields at interfaces when the charge and current densities are measures localized on the interface. From this analysis, the paper presents a derivation of the generalized Snell's law, along with formulas for the amplitudes of the reflected and transmitted waves in terms of the incident amplitude.

There has been a surge of recent interest in the Wigner's Friend paradox, sparking several novel thought experiments and no-go theorems. The main narrative has been that Wigner's Friend highlights a counterintuitive feature that is unique to quantum theory, and which is closely related to the quantum measurement problem. Here, we challenge this view. We argue that the gist of the Wigner's Friend paradox can be reproduced without assuming quantum physics, and that it underlies a much broader class of enigmas in the foundations of physics and philosophy. To show this, we first consider several recently proposed Extended Wigner's Friend scenarios, and demonstrate that some of their implications for the absoluteness of observations can be reproduced by classical thought experiments that involve the duplication of agents. Crucially, some of these classical scenarios are technologically much easier to implement than their quantum counterparts. Then, we argue that the essential structural ingredient of all these scenarios is a feature that we call "Restriction A": that a physical theory cannot give us a probabilistic description of the observations of all agents. Finally, we argue that this difficulty is at the core of other puzzles in the foundations of physics and philosophy, and demonstrate this explicitly for cosmology's Boltzmann brain problem. Our analysis suggests that Wigner's Friend should be studied in a larger context, addressing a frontier of human knowledge beyond quantum foundations: to obtain reliable predictions for experiments in which these predictions can be privately but not intersubjectively verified.

Direct observation of individual fluorescent emitters is crucial for understanding and studying quantum materials, chemical reactions, and biological systems. However, current single-molecule tracking methods only focuses on the localizations of molecules, overlooking molecular configuration and orientation. In this work, we introduce a high-throughput polarized single-molecule localization microscopy that simultaneously resolves the locations and emission dipole orientations of single fluorescent emitters with nanometer precision. Using the interface between pristine hexagonal boron nitride (h-BN) and an organic solvent as a challenging platform, we capture over 10^5 fluorescent events and reveal distinct molecular interaction dynamics at room temperature. The measured dipole orientations align with the three-fold rotational symmetry of the h-BN lattice, and molecular dynamics in the liquid enivronment can be modulated electrochemically, suggesting a route for on-demand control of quantum emitters. We also find that lateral diffusion at the solid-liquid interface is far more dynamic than that of solid-state emitters. This simultaneous tracking of molecular conformation and photophysics advances the understanding of single-molecule interactions and enables real-time sensing through two-dimensional materials.

Data assimilation (DA) combines partial observations with dynamical models to improve state estimation. Filter-based DA uses only past and present data and is the prerequisite for real-time forecasts. Smoother-based DA exploits both past and future observations. It aims to fill in missing data, provide more accurate estimations, and develop high-quality datasets. However, the standard smoothing procedure requires using all historical state estimations, which is storage-demanding, especially for high-dimensional systems. This paper develops an adaptive-lag online smoother for a large class of complex dynamical systems with strong nonlinear and non-Gaussian features, which has important applications to many real-world problems. The adaptive lag allows the utilization of observations only within a nearby window, thus reducing computational complexity and storage needs. Online lag adjustment is essential for tackling turbulent systems, where temporal autocorrelation varies significantly over time due to intermittency, extreme events, and nonlinearity. Based on the uncertainty reduction in the estimated state, an information criterion is developed to systematically determine the adaptive lag. Notably, the mathematical structure of these systems facilitates the use of closed analytic formulae to calculate the online smoother and adaptive lag, avoiding empirical tunings as in ensemble-based DA methods. The adaptive online smoother is applied to studying three important scientific problems. First, it helps detect online causal relationships between state variables. Second, the advantage of reduced computational storage expenditure is illustrated via Lagrangian DA, a high-dimensional nonlinear problem. Finally, the adaptive smoother advances online parameter estimation with partial observations, emphasizing the role of the observed extreme events in accelerating convergence.

Recent studies have demonstrated the great potential of reduced order modeling for parametric dynamical systems using low-rank tensor decompositions (LRTD). In particular, within the framework of interpolatory tensorial reduced order models (ROM), LRTD is computed for tensors composed of snapshots of the system's solutions, where each parameter corresponds to a distinct tensor mode. This approach requires full sampling of the parameter domain on a tensor product grid, which suffers from the curse of dimensionality, making it practical only for systems with a small number of parameters. To overcome this limitation, we propose a sparse sampling of the parameter domain, followed by a low-rank tensor completion. The resulting specialized tensor completion problem is formulated for a tensor of order $C + D$, where $C$ fully sampled modes correspond to the snapshot degrees of freedom, and $D$ partially sampled modes correspond to the system's parameters. To address this non-standard tensor completion problem, we introduce a low-rank tensor format called the hybrid tensor train. Completion in this format is then integrated into an interpolatory tensorial ROM. We demonstrate the effectiveness of both the completion method and the ROM on several examples of dynamical systems derived from finite element discretizations of parabolic partial differential equations with parameter-dependent coefficients or boundary conditions.

Accurate and rapid prediction of wildfire trends is crucial for effective management and mitigation. However, the stochastic nature of fire propagation poses significant challenges in developing reliable simulators. In this paper, we introduce PyTorchFire, an open-access, PyTorch-based software that leverages GPU acceleration. With our redesigned differentiable wildfire Cellular Automata (CA) model, we achieve millisecond-level computational efficiency, significantly outperforming traditional CPU-based wildfire simulators on real-world-scale fires at high resolution. Real-time parameter calibration is made possible through gradient descent on our model, aligning simulations closely with observed wildfire behavior both temporally and spatially, thereby enhancing the realism of the simulations. Our PyTorchFire simulator, combined with real-world environmental data, demonstrates superior generalizability compared to supervised learning surrogate models. Its ability to predict and calibrate wildfire behavior in real-time ensures accuracy, stability, and efficiency. PyTorchFire has the potential to revolutionize wildfire simulation, serving as a powerful tool for wildfire prediction and management.

Ion traps are used for a wide range of applications from metrology to quantum simulations and quantum information processing. Microfabricated chip-based 3D ion traps are scalable to store many ions for the realization of a large number of qubits, provide deep trapping potentials compared to surface traps, and very good shielding from external electric fields. In this work, we give an overview of our recent developments on chip-based 3D ion traps. Different types of chip materials, the integration of electronic filter components on-chip and compact electrical connections in vacuum are discussed. Further, based on finite element method (FEM) simulations, we discuss how integrating micro-optics in 3D ion traps is possible without disturbing the trapped ions.

We review changes made in recent years to the development process of the LAMMPS simulation software package and the software itself. We discuss how those changes have impacted the effort and workflow required to develop and maintain a software package that has been in existence for more than 30 years and where a significant part of the code base is contributed by external developers. We also look into how those changes in the process have affected the code quality and ease of modifying and extending the software while at the same time its audience has changed from a cohort with a strong software development background to a group including many researchers with limited software development skills. We explore how this contributes to LAMMPS' continuing growth in popularity over that period. Specific topics include the source-code management and contribution workflow, automated testing and static analysis, security and supply-chain integrity, the refactoring and modernization of the code base (including the adoption of modern C++), the C, Python, and Fortran library interfaces, the build system and deployment, the documentation, the LAMMPS-GUI graphical interface, and our recent experiences using AI tools. We highlight changes of the most recent development cycles and close with an outlook on future steps.

The non-Hermitian bulk-boundary correspondence features an interplay between the non-Hermitian skin effect and anomalous boundary-mode behavior. Whereas the skin effect is known to manifest itself in quantum dynamics in the form of chiral damping, it has remained less clear what impact the boundary modes may have. Here we derive experimentally accessible signatures of the boundary modes. We also establish clear criteria, based on the generalized Brillouin zone, that determine when bulk and boundary effects can be dynamically discerned using the Liouvillian separation gap. This leads to telltale signatures in both stable regimes -- where particle number remains finite -- and in the unstable regimes -- where a macroscopic boundary mode population occurs.

We propose a time- and wavelength-multiplexed remote atom-atom entanglement generation protocol based on cavity-assisted photon scattering (CAPS). This is designed to achieve a high rate and high fidelity with robustness to operational imperfections, parameter fluctuations, and auxiliary time costs, such as percent-level photon impurity, timing and cavity parameter jitter, and atom shuttling time costs. We benchmark this protocol using comprehensive analytical and numerical modeling of the atom-cavity dynamics, including state-dependent pulse delay effects, photon temporal impurity, atom-cavity system parameter fluctuations, and crosstalk among atoms through a shared cavity mode. With realistic atom-cavity system performance, we predict $2\times 10^{5}\,\mathrm{s}^{-1}$ successful atom-atom Bell pair generation even without in-cavity qubit reset, substantially enhanced from two-photon-interference-based protocols, at a predicted heralded fidelity of 0.999.

The coexistence of ferroelectricity and magnetism in a single-phase oxide is rare because the electronic requirements for these two orders are often incompatible. Here, using first-principles calculations and parallel-tempering Monte Carlo simulations, we propose stoichiometric VHfO$_4$ as a hafnia-derived multiferroic that overcomes this constraint through ordered cation design rather than dilute magnetic doping. We found that VHfO$_4$ could stabilize in a polar \(Pca2_1\)-like structure with layered V/Hf ordering, remains dynamically stable, and preserves switchable ferroelectricity with a large spontaneous polarization. The ordered V sublattice introduces competing exchange interactions that favor an antiferromagnetic ground state at zero strain. Epitaxial strain further drives transitions into additional phases, including a noncollinear spiral-like state and a predominantly in-plane antiferromagnetic state. We also find that out-of-plane lattice distortions along the polar axis strongly modify the exchange interactions and magnetic phase stability, indicating a strain-mediated pathway for electric-field control of magnetism. These results establish VHfO$_4$ as a promising platform for exploring multiferroicity and magnetoelectric coupling in hafnia-based oxides.

Qualitative content analysis of open-ended survey responses is a commonly used research method in science education. However, traditional coding approaches are often time-consuming and prone to inconsistency, especially when applied to large datasets. Existing solutions from Natural Language Processing such as supervised classifiers, topic modeling techniques, and generative large language models have limited applicability in analysis of open-ended survey responses, since they demand extensive labeled data, disrupt established qualitative workflows, and/or yield variable results. In this paper, we introduce a text embedding-based classification framework called Deductive Semantic Content Analysis (DeSCA) that requires only a handful of examples per category to run, is transparent and replicable, and fits well with standard qualitative workflows. When benchmarked against human analysis of a physics education survey consisting of 2899 open-ended responses, the method described by our framework achieves high agreement with expert human coders across ten embeddings models on a simulated exhaustive coding task, using approximately 1-2% of the total dataset for training. The method achieves lower agreement on a complete selective coding task; this performance, however, improves with fine-tuning of the text embedding model, which can be done with a small amount of additional data. We unpack these results in terms of the theoretical assumptions of text embeddings, and further demonstrate how embeddings can be used to audit previously-analyzed datasets for coding consistency. These findings demonstrate that text embedding-assisted coding can flexibly scale to thousands of responses without sacrificing interpretability, opening avenues for deductive qualitative analysis at scale.

Conventional \textit{ab initio} approaches are unable to describe phonon time-reversal symmetry ($\mathcal{T}$) breaking. Here, we develop an \textit{ab initio} framework, grounded in molecular Berry curvature (MBC) theory, that captures electronic-order-driven symmetry breaking in lattice dynamics. Using Co$_3$Sn$_2$S$_2$ as a model system, our \textit{ab initio} framework yields phonon spectra that break both $\mathcal{T}$ and mirror symmetries, quantitatively reproduce the observed phonon splittings observed in experiments, and reveal distinct microscopic origins for the $E_g$ and $E_u$ modes: $E_g$ splitting is governed by MBC and is accurately captured by our algorithm, whereas $E_u$ splitting is enhanced by the Fano resonance and matches the experimental data once the Fano-factor correction is included. Leveraging this algorithm, we predict several candidate materials with nonzero electronic-order-driven symmetry breaking in lattice dynamics, establishing a first-principles route to understand electron-phonon coupling, phonon magnetism, and related Hall-type lattice responses.

Cavity optomechanical systems have recently been proposed as detectors for ultralight dark matter, leveraging their ability to cool and probe mechanical oscillators at the quantum limit. Here we present a resonant search for ultralight dark matter using a cavity optomechanical accelerometer. The detector consists of a cryogenic Si$_3$N$_4$-membrane cavity mounted to a 4 K copper plate, with photothermal tuning used to scan its 39 kHz mechanical resonance. Shot-noise-limited displacement readout and radiation-pressure feedback cooling yield an acceleration sensitivity of $\sim 10\;\text{n}g_0/\sqrt{\text{Hz}}$ over 30 Hz near resonance. The detector's material inhomogeneity gives access to direct vector coupling to the dark-matter field. We conduct a resonant search based on matched-filter statistics, yielding upper bounds consistent with thermal noise and above those set by equivalence principle tests. No signal is observed, but the experiment demonstrates stable, quantum-limited operation and validates a scalable approach to resonant detection. With optimized test masses, lower temperature, and multiplexed arrays, the platform offers a path toward competitive constraints on vector-mediated dark-matter interactions.

Coincidence imaging, also known as ghost imaging, is a technique that exploits correlations between two particles to reconstruct information about a specimen. The particle that relays the spatial information about the object remains completely non-interacting, while the particle used to probe the object is not spatially resolved. While ghost imaging has been primarily implemented on photonic platforms, it becomes particularly intriguing when applied to particles with fundamentally different properties, such as massive, charged electrons and massless, neutral photons, especially considering the role of both particles as cornerstones of highly advanced microscopic platforms. In this work, we investigate coincidence imaging using electron-cathodoluminescence photon pairs generated within a transmission electron microscope. Utilizing a custom-built free-space cathodoluminescence setup, we demonstrate ghost imaging of complex patterns. We are able to obtain a spatial resolution down to 2 $\mu$m, paving the way for adaptation of quantum-enhanced imaging techniques from photonic quantum optics to electron microscopy.

Single-cell RNA sequencing (scRNA-seq) data simulation is limited by classical methods relying on linear correlations, failing to capture nonlinear dependencies. No existing simulator jointly models gene-gene regulatory interactions and cell-cell communication. We introduce qSimCells, a quantum computing-based simulator that uses entanglement to model intra- and inter-cellular interactions, generating realistic single-cell transcriptomic data from heterogeneous cell populations. Its quantum kernel uses a parameterized circuit with CNOT gates to encode gene regulatory networks (GRNs) and cell-cell communication topologies. By programming the entanglement architecture, the simulator establishes a known generative ground truth for both regulatory and communication pathways. The resulting synthetic data exhibits dependencies arising from the joint probability structure of the quantum circuit. Notably, standard correlation-based analyses (Pearson and Spearman) fail to recover the programmed causal relationships and instead report spurious associations driven by high baseline gene-expression probabilities. Applying cell-cell communication detection serves as an internal consistency check: CellChat correctly identifies the true ligand-receptor pairs when inter-state entanglement is active, revealing a robust, up to ~98-fold relative increase in inferred communication probability. These results demonstrate that the quantum kernel produces high-fidelity benchmark datasets with known ground truth, highlighting the limitations of correlation-based inference and the need for approaches capable of capturing complex structural dependencies underlying gene regulation and cell-cell communication.

We present a physics-based fluid control method utilizing localized spacetime windows, extending force-based fluid control to substantially larger simulation scales. In many practical editing scenarios, user-specified objectives affect only a small region of an otherwise satisfactory simulation, resulting in optimal control force distributions that are highly sparse in both space and time. However, existing optimization-based fluid control methods typically solve for control forces over the entire spacetime domain, leading to unnecessarily high computational cost and poor scalability. Motivated by this observation, we restrict optimization to localized spacetime regions surrounding the edit of interest, significantly reducing the dimensionality of the control problem. Within this framework, control forces are parameterized on a coarse "floating" background grid, decoupling control degrees of freedom from simulation resolution and promoting smooth, physically plausible forces. We further analyze spacetime-window selection as a joint spatial-temporal problem. While the full problem can be formulated as a 2D search over spatial and temporal window extents, practical workflows can often leverage user-specified spatial regions and lightweight temporal-window selection strategies to reduce search cost. Our method enables a range of intuitive editing tasks, where sparse user inputs can induce coherent motion in surrounding fluid structures. We demonstrate the effectiveness and efficiency of our method with various 2D and 3D particle-based free-surface simulation examples.

Understanding the quantum evolution of light in nonlinear media is central to the development of next-generation quantum technologies. Yet modeling these processes remains computationally demanding, as the required resources grow rapidly with photon number and phase-space resolution. Here we introduce a tensor-network approach that efficiently captures the dynamics of nonlinear optical systems in a continuous-variable representation. Using the matrix product state (MPS) formalism, both quantum states and operators are encoded in a highly compressed form, enabling direct numerical integration of the Schrödinger equation. We demonstrate the method by simulating degenerate spontaneous parametric down-conversion (SPDC) and show that it accurately reproduces established theoretical benchmarks - energy conservation, pump depletion, and quadrature squeezing - even in regimes where conventional Fock-basis simulations become infeasible. For high-intensity pump fields ($\alpha = 100$), the MPS representation achieves compression ratios above $3\cdot 10^3$ while preserving physical fidelity. This framework opens a scalable route to modeling multimode quantum light and nonlinear optical phenomena beyond the reach of traditional methods.

We present high-precision \emph{ab initio} calculations of the four-point vertex function for the three-dimensional uniform electron gas using variational diagrammatic Monte Carlo. From these results, we extract Landau parameters that reveal a density-driven crossover from underscreening to overscreening, and obtain the full two-electron scattering amplitude on the Fermi surface with controlled accuracy. A residual analysis of the scattering amplitude against the charge-channel Kukkonen--Overhauser (KO$^+$) interaction shows that only a minimal s-wave correction in the antiparallel-spin channel is needed, defining the sKO$^+$ ansatz: KO$^+$ within the local-density approximation plus this short-range correction. Using both our direct VDMC amplitudes and the sKO$^+$ ansatz, we compute the electron-electron contribution to the thermal resistivity, obtaining quantitative agreement with experiments on simple metals (Al, Na, K, Rb). sKO$^+$ thus provides a controlled UEG-based effective interaction for simple-metal transport and future first-principles extensions.

Machine learning-based anomaly detection methods are able to search high-dimensional spaces for hints of new physics with much less theory bias than traditional searches. However, by searching in many directions all at once, the statistical power of these search strategies is diluted by a variant of the look elsewhere effect. We examine this challenge in detail, focusing on weakly supervised methods. We find that training and testing on the same data results in badly miscalibrated $p$-values due to the anomaly detector searching everywhere in the data and overfitting on statistical fluctuations. However, if these $p$-values can be calibrated, they may offer the best sensitivity to anomalies, since this approach uses all of the data. Conversely, training on half of the data and testing on the other half results in perfectly calibrated $p$-values, but at the cost of reduced sensitivity to anomalies. Similarly, regularization methods such as early stopping can help with $p$-value calibration but also possibly at the expense of sensitivity. Finally, we find that k-folding strikes an effective balance between calibration and sensitivity. Our findings are supported by numerical studies with Gaussian random variables as well as from collider physics using the LHC Olympics benchmark anomaly detection dataset.

Material scientists and condensed matter physicists have long been divided on the issue of choosing the conceptual framework for explaining why open-shell transition-metal oxides tend to be insulators, whereas otherwise successful theories such as DFT often predict them to be (false) metals. Strong correlation becomes the recommended medicine. We point out that strong correlation can be mitigated by allowing DFT to lower the energy by breaking structural, magnetic or dipolar symmetries. Such local motifs are observed experimentally by local probes beyond the 'average structure' determined by X-Ray diffraction. Observed broken symmetries can arise from slow fluctuations that persist over the observation time or longer. The surprising fact is that when symmetry breaking motifs are used as input to electronic structure calculations, false metals are converted into real insulators without the recommended medicine of strong correlation. Consistently, DFT calculations that show energy lowering symmetry breaking correct most cases where DFT, even with advanced exchange-correlation functionals, previously missed the correct metal vs insulator designation. Total energy calculations distinguish systems that support energy-lowering symmetry breaking from those that do not. This approach distinguishes between paramagnetic insulating and metallic phases and shows mass enhancement in Mott metals. The reason is that symmetry breaking removes many of the degeneracies that exist in a symmetry-unbroken system, reducing significantly the need for strong correlation. If one chooses to ignore symmetry breaking, the persistent degeneracies often call for strong correlation treatment. Thus, symmetry breaking transforms strong to normal correlation and false metals to true insulators. This view sheds light on the historic controversy between Mott and Slater that still reverberates today.

We present GeoTransolver, a multiscale geometry-aware physics attention transformer for Computer Aided Engineering (CAE). GeoTransolver extends the Transolver backbone with GALE (Geometry-Aware Latent Embeddings) attention, which pairs physics-aware self-attention on learned state slices with cross-attention to a shared geometry and global context computed via multi-scale ball queries (inspired by Domino) and reused in every block. Implemented and released in NVIDIA PhysicsNeMo, GeoTransolver persistently projects geometry and global parameters, into physical state spaces to anchor computations to domain structure and operating regimes. We benchmark on DrivAerML, SHIFT-SUV, and SHIFT-Wing against Domino, Transolver (PhysicsNeMo implementation), and literature-reported AB-UPT, evaluating drag/lift R2 and relative L1 errors on field variables. As an additional nonlinear structural mechanics application, we also report Transolver and GeoTransolver results on bumper-beam and full-vehicle Body-in-White (BIW) crash-dynamics benchmarks, evaluating relative L2 trajectory error and probe-level kinematic MSE. GeoTransolver delivers improved accuracy, robustness to geometry and regime shifts, and favorable data efficiency; we include DrivAerML ablations and qualitative contour and design-trend results, advancing operator learning for high-fidelity surrogates on complex, irregular, non-linear domains.

Recent observations by the IceCube Neutrino Observatory have revealed a significant excess of high-energy neutrinos from nearby Seyfert galaxies, such as NGC~1068, without a corresponding flux of high-energy gamma-rays. This suggests that neutrinos are produced via hadronic interactions in a region opaque to gamma-rays, likely a hot corona surrounding the central supermassive black hole. However, the mechanism responsible for accelerating the parent protons to the required energies ($\sim 100$ TeV) remains an open question. In this study, we investigate diffusive shock acceleration (DSA) in active galactic nucleus (AGN) coronae using a suite of one-dimensional Particle-in-cell (PIC) simulations spanning a broad range of plasma parameters. We find that DSA is a robust and efficient mechanism for proton acceleration, consistently channeling approximately 10\% of the shock's kinetic energy into non-thermal ions, even for shocks with sonic Mach number as low as $ M_s \approx 2$. In contrast, the efficiency of electron acceleration is highly variable and less efficient ($<1\%$) in our parameter survey. These findings provide strong, first-principles support for the hadronic models of neutrino production in AGN and offer quantitative constraints that can explain the observed gamma-ray deficit.

People learn about opportunities and actions by observing the experiences of their friends. We model how homophily -- the tendency to associate with similar others -- affects both the endogenous quality and diversity of the information accessible to decision makers. Homophily provides higher-quality information, since observing the payoffs of another person is more informative the more similar that person is to the decision maker. However, homophily can lead people to take actions that generate less information. We show how network connectivity influences the tradeoff between the endogenous quantity and quality of information. Although homophily hampers learning in sparse networks, it enhances learning in sufficiently dense networks.

Unidentified Falling Objects (UFOs) refer to sporadic beam losses observed during LHC operation. The prevailing explanation is that micrometer-sized dust particles released from the beam screen produce beam losses through interactions with the protons. However, the release mechanism of these particles remains unknown. We propose that roughly $(1-10)$% of UFOs may be caused by axion quark nuggets (AQNs), macroscopic dark matter (DM) candidates with masses of order $(5-1000)\,$g. The AQN model naturally relates the dark- and visible-matter abundances ($\Omega_\mathrm{DM}\sim\Omega_\mathrm{visible}$) and provides a mechanism for generating the baryon-antibaryon asymmetry, with DM composed of both matter and antimatter AQNs. When passing underground within approximately 100km of the LHC, an antimatter AQN generates acoustic waves strong enough to trigger multiple UFO events within $2\,$s. If three correlated UFOs (placed at different locations along the LHC ring) are detected, the signal-to-noise ratio can exceed 5 across the entire allowed AQN mass range for a measurement time of about 360 hours. Practically, the LHC can serve as a large broadband acoustic detector for AQNs.

Radio relics in merging galaxy clusters are widely interpreted as synchrotron emission from relativistic electrons accelerated at large-scale shocks. However, the efficiency of diffusive shock acceleration (DSA) is expected to be suppressed in the low-Mach-number, weakly turbulent environments of cluster mergers; furthermore, recent theoretical insights suggest that DSA may not constitute a viable physical mechanism for such environments. In this work, we investigate ballistic surfing acceleration (BSA) as an electrodynamically grounded alternative for electron energization that bypasses the need for prescribed diffusion coefficients. We formulate BSA under typical cluster shock conditions, deriving the balance between coherent acceleration by the convective electric field and radiative losses from synchrotron and inverse-Compton cooling. This equilibrium defines the maximum reachable electron energy and constrains the resulting steady-state spectrum. By forward-modeling the associated synchrotron emission and comparing it with integrated radio observations of the `Sausage' (CIZA J2242.8+5301) and `Toothbrush' (1RXS J0603.3+4214) relics, we find that the observed spectral curvature and high-frequency steepening are consistent with BSA-limited energies, provided that active acceleration involves only a minute participation fraction ($10^{-9}$-$10^{-8}$) of the radiating electrons. Despite this high selectivity, BSA effectively produces Lorentz factors of $\gamma \sim 10^4$-$10^5$. Our results suggest that radio relics serve as prime astrophysical laboratories for probing coherent acceleration, with the BSA framework providing a robust and physically consistent explanation for electron energization in cluster shocks.

Data-driven surrogate models can significantly accelerate the simulation of continuous dynamical systems, yet the step-wise accumulation of errors during autoregressive time-stepping often leads to spectral blow-up and unphysical divergence. Existing global regularization techniques can enforce contractive dynamics but uniformly damp high-frequency features, causing over-smoothing; meanwhile, long-horizon trajectory optimization methods are severely constrained by memory bottlenecks. This paper proposes Jacobian-Adaptive Weighting for Stability (JAWS), which reformulates operator learning as a Maximum A Posteriori (MAP) estimation problem with spatially heteroscedastic uncertainty, enabling the regularization strength to adapt automatically based on local physical complexity: enforcing contraction in smooth regions to suppress noise while relaxing constraints near singular features such as shocks to preserve gradient information. Experiments demonstrate that JAWS serves as an effective spectral pre-conditioner for trajectory optimization, allowing short-horizon, memory-efficient training to match the accuracy of long-horizon baselines. Validations on the 1D viscous Burgers' equation and 2D flow past a cylinder ($\text{Re}=400$ out-of-distribution generalization) confirm the method's advantages in long-term stability, preservation of physical conservation properties, and computational efficiency. This significant reduction in memory usage makes the method particularly well-suited for stable and efficient long-term simulation of large-scale flow fields in practical engineering applications.

Time-slicing has emerged as a strategy for incorporating semiclassical propagation into real-time path integral formulation and recovering full quantum dynamics. A central step is the decomposition of a time-evolved wave function into a superposition of Gaussian wave packets (GWPs). Here we introduce a quadrature-free variational framework for GWP decomposition, reformulating it as an optimization problem in which the GWP parameters are chosen to maximize the overlap with the time-evolving wave function. An autoencoderdecoder neural network is used for this optimization, with the representation being adaptively reoptimized during propagation. Each wave packet in this decomposition represents a localized patch of the underlying semiclassical manifold, while retaining full correlations between all degrees of freedom. This variational adaptive Gaussian decomposition (VAGD) approach yields a compact Gaussian expansion, providing a scalable route to time-sliced semiclassical quantum dynamics. While general, applying VAGD to facilitate time-slicing of thawed Gaussian dynamics allows a route to improving the semiclassical treatment to the full quantum mechanical result in a systematic manner.

Continuous-variable quantum key distribution (CV-QKD) enables secure communication over standard telecom infrastructure, but scaling is stalled by bulky, discrete optical hardware. We address this bottleneck by demonstrating a real-time CV-QKD system driven by a chip-scale hybrid transmitter using commercial telecom components. Combining a micro-optic external-cavity laser with a monolithic photonic integrated IQ modulator we enable secure secret-key generation over 102 km of optical fiber while reducing optical volume by 95% relative to the commercial discrete-component counterpart. Moreover, real-time operation overcomes offline post-processing bottlenecks of experimental setups. This work bridges laboratory demonstrations and field-deployable technology for cost-effective quantum networks.

Measuring the mechanical response of liquid interfaces without direct contact remains a major experimental challenge, particularly in liquid-liquid systems where no solid reference exists. Here, we develop a frequency-modulation atomic force microscopy (FM-AFM) method that probes liquid interfaces through the hydrodynamic confinement of a viscous liquid film between an oscillating probe and the interface. This approach provides simultaneous access to the in-phase and dissipative components of the effective mechanical response under confinement. The method is first validated on a liquid-solid interface, where the measured confinement thickness and the evolution of the mechanical impedance are consistent with elastohydrodynamic theory over nearly one decade in elastic modulus. It is then applied to a liquid-liquid interface, which exhibits a predominantly viscous response with a finite in-phase contribution and a confinement thickness in the micrometric range. These results show that hydrodynamic confinement provides a sensitive, non-contact approach to compare the mechanical response of soft and liquid interfaces, and opens new perspectives for investigating complex and highly deformable systems such as polymer films, biological membranes, and rafts of nanoparticles.

Neural Physics recasts local discretisations of partial differential equations (PDEs) as fixed convolutional operators, providing a physics-preserving alternative to data-driven surrogate modelling in scientific machine learning. However, existing realizations remain largely confined to classical AI hardware and do not directly connect to quantum structured operator design. To bridge this gap, we introduce a \emph{Quantum Neural Physics} framework and develop a Hybrid Quantum-Classical CNN Multigrid Solver (HQC-CNNMG). The proposed method maps analytically prescribed stencil operators to local quantum convolutional primitives and embeds them within a classical multilevel W-cycle architecture, combining the operator-centric view of scientific ML with the numerical rigor of multigrid solvers. Using amplitude encoding together with the Linear Combination of Unitaries (LCU) and the Quantum Fourier Transform (QFT), the resulting local quantum operators admit logarithmic-depth implementation, with circuit depth scaling as $\mathcal{O}(\log K)$ for an encoded block of size $K$ under the idealized parallel circuit model considered here. Numerical experiments on Poisson, transient diffusion, convection--diffusion, and incompressible Navier--Stokes problems demonstrate numerical consistency, stable multilevel behaviour, and workflow-level feasibility on noiseless simulators. Comparisons with representative quantum linear solver paradigms further show that the main strength of HQC-CNNMG lies in its balanced trade-off among local circuit depth, numerical robustness, and compatibility with PDE structure, rather than in fully quantum global inversion.

Continuous control over lattice geometry, when combined with long-range interactions, offers a powerful yet underexplored tool to generate highly frustrated quantum spin systems. By considering long-range dipolar antiferromagnetic interactions on a breathed Kagome lattice, we demonstrate how these tools can be leveraged to stabilize a chiral spin liquid. We support this prediction with large-scale density-matrix renormalization group calculations and explore the surrounding phase diagram, identifying a route to adiabatic preparation via a locally varying magnetic field. At the same time, we identify the relevant low-energy degrees of freedom in each unit cell, providing a complementary language to study the chiral spin liquid. Finally, we carefully analyze its stability and signatures in finite-sized clusters, proposing direct, experimentally viable measurements of the chiral edge mode in both Rydberg atom and ultracold polar molecule arrays.

We investigate numerically the blocking of two-dimensional bistable reaction diffusion fronts by geometric obstacles. Our goal is to derive quantitative criteria for front propagation in the presence of spatial heterogeneities. Using a conservation law approach, we show that the integral of the reaction term acts as an effective driving force for the front. Combining this insight with the exact one-dimensional traveling wave solution, we construct a reduced analytical model that predicts blocking thresholds. In particular, we obtain explicit conditions for front propagation in a waveguide connected to a conical region of angle theta, valid for widths w less than 4. The model captures the influence of both geometry and nonlinearity, and shows good agreement with numerical simulations. Finally, we extend the analysis to more complex geometries, including checkerboard-like obstacles, and derive simple heuristic rules governing front propagation. ~

A crucial question throughout statistics is whether an observed correlation between two variables is a direct correlation or only an indirect one mediated by a confounder. We organize the existing nonlinear measures of direct correlation into two families, each with a systematic construction: (i) removing the direct correlation from the joint distribution and quantifying the resulting distributional shift, and (ii) intervening on one variable via do-calculus and quantifying the response of the other. For every Kullback-Leibler-based measure in either family we propose a Jensen-Shannon-based regularized analogue; the regularized measures take values in $[0,1]$, satisfy the metric property, and are free of the singularities of the Kullback-Leibler divergence. We analyze the achievable upper bound of each regularized measure under the observed marginals, and derive the maximal value each measure can attain when only the alphabet sizes of the variables are fixed; the maxima admit closed forms built on a single binary-entropy function. The measures are compared on a decision-making model and on three public datasets (Titanic survival, UCI Adult income, and the 1973 Berkeley graduate admissions), with bootstrap confidence intervals for every reported value.

We show that all existing methods quantifying rotational motion in molecular fluids eventually have severe limitations in systems undergoing complex rotational motion characterized by slow, heterogeneous, or intermittent dynamics. This impacts in particular the study of rotational dynamics in molecular supercooled liquids near their glass transition, as well as discussions of the decoupling between rotational and translational motion and violations of the Debye-Stokes-Einstein relation. We present a brief overview of existing methods and explain why none of them can accurately capture the evolution of rotational dynamics from a diffusive fluid to an arrested solid, thus resolving inconsistent literature results. We then introduce an empirical method that efficiently solves all issues. We benchmark our method devising a family of continuous time random walk models for rotational dynamics. Our method correctly quantifies the statistics of free and caged rotational motion, as well as non-Gaussian and non-Fickian rotational dynamics, and should allow a better characterization of dynamic heterogeneity in the rotational motion of supercooled molecular fluids.

We study classical energy-condition admissibility for subluminal, positive-energy warp shells. For the constructions examined here, the energy-condition failures are localized at the smooth source--vacuum transition rather than in the bulk interior. We introduce two \emph{source-first} shell ans"atze whose metric potentials are obtained from the Einstein constraints for a prescribed matter model: a shift-free S-shell and a T-shell whose shift is derived from the momentum constraint. We assess them with a five-criterion standard comprising regularity, constraint satisfaction, an explicit matter model, frame-independent energy-condition margins, and global diagnostics; the standard responds to the source-consistency critique of Barzegar, Buchert, and Vigneron. Applied to eight constructions spanning the canonical warp-drive classes, none passes the full standard. An independent frame-independent verification of the Fuchs constant-velocity shell confirms interior energy-condition compliance (0 of 13 interior probes violate) but reveals Hawking--Ellis Type~IV violations in the smoothing tail beyond the nominal shell. A frame-independent scan over shell compactness and thickness (600 configurations) yields no admissible configuration in either source-first class. The same boundary deficit appears in the shift-free S-shell and persists in the static $v_0=0$ limit, which ties it to the transition geometry rather than to the shift. Along a representative off-axis null ray the null-energy line integral is nevertheless positive for every source-prescribed shell; this is an exploratory diagnostic rather than a proof of the averaged null energy condition, but it shows that the pointwise boundary failures need not appear in that integral.

Active fluids generate motion and stress internally, but in living systems this activity is often regulated by environmental fields that the organisms consume or produce. Here we show that oxygen gradients organise dense suspensions of the flagellated microswimmer \textit{Euglena gracilis} and trigger an active interfacial instability. In circular chambers open to air at the periphery, oxygen exchange and cellular consumption generate a radial chemical gradient. Starting from an initially homogeneous suspension, cells spontaneously localise into a dense annular band through oxygen-dependent motility and bidirectional oxytaxis. This oxytactically formed ring then deforms and undergoes collective azimuthal motion, rotating as a long-lived corona of protrusions. We reproduce this sequence with an oxygen-coupled polar active-fluid model in which oxygen regulates both cell reorientation and motility, while dipolar active stresses drive the deformation and flow of the dense interface. The simulations show that oxygen taxis creates and positions the annular active interface, whereas the subsequent corona is an activity-driven interfacial instability. Our results reveal how a self-generated chemical gradient can position and activate a living fluid, providing a route to environmental control of active-matter flows and interfaces.

Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios. Neural emulators promise orders-of-magnitude speedups, yet existing ocean emulators have not combined fine spatial resolution with multi-year autoregressive rollouts. Samudra, the first autoregressive neural ocean emulator to produce multi-decade global rollouts, is limited to $1^\circ$ resolution and exhibits two long-horizon failure modes: \emph{variance collapse}, the loss of temporal variability, and \emph{imprinting artifacts}, in which velocity patterns leak into deep-ocean fields. We present Samudra 2, which introduces a wider U-Net backbone with modified ConvNeXt-style blocks and a reduced block-internal expansion factor, together with a dynamic loss that reweights output channels according to their prediction errors, strengthening gradients for slow-evolving deep-ocean fields. At $1^\circ$, Samudra 2 increases upper-ocean global-mean temperature $R^2$ from 0.56 to 0.87 and reduces deep-ocean temperature error by roughly sevenfold. The same architecture scales to $1/2^\circ$ and $1/4^\circ$ over approximately 8-year autoregressive rollouts, recovering mesoscale eddies and sharp western boundary currents. Running on a single GPU, Samudra 2 enables larger ensembles for sea-level projections, ocean heat uptake, and climate variability studies. All artifacts are publicly available: project page, code, checkpoints, documentation.

The energy-based approach to operator selection in ADAPT-VQE relies on reconstructing the one-parameter energy landscape for each operator in the pool. In fermionic implementations, the cost of reconstructing this energy landscape often becomes a bottleneck. We address this issue through an exact Hamiltonian transformation that reformulates the one-parameter energy landscape according to a generator-dependent fragmentation of the transformed Hamiltonian. While our method is mathematically identical to standard fermionic Rotoselect, it effectively reduces its cost by about a factor of two, bringing it close to that of gradient-based ADAPT-VQE. We use this formulation to benchmark the gradient-based and energy-based selection approaches in combination with two ansatz-optimization strategies -- "last", where only the appended operator is optimized, and "full", where the full ansatz is re-optimized -- and with both fixed-orbital and orbital-optimized formulations. The benchmark comprises $\text{LiH}$, $\text{BeH}_2$, and $\text{H}_2\text{O}$ at both equilibrium and stretched geometries. In the most weakly correlated system, pairing energy-based selection with "last" optimization enables the efficient construction of an accurate ansatz, which avoids any VQE optimization. As correlation increases, full ansatz re-optimization and orbital optimization become the main factors governing convergence and overall resource cost. This study shows how exact Hamiltonian transformations provide an effective route to reducing the measurement overhead of fermionic energy-based ADAPT-VQE. Moreover, the benchmark clarifies the relative role of operator scoring approach, re-optimization strategy, and orbital treatment in the performance of ADAPT-VQE.

We exhibit an exact correspondence between sampling with score-based diffusion models and adiabatic transport of ground states for a family of Schrödinger operators we call Score Hamiltonians, built from the learned score's quantum potential. We obtain novel density reconstruction bounds and principled annealing schedules via adiabatic theorems for Fokker-Planck equations with time-varying potentials. We find the fundamental limit of sampling is set by the ratio of squared score-matching error to Score Hamiltonian spectral gap - the inverse Poincaré constant of the data density.

We tackle two key difficulties in the simulation of the viscous hydrodynamics of a large dense collection of rigid particles: (i) the poor convergence rate of an iterative solution of the discretized linear system as particle gaps shrink, and (ii) the large number of unknowns needed to accurately discretize the resulting lubrication-driven flows. Our focus is the 2D Stokes resistance and mobility boundary value problems for nearly-touching disks. To address both challenges, we introduce a general two-body preconditioning strategy, and implement it with the method of fundamental solutions. For each close particle pair, the hard-to-resolve interaction is represented in a basis precomputed by solving a local boundary value problem on a fine grid. In an iterative solve, the resulting flow field corrects that obtained from a coarse representation of all particles. The local fine-grid correction can even be compressed so that all particles except the pair itself are affected by an equivalent set of coarse sources. Numerical experiments demonstrate rapid GMRES convergence in challenging multi-particle settings, with iteration counts remaining low even in densely packed suspensions. For example, the mobility problem is solved for a random close packing with area fraction $\varphi = 0.65$, $P = 10000$ monodisperse disks, and minimum separation $10^{-3}$, in just 47 GMRES iterations, achieving five digits of accuracy with 72 vector unknowns per body.

Complex neural quantum states are difficult to optimize when their wavefunction phase carries gauge, chiral, fermionic, or topological structure. We show that the major failure mode is not only ansatz expressivity, but the Monte Carlo estimator used to learn this phase. For separated amplitude-phase states, differentiating the local energy at fixed samples gives a different unbiased estimator of the same variational Monte Carlo phase force, without changing the objective. We further extend the construction to coupled two-head networks by keeping the amplitude-gradient contribution and applying the direct derivative only to the phase path. An adaptive minimum-variance mixture interpolates between standard and direct estimators during training. Across flux ladders, chiral chains, two-dimensional flux cylinders, an interacting fermion ladder, shared-network controls, and a fractional quantum Hall benchmark, the resulting estimators reduce phase-gradient variance, suppress seed failures, and often move multi-percent standard-gradient plateaus to sub-percent accuracy.

Anderson localization has evolved far beyond the conventional dichotomy between extended and localized states. Modern localization theory predicts a complete transport hierarchy comprising extended, critical, and localized phases together with all coexistence phases among them, forming a seven-phase Anderson-localization landscape. Despite its fundamental importance, this hierarchy has never been experimentally realized within a single system. Here we realize the complete seven-phase Anderson-localization landscape in a one-dimensional Floquet photonic lattice. By engineering quasiperiodic hopping profiles containing inhomogeneously distributed hopping zeros, we generate critical states and enable their coexistence with extended and localized sectors. The resulting transport regimes are directly resolved through their distinct spatiotemporal dynamics, including ballistic expansion, confined critical oscillations, and persistent localization. We observe all seven phases, including the elusive triply coexisting extended-critical-localized phase, and experimentally track the phase transitions connecting them. Our results establish the first complete experimental map of the Anderson-localization landscape and provide a unified platform for investigating mobility edges, multifractality, and programmable coherent transport.

The Brody distribution, originally a phenomenological interpolation between Poisson and Wigner level-spacing statistics in quantum chaos, is calibrated here as a quantitative measure of short-range exclusion in 2D spatial point processes. Two results form the core. First, the 2D complete-spatial-randomness baseline is recalibrated to $\beta=0.96\pm0.15$, correcting the inappropriate 1D Poisson reference. Second, an empirical $\beta$--$r_{\text{excl}}$ calibration is validated against the effective hard-core radius with Spearman $\rho=0.988$. The framework is demonstrated on 58 manufactured surfaces (10 materials, 10 processes), phase-extracted interferometric profilometry of a certified roundness standard, and 2D binary embeddings of prime numbers. A sparse-integer control proves the prime $\beta=2.15$ signal is genuinely arithmetic ($\Delta\beta=+0.68$ over random-integer control), while a Cantor-embedding null result ($\beta=1.40$, TOST $p<0.01$) demonstrates that 2D exclusion is embedding-created rather than intrinsic. Density-thinning experiments establish that $\beta$ captures exclusion strength rather than point density, while absolute values are density-dependent. A distinct CSR baseline for binary fields at low fill fraction is identified, with a decision table provided. The $\beta$--$r_{\text{excl}}$ calibration, the CSR baseline correction, and the control protocols together constitute a calibrated measurement framework for reproducible characterisation of short-range exclusion in 2D spatial point processes.

Photonic higher-order topological insulators (HOTI) are characterized by a hierarchy of topologically-protected states with different dimensionalities, making them especially interesting for potential applications that combine strong localization of electromagnetic fields and their robust waveguiding. However, their practical implementation often requires expensive processing techniques and is limited by accessible material parameters. In this paper, we demonstrate that a radio-frequency photonic HOTI can be implemented as a metasurface composed of split-ring resonators with couplings between dipole and quadrupole modes. We verify, by numerical simulations and experimentally at frequencies of 1.5-1.7 GHz, that a proposed metasurface supports corner- and edge-localized states. Our results reveal a scalable and easily reconfigurable GHz-range platform that employs printed circuit board technology, thus making crucial steps required for further experimental studies of photonic HOTI and the development of their microwave applications.

Here we investigate the all-optical control of spectral correlations in spontaneous parametric down-conversion. We show that when photon pairs are projected onto high-order spatial modes, the spatial structure of the pump field defines the phase-matching function of the nonlinear interaction. Thus, by structuring the pump field in both space and spectrum, the biphoton spectral correlations are fully controlled. Considering a standard periodically-poled crystal as the nonlinear medium, we show that the Gouy phase matching method proposed here can generate both spectrally uncorrelated and high-dimensional spectrally entangled photon pairs, similarly to what is achieved with aperiodically-poled crystals. Furthermore, we show that our method can generate a wider class of quantum states if the pump field is a spatiotemporal wavepacket, that is, if its spatial and spectral structures are correlated.

Quantum computers promise to solve the electronic structure problem for a large class of molecules. However, the performance of relevant quantum algorithms hinges on preparing initial states with substantial overlap with the target eigenvector. For classically challenging molecules with strong electron correlation, starting from multi-reference states, such as complete active space (CAS) wavefunctions is necessary. Unfortunately, the most advanced state preparation protocols applied to such states result in a gate complexity that scales exponentially with the active space size $d$. In fact, even encoding a CAS state classically is traditionally believed to be intractable for chemically relevant systems. Here, we draw insights from the recently introduced Quantum Paldus Transform (QPT) to show that there exists an efficient classical representation of CAS states and to design a new state preparation routine outperforming previous ones. The QPT represents a transformation from the Fock basis to a friendlier symmetry-adapted basis. Our main contribution consists in showing that CAS states expanded in this basis can efficiently be represented as a matrix product state (MPS) with a bond dimension scaling as $O(d^2)$. One can then efficiently load the MPS on a quantum computer and use the inverse QPT to transform the state to the Fock basis. Moreover, our method can easily be extended to the efficient preparation of CAS states in first quantisation with similar complexity. Crucially, we demonstrate that the complexity of both state preparation protocols only grows polynomially as $O(d^3)$ , which constitutes to the best of our knowledge an exponential improvement over the state of the art.

We use density functional theory and model Hamiltonians to reveal large spin splitting of bands localized at low-symmetry, ferromagnetic surfaces of bulk antiferromagnets (AFMs). There is great interest in finding new material platforms combining the robustness and ultrafast dynamics of AFMs with large, functional spin splitting which is often restricted to ferromagnets. Here, we show that a subset of AFM surfaces which have symmetry-allowed magnetization can host large spin splitting via bulk degeneracy lifting of sublattice-resolved exchange splittings. Using model Hamiltonians, we show that the spin splitting is maximized for two ferromagnetic surface motifs: terminations with single uncompensated magnetic sublattices, and two-sublattice surfaces whose sublattices are magnetically and electronically compensated in the bulk, but acquire distinct crystal field environments via surface truncation. The latter case can yield FM-like spin splitting magnitudes while also having vanishingly small uncompensated magnetization. In contrast, when surface magnetization arises from relativistic canting on symmetry-connected sublattices, the spin splitting is expected to be small. We confirm these predictions with first-principles calculations of $\mathrm{Cr_2O_3}$ and $\mathrm{FeF_2}$, finding splittings from $\sim10\mathrm{meV}$-$\sim1\mathrm{eV}$ depending on the surface in question. Our findings point to intrinsic surface symmetry breaking as a route to large, functional spin splitting in an expanded range of AFM materials.