A repetitive-pulse magnet is a promising tool when combined with repetitive excitations, such as pulsed lasers. Technically, the repetition and the magnetic field values in a repetitive-pulse magnet are limited by the Joule heating in the coil. Here, we analytically examine the relationship between the coil's dimensions and its efficiency, assuming negligible heating of the coil, to design an optimized high-repetition, high-magnetic-field coil. We calculated the dependence of the maximum magnetic field, energy loss, pulse duration, form factor, impedance, and maximum current on the coil's geometry. We found that the smaller the coil, the more pulses and the more intense the magnetic fields we can obtain under a given condition. We argue that the obtained trend arises from a complex interplay among various parameters.

Geomagnetic field reversal sequences exhibit persistence times spanning a broad range, from a few $10^4$ years to superchrons lasting more than $10^7$ years. Despite extensive observational and theoretical work, the physical mechanisms governing how such reversals occur and how their broad temporal variability is organized are still not fully understood. Here we investigate the temporal variability of geomagnetic polarity in a thermally driven low-dimensional geodynamo model subject to a slow periodic modulation of the control parameter governing the large-scale induction, namely the $\alpha$-effect parameter. We find that the modulation generates a multipeaked probability density function of magnetic persistence times, with local maxima occurring at approximately integer multiples of the modulation timescale, as expected in a stochastic-resonance-like regime. The peak positions follow an approximately linear dependence on their index, showing that the characteristic timescales selected by the system are set by the imposed modulation period. These results provide a physically motivated numerical framework in which slow modulation of a geodynamo control parameter can organize reversal statistics through stochastic-resonance-like dynamics.

Socio-demographic factors influence social contact patterns and play a fundamental role in shaping the transmission dynamics of infectious diseases. However, compartment-based models of infectious disease dynamics commonly consider the dependence of contact patterns on age, but ignore other factors that are likely to have compounding effects. Methods that stratify the population by multiple socio-demographic factors are few and require social contact surveys that contain information about all factors of interest. Here we present a method that can stratify an existing social contact matrix with an additional socio-demographic factor using information about the population structure of the socio-demographic factors and assumptions about the aggregate mixing rates within and between groups. We then analyse hypothetical populations and a projection of a social contact survey onto Aotearoa New Zealand's age-ethnic structure to show how these extended social contact matrices can change epidemic dynamics and outcomes. The inclusion of the additional factor has a big impact on the model reproduction number and final epidemic size. We find that minority group epidemic outcomes are most sensitive to variation in model parameter values.

Eddy saturation -- the weak sensitivity of Antarctic Circumpolar Current (ACC) transport to wind stress -- is a fundamental feature of Southern Ocean dynamics, yet the processes that maintain this state remain debated. Previous studies have proposed different mechanisms, including adjustments of eddy diffusivity and standing meanders, but the conditions under which each mechanism dominates are unclear. Here we use an idealized reentrant channel model to examine how drag strength controls the eddy saturation. When the wind strength relative to friction is below a certain threshold, eddy saturation is governed by a combination of standing meander and eddy diffusivity adjustments; once the threshold is exceeded, it is governed solely by standing meander adjustment. These results suggest that changes in drag strength may account for the divergent eddy saturation mechanisms reported across studies.

Diamond detectors with electrodes orthogonal to the surface, engraved via laser-induced graphitization, are full-carbon sensors of interest for a wide range of applications, spanning from High Energy Physics to Nuclear Medicine and dosimetry. In recent years, significant progress has been made in graphitization techniques, enabling the fabrication of lower-resistance electrodes. This has resulted in faster sensors, achieving time resolutions better than 100 ps. However, simulating signal formation in these devices remains a challenge. The effects of fluctuations in energy deposition, carrier transport, signal propagation, and readout electronics intertwine in a way that is non-trivial to disentangle. We have developed an innovative simulation approach based on an extension of the Ramo-Shockley theorem, modeling propagation effects in a theoretically sound manner by introducing time-dependent weighting potentials. These are obtained by solving a third-order partial differential equation derived as a quasi-static approximation of Maxwell's laws. The numerical solution of this equation emerged as the main challenge of the new approach. In this contribution, we discuss an innovative solver that uses fundamental solutions to impose boundary conditions and spectral methods to extend the solution to the bulk of the diamond detector. We report on how the solver has recently been ported to GPUs and distributed across multiple computing sites, leveraging the TeRABIT HPC Bubbles and the InterLink protocol. This drastically reduces time-to-insight and effectively enables what-if studies on sensor geometry.

GaAs-based nanowires hosting active quantum heterostructures provide a promising route toward monolithic integration of single-photon sources on silicon, a key requirement for scalable quantum photonics. However, ultrathin axial quantum-emitter formation is often hindered by facet-dependent growth dynamics and rotational twins, which induce lateral overgrowth and compromise interface abruptness. Here, we develop InGaAs-based quantum emitters by tailoring facet evolution via dilute Sb incorporation, which efficiently suppresses twins and promotes confined axial insertion at the growth-front facet. This approach significantly enhances the probability of obtaining abrupt, few-nanometer-thin quantum dots at the nanowire tip. Single-nanowire optical spectroscopy reveals intense, spatially localized emission from the active region with lifetimes as short as (0.51 $\pm$ 0.02) ns, and second-order photon-correlation measurements consistently exhibit pronounced antibunching with $g^{(2)}(0)<0.4$, confirming single-photon emission. These results establish a strong correlation between twin density and axial heterostructure formation, identifying defect control as a key factor in realizing monolithically integrated nanowire single-photon sources.

Skin-friction drag induced by wall-bounded turbulent flows accounts for a substantial fraction of energy consumption across commercial aerospace, wind energy, and marine transport. Its active reduction is one of the highest-value targets in engineering fluid dynamics. Deep reinforcement learning (DRL) has emerged as the leading approach for real-time flow control, yet its performance ceiling is set not by algorithmic capability but by reward structure, the naive scalar objective does not optimally reflect the underlying physics. Policy-DRIFT bypasses this ceiling by relocating reward information from policy gradients to generative model inference: a conditional flow matching model (CFM) constructs a physically-grounded manifold of realisable flow states spanning multiple control regimes, Terminal Reward Guidance (TRG) steers samples toward reward-maximising targets at inference, and a lightweight DRL policy, structurally decoupled from reward quality, tracks these full-field targets via root-mean-squared error (RMSE) minimisation. The test case is turbulent channel flow simulated using direct numerical simulation (DNS) at friction Reynolds number of $\mathrm{Re}_\tau = 180$, which is the canonical benchmark for wall-bounded turbulence. Policy-DRIFT achieves $49\%$ drag reduction approaching the theoretical upper bound, which is $\approx 16\%$ higher than the DRL benchmark, while consuming 37$\times$ less actuation energy. Our approach combines generative methods with active flow control, marking a paradigm shift towards controlling complex physical systems efficiently.

The interaction of electrically charged particles with magnetic fields is a fundamental problem in several areas of physics. An example is the motion of energetic particles through a magnetized plasma. The most accurate and reliable way to explore theoretically the interactions between particles and fields is via test-particle simulations. In such simulations one creates the turbulent magnetic field and solves the Newton-Lorentz equation numerically by employing an integration scheme. In the current article we discuss exponential integrators and derive systematically from this the Rodrigues scheme as well as the famous Boris integrator. For an approach where one creates the magnetic field anew at each time step, both integrators are overall comparable. In theory the Rodrigues approach should be more accurate due to the fact that the occurring matrix exponential is evaluated without further approximations. Practically, both methods provide very similar results. It is argued in the current article that a Rodrigues based integrator is a very strong alternative because for the specific problem discussed here, it does not require longer computing times.

Merging direct and indirect-drive has long been viewed as an optimal hybrid laser-fusion scheme that combines the uniformity of x rays with the efficiency of direct illumination. We present the first integrated 2D simulations of hybrid shock drive (HSD) targets for the OMEGA laser. The HSD scheme [L. Ceurvorst et al., Phys. Rev. E 101 063207 (2020)] uses x rays from a thin Au-coated x-ray converter outer shell to drive the initial shock into a standard direct-drive capsule. Direct illumination is used to implode the target after the first shock. The design effectively suppresses laser-imprint seeding of hydrodynamic instabilities, maintaining shell integrity during the implosion. This scheme will enable fielding low-adiabat, high-convergence implosions on OMEGA with expected performance greatly exceeding those of current designs. HSD targets are projected to significantly enhance fusion yields, potentially increasing the record Lawson parameter by $\sim$85\% on OMEGA while effectively eliminating the requirement for laser smoothing. These results position HSD as a robust platform for high-performance implosions, paving the way for advanced high-gain inertial fusion energy targets.

Graph construction is an essential step in the Graph Neural Network (GNN) based tracking pipelines. The goal of the graph construction is to construct a graph that contains only the defined true edge connections between nodes (detector hits). A promising approach for the graph construction is through the Metric Learning approach, where a node representation in an embedding space is learned, and nodes are connected according to their distance in the embedding space. The loss function for the metric learning in this case is a contrastive loss encouraging the true pairs of nodes to be close to each other, and pulling away the false pairs of nodes. This approach presents a conflict of the learning objective for the hopping connections when a true edge is defined as a chain connection in a particle track. To address the conflict for this case, we propose a ``Double Metric Learning'' approach, where two node representations are learned. A directed graph can then be constructed based on the distance between the two representations from two nodes respectively. We test this idea with the ATLAS ITk detector at the HL-LHC using the ATLAS ITk simulation and show better graph construction performance particularly for particles with high transverse momentum compared to the Simple Metric Learning approach. We also show that Double Metric Learning is able to accurately predict edge direction.

Understanding the mutual influence between social behavior and physical health is crucial for designing effective epidemic mitigation strategies. Individual interactions drive the evolution of opinions, which in turn shape how infectious diseases are perceived and consequently how they spread within a population, for instance through the adoption or rejection of preventive measures. At the same time, the distribution and dynamics of physical contacts play a fundamental role in determining transmission patterns. To this end, we develop a mathematical framework to analyze the coupled dynamics of opinion formation, disease transmission, and physical contacts by employing graphon-based networks, which capture heterogeneous and large-scale connectivity patterns typical of realistic social structures. The epidemic compartmental model further incorporates a kinetic description of microscopic level physical contacts, allowing for a consistent multiscale representation of interaction patterns. Starting from a microscopic description governed by interpersonal compromise and intrinsic self-thinking processes, we derive a kinetic compartmental epidemic model on graphons via a mean-field limit. This formulation allows us to investigate the joint evolution of the disease state and the opinion distribution, with a particular focus on the role of social networks and physical contacts. Numerical experiments demonstrate that the graphon-kinetic approach provides a comprehensive representation of the coupled opinion-epidemic dynamics, revealing new possibilities for controlling disease spread by shaping population opinion patterns.

Identifying transition states (TSs) on potential energy surfaces is a central computational bottleneck in mechanistic studies of catalytic materials. A TS search is not a single calculation but a long-horizon, multi-step workflow of atomistic simulations with delayed, asynchronous feedback and heterogeneous failure modes that require a joint multimodal analysis of scalar convergence diagnostics and atomic geometries along the reaction path. To address this challenge, we propose TSAgent, an agentic workflow that automates TS search directly at the density functional theory (DFT) level of quantum chemical accuracy. TSAgent operates through a persistent plan-execute-analyze-replan loop, continuously adapting its strategy based on convergence diagnostics and geometric feedback without human intervention. We evaluate TSAgent on a diverse 100-example subset of the OC20NEB heterogeneous catalysis benchmark, where it successfully locates TSs with 83% accuracy. In a direct comparison against expert DFT practitioners on 10 held-out examples, TSAgent achieves a 70% success rate compared to a human-expert average of 73 +/- 12%. Finally, TSAgent independently reproduces Bronsted-Evans-Polanyi scaling relationships for NH3 dissociation on metal and single-atom alloy surfaces from a published heterogeneous catalysis study, demonstrating that its utility extends beyond curated benchmarks to real scientific investigations.

The robustness of XRD methods for the determination of the lattice parameters of crystals is well established. These methods have been extended to helical atomic structures using twisted x-rays \cite{friesecke_twisted_2016}. Building on an integral form used in \cite{friesecke_twisted_2016}, we construct integral representations of a broad class of time-harmonic solutions to Maxwell's equations in a vacuum or, more generally, in a homogeneous medium without source terms. The representation includes assignable generalized functions (distributions) that can be tailored to specific boundary or far-field conditions. When the assignable functions satisfy mild periodicity and smoothness conditions, the solutions can be approximated using multi-dimensional trapezoidal rules with exponentially fast convergence. This approximation can be physically interpreted as utilizing finite sources of plane waves to approximate the broad class of time-harmonic solutions to Maxwell's equations. Using these solutions, we show that radiation from suitably placed and oriented sources can serve as incoming radiation for structures with icosahedral symmetry to achieve constructive interference after interacting with the icosahedral structure. The finite source approximations are sufficiently general to satisfy the general Dirichlet conditions at an arbitrarily large number of assigned locations in a source-free domain. The integral representation also extends to a broad class of physical phenomena governed by Helmholtz-type equations. Examples include the scalar wave equation for acoustic waves and elastic wave propagation in linear isotropic solids, which involve both scalar and vector wave equations.

Photonic time crystals are electromagnetic media with periodically time-varying parameters, enabling momentum band gaps, parametric amplification, and frequency conversion beyond what is possible in time-invariant systems. So far, they have been explored mainly in single-mode systems, which limits the range of accessible physical phenomena. Here, we introduce an impenetrable metasurface waveguide as a multimode time-varying platform supporting both guided surface modes and higher-order guided volume modes. We show that temporal modulation in this platform gives rise not only to conventional intramodal band gaps associated with same-branch coupling, but also to tilted intermodal band gaps originating from coupling between different guided-mode branches. Unlike intramodal band gaps, these intermodal band gaps are not restricted to half the modulation frequency and can enable directional wave amplification, where the amplified field carries energy along the waveguide even inside the band gap. We further show that the modulation phase difference provides an effective symmetry-control parameter: by exploiting temporal glide symmetry, one can selectively suppress or enhance gap opening for interactions between modes of the same or different symmetry. These results establish a versatile multimode platform for photonic time crystals, offering one of the simplest and most experimentally accessible routes to tilted band gaps compared with volumetric dispersive PTC implementations and, more broadly, opening new opportunities for time-varying electromagnetic systems.

Conical intersections play central roles in photoinduced reactions. However, comprehensive conical-intersection datasets that could advance our understanding of excited-state reaction processes remain scarce. To address this gap, we constructed a quantum chemistry dataset containing ground-state and conical-intersection structures of small molecules (up to ten heavy atoms: C, N, O, F). Ground-state geometries were optimized at the semi-empirical OM2 level, with single-point energies calculated at the OM2/MRCI level. Conical-intersection geometries and energies were also computed at the OM2/MRCI level. This dataset is designed to enable a deep integration of photochemistry with machine learning, bridging the gap between photochemical insight and data-driven approaches.

Superconducting nanowire single-photon detectors (SNSPDs) have demonstrated timing jitter in the few-picosecond regime, yet their timing resolution deteriorates substantially under high-count-rate operation. Existing interpretations mainly attribute this degradation to deterministic waveform distortions, such as multiphoton responses and pulse pile-up, yet the experimentally observed jitter broadening at high count rates cannot be fully accounted for within this picture. Here, we show that stochastic baseline fluctuations arising from finite-memory readout dynamics constitute an intrinsic source of the count-rate-dependent timing jitter in SNSPD systems. For stochastically arriving photons, overlapping recovery responses accumulate in the readout chain and generate statistically fluctuating baselines, which are converted into timing uncertainty through threshold-based timing extraction. We develop a stochastic-process framework that quantitatively connects photon statistics, readout dynamics, and timing jitter. The framework predicts characteristic scaling behaviors, including a nonmonotonic dependence of baseline fluctuations under pulsed excitation with a maximum near half of the repetition frequency. These predictions are quantitatively verified through systematic variations of count rate, circuit time constant, and detector dynamical properties. Our results identify stochastic baseline dynamics as a fundamental mechanism limiting timing resolution in high-count-rate SNSPD operation and provide a general framework for optimizing finite-memory high-speed photon-counting systems.

Full-waveform inversion (FWI) estimates unknown parameters in the wave equation from limited boundary measurements. Recent advances in neural reparameterized FWI (NeurFWI) demonstrate that representing the parameters using a neural network can reduce the reliance on the high-quality initial model and wavefield data, at the cost of slow high-resolution convergence. However, its underlying theoretical mechanism remains unclear. In this study, we establish the neural sensitivity kernel (NSK) and the wave tangent kernel (WTK) to analyze their convergence behavior from both model and data domains. These theoretical frameworks show that the neural tangent kernel (NTK) induced by neural representation adaptively modulates the original sensitivity and wave tangent kernels. This modulation leads to several key outcomes, i.e., the spectral filtering effect, the gradient wavenumber modulation, and the wave frequency bias, connecting the convergence behavior of NeurFWI with the eigen-structures of NSK and WTK. Building on these insights, we propose several enhanced NeurFWI methods with tailored eigen-structures in NSK and WTK to improve inversion performances and efficiency. We numerically validate these theoretical claims and the proposed methods in seismic exploration, and firstly extend their application to medical imaging.

This paper investigates the nonlinear dynamics of Newton's problem of minimal resistance in radial fields. We move beyond classical translational symmetry to analyze two non-equilibrium scenarios: a scale-invariant free expansion and an incompressible source flow. Our analysis reveals that the scale-invariant model suffers from a symmetry-breaking instability (loss of ellipticity) that necessitates geometric truncation. Conversely, we prove that the incompressible flow acts as a structural regularizer, admitting unique, smooth, and strictly concave solutions. These findings provide new qualitative insights into how physical conservation laws ensure the regularity and symmetry of optimal configurations in high-speed central flows, bridging the gap between variational calculus and the physics of complex systems.

We present an implicit, fully-coupled hydro-mechanical solver for the three dimensional simulation of fluid-driven rupture propagation along existing discontinuities. The solver handles simultaneously frictional slip (shear failure) and tensile opening (hydraulic fracture) along arbitrary intersecting fractures and faults in a linearly elastic and impermeable rock matrix. The spatial discretization combines a collocation displacement discontinuity boundary element method for quasi-static elasticity with a Galerkin finite element method for nonlinear pore-fluid diffusion along the discontinuities. Frictional and tensile failure are governed by a poro-elastoplastic cohesive zone like interface law with slip-weakening friction, dilatancy, and tensile strength degradation, integrated via an elastic predictor-plastic corrector scheme. The strong nonlinear coupling between mechanical deformation and fracture permeability is handled via adaptive implicit time-stepping. Efficient block preconditioning of the coupled tangent system, leveraging hierarchical matrix representations of the boundary element operator, is essential to achieve robustness across the full range of fracture behaviors. Accuracy and convergence are demonstrated against a comprehensive suite of analytical and semi-analytical solutions of increasing complexity: fluid-driven frictional ruptures under constant and slip-weakening friction, dilatant ruptures with permeability changes, and penny shaped hydraulic fractures spanning the viscosity-to-toughness transition. The solver is further assessed on two multi-fracture configurations: injection into three intersecting fractures, and a height-confined hydraulic fracture intersecting a strike-slip fault. The proposed framework simultaneously captures frictional slip, dilatancy, permeability evolution, and tensile opening.

We propose a bilayer all-dielectric PhC for controlling Janus bound states in the continuum (BIC) and optical chirality through symmetry-selective perturbations. Starting from a symmetry-protected $\Gamma$-point BIC, we use interlayer displacement as one geometric control knob to generate different topological charges in the upward radiation and downward radiation channels. A subsequent diagonal in-plane displacement reconstructs the polarization topology around the BIC and generates a Janus-chiral BIC with strong handedness selectivity. In contrast, other in-plane perturbations generate chiral quasi-BICs with finite radiative coupling, for which the circular dichroism (CD) and resonance wavelength can be continuously tuned. We further show that material conductivity provides an additional dissipative degree of freedom for actively modulating the chiral response, with a switchable CD exceeding 0.89. Near-field optical-chirality distributions and multipole decompositions reveal that the chiral response originates from a symmetry-induced imbalance of local optical handedness and a spin-selective magnetic-dipole resonance. These results reveal the topological relationship between Janus radiation, polarization singularities and intrinsic chirality, thus paving a scalable route toward reconfigurable high-$Q$ chiral photonics.

Reliable precipitation monitoring is essential for disaster risk reduction, water resources management, and agricultural decision-making. Multi-source satellite observations, particularly the combination of geostationary infrared and passive microwave measurements, have become a primary means of precipitation detection. Traditional multi-source satellite precipitation estimation methods remain computationally inefficient, and many deep learning methods lack the flexibility to incorporate new sensors without retraining the full model. Here we introduce PRISMA (Precipitation Inference from Satellite Modalities via generAtive modeling), a plug-and-play latent generative framework for multi-sensor precipitation estimation. PRISMA learns an unconditional precipitation prior from IMERG Final fields and constrains it through independently trained, sensor-specific conditional branches, allowing new observation sources to be incorporated without retraining the generative backbone. Applied to FY-4B AGRI infrared and GPM GMI microwave observations, PRISMA improves Critical Success Index by up to 40.3% and reduces root-mean-square error by 22.6% relative to infrared-only estimation within microwave swaths, while also improving probabilistic skill and maintaining an average inference time of about 37 s. Independent rain-gauge validation across China confirms consistent gains, and typhoon case studies show that microwave conditioning restores eyewall and spiral rainband structures, reducing storm-core mean absolute error by up to 42.3%. PRISMA thus provides an extensible and efficient framework for multi-sensor precipitation estimation.

Optical Skyrmions have recently garnered much interest providing a potential avenue for high capacity, robust topological information transfer. Typically, Skyrmions are derived from the coupling of just two degrees of freedom (DoFs) limiting their versatility. In this work we realize spatio-spectral Skyrmions derived from the non-separability between three DoFs: wavelength, space and polarization. A compact and simple technique is used to generate the spatio-spectral vector beams (SSVB) carrying the desired Skyrmionic structure, offering simple pathways for complex Skyrmionic beam design. The topological structure, witnessed through a map between the spatio-spectral plane and the Poincaré sphere, exhibits an additional tunable $k\pi$ parameter thereby enhancing the number of controllable DoFs. Our three DoF construction allows us to propose a novel topological multiplexing strategy that independently encodes different Skyrmion numbers at different radii of the field. We experimentally demonstrate the practicality of this approach by transmitting and receiving three distinct Skyrmion numbers encoded into a single topological field, for the first form of mode division multiplexing with Skyrmion topology. This work opens up new avenues for dense information encoding using multiple topological channels encoded in a single light field.

This paper presents a novel method for measuring the Poynting vector characteristics of monochromatic electromagnetic waves. We outline a specific design for such a meter and provide experimental data to validate the approach. For testing purposes, we utilized vortex beams with both linear and circular polarization.

Solid-state Spatial Light Modulators (SLMs) are fundamentally limited in their ability to achieve high spatial complexity and high temporal bandwidth simultaneously. High-speed, low-energy modulation requires sub-wavelength active mode volumes, and sophisticated spatial wavefront engineering necessitates an ultra-fine pixel pitch. While small pixels can simultaneously solve both, in conventional architectures, the dense 2D electrical routing required for such pixels creates an insurmountable physical bottleneck. This results in a compromise between the SLM refresh rate, number of pixels and the field of view. Here, we demonstrate a hybrid architecture that overcomes this limit by spatially decoupling the electrical modulation plane from the optical output plane. By integrating a metasurface doublet with a photonic integrated circuit (PIC)-based optical phased array (OPA), we achieve independent 2D electrical control over each phase-element while simultaneously realizing a three-fold reduction in effective pixel pitch. This decoupling allows us to maintain the small active volume required for high-speed operation, while circumventing the routing constraints of dense spatial array of emitters. We utilize this platform to demonstrate tunable varifocal lensing, 2D beam steering, and 2D holography. Our work provides a scalable foundation for next-generation solid-state SLMs that simultaneously offer high speed, low power consumption, and large field of view.

Digital backpropagation (DBP) is one of the most effective techniques for compensating nonlinear distortions in coherent optical fiber communication systems. However, its practical application to wideband transmission remains limited by high computational complexity caused by large channel memory and the requirement for fine spatial discretization. In this work, we propose a subband-based learned digital backpropagation (SbL-DBP) framework for wideband optical transmission systems. The received signal is decomposed into multiple subbands, enabling independent frequency-domain compensation of the chromatic dispersion with reduced effective channel memory and lower computational complexity. Nonlinear intra- and inter-subband interactions are addressed in the time domain using a trainable multi-input multi-output filtering structure. The parameters of the proposed framework are jointly optimized using end-to-end gradient-based learning. In addition, sparsification techniques are employed to remove insignificant coefficients and further reduce computational complexity. Numerical simulations of an 11$\times$40~Gbaud WDM RRC-16QAM 20$\times$100 km transmission system demonstrate that the proposed method provides a superior performance--complexity trade-off compared to conventional DBP and enhanced DBP. In the low- and medium-complexity regimes, SbL-DBP provides higher signal-to-noise ratio gains while requiring fewer propagation steps.

The rigorous simulation of stratified turbulence remains challenging due to pronounced flow anisotropy, suppressed vertical transport, and high sensitivity to numerical dissipation. This study systematically evaluates the predictive capability of the lattice Boltzmann method (LBM) for a three-dimensional stratified Taylor--Green vortex. Within a double-distribution-function framework under the Boussinesq approximation, we examine the influence of stencil configurations, forcing formulations, and spatial resolutions up to $256^3$, with validation against spectral DNS benchmarks. The results demonstrate that the D3Q27$\times$19 configuration achieves an optimal balance between numerical accuracy and computational efficiency, accurately reproducing the temporal evolution of kinetic and potential energies as well as the characteristic double-peak dissipation structure. Grid-sensitivity analysis further reveals that potential energy and fine-scale turbulent structures are significantly more resolution-dependent than kinetic energy, requiring a minimum resolution of $256^3$ for quantitative convergence. Moreover, under strongly stratified conditions, the velocity-shift forcing schemes outperform discrete source-term approaches, reducing the overall error by approximately 45.54\%. Overall, this work provides practical guidelines for high-fidelity LBM simulations of stratified turbulence and highlights that the coordinated selection of stencil isotropy, spatial resolution, and force discretization is essential for accurately capturing energy cascade and mixing dynamics.

Implicit solvent models are widely used to decrease the number of solvent degrees of freedom and enable the calculation of solvation energetics without water molecules. However, its accuracy often falls short compared to explicit models. Recent advancements in neural potentials have shown promise in drug discovery, but transferability remains a persistent challenge. Here, we introduce the Protein Hydration Neural Network (PHNN), an implicit solvent model that extends analytical continuum solvation by learning transferable corrections to model parameters instead of applying post hoc adjustments to final energies. The model is explicitly designed to maximize data efficiency by leveraging physical priors embedded in the data. We demonstrate that PHNN improves accuracy relative to traditional analytical methods and maintains predictive accuracy on out-of-domain protein systems.

We introduce a modular silicon-photonic Fabry-Perot resonator lattice based on cascaded tunable Sagnac loop reflectors. Each SLR is controlled by a single directional-coupler cross-coupling coefficient, enabling modular control of the effective lattice hoppings. As a representative example, alternating two SLR types maps the lattice onto the Su-Schrieffer-Heeger model in the weak-coupling limit. We derive the Bloch dispersion via a transfer-matrix formulation and obtain an effective tight-binding Hamiltonian in the weak-coupling limit. S-parameter simulations of a 20-site lattice show an isolated midgap resonance with edge-localized power profiles in the topological phase, and disorder tests show robustness against symmetry-preserving hopping perturbations. Our results establish SLR-based FP lattices as a complementary platform for on-chip topological photonics.

Nanoscale quantum light sources are essential building blocks for integrated quantum photonic systems. Here, we report a wavelength-scale entangled-photon source based on van der Waals-engineered NbOBr$_2$, and benchmark its performance for telecom-wavelength quantum light generation. By exploiting the material's second-order nonlinearity, we generate quantum-correlated photon pairs via spontaneous parametric down-conversion. We then use a 90$^{\circ}$ twisted stacking to induce quantum interference in photon-pair generation, yielding polarization-entangled photons. This approach enables tunability of the quantum optical state via control of the excitation laser polarization. We experimentally obtain entanglement fidelities exceeding 95% for Bell states, along with a high coincidence-to-accidental ratio of $\sim$335, and a brightness approximately one order of magnitude higher than recently reported telecom sources based on transition metal dichalcogenide (TMD) 2D materials. These results establish twisted van der Waals engineering as a powerful platform for highly tunable, high-brightness quantum light sources at telecom wavelengths.

Measurements of positional coordinates and dimensions - whether by human vision or optical instrumentation - are fundamental to safety, industrial productivity, manufacturing quality/accuracy, and scientific discovery. The ultimate precision of such measurements is governed by the Fisher information conveyed from an object to a detector through the optical field, and strategies for enhancing measurement performance often focus on reducing detector noise and/or refining estimation algorithms. Building on the emerging understanding of Fisher information as a physical quantity that propagates through space in a wave-like fashion, we demonstrate that substantial gains in precision can also be made by engineering the electromagnetic environment of a measurement target to optimise the generation and transmission of Fisher information. Using nanowire position metrology based on light scattering at a wavelength {\lambda} = 640 nm as an architype system, we achieve a multifold enhancement in localisation precision, reaching beyond {\lambda}/10,000. Our results establish target environment engineering as a powerful and broadly applicable strategy for advancing measurement and sensing performance across platforms ranging from optical characterisation of micro- and nano-objects to microwave radars and optical LiDAR navigation systems.

In this paper, we study the effective rank of the canonical polyadic decomposition applied to the electron repulsion integrals, ubiquitous in quantum chemistry. We demonstrate, both mathematically and numerically, that in general the effective rank of this decomposition cannot grow linearly as a function of the system size. Moreover, we derive a lower bound for the effective rank in the form $\propto N_{\mathrm{AO}}^2/\log_2^7 N_{\mathrm{AO}}$, where $N_{\mathrm{AO}}$ is the number of atomic orbitals in the molecule, under mild conditions imposed on the decomposition threshold $\epsilon$. As a result, while a subquadratic growth of the CPD rank is not excluded, a linear relationship between the rank and $N_{\mathrm{AO}}$ cannot hold universally. The implications of these findings for the use of the canonical polyadic format to represent electron repulsion integrals in quantum chemistry are analyzed.

We present a flexible, automated, and basis-set insensitive domain-based charge-transfer (CT) decomposition framework that can be combined with any CI-type excited-state wavefunction. Our approach is not based on excited-state densities and allows the excited-state character to be dissected into local and domain-based CT excitations and measures the individual contributions to each excited state. To guarantee a broad applicability, we introduce two domain-accumulation strategies to translate hole-particle substitutions to domain-domain excitations: a strict domain partitioning and a weighted approach suitable for small molecules and a large number of domains. The performance of both schemes is assessed for inter- and intramolecular CT excitations and various basis sets using EOM-CCSD and its simplified counterpart EOM-pCCD+S. Most importantly, the CT character is, to a large extent, basis-set independent, and both domain-accumulation schemes give consistent results. Overall, our framework provides a robust CT analysis and a domain resolution of the excitation character for a variety of computational setups and excited-state models.

Fluctuations of nonequilibrium localized waves are shaped not only by direct stochastic forcing but also by deterministic transfer among coupled collective degrees of freedom. We develop a pathway-resolved stochastic collective-coordinate theory that makes this transfer explicit for stationary driven-dissipative solitons of the generalized Lugiato--Lefever equation with Raman response. The reduction yields a refined stationary phase-locking relation, providing a fixed point for the subsequent stochastic theory. Projecting field-level fluctuations onto four soliton coordinates: amplitude, frequency shift, temporal position, and global phase, yields a reduced Langevin model and, after linearization about a stable stationary state, an analytic power-spectral-density matrix. This framework separates direct stochastic injection from deterministic inter-coordinate conversion and thereby resolves how each observable spectrum is assembled from distinct internal fluctuation pathways. It shows that timing jitter is governed primarily by Gordon--Haus-type frequency-to-timing conversion, while phase noise is often dominated by amplitude-to-phase transfer rather than by direct phase diffusion. Raman response opens additional cascaded pathways, and the low-detuning hump in the intensity and phase spectra is traced to the driven response of an underdamped amplitude--phase subsystem preceding the breathing instability. Comparisons with stochastic simulations of both the reduced model and the full generalized Lugiato--Lefever equation show good agreement throughout most of the stable stationary single-soliton regime, with systematic deviations mainly near the Hopf boundary. The theory provides a general route for connecting internal fluctuation-transfer mechanisms of dissipative solitons to measurable noise observables.

Full-dimensional reactive potential energy surfaces (PESs) for the OCS$^+$ cation are constructed to describe S$^+$ loss in the electronic ground state and seven low-lying electronically excited states. High-level \textit{ab initio} reference energies were computed at the MRCI+Q/aug-cc-pVTZ level and were used to generate PESs employing reproducing kernel Hilbert space representations (RKHS). The PESs accurately reproduce the measured dissociation limits to CO(X$^1\Sigma^+$)+S$^+$ in different electronic states. The topology of the PESs reveals multiple linear and T-shaped minima, pronounced angular anisotropy, and state-crossing manifolds. Exploratory quasi-classical trajectory simulations on selected PESs confirm numerical stability and energy conservation, illustrating the suitability of the surfaces for dynamical applications. The present work represents the most comprehensive characterization to date of the lowest PESs of OCS$^+$ and provides a reliable foundation for future studies of the photodissociation of OCS$^+$ and the chem-ionization dynamics of OCS.

Electrodynamic balances (EDBs) have been widely used to investigate reactions between levitated particles and background gases. In this paper, we report the development of an EDB that exposes trapped particles to alkali-metal vapor. The apparatus was developed principally to investigate the interactions between such vapor and the paraffin used as a spin anti-relaxation coating for alkali-metal vapor cells by atomic physicists. The trap electrodes of the EDB were installed in a vacuum glass cell. Particles were loaded via laser launching, without venting or contaminating the cell. Alkali-metal vapor was released from a dedicated dispenser. We found changes in the charge-to-mass ratios of trapped particles irradiated with ultraviolet light after exposure to alkali-metal vapor. These results demonstrate the utility of the apparatus.

Synchronization is a pervasive collective phenomenon underlying the firing of neurons, the beating of the heart, and the coherent emission of lasers. Across these systems, dissipation plays an organizing role, suppressing microscopic differences and steering coupled units toward a common macroscopic order. Here we harness engineered non-Hermitian dissipation to synchronize light directly in the optical domain. Implementing non Hermitian transition matrices on a silicon photonic processor, we drive arbitrary multimode optical fields toward a unique collective state with equal modal intensities and a globally locked phase, a process we call dissipation-induced phase synchronization. The synchronization rate and total optical power throughput are independently programmable, enabling control over the dissipative dynamics without compromising reconfigurability. These results recast dissipation as a functional resource and open a route to reconfigurable on-chip synchronization for classical and quantum photonic technologies.

In the shallow water approximation, the cross-sectional profiles of laboratory rivers satisfy a differential equation here shown to be formally the Friedmann equation of cosmology ruling the evolution of Anti-de Sitter universe. The ensuing cosmic analogy provides a counterintuitive Lagrangian for the transverse river profile. Extremizing the corresponding action corresponds to extremizing the friction force on the river bottom and the energy dissipation rate. Analysis of the second variation establishes that this extremum is a maximum.

Dielectric metasurfaces have introduced a new paradigm for substance detection by exploiting their resonant properties to enhance light-matter interaction. This enhancement can be used for sensing either through refractive index changes or through absorption-based mechanisms. Most works focus on high-quality factor resonators, aiming to increase field confinement in the vicinity of the resonant structure to improve sensitivity. In this work, we explore an alternative approach based on low-quality factor, fully dielectric metasurfaces, with engineered modes to enhance near-field concentration. We investigate different topologies that, despite their low-quality factors, achieve sensitivity and detection performance beyond what is typically reported for low-Q structures in the literature. This improvement is enabled by near-field engineering of the evanescent modes, allowing us to control the spatial distribution of the electromagnetic field and maximize its overlap with the analyte. Our results show that careful mode engineering provides a powerful strategy to boost sensing performance without relying on ultra-high-Q resonances.

Modern implementations of path integral molecular dynamics (PIMD) simulations of distinguishable particles frequently make use of high order factorization schemes for the Boltzmann operator to expedite convergence of equilibrium averages. Among these methods is the generalized Suzuki-Chin factorization (GSF), which is accurate up to fourth order in the imaginary-time step. In this work, we show that the GSF decomposition of the Boltzmann operator is applicable to bosonic PIMD, and results in an improved convergence of estimators. In particular, we show that the recently developed quadratic scaling bosonic PIMD need not change when using the GSF. The GSF scheme is implemented as a re-weighting factor for observables, without affecting the sampling generated by the standard, second-order, primitive factorization. We study the effect of this factorization for bosons in a harmonic trap and a sinusoidal potential. We also assess the effectiveness of GSF in calculating fermionic expectation values for harmonically-trapped atoms. In all these cases, we find that the GSF speeds up convergence with the Trotter number by a factor of $\sim 2-4$ across a wide temperature range, at only a modest computational cost.

The rapid growth of artificial intelligence, coupled with the slowing of Moore's law, is straining computing infrastructure, as CMOS electronics face inherent limits in bandwidth, energy efficiency, and parallelism. Integrated photonic computing encodes and processes information using the phase, amplitude, spatial modes, wavelength channels, and polarisation of guided optical fields, offering a scalable and energy-efficient route beyond charge-based signalling. Here, we review on-chip photonic computing, emphasising the progression from low-dimensional to high-dimensional architectures. At the foundational level, low-dimensional approaches manipulate the phase and amplitude of guided light through Mach-Zehnder interferometers, diffractive structures, microring resonators, and absorptive elements, forming a programmable basis for optical matrix-vector multiplication. Crucially, high-dimensional architectures exploit spatial modes and wavelength channels to carry multiple independent data streams through a single waveguide, achieving higher throughput with moderate hardware overhead. Practical deployment, however, demands more than device innovation. We examine how system-level techniques, from time-wavelength interleaving to hardware-aware training, address energy efficiency, precision, and algorithm-hardware co-design. Five challenges nevertheless remain: electro-optic conversion efficiency, computing parallelism, spatial integration, reconfigurability, and robustness. We highlight emerging topological structures, such as optical skyrmions, as a promising route to fault-tolerant, topologically protected encoding that exploits the largely untapped polarisation degree of freedom. We argue that, by embracing the higher dimensionality of light, photonic computing can offer not merely an incremental improvement but a new paradigm for high-performance, energy-efficient information processing.

Fully compressible numerical simulations of two-dimensional laminar lean hydrogen-air premixed flames have been performed, with the flame front subjected to acoustic forcing through the specification of a monopole-type sound source at the inflow. Simulations have been performed for acoustic frequencies ranging from 35~kHz to 500~kHz at two equivalence ratios, $\phi = 0.4$ and $\phi = 0.7$. During the flame-acoustic interaction, the flame evolves from an initially weakly stretched state to exponential perturbation growth, wrinkle interaction, and the formation of non-linear cellular structures, with distinct linear and non-linear stages identified from Fourier mode analysis. The instability dynamics depend strongly on both forcing frequency and equivalence ratio. In the case of $\phi=0.4$, the flame behaviour is strongly influenced by thermodiffusive instability, with a characteristic sequence of uniform cells, cell splitting, and cell merging. For $\phi=0.7$, weaker thermodiffusive effects result in a response more strongly governed by hydrodynamic instability and large-scale wrinkle growth. At low forcing frequencies, flame corrugations remain relatively uniform, whereas at high frequencies the flame front becomes increasingly modulated and develops envelope-like structures, which can be interpreted as the interaction between an intrinsic standing cellular mode and the imposed acoustic disturbance. In the linear growth regime, the density-weighted displacement speed, $S_d^*$, shows a linear correlation with total stretch rate, $K$, for all forcing frequencies. While in the non-linear growth regime, two distinct branches appear, corresponding to weakly stretched flame segments and strongly negatively curved segments associated with flame pinch-off.

Statistical properties of light produced in spontaneous Raman scattering on an ensemble of molecules indicate the quantum nature of this phenomenon. The scattered light is non-classical and has high non-classical intensity correlations between Stokes and anti-Stokes components. The temporal coherence of this light is well investigated, while many questions related to spatial coherence remain open. Recent experiments reveal two peculiar features of the spatial coherence of the Stokes and anti-Stokes light. First, the intensity correlations between Stokes and anti-Stokes light remain non-classical even for macroscopic samples containing many molecules. Second, these correlations decrease when signal propagates through a multi-mode optical fiber: the more propagating fiber modes at Stokes and anti-Stokes frequencies the less the correlations. Moreover, the second-order autocorrelation function of Stokes and anti-Stokes light also decreases with the number of propagating modes in multi-mode fiber. In this paper, we build a model of spontaneous Raman scattering correlations of light produced by an ensemble of molecules and propagating through weakly guiding optical fiber that quantitatively explains all these observations. We show that spacial orthogonality of the fiber modes makes the light propagating through these modes uncorrelated in the standard detection scheme. This leads to suppression of non-classical intensity correlations of the total field in the multi-mode fiber. We find the degree of non-classical correlations on fiber parameters. The obtained results pave the way for engineering of non-classical Stokes -- anti-Stokes correlations.

Solid mineral-based particles have been proposed as alternatives to sulfates for climate intervention by stratospheric aerosol injection, as a means for improving optical or chemical characteristics and thereby minimize risks and uncertainties. However, the heterogeneous reactivity of solid particles with stratospheric trace gases, and possible implications to the ozone layer, is currently not fully constrained, particularly at stratospheric concentrations. We present a systematic comparative study of the uptake of HNO3, HCl, and NO2 on calcite, alumina, crystalline silica (quartz), and amorphous silica, using complementary Knudsen cell and flow reactor techniques. We find that NO2 uptake is weak on all surfaces, with estimated removal timescales indicating negligible impact on stratospheric nitrogen chemistry. Conversely, HCl uptake is substantial, with a pronounced concentration dependence consistent with surface site limited Langmuir adsorption. Extracting adsorption isotherms, we find that HCl surface coverage at stratospheric concentrations differs by four orders of magnitude between the surfaces, with calcite adsorbing the most and amorphous silica the least, suggesting a dominant role of surface acid-base character. Using HCl surface coverage as a proxy for ClONO2 reactive uptake, we estimate that amorphous silica could deplete substantially less ozone than calcite or alumina under equivalent injection scenarios. We find a marked difference between crystalline and amorphous silica, underscoring the sensitivity of heterogeneous chemistry to surface microstructure and the importance of selecting particles with low-reactivity surfaces in addition to considering bulk characteristics. Our findings motivate the development of particles with surfaces tailored for minimizing SAI risks and uncertainties, including minimal reactivity with stratospheric gases and background sulfate aerosols.

Single-photon detection possibility is a fundamental requirement for quantum technologies, including communication, computing and sensing. To achieve scalability and practical deployment, increasing attention is being directed toward integration of detectors with photonic integrated circuits, which offer compactness and compatibility with mass production. Superconducting nanowire single-photon detectors have emerged as the leading solution, combining near-unity efficiency, high temporal performance and the ability to be embedded across a wide range of photonic material platforms. In this review we trace the development of integrated superconducting nanowire single-photon detectors from early demonstrations to recent advances, outlining the progress in device architectures, material engineering and integration strategies. We also discuss performance benchmarks, emerging alternative designs, the future opportunities and challenges for this rapidly evolving field.

Attosecond electron pulses enable real-time probing of ultrafast matter dynamics, yet conventional modulation schemes suffer from drastically shortened longitudinal focal lengths when targeting sub-attosecond durations. To address this bottleneck, we propose and demonstrate a compact scheme utilizing two counter-propagating lasers that reveals a previously unidentified stable modulation regime. Contrary to established models of ponderomotive forces and stochastic acceleration in dual-laser fields, we show that a specific parametric resonance condition permits the electron beam to be stably modulated into highly periodic attosecond trains with rapid energy gain. Using a sub-relativistic electron beam, simulations confirm the generation of ~1 as pulses with a Lorentz factor up to 15 and a relative energy spread below 0.02%, extending the focal length by three orders of magnitude compared with conventional approaches. This work identifies the critical transition from ordered modulation to stochastic acceleration, offering a viable route to overcoming the focal-length barrier in attosecond electron-pulse applications.

Reliable position and shape control in tokamak plasmas requires accurate real-time regulation of several strongly coupled shape parameters. The control vectors that disentangle these couplings, referred to as \textit{virtual circuits} (VCs), enable independent shape parameter control for a specific Grad--Shafranov (GS) equilibrium. Numerical calculation of VCs is not currently feasible in real time, therefore VCs are usually computed prior to each experiment, using a small number of reference GS equilibria sampled along the desired scenario trajectory, with each VC used to control the plasma within a preset time interval. While effective near the reference equilibrium, this approach can lead to degraded performance as the plasma departs from the reference equilibrium and/or from the desired trajectory, and it complicates the design of robust control strategies for rapidly evolving plasma configurations. In this paper, we construct neural-network-based emulators of plasma shape parameters from which VCs can be derived, to provide the MAST Upgrade (MAST-U) plasma control system with state-aware VCs in real-time. To do this, we develop an extensive library of over a million simulated GS equilibria, covering a substantial portion of the MAST-U operational space. These emulators provide differentiable functions whose gradients can be rapidly computed, enabling the derivation of accurate VCs for real-time shape control. We perform extensive verification of the emulated VCs by testing whether they disentangle the control problem. The neural-network-based approach delivers high accuracy and orthogonality across a diverse range of equilibria. This work establishes the physical validity of emulated VCs as a scalable and general alternative to schedules of precomputed VCs.

The use of mobile robotics in radioactive source seeking has become an important part of modern radiation-safety practices, supporting timely mitigation of contamination risks and helping protect public health. However, measuring radiation is often time-consuming, rendering traditional gradient-based source-seeking methods less effective due to lower sample efficiency. This paper proposes a sample-efficient Bayesian-Optimisation source-seeking strategy that utilises a heteroscedastic Gaussian process surrogate to balance exploration and exploitation. Excessive inter-sample travel is discouraged through a movement switching cost. The strategy is shown to generate sublinear regret in the source-seeking task, while simulations demonstrate its effectiveness in localising radioactive sources.

Stratospheric aerosol injection (SAI) is a solar radiation modification technique, proposed as an interim measure to offset warming while greenhouse gas (GHG) emissions are reduced. This paper discusses a possible SAI implementation route - an alternative to sulfate aerosols formed in situ - based on engineered solid particles having dedicated properties such as size, composition, surface chemistry, and traceable origin, supporting safety, controllability, and functionality needed for SAI systems. These engineered properties also open up options for any future multi-state coordination of SAI through two technical building blocks: (1) the SAI-induced radiative forcing (SRF) - the magnitude of the cooling effect attributable specifically to the SAI layer - as an operator-independent quantity, derivable from direct aerosol-layer measurements; and (2) particle traceability through identifying signatures embedded at production. Both could feed into a shared, publicly accessible monitoring database open to independent interrogation, addressing several governance challenges by anchoring compliance assessments in measurable parameters. Drawing on precedents from the Montreal Protocol, IAEA safeguards, and other regimes, we show that shared technical metrics have historically enabled multi-state cooperation, and we argue the same could apply to SAI. We describe a phased pathway in which the technical capabilities and coordination practices that would use them are developed and tested together, at scales orders of magnitude below operational deployment. To be clear - we regard SAI deployment as premature; the conditions under which it might be considered have not been met. The paper does not propose a governance framework; rather, it identifies technical infrastructure that could support a wide range of such frameworks.

Proton Exchange Membrane Fuel Cell (PEMFC) is widely recognized for its cleanliness and high efficiency, but is still facing challenges in cold environments. At low temperatures, the formation of ice and repeated freezing/thawing cycles may cause cell performance reduction and irreversible degradation. The cathode flow field of PEMFCs has a significant effect on the performance. In contrast to the conventional ``channel-ridge'' flow field, the metal foam has the advantages of excellent pre-distribution of gases and water drainage, which make it a promising candidate for the cold start. This paper examines the cold start of PEMFCs with metal foam flow field (MFFF) and serpentine flow field (SFF), and the influence of constant current mode, constant voltage mode, and ramping current mode is investigated experimentally through performance test and electrochemical characterization. The results show that lowering the voltage and increasing the current can enhance the cold-start performance of fuel cells. The MFFF fuel cell has superior cold start performance compared to the SFF fuel cell under the constant voltage mode of 0.3 V. Furthermore, the variable current mode is developed by considering the distinct properties of heat and water production during various phases, and the results indicate that increasing the current density at the unsaturated stage leads to an elevated rate of heat production and a reduced rate of water production, which can improve the cold start of PEMFCs.

Plant sap-feeding insects must dispose of excess fluid, yet at millimeter scales droplet release is constrained by capillary adhesion and contact-line pinning. How phloem-feeding insects solve this puzzle, particularly as the excretory apparatus changes in size and form from nymph to adult, has remained unclear. Combining micro-CT, high-speed imaging, measurements of honeydew properties, and reduced-order modeling, we show that the spotted lanternfly (Lycorma delicatula) uses distinct release mechanics across ontogeny. Nymphs release honeydew with an anal stylus that acts as a capillary rectifier, imposing a curvature asymmetry that biases the attached droplet toward detachment through a Laplace-pressure difference. Adults use a longer stylus associated with an elastic basal region, maintain stylus-droplet contact through a finite compression phase, and release droplets with greater translational and rotational momentum. In both stages, stylus rotation is ultrafast, with peak angular accelerations of order $10^7$ rad/s$^{-2}$ and release unfolding on millisecond timescales, yet droplet ejection speed remains below stylus tip speed. Weber-Bond scaling based on measured honeydew properties places both stages at $We_d<1$ and $Bo_d<1$ at the outlet, but distinguishes their post-release states: nymphal droplets remain surface-tension dominated, whereas adult droplets enter deformation- and spin-influenced regimes. Development therefore maintains waste clearance across ontogeny under the same outlet-scale capillary constraint by changing how stylus motion is coupled to the droplet at release, linking life-stage biomechanics to honeydew placement in this invasive phloem feeder and suggesting bioinspired strategies for droplet ejection, antifouling, and self-cleaning surfaces.

Physical Unclonable Functions (PUFs) are hardware security primitives whose inherent physical complexity can be exploited for secure authentication and cryptographic key generation. Silicon photonic devices, owing to their suitability for quantum and artificial intelligence applications alongside standard CMOS fabrication processes, constitute a highly promising substrate for integrated multifunctional PUFs. Despite the advanced security guarantees offered by quantum cryptographic protocols and the central role of silicon photonics in quantum technologies, quantum readout strategies based on single-photon states for photonic PUFs remain largely unexplored. In this work, we experimentally demonstrate a silicon nitride (SiN) programmable photonic Mach Zehnder interferometer mesh that implements a unitary transformation and operates as a PUF, whose secret physical signature arises from uncontrollable waveguide variations during fabrication. Using experimentally derived parameters from the SiN integrated mesh, we further introduce and numerically evaluate a quantum readout protocol that combines single-photon states with PUFs. Maximally mixed quantum states are employed to conceal the underlying unitary transformation from passive eavesdropping. Security against adversaries possessing devices fabricated under similar conditions is assessed, with authentication performance quantified through Monte Carlo analysis of the false acceptance and false rejection rates as a function of the number of detected events and corrected errors. The results indicate exceptional performance with equal error rates as low as 10 to the minus 14, highlighting the potential of quantum secure PUFs for high security authentication applications.

We present THEMol (Torsion, Hessian, Energy of Molecules), a massive open-source collection of quantum mechanical properties tailored for closed-shell organic molecules, with up to 50 heavy atoms. THEMol includes a Hessian subset with more than 3 million relaxed geometries with Hessian matrices, a TorsionScan subset with nearly 100 million constrained relaxed geometries with energies and forces, and relaxation-trajectory subsets (HessianRelax and TorsionScanRelax) that together comprise about 3 billion DFT calculations. The chemical space sampling is comprehensive, spanning twelve essential elements and diverse molecular architectures relevant to drug discovery, electrolytes, ionic liquids, and beyond. The dataset also features exhaustive conformational sampling through the TorsionScan and TorsionScanRelax subsets, including comprehensive in-ring and non-ring torsional scans. Furthermore, it contains an extensive library of Hessian matrices, computed at relaxed geometries, to capture critical second-derivative information of the potential energy landscape. Additionally, we supply electron density-derived atomic multipoles computed via the Minimal Basis Iterative Stockholder partition scheme. Organized into five distinct subsets (Hessian, TorsionScan, HessianRelax, TorsionScanRelax, and MBIS), the data encompasses optimized geometries, relaxation trajectories, and derived molecular properties. We anticipate that this massive and diverse dataset will significantly empower the development of highly accurate and transferable molecular potentials.

Chemistry's rules carry exceptions: the octet rule, Hess's Law, detailed balance, orbital symmetry selection rules, all with disclaimers memorised separately. Their cause: a question from a richer structural level posed in the vocabulary of a simpler one, i.e. level incompleteness. This monograph makes the levels explicit, constructing a canonical tower of nine categorical levels from stoichiometry through thermochemistry, equilibrium, kinetics, electron-pushing mechanisms, stereochemistry, potential energy surfaces, and electronic structure to all-particle quantum mechanics. Each level emerges from pairs of reactions distinct yet indistinguishable at the previous level; the minimal extension resolving each ambiguity is provably unique, certified by a non-trivial cokernel in an automorphism exact sequence, and recovers Feinberg's deficiency theorems as homological corollaries. A perpendicular dimension: every ML model for chemistry (yield predictors, neural kinetic networks, equivariant force fields, learned wavefunctions) is a morphism in the Para-enrichment of one tower level, with equivariance and thermodynamic consistency as universal properties. Three incompleteness results (Eyring, Wegscheider, topological output gaps) apply to the current literature. The framework descends to code: an operational functor from a Para-enriched product of the first four levels into the Kleisli category of the probabilistic sub-monad of Haskell IO, instantiated as a simulator of the Briggs-Rauscher oscillating reaction: the first Kleisli semantics of Gillespie's next-reaction method and first Para application outside ML. The passage to all-particle quantum mechanics, Born-Oppenheimer as the classical limit of a continuous field of C*-algebras, remains the deepest open construction; four candidate conjectures including Woolley-Primas have obstructions the framework makes specific.

Positronium-based imaging requires realistic modelling of positronium (Ps) decay in matter. We introduce a modular Ps decay model implemented in GATE 9.4 and GATE 10, enabling the definition of an arbitrary number of decay channels characterised by lifetime, branching fraction, annihilation multiplicity (2g/3g), and optional prompt photon emission. The model is validated through analytical and numerical benchmarks, including lifetime distributions, branching fraction consistency, photon kinematics, and prompt photon emission. Its practical applicability is demonstrated using simulations of mixed annihilation scenarios and the NEMA IEC phantom with a large field-of-view PET system. The proposed model accurately reproduces input lifetime distributions as weighted sums of exponential components and correctly samples decay channel fractions. Simulated two- and three-photon annihilation kinematics are consistent with theoretical expectations. Complex mixtures of decay channels, including varying 3g-to-2g ratios and multi-component ortho-positronium lifetimes, are correctly modelled, with observable signatures reflected in both temporal and energy distributions. Phantom simulations demonstrate the capability to generate realistic positronium-sensitive datasets. This work provides the first general-purpose, multi-channel positronium decay model integrated into GATE, enabling realistic simulations of positronium behaviour in complex media. The model supports the development and optimisation of positronium-based imaging techniques, including PLI and multi-photon PET, and applies to medical imaging, industrial tomography, and fundamental physics studies. Its public availability and compatibility with standard GATE workflows make it a valuable tool for the broader research community.

Scintillators convert ionizing radiation into visible photons, enabling applications from cosmic ray detection to medical imaging. Two independent strategies for improving scintillator performance via nanoscale patterning have recently been demonstrated: engineering material properties to enhance absorption of ionizing radiation and integrating nanophotonic structures to enhance the spontaneous emission rate ("nanophotonic scintillators"). Here, we propose a nanophotonic scintillator that simultaneously enhances both the initial energy conversion and the spontaneous emission rate, by periodically stacking a fast-emitting scintillator and a visible-light-transparent material with strong X-ray attenuation ("stopping layer") to form a one-dimensional (1D) photonic crystal (PhC) scintillator. Photoelectric absorption in the stopping layer increases the number of photoelectrons that deposit energy in neighboring scintillator layers and contribute to scintillation. At the same time, the spontaneous emission rate is enhanced by the nanophotonic structuring itself. We design a 1D PhC comprising an organic scintillator and indium tin oxide (ITO) as the stopping layer and numerically simulate the enhancement in scintillation yield and decay rate. The total detected light output is enhanced by up to a factor of 700 compared to a bulk organic scintillator of equal thickness. We further investigate a 1D PhC structure integrating inorganic and organic scintillators for time-of-flight positron emission tomography (TOF-PET): replacing the non-scintillating stopping layer with an inorganic scintillator further increases the light yield, and the coincidence time resolution (CTR) is enhanced up to 3.5 times compared to a bulk inorganic scintillator of equal thickness. Our work presents a unified approach to improve key scintillation parameters within a single nanophotonic structure.

Rydberg atoms provide a powerful platform for exploring strongly interacting quantum systems, both in free space and in structured electromagnetic environments, with growing applications in quantum technology. Accurately modeling their single-atom properties and mutual interactions is essential for interpreting experiments and designing new architectures. We present a unified theoretical framework for Rydberg atoms and their interactions based on multi-channel quantum defect theory (MQDT) and static electromagnetic Green's tensors. MQDT provides a precise description of Rydberg states of divalent atoms such as strontium and ytterbium, while the Green's tensor formalism provides a general and flexible approach for calculating interactions between two Rydberg atoms in arbitrary geometries, including modifications induced by nearby surfaces. We implement this framework in an updated version of the open-source PairInteraction software [Weber et al., J.~Phys.~B~50 (2017)]. The implementation leverages high-performance libraries and achieves speedups of one order of magnitude for pair-potential calculations compared to prior software. We demonstrate the capabilities of the framework through example applications to divalent atoms and show excellent agreement with experimental data for an exemplary Stark map of $^{174}$Yb. The modular software architecture enables the community to extend it further.

We investigate numerically the time evolution of velocity and magnetic field fluctuations in a coronal loop, focusing on the dynamics due to both phase mixing and turbulent cascade. The intensity, doppler velocity and non-thermal broadening are synthesized from numerical results in order to establish if the upcoming Multi-slit Solar Explorer (MUSE) mission could reveal the presence of those phenomena in the solar corona through its unprecedented high-resolution spectroscopic observations. The loop is represented by a cylindrical pressure-balanced magnetic structure with a transverse density and magnetic field inhomogeneity. The initial perturbation is a superposition of a torsional Alfvén wave and a transverse turbulent component with different tunable weights. In order to reconstruct plasma emission features we calculate moments of the Fe IX 171 Å spectral line. 2D maps obtained by integrating the emission along the assumed line of sight are calculated for the emission intensity $I_0$, the Doppler shift $I_1$ and the non-thermal broadening $I_2$, for several values of the model parameters. Finally, we simulate MUSE spectrograph by considering a resolution of $312$ km $\times$ $312$ km. We observe how intensity maps show the formation of longitudinal threads. The generation of small-scale fluctuations mainly takes place in the inhomogeneity region at the loop boundary, where the effects of phase mixing and non-thermal broadening are stronger. 1D power spectra of intensity and Doppler shift maps are calculated and compared with the corresponding spectra of density and line-of-sight velocity component. The agreement observed between the spectral indexes of the intensity power spectra at MUSE resolution and the one computed from the full 3D density field indicates that spectra of $I_0$ can be used to infer information on the spectrum of density inside a loop.

Actively tunable nanophotonic platforms that control light-matter interactions enable reconfigurable optical systems and programmable photonic integrated circuits. Hydrostatic pressure provides a noninvasive and material-agnostic mechanism for modulating the refractive index and resonance conditions without introducing free carriers or structural damage. Here, we demonstrate multiple pressure-dependent functionalities in silicon nitride nanostructures, including resonance tuning, refractive index modulation, and polarization state conversion. Applying a pressure of up to 5 GPa, we observe a Fabry-Pérot resonance shift of up to 30 nm and a relative refractive index decrease of up to 4%. Based on the results, we design and examine, to the best of our knowledge, the first extreme-pressure-tunable, polarization-converting metasurface, which tunes the ellipticity and orientation angle of the output light. These findings establish pressure-controllable silicon nitride as a viable platform for reconfigurable photonics and extreme-environment nanophotonic systems, including deep-ocean exploration, planetary interiors, and space applications.

We present an $\mathcal{O}(L^3)$ algorithm for evaluating contracted Clebsch--Gordan tensor products in $\mathrm{O}(3)$-equivariant machine learning potentials at fixed Canonical Polyadic (CP) rank. Mapping the angular integral to a structured Gauss--Legendre and Fourier tensor-product grid decouples the radial channel contractions from the angular transforms. The antisymmetric parity-odd Clebsch--Gordan channels, unreachable by the symmetric pointwise product on a scalar $S^2$ grid, are recovered through the surface-curl pairing $\hat r \cdot [\nabla_{S^2} A \times \nabla_{S^2} B]$, the spherical Poisson bracket, which supplies the $L=1$ angular momentum on the grid while preserving rotational equivariance. The construction extends to parity-aware equivariant message passing in atomic-cluster-expansion-style architectures and is verified by direct numerical quadrature. The full uncontracted Clebsch--Gordan tensor product remains subject to the $\mathcal{O}(L^4)$ output-size lower bound. A benchmark shows wall-clock scaling empirically as $L^2$ across the practical $l_{\max}$ range. For the on-site contraction this is pre-asymptotic, giving way to $L^3$ at large $l_{\max}$. For message passing it is structural and the runtime is memory-bandwidth bound on $L^2$-sized grid tensors.

Classifying interactions is key in the physical sciences, and bonding mechanisms in matter-antimatter systems remain particularly enigmatic. Here we focus on a paradigmatic example of positronium hydride (PsH) dimer composed of two protons, two positrons, and four electrons, whose bonding nature has been previously described as either ionic, covalent, or van der Waals-like. Accurate quantum Monte Carlo calculations show that the two positrons occupy a delocalized molecular orbital that envelopes the two hydrogen anions and responds as a collective dipole to an applied electric field. This positronic bonding stems from quantum correlations that resemble a single covalent bond formed between negatively charged pseudo-nuclei, but with a bond strength commensurate with the traditional van der Waals interaction. Our findings suggest that the ability to form delocalized proto-bonds is a more general property of quantum systems, and could be present in a broader class of particles, antiparticles, and quasi-particles interacting with matter.

The possibility of laser cooling and the presence of closely spaced rovibrational doublets make polyatomic molecules an attractive platform for the $\mathcal{P}$, $\mathcal{T}$-violation searches. We study the spectrum of the lowest rovibrational state of the AcOCH$_3+$ symmetric top molecule. The electronic structure full-electron computation was performed within a relativistic coupled cluster method with double and perturbative triple excitations. The rovibrational wavefunctions are obtained using a coupled channel technique, taking into account all rovibrational effects and anharmonicities of the potential. As a result, the vibrational frequencies, as well as the values of the electric dipole moments for the rovibrational states, were computed.

Efficient and selective excitation of lattice vibrations is central to controlling energy flow at the nanoscale, yet remains challenging under conventional optical excitation. Here, we introduce a mid-infrared-assisted phonon amplification approach, termed MIRAPA, that enables efficient energy injection directly into vibrational bonds. Using surface-enhanced resonant Raman scattering in few-layer $\mathrm{MoS_2}$, we exploit strong exciton--phonon coupling to monitor phonon populations. When mid-infrared (MIR) light is introduced, it couples directly to out-of-plane lattice vibrations, leading to room-temperature phonon amplification exceeding $80\%$. Crucially, MIRAPA bypasses electronic excitation pathways, allowing the MIR power density to be nearly $300\times$ lower than that required for visible excitation to achieve comparable enhancement. The resulting phonon modulation is robust, persisting over more than $2800$ on/off cycles and exceeding $15$ hours of continuous-wave laser illumination without degradation. Quantitative analysis yields an effective noise-equivalent power of approximately $0.3\,\mathrm{nW}/\sqrt{\mathrm{Hz}}$ for MIR detection, highlighting the sensitivity of the approach. By combining vibrational selectivity, low-power operation, and long-term stability, MIRAPA provides a robust platform for probing and amplifying phonons in two-dimensional semiconductors. These results open new opportunities for nanoscale vibrational sensing, mid-infrared detection, and phonon-based coherent devices, including routes toward phonon lasing.

A silicon microring resonator with an anisotropic beryllium sulfide (BeS) cladding is proposed for polarization-selective detection of exhaled-breath volatile organic compound biomarkers. The anisotropic dielectric response of BeS enables the transverse-electric (TE) and transverse-magnetic (TM) modes to probe orthogonal components of the cladding permittivity tensor, generating two independent optical observables from a single device. Five clinically relevant biomarkers are investigated: acetone, isoprene, 4-hydroxyhexenal, 2-propenal, and benzene. First-principles optical constants are incorporated into three-dimensional finite-difference time-domain simulations to evaluate the sensing response. The TE mode exhibits a uniform resonance shift of 0.263 nm across all analytes and serves as a concentration reference channel, while the TM mode produces analyte-specific shifts ranging from 0.200 to 0.426 nm. A unique TM amplitude inversion is observed for benzene, enabling additional discrimination. The resulting dual-polarization response forms a two-dimensional optical fingerprint that distinguishes all five biomarkers without requiring a sensor array or multiple functionalized resonators. The device achieves quality factors of 4520 and 3151 for the TE and TM modes, respectively, with sensitivities up to 6.5 nm/RIU, figures of merit up to 14.9 RIU^-1, and detection limits as low as 1.5 mRIU. Cross-sensitivity analysis further shows that CO2 and H2O produce negative TM resonance shifts, separating interferents from target biomarkers in the fingerprint plane. The proposed platform demonstrates a compact route toward array-free photonic breath analysis using intrinsic cladding anisotropy.

We formulate axion magnetohydrodynamics beyond the ideal limit, retaining axion inertia and the essential physics of non-ideal plasmas from first principles. In this framework, regions where magnetic flux freezing breaks down acquire a new physical role: whenever $\mathbf{E} \cdot\ \mathbf{B} \neq 0$, magnetic dissipation acts as a localized source of axion radiation. We show that magnetic reconnection naturally excites mixed Alfvén-axion modes, enabling coherent energy exchange between magnetic fields and axions in magnetically dominated environments. In neutron stars and magnetars, this mechanism leads generically to transient axion bursts powered by reconnection--driven Alfvénic dissipation. We connect this production process to observational prospects and derive a characteristic sensitivity to the axion--photon coupling, complementary to searches based on static magnetic fields.

We show how dynamical equations for liquid films and drops on uneven surfaces, including contact line dynamics and evaporation/condensation effects, may be formulated as a variational dynamics, generated via Onsager's variational principle. The theory applies in the isothermal overdamped-dynamics limit. We apply this general approach to obtain several well-known results on contact line dynamics and to study drops pinning and sliding on inclined corrugated surfaces. This approach constructs the dynamical equations starting from the free energy of the system and therefore has the advantage that it naturally incorporates the correct equilibrium properties.

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

The steady incompressible Navier--Stokes equations pose significant computational challenges due to their nonlinear convective terms and pressure--velocity coupling. Physics-informed neural networks (PINNs) provide a mesh-free framework for approximating such systems, but classical PINNs can experience optimization difficulties in nonlinear flow regimes. In this work, we propose a quantum physics-informed neural network (QPINN) framework with a quantum neural network (QNN)-based trainable embedding for the lid-driven cavity problem. The proposed approach uses a QNN to learn data-adaptive quantum feature maps that encode spatial coordinates before they are processed by a variational quantum circuit within a physics-informed loss formulation. Numerical experiments show that the proposed QNN-TE-QPINN exhibits stable training behavior and competitive solution accuracy compared with classical PINNs and hybrid quantum models using classical embeddings, while requiring significantly fewer trainable parameters. Rather than claiming computational speedup, these results highlight the potential of trainable quantum embeddings for parameter-efficient physics-informed learning. The findings suggest that embedding design plays an important role in quantum-assisted PDE solvers and support further investigation of QNN-based trainable embeddings for nonlinear fluid dynamics benchmarks.

The numerical simulation of interaction between free flow and porous media, governed by coupled Stokes/Navier--Stokes--Darcy flows, is critical for understanding fluid filtration and physiological transport, yet it is hindered by the high computational cost of resolving interface heterogeneities and the instability of long-term predictions. While deep learning offers surrogate modeling potential, existing frameworks often suffer from exponential error accumulation and poor convergence in multi-physics regimes. To address these limitations, we propose ViT-K, a novel few-shot learning model designed to learn the spatiotemporal evolution of coupled flows from sparse datasets. The ViT-K framework effectively reconstructs the global flow physics on a low-dimensional manifold by combining Vision Transformers (ViT) to capture heterogeneous interfacial features with the Koopman operator to linearize temporal dynamics. By lifting nonlinear dynamics into a globally linear observable space, the ViT-K model provides stability by design, ensuring that prediction errors grow linearly rather than exponentially over time. This theoretical property enables reliable long-term extrapolation even in small-sample regimes. Numerical experiments on benchmark coupled systems demonstrate that ViT-K not only captures complex interface physics with high fidelity but also exhibits exceptional robustness against measurement noise by acting as an implicit spectral filter. The proposed method significantly outperforms traditional solvers in inference speed while maintaining physical consistency, offering a robust paradigm for real-time multiphysics forecasting.

The striking variety of macroscopic morphologies displayed by bacterial colonies depends on microscopic environmental and behavioural details in a manner that is currently not well understood. A surprising example is sibling inhibition, whereby isogenic bacterial colonies spreading in soft agar hydrogels tend to avoid each other and form sharp demarcation lines when growing nearby. Here we investigate this effect with the common pathogen \textit{Pseudomonas aeruginosa}, by combining quantitative density measurements with a minimal biophysical model. Our results show that the phenomenon does not depend on gel compression, lethal inhibition or quorum sensing-dependent cell communication. Instead, colony separation is driven by localised nutrient depletion through a dynamic feedback between growth and motility. The model, which is calibrated using experimental data, captures key observations including the dependence of inhibition strength on the initial nutrient concentration. This work establishes nutrient availability and non-lethal motility inhibition as central factors underlying sibling inhibition, providing a generalisable framework for microbial spatial dynamics with implications for understanding bacterial interactions in tissues, soils and engineered microbiomes.

The extended KdV equation is a nonlinear dispersive wave model that is asymptotically or variationally derived from the full dispersive Euler shallow water waves equations when gravity-capillary and higher order nonlinear effects are taken into account, under weakly nonlinear and long-wave approximations. This reduction introduces four additional terms beyond the classical KdV equation: a nonlinear term (quadratic nonlinearity), two nonlinear-dispersive terms, and a fully dispersive term (fifth order dispersion). In this paper, we employ a variational approach based on averaged Lagrangians to analyze the accuracy of single solitary wave solutions governed by a particular extended KdV equation where energy conservation is a key feature. Compared with solitary wave solutions previously obtained through higher order asymptotics and algebraic methods, the present variational solutions are notably simpler and more readily applicable to practical problems. The solitary wave solutions obtained through this method are then systematically compared with direct numerical simulations, and the corresponding results are critically discussed. We further demonstrate the applicability of these single solitary waves to problems in the field of non-convex dispersive hydrodynamics. These problems include shallow water classical undular bores, commonly known as dispersive shock waves, and non-classical (resonant) dispersive shocks which are additionally analyzed using the concept of Whitham shocks. Theoretical predictions show excellent agreement with numerical simulations.

We investigate how localized inhomogeneity affects the geometry and stability of migratory bird formations. We use a lifting-line model with a horseshoe-vortex representation to describe the longitudinal dynamics of aerodynamic interactions. As a reference case, we first analyze homogeneous formations and show that their steady states exhibit a U-shaped geometry with hierarchical streamwise spacing, in which adjacent birds become progressively closer toward the leader. We then introduce localized inhomogeneity by modifying the wingspan of a single bird, with its physical properties determined by scaling relations. We determine the range of wingspan variation that preserves a stable formation. The stability range depends strongly on the position of the modified bird, being narrower near the outer wing and broader near the leader. These findings provide a minimal dynamical framework for understanding how local aerodynamic interactions and localized individual differences affect collective flight structures.

We investigate the suppression of matter-wave Talbot interference under environmentally induced decoherence. The system is modeled as an atomic beam diffracted by a periodic grating, whose transverse dynamics is described within the paraxial approximation. Environmental coupling is introduced through an effective open-system model that exponentially damps spatial coherences between diffracted components, allowing a continuous interpolation between the coherent Talbot regime and the incoherent far-field diffraction limit. Besides the usual intensity and transverse-momentum distributions, we analyze the local probability flow associated with the diffracted matter wave. The corresponding Bohmian, or hydrodynamic, representation is used here as a diagnostic tool fully equivalent to the standard quantum description, with no additional assumptions beyond the probability current of the paraxial wave field. In the present Talbot geometry, this analysis shows how decoherence progressively suppresses the carpet structure and smooths the transverse-momentum distribution, while the flow may remain organized into channels determined by the grating periodicity. The results illustrate, in a periodic matter-wave Talbot geometry, that the loss of visible interference and the loss of dynamical pathway separation need not occur simultaneously. In particular, flux-channel structures can persist in parameter regimes where multi-slit interference features have already been strongly reduced. This distinction provides a local characterization of decoherence in matter-wave Talbot interferometry and complements previous trajectory-based analyses of coherence loss in simpler interference and confined geometries.

What does a book look like to a quantum computer? This paper takes eight classical works of the Renaissance and its late-antique inheritance -- from Augustine to Galileo -- and runs each through a neutral-atom quantum processor. The bridge is graphs: each textual unit becomes an atom, and graph edges are physical blockade constraints for engineered exact unit-disk designs, or a 2D approximation to the semantic graph for natural texts. Three contributions follow. First, we introduce rigidity rho, a metric for how unique a book's structural backbone is -- distinguishing Marguerite de Navarre's Heptameron (rigid, twelve-nouvelle hard core) from Boethius (fully fungible, every chapter substitutable). Second, we invert the pipeline: rather than extracting a graph from existing prose, we pick a target graph the hardware encodes natively, and write a book whose structure matches it. The twenty-nine texts written this way, collected under the name QOuLiPo, extend the OuLiPo tradition to graph-topological constraints and, together with the eight natural texts, form a benchmark distribution against which neutral-atom hardware can be tracked as it scales. Third, we run both natural and engineered texts on Pasqal's FRESNEL processor up to one hundred atoms; engineered texts reach high approximation ratios, the cleanest instances returning the exact backbone. A cloud-accessible quantum machine plus an agentic coding environment now lets a single investigator run this pipeline end-to-end. What is reported is an application layer, not a speedup -- humanistic instances ready to load onto neutral-atom processors as they scale, already complementing classical text analysis. The Digital Humanities community has a stake in building familiarity with this hardware now: the engineered-corpus design choices made today fix the benchmark distribution future hardware will be measured against.

The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self-harm, extremist acts, financial losses, or costly medical and military mistakes -- and no one can yet predict when. Shifts persist in even the newest AI models despite remarkable progress in AI modeling, post-training alignment and safeguards. Here we show that a vector generalization of fusion-fission group dynamics observed in living and active-matter systems drives -- and can forecast -- future shifts in the AI's behavior. The shift condition, which is also derivable mathematically, results from group-level competition between the conversation-so-far (C) and the desirable (B) and undesirable (D) basin dynamics which can be estimated in advance for a given application. It is neither model-specific nor driven by stochastic sampling. We validate it across six independent tests, including: 90 percent correct across seven AI models spanning two orders of magnitude in parameter count (124M-12B); production-scale persistence across ten frontier chatbots; and a priori time-stamped prediction eleven months before the Stanford 'Delusional Spirals' corpus appeared, and independently confirmed by that corpus of 207,443 human-AI exchanges. Because it sits architecturally below the current safety stack, the same formula provides a real-time warning signal that current alignment does not supply, portable across current and future ChatGPT-like AI architectures and instantiable in application domains where competing response classes can be defined.

Atom interferometers represent a promising approach for gravitational wave detection in the decihertz frequency band, complementary to existing light-based detectors. The South Pole offers unique advantages for such experiments: exceptionally low seismic noise, established infrastructure for large scientific projects, and a location that strengthens gravitational wave source localization through global triangulation. Here we discuss the scientific case and practical considerations for deploying a long-baseline atom interferometer at the South Pole, which has the potential to expand the global network of gravitational wave detectors while enabling precision tests of fundamental physics.

Frequency modes of light are one of the most promising platforms that provide access to high-dimensional quantum states amongst different photonic degrees of freedom capable of high-dimensionality, enabling robust, error-tolerant, and scalable quantum optical information systems. We demonstrate engineering of precisely controlled two-photon high-dimensional states entangled in frequency through time-domain Fourier optical synthesis. We generate and convert a continuous broadband frequency-entangled state into a large range of discrete frequency bins suitable for ITU standards, with spacings ranging from 12.5 GHz to 750 GHz, and observe spectral anticorrelations over 38 frequency bins, including intra-bin pure states at a 100 GHz bin spacing. We characterize the full quantum state dimensionality via Schmidt decomposition and observe lower bounds on the frequency-binned Hilbert-space dimensionalities of at least 289, formed by two entangled qudits with dimension 17. Furthermore, we demonstrate quantum nonlocality via frequency correlations in a transmission experiment over a campus-scale two-node fiber network. This work represents a crucial step towards building a versatile and relatively simple way of generating precisely controlled high-dimensional spectral qudits, with the potential of harnessing in wavelength-multiplexed quantum networks, high-dimensional information processing, and communication of quantum states specifically, and fiber-optic quantum remote sensing.

Extreme precipitation causes severe societal and economic damage, and weather control has long been discussed as a potential mitigation strategy. However, to the best of our knowledge, perturbation-based interventions for weather control using data-driven weather forecasting models have not yet been explored. While adversarial attacks also generate perturbations that alter forecasts, they aim to exploit model artifacts and do not account for physical plausibility. In this paper, we propose a gradient-based guidance framework for precipitation-reduction interventions through diffusion sampling in diffusion-based weather forecasting models. Instead of directly perturbing atmospheric states, our method steers the diffusion sampling trajectory, enabling precipitation reduction while maintaining consistency with the atmospheric distribution. To assess physical plausibility, we evaluate from three perspectives: (i) vertical and variable-wise perturbation profiles, (ii) latent-space trajectory deviation, and (iii) cross-model transferability. Experiments on extreme precipitation events from WeatherBench2 demonstrate that our method achieves effective precipitation reduction while yielding more physically plausible interventions than adversarial perturbations.

Recent theoretical and experimental advances in quantum ferroelectrics suggest that ferroelectricity can also emerge in non-polar space group, highlighting the limitations of conventional polar space group criteria in identifying ferroelectric materials. Here, we introduce a unified definition based on switchable polarization differences between energetically equivalent states, which naturally encompasses conventional and quantum ferroelectrics. Guided by this principle, we implement a high-throughput screening strategy that systematically identifies both conventional and quantum ferroelectrics among experimentally synthesized materials. In particular, we identify a new type of quantum ferroelectric in which the quantized polarization arises from arbitrary ionic displacements, in contrast to previous quantum ferroelectrics (including both fractional and integer quantum ferroelectrics) where quantized polarization results from fractional or integer ionic displacements. Notably, we find that materials such as Ba3I6 and Cs2PdC2 exhibit low switching barriers and robust insulating behavior, highlighting their experimental viability. Our results reconcile conventional and quantum ferroelectrics, expand the accessible materials landscape, and provide a practical roadmap for discovering next-generation ferroelectrics with advanced switchable functionalities.

First-principles density functional theory (DFT) is employed to evaluate the structural, electronic, optical, thermoelectric, and electrocatalytic properties of monolayer CrSi2N4. Its symmetric N-Si-N-Cr-N-Si-N septuple-layer structure exhibits dynamic, thermal (300 K), and mechanical stability, supported by a -8.76 eV/atom cohesive energy. PBE and HSE06 functionals reveal an indirect bandgap of 0.58 eV and 2.16 eV, respectively, driven by localized Cr-3d and N-2p states. The monolayer features 15.57 static dielectric constant and maximum absorption coefficients of 0.9 X 10^6 cm-1 (visible) and 1.4 X 10^6 cm-1 (deep-UV). Semiclassical Boltzmann calculations predict an outstanding room-temperature n-type thermoelectric power factor of 3.5 x mW/mK2. For hydrogen evolution (HER), the basal plane yields a baseline hydrogen adsorption free energy ({\Delta}GH) of 1.05 eV at the N-site. Applying +5% expansive biaxial strain improves HER kinetics, reducing {\Delta}GH to 0.46 eV. Thus, CrSi2N4 is a resilient, tuneable candidate for waste-heat recovery, photodetectors, and sustainable electrocatalysis.

Nitrogen-bearing polycyclic aromatic hydrocarbons (N-PAHs) are key precursors to complex organic molecules in both the interstellar medium and the nitrogen-rich planetary atmospheres. Despite the recent detections of nitrogen-functionalized astromolecules, their formation pathways remain an open question. The discrepancies between their predicted and observed abundances point to unknown mechanism that govern their evolution in the astrophysical environments. Employing an ion trap technique and electronic structure calculations, we unravel multiple barrier-less reactions between gas-phase pyrimidine cations (C$_4$H$_4$N$_2^+$) and acetylene (C$_2$H$_2$) which form an hitherto unreported endocyclic- N-PAHs (C$_8$H$_7$N$_2^+$). The present measurements on reactions involving a double-nitrogen subsituted aromatic heterocycle have implications to the astrochemistry of both the Titan's atmosphere and interstellar medium.

High-pressure research is a productive route to new structures and emergent properties. However, crucial high-pressure structural information remains highly fragmented across individual publications and heterogeneous computational repositories. This fragmentation creates a major bottleneck for data-driven materials design. To bridge this gap, we introduce the High-Pressure Crystal Structure Database (HPCSD), a traceable, pressure-resolved repository that integrates experimental and theoretical high-pressure structures. HPCSD is constructed from two complementary data streams: elemental high-pressure phases and a searchable configuration space of stable and metastable phases generated via CALYPSO crystal structure prediction. To ensure rigorous comparability, all retained structures underwent re-optimization under a unified density functional theory (DFT) framework , with continuous enthalpy curves systematically generated specifically for the elemental phases across their stability fields. The initial release encompasses 77,346 consistently evaluated structural entries spanning 89 elements. An analysis reveals that pressure-induced polymorphism is ubiquitous and exhibits pronounced family-dependent trends. Structural diversity is strongly influenced by an element's electronic adaptability , with the greatest structural complexity emerging at intermediate rather than highest pressures. By providing standardized, reusable, and rigorously evaluated high-pressure structural data, HPCSD establishes a robust infrastructure to accelerate experimental phase identification, facilitate cross-study thermodynamic comparisons, and support the development of machine-learning interatomic potentials and generative models for high-pressure systems.

This study provides an analytical foundation for adversarial synchronization control in Kuramoto oscillator networks, where small gradient-based perturbations applied repeatedly to oscillator phases can dramatically enhance or suppress collective synchronization. Using the Ott--Antonsen reduction, we derive an exact closed-form expression for the effect of a single adversarial perturbation (kick) on the order parameter. A key finding is that each kick produces a finite, coupling-independent increment in the order parameter even when synchronization is arbitrarily weak, which combined with slow relaxation near the critical coupling and mean-field feedback explains the disproportionate amplification previously observed in numerical simulations. Fixed-point analysis further reveals a fundamental asymmetry between enhancement and suppression, with the latter governed by noise-induced escape in finite systems. Extending the framework to networks via the annealed network approximation, we show that the theory captures the synchronization behavior of representative model networks and identify a decoupling between kick sensitivity and mean-field dominance in scale-free networks. These results offer a tractable theoretical basis for understanding and designing kick-based synchronization control in oscillator networks.

In living cells, proteins involved in specialized biochemical functions are often spatially organized within biomolecular condensates. Increasing evidence suggests that some of these condensates, including DNA repair condensates, emerge through liquid-liquid phase separation (LLPS). In the nucleus, however, condensates form within a highly heterogeneous environment composed of chromatin fibers, RNA, and additional protein scaffolds such as PAR chains, all of which may interact with phase-separating proteins. Moreover, condensate formation is frequently associated with specific chromatin conformations; for instance, loop extrusion has been proposed as a mechanism promoting DNA repair condensates. Here, we investigate how the surrounding fibrous environment controls the morphology and spatial organization of phase-separated condensates. Using Brownian dynamics simulations of minimal models combining Lennard-Jones particles with fixed fibrous substrates, we examine the respective roles of local fiber geometry and large-scale network organization, reflecting the multiscale architecture of chromatin. We show that protein-fiber interactions strongly influence droplet positioning relative to the substrate, in a manner analogous to wetting transitions in soft condensed matter systems. Both local geometric constraints and global network organization markedly affect droplet size, morphology, and multiplicity. In addition, large-scale asymmetries in fiber organization can induce robust spatial localization of the dense phase. Our results thus highlight how multiscale structural heterogeneity of the nuclear environment can regulate the emergence and organization of biomolecular condensates.

Developing machine learning interatomic potentials (MLIPs) for complex materials systems remains challenging because it requires expertise in atomistic simulations, machine learning, and workflow design, as well as iterative active learning procedures. Existing automated pipelines typically assume a fixed sequence of stages or depend on domain experts, which limits their adaptability to heterogeneous materials systems where the optimal curriculum is not known in advance. To lower the barrier to developing MLIPs for non-experts, we propose Lang2MLIP, a multi-agent framework that takes natural-language input and formulates end-to-end MLIP development as a sequential decision-making problem solved by large language models (LLMs). At each step, a decision-making agent observes the current dataset, model, evaluation results, and execution log, and then automatically selects an appropriate action to improve the model. This removes the need for a predefined pipeline and enables the agent to self-correct by revisiting earlier subsystems when new failures arise. We evaluate this approach on a solid electrolyte interphase (SEI) system with multiple components and interfaces. These results suggest that LLM-based multi-agent systems are a promising direction for automating MLIP development and making it more accessible to non-experts.

Rydberg atomic electrometry leverages the extreme sensitivity of highly excited atoms for calibration-free electric field measurements. The technique uses a non-metallic vapor cell to link properties of an RF field to a spectroscopic readout in the optical domain. Most demonstrations have so far focused on detecting linearly-polarized fields, for which the induced splitting of dressed atomic levels is rotationally invariant. Here we report on Rydberg atomic measurements of RF fields in a general state of polarization (SOP) which we map onto the Poincaré sphere through spectroscopic fingerprints. For a Stokes vector circumnavigating a Poincaré sphere meridian, we witness a continuous transformation of the atomic eigenenergy spectrum. Because the relative positions of eigenenergies are locked in place by quantization of angular momentum, the framework is universal and calibration free. We provide a specific demonstration in rubidium, which generalizes to all systems with a single valence electron.

Poroelastic wave simulations are important for many applications relating fluid flow and wave characteristics in porous rock formations. Reciprocity is a key physical property of wave propagation in porous media that is important for such applications, even when viscous dissipation is present. However, numerical poroelastic simulations often fail to reproduce reciprocal responses because the discretization does not preserve the balance between reversible wave dynamics and irreversible fluid-solid drag. To address this, we formulate the Biot equations in terms of a continuous evolution operator split into a reversible (skew-adjoint) wave part and an irreversible (self-adjoint, non-positive) Darcy part, including the leading-order Johnson-Koplik-Dashen correction. This structure clarifies why reciprocity holds in the continuous equations and how it is easily broken in discrete form. Guided by this interpretation, we construct a symmetric second-order Strang-splitting scheme with half-step source injection. The method conserves energy in the reversible subsystem, treats Darcy dissipation unconditionally stably, and retains Courant limits similar to elastic solvers. Using a staggered pseudo-spectral discretization, we model multimode propagation in 2D VTI media and obtain cross-component reciprocity with a relative L2 misfit approaching machine precision, demonstrating that the discrete scheme inherits the symmetry properties of the continuous evolution operator.

Hydrogen (H2) is a promising carbon-neutral energy carrier. However, its deployment is limited by the lack of lightweight, reversible storage media that operate under practical conditions. Here, we establish nitrogen-doped graphdiyne (N-GDY) as a programmable two-dimensional platform for stabilizing dispersed light-metal dopants and enabling high-capacity physisorption of molecular H2. The computational package involves density functional theory (DFT) combined with ab initio molecular dynamics (AIMD) and Langmuir-based statistical thermodynamic modeling. The results revealed that N-sites of N-GDY bind up to five Li, Na, K, and Ca atoms per primitive cell with binding energies of -2.27, -1.57, -1.80, and -2.13 eV, respectively, exceeding their respective bulk cohesive energies. AIMD simulations at 400 K further confirm the structural robustness of the decorated frameworks and the absence of metal aggregation. The polarised metal centres activate reversible H2 adsorption through electrostatic and dispersion interactions, with average adsorption energies falling within the optimal window (-0.15 to -0.35 eV per H2). Sequential adsorption analysis reveals uptake of up to 25 H2 molecules per primitive cell, achieving intrinsic gravimetric capacities of 13.08, 10.82, 9.23, and 9.15 wt% for Li-, Na-, K-, and Ca-functionalized systems, respectively. Thermodynamic analysis indicates favorable adsorption-desorption behavior under near-ambient conditions, with Li- and Ca-functionalized systems exceeding the 6.5 wt% U.S. Department of Energy's ultimate system-level target when considering intrinsic material capacity. These results identify N-GDY as a chemically tunable scaffold for dispersing lightweight metals and provide a mechanistic design strategy for achieving high-capacity, reversible hydrogen storage in porous two-dimensional materials.

We study the weakly nonlinear saturation of the flutter instability of a planar Cosserat rod in a viscous fluid driven by a terminal follower force. This instability, established in our preceding work as a Hopf bifurcation of a non-self-adjoint linear operator, produces stable limit-cycle oscillations in the fully nonlinear overdamped dynamics. Here we derive an analytical description of the emergence of this limit cycle near threshold. Working close to the critical follower force, we perform a multiple-scale expansion about the compressed straight base state and systematically remove secular growth at higher orders. Solvability at cubic order, enforced using the adjoint eigenmode of the non-Hermitian operator, yields a Stuart-Landau amplitude equation for the critical oscillatory mode. The Landau coefficients are expressed as explicit inner products involving the critical eigenmode, its adjoint, and quadratic corrections. The resulting reduced theory predicts a supercritical Hopf bifurcation with a steady-state tip oscillation amplitude scaling as the square root of the distance from threshold. These predictions rationalize the near-threshold scaling observed in nonlinear simulations and provide an analytical normal form for the onset of self-sustained beating in pressure-driven soft robotic arms at low Reynolds number.

We introduce a systematic analysis of density functional approximation errors in solids by separating functional-driven from density-driven contributions using quantum Monte Carlo densities of silicon, sodium chloride, and copper as reference. Typically, functional errors dominate, but we identify important exceptions where density-driven errors exceed functional errors by factors of 2-3, notably for SOGGA11 and {\tau}-HCTH in the semiconductor and the insulator. Material dependence is striking: 63% of functionals show error cancellation in silicon versus 18% in copper, and only five functionals surpass LDA accuracy for metallic copper even with exact densities. For silicon and sodium chloride, GILL or BECKE exchange combined with PBE, PW91, or P86 correlation achieves near-exact xc energies on QMC densities, while copper requires specialized functionals like PBEsol or PBELYP. High-quality densities consistently reduce density-driven errors across all systems. Historical analysis reveals that 1990s GGA functionals outperform many modern meta-GGAs, contradicting expectations of systematic improvement along Jacob's ladder. These results provide practical guidance for functional selection and highlight implications for machine learning potential development, where material-dependent error cancellation may compromise transferability.

Exciton-polaritons provide a versatile platform for the study of a wide range of phenomena, including polariton lasers, topological polaritons, and bosonic condensation. Transition metal dichalcogenide monolayers host excitons with large oscillator strength and binding energies constituting a robust matter constituent that forms polaritons from cryogenic to room temperature when embedded in optical microcavities. Plasmonic nanoparticles arranged in lattice geometries offer strong field-confinement and high quality factors. However, the high sensitivity of monolayer excitons to strain and dielectric disorder necessitates encapsulation in atomically flat hBN to ensure a high optical quality, rendering plasmonics more challenging. Here, we employ our recently developed fabrication method for embedding gold nanodisk arrays into van der Waals heterostructures and compare two samples with opposite layer order. We observe that strain and etching-induced surface contamination can reduce the exciton quality and thus the light-matter interaction strength significantly. Our fabrication approach reduces interfacial irregularities and enables homogeneous large-area polariton lattices for a wide range of applications, such as polarization-control or topological polaritonics.

Spectrally multiplexed telecom quantum networks require quantum memories that combine efficient storage with programmable frequency addressing. An ideal integrated implementation should therefore unite a native telecom transition, efficient storage and fast on-chip spectral control. Here we demonstrate a cavity-enhanced quantum memory in an isotopically purified $^{167}\mathrm{Er}^{3+}$-doped thin-film lithium niobate microring resonator. Long-lived hyperfine shelving states support persistent, high-contrast atomic frequency comb preparation, with a single-component comb lifetime of $277.6 \pm 52.6$s. Together with cavity impedance matching, this yields an on-chip storage efficiency of $23.3 \pm 0.5\%$ for 100-ns storage. The intrinsic electro-optic response of lithium niobate enables frequency-selective storage and routing of retrieved photons at rates up to 20~MHz with inter-channel crosstalk below $10^{-4}$. We further store and retrieve time-energy-entangled telecom photons, violating an entanglement-witness bound by more than 11 standard deviations and thus verifying the quantum nature of the storage process. Our results establish erbium-doped thin-film lithium niobate as a programmable light--matter interface for spectrally multiplexed quantum networks.

One of the most common approaches for coupling optical single-photon sources and photonic integrated circuits is to use a cavity. The cavity acts as a spectral filter that distorts the light spectrum and changes its statistical properties. But in the general case one should take into account not only spectral filtering of light but also the spectral filter influence on the single-photon source dynamics. We build an effective analytical model for description of the cavity influence on the photon statistics of light emitted by the single-photon source as spectral filtering only. We show that this model correctly describes the photon statistics even in a strong-coupling regime between the single-photon source and the spectral filter. Our results can be useful for analytical modeling of photon statistics of quantum emitters strongly coupled to various electromagnetic interfaces.

We study Lévy-like and truncated Lévy-like flights with step probability distribution of the form $r^{-1+\nu}$ for negative, positive, and zero $\nu$, focusing on the appearance of fractal geometry characteristics in the generated point sets. Forming ensembles of such point sets with fixed multiplicity, we develop simulation techniques leading to the desired value of correlation dimension in a vast continuous interval of scales. In particular, we demonstrate the possibility to produce ensembles of data sets with a low number of points with the needed properties. Furthermore, we show that the positive $\nu$ distributions, apart from a region near the upper scale limit, show fractal behaviour that extends to infinitesimally low scales. As an example, we apply our findings to producing simulations relevant to the search for critical fluctuations, related to QCD critical endpoint, in heavy-ion collision experiments.

During near-Sun crossings of the heliospheric current sheet (HCS), Parker Solar Probe (PSP) observed populations of high-energy protons and heavier ions indicating possible energization by magnetic reconnection up to 10s -- 100s keV nucleon$^{-1}$. Here we study ion acceleration by magnetic reconnection at the HCS. To estimate ion energization, we solve the Parker transport equation coupled to a large-scale 2D MHD reconnection simulation. We find that multiple ion species develop power-law distributions with both spectral index and high-energy cutoff $E_{\text{max}}$ consistent with in-situ data. By accounting for the injection physics determined by kinetic simulations, we confirm that the charge-to-mass ratio scales as $E_{\text{max}} \propto (Q/M)^{\alpha}$ with $\alpha \sim 0.8-1.1$, approximately consistent with PSP measurements in the broader range $\alpha \sim 0.6-1.7$. In the limit where ions are injected at the same energy per nucleon, $\alpha$ can be as low as $\sim 0.3$. These findings further support the role of magnetic reconnection in producing high-energy heavy ions at the HCS.

Nondestructive detection of single-electron motion is crucial for quantum information processing with electrons trapped in Paul traps. The standard approach in Penning traps is to detect the image current induced on the trap electrodes by the electron's oscillatory motion. However, applying this approach in Paul traps for single electrons is currently hindered by motional frequency fluctuations arising from trap anharmonicities and instabilities in the rf trapping field. In this work, we propose a robust detection scheme exploiting the transient dynamics of parametric driving to overcome these limitations. Distinct from traditional steady-state approaches, our method focuses on the transient regime to break the temporal constraints imposed by steady-state assumptions, thereby enabling fast readout. We show that a controlled ramp of the parametric drive effectively locks the frequency of the electron motion in the transient regime, rendering the signal highly resilient to realistic experimental noise and inherent micromotion. This work paves the way for the experimental realization of nondestructive detection of single-electron motion in Paul traps.

This paper presents the first systematic application of a material homotopy continuation framework for efficient, automated computation of dispersion curves in viscoelastic waveguides of arbitrary cross-section. A material homotopy continuously maps the original lossy problem to an auxiliary lossless one via an attenuation parameter s in [0,1], addressing the core challenges of the non-Hermitian eigenvalue problem. Grounded in analytic perturbation theory, the method guarantees branch identity continuity--a one-to-one correspondence between solutions at s=0 and s=1--provided the real-parameter path does not cross any exceptional points. Under a Type I exceptional point topology, physical mode labels established at the elastic stage remain valid at the viscoelastic stage without post-processing, yielding the characteristic real-part veering with imaginary-part crossing. The decoupling strategy performs reliable mode tracking in the Hermitian regime via adaptive wavenumber refinement, then propagates a sparse set of key solutions to the target viscoelastic state through predictor-corrector homotopy continuation. Numerical examples across symmetric and unsymmetric laminates validate the framework's robustness and efficiency, with the majority of cases verified at a loss factor of approximately 0.003 and a single symmetric laminate providing additional support at 0.02. For a challenging unsymmetric laminate at a loss factor of 0.05, the method still produces numerically accurate solutions; two complementary diagnostic signatures--an extremely sharp imaginary-part crossing and a discernible discrepancy between spectral group velocity and energy flux velocity--warn of potential label mismatch and guide further analysis.

In epithelia, how do collective cell migration and tissue spatial organization feedback on each other? We address this question through large-scale numerical simulations of the cellular Potts model. By accounting for both cell morphology and cytoskeletal activity, we uncover a remarkably rich phase diagram featuring multiple types of orientational order, either as distinct phases or coexisting across length scales. We identify a specific pathway in parameter space along which a gradual increase in the actin polymerization rate drives a phase transition into a long-range flocking state. Simultaneously, quasi-long-range nematic order emerges at length scales much larger than the cell size due to the combined effects of directed motion and lateral cell-cell interactions. At length scales comparible to cell size, however, cells adopt an approximatively hexagonal morphology, resulting in hexanematic order, similar to that observed in reconstituted Madin-Darby Canine Kidney (MDCK) cell monolayers. With further increases in actin polymerization, nematic order becomes fully long-range, while hexatic order remains quasi-long-range and confined to short length scales, but independent of cytoskeletal activity. When noise is sufficiently low to allow crystallization at finite actin polymerization rate, cycling the cell-monolayer across the melting transition yields an example of phenotypical hysteresis, reminiscent of that observed across the epithelial-mesenchymal transition.

Scaling Scientific Machine Learning (SciML) toward universal foundation models is bottlenecked by negative transfer: the simultaneous co-training of disparate partial differential equation (PDE) regimes can induce gradient conflict, unstable optimization, and plasticity loss in dense neural operators. In particular, broadband open-channel fluid dynamics and boundary-dominated porous media flows impose incompatible spectral and geometric demands on a single dense parameter path. We introduce Shodh-MoE, a sparse-activated latent transformer architecture for multi-physics transport. Shodh-MoE operates on compressed 16^3 physical latents produced by a physics-informed autoencoder with an intra-tokenizer Helmholtz-style velocity parameterization, restricting decoded states to divergence-free velocity manifolds. The model guarantees exact mass conservation, achieving a physically verifiable velocity divergence of ~2.8 x 10^-10 (evaluated post-hoc in FP64) on 128^3 grids. A Top-1 soft-semantic router dynamically assigns localized latent patches to expert subnetworks, enabling specialized parameter paths for distinct physical mechanisms while preserving shared experts for universal symmetries. In a 20,000-step distributed pretraining run over mixed three-dimensional physical tensors, routing telemetry shows autonomous domain bifurcation: held-out validation tokens from the open-channel domain route exclusively to Expert 0, while porous-media tokens route exclusively to Expert 1. The model converges simultaneously across both regimes, achieving latent validation MSEs of 2.46 x 10^-5 and 9.76 x 10^-6, and decoded physical MSEs of 2.48 x 10^-6 and 1.76 x 10^-6. These results support sparse expert routing as a practical architectural mechanism for mitigating multi-physics interference in universal neural operators.

We report a measurement of the radiative lifetime of the $^2F_{7/2}$ level of $^{171}$Yb$^+$ that is coupled to the $^2S_{1/2}$ ground state via an electric octupole this http URL radiative lifetime is determined to be $9.96(50)\times 10^7$~s, corresponding to 3.16(16) years. The result reduces the relative uncertainty in this exceptionally long excited state lifetime by one order of magnitude with respect to previous experimental estimates. Our method is based on the coherent excitation of the corresponding transition and avoids limitations through competing decay processes. The explicit dependence on the laser intensity is eliminated by simultaneously measuring the resonant Rabi frequency and the induced quadratic Stark shift. Combining the result with information on the dynamic differential polarizability permits a calculation of the transition matrix element to infer the radiative lifetime.

Artificial Neural Networks (ANNs) are becoming important tools in physics research and education because they help in data analysis and complement traditional analytical methods. In this work, ANN modeling is introduced in a standard compound pendulum experiment used to determine the acceleration due to gravity, g. The aim is not to replace the conventional analytical method, but to demonstrate how machine learning can support experimental data analysis in undergraduate physics laboratories. Students first measure parameters such as effective length, time period, and angular displacement, and determine g using standard analytical methods with uncertainty analysis. These experimentally obtained data are then used to train and test an ANN model. The dataset is divided into training (70%), validation (15%), and testing (15%) groups. The experimentally determined value of gravitational acceleration was 1009.03 +/- 6.82 cm/s^2, while the ANN predicted a mean value of 1009.029858 cm/s^2 with a mean absolute error of 0.000592 cm/s^2. The close agreement between the experimental and ANN-predicted values shows that the ANN successfully learned the relationship between the pendulum parameters and g. However, the ANN prediction error should not be considered as an improvement in experimental accuracy because the model is trained using experimentally derived data. Instead, the ANN serves as a useful computational and educational tool that introduces students to regression, validation, overfitting, and data-driven analysis alongside traditional experimental physics.

Accurate real-time forecasting of cryogenic tank behavior is essential for the safe and efficient operation of propulsion and storage systems in future deep-space missions. While cryogenic fluid management (CFM) systems increasingly require autonomous capabilities, conventional simulation methods remain hindered by high computational cost, model imperfections, and sensitivity to unanticipated boundary condition changes. To address these limitations, this study proposes an Adaptive Real-Time Forecasting Framework for Cryogenic Propellant Management in Space Systems, featuring a lightweight, non-intrusive method named ARCTIC (Adaptive Real-time Cryogenic Tank Inference and Correction). ARCTIC integrates real-time sensor data with precomputed nodal simulations through a data-driven correction layer that dynamically refines forecast accuracy without modifying the underlying model. Two updating mechanisms, auto-calibration and observation and correction, enable continuous adaptation to evolving system states and transient disturbances. The method is first assessed through synthetic scenarios representing self-pressurization, sloshing, and periodic operations, then validated using experimental data from NASA's Multipurpose Hydrogen Test Bed and K-Site facilities. Results demonstrate that ARCTIC significantly improves forecast accuracy under model imperfections, data noise, and boundary fluctuations, offering a robust real-time forecasting capability to support autonomous CFM operations. The framework's compatibility with existing simulation tools and its low computational overhead make it especially suited for onboard implementation in space systems requiring predictive autonomy.

We report an advanced formulation of the Fourier modal method developed for two-dimensionally periodic multilayered structures containing materials with non-zero macroscopic magneto-electric coefficients (also known as coefficients of chirality and bi-anisotropy) represented as arbitrary 3 by 3 tensors. We consider two numerical schemes for this formulation: with and without generalized Fourier factorization rules. For both schemes, we provide explicit expressions for the Fourier tensors of macroscopic material parameters and demonstrate that, in the absence of magneto-electric coupling, they reduce to the conventional factorization rules. We show that the scheme employing factorization rules facilitates improved convergence, even when the macroscopic chirality coefficient is large. The described formulation represents a fast and rigorous technique for theoretical studies of periodic structures with chiral, bi-anisotropic, or non-reciprocal materials in the widely used framework of the Fourier modal method.

The phase offset between surface charge modulation and geometric undulations in a corrugated nanochannel provides a tunable mechanism for rectified, diode-like ion transport under purely pressure-driven conditions: reversing the applied pressure gradient selectively activates transport of opposite ionic species, generating a net ionic current whose sign and magnitude are set by the charge-geometry alignment. Fully coupled Poisson-Nernst-Planck-Stokes simulations reveal the underlying two-regime structure: at low driving force (Regime I), throughput is suppressed below the Poiseuille limit by a localized streaming potential that pins counterions within the electric double layer; above a threshold pressure (Regime II), the mechanical force overcomes electrostatic resistance, producing an abrupt, orders-of-magnitude rise in mean velocity. Electroosmotically driven flow undergoes a qualitatively similar but smoother transition. Peak charge selectivity is achieved at near-complete electric double layer overlap and driving forces just below the Regime I-Regime II transition. Random walk particle tracking confirms selective rectification and quantifies the dependence of ion dispersion on surface charge placement across both regimes.

Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics directly from data, while strictly adhering to the Arrhenius and mass action laws. However, standard CRNNs cannot represent pressure-dependent or mixture-based rate behavior, which is critical in many combustion and chemical systems and typically requires empirical falloff formulations such as Troe or SRI, or data-based interpolation or polynomial fits such as PLOG or Chebyshev Polynomials. Here, we develop Kolmogorov-Arnold Chemical Reaction Neural Networks (KA-CRNNs) that generalize CRNNs by modeling each kinetic parameter as a learnable function of third-body concentrations using Kolmogorov-Arnold activations. This structure maintains the Arrhenius and mass action interpretability and physical constraints of a vanilla CRNN while enabling assumption-free inference of global and collider-specific pressure effects directly from data. Two proof-of-concept reaction studies are presented to highlight the capability of KA-CRNNs to accurately reproduce pressure-dependent and collider-specific kinetics across a range of temperatures, pressures, and bath gas mixtures, extracting meaningful and generalizable models from sparse training data and significantly outperforming interpolative approaches (2.88x reduction in MSE). The framework establishes a foundation for data-driven discovery of extended kinetic behaviors in complex reacting systems, advancing interpretable and physics-constrained approaches for chemical model inference.

Many philosophers of physics maintain that a physical theory that exhibits (certain kinds of) symmetries is flawed, on the grounds that such theories posit "excess structure". In an influential paper, Dewar [2019, "Sophistication about Symmetries", \emph{Brit. J. Phil. Sci.} \textbf{70}: 485-521] introduces a distinction between "reduction" and "sophistication" as alternative ways of removing excess structure. In this paper we re-examine the distinction as Dewar draws it, and we argue that there is no physically or philosophically important distinction between what Dewar calls "reduction" and what he calls "internal sophistication". We then argue that there are multiple notions of "reduction" in the literature that ought to be distinguished, both in motivation and in outcome.

Physics-informed neural networks (PINNs) have shown promise for solving partial differential equations, yet their success in simulating incompressible flows at high Reynolds numbers remains limited. Existing approaches rely on auxiliary labeled data, supervised pretraining, or reference solutions, and no purely unsupervised method comparable to conventional finite-difference or finite-volume solvers has been demonstrated. We attribute this gap to the absence of a mechanism for enforcing the divergence-free constraint and boundary conditions to strict tolerances. To address this, we adopt the physics- and equality-constrained artificial neural network (PECANN) framework with a conditionally adaptive augmented Lagrangian method (CA-ALM), and introduce a pressure-Poisson-based objective. The residual of the pressure Poisson equation is minimized subject to the momentum and continuity equations and boundary conditions on the primitive variables as equality constraints, with CA-ALM enforcing all constraints tightly. For advection-dominated, high-Reynolds-number flows, we further propose an adaptive vanishing entropy viscosity that stabilizes early training without influencing the converged solution. A baseline that instead uses the momentum residual as the objective proves ineffective under the same machinery, underscoring the critical role of the pressure-Poisson objective. The method is assessed on lid-driven cavity flow up to $Re=7{,}500$, three-dimensional unsteady Beltrami flow, and steady and unsteady flow past a circular cylinder with general inflow-outflow boundary conditions, including an ablation study identifying admissible outlet conditions -- all without labeled data or supervised pretraining. Notably, it captures the spontaneous onset of periodic vortex shedding in unsteady cylinder flow without external perturbations, starting from a randomly initialized network.

High-fidelity lattice-Boltzmann very-large-eddy simulations are performed to describe the noise dissipation mechanisms in a single cavity acoustic liner subjected to grazing turbulent flow at a centreline Mach number of 0.3 and plane acoustic waves. The study examines the effects of sound pressure level (ranging from 130 to 160 dB) and source frequency, as well as of the direction of acoustic-wave propagation relative to the grazing flow. The acoustic energy dissipation mechanisms are the viscous losses within the shear layer forming along the internal walls of the orifice and the vortex-shedding. The latter is quantified through Howe's energy corollary. In the absence of grazing flow, acoustic energy is dissipated almost equally during both inflow and outflow phases, with vortex shedding dominating at high SPL and viscous losses at low SPL. The introduction of a grazing flow alters the flow topology; in particular, the shear layer past the orifice generates a quasi-steady vortex that confines the acoustic-induced flow to the downstream half of the orifice. This topological change alters the two noise dissipation mechanisms: viscous losses increase at low SPL because the grazing flow pushes the fluid toward the downstream lip of the orifice; vortex shedding becomes phase dependent, dissipating acoustic energy during the inflow phase and generating acoustic energy during the outflow phase. This explains why the net acoustic dissipation decreases in the presence of grazing flow, highlighting the crucial role of near-wall flow topology on liner performances.

A data-driven methodology is proposed to model the distribution of multivariate stochastic trajectories from an observed sample. As a first step, each trajectory in the sample is reduced to a vector of features by means of Functional Principal Component Analysis. Next, the joint distribution of features is modeled using (i) a non-parametric vine copula approach for the bulk of the distribution, and (ii) the conditional modeling framework of Heffernan and Tawn (2004) for the multivariate tail. The method is applied to the modeling of water waves. The dataset used is the DeRisk database, which consists of numerical simulations of water waves. The analysis is restricted to the portion of the wave period between the free-surface zero-upcrossing and the wave crest. The kinematic variables considered are the free-surface slope, the normal component of the fluid velocity at the free surface, and the vertical Lagrangian acceleration of the fluid at the free surface. The stochastic trajectories of these three variables are modeled jointly. The vertical Lagrangian acceleration of the fluid is employed to enforce a wave-breaking filter in the stochastic model. The number of hyperparameters in the stochastic framework is reduced to three, and a stepwise calibration strategy is proposed for their adjustment. The capabilities of the model are illustrated by predicting the distributions of selected response variables and by generating synthetic trajectories.

We calculate the parity non-conserving (PNC) electric-dipole ($E1$) transition amplitudes for the $5s - 6s$ and $5s - 4d_{3/2}$ transitions in Rb and Sr$^+$. Our results include both the nuclear-spin-independent and nuclear-spin-dependent contributions, with particular emphasis on the potential effects of a hypothetical additional $Z'$-boson. We highlight possible advantages of using light atoms in searches for such new interaction. The ratio of the contribution of a low mass $Z'$-boson to the contribution of the Standard model $Z$ -boson to PNC effects increases rapidly (faster than $1/Z^2$) with decreasing nuclear charge $Z$. Another advantage is that theoretical interpretations of experiments in lighter systems may be carried out with a higher accuracy than that in Cs, Ba$^+$, Fr and Ra$^+$.

Long Short-Term Memory (LSTM) models are trained to predict forecast errors for the High-Resolution Rapid Refresh (HRRR) model using the New York State Mesonet and Oklahoma State Mesonet near-surface weather observations as ground truth. When evaluated using mean-absolute-error and percent improvement relative to the HRRR, LSTMs predict precipitation error most accurately, providing, on average, a 48% improvement relative to the HRRR forecast, followed by wind error, providing, on average, a 15% improvement, and then temperature error, providing, on average, a 25% improvement. Precipitation errors exhibit an asymmetry, with overforecast precipitation detected more accurately than underforecast, while wind error predictions are consistent across over- and underforecast predictions. Temperature error predictions are relatively accurate but smoother, with respect to variance, than true observations. This paper describes an overview of LSTM performance with the expressed intent of providing forecasters with real-time predictions of forecast error at the point of use within the New York State and Oklahoma State Mesonets. In practice, the predicted errors can be used to adjust deterministic HRRR forecasts at the point of use, identify locations and variables with elevated uncertainty, and provide supplemental guidance for high-impact decision-making. This research demonstrates the potential of LSTM-based machine learning models to provide actionable, location-specific predictions of forecast error for high-resolution operational numerical weather prediction (NWP) systems. However, model performance is variable-dependent, and the approach relies on the availability of dense mesonet observations, which may limit applicability in data-sparse regions.

This study presents a self-consistent, quantum-informed model for the decay dynamics of localized surface plasmons (LSPs) in spherical metal nanoparticles (NPs), described as plasmonic quasi-particles (PQPs). By bridging classical electrodynamics description for quasi-normal modes (retardation effects included) with a quantum emitter perspective, this framework provides an analytically tractable description of the damping of the dissipative confined plasmonic systems. In addition to its significance for emission control, the model emphasizes the bosonic characteristics of plasmonic quasi-particles, which are coherent many-electron excitations of the states of quasi-normal modes. Unlike conventional cavity quantum electrodynamics (CQED), where the emitter and cavity exist as separate systems, a plasmonic quasi-particle functions as a quantum emitter embedded within a self-created resonant near-field nano-cavity of confined radial fields, sharing the spectral characteristics of the surface transverse-magnetic (TM) modes, which include nonradiative damping effects resulting from, e.g., ohmic losses in a metal. This work extends Fermi's Golden Rule to include the coupling between the emission process and the self-generated cavity impact. The derived self-consistent formulation offers analytical expressions for the total damping rates, which demonstrate a size-dependent suppression displayed in higher multipolarity modes attributed to the impact of the self-quenching effect resulting from the coaction of radiative and non-radiative channels.

Solar-pumped lasers, predominantly based on neodymium gain media, offer a promising route to renewable laser-energy conversion and space-based photonics; however, their performance has been constrained by thermal loading and limited power scalability. Here, we propose and numerically investigate a solar-pumped ytterbium thin-disk gain medium combined with a dome concentrator, which enables multipass solar pumping and enhanced absorption. The design yields comparably low lasing thresholds for neodymium- and ytterbium-doped media, while ytterbium provides superior power scalability, enabling up to threefold higher output power. We further identify ytterbium-doped medium combined with a spherical concentrator as a viable solar-pumped, radiation-balanced configuration, achieving self-cooled lasing at solar pump intensities of 28.5 kW cm-2 within the 1020-1033 nm window of the solar spectrum. We further demonstrate that dual-wavelength pumping overcomes the limitations imposed by low solar intensity and concentration constraints, enabling radiation-balanced lasing at orders-of-magnitude lower solar pump intensities. The proposed spherical-concentrator-based design enhances pump absorption while allowing efficient escape of anti-Stokes fluorescence. These results establish multi-pass, solar-pumped ytterbium lasers as a compact, scalable, and sustainable platform for high-performance solar-pumped lasers.

In this paper, we propose improved wall-treatment strategies for meshfree methods applied to turbulent flows. The goal is to enhance wall-function handling in simulations of high-Reynolds-number turbulent flows and to understand the performance of turbulence models within these frameworks. While wall-function techniques are well established for mesh-based methods, their implementation in meshfree methods faces unique challenges. The main difficulties arise from scattered point distributions and dynamic point movement in Lagrangian frameworks. To address these issues, we evaluate a baseline closest-neighbor approach alongside two novel techniques: the nearest-band neighbor (NBN) method and the shifted boundary (SB) method. The NBN method enforces wall functions on a band of interior points, helping to maintain uniform point selection. On the other hand, the SB method virtually moves boundary points to a fixed wall-normal distance, eliminating the spatial noise associated with point movement. We evaluate these methods using turbulence closures: Spalart--Allmaras, $k-\varepsilon$, and $k-\omega$ turbulence models. These methods are validated on 1D Couette flow, a turbulent flat plate, and a 3D NACA 0012 airfoil at high Reynolds numbers. Results demonstrate that both novel methods outperform the standard closest-neighbor approach on flat geometries. For flat plates, the SB method provides stability and perfectly smooth $y^+$ distributions. However, when applied to a curved NACA 0012 profile, the NBN method proves to be robust and flexible. In contrast, the SB method exhibits setbacks in numerical diffusion and premature flow separation on curved geometries. This is due to uncorrected normal-vector shifting and adverse pressure gradients. This work establishes the NBN method as a reliable, robust foundation for simulating turbulent flows over practical geometries using meshfree methods.

A size-extensive, converging, black-box, ab initio coupled-cluster ($\Delta$CC) ansatz is introduced that computes the energies and wave functions of stationary states from any degenerate or nondegenerate Slater-determinant references with any numbers of $\alpha$- and $\beta$-spin electrons, any patterns of orbital occupancy, any spin multiplicities, and any spatial symmetries. For a nondegenerate reference, it reduces to the single-reference coupled-cluster ansatz. For a degenerate multireference, it is a natural coupled-cluster extension of degenerate Rayleigh-Schrödinger perturbation ($\Delta$MP) theory. For ionized and electron-attached references, it can be viewed as a coupled-cluster Green's function, although the present theory is convergent toward the full-configuration-interaction (FCI) limits, while Feynman-Dyson many-body Green's function (MBGF) theory generally is not. Additionally, a new state-universal multireference coupled-cluster theory for general model spaces is developed by slightly modifying the $\Delta$CC ansatz. This quasidegenerate coupled-cluster (QCC) theory is size-extensive, converging, but not black-box, which is expected to be well suited for strong correlation. Determinant-based, general-order algorithms of $\Delta$CC and QCC theories are implemented, which are compared with configuration-interaction (CI) and equation-of-motion coupled-cluster (EOM-CC) theories through octuple excitations and with $\Delta$MP and MBGF theories up to the nineteenth order. For transition energies, the order of performance is: QCC $\approx$ $\Delta$CC $>$ EOM-CC $>$ CI at the same excitation order or QCC $\approx$ $\Delta$CC $>$ $\Delta$MP $>$ MBGF at the same cost scaling.

People often express opinions that differ from their privately held views, a phenomenon known in economy as preference falsification. Expressed-private opinion (EPO) models capture this by assigning each agent two dynamical variables: a private (internal) and an expressed (external) opinion. Within the nonlinear $q$-voter model, two EPO variants have been studied so far: with and without self-anticonformity. In both formulations, agents update private and expressed binary opinions, one after another and at the same rate, which has led to two update schemes studied previously: AT (act then think), in which an agent first updates its expressed and then its private opinion, and TA (think then act), in which the order is reversed. To eliminate this ad hoc distinction and quantify the interplay between private and expressed opinions, we introduce the $\alpha$-EPO $q$-voter model with asynchronous updating -- in each elementary step, an agent updates its private opinion with probability $\alpha$ or its expressed opinion with complementary probability $1-\alpha$. We derive mean-field theory and, for the first time for EPO $q$-voter dynamics, a pair approximation, and validate them with Monte Carlo simulations on artificial and real organizational networks. Comparing the two model variants, we show that the collective outcome controlled by $\alpha$ strongly depends on self-anticonformity: with self-anticonformity the results are robust to $\alpha$, whereas without it $\alpha$ shifts the agreement-disagreement threshold and can change the type of phase transition. In the mean-field limit this change occurs only for $q=3$, but the pair approximation reveals an additional low-connectivity regime in which both $\alpha$ and the average degree $k$ control the emergence and width of hysteresis also for larger influence groups.

The origin of the "theoretical limit of time-frequency resolution of Fourier analysis" is from its numerical implementation, especially from an assumption of "Periodic Boundary Condition (PBC)," which was introduced a century ago. We previously proposed to replace this condition with "Linear eXtrapolation Condition (LXC)," which does not require periodicity. This feature makes instantaneous spectra analysis of pulse series available, which replaces the short time Fourier transform (STFT). We applied the instantaneous spectra analysis to two lung sounds with abnormalities (crackles and wheezing) and to a normal lung sound, as a demonstration. Among them, crackles contains a random pulse series. The spectrum of each pulse is available, and the spectrogram of pulse series is available with assembling each spectrum. As a result, the time-frequency structure of given pulse series is visualized.

Transition interface sampling (TIS) and replica exchange TIS (RETIS) are powerful methods for computing rates of rare events inaccessible to straightforward molecular dynamics (MD) simulations. Path reweighting extends their output, enabling the evaluation of diverse thermodynamic and kinetic quantities, including reaction prediction metrics, activation barriers, committor functions, and free energies. The recently developed Infinity-RETIS algorithm boosts parallel efficiency through asynchronous replica exchanges in the infinite-swap limit, eliminating the wall-time bottlenecks of conventional RETIS. This approach introduces fractional samples and biased sampling distributions, requiring a generalized path reweighting framework, for which we derive expressions demonstrating how exact dynamic and thermodynamic variables can be computed. We then focus on a special class of free energy surfaces defined by history-dependent conditions, whose values are influenced by kinetic factors such as particle mass and friction, unlike standard unconditional free energy surfaces. Even with suboptimal reaction coordinates, these conditional free energies can reveal kinetically relevant barriers that may be misrepresented by standard unconditional free energies, thereby providing a rigorous and versatile tool for characterizing complex molecular transitions.

Various extensions to Riemann geometry have been proposed since the inception of general relativity (GR). The aim has been and continues to be to construct a quantum and dynamic spacetime that incorporates the well-known classical (static) spacetime. Apparently, this seems to enable the principles of GR and quantum mechanics (QM) to be reconciled into a coherent relativity and quantum theory. A canonical geometric quantization approach that presents kinematics of free-falling quantum particles within a tangent bundle, expands QM to incorporate relativistic gravitational fields, and generalizes the four-dimensional Riemann manifold into an eight-dimensional one likely discretizes, if not fully quantizes, the Finsler and Hamilton structures. The Finsler and Hamilton metrics can be directly derived from the Hessian matrix. As introduced in [Physics, 7 (2025) 52], the quantized four-dimensional metric tensor can be deduced by means of approximations including proper parameterization of coordinates and the equating line elements on all these manifolds including Riemann manifold. This research, on the contrary, goes beyond all these approximations and proposes the incorporation of a phase-space metric tensor into GR. The derivation of a quantized eight-dimensional metric tensor is not only presented, but also the implications of it and the corresponding relative spacetime are examined.

Structured illumination microscopy (SIM) has attained high spatiotemporal delineation of subcellular architecture, yet offers limited insight into chemical composition. We develop Chem-SIM, a structured-illumination fluorescence detected mid-infrared photothermal microscopy, for super-resolved chemical imaging of microorganisms and mammalian cells. Poisson maximum-likelihood demodulation and spectral normalization across wavenumber recover the weak IR-induced fluorescence intensity change under low photon budgets and convert the fluorescence intensity modulation to chemical fingerprints. Photothermal gating further rejects water backgrounds in aqueous samples, while the IR pump maintains cellular activity at near-physiological temperature. Chem-SIM preserves full vibrational fingerprints, achieves SIM-grade lateral resolution in a high-throughput camera-based format. Here, we show that this platform distinguishes stationary- from log-phase bacteria through chemical content mapping, reports deuterated fatty-acid incorporation in ovarian cancer cells, and resolves lipid-droplet dynamics in live cells, establishing a high-throughput route to super-resolved imaging of organelle chemistry, metabolism, and dynamics.

Electromagnetic resonances play a central role in nanophotonics by enabling efficient confinement of electromagnetic energy and enhanced light-matter interaction. Traditionally, resonant phenomena have been described using platform-specific concepts developed within distinct research communities, including photonic crystals, plasmonics, and dielectric metasurfaces. In this Perspective, we propose a unified framework that distinguishes electromagnetic resonances as eigenmodes of open systems from their experimentally observed manifestations as scattering features. We show how resonances evolve from isolated particles to coupled oligomers and periodic structures, highlighting the roles of geometry, material response, and dimensionality. Particular attention is given to interference-driven phenomena such as bound states in the continuum, lattice resonances, anapoles, and superscattering, some of which cannot always be associated with a single eigenmode. By clarifying the relationship between eigenmodes, scattering channels, and interference effects, this Perspective provides a coherent language for interpreting resonant phenomena and identifies key challenges and opportunities for designing robust resonant photonic systems.

Although continuous symmetry theory has attracted increasing attention in modern chemistry, local symmetry remains under-investigated. As a consequence, the relationship between symmetry and chemical behavior is often obscured, limiting the practical use of fuzzy symmetry measures. In this study, we introduce a novel framework for evaluating local symmetry based on electron density localization, and present continuous symmetry representations for several representative molecules. Our approach not only quantitatively captures global symmetry, but also reveals distinctive features of symmetry in a local chemical environment. The related concept, local chirality or chirotopicity, is also discussed. Overall, the proposed local symmetry and chirality measures provide valuable insights into molecular structure and structure-property relationships.

Topological transitions in non-Hermitian systems are generally boundary sensitive: a point-gap winding transition under periodic boundary condition (PBC) and a non-Bloch bulk real-line-gap transition under open boundary condition (OBC) at $\mathrm{Re}(E)=0$ are governed by different spectra and therefore need not coincide. Here we show, for a class of chiral non-Hermitian Su--Schrieffer--Heeger (SSH)-type lattices, that these two criticalities can be locked by an exceptional-point-constrained (EP-constrained) parameter evolution. The key requirement is not the occurrence of isolated exceptional points, but the persistence of a zero-energy Bloch degeneracy along the entire sweep, which is generically exceptional in the non-Hermitian regime. In an analytically tractable limit of an extended non-Hermitian SSH chain, the EP-constrained manifolds and both transition boundaries are obtained in closed form, making the locking explicit. Away from this limit, numerical generalized-Brillouin-zone (GBZ) calculations confirm the correspondence for representative constrained sweeps, whereas unconstrained paths show that isolated exceptional points or Hermitian degeneracies do not enforce locking. We further verify the mechanism in a spinful four-band extension with branch-resolved GBZs, including strongly branch-imbalanced regimes. These results establish a path-dependent diagnostic principle: along EP-constrained sweeps in this SSH-type class, changes in PBC point-gap winding can indicate OBC non-Bloch bulk real-line-gap transitions and the corresponding changes in zero-energy boundary modes.

We report results from a 13-liter purified liquid argon test stand at Wellesley College. The system includes a single-pass liquid-phase purification column, a double-gridded purity monitor to assess the electron lifetime, and a slow control and data acquisition system. Initial measurements demonstrate an O$_2$-equivalent impurity concentration of 0.25 ppb, corresponding to an electron lifetime of 1.5 ms at a drift field of 500 V/cm. This test stand supports ongoing detector R&D on charge and light readout technologies for future large-scale liquid argon time projection chambers, such as Q-Pix and other cold electronics systems, as part of a facility at Wellesley College for fundamental studies of LArTPC readouts.

Turbophoresis in inhomogeneous turbulent flows leads to the formation of large-scale nonuniform particle number density distributions of inertial particles. This effect is associated with an effective drift velocity directed toward regions of lower turbulence intensity and proportional to the particle Stokes time and the spatial gradient of the turbulence intensity. In the present study, turbophoretic transport is experimentally investigated in air flows generated by one-grid and two-grid oscillating turbulence systems. The flow velocity field and particle spatial distribution are measured using Particle Image Velocimetry. To isolate the effect of particle accumulation due to turbophoresis from that associated with mean fluid flow, the measured particle number density of inertial particles is normalized by the corresponding distribution obtained for noninertial tracer particles under identical flow conditions. The measurements show preferential accumulation of inertial particles in regions of lower turbulence intensity, consistent with the expected behavior of turbophoretic transport.

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces a fundamental difficulty: prior distributions are typically defined in the weight space of neural networks, whereas physically meaningful prior assumptions are more naturally expressed in function space. In this study, we introduce a unified framework, termed functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks (fpBPINN), to incorporate functional priors into Bayesian PINN-based inversion. We consider two complementary approaches. The first is a functional-prior-informed Bayesian PINN (FPI-BPINN), in which a neural network weight prior is learned to be consistent with a prescribed functional prior, and Bayesian inference is subsequently performed in weight space. The second is function-space particle-based variational inference for PINNs (fParVI-PINN), which performs Bayesian estimation using ParVI directly in function space. We also show that random Fourier features (RFF) play an important role in representing Gaussian functional priors with neural networks and in improving posterior approximation. We applied the proposed approaches to one-dimensional seismic traveltime tomography and two-dimensional Darcy-flow permeability inversion. These numerical experiments showed that both approaches accurately estimated posterior distributions, highlighting the significance of introducing physically interpretable functional priors into Bayesian PINN-based inverse problems. We also identified the contrasting advantages of FPI-BPINN and fParVI-PINN, namely flexibility and accuracy, respectively.

Two-body reduced density matrices (2RDMs) encode the essential two-electron physics of electronic states, but their quartic storage cost poses a major limitation in practical workflows. We investigate a simple protocol to compress both transition and non-transition 2RDMs into a lower-rank representation that preserves their wedge-product structure and physical symmetries under truncation. The resulting decomposition couples Coulomb and exchange channels through a common set of low-rank factors, yielding a more compact rank-sparse representation than single-channel factorizations. For correlated states, the effective rank scales linearly with system size, achieving a $\sim99$\% compression for the coupled-cluster 2RDM of octane while retaining chemical accuracy. We apply this to the recently introduced {\em ab initio} eigenvector continuation workflows, where many-body wave functions are interpolated across nuclear geometries with mean-field cost. Here, 2RDMs between training states act as projectors into a subspace but their memory scaling limits applications to larger systems. The compression scheme reduces the memory cost from quartic to quadratic for a fixed error per electron. Metrics to systematically control the decomposition are investigated, enabling statistically resolved structural, dynamical and spectroscopic observables from nonadiabatic molecular dynamics simulations of photoexcited H$_{28}$ chains, interpolating from compressed near-exact DMRG training data. This establishes these structure-preserving compressed intermediates for practical correlated electronic structure workflows.

Objective. Hypoxic tumor subvolumes resist radiation through elevated oxygen enhancement ratios (OER), yet no computational OER model is simultaneously particle-specific, mechanistically grounded, and fast enough for voxel-scale treatment planning. We present the VOxel-Aware Oxygen Model (VOxA) to address all three requirements. Approach. An Oxygen Model (OM) encodes particle-specific LET-OER dependence through dual sigmoidal transitions constrained to increase monotonically with atomic number Z, combined with Michaelis-Menten oxygen kinetics. A Voxel-Aware (VA) extension resolves per-DSB local energy heterogeneity via a calibrated particle-specific sensitivity parameter. Calibrated on 233 OER observations from 29 sources across 10 particle types (LET = 0.2-654 keV/um); DSB coordinates from TOPAS-nBio simulations. Main results. The OM achieves $R^2 = 0.719$ and MAE = 0.300 retention OER units; theoretical OER maximum 3.32 (2.4% from measurement), bootstrap median 3.37 [3.18, 4.09]. The composite $K_{\rm fix} + K_{\rm repair} = 2.82$ mmHg is tightly constrained despite high collinearity (r = 0.935). On the Furusawa heavy-ion subset, VOxA achieves 28.4% lower survival OER MAE than the clinical standard (63.1% on helium, 24.0% on carbon) and reproduces He < C < Ne Z-ordering that universal models cannot capture. The VA extension passes 18 tests confirming sample-size-invariant within-nucleus coefficient of variation of the per-DSB retention probability. VOxA evaluates in under $10^{-3}$ ms per voxel, more than $10^6$ times faster than Monte Carlo chemistry. Significance. VOxA is the first particle-specific OER model to reproduce Z-ordering analytically at clinical planning speed, validated on the largest OER calibration dataset for this model class. Committed-break coordinates at whole-nuclear scale provide the input for inter-break topological analysis and hypoxic LET painting.

We reveal a previously unknown continuous symmetry and conservation law in the equations of linear isotropic elasticity, which describe the chirality of elastic waves. We show that the integral chirality is determined by the population imbalance between right- and left-handed transverse phonons, whereas the local chirality density generally involves both transverse and longitudinal wave components. We also introduce the related concepts of acoustic helicity and ``false chirality''. The theory is illustrated with simple interference fields exhibiting distinct distributions of chirality, spin angular momentum, and false chirality. Our results establish chirality as a fundamental property of elastic waves and provide a general theoretical framework for chiral acoustic phenomena.

We show topological configurations of the complex-valued spectra in gapped non-Hermitian systems. These arise when the distinctive EPs in the energy Riemann sheets of such models are annihilated after threading them across the boundary of the Brillouin zone. This results in a non-trivially closed branch cut that is protected by an energy gap in the spectrum. Their presence or absence establishes topologically distinct configurations for fully non-degenerate systems and tuning between them requires a closing of the gap, forming exceptional point degeneracies. We provide an outlook toward experimental realizations in metasurfaces and single-photon interferometry.

This work presents the application of parametric Operator Inference (OpInf) -- a nonintrusive reduced-order modeling (ROM) technique that learns a low-dimensional representation of a high-fidelity model -- to the numerical model of the purging process in semiconductor manufacturing. Leveraging the data-driven nature of the OpInf framework, we aim to forecast the flow field within a plasma-enhanced chemical vapor deposition (PECVD) chamber using computational fluid dynamics (CFD) simulation data. Our model simplifies the system by excluding plasma dynamics and chemical reactions, while still capturing the key features of the purging flow behavior. The parametric OpInf framework learns nine ROMs based on varying argon mass flow rates at the inlet and different outlet pressures. It then interpolates these ROMs to predict the system's behavior for 25 parameter combinations, including 16 scenarios that are not seen in training. The parametric OpInf ROMs, trained on 36\% of the data and tested on 64\%, demonstrate accuracy across the entire parameter domain, with a maximum error of 9.32\%. Furthermore, the ROM achieves an approximate 142-fold speedup in online computations compared to the full-order model CFD simulation. These OpInf ROMs may be used for fast and accurate predictions of the purging flow in the PECVD chamber, which could facilitate effective particle contamination control in semiconductor manufacturing.

Convection in Earth's outer core is driven by the release of heat and light elements at the inner core boundary. A key question is whether these buoyancy sources drive convection throughout the core, or whether a stable layer exists just below the core-mantle boundary (CMB). Recent simulations incorporating CMB heat flux heterogeneities propose locally stable ``regional inversion lenses'' (RILs) rather than a global layer, allowing stable and unstable regions to coexist. However, these simulations combine thermal and compositional anomalies, ignoring differences in diffusivities and boundary conditions. Here we simulate thermal, chemical, and thermochemical convection at Ekman number $E=10^{-5}$, with thermal and chemical flux Rayleigh numbers $\widetilde{Ra}_T=30-4000$ and $\widetilde{Ra}_\xi=30-100000$, and Prandtl numbers $Pr_T=1$ and $Pr_\xi=10$. Purely chemical simulations accumulate light elements below the CMB, forming locally stable regions near the poles or global layers, depending on $\widetilde{Ra}_\xi$. These chemically stratified regions persist in thermochemical simulations even when thermal forcing is destabilising. Introducing heterogeneous CMB heat flux produces thermally stratified RILs even with strongly destabilising compositional buoyancy. Our simulations reveal a diverse range of locations, properties, and morphologies of stable regions depending on $\widetilde{Ra}_T$ and $\widetilde{Ra}_\xi$, they can have a seismically detectable thickness and strength and might also have a signature in geomagnetic observations.

A new scheme for detecting wave-like dark matter (DM) using Rydberg atoms is proposed. Recent advances in trapping and manipulating Rydberg atoms make it possible to use Rydberg atoms trapped in optical tweezer arrays for DM detection. We propose to prepare a large ensemble of Rydberg atoms and to observe the excitations between Rydberg states by the DM-induced effective electric field. A scan over DM mass is enabled with the use of the Zeeman and diamagnetic shifts of energy levels under an applied external magnetic field. Taking dark-photon DM as an example, we demonstrate that our proposed experiment can have high enough sensitivity to probe previously unexplored regions of the parameter space of dark-photon coupling strengths and masses.

We study the dissipation measure arising in the inviscid limit of two-dimensional incompressible fluids. It is proved that the dissipation is Lebesgue in time and, for almost every time, it is absolutely continuous with respect to the defect measure of strong compactness of the solutions. When the initial vorticity is a measure, the dissipation is proved to be absolutely continuous with respect to a ''quadratic'' space-time vorticity measure. This results into the trivial measure if the initial vorticity has singular part of distinguished sign, or a spatially purely atomic measure if wild oscillations in time are ruled out. In fact, the dynamics at the Batchelor-Kraichnan dissipative scale is the only relevant one, in turn offering new criteria for anomalous dissipation. We provide kinematic examples highlighting the strengths and the limitations of our approach. Quantitative rates, dissipation life-span and steady fluids are also investigated.

We identify a new family of boron-rich compounds consisting of interconnected B$_{12}$ icosahedra, and electropositive guest atoms ($X$) in interstitial sites. These structures were found through first-principles crystal structure prediction at 50 GPa, where they could form, and are dynamically stable down to ambient pressure, so they could be formed under pressure, and brought back. When $X$ is a mono- or trivalent element the structures are metallic and superconducting. Predicted critical temperatures reach up to 42 K for CsB$_{12}$, rivaling MgB$_2$, the highest-$T_c$ ambient-pressure conventional superconductor. We interpret the XB$_{12}$ phase as a superatomic crystal: the B$_{12}$ units retain the icosahedral shape that they also exhibit in isolation, while forming an extended crystalline network. When X is a mono- or tri-valent atom, the system is metallic, and the B--B covalent bonding promotes strong electron-phonon coupling. Unlike MgB$_2$, where superconductivity is driven by a narrow subset of phonon modes, the XB$_{12}$ compounds exhibit broad, mode- and momentum-distributed coupling through both intra- and inter-superatomic vibrations. Our results highlight the XB$_{12}$ family as a promising platform for superconductivity and demonstrate the potential of superatoms as functional building blocks in solid-state materials design.

Linear operations, e.g., vector-matrix and vector-vector multiplications, are core operations of modern neural networks. To diminish computational time, these operations are implemented by parallel computations using different coprocessors. In this work we show that an open quantum system consisting of bosonic modes and interacting with bosonic reservoirs can be used as an analog thermodynamic coprocessor implementing multiple vector-matrix multiplications with stochastic matrices in parallel. Input vectors are encoded in occupancies of reservoirs, and the output result is presented by stationary energy flows. The operation takes time needed for the system's transition to a non-equilibrium stationary state independently on the number of the reservoirs, i.e., on the input vector dimension. With technological limitations being considered, a device of $5\times5$ cm$^2$ area covered with the coprocessors can conduct of the order of $10^{11}$ operations per second per a mode of the OQS. The computations are accompanied by an entropy growth. We construct a direct mapping between open quantum systems and electrical crossbar structures frequently used in analog vector-matrix multiplication, showing that dissipation rates multiplied by open quantum system's modes frequencies can be seen as conductivities, reservoirs' occupancies can be seen as potentials, and stationary energy flows can be seen as electric currents.

Creation and manipulation of non-classical states of light is rapidly becoming the focus of modern attosecond science. Here, we demonstrate numerically how interaction with such states can trigger the emergence of a many-body system with spontaneously broken symmetry by considering a modification of the well-known problem of superradiance encountered already by Dicke. Similarly to him, we investigate photon emission by ensembles of indistinguishable atoms. In contrast to him, however, we leverage symmetry-based selection rules to suppress emission of single photons by single atoms. A steady state is therefore only reached following a spontaneous transition into a collective symmetry-broken state of atoms and photonic modes. This transition permanently locks the atomic dipoles to the quantum field experienced by the system at a particular instant, transforming the entire setup into a potent quantum sensor reproducing the phase of the recorded quantum fluctuation.

We investigate the influence of magnetic order on the proximity-induced superconducting state in the Mn layers of a Mn-Nb(110) heterostructure by using a first-principles method. For this study, we use the recently developed Bogoliubov-de Gennes (BdG) solver for superconducting heterostructures [Csire et al., Phys. Rev. B 97, 024514 (2018)] within the first-principles calculations based on multiple scattering theory and the screened Korringa-Kohn-Rostoker (SKKR) Green's function method. In our calculations, we first study the normal-state density of states (DOS) in the single- and double-Mn-layer heterostructures, and calculate the induced magnetic moments in the Nb layers. Next, we compute the momentum-resolved spectral functions in the superconducting state for the heterostructure with a single Mn layer, and find bands crossing the Fermi level within the superconducting (SC) gap. We also study the SC state DOS in the single- and double-Mn-layer heterostructures and compare some of our results with experimental findings, revealing secondary gaps, plateau-like regions, and central V-shaped in-gap states within the bulk SC Nb gap that are magnetic-order-dependent. Finally, we compute the singlet and internally antisymmetric triplet (IAT) order parameters for each layer for both heterostructures, and find an order of magnitude difference in the induced singlet part of the SC order parameter in the Mn layer/s between the FM and AFM cases in favor of the AFM pairing with the maximum still being only 4.44% of the bulk Nb singlet order parameter value. We also find a negligible induced triplet part, yet comparable to the induced singlet values, indicating some singlet-triplet mixing in the Mn layer/s.

Recent machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms-advection (long-range transport), diffusion-like mixing, thermodynamic processes, and forcing-are represented implicitly within a single large network. This is particularly problematic for advection, where long-range transport typically requires expensive global interaction mechanisms or deep stacks of local convolutional layers. To mitigate this, we present PARADIS, a physics-inspired global weather prediction model that enforces inductive biases on network behavior through a functional decomposition into advection, diffusion, and reaction blocks acting on latent variables. We implement advection through a Neural Semi-Lagrangian operator that performs trajectory-based transport via differentiable interpolation on the sphere, enabling end-to-end learning of both the latent modes to be transported and their characteristic trajectories. Diffusion-like processes are modeled by depthwise-separable spatial mixing, whereas local source terms and vertical interactions are handled via pointwise channel interactions, yielding a physically structured operator decomposition. Evaluated on ERA5 benchmarks, PARADIS achieves competitive deterministic forecast skill, with particularly strong short-lead performance, while preserving substantially better spectral fidelity and forecast activity during medium-range rollouts.

Learning solution operators for systems with complex, varying geometries and parametric physical settings is a central challenge in scientific machine learning. In many-query regimes such as design optimization, control and inverse problems, surrogate modeling must generalize across geometries while allowing flexible evaluation at arbitrary spatial locations. In this work, we propose Arbitrary Geometry-encoded Transformer (ArGEnT), a geometry-aware attention-based architecture for operator learning on arbitrary domains. ArGEnT employs Transformer attention mechanisms to encode geometric information directly from point-cloud representations with three variants-self-attention, cross-attention, and hybrid-attention-that incorporates different strategies for incorporating geometric features. By integrating ArGEnT into DeepONet as the trunk network, we develop a surrogate modeling framework capable of learning operator mappings that depend on both geometric and non-geometric inputs without the need to explicitly parametrize geometry as a branch network input. Evaluation on benchmark problems spanning fluid dynamics, solid mechanics and electrochemical systems, we demonstrate significantly improved prediction accuracy and generalization performance compared with the standard DeepONet and other existing geometry-aware saurrogates. In particular, the cross-attention transformer variant enables accurate geometry-conditioned predictions with reduced reliance on signed distance functions. By combining flexible geometry encoding with operator-learning capabilities, ArGEnT provides a scalable surrogate modeling framework for optimization, uncertainty quantification, and data-driven modeling of complex physical systems.

This work investigates the geometrical properties of self-gravitating $N$-body systems from the perspective established by Henri Poincaré and Albert Einstein concerning the operational nature of measured geometry. Utilizing recent numerical analyses of central configurations--special equilibrium solutions to the Newtonian $N$-body problem--we uncover systematic spatial variations in nearest-neighbor particle separations correlated with the radial distance from the system's center of mass. We argue that these variations reflect a context-dependent, emergent effective geometry shaped by gravitational interactions, in accordance with Poincaré's assertion that measured geometry depends on the forces influencing measuring devices, and Einstein's view that rods and clocks define physical geometry through their local dynamics. By revisiting these foundational insights within a modern computational framework, we provide evidence that geometry in self-gravitating Newtonian systems is not a fixed background, but an emergent construct arising from internal physical interactions.

Inverse problems for stiff parabolic partial differential equations (PDEs), such as the inverse heat conduction problem (IHCP), are severely ill-posed: the forward map rapidly damps high-frequency interior structure before it reaches the boundary. Soft-constrained physics-informed neural networks (PINNs), which embed the PDE as a residual penalty, suffer from gradient pathology in this regime and tend to fit boundary measurements while leaving the interior field essentially untouched. We propose Neural Field Thermal Tomography (NeFTY), a hard-constrained neural field framework for label-free three-dimensional inverse heat conduction. NeFTY represents the unknown diffusivity as a continuous coordinate-based neural network, and at every optimization step passes the candidate field through a differentiable implicit-Euler heat solver with harmonic-mean interface flux, so that the governing PDE holds exactly on the discretization rather than as a soft penalty. Adjoint gradients propagate the surface reconstruction error back to the network weights at solver-level memory cost, making test-time inversion tractable on a single GPU. Across synthetic 3D benchmarks, NeFTY substantially outperforms soft-constrained PINN variants and a voxel-grid baseline on label-free volumetric recovery, and it transfers to real thermography data, surpassing classical signal-processing baselines in both defect segmentation and depth estimation. Additional details at this https URL

Control over crystallographic orientation in metal-organic framework (MOF) thin films is crucial for exploiting their anisotropic properties in sensing, catalysis, and separation. Achieving reproducible, highly oriented films remains challenging, especially for mixed-linker, pillared-layered frameworks. Here we present an automated, spin-assisted layer-by-layer liquid-phase epitaxy (LbL-LPE) strategy that enables rapid, ambient-condition fabrication of highly oriented, mixed-linker MOF thin films, demonstrated for Zn2BDC2DABCO (BDC = terephthalate, DABCO = 1,4-diazabicyclo[2.2.2]octane). Correlative process monitoring by grazing-incidence wide-angle X-ray scattering (GIWAXS), grazing-angle infrared (GI-IR) and UV-Vis spectroscopy, contact-angle measurements, scanning electron microscopy (SEM), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) ensure formation of uniform films with exceptional out-of-plane (001) orientation (degree of orientation >85%, Hermans parameter ~0.95) and excellent reproducibility. This strategy enables highly reproducible, high-throughput fabrication of orientation-controlled MOF thin films, providing a generalizable alternative to conventional LbL approaches. By enabling reproducible and entirely automated fabrication of highly anisotropic architectures, this work establishes a platform for integrating oriented MOFs into next-generation optoelectronic, sensing, and membrane devices where directional transport and ordered pore alignment are essential.