Identifying physical mechanisms that protect the information carried by various forms of structured light is one of the cornerstones of today's classical and quantum communications. Here we show that the purity of orbital angular momentum (OAM) modes can be protected against degradation in random media by leveraging two fundamental features of their own Schrödinger Hamiltonian dynamics, namely, Zeno effect -- frequent observations slow down the evolution -- , and Gouy phase -- the back-action of the observation. Repeated, OAM-dependent Gouy phase kicks imparted along the disturbing path by simple imaging systems trigger the optical Zeno effect that protects the input OAM mode against mode cross-talk that would broaden the OAM spectrum. Given the universality of the mechanism, the Gouy phase-assisted Zeno effect would protect propagation modes other than those of OAM, and the diverse forms of structured light built with them.

Collective organization in physical, biophysical, and biological systems often emerges from many weak, local interactions, yet the resulting global structures display striking regularities and apparent limits in diversity. Existing theoretical approaches typically emphasize specific mechanisms, detailed dynamics, or energetic optimization, making it difficult to identify constraints that are independent of microscopic realization. Here we develop a general theoretical framework showing that, when effective interactions among system components compress into a low-dimensional interaction space, global organization is governed by geometric constraints rather than detailed dynamics. We formalize interaction spaces as metric manifolds derived from coarse-grained effective couplings and show that low interaction dimensionality imposes upper bounds on the number, separability, and robustness of distinct collective organizations. These results yield impossibility statements: many conceivable macroscopic organizations are excluded a priori, even when locally compatible interactions exist. The framework applies across equilibrium and nonequilibrium systems without assuming specific symmetries or conservation laws. By shifting the explanatory focus from generative mechanisms to structural constraints, this work establishes a general, geometry-based perspective on collective organization.

The common New England sight of a cranberry bog presents a rich tapestry of fluid dynamics and soft matter phenomena. Here, we present four connected problems exploring the behavior of cranberries in their stages of harvest: the buoyant rise of a cranberry in a flooded bog, the stable floating configuration of a cranberry on the surface, the aggregation and interaction between many floating cranberries collected with a boom, and the piling of cranberries onto a truck for transportation. We model these phenomena from first principles and develop simple computational simulations of their collective behaviors. Additionally, we describe tabletop experiments to accompany these problems, either as in-class demonstrations or lab activities. Throughout, we draw connections to broader physical principles in soft condensed matter and fluids, allowing the real-world example of the cranberry bog to serve as a bridge between the undergraduate curriculum and topics in soft matter research.

Complex dynamical systems frequently encounter a recurrent structural instability: the collapse of the spectral gap, driving the system toward a low-dimensional "Zero-Mode Attractor" (e.g., spectral pile-up or over-smoothing). Building upon recent global well-posedness estimates [Hou, arXiv:2601.00638], this work generalizes the Multi-Scale Negative Coupled Information System (MNCIS) framework. We postulate that global stability requires an active topological operator -- Adaptive Spectral Negative Coupling (ASNC) -- functioning as a state-dependent high-pass filter that penalizes entropy accumulation at spectral boundaries. We validate this unified framework via three implementations:(1) Hydrodynamics: In 3D Navier-Stokes turbulence ($N=256^3$), ASNC acts as a global-enstrophy adaptive sub-grid scale (SGS) model, stabilizing the inviscid limit and preserving the Kolmogorov $-5/3$ inertial range without artificial hyper-viscosity.(2) Artificial Intelligence: Addressing Over-smoothing in Graph Neural Networks (GNNs), we implement ASNC as a parameter-free topological constraint. Unlike baselines (e.g., DeepGCNs) relying on dense residual connections to bypass signal decay, our framework enables the training of ultra-deep 64-layer networks without residual connections, maintaining perfectly stationary feature variance ($\sigma^2 \equiv 1.0$) on the ogbn-arxiv benchmark. (3) Biological Physics: In reaction-diffusion morphogenesis, it stabilizes Turing patterns against diffusive washout in high-entropy regimes. Our results suggest that the MNCIS framework provides a base-independent topological condition for distinguishing viable complex systems from those collapsing into thermal equilibrium, bridging physical stability and information persistence.

The answer to the title-question is positive. The analysis of the geometry of continuous and differentiable curves in three-dimensional Euclidean space suggests that the point represents the location of the center of charge of the electron, satisfies a system of ordinary differential equations of fourth order, and moves at the speed of light. The center of mass of the electron is a different point and will be determined by the evolution of the center of charge. It is the relative motion of the center of charge around the center of mass that gives rise to the spin and magnetic properties.

Computation is commonly defined as the execution of abstract algorithms over symbolic representations, with physical systems treated as substrates that realise predefined operations. While effective for engineered machines, this separation becomes problematic when applied to living systems, where persistence, adaptation, and failure occur without symbolic instruction or central control. Here, computation is reformulated as a structural property of organised matter. Organisation is defined as the persistence of relational constraints that delimit admissible state transitions. Information is not encoded content but relational invariance: differences that influence future behaviour by reshaping what transitions remain possible. Computation is identified with the ongoing enactment of such organisation, integrating memory, processing, and execution as inseparable aspects of material dynamics. Within this framework, algorithms correspond to internally embedded regularities enabled by constraint, and computational limits arise from organisation itself. The account provides experimentally accessible criteria for computation based on persistence, recovery, and structural failure under perturbation.

The study aimed to evaluate the applicability of environmental indices in the monitoring of smouldering coal-waste dumps. A dump located in the Upper Silesian Coal Basin served as the research site for a multi-method analysis combining remote sensing and field-based data. Two UAV survey campaigns were conducted, capturing RGB, infrared, and multispectral imagery. These were supplemented with direct ground measurements of subsurface temperature and detailed vegetation mapping. Additionally, publicly available satellite data from the Landsat and Sentinel missions were analysed. A range of vegetation and fire-related indices (NDVI, SAVI, EVI, BAI, among others) were calculated to identify thermally active zones and assess vegetation conditions within these degraded areas. The results revealed strong seasonal variability in vegetation indices on thermally active sites, with evidence of disrupted vegetation cycles, including winter greening in moderately heated root zones - a pattern indicative of stress and degradation processes. While satellite data proved useful in reconstructing the fire history of the dump, their spatial resolution was insufficient for detailed monitoring of small-scale thermal anomalies. The study highlights the diagnostic potential of UAV-based remote sensing in post-industrial environments undergoing land degradation but emphasises the importance of field validation for accurate environmental assessment.

We propose a novel level set-based topology optimization for micropolar solids subjected to thermo-mechanical loading. To capture the size effects, we have incorporated the microstructural length-scale information into the level set-based topology optimization method by adopting a micropolar theory. The proposed non-local topology optimization method can provide accurate topology optimization for size-dependent solids under thermo-mechanical loading. We have demonstrated the effectiveness of the proposed method through a few representative two-dimensional benchmark problems. The numerical results reveal the substantial influence of underlying micro-structures, incorporated in the model through micropolar parameters, and temperature on topology optimization, highlighting the necessity of the proposed thermo-mechanical micropolar formulation for materials with pronounced non-local effects. For the numerical implementation of the proposed model, we have used open-source finite element libraries, \texttt{this http URL}, and \texttt{this http URL}, available in Julia, to ensure transparency and reproducibility of the reported computational results.

The environmental impact of electricity consumption is commonly assessed through its carbon footprint (CF), while water-related impacts are often overlooked despite the strong interdependence between energy and water systems. This is particularly relevant for electricity-intensive activities such as data center (DC) operations, where both carbon emissions and water use occur largely off-site through electricity consumption. In this work, we present Wattnet, an open-source tool that jointly assesses the CF and water footprint (WF) of electricity consumption across Europe with high temporal resolution. Wattnet implements an electricity flow-tracing methodology that accounts for local generation mixes, as well as for cross-border electricity imports and exports at a 15-minute resolution. Operational and life-cycle impact factors are used to quantify and compare local (generation-based) and global (consumption-based) footprints for multiple European regions during 2024. The results demonstrate that neglecting electricity flows and temporal variability can lead to significant misestimations of both CF and WF, particularly in countries with high levels of electricity trade or hydropower dependence. Furthermore, the joint analysis reveals trade-offs between decarbonisation and water use, highlighting the prominent role of reservoir-based hydropower in increasing WF even in low-carbon systems. Wattnet facilitates informed decision-making for workload scheduling and energy-aware operation of DCs, while also enhancing transparency regarding the environmental impacts of electricity consumption for end users and policymakers.

Machine learning interatomic potentials (MLIPs) enable efficient modeling of molecular interactions with quantum mechanical (QM) accuracy. However, constructing robust and representative training datasets that capture subtle, system-specific interaction motifs remains challenging. We introduce PANIP (PAirwise Non-covalent Interaction Potential), an ensemble MLIP model built upon the NequIP framework and trained on non-covalent interactions (NCIs) between protein-derived fragments. PANIP is trained using an automated multi-fidelity active learning (MFAL) workflow, in which a representative training subset, termed PDB-FRAGID (PDB Fragment Interaction Dataset), was distilled from an otherwise prohibitively large pool of fragment dimers extracted from the Protein Data Bank (PDB). PANIP retains {\omega}B97X-D3BJ/def2-TZVPP-level accuracy and achieves mean absolute errors below 0.2 kcal/mol on out-of-distribution systems, demonstrating excellent transferability across diverse NCI motifs. Compared to the widely used ANI-2x potential, PANIP delivers substantially lower errors, particularly for charged and strongly interacting dimers. Coupled with a fragmentation-based energy decomposition scheme, PANIP estimates protein-ligand binding energies at near force-field computational cost yet QM-level accuracy, enabling its use as a fragment-based scoring function that rivals specialized docking scoring functions.

Modern process simulators enable detailed process design, simulation, and optimization; however, constructing and interpreting simulations is time-consuming and requires expert knowledge. This limits early exploration by inexperienced users. To address this, a large language model (LLM) agent is integrated with AVEVA Process Simulation (APS) via Model Context Protocol (MCP), allowing natural language interaction with rigorous process simulations. An MCP server toolset enables the LLM to communicate programmatically with APS using Python, allowing it to execute complex simulation tasks from plain-language instructions. Two water-methanol separation case studies assess the framework across different task complexities and interaction modes. The first shows the agent autonomously analyzing flowsheets, finding improvement opportunities, and iteratively optimizing, extracting data, and presenting results clearly. The framework benefits both educational purposes, by translating technical concepts and demonstrating workflows, and experienced practitioners by automating data extraction, speeding routine tasks, and supporting brainstorming. The second case study assesses autonomous flowsheet synthesis through both a step-by-step dialogue and a single prompt, demonstrating its potential for novices and experts alike. The step-by-step mode gives reliable, guided construction suitable for educational contexts; the single-prompt mode constructs fast baseline flowsheets for later refinement. While current limitations such as oversimplification, calculation errors, and technical hiccups mean expert oversight is still needed, the framework's capabilities in analysis, optimization, and guided construction suggest LLM-based agents can become valuable collaborators.

Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast machine learning surrogate models have been proposed. Traditional surrogate models for calorimeter shower modeling train separate networks for each particle species, limiting scalability and reuse. We introduce AllShowers, a unified generative model that simulates calorimeter showers across multiple particle types using a single generative model. AllShowers is a continuous normalizing flow model with a Transformer architecture, enabling it to generate complex spatial and energy correlations in variable-length point cloud representations of showers. Trained on a diverse dataset of simulated showers in the highly granular ILD detector, the model demonstrates the ability to generate realistic showers for electrons, photons, and charged and neutral hadrons across a wide range of incident energies and angles without retraining. In addition to unifying shower generation for multiple particle types, AllShowers surpasses the fidelity of previous single-particle-type models for hadronic showers. Key innovations include the use of a layer embedding, allowing the model to learn all relevant calorimeter layer properties; a custom attention masking scheme to reduce computational demands and introduce a helpful inductive bias; and a shower- and layer-wise optimal transport mapping to improve training convergence and sample quality. AllShowers marks a significant step towards a universal model for calorimeter shower simulations in collider experiments.

Two-phase surfactant-laden flow and transport in porous media are central to many natural and engineering applications. Surfactants alter two-phase flow by modifying interfacial tension and wettability, while two-phase flow controls surfactant transport pathways and interfacial adsorption. These coupled processes are commonly modeled using Darcy-type two-phase flow equations combined with advection--dispersion--adsorption transport equations, with capillary pressure--saturation relationships scaled by the Leverett $J$-function. However, the Leverett $J$-function idealizes porous media as bundles of cylindrical tubes and decouples interfacial tension and wettability, limiting its ability to represent angular pore geometries and interfacial tension--wettability coupling effects. We present a modeling framework that explicitly incorporates pore angularity and interfacial tension--wettability coupling into Darcy-scale surfactant-laden flow and transport models. Two-phase flow properties are derived for angular pores, upscaled across pore size distributions, and formulated as explicit and closed-form expressions. These upscaled relationships are integrated into a coupled flow--transport model to simulate transient two-phase flow and surfactant transport. Results reveal a nonlinear and nonmonotonic dependence of two-phase flow properties on pore angularity, pore size distribution, and interfacial tension. Example simulations of water flow and PFAS migration in unsaturated soils indicate that surfactant-induced flow effects on PFAS leaching are generally minor under typical conditions, whereas pore angularity strongly controls water flow, interfacial area, and PFAS retention. Overall, the proposed framework provides a more physically grounded approach for modeling two-phase surfactant-laden flow and transport in porous media.

Pencil beam scanning (PBS) proton therapy provides highly conformal dose distributions that are increasingly leveraged for postmastectomy radiation therapy (PMRT) to reduce cardiopulmonary exposure. However, implant-based reconstruction in the setting of PMRT remains vulnerable to capsular contracture, and biological mechanisms of possible high linear energy transfer (LET) in PBS have not been well characterized. A retrospective case-control study was conducted on consecutive breast cancer patients who underwent mastectomy followed by implant-based reconstruction and proton PMRT (50 Gy in 25 fractions) between 2015 and 2021. Dose-LET volume histograms (DLVHs) were calculated for peri-implant tissue (5-mm shell around the implant). Generalized linear mixed-effects regression (GLMER) was employed to identify DLVH indices significantly associated with capsular contracture. Spearman correlation analysis was used to eliminate redundance. DLVCs were derived from receiver operating characteristic (ROC) analysis and validated using support vector machine (SVM)-based normal tissue complication probability (NTCP) model. Eight capsular contracture and 16 matched controls patients were analyzed. Three independent and significant DLVH indices were identified(p<0.01). The corresponding DLVCs were: V(55.8 Gy[RBE=1.1], 2.2 keV/{\mu}m) < 0.0033%, V(50.3 Gy[RBE=1.1], 5.4 keV/{\mu}m) < 0.0017%, and V(32.8 Gy[RBE=1.1], 0.9 keV/{\mu}m) > 96.98%. The SVM-based NTCP model achieved an area under the ROC curve (AUROC) of 0.867, with 91.7% accuracy, 87.5% sensitivity, and 93.8% specificity. Capsular contracture following proton PMRT is significantly associated with the synergistic interplay between dose and LETd in peri-implant tissue. The derived DLVCs provide actionable dosimetric constraints that can be integrated into treatment planning to minimize capsular contracture risk in proton PMRT.

Infrared detectors have traditionally been divided into two fundamental classes, mid-wave (MWIR, 3-5 um) and long-wave (LWIR, 8-14 um). Integrating MWIR and LWIR within a single device is challenging due to distinct materials, cooling needs, and detection mechanisms, while such integration is critical for improved object recognition, temperature estimation, and environmental sensing. In this work, we demonstrate a Spintronic Poisson (SP) bolometer enabling room-temperature ultra-broadband sensing across 3-14 um. Unlike conventional bolometers that rely on continuous analog signals, the SP bolometer implements a Poisson-counting detection paradigm, encoding temperature in discrete stochastic events, which turns thermal noise from a limitation into the basis of the estimator itself. We fabricate the SP bolometer using a spintronic transduction layer integrated with a plasmonic nanoantenna array to enhance broadband infrared absorption. Using spintronic transduction, the device achieves the noise-equivalent temperature difference (NETD, thermal sensitivity metric) of 80-100 mK at 300 K, surpassing uncooled detectors and approaching cooled technologies. This work establishes a statistical detection paradigm for room-temperature infrared sensing with broad application potential.

We present a terahertz time-domain spectroscopy (THz-TDS) system which accesses a broadband spectrum, efficiently covering the so-called "new THz gap" between 5 and 15 THz and extending beyond 25 THz. The system exploits nonlinear interactions within the organic crystal BNA (N-benzyl-2-methyl-4-nitroaniline) to generate and detect THz radiation upon excitation by a near-infrared (NIR) pulse centered at 1.03 $\mu$m. To enable broadband THz spectral monitoring, the NIR pulse from a Yb-based solid-state laser undergoes spectral broadening in a gas-filled single-ring hollow-core photonic crystal fiber, followed by a pulse compression to achieve durations as short as 31 fs. This approach paves the way for broadband spectroscopy in hard-to-access THz regions using widely available near-infrared ultrafast sources.

Chiroptical enantioselective sensing is gaining traction across various applications. However, intrinsic molecular chiroptical responses are weak, and existing amplification approaches add synthesis, manufacturing, or operational complexity that limits sensitivity, scalability, and dynamic control. Here, we present a fundamentally new sensing paradigm merging adsorption-driven chirality induction with wafer-scale optical transduction in a programmable heterostructure containing twisted aligned carbon nanotubes (CNTs) and phase change materials (PCMs). Chiral molecules adsorb onto CNTs to form chiroptically active composites that are macroscopically assembled by alignment and rotational stacking, yielding large ultraviolet circular dichroism (CD). We resolve molecule concentration and handedness in a single device without lithography, hotspot delivery, or differential protocols, achieving sub-$\mu$M sensitivity for CD-silent glucose and chiral amino acids enabled by $>10^5\,\mathrm{M^{-1}}$ adsorption constants. We validate adsorption using molecular dynamics simulations, reproduce experimental results using chiral transfer matrix simulations, and realize sensor programmability by tuning the PCM layer. This platform enables cost-effective in-situ enantiomer monitoring in aqueous environments.

We present a channel-resolved interpretation of laser-driven Coulomb explosion of the HCl dimer from an ensemble of trajectories. Three dominant outcomes are identified: a minor three-body channel and two four-body channels (sequential and near-simultaneous dissociation of both molecules). The key result is that pathway selection is strongly correlated with the degree of ionization during the laser interaction, which is in turn strongly modulated by laser-molecule orientation. Higher early-time ionization predisposes the system toward near-simultaneous four-body breakup, whereas lower ionization favors sequential and three-body fragmentation; for low-ionization cases, a fragment-resolved charge metric further differentiates three-body and sequential behavior. These charge-dependent trends consistently map onto experimentally accessible observables: the simultaneous mechanism dominates the high-energy tail of the kinetic energy release (KER) spectrum and populate distinct regions of the emission-angle distributions, while sequential events concentrate at lower KER. Overall, early-time charge evolution provides a unifying explanation for channel branching and for the channel-resolved fragmentation signatures.

Programmability and precise control of laser frequency are essential for quantum experiments and applications such as atomic clocks, quantum computers, and cold-atom sensors. Current systems use bulky, power-hungry modulators and frequency shifters which are difficult to integrate and limit portability and scalability. We report an electrically controllable, agile optical frequency source based on a semiconductor laser stabilized to a photonic-integrated, lead zirconate titanate (PZT)-actuated resonator cavity. We demonstrate this approach with precision programmable frequency control of a 780-nm laser that can periodically reference to rubidium spectroscopy followed by fast, programmable, arbitrary frequency tuning sequences for quantum control. We use this approach to demonstrate sub-Doppler cooling of rubidium-87 without any external modulators, achieving atom-cloud temperatures as low as 16 $\mu$K. The device achieves a tuning strength up to 1 GHz/V with 11 MHz modulation bandwidth while consuming only 10 nW of electrical power. This work establishes a route toward compact, low-power, and chip-scale laser systems for next-generation quantum and atomic sensing technologies.

DarkSide--20k (DS--20k) is a next--generation dual--phase liquid argon (LAr) time projection chamber (TPC) devoted to the direct--detection of dark matter. The detector is currently under construction in Hall--C at the Laboratori Nazionali del Gran Sasso, Italy, at a depth of approximately 3500 m water equivalent. The detector will instrument 49.7~t of low--radioactivity underground LAr contained within an acrylic TPC and is designed to reach a WIMP--nucleon spin--independent cross--section sensitivity down to $10^{-48}\,\mathrm{cm}^{2}$ for a WIMP mass of $0.1\,\mathrm{TeV}/c^{2}$ in a 200~tonne--year run. In DS--20k a uniform electric drift field is established in the active volume to transport ionization electrons toward the electroluminescence region, with the required high voltage delivered to the TPC cathode through a custom cable and stress--cone assembly. At the University of California, Davis, a dedicated test setup was developed to reproduce the DS--20k cathode high--voltage connection in LAr, matching the local electric--field conditions. This work summarizes the results of a comprehensive test campaign validating the operation of the DS--20k cathode HV system in LAr up to $-100$~kV.

We extend the CIPSI-driven CC($P$;$Q$) methodology [K. Gururangan et al., J. Chem. Phys. 155 (2021) 174114], in which the leading higher-than-doubly excited determinants are identified using the selected configuration interaction (CI) approach abbreviated as CIPSI, to excited electronic states via the equation-of-motion (EOM) coupled-cluster (CC) formalism. By examining vertical excitations in CH+ at equilibrium and stretched geometries, adiabatic excitations in CH, and ground- and excited-state potential cuts of water, we demonstrate that the CIPSI-driven CC($P$;$Q$) method converges parent CC/EOMCC singles, doubles, and triples energetics from relatively inexpensive Hamiltonian diagonalizations in CI spaces smaller than the corresponding triples manifolds.

Neuromorphic computing offers a pathway toward energy-efficient processing of data, yet hardware platforms combining nanoscale integration and multimodal functionality remain scarce. Here we demonstrate a gallium-phosphide electro-optomechanical spiking neuron that integrates optical and electromechanical interfaces within a single nanostructure on a silicon photonic chip operating at telecommunication wavelengths (1550 nm) and exploiting a 3 gigahertz-frequency mechanical mode. Our device displays excitable dynamics, generating optical spikes at its output, as in the spiking activity of neurons and cardiac cells and defined by the calibrated all-or-none response to external perturbations. This dynamic is consistent with the saddle-node on invariant circle scenario and associated features are demonstrated including control of excitable threshold, temporal summation and refractory period. Our device compact footprint and its CMOS-compatible platform make it well suited for edge-computing applications requiring low latency and establish a foundation for versatile brain-inspired optomechanical computing and advanced on-chip optical pulse sources.

DEAP--3600 is a single--phase liquid argon (LAr) direct--detection dark matter experiment operating 2~km underground at SNOLAB (Sudbury, Canada). The detector consists of 3.3~tons of LAr contained in a spherical acrylic vessel. At a WIMP mass of 100~GeV, DEAP--3600 has a projected sensitivity of $10^{-46}\,\mathrm{cm}^{2}$ for the spin--independent elastic scattering cross section of WIMPs. Radioactive sources have been used for the energy calibration and to test the detector performance. One of the most effective calibration runs has been taken with a $^{22}\mathrm{Na}$ source deployed in a tube located around the DEAP--3600 steel shell. The simultaneous emission of three $\gamma$'s by the source provides an excellent tagging for the $^{22}\mathrm{Na}$ decay. The results concerning the energy response of the detector and the agreement between data and Monte Carlo simulations in DEAP--3600 are investigated in this study.

Tip-enhanced Raman spectroscopy (TERS) typically relies on high-NA excitation to generate a strong axial field at the tip apex, which shortens the working distance and constrains sample geometries. We show that a plasmonic bi-cavity tip, the plasmon-tunable tip pyramid (PTTP), co-tuned in nanopyramid length L and plateau length W, supports a hybrid antenna-cavity mode that funnels energy to the apex under radially polarized, on-axis excitation, even with a dry objective of NA = 0.75. Finite-element simulations identify W as a design-critical parameter that sets an in-plane surface-plasmon-polariton (SPP) Fabry-Pérot-like resonance; co-tuning (L,W) yields a periodic series of maximal apex |E|^2. Experiments on monolayer graphene confirm near-field enhancement and reproduce the characteristic annular TERS point spread function (PSF) with NA = 0.75. Relaxing the NA requirement increases working distance and compatibility with constrained environments, pointing to practical, deployment-ready nano-Raman instrumentation.

Metasurface coupling constitutes a fundamental yet intricate electromagnetic interaction that occurs within a lattice of artificial subwavelength unit cells. Despite its prevalence, such coupling is typically ignored in conventional metasurface design frameworks due to the high characterization complexity, leading to suboptimal device performance. Here, we reveal a distinctive long-range coupling that exceeds an order of magnitude compared with the interaction range of evanescent waves, substantially enriching the metasurface design landscapes. This coupling exhibits pronounced graph topological features, and we design a graph neural network (GNN) to accurately abstract its inherent physics. Through strategic enhancement of the coupling effects, the discrete metasurface responses are transformed into continuous states, thereby unlocking diverse multiplexing channels. By further integrating the GNN into an inverse design agent, we tailor the multi-channel global response of metasurface to support simultaneous multiplexing across angle, frequency, and polarization domains. Experimentally, we demonstrate a compact metasurface multiplexer with eight independent channels, showcasing its potential for next-generation vehicular networks. This work establishes a new paradigm for highly integrated multifunctional metasurfaces, with promising prospects for high-density optical storage, information encryption, and high-capacity wireless communication.

Vibrational-encoded fluorescence spectro-microscopies are emerging as powerful tools for studying molecular vibrations with the unparalleled sensitivity of fluorescence spectroscopy. We recently described one such technique, termed bond-selective fluorescence-detected infrared-excited (BonFIRE) spectro-microscopy. Currently, prospects of BonFIRE towards rational molecular design are limited, but they have the potential to be assisted by computational tools. In this Perspective, we provide a brief overview of the theory of BonFIRE spectroscopy. We then describe a fully automated computational pipeline for calculating BonFIRE spectra, reproducing key features of experimental results. Finally, we highlight a few potential applications of computational methods for vibrational-encoded fluorescence spectro-microscopies and their broader implications for chemistry and biology.

Baseball consists of two teams alternating between batting and fielding while competing to score runs through sequential pitching events. Recent advances in tracking technology have enabled all Major League Baseball (MLB) clubs to record every pitch with high resolution, yet most quantitative studies have primarily emphasized single-pitch metrics, leaving the role of sequential structure less explored. Here, we examine pitch-pattern motifs of multiple lengths using approximately 12.4 million Statcast pitch recordings from the 2008-2025 MLB regular seasons at two complementary scales. At the macroscale, we quantify pitch-sequence diversity using the Shannon entropy and inverse Simpson index and examine their relationships with earned run average and win totals. At the microscale, we compare hit and out frequencies across pitch-pattern motifs. Rather than identifying outcome-determining sequences, we find that motif usage exhibits stable, non-random organization, as reflected in Zipf s and Heaps' laws, while showing limited association with conventional performance measures. While language-like scaling (Zipf's and Heaps' laws) clearly reveals an underlying 'grammar' of MLB pitch sequences, that grammar alone is insufficient to account for performance indicators such as ERA or wins. These results suggest that sequence-based analyses clarify the structural organization of pitch usage, while also delineating the limits of motif-based approaches for explaining performance without richer contextual information.

Branching ratios for fragmentation channels of important meta- and unstable species are essential for a molecular-level characterization of atmospheric chemistry. Here, the molecular product channels for the decomposition dynamics of the smallest Criegee intermediate, H$_2$COO, are quantitatively investigated. Using a high-quality, full-dimensional machine learned potential energy surface (CASPT2/aug-cc-pVTZ), the translational, rotational, and vibrational energy distributions of the CO$_2$+H$_2$, H$_2$O+CO, and HCO+OH fragmentation channels were analyzed to elucidate partitioning of the available energy. The CO$_2$ + H$_2$ product forms through two different pathways that bifurcate after formation of the OCH$_2$O intermediate. Along the direct pathway, CO$_2$ is preferentially vibrationally excited with H$_2$in its vibrational ground state, whereas for the indirect pathway going through formic acid, H$_2$ can populate levels with $v > 0$. For all product channels passing through energized formic acid, the lifetime distributions are described by stretched exponentials with $\beta$ ranging from 1.1 to 1.7. This is a clear signature of non-RRKM effects and suggests that the explicit molecular dynamics needs to be followed for a quantitative and realistic description of the photodissociation dynamics.

Fluid dynamics, for its strength in describing physical phenomena across vastly different scales from the cheerios effect on the breakfast table to the evolution of cosmic and quantum systems, has been called the 'queen mother' of science (Bush, 2015). However, a central challenge remains: ensuring the generalisability, interpretability and reliability of the machine learned models when applied to physical systems. To address this, we present a transparent approach that provides insights into how data-driven fluid dynamics and machine learning (ML) work. This is achieved by training a convolutional neural network (CNN) on data from a simple laminar fluid flow to behave as an operator that exactly matches the finite-difference numerics. Importantly, the model demonstrates strong generalisation capability by reproducing the dynamics for a wide range of distinct and unseen flow conditions within the same flow category. The CNN learns the forward Euler three-point stencil weights, capturing physical principles such as consistency and symmetry despite having only three tunable weights. Going beyond pure numerical training (numCNN), the approach is shown to work when trained on analytical (anCNN) and even molecular dynamics (mdCNN) data. In some cases, the physics is not captured, and thanks to the simple and interpretable form, these CNNs provide insight into the limits, pitfalls and best practice of data-driven fluid models. Because the approach is based on finite-difference operators and demonstrated with diffusive flow, it naturally extends to many structured-grid computational fluid dynamics (CFD) problems, including turbulent, multiphase, and multiscale flows.

Biological matrices are rich in information related to life processes, serving as invaluable media for assessing an individual's overall physiological status and its dynamic fluctuations, as well as crucial foundations for disease diagnosis. However, the inherent complexity of these matrices, coupled with our incomplete understanding of their full composition, presents significant challenges for comprehensive analysis and accurate diagnostic interpretation. The advent of single-molecule technologies has revolutionized biomedical research, enabling the direct observation of life processes at the molecular scale. We have proposed an Intelligent Nano-Fingerprinting strategy based on single-molecule nanopore technology, designed to capture the global molecular fingerprints of complex plasma matrices. Furthermore, we developed an intelligent algorithmic model capable of achieving precise classification of plasma samples. This approach is characterized by its simplicity, efficiency, and considerable potential for large-scale adoption and transferable applications.

A broadband silicon polarization beam splitter (PBS) is proposed and experimentally demonstrated based on Floquet-engineered directional couplers. The total length of the coupling structure is 20 um . By periodically modulating the waveguide width of the directional couplers, the power exchange between the two waveguides for the transverse-electric (TE) mode is suppressed, whereas the power coupling for the transverse-magnetic (TM) mode is enhanced. The fabricated PBS exhibits polarization extinction ratios (PERs) > 20 dB for both polarizations over a broad wavelength range of 1483 nm-1620 nm. Additionally, the measured insertion losses (ILs) are 0.15 dB and 1.2 dB at 1550 nm for TE and TM polarizations, respectively.

This study investigates the minimal seed for laminar-to-turbulent transition in a supersonic boundary layer at $M=3.0$ and $Re=300$ using adjoint-based nonlinear non-modal analysis. While linear theory identifies oblique waves as the optimal disturbances for transient growth, we demonstrate that nonlinear effects fundamentally alter the optimal perturbation structure as the initial amplitude exceeds a critical threshold. Our analysis reveals that the nonlinear optimal perturbation exhibits a distinctive spatial distribution characterized by flattened structures in the outer layer and streamwise vortices near the wall, leading to a more rapid transition compared to the linear counterpart. A key finding is that this nonlinear amplification mechanism remains robust even under wall-cooled conditions ($T_w = 0.6 T_{ad}$), where the disappearance of the generalized inflection point (GIP) suppresses linear instabilities of Mack's first mode. This rapid growth is driven by the nonlinear interaction between two-dimensional planar waves near the wall and staggered vortex patterns in the outer layer, facilitating streak formation through vortex self-induction. Although the late-stage evolution eventually converges to the classical oblique breakdown scenario as characterized by the formation of $\Lambda$-vortices, the identified nonlinear path significantly reduces the disturbance energy required for transition.

Neutrons produced by cosmic ray muons constitute a significant background for underground experiments investigating neutron oscillations, neutrinoless double beta decay, dark matter, and other rare event signals. This work benchmarks measured neutron yields and neutron multiplicities--with a focus on data from the Daya Bay Reactor Neutrino Experiment--against comprehensive simulations using three GEANT4 hadronic physics lists. These simulations are further refined via a TALYS-based adjustment of hadronic cross sections. For the BERT-based models, the adjustment reduces the discrepancy in the total neutron yield from about 20% to approximately 6%, while for the BIC-based models it improves the agreement from roughly 13% to the sub-percent level (~0.3%), indicating a markedly better consistency of the BIC-based models with the experimental data. Nevertheless, a clear tension persists: simulations systematically underproduce single-neutron events while overproducing multi-neutron events. The study establishes a reproducible benchmark for cosmogenic neutron modeling and proposes a targeted refinement strategy--including channel-specific reweighting and intranuclear cascade parameter tuning--to guide future model development.

We discuss the relationship between the zero modes of electromagnetic fields in a cavity resonator and the cavity's topological characteristics. We show that the dimension of the electromagnetic zero-mode space coincides with the dimension of the corresponding homology group of the cavity. Moreover, we prove that the alternating sum of the dimensions of the electromagnetic zero-mode spaces is closely related to the Euler characteristic of the cavity boundary, and hence to the integral of the curvature.

In this paper we analyze the Finnish road network as a graph in order to measure whether the "rurality" or "urbanity" of an area correlates with local geometrical properties of the graph. Our primary motivation is the observation that the road systems in rural areas look similar to hyperbolic graphs, while in large cities they resemble more the Cayley graph of $\mathbb{Z}^2$. We do not aim for a comprehensive analysis, but rather wish to demonstrate that this observation can be measured and analyzed through looking at various "hyperbolicity measures" of randomly sampled geodesic triangles in the road graph.

We present the design and simulation of a prototype gas cell for in-gas-jet laser-ionization and spectroscopy studies using the low energy branch of the SPIRAL2-S$^3$ radioactive-ion-beam facility. The prototype aims to demonstrate the possibility to reduce the extraction time of radioactive ions from the gas cell, while implementing a controlled neutralization mechanism, necessary for laser-spectroscopy studies. Different simulation methods of ion processes in gas are comparatively discussed. Design considerations and detailed simulations of the ion extraction time and efficiency are presented. A study of the dynamics of electrons obtained in the gas cell by ionization is also performed to assess the achievable electron densities.

Fisher zeros play a central role in the theoretical understanding of phase transitions. However, their computation requires knowledge of the density of states, which limits their practical applicability. Alternative approaches based on the Energy Probability Distribution (EPD) and Moment Generating Function (MGF) alleviate the computational cost but suffer from convergence issues in the two-dimensional \textbf{anisotropic Heisenberg} model (XY model). In this work, we introduce a Padé approximation to systematically reduces the number of zeros required in the Fisher, EPD, and MGF formulations without loss of accuracy. Moreover, since the Fisher zeros formulation does not rely on a convergence algorithm, their combination with a Padé approximation enables a reliable analysis of the XY model while significantly reducing computational cost. Applications to the two-dimensional Ising and XY models demonstrate substantial reductions in polynomial degree and computation time while preserving accurate estimates of the critical temperature.

The spatial-temporal beam quality of laser sources is crucial for applications such as nonlinear spectroscopy and master oscillator power amplification systems. However, the temporal stability remains challenged by issues like line-width broadening and high-power demand in efforts to improve it. In this work, we investigate the effect of the second-harmonic generation process on the laser characteristics under three longitudinal mode regimes: single-longitudinal-mode, dual-longitudinal-mode, and multi-longitudinal-mode. The results demonstrate that the second-harmonic generation process effectively stabilizes the temporal characteristics of the laser and enhances its correlation, leading to a temporally clean output beam. The physical mechanism of the observed temporal stabilization effect can be attributed to a high-peak-pulse attenuation effect, jointly induced by nonuniform longitudinal-mode depletion and phase preservation in the residual fundamental wave. Statistical analysis indicates that at the maximum fundamental-wave power in the multi-longitudinal-mode regime, the standard deviation and peak-to-valley values derived from the normalized temporal profile decrease from 0.6122 and 5.6846 for the fundamental wave to 0.189 and 0.8847 for the residual fundamental wave. Meanwhile, the background level of the intensity auto-correlation function rises from ~0.72 to ~0.96, revealing its evolution toward a more coherent state. To the best of our knowledge, this research presents the first demonstration of laser temporal stabilization and correlation enhancement via second-harmonic generation. It not only deepens the comprehension of second-harmonic generation mechanisms, but also opens up a new avenue for realizing temporal beam self-cleaning of light.

In this brief, a novel harmonic-backscatter-rectifier (HBR) is introduced for simultaneous rectification and harmonic-based uplink. The proposed (HBR) employs a rectifier to generate both DC power and the harmonic carriers for backscattering communication, and a dual-band reconfigurable band-stop filter using defected ground structure(DGS) to modulate the second and third harmonics with low-power consumption and same input impedance at f0 all the time. As a proof of concept, an HBR prototype operating at 1.85 GHz was designed and experimented. An uplink data rate of 8Mbps (4Mbps x 2 ) was achieved when the HBR was fed with -10 dBm RF power, and the data modulation consumed less than 27.7pJ/bit. In addition, a passive harmonic tag was implemented with the proposed HBR and a low-power binary sequence generator, which demonstrated a continuous uplink of 12 kbps at -6 dBm RF power.

The exploration of quantum phenomena in complex materials such as moiré superlattices is limited by the O(N^3) scaling of conventional electronic structure methods. Here we introduce a high-performance tight-binding framework that reduces the complexity to O(N^1.5) by transforming the Hamiltonian into a real symmetric form and combining Sylvester's inertia law with LDL decomposition. This approach enables efficient band structure calculations for large systems: solving magic angle twisted bilayer graphene in minutes on a laptop and scaling to 1.5 million atoms within days on a workstation. We apply it to the previously inaccessible ultra-low twist-angle regime (less than 0.16 degree) with mechanical strain relaxation and find robust flat bands persisting down to 0.09 degree. Our framework bridges density functional theory accuracy with large-scale quantum simulation, opening a route to systematic data-driven exploration of mesoscale quantum materials.

Vaccination games in higher-order settings remain underexplored, despite their importance in shaping opinions and collective decisions. Here, we introduce a parsimonious behavioral-epidemiological model to evaluate how peer reinforcement pressure influences vaccination uptake. The framework consists of a two-layer multiplex: an epidemic layer governed by the SIR process on a square lattice, and a behavioral layer represented by a hypergraph of triadic interactions. Individuals update their vaccination strategy via imitation, modulated by a reinforcement parameter $\alpha$ when peer support is present. We find that higher-order structure alone induces clusters of vaccinated individuals that act as protective barriers. Low but nonzero reinforcement ($\alpha \approx 0.5$) maximizes coverage and suppresses outbreaks, while both negligible ($\alpha \approx 0$) and moderate ($\alpha > 0.1$) reinforcement reduce uptake, as excessive confirmation lowers adaptability and enables non-vaccinators to re-emerge. Our work bridges complex contagion theory with evolutionary game dynamics, offering insights into how contact structure and peer reinforcement jointly shape vaccination behavior.

Light carries rich information across space, spectrum, polarization, and time, yet conventional cameras capture only a narrow projection of this multidimensional structure. A thin diffuser encodes wavelength-dependent information into single-shot scatterograms, captured by a polarization-resolving CMOS sensor that simultaneously measures four linear polarization states. We use 4DCam to image a live Betta splendens fish, uncovering polarization-dependent color modulations that remain invisible to conventional cameras. We experimentally show that the 4D information encoded in the scatterograms markedly improves material discrimination, achieving 96% accuracy for textile classification and 90% for camouflage detection, compared with 70% and 80%, respectively, using 3D hyperspectral imaging alone. Built entirely from passive optics, 4DCam seamlessly integrates physical encoding, generative decoding, and direct inference, enabling real-time, information-complete optical sensing.

For any kind of wave phenomenon one can find ways to derive the respective dispersion relation from experimental observations and measurements. This dispersion relation determines the structure of the wave equation and thus characterizes the dynamics of the respective wave. Different wave phenomena are thus governed by different differential equations. Here we want to emphasize the experimental approach to matter waves, but before doing so we will discuss and test the procedure for other types of waves, in particular water waves.

The accuracy of one-shot $G_0W_0$ and the Bethe-Salpeter equation (BSE) is well known to be highly sensitive to the choice of the starting-point eigensystem, typically obtained from mean-field theory. A highly effective method explored is the use of density functional approximation (DFA) with a range-separated hybrid (RSH) approach. In this work, we evaluate the performance of $G_0W_0$ in predicting ionization potentials and the BSE for describing neutral excitations, employing a recently proposed, broadly applicable, and computationally efficient range-separation tuning scheme [Singh \textit{et. al.}, Journal of Physical Chemistry Letters, 16, 32, 8198-8208, (2025)]. Our results demonstrate that this simplified tuning protocol provides an accurate starting point for many-body perturbation theory, thereby eliminating the need for conventional, multi-step optimally tuned RSH optimization procedure. The resulting quasiparticle energies from $G_0W_0$ closely reproduce reference ionization potentials, while BSE calculations based on the same tuned RSH orbitals yield quantitatively accurate optical absorption spectra and excitonic properties across a range of molecular systems and clusters.

In a recent letter [T. D. Kühne, M. Krack, F. Mohamed and M. Parrinello, Phys. Rev. Lett. 98, 066401 (2007)], we outlined a new Car-Parrinello-like approach to Born-Oppenheimer molecular dynamics. Here, we provide a guide to performing actual calculations using our method and demonstrate this on liquid water at ambient conditions. We do not go into methodological details beyond those necessary for applying this approach, but focus on practical details pertinent to our particular implementation within the CP2K/Quickstep code [T. D. Kühne et al., J. Chem. Phys. 152, 194103 (2020)].

Shock dynamics and nonlinear wave propagation are fundamental to computational fluid dynamics (CFD) and high-speed flow modeling. In this study, we developed explicit and implicit finite-difference solvers for the one-dimensional Burgers viscous equation to model shock formation, propagation, and dissipation. The governing equation, which incorporates convective and diffusive effects, serves as a simplified analogue of the Navier-Stokes equations. Using the Finite-JAX framework, each solver is implemented with upwind and central finite-difference schemes for the convective and diffusive terms, respectively. Time integration is performed using explicit forward Euler and implicit backward-time central space (BTCS) schemes under periodic and Dirichlet boundary conditions. Stability is ensured by the Courant-Friedrichs-Lewy (CFL) criteria for the convective and diffusive components. Numerical experiments quantify the accuracy, convergence, and real-time performance of JAX across CPUs, GPUs, and TPUs, demonstrating that JAX maintains fidelity while achieving portability. The results show that the explicit scheme captures impact accurately under strict time-step constraints, while the implicit formulation provides greater stability and accuracy at a higher computational cost. Taken together, these results establish a reproducible dataset for benchmarking CFD solvers and training machine learning models for nonlinear transport and impact-driven phenomena. Our new implementation of FiniteJAX enhances the portability, scalability, and performance of solvers based on the JAX framework developed by Google DeepMind.

Compact spectrometers promise to revolutionize sensing applications, offering a unique pathway to laboratory-grade analysis within a miniaturized footprint. Central to their performance is the encoding strategy to unknown spectra, which determines the efficiency, accuracy, and adaptability of spectral reconstruction. However, the absence of a unified spectral encoding framework has hindered the realization of optimal, high-performance compact spectrometers. We propose a transformative approach: an information-theoretic framework grounded in bio-inspired Bayesian expected information gain that defines the first generic light encoder for computational spectrometers. By optimizing three fundamental attributes at the lowest level of physical hierarchy, (1) orthogonality, (2) completeness, and (3) sparsity, we establish a design paradigm that transcends conventional encoding hardware limitations. We validate this paradigm with the first generic encoder capable of dynamically reconfiguring its response matrices. Experiments show superior reconstruction fidelity across diverse spectral regimes, enabling tunable spectral encoding tailored to varied input features. An ultra-high resolution of 6 pm and a broad measurable bandwidth of 30 nm are experimentally validated. By bridging the gap between theoretical encoding principles and reconfigurable hardware, our framework defines a coherent basis for future advances in compact spectrometry.

In this paper we examine the flow generated by coupled surface and internal small-amplitude water waves in a two-fluid layer model, where we take the upper layer to be rotational (constant vorticity) and the lower layer to be irrotational. The presence of vorticity greatly complicates the underlying analysis, yet it generates a rich array of otherwise unobservable phenomena such as the presence of critical layers, and stagnation points, in the fluid interior. We employ a phase-plane analysis to elucidate the qualitative behaviour of streamlines for a range of different coupled-wave, and vorticity, regimes. Although the water waves considered are linear in the fluid dynamics sense, the dynamical systems which govern their motion are nonlinear.

The El Niño-Southern Oscillation (ENSO) is a dominant mode of interannual climate variability, yet the mechanisms limiting its long-lead predictability remain unclear. Here we develop a physics-guided Deep Echo State Network (DESN) that operates on physically interpretable climate modes selected from the extended recharge oscillator (XRO) framework. DESN achieves skillful Niño3.4 predictions up to 16-20 months ahead with minimal computational cost. Mechanistic experiments show that extended predictability arises from nonlinear coupling between warm water volume and inter-basin climate modes. Error-growth analysis further indicates a finite ENSO predictability horizon of approximately 30 months. These results demonstrate that physics-guided reservoir computing provides an efficient and interpretable framework for diagnosing and predicting ENSO at long lead times.

Predicting non-covalent host-guest recognition remains challenging due to the complex interplay of electrostatics, dispersion, and steric effects, and the limited transferability of existing docking approaches to synthetic supramolecular systems. Here we present DeepHostGuest, a geometric deep-learning framework that learns generalizable recognition principles directly from experimentally resolved host-guest structures. Hosts are encoded as electrostatic surfaces and guests as molecular graphs, enabling transferable learning across diverse supramolecular systems. DeepHostGuest achieves high-accuracy predictions (RMSD $\leq 2$ Angstrom for 80.8% of test cases), substantially outperforming classical docking without case-specific tuning. Notably, the method generalizes beyond its training domain to crystalline sponge systems, accurately capturing the binding of large amphiphilic molecules within metal-organic cages. Beyond predicting binding conformations, the structures generated by DeepHostGuest serve as a reliable basis for accurate binding free-energy calculations. Density Functional Theory (DFT)-calculated affinities correlate well with experiment, enabling structure-property relationships across 876 host-guest complexes spanning 34 host families. Interpretable feature analysis reveals that binding strength arises from a cooperative interplay of host polarity, guest hydrophobicity, and geometric complementarity, with distinct design regimes across supramolecular classes. Together, these results establish data-driven molecular recognition as a practical route to predictive supramolecular design, enabling high-throughput virtual screening and rational optimization of functional host-guest systems.

We uncover a universal scaling law governing the dispersion of collective attention and identify its underlying stochastic criticality. By analysing large-scale ensembles of Wikipedia page views, we find that the variance of logarithmic attention grows ultraslowly, $\operatorname{Var}[\ln{X(t)}]\propto\ln{t}$, in sharp contrast to the power-law scaling typically expected for diffusive processes. We show that this behaviour is captured by a minimal stochastic differential equation driven by fractional Brownian motion, in which long-range memory ($H$) and temporal decay of volatility ($\eta$) enter through the single exponent $\xi\equiv H-\eta$. At marginality, $\xi=0$, the variance grows logarithmically, marking the critical boundary between power-law growth ($\xi>0$) and saturation ($\xi<0$). By incorporating article-level heterogeneity through a Gaussian mixture model, we further reconstruct the empirical distribution of cumulative attention within the same framework. Our results place collective attention in a distinct class of non-Markovian stochastic processes, with close affinity to ageing-like and ultraslow dynamics in glassy systems.

Low-scattering, deep-penetration light transport in biological media remains a pivotal challenge for biophotonic technologies, including biomedical imaging, optical diagnostics, and photodynamic therapy. This review builds upon and extends our earlier studies of nonlinear optical self-trapping and optically induced waveguiding in biological suspensions, such as human erythrocytes and cyanobacteria, where light-matter coupling is governed by optical-force-mediated particle redistribution. Recent progress has revealed increasingly rich and complex regimes, including the propagation and nonlinear self-action of structured (vortex) beams in biological environments, as well as nonlinear responses dominated by thermally driven mechanisms in absorptive biomolecular solutions (e.g., heme and chlorophyll). We place particular emphasis on distinctive nonlinear phenomena observed in these systems, including spatial self-phase modulation, optical-force-induced sculpturing of effective energy landscapes, and quasi-waveguide formation in soft, heterogeneous biological media. We conclude by highlighting emerging opportunities to harness these nonlinear behaviors for deep-tissue imaging, label-free biosensing, and the realization of biocompatible photonic structures and devices assembled directly from living or hybrid biological matter.

The parameters of uniformly accelerated reference frame s three equivalent ways is calculated. The article also found explicitly transformation to uniformly accelerated reference frame and proved the assertion that Thomas precession and Wigner rotation s in opposite directions and cancel each other out.

The next-generation liquid xenon experiments like PandaX-xT target an energy range from sub-keV to multi-MeV to address the requirement of multiple physics searches. The Hamamatsu R12699-406-M4 photomultiplier tubes (PMTs) were developed and selected as photon sensors for PandaX-xT. Their voltage-divider base is optimized for a broad dynamic range, from single-photoelectron (SPE) sensitivity to 30~nC collected charge (matching the 2.5~MeV Q-value of $^{136}$Xe neutrinoless double beta decay~(NLDBD)). Using a dedicated test bench, we characterize the saturation and suppression responses of R12699-406-M4 PMTs with this base design. Based on measured PMT-base responses, we develop a circuit simulation model that accurately reproduces the physical mechanisms underlying these effects with key parameters tuned via experimental data. The combined simulation and bench-test approach guides base design and optimization, enabling improved detector dynamic range and supporting future saturation and suppression correction studies in data analysis.

This work proposes for the first time a novel optimization method for numerical algorithms, which takes advantages of machine learning frameworks PyTorch and TensorRT, leveraging their modularity, low development threshold, and automatic tuning characteristics to achieve a real-time plasmas reconstruction algorithm called PTEFIT as an application in tokamak-based controlled fusion that combines performance, flexibility, and usability. The algorithm has been deployed and routinely operated on the EXL-50U spherical tokamak, with an average inference time of only 0.268ms per time slice at $129\times 129$ resolution, and has successfully driven feedback control of the maximum radial position of plasmas and isoflux control. We believe that its design philosophy has sufficient potential to accelerate development and optimization in GPU parallel computing, and is expected to be extended to other numerical algorithms.

A microwave rectifier at 5.8 GHz without any capacitors is presented, which owns a measured MW-to-DC conversion efficiency of 68.1%. A harmonic rejection filter and a DC pass filter, which replace lumped capacitors in conventional microwave rectifiers, are applied to suppressing the harmonics produced by an HSMS-286 Schottky diode during rectifying. At the fundamental frequency, a microstrip impedance transformer which contains a shunt {\lambda}g/8 short-ended microstrip transmission line and two short series microstrip transmission lines are applied to compensating the imaginary impedance of the diode and matching the input impedance of the rectifier. The measured MW-to-DC conversion efficiency agrees well to the simulated results. The novel rectifier without any lumped passive elements may be applied for power transmission system at higher microwave frequencies.

Complex systems exhibit rich equilibrium states, yet the universal principles governing these systems remain unrevealed, motivating the search for novel experimental platforms. Random fiber lasers (RFLs), which generate partially-coherent light-wave through feedback from Rayleigh scattering, provide a photonic realization of such systems. Here we report a comprehensive theoretical and experimental investigation of photon statistics for RFLs based on classical second-order temporal correlation function \( g^{(2)}(\tau) \), revealing unique statistical properties and introduce a two-dimensional framework for controlling photon statistics. Remarkably, we establish a unified landscape between photon correlation, intensity statistics governed by Levy statistics, and phase transitions with replica symmetry breaking. This multifaceted relationship, observed for the first time, bridges disordered photonics with statistical physics of complex system. Our results offer new pathways for engineering laser emission with controllable photon statistics, and more broadly, this work positions RFLs as a fertile land for exploring emergent behaviors in disordered systems.

Surface acoustic wave platforms based on piezoelectric thin-film heterostructures provide sub-wavelength acoustic confinement, making them attractive for compact nonlinear phononic systems with applications including frequency conversion, parametric interactions, and nonlinear signal processing. Here, we investigate guided surface acoustic wave phononic four-wave mixing at gigahertz frequencies in an aluminum scandium nitride/4H-silicon carbide heterostructure operated at both room temperature (295 K) and cryogenic temperature (4 K). The 500 nm thick aluminum scandium nitride film supports guided Rayleigh and Sezawa modes with distinct displacement and strain energy density distributions, allowing a direct comparison of mode-dependent nonlinear behavior within the same device. Continuous-wave four-wave mixing measurements reveal an enhancement in the extracted modal nonlinear coefficient at 4 K relative to 295 K for both modes. In addition, the Rayleigh mode exhibits a modal nonlinearity approximately two orders of magnitude larger than that of the Sezawa mode across both temperature regimes. These results demonstrate that phononic four-wave mixing is strongly influenced by temperature, mode confinement, and strain localization while establishing aluminum scandium nitride on silicon carbide heterostructures as a promising platform for engineering enhanced nonlinear phononic interactions for future classical and quantum acoustic on-chip signal processing systems.

We investigate nonlinear optical absorption in diamond crystals containing high densities of nitrogen vacancy (NV) centers using open-aperture Z-scan measurements with 230 fs laser pulses at 1032 nm, within the transparency window of diamond. While high-purity electronic-grade diamond exhibits third-order nonlinear absorption, NV-doped samples display pronounced saturable absorption that strengthens with increasing defect concentration. Linear transmission spectroscopy reveals that, in addition to NV centers, the crystals host significant populations of H2 (NVN-) defect complexes whose absorption band partially overlaps the excitation wavelength. By correlating spectroscopic data with nonlinear measurements and modeling the response using an effective two-level system, we show that the observed saturation cannot be attributed solely to NV centers but arises from the combined contribution of NV-related and H2 defects. For the highly doped sample, we determine an effective linear absorption coefficient of alpha0 = 6.52 cm-1 and a saturation intensity of Is = 40.0 GW/cm2. These findings highlight the critical role of the complex defect landscape in governing the nonlinear optical response of NV-doped diamond and underscore the necessity of accounting for ancillary defect species in the design of diamond-based nonlinear and quantum photonic devices.

Ultrasound vortices have rapidly expanded their applications to areas like particle trapping, contactless manipulation, acoustic communications. In ultrasonic imaging and therapy involving bone tissues, these vortex beams offer intriguing possibilities but transmitting them through bone (especially the skull) poses challenges. Traditional acoustic lenses were engineered to rectify skull-induced beam aberration, and their capacity was limited to generating only static ultrasound fields within the brain. To overcome this constraint, our study presents a novel method for transcranially steering focused ultrasound vortex using 3D printed binary acoustic metasurfaces (BAMs) with a thickness of 0.8 {\lambda}. We tackled the challenge of skull-induced phase aberration by computing the phase distribution via a time reversal technique, which concurrently enabled the generation of a steerable focused vortex inside an ex vivo human skull by adjusting the operating frequency. Both numerical and simulations experiments were conducted to validate the capabilities of BAMs. Furthermore, we explored the generation of higher-order topological charge acoustic vortices within the brain utilizing the BAM. This development paves the way for designing cost-effective particle-trapping systems, facilitating clot manipulation, and applying acoustic-radiation forces and torques within or across bone structures, thus presenting a new frontier for potential biomedical applications.

A nonlinear rectangular patch antenna (RPA) is presented in which back-to-back Schottky diodes are embedded at high-field regions to enable passive, bias-free harmonic generation. The self-biased diodes introduce a power-dependent impedance that drives efficient frequency up-conversion and selective third-harmonic radiation. A tailored matching network enhances third-harmonic excitation and coupling while preserving radiation efficiency at the fundamental frequency. Analytical modeling combined with SPICE-assisted full-wave time-domain simulations predicts strong odd-harmonic content, and measurements on RPA prototypes employing SMS7630 diodes confirm these results. Simulated and measured S-parameters and far-field patterns at 925 MHz and 2.775 GHz show excellent agreement. The demonstrated approach establishes nonlinear loading as an effective mechanism for passive harmonic control in compact radiators, enabling frequency-agile and spectrum-efficient antenna systems.

We investigate the nonlinear formation of plasmoids in 2D low-beta current sheets through the interplay between the Kelvin-Helmholtz instability (KHI) and the lower-hybrid drift instability (LHDI). Using a hybrid kinetic-gyrokinetic model-based Super Simple Vlasov (ssV) code with fully kinetic ions and drift-kinetic electrons, we simulate Harris-type current sheets and velocity shear layers with strong cross-field density gradients. Our central hypothesis is that steep density gradients drive LHDI, which can grow faster than KHI and initiate an inverse cascade from kinetic to fluid scales, potentially suppressing KHI. Our simulations confirm that, in thin current sheets, LHDI develops rapidly at the sheet edges and nonlinearly merges into larger-scale magnetic islands before KHI can evolve. These LHDI-driven structures distort the velocity shear and suppress classical KH vortices. In contrast, for thicker current sheets or weaker density gradients, KHI dominates and produces the expected rolled-up vortices and associated plasmoids. These findings demonstrate that LHDI-induced turbulence can act as both a seed and a regulator of plasmoid-generating instabilities, mediating cross-scale energy transfer. This mechanism is relevant to thin boundary layers in space plasmas, such as the solar wind magnetosphere interface, and suggests that microturbulence can govern large-scale magnetic topology during collisionless reconnection.

Non-axisymmetric magnetic field coils have been designed to provide efficient error field correction and suppress edge localized modes in SPARC - a compact high-field tokamak that is presently under construction at Commonwealth Fusion Systems. These designs utilize the Generalized Perturbed Equilibrium Code's (GPEC's) representation of the multi-modal, non-axisymmetric plasma response to optimize the geometric coupling between 3D coil arrays and the desired core or edge plasma response. Error field correction coils are designed to couple to the plasma-amplified kink that dominates the drive of core resonances. The maximum allowable error field is projected to SPARC using an empirical scaling that is consistent with linear and nonlinear MHD modeling expectations. Asymmetric construction and assembly tolerances are then balanced against the corresponding kA-turns needed for correction to levels below the allowable limit. These physics-driven coil designs provide confidence in our ability to operate SPARC in new high field tokamak regimes without error field induced locked modes.

Niobium oxide diiodide (NbOI2) is an emerging material for photonics and electronics, distinguished by its exceptional second-order nonlinearity and pronounced in-plane ferroelectricity, both originating from its highly anisotropic ABC-stacked crystal structure. Its broken inversion symmetry enables its optical nonlinear efficiency to scale with thickness, making multilayer NbOI2 highly promising for nonlinear frequency conversion like second harmonic generation or and spontaneous parametric down-conversion in bulk or waveguides. However, under ambient conditions NbOI2 degrades into an amorphous oxide within weeks, severely diminishing its nonlinear response. To overcome this, we investigate SiO2 encapsulation via physical vapor deposition to protect NbOI2 multilayers from environmental degradation. Our systematic study reveals that encapsulation preserves structural integrity and nonlinear optical performance, establishing NbOI2 as a stable candidate for heterogeneous integration in foundry-compatible photonic platforms and quantum technologies.

A dedicated R\&D is ongoing for the charged particle identification system of the \mbox{ALICE 3} experiment proposed for the LHC Run 5 and beyond. One of the subsystems for the high-energy charged particle identification will be a Ring-Imaging Cherenkov (RICH) detector. The possibility of integrating Cherenkov-based charged particle timing measurements is currently under study. The proposed system is based on a proximity-focusing RICH configuration including an aerogel radiator separated from a SiPM array layer by an expansion gap. A thin high-refractive index window of transparent material, acting as a second Cherenkov radiator, is glued on the SiPM array to enable time-of-flight measurements of charged particles by exploiting the yield of Cherenkov photons in the thin window. We assembled a small-scale prototype instrumented with different Hamamatsu SiPM array sensors with pitches ranging from 1 to 3 mm, readout by custom boards equipped with the front-end Petiroc 2A ASICs to measure charges and times. The primary Cherenkov radiator consisted of a 2 cm thick aerogel tile, while various window materials, including SiO$_2$ and MgF$_2$, were used as secondary Cherenkov radiators. The prototype was successfully tested in a campaign at the CERN PS T10 beam line with pions and protons. This paper summarizes the results achieved in the 2023 test beam campaign.

The ALICE Collaboration is proposing a completely new apparatus, ALICE 3, for the LHC Run 5 and beyond. A key subsystem for charged particle identification will be a Ring-Imaging Cherenkov (RICH) detector consisting of an aerogel radiator and a photosensitive surface based on Silicon Photomultiplier (SiPM) arrays in a proximity-focusing configuration. A thin high-refractive index slab of transparent material (window), acting as a second Cherenkov radiator, is glued on the entrance face of the SiPM arrays to achieve precise charged particle timing. Requiring time matching between aerogel Cherenkov photon and track hits leads to an improvement of pattern recognition by discarding the uncorrelated SiPM dark count hits. In this work we present the current status of the R\&D performed for the ALICE 3 RICH detector prototype and the expected full scale system performance. A special focus will be given to the beam test results obtained with a small-scale prototype instrumented with various array of Hamamatsu SiPMs with pitches ranging from 1 to 3 mm. The Cherenkov radiator consisted of a 2 cm thick aerogel tile with a refractive index of 1.03 at 400 nm wavelength. For timing measurements SiPM arrays coupled with two different window materials (SiO$_2$ and MgF$_2$) were used. The prototype was successfully tested in beam test campaigns at the CERN PS T10 beam line. The data were collected with a complete chain of front-end and readout electronics based on the Petiroc 2A and Radioroc 2 together with a picoTDC to measure charges and times. We measured a charged particle detection efficiency above 99\% and a single photon angular resolution better than 4.2 mrad at the Cherenkov angle saturation with a time resolution better than 70 ps for charged particles.

The ALICE Collaboration is proposing a completely new apparatus, ALICE~3, for the LHC Runs~5 and beyond. In this context, a key subsystem for high-energy charged particle identification will be a proximity-focusing ring-imaging Cherenkov detector using aerogel as radiator and silicon photomultipliers (SiPMs) as photon sensors. We assembled a small-scale prototype instrumented with Hamamatsu S13352 and S13361-3075AE-08 SiPM arrays, readout by custom boards equipped with front-end Petiroc 2A ASICs. The Cherenkov radiator consisted of a 2 cm thick hydrophobic aerogel tile with a refractive index of 1.03 separated from the SiPM plane by a 23 cm expansion gap. The prototype was successfully tested in a campaign at the CERN PS T10 beam line with the goal of validating the design bRICH specifications in terms to achieve the target separation power. We measured a single photon angular resolution of 3.8~mrad at the Cherenkov angle saturation value of 242~mrad, as well as the expected scaling of the angular resolution with the increasing number of detected photons. We also studied the contribution of uncorrelated and correlated background sources with respect to the signal and proved the effectiveness of time matching between charged tracks and photon hits to achieve efficient suppression of the SiPM dark count rate background. In this paper, the detector concept, the description of the tested prototype layout and the main beam test results are reported.

Small-scale intermittency is a defining feature of fully developed fluid turbulence, marked by rare and extreme fluctuations of velocity increments and gradients that defy mean-field descriptions. Existing multifractal descriptions of intermittency focus primarily on longitudinal increments and gradients, despite mounting evidence that transverse components exhibit distinct and stronger intermittency. Here, we develop a unified multifractal framework that jointly prescribes longitudinal and transverse velocity increments, and extends to gradients. We derive explicit relations linking inertial-range scaling exponents of structure functions to moments of velocity gradients in dissipation range. Our results reveal that longitudinal gradient scaling is solely prescribed by longitudinal structure functions, as traditionally expected; however, transverse gradient scaling is prescribed by mixed longitudinal-transverse structure functions. Validation with high-resolution direct numerical simulations of isotropic turbulence, at Taylor-scale Reynolds number up to $1300$ demonstrates excellent agreement, paving way for a more complete and predictive description of intermittency faithful to the underlying turbulence dynamics.

Micro-Pattern Gas Detectors (MPGDs) are extensively employed in modern high-energy and nuclear Physics experiments because of their excellent spatial resolution, high rate capability, and operational stability. Among these, the Gas Electron Multiplier (GEM) has emerged as one of the most widely adopted MPGD technologies. Despite their widespread adoption, GEM detectors based on the conventional bi-conical hole geometry do not always achieve optimal performance, particularly in maximizing effective gain while suppressing ion backflow. One of the primary factors limiting a GEM's performance is ion backflow. The accumulation and gradual discharge of these ions might alter the local electric field, resulting in a temporary dead time and complicating responses to subsequent events. These limitations pose challenges for applications requiring high precision and stable long-term operation. In this work, we address these issues by investigating modified GEM geometries designed to enhance gain performance and reduce ion backflow, thereby improving overall detector performance. The current study investigates geometric optimization strategies for a triple-GEM detector to enhance performance, mitigate ion backflow, and augment gain. The detector structures were designed using the ANSYS Mechanical APDL, and the associated electrostatic field configurations were computed using the ANSYS Maxwell. A thorough investigation of gain and ion backflow calculations was carried out when the generated field maps were interfaced with Garfield$^{++}$. The potential enhancements in detector efficiency and stability that the proposed modifications to the GEM foil geometry offers a valuable insights for the design of next-generation gaseous detectors.

Photonic crystals allow unprecedented control over how light is confined, propagates, and interacts with matter. Their development has had a transformative impact on optics and physics, and they remain the central platform for both fundamental discoveries and practical photonic technologies. However, the relatively large footprint and substrate-bound nature of photonic crystal structures have so far strongly limited their use as miniature optical devices or biointegrated sensors. Here, we overcome these limitations by identifying the minimal size of a 2D photonic crystal array needed to achieve lasing and describe the fabrication of substrate-less hexagonal laser particles with an active area as small as 30 {\mu}m2. Massively parallel fabrication, robust detachment, and integration of the nanolaser particles into live cells is demonstrated. Crucially, by engineering spatial and spectral mode characteristics, we designed NIR-II probes with mode volumes on the order of tens of attolitres, an order of magnitude smaller than whispering gallery probes of similar dimensions. Such high light localization is comparable in scale to different organelles of eucaryotic cells. In the future, we expect that chemical or plasmonic functionalization of the device will enable label-free sensing of nanoscale intracellular processes, and that it shall serve as a miniature platform to exploit developments in optical and quantum sensing for chemical and biological applications.

Modern cryopreservation exists at the convergence of diverse disciplines--materials science, physical chemistry, mechanical engineering, biological engineering, etc.--and emerging technologies often draw from many of these disciplines simultaneously. Thermodynamics, as one of the foundational theories underlying both physical and biological science, provides a framework through which to understand these interdisciplinary technologies, yet the full kit of requisite thermodynamic tools is not housed within any one discipline. This Chapter aims to articulate a foundational thermodynamic approach to the description, interrogation, and design of modern cryopreservation technologies, and to review the state of the art in emerging cryopreservation technologies through the lens of this approach. We focus in particular on the management of phase change across equilibrium-driven techniques (e.g., liquidus tracking, partial freezing, isochoric freezing), kinetics-driven techniques (e.g. supercooling, ice seeding), and transport-driven techniques (e.g. directional freezing, droplet approaches), and we hope to equip the reader with a self-consistent theoretical toolkit that enables meaningful comparison of these techniques from a thermodynamic perspective.

The design-make-test-analyze cycle in early-stage drug discovery remains constrained primarily by the "make" step: small-molecule synthesis is slow, costly, and difficult to scale or automate across diverse chemotypes. Enumerated chemical spaces aim to reduce this bottleneck by predefining synthesizable regions of chemical space from available building blocks and reliable reactions, yet existing commercial spaces are still limited by long turnaround times, narrow reaction scope, and substantial manual decision-making in route selection and execution. Here we present the first version of onepot CORE, an enumerated chemical space containing 3.4B molecules and corresponding on-demand synthesis product enabled by an automated synthesis platform and an AI chemist, Phil, that designs, executes, and analyzes experiments. onepot CORE is constructed by (i) selecting a reaction set commonly used in medicinal chemistry, (ii) sourcing and curating building blocks from supplier catalogs, (iii) enumerating candidate products, and (iv) applying ML-based feasibility assessment to prioritize compounds for robust execution. In the current release, the space is supported by seven reactions. We describe an end-to-end workflow - from route selection and automated liquid handling through workup and purification. We further report validation across operational metrics (success rate, timelines, purity, and identity), including NMR confirmation for a representative set of synthesized compounds and assay suitability demonstrated using a series of DPP4 inhibitors. Collectively, onepot CORE illustrates a path toward faster, more reliable access to diverse small molecules, supporting accelerated discovery in pharmaceuticals and beyond.

Hybrid-Vlasov simulations resolve ion-kinetic effects for modeling the solar wind-magnetosphere interaction, but even 5D (2D + 3V) simulations are computationally expensive. We show that graph-based machine learning emulators can learn the spatiotemporal evolution of electromagnetic fields and lower order moments of ion velocity distribution in the near-Earth space environment from four 5D Vlasiator runs performed with identical steady solar wind conditions. The initial ion number density is systematically varied, while the grid spacing is held constant, to scan the ratio of the characteristic ion skin depth to the numerical grid size. Using a graph neural network architecture operating on the 2D spatial simulation grid comprising 670k cells, we demonstrate that both a deterministic forecasting model (Graph-FM) and a probabilistic ensemble forecasting model (Graph-EFM) based on a latent variable formulation are capable of producing accurate predictions of future plasma states. A divergence penalty is incorporated during training to encourage divergence-freeness in the magnetic fields and improve physical consistency. For the probabilistic model, a continuous ranked probability score objective is added to improve the calibration of the ensemble forecasts. When trained, the emulators achieve more than two orders of magnitude speedup in generating the next time step relative to the original simulation on a single GPU compared to 100 CPUs for the Vlasiator runs, while closely matching physical magnetospheric response of the different runs. These results demonstrate that machine learning offers a way to make hybrid-Vlasov simulation tractable for real-time use while providing forecast uncertainty.

Measuring radon emanation from detector materials is a key method for controlling radon, a significant background in rare-event physics experiments. Methods for measuring radon emanation are well-established but have predominantly focused on the 222Rn isotope, the dominant radon isotope for these backgrounds. However, measurements of 220Rn (thoron), the second most abundant radon isotope, remain relatively unexplored. 220Rn emanation measurements are challenging because the 220Rn must be transferred from the emanation chamber to the active detector within its short 55 s half-life. In this study, a direct in-chamber approach for measuring 220Rn emanation is presented in which the sample is placed directly within the active detector chamber, thereby minimising losses during transfer. The method was demonstrated with a DURRIDGE RAD8 electrostatic radon detector, which measured 220Rn emanation from low-activity thoriated rods with an activity of 76 +/- 20 mBq. Compared with a conventional flowthrough 220Rn emanation setup, the in-chamber method increased sensitivity by a factor of 3. Using helium as the carrier gas provided a further sensitivity increase, giving an overall sensitivity gain of ~5. These results indicate that in-chamber 220Rn emanation measurements provide an effective tool for low-background experiments and have the potential to accelerate radon studies by exploiting the shorter half-life of 220Rn.

Accurate determination of transition states remains central to understanding reaction kinetics. Double-ended methods like the Nudged Elastic Band (NEB) ensure relevant transition states and paths, but incur high computational costs and suffer stagnation on flat or rough potential energy surfaces. Conversely, single-ended eigenmode-following techniques offer efficiency but cannot often be constrained between specific states. Here, we present the Reorienting Off-path Nudged Elastic Bands (RONEB), an adaptive hybrid algorithm that integrates the double ended nature of the NEB with the acceleration of single ended Min-Mode Following methods. RONEB provides stability based on the history of the path optimization, relative force triggering, and an alignment-based back-off penalty to dynamically decouple the climbing image from the elastic band constraints. We benchmark the method against the standard Climbing Image NEB (CI-NEB) across the Baker-Chan transition state test set using the PET-MAD machine-learned potential and the OptBench Pt(111) heptamer island surface diffusion set. A Bayesian analysis of the performance data quantifies a median reduction in gradient calls of 46.3% [95% CrI: -54.7%, -36.9%] relative to the baseline, while surface diffusion tests reveal a 28% reduction across 59 metallic rearrangement mechanisms. These results establish RONEB as a highly effective tool for high-throughput automated chemical discovery.

Active arterial mechanics, governed by vascular smooth muscle contraction, are critical to physiological regulation, cardiovascular disease progression, and clinical diagnosis. Although various in vivo methods have been developed to assess arterial stiffness, most cannot distinguish the contribution of smooth muscle tone; therefore, quantitative characterization of arterial activity remains challenging. In this study, we developed a pressure-area analysis framework integrating ultrasound imaging, blood pressure measurement, neural network-based segmentation of arterial cross-sectional area, and biomechanical model-driven inversion to infer active mechanical properties. A total of 233 volunteers (aged 18-65 year) were recruited to acquire cross-sectional ultrasound videos of the right common carotid artery for training the neural network. The segmentation results demonstrate good spatial and temporal performance of the neural network. We further recruited 10 additional volunteers (aged 25 +/- 3 year) to perform a 1-minute step test, followed by pressure-area measurements over a 30-minute recovery period. Using the proposed approach, we quantified post-exercise changes in carotid arterial active mechanics relative to baseline (i.e., the resting state). Results showed that active mechanics remained elevated for approximately 15 minutes compared to baseline (p < 0.05), whereas systolic pressure differed significantly only within the first approximately 5 minutes post-exercise (p < 0.001). These results indicate a dissociation between blood pressure and smooth muscle recovery, which may offer new insight into vascular smooth muscle regulation during physiological stress.

Combining high-speed photography with direct force measurements, we investigate the impact dynamics of drops of cornstarch-water mixtures -- a premier example of shear-thickening fluids -- across a wide range of impact conditions. Our study identifies three distinct impact regimes. In addition to the liquid-like and solid-like behaviors generally expected for the impact-induced response of shear-thickening fluids, we uncover a counterintuitive regime in which high-concentration cornstarch-water mixtures display a liquid-like response at the onset of impact when shear rates are high and only transition to a solid-like behavior at later times as shear rates reduce. By integrating the classic drop-impact theory with the Reynolds-Darcy mechanism for dilatancy, we develop a unified model that quantitatively describes the impact dynamics of shear-thickening drops across all regimes. Our work reveals the unexpected response of shear-thickening fluids to ultra-fast deformation and advances fundamental understanding of drop impact for complex fluids.

The problem of spatially selective radio-frequency (RF) pulse design in magnetic resonance imaging (MRI) is typically stated in the form of determining, analytically or numerically, RF waveforms to be applied in synchrony with one or more predetermined gradient waveforms. In most cases, the dynamics of the nuclear spin magnetization under the RF and gradient fields is described in a global rotating frame that cancels the effect of the static (main) magnetic field B0. In this work, we consider an alternative frame of reference, which can be called a local rotating frame where total longitudinal magnetic field (B0 plus gradient) in every voxel is zero. In this frame, the effect of time-dependent gradient field is integrated out, and the remaining magnetization dynamics, governed by much weaker RF fields, becomes both simpler and slower. We show that recasting existing RF design methods in such a frame provides useful insights and techniques that are not obvious in the conventional description. The methods we consider include (i) two-dimensional spatial RF pulse design in the excitation k-space, (ii) Shinnar-Le Roux RF design, (iii) residual phase calculation in slice-selective excitation, and (iv) iterative and numerical solutions for multi-coil RF pulse design. In particular, we show that the new formalism can substantially reduce the Bloch simulation time which can greatly benefit iterative pulse designs in parallel transmit. In all, the proposed framework provides considerable theoretical insights and practical utility for RF pulse design in MRI.

Scientific attention is unevenly distributed, creating inequities in recognition and distorting access to opportunities. Using citations as a proxy, we quantify disparities in attention by gender and institutional prestige. We find that women receive systematically fewer citations than men, and that attention is increasingly concentrated among authors from elite institutions -- patterns not fully explained by underrepresentation alone. To explain these dynamics, we introduce a model of citation network growth that incorporates homophily (tendency to cite similar authors), preferential attachment (favoring highly cited authors) and group size (underrepresentation). The model shows that disparities arise not only from group size imbalances but also from cumulative advantage amplifying biased citation preferences. Importantly, increasing representation alone is often insufficient to reduce disparities. Effective strategies should also include reducing homophily, amplifying the visibility of underrepresented groups, and supporting equitable integration of newcomers. Our findings highlight the challenges of mitigating inequities in asymmetric networks like citations, where recognition flows in one direction. By making visible the mechanisms through which attention is distributed, we contribute to efforts toward a more responsible web of science that is fairer, more transparent, and more inclusive, and that better sustains innovation and knowledge production.

A gauge theory with an indefinite metric without negative probabilities is given by extending quantum mechanics, where a general metric is introduced, and the invariance under the general linear transformation is imposed on the space of quantum states. On this basis, we construct and investigate a chiral sextet model, which has one more Lorentz symmetry in the gauge space, to derive much properties of the standard electroweak theory, and also Einstein gravity, when the double Lorentz symmetry spontaneously fuses into one.

Liquid crystal whispering gallery mode microlasers show high sensitivity to external stimuli and distinct spectral features, rendering them ideally suited for various sensing applications. They also offer intrinsic anisotropic optical properties, which can be used to shape and manipulate light even inside spatially highly symmetric structures. Here, we report the synthesis and detailed optical characterization of a spherical bipolar liquid crystal polymer microlaser that tightly confines the optical path of whispering gallery modes to the equatorial plane. By controlled anchoring of the liquid crystal mesogens followed by polymerization, a fixed refractive index gradient is formed within the spherical microcavity. Consequently, only transverse electric (TE) modes oscillating in the equatorial plane experience the high extraordinary refractive index, allowing to confine lasing into a single plane. Furthermore, we observe that the refractive index gradient causes a characteristic splitting of the TE modes. By combining hyperspectral imaging and analytical modeling, we demonstrate that the observed splitting is caused by lifting of the energy degeneracy of higher order azimuthal laser modes, enabling direct insights into the complex interplay of refractive index gradients and resulting whispering gallery mode confinement. In addition, the unique ability to confine lasing of a spherical microbead into only a single plane makes these microlasers independent of the exact position of the pump beam, which allows consistent localized sensing especially in combination with fast point scanning microscopes or inside highly dynamic biological environments.

Metasurfaces are extremely useful for controlling and manipulating electromagnetic waves. Full-wave numerical simulation is highly desired for their design and optimization, but it is notoriously difficult, even for two-dimensional metasurfaces, when they comprise a huge number of subwavelength elements. This paper focuses on two-dimensional non-periodic metasurfaces that contain only a relatively small number of distinct subwavelength elements. We develop an efficient numerical method based on Neumann-to-Dirichlet operators, the finite element method and local function expansions. Our method drastically reduces the total number of unknowns and is capable of simulating two-dimensional metasurfaces with $10^{5}$ subwavelength elements on a personal computer. Numerical examples demonstrate that the method maintains high accuracy while offering significant advantages in both computational time and memory usage compared to the classical full-domain finite element method, making it particularly suited for the analysis of large metasurfaces.

The physical behavior of discharges in Z pinch devices can be completely deciphered in terms of only three dimensionless parameters. These parameters can be arranged in a way that draw a surface in 3D space. This surface compiles all the accessible information on the macroscopic physical behavior of each possible Z pinch discharge. We analyze the practical problems the drawing of this surface encounters and in view of the situation, we devote the remainder of the article to outline a feasible method for estimating the plasma temperature in Z pinch discharges.

Although solid-state platforms underpin modern electronics, little is known about how intense ultrashort light pulses carrying orbital angular momentum (OAM) interact with solids. This gap persists even though, for more conventional light-matter interactions, the complex underlying electron dynamics can often be confined to a single Brillouin zone and described well within the dipole approximation. Previous studies were restricted to nonlinear, perturbative regimes, largely because the generation of intense ultrashort vortex pulses, particularly in the mid-infrared spectral regime, has remained a long-standing challenge. Consequently, the role of structured light in driving nonlinear, non-perturbative processes in solids, and the associated transfer of angular momentum during these interactions, has not been systematically explored. Here, we investigate solid-state high-harmonic generation (HHG) driven by intense ultrashort structured light using a versatile experimental approach applicable to different materials and geometries. We demonstrate that the OAM of the driving field is coherently transferred to the emitted harmonics. In particular, we show that the OAM is conserved independently of the crystal symmetry, the range of electronic interactions, and the presence of strong spin-orbit coupling. These results establish OAM-resolved HHG as a robust framework for characterizing and controlling angular momentum transfer in solid-state HHG and open new avenues for structured-light-driven quantum technologies and topological materials investigations.

Non-Hermitian photonics provides a fertile platform for exploring phenomena with no Hermitian counterparts, including the non-Hermitian skin effect and exceptional points, with direct relevance for integrated photonic technologies. In this work, we investigate the properties of non-Hermitian second-order topological phases by constructing a photonic kagome crystal with balanced gain and loss, and reveal the interplay between higher-order topology and the non-Hermitian skin effect. We demonstrate that non-Hermiticity not only lifts the degeneracy of the topological corner modes but also drives bulk states to accumulate at corners, giving rise to bipolar non-Hermitian skin effect. By defining the point-gap topology, we uncover the fundamental topological origin of the non-Hermitian skin effect. More interestingly, the non-Hermitian skin effect induces a fundamental breakdown of the conventional bulk-boundary correspondence based on the Bloch band theory. Our findings establish a general framework for non-Hermitian higher-order photonic systems and open avenues toward tailorable topological photonic devices exploiting non-Hermitian enhanced localization.

The COSINE-100U (upgrade) will enhance the sensitivity of the COSINE-100 dark matter search by operating the detector array immersed in liquid scintillator (LS) at $-30^oC$. To validate the detector design for these conditions, we constructed a module using the COSINE-100U encapsulation and performed a dedicated long-term stability study. The module was first monitored at room temperature for ~110 days in air, followed by a one-week immersion in LAB-based LS to verify initial compatibility. Upon confirming stable optical performance, the temperature was lowered to $-33^oC$. During approximately 150 days of continuous operation at low temperature, we observed no degradation in performance. These results demonstrate the chemical and mechanical robustness of the encapsulation, confirming its suitability for the COSINE-100U physics run.

The current study reports the design of advanced light-weight materials for high-energy accelerator beampipe applications. The objective is to optimize the combined requirements of high radiation length and stiffness properties of the designed materials. The present study targets conventional beampipe materials such as aluminum, titanium, and stainless steel as primary performance benchmarks. These conventional beampipes are used at synchrotron radiation sources, such as Indus-1 and Indus-2 in India, the Nuclotron-based Ion Collider Facility in Russia, and the ring synchrotron facility SIS 100/300 at the Facility for Antiproton and Ion Research in Germany. In this context, a series of ternary Mg--Al--Li alloys is systematically investigated to enhance the figure of merit. Two aluminum--rich alloys, A1 ($\mathrm{Al_{61.5}Li_{10.8}Mg_{27.7}}$) and A2 ($\mathrm{Al_{66}Li_{19.4}Mg_{14.6}}$), along with three magnesium-rich alloys, M1 ($\mathrm{Al_{23.9}Li_{29.3}Mg_{46.8}}$), M2 ($\mathrm{Al_{19}Li_{20.6}Mg_{60.4}}$), and M3 ($\mathrm{Al_{39.8}Li_{20.1}Mg_{40.1}}$) are explored. Thermodynamic stability, density, liquidus temperature, and phases are evaluated using Latin hypercube sampling within the Thermo-Calc TC-Python framework. Elastic properties are obtained from density functional theory calculations performed using the Vienna \textit{Ab Initio} Simulation Package. Our results show that, although the elastic moduli ($E$) of the investigated Mg-Al-Li alloys are comparable to those of conventional beampipe materials, their significantly higher radiation lengths ($X_0$) lead to an overall improvement in the figure of merit $X_0 E^{1/3}$.

Single-Photon Avalanche Diodes (SPADs) are new and promising imaging sensors. These sensors are sensitive enough to detect individual photons hitting each pixel, with extreme temporal resolution and without readout noise. Thus, SPADs stand out as an optimal choice for low-light imaging. Due to the high price and limited availability of SPAD sensors, the demand for an accurate data simulation pipeline is substantial. Indeed, the scarcity of SPAD datasets hinders the development of SPAD-specific processing algorithms and impedes the training of learning-based solutions. In this paper, we present a comprehensive SPAD simulation pipeline and validate it with multiple experiments using two recent commercial SPAD sensors. Our simulator is used to generate the SPAD-MNIST, a single-photon version of the seminal MNIST dataset, to investigate the effectiveness of convolutional neural network (CNN) classifiers on reconstructed fluxes, even at extremely low light conditions, e.g., 5 mlux. We also assess the performance of classifiers exclusively trained on simulated data on real images acquired from SPAD sensors at different light conditions. The synthetic dataset encompasses different SPAD imaging modalities and is made available for download. Project page: this https URL.

We report the creation of optically trapped ultracold heteronuclear p-wave Feshbach molecules in a mixture of 23Na and 87Rb atoms. With loss spectroscopy and binding energy measurements, we systematically characterize the interspecies p-wave Feshbach resonances near 284 G. Leveraging this understanding, we use magneto-association to form p-wave NaRb Feshbach molecules, producing both pure samples and mixtures of molecules in different angular momentum states. Additionally, we investigate the inelastic loss of these molecules, primarily influenced by atom-molecule and molecule-molecule collisions. Our results represent a significant step toward realizing tunable p-wave interactions in heteronuclear ultracold systems and provide a foundation for exploring non-zero angular momentum molecules.

Additional clock transitions of ytterbium atoms based on inner-shell orbital transition could benefit the search for new physics beyond the Standard Model. Observation of these transitions with high resolution is a prerequisite for making precise frequency measurements. Here, we observe 4.3 Hz-linewidth spectra of the inner-shell orbital transition at 431 nm in lattice-trapped 174Yb. With high-resolution spectra, we precisely determine the differential static polarizability of the transition to be -2.10(4) kHz/(kV/cm)^2. The magnitude of this polarizability is approximately 1/17 of that of the well-known clock transition in 171Yb at 578 nm, indicating a reduced sensitivity to blackbody radiation. We carry out a frequency ratio measurement between the two clock transitions of ytterbium atoms with an uncertainty of 9E-15. The frequency of the 431 nm transition is determined to be 695 175 030 801 776.5(6.3) Hz. These results represent a step forward in future studies on the search for new physics beyond the Standard Model.

In parts of Himachal Pradesh (Kullu and Mandi) and the Western Himalaya, village deities (\emph{devtā}) are carried through the landscape on shoulder-borne palanquins or ``raths.'' Participants often describe these raths as agents: they \emph{choose} routes, signal assent or refusal, and sometimes ``move on their own'' as if people are not moving them but are instead being moved. This paper offers : (i) a mechanistic model in which a palanquin interacts with human carriers modeled as coupled limit-cycle oscillators, and (ii) a philosophical analysis of how music and gurus/oracular specialists (\emph{gūr}/``guru'' in local English) function as couplings that stabilize collective interpretation, producing what we call \emph{distributed agency}. On the physics side we build a six-degree-of-freedom rigid-body model with unilateral handle contacts, base excitation from walking, and a Kuramoto-Adler phase description for interpersonal coupling and musical entrainment. We prove standard phase-locking conditions (Adler-type capture range) and show how unilateral contact can rectify periodic forcing and inject harmonics, creating parameter regimes in which near-perfect synchrony produces large-amplitude roll. On the simulation side we report an ensemble study (30 seeds per condition) from an archived ``Palanquin Simulator'' package: a ``baseline'' condition produces small roll (\(\mathrm{RMS}\approx 0.15^{\circ}\)) and moderate synchrony, whereas a ``music'' entrainment condition produces near-unity synchrony (\(\approx 0.99\)) but also robust roll instability (\(\mathrm{RMS}\approx 18^{\circ}\)) and frequent contact loss. We do \emph{not} claim to have validated the model against field data; we treat the simulations as a \emph{proof of plausibility} and as a generator of falsifiable predictions.

Long arrays of identical, self-propelling flapping flyers are inherently unstable and thus unlikely to exist without active control mechanisms. One approach to enable long in-line formations is to enforce a constant separation between the group members. The objective then becomes to determine the flapping strategies the flyers should adopt to achieve a certain separation. Using an aerodynamic model of vortex wake production and inter-flyer effects, we explore different flapping strategies for followers given the motion of the leader. The choice of tactic is dependent upon the aerodynamic, kinematic, and physical parameters of the system, and reflects an interplay between efficiency and stability. We find that whether a flyer flaps in or out of phase with its upstream neighbour, together with the target separation, strongly affect the flapping amplitude and, therefore, the energetic cost of group flight. In certain regimes, group flight is energetically favourable compared to isolated flight, while in others, flying in formation becomes less efficient. We also identify "goldilocks zones", ranges of separation in which one of the in- or out-of-phase motions can be simultaneously energetically efficient and dynamically stable. Outside these regions, energetically favourable flight is unstable and therefore unlikely to occur.

Laser hardening of metals occurs under the influence of a shock wave, which changes the distribution and density of one-dimensional defects - dislocations. There is a relationship between the density of dislocations, the grain size and the resistance of a single crystal to shear loading. The mechanism of hardening processes continues to be intensively studied, and the dynamics of defects plays a central role here. In this paper, the dislocation entropy is analyzed from a combinatorial point of view and from the point of view of a physical oscillator with a given energy reserve. Both contributions play an important role in describing the free energy of a one-dimensional ensemble of dislocations, and are necessary to take into account the dynamic processes inside the grain of a polycrystalline structure. Keywords: Laser Shock Peening, statistical mechanics

Surface-enhanced Raman scattering (SERS) provides critical insights into analyte structure, dynamic processes, and intermolecular interactions at the single-molecule level. By exploiting the hotspot formation in the vicinity of plasmonic structures, SERS constitutes an established tool for fundamental biological research, particularly for early-stage disease diagnostics. In this context, the DNA Origami technique, with its high addressability, enables both the assembly of plasmonic nanostructures with nanometric accuracy, and the deterministic placement of a single analyte molecule precisely at the generated hotspot within them. To date, most DNA Origami based nanoantennas rely on gold or silver nanoparticles (NPs), whose plasmonic resonances are confined to the visible spectrum, severely limiting their use in other spectral ranges. To extend the operating range, we have recently established a robust strategy for self-assembling programmable ultraviolet (UV)-plasmonic dimer antennas using rhodium nanocubes. Herein, we leverage this tailored architecture to systematically investigate its performance for single-molecule UV-SERS. We demonstrated how biofabricated Rh-dimers can be used to detect the characteristic SERS signal of a single streptavidin molecule linked at the dimer s gap. Our results are validated through polarization dependent measurements that yield the expected signal modulation depending on the the dimer orientation only for the DNA origami with a protein at the hotspot. This work establishes a highly sensitive and polarization-tunable UV-SERS platform, laying a solid foundation for label-free optical investigation and bio-spectroscopy of individual biomolecules in the UV spectral range.

We have studied radiofrequency capacitively coupled plasmas in pure O2 using single mode laser cavity ringdown spectroscopy of oxygen atoms at 630 nm. The absolute atom densities and translational temperatures were determined over a range of pressures and RF power . At pressures of 267 Pa and above, the O atom mole fraction increases with RF power and decreases with pressure, reaching a maximum of 15 percent. However, at 133 and 67 Pa it passes through a distinct maximum with power before decreasing significantly. The atom recombination processes are probed by time resolved measurements in the afterglow of pulse modulated plasmas. At 133 and 67 Pa the atom loss is dominated by surface recombination, and we see clear evidence that this rate is increased by energetic ion bombardment, in agreement with a study from Bill Graham group. This effect partially explains the observed decrease in dissociation at high RF power. The time-resolved results also allow the O negative ion density to be determined and indicate the creation of ozone in the afterglow. At 133 Pa, the trends with RF power of the O2 dissociation, O negative ion density and gas temperature suggest a transition at high power to a plasma mode with fewer high energy electrons. At higher pressures gas phase recombination mechanisms become dominant, however gas convection driven by gas cooling in the afterglow makes it complex to analyse the time-resolved data.

Modern precision manufacturing faces the challenge of integrating accuracy requirements into a framework of agile and sustainable production technologies. This development leads to numerous further challenges, affecting almost all areas of precision manufacturing industry. To overcome those challenges, this article presents promising technologies and innovative solution concepts from the field of temperature measurement in the cutting zone, process design for advanced materials, the development of reconfigurable machine tools, the optimization of the temperature behaviour of high-speed spindles and the use of edge computing in machine tools. Using the presented solutions, modern precision machining can be made more future-oriented in terms of sustainability and resilience.

This paper gives an overview about aspects of mechanical engineering of undulators. It is based mainly on two types that are used in the SwissFEL facility. The U15 Undulator is an example of an in-vacuum type and the UE38 is an APPLE-X type. It describes the frame, the adjustment of the magnets with flexible keepers and the adjustment of the whole device with eccentric movers.

Collisions of two weakly nonlinear, $a_0 \ll 1$, counter-propagating EM pulses in pair plasma leave behind a long-surviving collection of localized waves, {\it an electromagnetic ghost}. Waves are trapped (localized) by the random large density fluctuations created by the beat between the pulses. The process is similar to random plasma density grating and/or Anderson-like wave localization. Structures survive for long, mesoscale times, while the EM energy slowly bleeds through high density walls of the density trap. Large guide magnetic field, $\omega_B \geq $ few $\omega$, suppresses the formation of the ghosts.

The ion beam induced charge technique with proton microprobe is used to characterise newly developed p-n junction large area silicon carbide detectors. They were recently produced as part of the ongoing program to develop a new particle identification wall for the focal plane detector of the MAGNEX magnetic spectrometer at INFN - Laboratori Nazionali del Sud in view of the NUMEN experimental campaigns. Four silicon carbide devices are studied. Proton beams over a 1.26 to 6.00 MeV incident energy range are used to probe the active area and the depletion depth of each device. The energy loss tables for the silicon carbide material are checked, finding an empirical correction that is then used to quantify the depletion depth at the full depletion voltage through energy loss measurements of 3.40 MeV proton beams irradiating the back side of the devices. It is possible to fully deplete the devices provided that the epitaxial layer is grown properly on the substrate.

Polarisation-maintaining ZBLAN optical fibres with small elliptical cores are attractive for nonlinear frequency conversion, for example in supercontinuum generation (SCG), and for dispersion management in ultrafast fibre laser systems operating in the mid-infrared (MIR) spectral region. Accurate characterisation of group birefringence and group velocity dispersion (GVD) is essential for modelling ultrafast pulse propagation in such fibres, yet experimental data remain scarce. Here, we present broadband measurements of group birefringence and GVD in four elliptical-core ZBLAN fibres, selected for their potential for low-noise MIR SCG, over the wavelength range 1.4 to 4.3 $\mu$m. Using polarisation-resolved white-light spectral interferometry, we quantify the dispersion characteristics of both fundamental modes in each fibre. Measurement uncertainties are evaluated using a standard deviation analysis to ensure reliability. This work aims to support the development of MIR supercontinuum sources and to facilitate the understanding of nonlinear optical phenomena in ZBLAN fibres.

We propose a financial liquidity policy sharing method for firm-to-firm supply networks, introducing a scalable autonomous control function for viable complex adaptive supply networks. Cooperation and competition in supply chains is reconciled through overlapping collaborative sets, making firms interdependent and enabling distributed risk governance. How cooperative range - visibility - affects viability is studied using dynamic complex adaptive systems modelling. We find that viability needs cooperation; visibility and viability grow together in scale-free supply networks; and distributed control, where firms only have limited partner information, outperforms centralised control. This suggests that policy toward network viability should implement distributed supply chain financial governance, supporting interfirm collaboration, to enable autonomous control.

The General Lagrangian Mean (GLM) theory uses a version of the averaged equations of fluid dynamics, designed to examine interactions between small-amplitude waves and mean flows. These equations are formulated in coordinates following the fluid's average velocity and are often referred to as `pseudo-Lagrangian'. This paper focuses on the principles for deriving the GLM equations, using an inviscid, incompressible, homogeneous fluid as a demonstration case. Our exposition methodically differs from others and is aimed at the learners of this theory.

Multilayer metasurfaces provide substantially greater spectral design freedom than single-layer devices, yet their implementation in the visible and near-infrared remains limited by the complexity, cost, and low throughput of conventional nanofabrication. Here, we establish a recently proposed direct-write electron-beam lithography approach as a high-throughput fabrication platform for multilayer resonant metasurfaces, based on an antimony precursor that decomposes in situ into high-index antimony sulfide. This method eliminates deposition-etch cycles and reduces each layer to only two fabrication steps, enabling efficient realization of multilayer architectures. Using this platform, we demonstrate multilayer q-BIC-derived metasurfaces with independently tunable resonance wavelengths and linewidths, allowing the construction of compact multi-resonant filters with spectrally decoupled layers. We experimentally demonstrate three-layer devices supporting three resonances and show independent control of resonance wavelength and Q factor across layers. Leveraging this capability, we generate decorrelated filter arrays for compressive sensing and hyperspectral reconstruction, achieving sets of 9 and 36 filters with average absolute Pearson correlation coefficients of 0.11 and 0.21, surpassing prior metasurface and photonic-crystal implementations. These results establish a practical route toward scalable multilayer resonant metasurfaces for spectral filtering, on-chip spectroscopy, and computational imaging.

We investigate the impact of magnetic-field-induced feedback on the dynamics of a Hindmarsh-Rose neuron model exhibiting a blue-sky catastrophe. By introducing a magnetic flux variable that couples nonlinearly to the membrane potential, we demonstrate that electromagnetic effects profoundly reshape neuronal firing patterns and bifurcation structure. Interspike-interval bifurcation analysis reveals a nonmonotonic dependence on the magnetic coupling strength, with weak coupling preserving regular spiking and bursting, intermediate coupling promoting chaotic bursting, and strong coupling yielding structured irregular dynamics. These transitions are quantitatively characterized using the largest Lyapunov exponent computed via the Wolf algorithm and supported by Poincaré sections and time-series analysis. Our results establish electromagnetic feedback as a robust and tunable mechanism for controlling instability and chaos in slow-fast neuronal systems.

The AMACA project (Astronomy education with a Multi-sensory, Accessible, and Circular Approach) develops multi-sensory activities for accessible education and engagement in astronomy. Despite promising innovations, existing resources are often poorly documented, designed for one-time events, expensive, and lack interdisciplinary collaboration, user testing, and broad dissemination. AMACA addresses these challenges by creating multi-sensory activities for education and outreach, with a particular focus on accessibility for people with sensory disabilities. A circular approach informs its educational structure: (1) a PhD course on multi-sensory astronomy outreach develops hands-on activities with the support of astronomers, psychologists, and organizations for the visually impaired and the deaf; (2) PhD candidates teach High School (HS) students how to deliver the activities; (3) HS students lead the activities at the Astronomy Festival "The Universe in All Senses"; (4) HS students train teachers to implement the activities in their classrooms. AMACA also develops tools to guide project development and track participants' learning. Key findings show improved communication and accessibility awareness among PhD candidates, increased emotional engagement with astronomy among HS students, enhanced public engagement with research and accessibility awareness, and high teacher satisfaction with the flipped-roles, hands-on approach. Overall, AMACA enhances accessibility and engagement in astronomy education across audiences.

Packed-bed sensible heat storage (SHS) is important for balancing energy supply and demand over time. To improve the efficiency of a packed-bed SHS system through second law analysis (SLA), we developed macroscopic entropy and exergy transport equations for fluid flow and heat transfer in porous media based on microscopic transport equations. These equations enable us to identify where and how much exergy is destroyed. Using a packed-bed SHS system developed at the PROMES-CNRS laboratory as a test case, we demonstrated how to apply SLA to optimize an SHS system. Our analysis revealed that, in addition to exit and heat leakage losses at tank surfaces, thermal and solid conduction losses inside the tank significantly contribute to total loss in the studied SHS system. These internal losses occur close to the thermocline. However, their slower transport causes a delay in their emergence. The SLA suggests an optimal tank aspect ratio of D/H = 0.75, at which the total exergy loss coefficient reaches its minimum value when exit loss is not considered. As particle size decreases, the exergy loss coefficient also decreases due to enhanced heat transfer between the fluid and solid phases. The pressure loss for the studied SHS system is negligible. The SLA favors a truncated cone-shaped tank with a slightly larger upper surface. Through the SLA, the exergy loss coefficient is reduced from 4.9% for the original design to 4.1% for the optimized design. This study demonstrates that, when used in conjunction with energy analysis, the SLA is an effective tool for optimizing energy storage systems.

In this study we searched for shadow bands associated with the total solar eclipse of April 8, 2024. Our aim was to improve our understanding of their origin. Shadow bands are debated to arise either from atmospheric turbulence within Earth's planetary boundary layer (PBL) or from a diffraction-interference effect occurring above the atmosphere. To test these theories, high altitude balloons (HABs) equipped with light sensors, similar ground light sensors, radiosondes launched with weather balloons, and an aircraft-mounted light sensor were deployed. Our team was located in Concan, TX, except for the plane which flew to NE Vermont to find clear weather. Unlike Pitt's 2017 HAB study, which detected a 4.5 Hz signal attributed to shadow bands above the PBL and on the ground, no shadow bands were detected above the PBL in Texas or in northeast Vermont, despite the use of improved instrumentation. Cloud cover prevented useful ground based measurements in Texas, limiting our conclusions about the nature of shadow bands. These findings suggest that shadow bands may not always be present or, if they are, may be primarily due to atmospheric turbulence. The results of this study and Pitt's 2017 study emphasize the need for future work.

Using orbital angular momentum (OAM) in the terahertz (THz) range provides a new degree of freedom for communication and imaging systems. This study presents a compact diffractive optical neural network designed to recognize discrete and superposed OAM states at THz frequencies. The network consists of six diffractive layers trained to spatially separate nine OAM modes with topological charges from 1 to 9. Each mode is projected to a distinct position on the output plane, enabling direct recognition of its state. The structure was fabricated through low-cost 3D printing techniques with high-impact polystyrene (HIPS), allowing for scalable and practical implementations. Experimental validation at 0.3 THz demonstrates good fidelity of mode discrimination and mapping. The proposed approach offers a robust and economical pathway for OAM decoding, offering new opportunities for beam manipulation through THz systems based on diffractive optical neural networks.

Collisional and stochastic wave-particle dynamics in plasmas far from equilibrium are complex, temporally evolving, stochastic processes which are challenging to model. In this work, we extend previous methods coupling differentiable kinetic simulators and plasma phase space diagnostics to learn collision operators that account for time-varying background distributions. We also introduce a more general integro-differentiable operator formulation to probe relevant terms in the collision operator. To validate the proposed methodology we use data generated by self-consistent electromagnetic Particle-in-Cell simulations. We show that both approaches recover operators that can accurately reproduce the plasma phase space dynamics while being more accurate than estimates based on particle track statistics. These results further demonstrate the potential of using differentiable simulators to infer collision operators for scenarios where no closed form solution exists or deviations from existing theory are expected.

We made a detailed review of the difference measures which have been used to judge the differences between experimentally determined color differences and theoretically defined ones, so-called line elements, for the human visual system. To eliminate the statistical errors due to variable and usually arbitrary sampling of the directions in a color point, we integrate the measures over a complete ellipsoid/ellipse. It turns out that in the limit for small deviations from circularity all proposed measures ($V_{AB}$, $\gamma-1$, $CV$ and $\mathrm{STRESS}$) are equivalent. For greater deviations the measures become distinct with $\gamma-1$ the most sensitive and $\mathrm{STRESS}$ the least. Ideally a difference measure should be coordinate independent and then it is advantageous to apply an affine transformation to both sets, e.g. turning the theoretical one into the unit ball. Although MacAdam already used this method but sampled the transformed ellipse, we integrate over the ellipsoid/ellipse. Comparing the results with the base measures we show that only $\mathrm{STRESS}$ is coordinate independent. Judging whether a single ellipsoid/ellipse resembles a unit ball can easily be done by comparing the eigenvalues with one and we show that our previously proposed error measure $d_{ev}$ (Candry e.a. Optics Express, 30, 36307, 2022) is the eigenvalue version of $\gamma-1$. We show why the short lived correlation coefficient $r$ was justly abandoned, being very coordinate dependent, but that Pant's recent geometric measure $1-R$ on the other hand is coordinate independent. All measures are routinely made scale invariant by the introduction of a scaling parameter, to be optimized. Lastly we show that from all measures the $\gamma-1$ ones are the only ones permitting the simple derivation of the globally optimized difference measure from the locally defined ones.

Nitrogen vacancy centres (NVC) in diamond have been widely used for near-dc magnetometry. The intrinsic properties of diamonds make them potential candidates for tokamak fusion power diagnostics, where radiation-hard magnetometers will be essential for efficient control. An NVC magnetometer placed in a tokamak will need to operate within a $\geq$ 1 T magnetic field. In this work, we demonstrate fibre-coupled ensemble NVC optically detected magnetic resonance (ODMR) and magnetometry measurements at magnetic fields up to 1.2 T. Sensitivities of approximately 240 to 600 nT/$\sqrt{\textrm{Hz}}$ and 110 nT/$\sqrt{\textrm{Hz}}$ are achieved in a (10-150) Hz frequency range, for non-degenerate and near-$\langle$111$\rangle$ field alignments respectively.

Photopolymerization-based additive manufacturing enables cost-effective, high-speed fabrication of complex 3D structures but is constrained by a trade-off between resolution and printing speed. Single-photon polymerization ensures rapid polymerization of centimeter-scale structures with features on the order of tens of micrometers, whereas 2PP provides sub-micrometer features at sub-millimeter scales. Here, we introduce a hybrid unified 3D printer that leverages the complementary strengths of both printing mechanisms to bridge this scale-resolution gap. We propose integrating 2PP for high-resolution, localized spatial control with single-photon TVAM for enabling rapid, high-throughput 3D fabrication. In this approach, TVAM first forms millimeter-scale volumetric structures attached on a glass rod, via overprinting, which is then accessible for subsequent high-resolution 2PP. Without needing to change the photoresin or introducing intermediate post-processing steps, we demonstrate finely printed structures via 2PP using a tightly focused femtosecond laser beam, fabricated both inside and on the surface of the millimeter-scale 3D objects printed with TVAM. Here, TVAM contributes by generating a pre-polymerized volume that facilitates subsequent 2PP and by directly driving volumetric polymerization in designated regions within seconds. We experimentally demonstrate that this dual-mode strategy provides a scalable approach for rapidly fabricating millimeter-scale 3D structures featuring sub-micrometer details. For applications like bioscaffolds and tissue engineering, tens of micrometer-scale features are sufficient across the majority of the volume with higher resolution confined to localized functional regions. For optical component manufacturing, the distinct refractive indices of the 2PP and TVAM regions can be exploited for light propagation and other micro-optical functionalities.

This study investigates the elastic response of a two-dimensional semi-infinite medium subjected to a moving surface load with a prescribed displacement profile. As a fundamental step, we derive analytical Green's functions for the displacement and stress fields generated by a point load traveling at a constant velocity along the surface, explicitly incorporating elastodynamic effects through Mach number dependence. These moving-load solutions serve as building blocks for constructing more general loading scenarios via linear superposition. Based on Green's functions, an inverse problem is formulated to reconstruct the unknown surface traction responsible for a given surface displacement. The inverse analysis is performed through a Fourier-domain inversion with regularization, which enables a direct and computationally efficient determination of the contact pressure without iterative forward simulations. This framework is applied to a rigid wheel-ground contact problem, where the imposed displacement is dictated by the wheel geometry. The reconstructed surface traction exhibits a smooth, symmetric distribution within the contact region, while the resulting subsurface stress fields are obtained in closed analytical form and involve dilogarithm functions. The principal stress difference reveals characteristic spatial patterns similar to photoelastic fringes, and their asymmetry increases with the Mach number, reflecting the dynamic nature of the moving contact.

Plasmonic nanoparticles generate strongly localized and enhanced light field through localized surface plasmon resonance, thereby playing a central role in plasmonics and nanophotonics. Because the optical properties of plasmonic nanoparticles are highly sensitive to their size and shape, nanoscale visualization of nanoparticle growth is crucial for detailed understanding of growth mechanisms and precise control of particle geometry. However, it is not possible to visualize the rapid growth dynamics using conventional imaging techniques. In this study, we demonstrate in-situ real-time observation of silver nanoparticle (AgNP) growth dynamics at the single-particle level using high-speed atomic force microscopy (HS-AFM). We employed a photoreduction method, which enables reliable control of AgNP formation by laser irradiation. By integrating a stand-alone tip-scan HS-AFM with an optical setup for photoreduction, we successfully captured real-time movies showing the nucleation and subsequent growth of AgNPs at the single-particle level. Furthermore, quantitative single-particle analysis revealed particle-to-particle variations in growth dynamics. The growth dynamics were further studied at different laser intensities, revealing intensity-dependent growth rates and the balance between nucleation and growth. This study establishes HS-AFM as a novel microscopic platform for in-situ visualization of plasmonic nanoparticle growth and will contribute to advances in plasmonics and materials science.

Multi-turn charge-exchange injection is the primary method of creating high-intensity hadron beams in circular accelerators, and phase space painting during injection enables tailoring of the accumulated phase space distribution. A technique we call eigenpainting allows injection of particles into a single mode of a coupled ring, providing full four-dimensional control of the phase space distribution. Under ideal conditions, uniform eigenpainting generates a linear-force equilibrium distribution in the transverse plane, with zero volume in four-dimensional transverse phase space, even including space charge. We have implemented eigenpainting for the first time in the Spallation Neutron Source (SNS) Accumulator Ring. Injecting 8.8 $\mu$C of 800 MeV beam, we obtain a final ratio of intrinsic transverse emittances of $\approx$2.4. We analyze the effect of space charge on the final distribution through comparison of the reconstructed phase space to particle-in-cell simulations.

The replica-exchange Monte-Carlo (RXMC) method is a powerful Markov-chain Monte-Carlo algorithm for sampling from multi-modal distributions, which are challenging for conventional methods. The sampling efficiency of the RXMC method depends highly on the selection of the temperatures, and finding optimal temperatures remains a challenge. In this study, we propose a refined online temperature selection method by extending the gradient-based optimization framework proposed previously. Building upon the existing temperature update approach, we introduce a reparameterization technique to strictly enforce physical constraints, such as the monotonic ordering of inverse temperatures, which were not explicitly addressed in the original formulation. The proposed method defines the variance of acceptance rates between adjacent replicas as a loss function, estimates its gradient using differential information from the sampling process, and optimizes the temperatures via gradient descent. We demonstrate the effectiveness of our method through experiments on benchmark spin systems, including the two-dimensional ferromagnetic Ising model, the two-dimensional ferromagnetic XY model, and the three-dimensional Edwards-Anderson model. Our results show that the method successfully achieves uniform acceptance rates and reduces round-trip times across the temperature space. Furthermore, our proposed method offers a significant advantage over recently proposed policy gradient method that require careful hyperparameter tuning, while simultaneously preventing the constraint violations that destabilize optimization.

This paper constructs a multilayer recursive game model to demonstrate that in a rule vacuum environment, hierarchical predatory structures inevitably collapse into a monolithic political strongman system due to the conflict between exponentially growing rent dissipation and the rigidity of bottom-level survival constraints. We propose that the rise of a monolithic political strongman is essentially an "algorithmic entropy reduction" achieved through forceful means by the system to counteract the "informational entropy increase" generated by multilayer agency. However, the order gained at the expense of social complexity results in the stagnation of social evolutionary functions.

Treatment delivery is largely determined by capabilities of the beam delivery system (BDS), where faster delivery can have many potential benefits including improved dosimetric quality, utility, cost effectiveness, patient throughput and comfort. Despite significant developments in accelerators, delivery methodologies, dose optimisation and more, the energy layer switching time (ELST) is still a persisting limitation in existing BDS. The ELST can contribute significantly to beam delivery time (BDT) and extend treatment times, requiring compensation by optimisation planning approaches, motion mitigation strategies, or active beam modification. This fundamental constraint can be addressed by increasing the narrow energy acceptance range of conventional beamlines to minimise the ELST, enabling ultra-fast delivery. A large energy acceptance (LEA) BDS has the potential to revolutionise PBT through immediate improvements to current treatment delivery and emerging delivery modalities: the complete exploitation of PBT - and unlocking its full potential - can only be made possible with advances in beam delivery technologies. We review the abundant opportunities offered by an ultra-fast BDS: shorter treatment times, reduced motion induced dose degradation, improved effectiveness of motion management techniques, possibilities for volumetric rescanning, bidirectional delivery, further planning optimisation, and novel delivery strategies. We overview the design concepts of several LEA proposals, technology requirements, and also discuss the remaining challenges and considerations with realising a LEA BDS in practice. There are multiple avenues requiring further development and study, however the clinical potential and benefits of this enabling technology are clear: ultra-fast delivery offers both immediate and future improvements to PBT treatments.

Multiphase fluid flows comprising of mesoscale solid particles, liquid droplets, or gas bubbles are common in both natural and man-made systems, but quantifying the energy transfer is challenging due to complex bubble-particle interactions. In this study, we used ultrafast synchrotron X-ray imaging to study the mesoscale dynamic interactions among ultrasonic cavitation bubbles and hydrophobic particles or clusters. Critical dynamic information and data were extracted from the vast amount of X-ray images and then fed into the hybrid analytical-numerical model for calculating the energy transfer from the oscillating bubble and the imploding bubble to the nearby hydrophobic particles. Using the Ni spherical microparticles as an example, at bubble oscillation approximately 16% (80-320 nJ) of the local energy was transferred to the particle. At bubble implosion, the transferred energy increased approximately 26% (0.135-1.09 uJ). Local energy transfer occurred on timescales of 1 us to 1 ms and length scales of 1 um to 1 mm. Within each ultrasound cycle, kinetic and potential energy underwent complex exchanges, with local energy exhibiting a stepwise decay at the end of each cycle. The transferred energy was mainly consumed for enabling highly efficient particle dispersion. This research provides quantitative insights into optimizing hydrophobic nanomaterial dispersion and has broader implications for interfacial energy transfer processes such as making suspensions, composite materials and exfoliated 2D materials.

This paper presents AAFIYA (Antenna Analysis in Frequency-domain for Impedance and Yield Assessment), a modular Python toolkit for automated characterization of radio-frequency antennas using measurement and simulation data. The toolkit provides a unified workflow for processing S-parameters, impedance, realized gain, beam patterns, polarization metrics, and calibration-based yield estimation, with support for standard Touchstone files and beam pattern data. AAFIYA is validated using measurements from an electromagnetic anechoic chamber involving Log Periodic Dipole Array (LPDA) reference antennas and Askaryan Radio Array (ARA) Bottom Vertically Polarized antennas over 100-850 MHz. Extracted metrics, including impedance matching, realized gain patterns, vector effective lengths, and cross-polarization ratio, are compared against full-wave simulations from HFSS and WIPL-D, showing good agreement across frequency and angle. The results demonstrate that AAFIYA enables accurate, reproducible, and publication-ready antenna analysis, and provides a flexible foundation for future extensions, including automated optimization and data-driven antenna design.

A large-scale molecular dynamics (MD) simulation of acoustic cavitation in a simple liquid was performed using the supercomputer Fugaku. The system, consisting of approximately 100 billion atoms, was subjected to ultrasonic irradiation. Direct observation of multi-bubble dynamics has been challenging in both experimental measurements and conventional numerical fluid mechanics simulations. Moreover, previous MD simulations involving only hundreds of millions of atoms were unable to generate multiple bubbles within a system. Our results reveal that cavitation bubbles nucleate and grow near the ultrasonic horn, forming a large bubble cluster that periodically splits into multiple small clusters and subsequently merges again. This cycle is synchronized with the oscillation period of the horn. Pressure and temperature inside the bubbles exhibit sharp increases during cluster fragmentation, and their oscillation amplitudes vary on a timescale longer than the driving period of the horn, indicating the presence of subharmonic behavior consistent with experimental observations. Despite bubble formation, the effect on the acoustic properties of the sound wave was almost negligible, indicating that cavitation near the horn surface has limited influence on bulk acoustic properties. These findings provide new insights into the molecular-scale mechanisms of cavitation and offer guidance for optimizing ultrasonic systems in chemical and biomedical applications. Future work will focus on quantifying long-period oscillations, analyzing attenuation effects, and extending simulations to complex fluids.

Although the classification of crystalline materials can be generally handled by momentum-space-based approaches, topological classification of aperiodic materials remains an outstanding challenge, as the absence of translational symmetry renders such conventional approaches inapplicable. Here, we present a numerically efficient real-space framework for classifying parity-based $\mathbb{Z}_2$ topology in aperiodic systems based on the spectral localizer framework and the direct computation of the sign of a Pfaffian associated with a large sparse skew-symmetric matrix. Unlike projector-based or momentum-space-based approaches, our method does not rely on translational symmetry, spectral gaps in the Hamiltonian's bulk, or gapped auxiliary operators such as spin projections, and instead provides a local, energy-resolved topological invariant accompanied by an intrinsic measure of topological protection. A central contribution of this work is the development of a scalable sparse factorization algorithm that enables the reliable determination of the Pfaffian's sign for large sparse matrices, making the approach practical to realistic physical materials. We apply this framework to identify the quantum spin Hall effect in quasicrystalline class AII systems, including gapless heterostructures, and to diagnose fragile topology in a large $C_2 \mathcal{T}$-symmetric photonic quasicrystal. Overall, our results demonstrate that the spectral localizer, combined with efficient sparse numerical methods, provides a unified and robust tool for diagnosing parity-based topological phases in aperiodic electronic, photonic, and acoustic materials where conventional band-theoretic indexes are inapplicable.

We demonstrate deterministic control of branched flow of light using anisotropic nematic liquid crystals. By sculpting the director field via photoalignment, we create spatially programmable optical potentials that govern light scattering and propagation. This platform enables configurable, anisotropic branched flow of light and reveals a universal scaling law for its characteristic features, directly connecting disordered photonics with mesoscopic wave transport. Under extreme anisotropy, we observe a pronounced directional channeling effect, driven by anomalous symmetry-breaking velocity diffusion, which concentrates light propagation along preferential directions while suppressing transverse spreading. These findings establish a tunable material platform for harnessing branched flow of light, opening pathways toward on-chip photonic circuits that exploit disorder-guided transport, scattering-resilient endoscopic imaging, and adaptive optical interfaces in complex media.

Near exceptional degenerate points in parameter space, coupled oscillator systems display enhanced sensitivity of their saturated steady-state (SS) oscillation frequencies to small changes in system parameters. Linear $\mathcal{PT}$-symmetric systems made of two coupled resonators have exceptional point of degeneracy (EPD), around which square-root sensitivity is observed. However, realistic systems with gain are inherently saturable and nonlinear, thereby invalidating linear assumptions, and when $\mathcal{PT}$-symmetry is broken the coupled resonator system becomes unstable, hence it seems that the best working regime is to use such instability to make an SS-EPD-based oscillator. We study the saturated steady-state of a general system of two coupled oscillators with saturable nonlinear gain. Extending previous analyses, we find the steady-state oscillation frequency-gain pairs, and we analytically and numerically derive the sensitivity of the oscillation frequency to system's perturbations around a unique third-order degeneracy which corresponds to SS$\mathcal{PT}$ symmetry because it is the saturated gain that is symmetric to losses. In general, unlike linear systems, we find that at SS, the sensitivity of the oscillation frequency to exhibit linear, square-root, or cube-root dependence on small perturbations. We additionally study the energy and stability of each SS, and demonstrate the application and limitations of this analysis to coupled RLC circuits. We give a comprehensive outlook for exploiting exceptional degeneracy-enhanced sensitivity in nonlinear coupled oscillators and suggest the best operative conditions.

Nonlinear manifold learning (ML) based reduced-order models (ROMs) can substantially improve the quality of nonlinear flow-field modeling. However, noise and the lack of physical information often distort the dimensionality-reduction process, reducing the robustness and accuracy of flow-field prediction. To address this problem, we propose a novel manifold learning ROM with implicit physics embedding (IPE-ML). Starting from data-driven manifold coordinates, we incorporate physical parameters (e.g., angle of attack, Mach number) into manifold coordinates system by minimizing the prediction error of Gaussian process regression (GPR) model, thereby fine-tuning the manifold structure. These adjusted coordinates are then used to construct a flow-fields prediction model that predict nonlinear flow-field more accurately. The method is validated on two test cases: transonic flow-field modeling of the RAE2822 and supersonic flow-field modeling of the hexagon airfoil. The results indicate that the proposed IPE-ML can significantly improve the overall prediction accuracy of nonlinear flow fields. In transonic case, shock-related errors have been notably reduced, while in supersonic case the method can confine errors to small local regions. This study offers a new perspective on embedding physical information into nonlinear ROMs.

The climate system's nonlinear dynamics is influenced by various external forcings and internal feedbacks that can give rise to regional and even global tipping points that may lead to significant and potentially irreversible changes. Paleoclimatic records reveal that Earth's climate has shifted between distinct equlibria, including a "hothouse Earth" state with temperatures about 10 K higher than present. However, a specific mechanism for a sudden tipping to an alternate stable state, several degrees warmer than the present climate, has yet to be presented. We introduce a temperature-carbon-vegetation (TCV) model comprising an energy balance model of global temperature, coupled with global terrestrial and ocean CO2 dynamics, and with vegetation ecosystem change. Our model exhibits a new tipping mechanism that leads to a hothouse Earth under a high-emissions scenario. Its simulations align with both observations and IPCC-class global climate models prior to tipping. The two processes that produce global tipping are: (i) temperature-albedo feedback due to darkening of the terrestrial cryosphere by glacial microalgae; and (ii) limits to vegetation adaptation that lead to reduced carbon absorption.

The orbital angular momentum (OAM) of light provides an infinite orthogonal basis for information capacity in optical communications and high-dimensional quantum processing. However, harnessing this potential in integrated systems is hindered by the lack of compact devices capable of direct electrical readout of high-order OAM modes. Here, we report an on-chip silicon-based integrated photodetector that directly converts optical OAM into distinguishable electrical signals without bulky interferometric or imaging optics. By leveraging a momentum-matched plasmonic coupling mechanism, the device maps vortex beams onto surface plasmon polaritons (SPPs) with OAM-dependent splitting angles, thereby generating photocurrents that uniquely encode the topological charge. The incorporation of a surface dielectric lens and a split-electrode architecture further enhances mode resolution and enables chirality discrimination. The device demonstrates a wide topological charge detection range from m = -9 to 9 and achieves a record-high average OAM responsivity of 226 nA/W. By bridging the gap between vortex beams and electronic readout on a scalable platform, this work paves the way for on-chip OAM direct detection for next-generation high-capacity optical networks.

Coherent detection ghost imaging lidar (CD-GI lidar) integrates ghost imaging with coherent detection, thereby achieving enhanced anti-interference and phase-resolved imaging capability. Here, we propose a bucket-detector-based multi-mode coherent detection scheme for CD-GI lidar, where the reflected multi-mode light fields are coherently mixed with a single-mode local oscillator (LO) at the bucket detector photosensitive plane. The bucket-detector-based multi-mode CD-GI lidar system breaks the constraints of Siegman antenna theorem by utilizing field correlation to decouple the reflected multi-mode light fields and reconstructs the spatial distribution of targets' vibration modes. Theoretical analysis of the bucket-detector-based multi-mode CD-GI lidar system is presented in this work, and its feasibility is verified through a series of experiments.

Two-fluid spray nozzles are widely used in combustion, chemical processing, pharmaceutical coating, environmental control, and spray drying to atomize liquids with pressurized gas. However, the adiabatic cooling and resulting flash freeze--thaw exposure of atomized droplets remain underexplored. Using high-fidelity computational fluid dynamics coupled with droplet-scale nucleation modeling, we show that the atomizing gas temperature at the nozzle exit can fall from $22\,^{\circ}\mathrm{C}$ to below $-130\,^{\circ}\mathrm{C}$, initiating rapid ice nucleation and freezing in micro-scale droplets. For atomizing gas at $5\,\mathrm{bar}$ (gauge) and $22\,^{\circ}\mathrm{C}$, all droplets smaller than $1.5\,\mu\mathrm{m}$ freeze, whereas droplets larger than $3\,\mu\mathrm{m}$ remain liquid. These frozen droplets thaw within $O(10)\,\mu\mathrm{s}$ upon leaving the cold zone, subjecting sensitive actives to intense freeze--thaw thermomechanical stresses near the nozzle even when the bulk drying gas is warm. Parametric studies show that ice formation is eliminated at atomizing gas temperatures above $110\,^{\circ}\mathrm{C}$ for all gas-to-liquid mass ratios (GLRs) between 8 and 25, or at $\mathrm{GLR}<12$ for all atomizing gas temperatures; the chamber drying gas does not influence near-nozzle freezing. Additionally, we demonstrate that swirling flow intensifies flash freeze--thaw by deepening gas cooling, whereas non-swirling flow extends cold-zone residence time, yet both designs produce similar iced-droplet fractions. We construct an operating map delineating conditions that avoid flash freeze--thaw and show that the no-ice boundary provides a conservative criterion for both swirl and non-swirl nozzles. These findings identify a previously unrecognized freeze--thaw stress mechanism that can compromise spray-dried pharmaceutical product stability.

The rarely used Hamilton-Jacobi equation has been utilized as an elegant way to find the trajectories of mechanical systems and to derive symplectic maps. Further, the exact solution in kick approximation of Hamilton's equations of motion in interaction representation is written as a generalized one-turn twist map. One can imagine that the nonlinear kick comes first, followed by the one-period rotation along the machine circumference, or a second alternative in which the one-period rotation occurs before the kick. There is a difference in the result of solving Hamilton's equations between the two cases, which is expressed in obtaining a standard forward twist map in the first case, or alternatively a backward map in the second one. This nontrivial and intuitively unclear peculiarity is usually ignored/overlooked in practically all specialized references on the topic. Finally, the statistical properties and the behavior of the density distribution of a particle beam in configuration space under the influence of an isolated sextupole have been studied.

BULLKID-DM is an experiment designed for the direct searches of particle dark matter candidates with mass around 1 GeV, or below, and cross-section with nucleons smaller than $10^{-40}$ cm$^2$. The detector consists of a stack of diced silicon wafers, acting as arrays of particle absorbers, sensed by multiplexed Kinetic Inductance Detectors. The target will amount to 800 g subdivided in more than 2000 silicon dice, with the aim of controlling the background from natural radioactivity by creating a fully active structure and by applying fiducialization techniques. In this work we present the projected sensitivity of BULLKID-DM to light WIMP-like particles considering also the other future experiments in the field.

Generative artificial intelligence (AI) systems can now reliably solve many standard tasks used in introductory physics courses, producing correct equations, graphs, and explanations. While this capability is often framed as an opportunity for efficiency or personalization, it also poses a subtle ethical and educational risk: students may increasingly submit correct results without engaging in the epistemic practices that define learning this http URL challenge has recently been described as the "boiling frog problem" because we may not fully recognize how rapidly AI capabilities are advancing and fail to respond with commensurate urgency. In this article, we argue that the central challenge of AI in physics education is not cheating or tool selection, but instructional design. Drawing on research on self-regulated learning, cognitive load, multiple representations, and hybrid intelligence, we propose a practical framework for cognitively activated learning activities that structures student activities before, during, and after AI use. Using an example from an introductory kinematics laboratory, we show how AI can be integrated in ways that preserve prediction, interpretation, and evaluation as core learning activities. Rather than treating AI as an answer-generating tool, the framework positions AI as an epistemic partner whose contributions are deliberately bounded and reflected upon.

This paper extends the distributed rolling contact FrBD framework to linear viscoelasticity by considering classic derivative Generalised Maxwell and Kelvin-Voigt rheological representations of the bristle element. With this modelling approach, the dynamics of the bristle, generated friction forces, and internal deformation states are described by a system of 2(n+1) hyperbolic partial differential equations (PDEs), which can capture complex relaxation phenomena originating from viscoelastic behaviours. By appropriately specifying the analytical expressions for the transport and rigid relative velocity, three distributed formulations of increasing complexity are introduced, which account for different levels of spin excitation. For the linear variants, well-posedness and passivity are analysed rigorously, showing that these properties hold for any physically meaningful parametrisation. Numerical experiments complement the theoretical results by illustrating steady-state characteristics and transient relaxation effects. The findings of this paper substantially advance the FrBD paradigm by enabling a unified and systematic treatment of linear viscoelasticity.

In this work, we demonstrate carrier-envelope-offset (CEO) frequency stabilization of a Kerr-lens mode-locked thin-disk oscillator delivering 180 W average power, 80 MW output peak power, and >500 MW intra-cavity peak power, the highest peak power achieved in any stabilized thin-disk oscillator system. The CEO frequency detection is performed with an f-2f interferometer based on supercontinuum generation in a YAG crystal. Intra-cavity loss modulation using an acousto-optic modulator, that simultaneously provides the Kerr lens, yields 50 mrad of residual phase noise with a 250 kHz control bandwidth. After pulse compression to 0.9 GW peak power in a dual-stage multipass cell, the system directly enables high harmonic generation in noble gases. These results represent a significant step in realizing a compact and robust high-repetition-rate driver system suitable for vacuum ultraviolet and extreme ultraviolet frequency comb generation.

Modulating retroreflectors (MRRs) offer a promising pathway to low-complexity and energy efficient asymmetric optical inter-satellite link (OISL) for small spacecrafts, such as CubeSats. In this paper, we develop a unified statistical channel model for an on off keying modulated, retroreflector-enabled OISL. The model captures both stochastic and deterministic pointing losses, as well as signal-dependent noise. Stochastic channel distributions are approximated via Monte Carlo simulation, and system optimization is carried out under CubeSat constraints using the achievable information rate as the primary metric. In addition, we derive bit-error ratio and outage probability to evaluate communication reliability. The proposed architecture is benchmarked against three state-of-the-art CubeSat laser terminals, i.e., NASA's Optical Communications and Sensors Demonstration (OCSD), DLR's OSIRIS4CubeSat, and NASA's CLICK BC. Results indicate that an optimized MRR-based transmitter can outperform OCSD and achieve performance comparable to OSIRIS4CubeSat at ranges below 500 km, while consuming only 2.5 W of power during transmission, significantly less than conventional CubeSat optical terminals.

Transition-metal nanoclusters exhibit structural and electronic properties that depend on their size, often making them superior to bulk materials for heterogeneous catalysis. However, their performance can be limited by sulfur poisoning. Here, we use dispersion-corrected density functional theory (DFT) and physics-informed machine learning to map how atomic sulfur adsorbs and causes poisoning on 13-atom icosahedral clusters from 30 different transition metals (3$d$ to 5$d$). We measure which sites sulfur prefers to adsorb to, the thermodynamics and energy breakdown, changes in structure, such as bond lengths and coordination, and electronic properties, such as $\varepsilon_d$, the HOMO-LUMO gap, and charge transfer. Vibrational analysis reveals true energy minima and provides ZPE-based descriptors that reflect the lattice stiffening upon sulfur adsorption. For most metals, the metal-sulfur interaction mainly determines adsorption energy. At the same time, distortion penalties are usually moderate but can be significant for a few metals, suggesting these are more likely to restructure when sulfur is adsorbed. Using unsupervised \textit{k}-means clustering, we identify periodic trends and group metals based on their adsorption responses. Supervised regression models with leave-one-feature-out analysis identify the descriptors that best predict adsorption for new samples. Our results highlight the isoelectronic triad \ce{Ti}, \ce{Zr}, and \ce{Hf} as a balanced group that combines strong sulfur binding with minimal structural change. Additional DFT calculations for \ce{SO2} adsorption reveal strong binding and a clear tendency toward dissociation on these clusters, linking electronic states, lattice response, and poisoning strength. These findings offer data-driven guidelines for designing sulfur-tolerant nanocatalysts at the subnanometer scale.

Phase transitions are the macroscopic manifestation of microscopic processes that drive a system towards a new state. The detailed evolution of these processes, particularly in abrupt phase transitions, are currently not fully understood. Here, we introduce a theoretical framework based on internal node dependencies within a single-layer lattice. Crucially, we demonstrate that the fundamental mechanism underlying abrupt transitions is nucleation propagation preceded by a slow cascading process which scales with the range of dependencies. Our findings show that the synergy between these two distinct stages is essential for the occurrence of an abrupt transition. The first stage of a slow cascading mechanism was recently observed experimentally in superconducting layered materials, where heat acts as the dependency links, for the limit of infinite dependency range. Our model thus generalizes the framework to include finite dependency ranges, revealing previously unobserved mechanisms that could be experimentally verified through controlling the range of thermal diffusion in the material. As a universal mechanism, our model provides a robust method to test nucleation-controlled phase transitions in multiple systems, providing a path to discover and understand microscopic mechanisms in phase transitions.

Accurate representations of the World Air Transportation Network (WAN) are fundamental inputs to models of global mobility, epidemic risk, and infrastructure planning. However, high-resolution, real-time data on the WAN are largely commercial and proprietary, therefore often inaccessible to the research community. Here we introduce a generative model of the WAN that treats air travel as a stochastic process within a maximum-entropy framework. The model uses airport-level passenger flows to probabilistically generate connections while preserving traffic volumes across geographic regions. The resulting reconstructed networks reproduce key structural properties of the WAN and enable simulations of dynamic spreading that closely match those obtained using the real network. Our approach provides a scalable, interpretable, and computationally efficient framework for forecasting and policy design in global mobility systems.

Heat-flux boundary conditions are challenging to implement efficiently in rarefied gas flow simulations because the wall-reflected gas temperature and density must be determined dynamically during the computation. This paper aims to tackle this problem within the general synthetic iterative scheme (GSIS), where the Boltzmann kinetic equation is solved deterministically in an outer loop and macroscopic synthetic equations are solved in an inner loop. To avoid kinetic-macroscopic boundary-flux mismatch and the resulting convergence bottlenecks, for the macroscopic boundary flux at every inner iteration, the incident increment is estimated using a Maxwellian distribution, and then the reflected contribution is obtained by boundary conditions consistent with those in the kinetic solver. In addition to retaining the fast-converging and asymptotic-preserving properties of GSIS, the proposed method significantly reduces the iterations required to determine the wall-reflected gas parameters. Numerical simulations of rarefied gas flows in and around a 3D nozzle, a 2D adiabatic cylinder, and a 2D annular heat-transfer configuration show good agreement with the direct simulation Monte Carlo method, while achieving substantial efficiency gains over conventional iterative schemes.

We demonstrate a fully bidirectional 1 Gbs Ethernet over OWC link over a 1m free space path using a VCSEL-PIN pair and only commercially available components. The unamplified, transparent system achieves error-free operation, with a latency of less than 25 ns, and a centimetre-scale alignment tolerance.

In recent years, significant progress has been made in the velocity-moment-based quasi-linear (QL) theory of waves and instabilities in plasmas with nonequilibrium velocity distributions (VDs) of the Kappa (or $\kappa$) type. However, the temporal variation of the parameter $\kappa$, which quantifies the presence of suprathermal particles, is not fully captured by such a QL analysis, and typically $\kappa$ remains constant during plasma dynamics. We propose a new QL modeling that goes beyond the limits of a previous approach, realistically assuming that the quasithermal core cannot evolve independently of energetic suprathermals. The case study is done on the electron-cyclotron (EMEC) instability generated by anisotropic bi-Kappa electrons with $A=T_\perp/T_\parallel > 1$ ($\parallel, \perp$ denoting directions with respect to the background magnetic field). The parameter $\kappa$ self-consistently varies through the QL equation of kurtosis (fourth-order moment) coupled with temporal variations of the temperature components, relaxing the constraint on the independence of the low-energy (core) electrons and suprathermal high-energy tails of VDs. The results refine and extend previous approaches. A clear distinction is made between regimes that lead to a decrease or an increase in the $\kappa$ parameter with saturation of the instability. What predominates is a decrease in $\kappa$, i.e., an excess of suprathermalization, which energizes suprathermal electrons due to self-generated wave fluctuations. Additionally, we found that VDs can evolve toward a quasi-Maxwellian shape (as $\kappa$ increases) primarily in regimes with low beta and initial kappa values greater than five. Instability-driven relaxation only partially resolves temperature anisotropy in bi-Kappa electron VDs, as wave fluctuations generally act to further energize suprathermal electrons.

Distributed optical fiber sensing (DOFS), along with its capabilities of long-range coverage, multi-parameter monitoring, and completely passive detection, emerges as one of the most promising non-destructive detection techniques for structural health monitoring (SHM) and operational assessment of linear transportation infrastructures. In this paper, we provide a state-of-the-art review on DOFS applications across typical linear infrastructure systems, encompassing highways, long-span bridges, rail transit networks, airport runways, and analogous linear structures. The comprehensive discussion consists of four critical research dimensions: 1) optical fiber selection for multi-parameter sensing and robust cable packaging techniques, 2) distributed sensing principles and signal processing algorithms, 3) diverse application scenarios in SHM and related fields, and 4) anomaly detection and event classification methodologies. Building upon the foundational introduction of DOFS technical principles and monitoring solutions for intelligent transportation infrastructure, this paper elaborates on system design approaches, sensing data analytics algorithms, and future research directions.

We present an analytical and numerical study of how Doppler-induced spectral broadening in laser-heated plasmas degrades the coherence of small-angle X-ray scattering (SAXS) signals, and show that the resulting loss of temporal coherence reduces the SAXS intensity. Applying this formalism to two benchmark geometries - single density steps (wires) and periodic gratings -- we obtain analytic estimates. For gratings, finite coherence simultaneously lowers Bragg-peak heights and broadens their widths, whereas for isolated steps only the overall scaling with q affected. We map the parameter space relevant to current SASE and self-seeded XFELs, revealing that Doppler effects remain managable for the trieval of geometry parameters (less than few 10 % error) for SASE bandwidths but become the dominant error source in seeded configurations or above-keV temperatures. Practical consequences for density-gradient retrieval and interface-sharpness measurements are quantified. The results supply clear criteria for when Doppler broadening must be included in SAXS data analysis and offer a route to infer electron temperature directly from coherence-loss signatures.

Spider dragline silk supercontracts as humidity increases, displaying large axial shortening together with a reproducible macroscopic twist. The physical origin of this torsion remains debated and is often attributed to helically arranged load-bearing elements, despite the lack of direct evidence for helicity in the native fiber. Here we show that torsion can arise generically from nonlinear anisotropic elasticity: humidity-driven shortening of the amorphous matrix, mechanically constrained by stiff, axially aligned $\beta$-sheet--rich load-bearing segments and their experimentally induced prestretch, drives the system into a dual Poynting regime in which axial shortening couples to spontaneous twist. Coupling a diffusion-based water-uptake law to irreversible matrix remodeling and fiber plasticity, the model quantitatively reproduces monotonic and cyclic torsional measurements using parameter values consistent with available experimental material parameters. These results identify supercontraction-induced torsion in spider silk as a manifestation of a dual Poynting effect and provide a minimal, physically grounded framework for humidity-driven torsional actuation in matrix--fiber architectures.

Reaching consensus on massive discussion networks is critical for reducing noise and achieving optimal collective outcomes. However, the natural tendency of humans to preserve their initial ideas constrains the emergence of global solutions. To address this, Collective Intelligence (CI) platforms facilitate the discovery of globally superior solutions. We introduce a dynamical system based on the standard $O(N)$ model to drive the aggregation of semantically similar ideas. The system consists of users represented as nodes in a $d=2$ lattice with nearest-neighbor interactions, where their ideas are represented by semantic vectors computed with a pretrained embedding model. We analyze the system's equilibrium states as a function of the coupling parameter $\beta$. Our results show that $\beta > 0$ drives the system toward a ferromagnetic-like phase (global consensus), while $\beta < 0$ induces an antiferromagnetic-like state (maximum dissent), where users maximize semantic distance from their neighbors. This framework offers a controllable method for managing the tradeoff between cohesion and diversity in CI platforms.

Low-dimensional media have exhibited optical anisotropy that is unachievable in traditional 3D media due to the asymmetry of their strong, in-plane covalent bonds and weak out-of-plane van der Waals interactions. As a result, 2D media are promising building blocks for ultrathin devices such as polarimeters, polarized light sources, and active polarizers. III-VI semiconductors possess a rare property in the class of multilayered semiconductors, which is that their fundamental excitons are oriented out-of-plane. This allows them to exhibit phenomena such as transparency in the visible range while also being emissive in the visible and near-infrared ranges. Here, we report the first experimental values for the anisotropic refractive indices of {\gamma}-InSe and {\epsilon}-GaSe, and we observe the effects of the out-of-plane excitons on the c-axis refractive index. It is found that both materials exhibit moderate optical anisotropy for multilayered semiconductors. The complex, anisotropic refractive index of {\gamma}-InSe and {\epsilon}-GaSe enables the accurate simulation of these media, allowing for the design of high-performance, ultra-compact optoelectronic devices.

We present a novel framework for rigid body dynamics in ambient media, such as air or water, enabling accurate motion prediction of objects without requiring computational fluid dynamics simulations. Our method computes the added mass of the fluid and replaces heuristic models for shape-dependent lift and drag with a generalized estimate of flow separation and dynamic pressure. Our method is the first within the rigid body dynamics context to reproduce the full range of falling plate behaviors: fluttering, tumbling, chaotic and steady modes, as well as phenomena such as the Magnus effect and the flight dynamics of an American football (tight spiral pass paradox). The resulting algorithm is simple to implement, robust, does not rely on specialized integrators and incorporates seamlessly into existing physics engines for real-time simulation.

Quasi-poloidal (QP) magnetic fields have desirable properties for confining plasma: no radial drift of guiding centres (with positive implications for neoclassical transport), zero Pfirsch-Schlüter current, a lower level of damping for poloidal flows, leading to reduced anomalous transport, and possible stability benefits. Despite their attractive properties, QP fields are not amenable to the near-axis expansion, a major theoretical tool for understanding toroidal fields. In this paper, we provide a novel framework for defining and understanding QP flux surfaces. This framework relies on a simplification that transforms the task of finding a quasi-poloidal flux surface from a 3D problem to a 2D problem. This simplification also applies to asymmetric magnetic mirrors with desirable properties. We sketch how this 2D problem can form the basis of an efficient optimisation problem for finding QP flux surfaces. We leverage this 2D problem for theoretical understanding: for instance, we identify one class of QP flux surfaces that are naturally flat mirrors (Velasco et al. 2023). The reduced model is validated against numerically optimised QP equilibria. We further utilise the reduced model to explain the prevalence of cusps, high mirror ratios, and narrow pinch points in these numerical equilibria.

We present an experimental study on steady gravity currents advancing along a heated wall. The current is generated by a mixture of air and carbon dioxide continuously supplied at the channel inlet. To have a complete point-wise characterization of the flow, simultaneous high-frequency measurements of two velocity components, CO_2 concentration, and temperature are performed. An experimental protocol is presented to reconstruct the local fluid density and to estimate turbulent vertical and horizontal fluxes of CO_2, temperature, and buoyancy. The reliability of both the flow measurements and of the estimate of convective heat flux exchanged at the wall is assessed through integral balances of \textnormal{CO}$_2$ mass, enthalpy, and buoyancy, performed at different distances from the source. Three wall-heating conditions are considered: an adiabatic case, a moderately heated case, and a strongly heated case. In the heated experiments, a convectively unstable boundary layer forms near the wall, capped by a stably stratified region. The influence of this condition on the first- and second-order flow statistics profiles is examined. Although wall heating influences the vertical shear, the Brunt-Vaisala frequency, and both shear and buoyancy production of turbulent kinetic energy within the stably-stratified region characterized by an almost constant vertical gradient of streamwise velocity, neither the gradient Richardson number nor the flux Richardson number exhibits a clear trend in this region with the imposed wall heat flux.

The imaging platform developed at the High Energy Density - Helmholtz International Beamline for Extreme Fields (HED-HiBEF) instrument at the European XFEL and its applications to high energy density and fusion related research are presented. The platform combines the XFEL beam with the high-intensity short-pulse laser ReLaX and the high-energy nanosecond-pulse laser DiPOLE-100X. The spatial resolution is better than 500 nm and the temporal resolution of the order of 50 fs. We show examples of blast waves and converging cylindrical shocks in aluminium, resonant absorption measurements of specific charged states in copper with ReLaX and planar shocks in polystyrene material generated by DiPOLE-100X. We also discuss the possibilities introduced by combining this imaging platform with a kJ-class laser.

We investigate the robustness of a curvature-weighted spectral precursor to dissipation in freely decaying three-dimensional incompressible turbulence. Building on our recent work in Physical Review Fluids on the Taylor--Green vortex, we analyze direct numerical simulations using the curl-of-vorticity spectrum $|\nabla\times \boldsymbol{\omega}|^2(k)$, equivalent to a $k^4$-weighted energy spectrum for solenoidal flow. Extending the study across multiple initial conditions -- multi-mode ABC flows, a randomized low-wavenumber ABC field, the Taylor--Green vortex, and the Kida--Pelz flow -- we find a consistent temporal ordering: the characteristic time associated with the advance and saturation of the peak wavenumber of $|\nabla\times \boldsymbol{\omega}|^2(k)$ precedes the dissipation-peak time, which in turn precedes the characteristic time associated with the peak scale of the nonlinear energy-flux spectrum. We further probe viscosity effects in Taylor--Green turbulence: the precursor persists at lower viscosity when adequate resolution is employed, but weakens and can break at higher viscosity, consistent with stronger viscous damping of curvature-dominated small-scale content. Throughout, we use explicit inspection of curvature-weighted spectra to distinguish physical peak evolution from cutoff-proximate artifacts. These results establish robustness across initial conditions and clarify the practical role of viscosity and resolution for deploying curvature-weighted spectral precursors in decaying turbulence.

We present the data-driven coupled-cluster deep network (DDCCNet), a family of multitask, physics-enhanced deep learning architectures designed to predict coupled-cluster singles and doubles (CCSD) amplitudes and correlation energies from lower-level electronic structure methods. The three DDCCNet variants (termed as v1, v2, and v3) progressively incorporate architectural refinements ranging from parallel subnetworks for t_1 and t_2 amplitudes to feature-partitioned blocks and physics-enhanced intermediate prediction layers that are structured in accordance with coupled-cluster equations to enhance physical consistency and multitask learning efficiency. These models jointly learn correlated amplitude patterns while embedding symmetry and orbital-level interactions directly into the network structure. Applied to methanol conformers, CO2 clusters, and small organic molecules, DDCCNet_v2 delivered the most accurate and transferable performance, achieving chemically precise correlation energies across diverse molecular systems. Collectively, DDCCNet establishes a scalable, physically grounded framework that unifies machine learning and ab initio theory for efficient, data-driven electronic structure prediction.

Tip-Enhanced Raman Spectroscopy (TERS) combines Raman spectroscopy with scanning probe microscopy to overcome the spatial resolution limitation imposed by light diffraction, offering a primary optical technique for the comprehensive study of two-dimensional (2D) materials. In this work, we investigate an anomalous decay profile of the TERS intensity of the graphene 2D band as the tip-sample separation changes, observations enabled by high TERS efficiency and accuracy in tip-approach and tip-retract procedures. The anomalous results can be properly described by the addition of an ad hoc deformation to the effective tip-sample distance, rationalized here as due to the presence of a liquid meniscus formed via capillary forces.

Estimating collision rates is of immense importance in particle-laden flows. An economical way of doing this is to directly identify incidences of caustics, or extreme clustering, by tracking particle velocity gradients in the neighborhoods of individual particles. The objective of this work is two-fold. (i) We find conditions under which caustics form, in point-vortex flow and in two-dimensional turbulence. While caustics are known to form in regions of strain, we show that the type of strain is key. Particles must remain in compressional strain throughout the process to form caustics, whereas survivor particles: which visit high strain but do not form caustics, briefly go through extensional strain during the early part of the process. This enables survivor particles to attain significantly straighter paths, and to move faster, whereas caustics particles follow paths of high curvature and move slower. As a result, caustics particles stay longer in high-strain regions than survivors. (ii) We ask about the effect of finite particle density, where the particle is denser than the background fluid. We show that finite-density particles need to sample stronger background strain than infinite-density ones to trigger caustics, but our other findings are universal across particle density.

Stochastic kinetic models describe systems across biology, chemistry, and physics where discrete events and small populations render deterministic approximations inadequate. Parameter inference and inverse design in these systems require optimizing over trajectories generated by the Stochastic Simulation Algorithm, but the discrete reaction events involved are inherently non-differentiable. We present an approach based on straight-through Gumbel-Softmax estimation that maintains exact stochastic simulations in the forward pass while approximating gradients through a continuous relaxation applied only in the backward pass. We demonstrate robust performance on parameter inference in stochastic gene expression, accurately recovering kinetic rates of telegraph promoter models from both moment statistics and full steady-state distributions across diverse and challenging parameter regimes. We further demonstrate the method's applicability to inverse design problems in stochastic thermodynamics, characterizing Pareto-optimal trade-offs between non-equilibrium currents and entropy production. The ability to efficiently differentiate through exact stochastic simulations provides a foundation for systematic inference and rational design across the many domains governed by continuous-time Markov dynamics.

Harnessing strong light-matter interactions to control chemical reactions in confined electromagnetic fields offers a promising route toward deepening our understanding of chemical dynamics at the collective quantum-mechanical level, with potential implications for future chemical synthesis paradigms. Achieving this goal, however, requires an in-depth mechanistic understanding of the underlying dynamical processes. As a step in this direction, we present a systematic and numerically exact quantum dynamical study of cooperative reaction dynamics inside an optical microcavity. Using a hierarchy of model systems with increasing complexity, we elucidate how cavity-modified reactivity emerges from-and is highly sensitive to-subtle structural and environmental variations. Our models consist of optically dark reactive molecules, each represented by a symmetric double well potential, coupled to infrared-active non-reactive intramolecular or solvent vibrational modes, as well as their respective dissipative environments. Our results demonstrate that cavity-induced rate modifications arise from a delicate interplay among mode hybridization in strong-coupling regimes, the dynamical balance of all participating energy exchange processes, and quantum interference between multiple fluctuation-dissipation-mediated reaction pathways enabled by collective cavity coupling. By continuously tuning a single system parameter or introducing molecular collectivity, we observe qualitatively distinct rate modification profiles as functions of the cavity frequency, including resonant rate enhancement, resonant rate suppression, hybridization-induced peak splitting, and, notably, asymmetric Fano-type line shapes in which enhancement peaks and suppression dips coexist within a narrow resonance window, highlighting the important role of quantum interference in cavity-modified chemical reactivity.

Understanding the relationship between molecular structure and chemical reactivity or properties is fundamental to rational molecular design. Linear free energy relationships (LFERs), particularly Hammett analysis, have long served as powerful tools in organic chemistry. Recently, these approaches have been enhanced by incorporating computationally derived parameters, enabling broader applicability across diverse molecules and reactions. To facilitate and scale this process, we present DFTDescriptorPipeline, a fully automated workflow for extracting quantum chemical descriptors from Gaussian log files and constructing structure-property and structure-reactivity relationships using multivariate linear regression (MLR) models. We validate the workflow across four case studies, including photoswitchable molecules and catalytic reactions. In each case, the models provide interpretable results, demonstrating the versatility of this approach and its relevance to a wide range of chemical contexts. We anticipate that this platform will serve as a generalizable framework for integrating quantum chemical calculations into data-driven molecular design.

This study presents a robust methodology for the 3D volumetric reconstruction of native Chileanpollen grains, specifically Gevuina avellana (hazel),Conium maculatum (hemloc) and Anthemis cotula (chamomile). Using a lens-free Digital In-line Holographic Microscopy (DLHM) system, we capture complex interference patterns that are numerically reconstructed using the Kirchhoff-Helmholtz transform. Our results demonstrate that this label-free approach provides high-fidelity morphological characterization and nanometric precision in biophysical parameter extraction, offering a scalable alternative for automated melissopalynology and environmental monitoring.

Extending corresponding results for matrix product states [Verstraete and Cirac, PRB 73, 094423 (2006); Schuch et al. PRL 100, 030504 (2008)], it is shown how the approximation error of tree tensor network states (TTNS) can be bounded using Schmidt spectra or Rényi entanglement entropies of the target quantum state. Conversely, one obtains bounds on TTNS bond dimensions needed to achieve a specific approximation accuracy. For tree lattices, the result implies that efficient TTNS approximations exist if $\alpha<1$ Rényi entanglement entropies for single-branch cuts obey an area law, as in ground and low-energy states of certain gapped systems.

We formulate the covariant hydrodynamics equations describing the fluid dynamics as the problem of intersection theory on the infinite dimensional symplectic manifold associated with spacetime. This point of view separates the structures related to the equation of state, the geometry of spacetime, and structures related to the (differential) topology of spacetime. We point out a five-dimensional origin of the formalism of Lichnerowicz and Carter. Our formalism also incorporates the chiral anomaly and Onsager quantization. We clarify the relation between the canonical velocity and Landau $4$-velocity, the meaning of Kelvin's theorem. Finally, we discuss some connections to topological strings, Poisson sigma models, and topological field theories in various dimensions.

This is an editorial article based on the reseaches on the Ising model over the last 100 years.

We investigate the wrinkling dynamics of a thin elastic sheet that is indented or compressed while floating on a viscous liquid. We show that the deformation speed controls the dynamics, leading to a wrinkle wavelength significantly smaller than that selected under quasistatic compression. Once active compression ceases, the wrinkles coarsen until their wavelength relaxes toward the equilibrium value. We develop a theoretical model coupling Stokes flow in the liquid to elastic bending of the sheet, which quantitatively predicts both the initial wavelength selection and its subsequent coarsening. We demonstrate that the same mechanism governs two dimensional and axisymmetric geometries, thereby extending classical static wavelength selection laws to dynamic situations. Although developed from controlled laboratory experiments, the model captures a generic viscous-elastic coupling and applies broadly to thin elastic films interacting with viscous environments, including the formation of surface wrinkles in pahoehoe lava flows.

In many real-world applications of data assimilation (DA), the strategic placement of observers is crucial for effective and efficient forecasting. Motivated by practical constraints in sensor deployment, we show that global recovery of the flow field can be achieved using observations available only in a subregion of the domain, possibly far from the boundary. We focus on the two-dimensional incompressible Navier-Stokes equations posed in a bounded physical domain with Dirichlet boundary conditions. Building on the continuous data assimilation framework of Azouani, Olson, and Titi (2014), we rigorously prove that the assimilated solution converges globally to the true solution under suitable conditions on the nudging parameter, spatial resolution, and the geometry of the observation region, specifically, when the maximum distance from any point in the domain to the observational subregion is bounded by a constant multiple of \( \nu^{1/2} \) (in terms of scaling). Our computational results, conducted via finite element methods over complex geometries, support the theoretical findings and reveal even greater robustness in practice. Specifically, synchronization with the true solution is achieved even when the observational subregion lies farther from the rest of the domain than the theoretical threshold permits. Across all three tested scenarios, the local nudging algorithm performs comparably to full-domain assimilation, reaching global accuracy up to machine precision. Interestingly, observational data near the boundary are found to be largely uninformative. This demonstrates that full observability is not necessary: carefully chosen interior observations, even far from the boundary, can suffice.

Low-temperature plasma processing is a promising technique for tailoring the properties of transition metal dichalcogenides (TMDs) because it allows for precise control of radical and ion energies and fluxes. For chalcogen substitution, a key challenge is to identify the ion energy window that enables selective chalcogen removal while preserving the metal lattice. Using ab-initio molecular dynamics (AIMD), we demonstrate that oxygen and fluorine functionalization through thermal chemisorption significantly lowers the sputtering energy threshold ($E_{sputt}$) of MoS${}_2$ from $\sim 35$ eV to $\sim 10$ eV. In addition, we find that a non-orthogonal impact angle $\sim 30{}^{\circ}$ reduces the sputtering energy threshold, while cryogenic-range TMD temperatures may increase. To explain the observed trends, a multi-step sputtering mechanism is proposed. Our results show that oxygen/fluorine functionalization, impact angle, and material temperature are key control parameters for selective, damage-free chalcogen removal in TMD processing.

CP-PAW is a combined electronic structure and ab-initio molecular dynamics code to perform mixed quantum and classical simulations of atomistic condensed phase systems, such as solids, liquids, and molecular systems. As the name suggests, the CP-PAW code unifies the all-electron projector augmented-wave method with the Car-Parrinello approach to determine not only the electronic and nuclear ground state of condensed matter, but also to study their properties and dynamics. In addition to briefly outlining the underlying theory, the focus will be on unique aspects of CP-PAW and how to correctly employ them as a user. How to install CP-PAW using the new build system will also be briefly mentioned.

This study examines the dynamics of the third body in an elliptic restricted three-body problem (ERTBP) framework, taking into account perturbations from radiation pressure, oblateness, and elongation of the primary bodies, as well as disk-like structures. The objectives are to determine the positions and stability of the equilibrium points, asses how these points shift under the influence of perturbations, and evaluate the dependence of their stability on the orbital eccentricity and perturbation parameters. The ERTBP model is modified to include a radiating, oblate primary body and an elongated secondary body modeled as a finite straight segment, alongside perturbations from a surrounding disk. The system's equations of motion are numerically solved using parameters from perturbed and classical cases. Equilibrium positions are computed over a range of eccentricities and perturbation values, and stability is analyzed using linearized equations and eigenvalue methods. In all cases, we have found three collinear ($L_1$, $L_2$, $L_3$) and two non-collinear ($L_4$, $L_5$) equilibrium points solutions. The inclusion of radiations, oblateness, elongation using a finite straight segment, and disk perturbation systematically displaces each equilibrium point from its classical location, with the magnitude and direction of the displacement varying with the perturbation parameter. Stability analysis confirms that the collinear points remain linearly stable under all tested conditions. Meanwhile, non-collinear points are stable under a specific condition. We investigate the stability boundary of these points as a function of orbital eccentricity and we found there is a critical range of eccentricity values within which stability is preserved.

The Korteweg-de Vries (KdV) equation serves as a foundational model in nonlinear wave physics, describing the balance between dispersive spreading and nonlinear steepening that gives rise to solitons. This article introduces sangkuriang, an open-source Python library for solving this equation using Fourier pseudo-spectral spatial discretization coupled with adaptive high-order time integration. The implementation leverages just-in-time (JIT) compilation for computational efficiency while maintaining accessibility for instructional purposes. Validation encompasses progressively complex scenarios including isolated soliton propagation, symmetric two-wave configurations, overtaking collisions between waves of differing amplitudes, and three-body interactions. Conservation of the classical invariants is monitored throughout, with deviations remaining small across all test cases. Measured soliton velocities conform closely to theoretical predictions based on the amplitude-velocity relationship characteristic of integrable systems. Complementary diagnostics drawn from information theory and recurrence analysis confirm that computed solutions preserve the regular phase-space structure expected for completely integrable dynamics. The solver outputs data in standard scientific formats compatible with common analysis tools and generates visualizations of spatiotemporal wave evolution. By combining numerical accuracy with practical accessibility on modest computational resources, sangkuriang offers a platform suitable for both classroom demonstrations of nonlinear wave phenomena and exploratory research into soliton dynamics.

We develop an information-theoretic formulation of stochastic dynamics in which the fundamental stochastic variable is the total action connecting spacetime points, rather than individual paths. By maximizing Shannon entropy over a joint distribution of actions and endpoints, subject to normalization and a constraint on the mean action, we obtain a Boltzmann-like distribution in action space. This framework reproduces the standard Brownian propagator in the nonrelativistic limit and naturally extends to relativistic regimes, where the Wiener construction fails to preserve Lorentz covariance. The approach bypasses functional integration over paths, makes the role of entropic degeneracy explicit through an action-space density of states, and provides a transparent connection between the principle of least action and statistical inference. We derive the density of states explicitly using large deviation theory, showing that it takes a Gaussian form centered at the minimal action, and rigorously justify the saddle-point approximation in the diffusive regime. The Markovian property of the resulting propagator is verified to hold via the Chapman--Kolmogorov equation, following from the additivity of the minimal action for free-particle dynamics. In the diffusive regime, the resulting dynamics are governed by a competition between extremization of the action and entropic effects, which can be interpreted in terms of an effective action free energy. Our results establish an unified, covariant, and information-based foundation for classical and relativistic stochastic processes.

I define a notion of locality LOC, closely modelled on the Bell principle of Local Causality, construed as the condition that single case probabilities cannot be modified by actions at spacelike separation. The new principle, like that of Bell, forces Bell inequalities, but with two loopholes: one is violation of measurement independence, known to Bell, but the other is non-uniqueness of remote outcomes, a loophole only for LOC, not for Local Causality. I also set out a theory of physical probability, applicable to the Everett interpretation, in which the Born rule is derived, and which therefore violates Bell inequalities. I show it is consistent with LOC. Surprisingly, both loopholes are exploited. I conclude not only that physical probability in the Everett interpretation involves no action at a distance, but that the observed violation of Bell inequalities is powerful evidence for many worlds.

Projection-based model reduction enables efficient simulation of complex dynamical systems by constructing low-dimensional surrogate models from high-dimensional data. The Operator Inference (OpInf) approach learns such reduced surrogate models through a two-step process: constructing a low-dimensional basis via Singular Value Decomposition (SVD) to compress the data, then solving a linear least-squares (LS) problem to infer reduced operators that govern the dynamics in this compressed space, all without access to the underlying code or full model operators, i.e., non-intrusively. Traditional OpInf operates as a batch learning method, where both the SVD and LS steps process all data simultaneously. This poses a barrier to deployment of the approach on large-scale applications where dataset sizes prevent the loading of all data into memory at once. Additionally, the traditional batch approach does not naturally allow model updates using new data acquired during online computation. To address these limitations, we propose Streaming OpInf, which learns reduced models from sequentially arriving data streams. Our approach employs incremental SVD for adaptive basis construction and recursive LS for streaming operator updates, eliminating the need to store complete data sets while enabling online model adaptation. The approach can flexibly combine different choices of streaming algorithms for numerical linear algebra: we systematically explore the impact of these choices both analytically and numerically to identify effective combinations for accurate reduced model learning. Numerical experiments on benchmark problems and a large-scale turbulent channel flow demonstrate that Streaming OpInf achieves accuracy comparable to batch OpInf while reducing memory requirements by over 99% and enabling dimension reductions exceeding 31,000x, resulting in orders-of-magnitude faster predictions.

Many protocols within quantum cryptography, communications, and computing require the ability to generate entangled states as well as spectral qudits. Nonlinear interferometry is a viable way to engineer these complex quantum states of light. However, it is difficult to achieve a high level of control over spectral correlations. Here, we present a protocol utilizing a nonlinear interferometer with linear spectral phases that can generate both high-dimensional spectral qudits and high-dimensional entangled states. We model the effect of loss and loss of overlap on interference visibility and thereby on the states generated.

Nanocrystals (NCs) serve as versatile building blocks for the creation of functional materials, with NC self-assembly offering opportunities to enable novel material properties. Here, we demonstrate that PbS NCs functionalized with strongly negatively charged metal chalcogenide complex (MCC) ligands, such as $Sn_2S_6^{4-}$ and $AsS_4^{3-}$, can self-assemble into all-inorganic superlattices with both long-range superlattice translational and atomic-lattice orientational order. Structural characterizations reveal that the NCs adopt unexpected edge-to-edge alignment, and numerical simulation clarifies that orientational order is thermodynamically stabilized by many-body ion correlations originating from the dense electrolyte. Furthermore, we show that the superlattices of $Sn_2S_6^{4-}$-functionalized PbS NCs can be fully disassembled back into the colloidal state, which is highly unusual for orientationally attached superlattices with atomic-lattice alignment. The reversible oriented attachment of NCs, enabling their dynamic assembly and disassembly into effectively single-crystalline superstructures, offers a pathway toward designing reconfigurable materials with adaptive and controllable electronic and optoelectronic properties.

We investigate quantum correlations in a hybrid qubit-qutrit system subject to both axial and planar single-ion anisotropies, dipolar spin-spin interactions, and Dzyaloshinskii-Moriya (DM) coupling. Using Negativity, Measurement-Induced Non-locality (MIN), Uncertainty-Induced Nonlocality (UIN), and Bell nonlocality (as quantified by the CHSH inequality) as measures, we analyze the interplay between anisotropy parameters, magnetic fields, and temperature on the survival of quantum correlations. Our results demonstrate that Bell nonlocality and entanglement (Negativity) are highly sensitive to temperature and anisotropy, exhibiting sudden death under thermal noise, whereas MIN and UIN are significantly more robust. In particular, these discord-like and information-theoretic measures provide the largest baseline and persist even in parameter regions where entanglement vanishes, highlighting their suitability as a quantumness witness in realistic conditions. Notably, our Bell nonlocality study is tailored to the asymmetric qubit-qutrit setting by exploiting a recently developed qubit-qudit CHSH maximization framework. However, Bell nonlocality is confirmed to be the most fragile, surviving only in narrow parameter windows at low temperature. A key finding of this work is that we observe the fragility hierarchy: Bell nonlocality < Negativity < UIN(MIN) in the qubit-qutrit setting. These results provide deeper insight into the relative robustness of distinct quantum resources in anisotropic qubit-qutrit models, suggesting that quantum discord-like measures, such as MIN and UIN, may serve as more practical resources than entanglement for quantum information tasks in thermally active spin systems.

This study investigates the economic complexity of Indian states by constructing a state-industry bipartite network using firm-level data on registered companies and their paid-up capital. We compute the Economic Complexity Index and apply the fitness-complexity algorithm to quantify the diversity and sophistication of productive capabilities across the Indian states and two union territories. The results reveal substantial heterogeneity in regional capability structures, with states such as Maharashtra, Karnataka, and Delhi exhibiting consistently high complexity, while others remain concentrated in ubiquitous, low-value industries. The analysis also shows a strong positive relationship between complexity metrics and per-capita Gross State Domestic Product, underscoring the role of capability accumulation in shaping economic performance. Additionally, the number of active firms in India demonstrates a persistent exponential growth at an annual rate of 11.2%, reflecting ongoing formalization and industrial expansion. The ordered binary matrix displays the characteristic triangular structure observed in complexity studies, validating the applicability of complexity frameworks at the sub-national level. This work highlights the usefulness of firm-based data for assessing regional productive structures and emphasizes the importance of capability-oriented strategies for fostering balanced and sustainable development across Indian states. By demonstrating the usefulness of firm registry data in data constrained environments, this study advances the empirical application of economic complexity methods and provides a quantitative foundation for capability-oriented industrial and regional policy in India.

Control valve stiction, a friction that prevents smooth valve movement, is a common fault in industrial process systems that causes instability, equipment wear, and higher maintenance costs. Many plants still operate with conventional valves that lack real time monitoring, making early predictions challenging. This study presents a machine learning (ML) framework for detecting and predicting stiction using only routinely collected process signals: the controller output (OP) from control systems and the process variable (PV), such as flow rate. Three deep learning models were developed and compared: a Convolutional Neural Network (CNN), a hybrid CNN with a Support Vector Machine (CNN-SVM), and a Long Short-Term Memory (LSTM) network. To train these models, a data-driven labeling method based on slope ratio analysis was applied to a real oil and gas refinery dataset. The LSTM model achieved the highest accuracy and was able to predict stiction up to four hours in advance. To the best of the authors' knowledge, this is the first study to demonstrate ML based early prediction of control valve stiction from real industry data. The proposed framework can be integrated into existing control systems to support predictive maintenance, reduce downtime, and avoid unnecessary hardware replacement.

The primordial 7Li abundance predicted by standard Big Bang Nucleosynthesis (BBN) exceeds that inferred from old, metal-poor stars by a factor of about 3-4. In standard BBN, most primordial 7Li is produced as 7Be in the early Universe and later converted by electron capture. Additional production or destruction channels of 7Be, such as proton capture or antineutrino capture during BBN, may therefore affect the final lithium yield. We quantify the depletion of 7Be due to in-situ electron capture, including the associated antineutrino channel, positron decay from nuclear excited states, and proton capture through the radiative 7Be(p,gamma)8B reaction. We also investigate stimulated emission induced by the dense photon background during the nuclear statistical equilibrium epoch, as well as a three-body Auger-like variant transferring the capture energy to a continuum electron. Decay rates are computed using first-order perturbation theory, modelling weak interactions with a Fermi contact term and factorising hadronic and leptonic currents. Thermally averaged rates are obtained by folding cross-sections with Maxwell-Boltzmann distributions and accounting for particle densities in the temperature range 10-100 keV. We find that the electron-capture rate decreases rapidly with temperature and is significantly enhanced by the inclusion of the antineutrino channel. Stimulated emission and plasma screening increase the radiative proton-capture rate by only 1-3 percent at temperatures around 87 keV. The Auger-like channel contributes at the level of a few thousandths of a percent and becomes negligible at lower temperatures. Overall, our total rate revises previous estimates by nearly an order of magnitude. Electron capture, proton capture, and positron decay provide corrections to the dominant depletion channel 7Be(n,p)7Li.

Intervals of enhanced turbulent fluctuations are typically less frequent within the magnetic cloud region of an interplanetary coronal mass ejection (ICME). We investigate two such intervals inside an ICME observed by the \textit{Wind} spacecraft on 8--9 June 2000 and characterize their associated wave populations. We focus on spectral analysis and plasma instability analysis, using ion-scale normalized magnetic helicity and polarization properties with respect to the background magnetic field $B_0$. In the first interval, the ion-scale normalized magnetic helicity shows a left-handed circularly polarized signature. In the second interval, the left-handed signature persists and an additional high-frequency right-handed population appears. The propagation is approximately parallel to $B_0$. The left-handed fluctuations are compatible with Alfvén ion-cyclotron (AIC) waves, while the right-handed fluctuations are consistent with fast magnetosonic/whistler (FM/W) waves. The ICME plasma accesses resonance conditions that support multiple ion-scale wave modes. Evolving anisotropies in the plasma and the approach to marginal stability allow the coexistence of AIC-like and fast-magnetosonic/whistler-like fluctuations, with enhanced electron heating favoring the growth of the FM/W contribution and strengthening the density--magnetic-field magnitude correlation.

Single-atom catalysts (SACs) maximize atom efficiency and exhibit unique electronic structures, yet realizing precise and scalable atomic dispersion remains a key challenge. Here, we report a non-equilibrium strategy for the scalable synthesis of SACs via ion implantation, enabling precise stabilization of metal atoms on diverse supports. Using an industrial-grade ion source, wafer-scale ion implantation with milliampere-level beam currents enables high-throughput fabrication of SACs, while the synergistic energy-mass effects stabilize isolated metal atoms in situ. A library of 36 SACs was constructed, and the resulting Pt/MoS2 exhibits outstanding hydrogen evolution performance with an overpotential of only 26 mV at 10 mA cm-2 and exceptional long-term stability, surpassing commercial Pt/C. This work demonstrates ion implantation as a versatile platform bridging fundamental SACs design and scalable manufacturing, providing new opportunities for high-performance catalysts in energy conversion applications.

Artificial Intelligence is rapidly transforming materials science and engineering, offering powerful tools to navigate complexity, accelerate discovery, and optimize material design in ways previously unattainable. Driven by the accelerating pace of algorithmic advancements and increasing data availability, AI is becoming an essential competency for materials researchers. This review provides a comprehensive and structured overview of the current landscape, synthesizing recent advancements and methodologies for materials scientists seeking to effectively leverage these data-driven techniques. We survey the spectrum of machine learning approaches, from traditional algorithms to advanced deep learning architectures, including CNNs, GNNs, and Transformers, alongside emerging generative AI and probabilistic models such as Gaussian Processes for uncertainty quantification. The review also examines the pivotal role of data in this field, emphasizing how effective representation and featurization strategies, spanning compositional, structural, image-based, and language-inspired approaches, combined with appropriate preprocessing, fundamentally underpin the performance of machine learning models in materials research. Persistent challenges related to data quality, quantity, and standardization, which critically impact model development and application in materials science and engineering, are also addressed.

Alumina and aluminum oxyhydroxides underpin chemical-engineering technologies from heterogeneous catalysis, corrosion protection, functional coatings, energy-storage devices, to biomedical components. Yet molecular models that predictively connect phase structure, pH-dependent surface chemistry, electrolyte organization, and adsorption across operating conditions remain limited. Here we introduce a unified INTERFACE Force Field (IFF) parameterization together with a curated, ready-to-use pH-resolved surface model database that provides the most accurate and transferable atomistic description of major alumina phases to date. The framework covers a-Al2O3, g-Al2O3, boehmite, diaspore, and gibbsite using a single, physically interpretable parameter set that is directly compatible with CHARMM, AMBER, OPLS-AA, CVFF, and PCFF. Across structural, thermodynamic, mechanical, and interfacial benchmarks, simulations reproduce experimental reference data with more than 95 percent accuracy, exceeding existing force fields and the reliability of current density-functional approaches. A key advance is the first transferable treatment of surface ionization and charge regulation across alumina phases over a broad range of pH values, enabling simulations of realistic solid electrolyte interfaces without phase-specific reparameterization. Quantitative reliability is demonstrated by reproducing trends in zeta potentials and pH-dependent adsorption of a corrosion inhibitor at alumina-water interfaces. Predicted adsorption free energies and surface contact times correlate with experiments across more than an order of magnitude. Relative to ML-DFT workflows, the speed 100 to 1000 times faster, reaching system sizes and time scales inaccessible to quantum methods. The results establish a predictive computational platform to design alumina-containing functional materials under realistic process conditions.

The use of polymer nanocomposites as gas barrier materials has seen increasing interest, including applications involving hydrogen transport and storage. Better understanding of gas transport through those polymeric systems requires 3D nanoscale detection of distributions and the possible trapping of gas molecules within nanoparticles and polymer/nanoparticle interfaces While atom probe tomography (APT) offers promising means for such nanoscale characterisation, its use for polymers has been mainly limited to thin organic layers deposited onto substrates or pre-fabricated metal needle shaped specimens. This work provides the very first application of APT to bulk polymer nanocomposites. Particularly, site specific atom probe sample preparation by Focused Ion Beam (FIB) liftout has been shown for the first time in a model system of hexagonal boron nanoparticles within a PVDF polymer matrix, using a variety of FIB workflows including Xe FIB, Ga FIB, cryogenic Ga FIB and deuterium charging. Mass spectra from the bulk polymer and the nanoparticle were collected using pulsed laser atom probe using standard conditions and compared. Several challenges encountered during this research including damage of the polymeric matrix during sample preparation were extensively discussed in this paper. Once those challenges have been resolved (e.g. by developing site specific sample preparation protocols), the application of APT to polymer nanocomposites can open new options for nanoscale characterisation of those systems.

The global food system has metamorphosed from a loose aggregation of bilateral exchanges into a highly intricate, interdependent Global Food Trade Network (FTN). This comprehensive review synthesizes the extant literature to examine the FTN through the rigorous lens of complex network science, moving beyond traditional economic trade models to quantify the system's topological architecture. We delineate the network's historical transition from a unipolar, efficiency-driven system dominated by Western hegemony to a multipolar, regionalized structure characterized by high clustering and scale-free heterogeneity. Special emphasis is placed on the dual nature of connectivity, which functions simultaneously as a buffer against local production variances and a conduit for global contagion. By conceptualizing the FTN as a multiplex system-distinguishing between the robust topology of wheat, the brittle regionalism of rice, and the polarized "dumbbell" structure of soy-we elucidate the distinct structural vulnerabilities inherent in modern food security. Furthermore, we analyze the impact of recent high-magnitude shocks, specifically the COVID-19 pandemic and the Russia-Ukraine conflict, illustrating the critical trade-off between logistical efficiency and systemic resilience. The review concludes by assessing the future trajectory of the network under anthropogenic climate change, predicting a poleward migration of comparative advantage that necessitates a paradigm shift from isolationist protectionism to cooperative network redundancy.

We introduce a novel spatial discretization technique for the reliable and efficient simulation of magnetization dynamics governed by the Landau-Lifshitz (LL) equation. The overall discretization error is systematically decomposed into temporal and spatial components. The spatial error analysis is conducted by formulating the LL equation within the framework of the Localized Orthogonal Decomposition (LOD) method. Numerical examples are presented to validate the accuracy and approximation properties of the proposed scheme.

This work develops low-energy spacecraft (SC) trajectories using Venus gravity assists to study asteroids during heliocentric transfer segments between planetary encounters. The study focuses on potentially hazardous asteroids (PHAs) as primary exploration targets. This paper proposes a method for calculating SC trajectories that enable asteroid flybys after a Venus gravity assist. The method involves formulating and solving an optimization problem to design trajectories incorporating flybys of selected asteroids and Venus. Trajectories are calculated using two-body dynamics by solving the Lambert problem. A preliminary search for candidate asteroids uses an algorithm to narrow the search space of the optimization problem. This algorithm uses the V-infinity globe technique to connect planetary gravity assists with resonant orbits. The resonant orbit in this case serves as an initial approximation for the SC's trajectory between two successive planetary flybys. Four flight schemes were analyzed, including multiple flybys of Venus and asteroids, with the possibility of an SC returning to Earth. The proposed solutions reduce flight time between asteroid approaches, increase gravity assist frequency, and enhance mission design flexibility. The use of Venus gravity assists and resonant orbits ensures a close encounter with at least one asteroid during the SC's trajectory between two consecutive flybys of Venus, and demonstrates the feasibility of periodic Venus gravity assists and encounters with PHAs. The developed method was applied to construct trajectories that allow an SC to approach both Venus-resonant asteroids and PHAs via multiple Venus gravity assists. An additional study was carried out to identify asteroids accessible during the Earth-Venus segment in launch windows between 2029 and 2050.

We show that the relativistic energy-momentum relation can emerge as an effective ensemble-averaged structure from a multiplicative Hamiltonian when fluctuations of an auxiliary parameter are treated using maximum entropy inference. The resulting probability distribution is uniquely fixed by scale-invariant constraints, which are shown to arise naturally from the Fisher-Rao geometry of the associated statistical manifold. Within this information-geometric framework, the relativistic dispersion relation appears without initially imposing Lorentz symmetry, but as a consequence of statistical averaging and geometric invariance.

Pigmentation abnormalities, ranging from hypo- to hyperpigmentation, can serve as biomarkers of developmental disruption in fish exposed to environmental contaminants. However, the mechanistic pathways underlying these alterations remain poorly understood. Studies have shown that pattern formation in fish development requires specific pigment cell interactions. Motivated by experimental observations of pigmentation alterations following contaminant exposure, we investigate how pollutants influence pigment cell self-organization using a continuum reaction-diffusion-advection framework. The model incorporates nonlocal Morse-type kernels to describe short- and long-range interactions among melanophores and xanthophores. Our results show that perturbations to the strengths of adhesion or repulsion can drive transitions between stripes, spots, and mixed patterns, reproducing phenotypes characteristic of fish pigmentation mutants. In particular, homotypic interactions are sensitive to contamination, leading to pronounced changes in melanophore density and resulting pigmentation patterns. Time-dependent simulations indicate that pigment changes from early short-term contaminant exposure may be recoverable, whereas prolonged exposure can lead to sustained pigment loss. In a growing fish, contaminant-induced changes in cell-cell interactions directly influence stripe formation rate, stripe number, and pigmentation levels. Overall, our study provides insight into the mechanistic link between experimentally observed pigmentation alterations and the changes in spatial patterns of adult fish.

Cyclic molecules are ubiquitous across applications in chemistry and biology. Their restricted conformational flexibility provides structural pre-organization that is key to their function in drug discovery and catalysis. However, reliably sampling the conformer ensembles of ring systems remains challenging. Here, we introduce PuckerFlow, a generative machine learning model that performs flow matching on the Cremer-Pople space, a low-dimensional internal coordinate system capturing the relevant degrees of freedom of rings. Our approach enables generation of valid closed rings by design and demonstrates strong performance in generating conformers that are both diverse and precise. We show that PuckerFlow outperforms other conformer generation methods on nearly all quantitative metrics and illustrate the potential of PuckerFlow for ring systems relevant to chemical applications, particularly in catalysis and drug discovery. This work enables efficient and reliable conformer generation of cyclic structures, paving the way towards modeling structure-property relationships and the property-guided generation of rings across a wide range of applications in chemistry and biology.

Polychromatic electromagnetic fields are typically expanded as integrals over monochromatic fields, such as plane waves, multipolar fields, or Bessel beams. However, monochromatic fields do not belong to the Hilbert space of free Maxwell fields, since their norms diverge. Moreover, the continuous frequency integrals involved in such expansions complicate the treatment of light--matter interactions via the scattering operator. Here, we identify and study a polychromatic basis for free Maxwell fields whose basis vectors belong to the Hilbert space. These vectors are defined as simultaneous eigenstates of four commuting operators with integer eigenvalues. As a consequence, the basis set is countable, and the Hilbert space is separable and isomorphic to $\ell^2$, the Hilbert space of square-summable sequences. Each basis vector represents a polychromatic single-photon wave with quantized energy and a wavelet--like temporal dependence. Three versions of this basis are defined: Regular, incoming, and outgoing. The fields of the regular basis are smooth in both space and time. The incoming and outgoing fields are likewise smooth, except at the spatial origin. These results support and motivate the use of countable bases for both the theoretical description and the practical computation of light--matter interactions.

Fluctuation-induced forces, such as the Critical Casimir Effect (CCE), are fundamental mechanisms driving organization and self-assembly near second-order phase transitions. The existence of a comparable, universal force for systems undergoing a first-order transition has remained an unresolved fundamental question. The proposed Orderphobic Effect is one such potential mechanism. It arises from minimisation of the interfacial free energy between solutes that locally nucleate a disordered phase. Here, we report the first experimental demonstration and quantitative measurement of the Orderphobic Effect. Using a driven, non-equilibrium quasi-2D granular fluid undergoing a first-order order-disorder transition, we show that specifically designed solutes in an ordered phase nucleate a coexisting ``bubble'' of the disordered phase. By analysing its capillary fluctuations, we confirm that this phenomenon occurs due to the proximity to phase-coexistence, and we directly quantify the attractive force by measuring the interaction free energy between solutes. The observation of this general fluctuation-mediated force in a non-equilibrium steady state strongly supports its claimed universality. Our work establishes the Orderphobic Effect as the first-order equivalent to the CCE, providing a new, general route for controlling self-assembly and aggregation in soft matter and non-equilibrium systems.

Pyvale is an open-source software package that aims to become an all-in-one tool for sensor simulation, sensor uncertainty quantification, sensor placement optimization, and calibration/validation. Central to this is support for image-based sensors, with a dedicated Digital Image Correlation (DIC) module designed for both standalone use and integration within broader experimental design workflows. The design philosophy behind the DIC engine in Pyvale prioritizes a user-friendly Python interface with performant compiled code under the hood. This paper covers Pyvale's 2D DIC engine design, implementation, metrological performance compared to other DIC codes, and the unique ability to handle gigapixel size scanning electron microscope (SEM) images. Finally, we compare runtimes between Pyvale and other open-source DIC codes and show strong computational performance across a range of image resolutions and thread counts.

We present a multiscale modeling approach that integrates molecular dynamics simulations, machine learning, and the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to investigate the glass transition dynamics of polymer systems. The glass transition temperatures (Tg) of four representative polymers are estimated using simulations and machine learning model trained on experimental datasets. These predicted Tg values are used as inputs to the ECNLE theory to compute the temperature dependence of structural relaxation times and diffusion coefficients, and the dynamic fragility. The Tg values predicted from simulations show good quantitative agreement with experimental data. While machine learning tends to slightly overestimate Tg, the resulting dynamic fragility values remain close to experimental fragilities. Overall, ECNLE calculations using these inputs agree well with broadband dielectric spectroscopy results. Our integrated approach provides a practical and scalable tool for predicting the dynamic behavior of polymers, particularly in systems where experimental data are limited.

Clubroot, a major soilborne disease affecting canola and other cruciferous crops, is characterized by the development of large galls on the roots of susceptible hosts. In this study, we present the first application of terahertz time-domain spectroscopy (THz-TDS) as a non-invasive diagnosis tool in plant pathology. Compared with conventional molecular, spectroscopic, and immunoassay-based methods, THz-TDS offers distinct advantages, including non-contact, non-destructive, and preparation-free measurement, enabling rapid in situ screening of plant and soil samples. Our results demonstrate that THz-TDS can differentiate between healthy and clubroot-infected tissues by detecting both structural and biochemical alterations. Specifically, infected roots exhibit a blue shift in the refractive index in the low-frequency THz range, along with distinct peaks-indicative of disruptions in water transport and altered metabolic activity in both roots and leaves. Interestingly, the characteristic root swelling observed in infected plants reflects internal tissue disorganization rather than an actual increase in water content. Furthermore, a physics-constrained neural network is proposed to extract the main feature in THz-TDS. A comprehensive evaluation, including time-domain signals, amplitude and phase images, refractive index and absorption coefficient maps, and principal component analysis, provides enhanced contrast and spatial resolution compared to raw time-domain or frequency signals. These findings suggest that THz-TDS holds significant potential for early, non-destructive detection of plant diseases and may serve as a valuable tool to limit their spread in agricultural systems.

Spike timing offers a combinatorial address space, suggesting that timing-based spiking inference can be executed as lookup and routing rather than as dense multiply--accumulate. Yet most neuromorphic and photonic systems still digitize events into timestamps, bins, or rates and then perform selection in clocked logic. We introduce Polychronous Wave Computing (PWC), a timing-native address-selection primitive that maps relative spike latencies directly to a discrete output route in the wave domain. Spike times are phase-encoded in a rotating frame and processed by a programmable multiport interferometer that evaluates K template correlations in parallel; a driven--dissipative winner-take-all stage then performs a physical argmax, emitting a one-hot output port. We derive the operating envelope imposed by phase wrapping and mutual coherence, and collapse timing jitter, static phase mismatch, and dephasing into a single effective phase-noise budget whose induced winner--runner-up margin predicts boundary-first failures and provides an intensity-only calibration target. Simulations show that nonlinear competition improves routing fidelity compared with noisy linear intensity readout, and that hardware-in-the-loop phase tuning rescues a temporal-order gate from 55.9% to 97.2% accuracy under strong static mismatch. PWC provides a fast routing coprocessor for LUT-style spiking networks and sparse top-1 gates (e.g., mixture-of-experts routing) across polaritonic, photonic, and oscillator platforms.

We investigate strategies for simulating open quantum systems coupled to dissipative baths by comparing explicit wave function-based discretization [via multi-layer multi-configuration time-dependent Hartree (ML-MCTDH)] and the implicit density matrix-based master equation method [via tree tensor network hierarchical equations of motion (TTN-HEOM)]. For dissipative baths characterized by exponentially decaying bath correlation functions, the implicit discretization approach of HEOM -- rooted in bath correlation function decompositions -- proves significantly more efficient than explicit discretization of the bath into discrete harmonic modes. Explicit methods, like ML-MCTDH, require extensive mode discretization to approximate continuum baths, leading to computational bottlenecks. Case studies for two-level systems and a Fenna--Matthews--Olson complex model highlight TTN-HEOM's superiority in capturing dissipative dynamics with relaxations with a minimal number of auxiliary modes, while the explicit methods are as exact as the HEOM in pure dephasing regimes. This comparison is enabled by the TENSO package, which has both ML-MCTDH and TTN-HEOM implemented using the same computational structure and propagation strategy.

Feynman famously said that single-particle interference is ``a phenomenon which is impossible to explain in any classical way, and which has in it the heart of quantum mechanics.'' In this paper we show that some of the phenomenology of interference can be reproduced in a ``classical'' way, by reproducing the Elitzur-Vaidman Bomb Tester (including their improved version) using an extension of the quantum simulation logic (QSL) formalism. Our result improves and simplifies a previous result by Catani \emph{et al}, which relies on a much more complicated extension involving a ``toy field theory.'' We also show that not all single-particle interference can be explained by such a simple extension (including that of Catani et al), by showing that Hofmann's three-path interferometer is ``nonclassical'' in a very specific sense: it violates a Kochen-Specker-noncontextual inequality. Given that both our extension of QSL and Catani et al's extension are \emph{noncontextual} -- so do not reproduce the contextual behaviour of Hofmann's three-path interferometer -- the behaviour of that interferometer is a proper example of a phenomenon that has in it the heart of quantum mechanics, according to Feynman.

Accurately predicting the Curie temperature ($T_c$) of ferroelectrics from first principles remains a major challenge, as theoretical estimates often fall significantly below experimental values. In this work, we investigate the origin of these discrepancies in the prototypical ferroelectric PbTiO$_3$ by performing extensive constant-pressure ab initio molecular dynamics (AIMD) simulations and benchmarking them against classical molecular dynamics (MD) using machine learning force fields (MLFFs) derived from first-principles data. Our results show that the underestimation of $T_c$ primarily stems from the limitations of the exchange-correlation functional, rather than inaccuracies in the MLFF fitting. We uncover a critical interplay between finite-size effects and the range of interatomic interactions: although short-range MLFFs appear to yield better agreement with experimental $T_c$, this improvement results from a fortuitous cancellation of errors. Incorporating explicit long-range interactions improves accuracy for larger supercells but ultimately leads to lower predicted $T_c$ values. These findings highlight that accurate finite-temperature predictions require not only high-quality training data and sufficiently large simulation cells, but also the explicit treatment of long-range interactions and improved exchange-correlation functionals.

Large Language Models (LLMs) are rapidly reshaping scientific research. We analyze these changes in multiple, large-scale datasets with 2.1M preprints, 28K peer review reports, and 246M online accesses to scientific documents. We find: 1) scientists adopting LLMs to draft manuscripts demonstrate a large increase in paper production, ranging from 23.7-89.3% depending on scientific field and author background, 2) LLM use has reversed the relationship between writing complexity and paper quality, leading to an influx of manuscripts that are linguistically complex but substantively underwhelming, and 3) LLM adopters access and cite more diverse prior work, including books and younger, less-cited documents. These findings highlight a stunning shift in scientific production that will likely require a change in how journals, funding agencies, and tenure committees evaluate scientific works.

Modeling and forecasting subsurface multiphase fluid flow fields underpin applications ranging from geological CO2 sequestration (GCS) operations to geothermal production. This is essential for ensuring both operational performance and long-term safety. While high fidelity multiphase simulators are widely used for this purpose, they become prohibitively expensive once many forward runs are required for inversion purposes and quantify uncertainty. To tackle this challenge we propose LAViG-FLOW, a latent autoregressive video generation diffusion framework that explicitly learns the coupled evolution of saturation and pressure fields. Each state variable is compressed by a dedicated 2D autoencoder, and a Video Diffusion Transformer (VDiT) models their coupled distribution across time. We first train the model on a given time horizon to learn their coupled relationship and then fine-tune it autoregressively so it can extrapolate beyond the observed time window. Evaluated on an open-source CO2 sequestration dataset, LAViG-FLOW generates saturation and pressure fields that stay consistent across time while running orders of magnitude faster than traditional numerical solvers.

We report Beta-AlGaO/Ga2O3 tri-gate heterostructure MOSHEMTs incorporating a thin 5 nm Al2O3 gate oxide layer for improved gate control and reduced leakage. The devices were fabricated on AlGaO/GaO heterostructures grown by ozone MBE on Fe-doped Ga2O3 (010) substrates. The tri-gate MOSHEMTs, with 1 micron-wide fins and Lg=155 nm, exhibit a peak current-gain cut-off frequency fT=70 GHz and a power-gain cut-off frequency fMAX=55 this http URL fT.L product of 10.85 GHz-micron is the highest among reported Ga2O3 FETs to date. The devices show Vth =-0.5 V, an on/off ratio 10^6 I=80 mA/mm, a peak gm=60 mS/mm, and a low gate leakage current of 10^(-10) mA/mm at Vgs=0.5 V. Passivation with a 100 nm ALD Al2O3 layer effectively removes DC/RF dispersion and maintains stable operation under pulsed IV and repeated RF measurements. These results demonstrate the potential of tri-gate AlGaO/GaO MOSHEMTs for next-generation high-frequency and high-power applications.

The concept of random walk, in which particles or waves undergo multiple collisions with the microscopic constituents of a surrounding medium, is central to understanding diffusive transport across many research areas. However, this paradigm may break down in complex systems, where quantum interference and memory effects render the particle propagation anomalous, often fostering localization. Here we report on the observation of such anomalous dynamics in a minimal setting: an ultracold mass-imbalanced mixture of two fermionic gases in three dimensions. We release light impurities into a gas of heavier atoms and follow their evolution across different collisional regimes. Under strong interspecies interactions, by lowering the temperature we unveil a crossover from normal diffusion to subdiffusion. Simultaneously, a localized fraction of the light gas emerges, displaying no discernible dynamics over hundreds of collisions. Our findings, incompatible with the conventional Fermi-liquid picture, are instead captured by a model of an atom propagating through a (quasi-)static disordered landscape of point-like scatterers. These results highlight the key role of quantum interference in our mixture, which emerges as a versatile platform for exploring disorder-free localization phenomena.

Parallel imaging techniques reduce magnetic resonance imaging (MRI) scan time but image quality degrades as the acceleration factor increases. In clinical practice, conservative acceleration factors are chosen because no mechanism exists to automatically assess the diagnostic quality of undersampled reconstructions. This work introduces a general framework for pixel-wise uncertainty quantification in parallel MRI reconstructions, enabling automatic identification of unreliable regions without access to any ground-truth reference image. Our method integrates conformal quantile regression with image reconstruction methods to estimate statistically rigorous pixel-wise uncertainty intervals. We trained and evaluated our model on Cartesian undersampled brain and knee data obtained from the fastMRI dataset using acceleration factors ranging from 2 to 10. An end-to-end Variational Network was used for image reconstruction. Quantitative experiments demonstrate strong agreement between predicted uncertainty maps and true reconstruction error. Using our method, the corresponding Pearson correlation coefficient was higher than 90% at acceleration levels at and above four-fold; whereas it dropped to less than 70% when the uncertainty was computed using a simpler a heuristic notion (magnitude of the residual). Qualitative examples further show the uncertainty maps based on quantile regression capture the magnitude and spatial distribution of reconstruction errors across acceleration factors, with regions of elevated uncertainty aligning with pathologies and artifacts. The proposed framework enables evaluation of reconstruction quality without access to fully-sampled ground-truth reference images. It represents a step toward adaptive MRI acquisition protocols that may be able to dynamically balance scan time and diagnostic reliability.

The rotating magnetocaloric effect (RMCE) induced by self-demagnetization has been investigated in the giant magnetocaloric effect (GMCE) material Gd$_5$Si$_2$Ge$_2$. This shape-dependent effect had thus far only been reported in pure Gd, marking this as the first analysis of the effect in a sample with a magnetostructural first-order phase transition. By rotating the applied magnetic field vector while keeping its intensity constant, the demagnetizing field within a high-aspect ratio sample changes significantly, resulting in a RMCE. We characterize RMCE by determining the adiabatic temperature change ($\Delta T_{ad}^{rot}$) directly through temperature measurements, and the isothermal entropy change ($\Delta S_M^{rot}$) via magnetometry and magnetostatic simulations. We obtain a remarkable maximum $\Delta T_{ad}^{rot}$ of 1.77 K for a constant external field of 0.8 T, higher than that obtained under 1.0 T. The magnetostatic simulations not only corroborate the highly non-monotonous field-dependence of $|\Delta S_{M}^{rot}|$, which reaches 95\% of its maximum value at 0.8 T, 6.12 J K$^{-1}$ kg$^{-1}$ for the experimentally measured shape, but also estimate a 35\% increase in the maximum $|\Delta S_{M}^{rot}|$ up to 8.67 J K$^{-1}$ kg$^{-1}$ in a simulated shape with higher aspect ratio.

We report the magnetic and magnetotransport properties of the layered CaAl$_2$Si$_2$-type diluted magnetic semiconductor (Ba$_{1-x}$K$_x$)(Cd$_{1-y}$Mn$_y$)$_2$As$_2$ over a broad Mn (spin) substitution range of $0.05 \le y \le 0.5$. K substitution introduces hole carriers, whereas Mn provides local moments, resulting in bulk ferromagnetism with Curie temperatures up to $\sim 17$ K. Intrinsic magnetic ordering is further supported by an anomalous Hall contribution and a specific-heat anomaly near $T_{\mathrm{C}}$. A key performance feature is a colossal negative magnetoresistance: for heavily Mn-doped compositions ($y \ge 0.3$), $\mathrm{MR}=[\rho(H)-\rho(0)]/\rho(0)$ reaches approximately $-100\%$ at 2 K and nearly saturates at a relatively low magnetic field of $\sim 0.35\,\mathrm{T}$. The combination of soft ferromagnetism, strong spin-charge coupling, and low-field MR saturation highlights (Ba,K)(Cd,Mn)$_2$As$_2$ as a promising bulk platform for low-temperature magnetoresistive functionalities.

Optimal Causation Entropy (oCSE) is a robust causal network modeling technique that reveals causal networks from dynamical systems and coupled oscillators, distinguishing direct from indirect paths. CausationEntropy is a Python package that implements oCSE and several of its significant optimizations and methodological extensions. In this paper, we introduce the version 1.1 release of CausationEntropy, which includes new synthetic data generators, plotting tools, and several advanced information-theoretical causal network discovery algorithms with criteria for estimating Gaussian, k-nearest neighbors (kNN), geometric k-nearest neighbors (geometric-kNN), kernel density (KDE) and Poisson entropic estimators. The package is easy to install from the PyPi software repository, is thoroughly documented, supplemented with extensive code examples, and is modularly structured to support future additions. The entire codebase is released under the MIT license and is available on GitHub and through PyPi Repository. We expect this package to serve as a benchmark tool for causal discovery in complex dynamical systems.

Four-dimensional (4D) Flow MRI can noninvasively measure cerebrovascular hemodynamics but remains underused clinically because current workflows rely on manual vessel segmentation and yield velocity fields sensitive to noise, artifacts, and phase aliasing. We present VAST (Vascular Flow Analysis and Segmentation), an automated, unsupervised pipeline for intracranial 4D Flow MRI that couples vessel segmentation with physics-informed velocity reconstruction. VAST derives vessel masks directly from complex 4D Flow data by iteratively fusing magnitude- and phase-based background statistics. It then reconstructs velocities via continuity-constrained phase unwrapping, outlier correction, and low-rank denoising to reduce noise and aliasing while promoting mass-consistent flow fields, with processing completing in minutes per case on a standard CPU. We validate VAST on synthetic data from an internal carotid artery aneurysm model across SNR = 2-20 and severe phase wrapping (up to five-fold), on in vitro Poiseuille flow, and on an in vivo internal carotid aneurysm dataset. In synthetic benchmarks, VAST maintains near quarter-voxel surface accuracy and reduces velocity root-mean-square error by up to fourfold under the most degraded conditions. In vitro, it segments the channel within approximately half a voxel of expert annotations and reduces velocity error by 39% (unwrapped) and 77% (aliased). In vivo, VAST closely matches expert time-of-flight masks and lowers divergence residuals by about 30%, indicating a more self-consistent intracranial flow field. By automating processing and enforcing basic flow physics, VAST helps move intracranial 4D Flow MRI toward routine quantitative use in cerebrovascular assessment.

We study a family of lattice-gas systems defined on semiregular grids, obtained by projecting the vertices of three different geodesic icosahedra onto a spherical surface. By using couplings up to third neighbors we explore various interaction patterns, ranging from core-corona repulsion to square-well attraction and short-range attractive, long-range repulsive potentials. The relatively small number of sites in each grid ($\sim 100$) enables us to compute the exact statistical properties of the systems as a function of temperature and chemical potential by Wang-Landau sampling. For each case considered we highlight the existence of distinct low-temperature ``phases'', featuring, among others, regular-polyhedral, cluster-crystal, and worm-like structures. We highlight similarities and differences between these motifs and those observed on the triangular lattice under the same conditions. Finally, we discuss the relevance of our results for the bottom-up realization of spherical templates with desired functionalities.

We demonstrate a neutral atom networking node that combines high photon collection efficiency with high atom photon entanglement fidelity in a compact, fiber integrated platform. A parabolic mirror is used both to form the trap and to collect fluorescence from a single rubidium atom, intrinsically mode matching $\sigma$ polarized emitted photons to the fiber and rendering the system largely insensitive to small imperfections or drifts. The core optics consist of millimeter scale components that are pre aligned, rigidly bonded on a monolithic invacuum assembly, and interfaced entirely via optical fibers. With this design, we measure an overall photon collection and detection efficiency of $3.66\%$, from which we infer an overall collection efficiency of $6.6\%$ after the single--mode fiber coupling. We generate atom photon entangled states with a raw Bell state fidelity of 0.93 and an inferred fidelity of 0.98 after correcting for atom readout errors. The same node design has been realized in two independent setups with comparable performance and is compatible with adding high NA objective lenses to create and control atomic arrays at each node. Our results establish a robust, cavity free neutral atom interface that operates near the limit set by the collection optics numerical aperture and provides a practical building block for scalable quantum network nodes and repeaters.

In this work, we investigate $CO_{2}$ activation on ZnS using Near Ambient-Pressure X-ray photoelectron spectroscopy measurements (NAP-XPS) and density functional theory calculations (DFT). Our NAP-XPS experiments reveal that $CO_{2}$ adsorbs onto a defective ZnS surface upon heating above $473 \ K$ in a $CO_{2}$ atmosphere (up to $0.55 \ mbar$). The $CO_{2}$ adsorption fingerprint is detectable even after cooling to room temperature under ultra-high vacuum. Our DFT calculations suggest that $CO_{2}$ adsorption is energetically favorable on ZnS surfaces containing zinc vacancies, highlighting defect sites as key adsorption centers. Additionally, oxygen adsorption on a defective ZnS surface is exothermic, in contrast to the endothermic behavior observed on a defect-free surface. These findings contribute to a deeper understanding of defect-driven surface reactivity and may inform ZnS-based catalyst's design for $CO_{2}$ capture and reutilization.

Generative AI has emerged as a transformative paradigm in engineering design, enabling automated synthesis and reconstruction of complex 3D geometries while preserving feasibility and performance relevance. This paper introduces a domain-specific implicit generative framework for turbine blade geometry using DeepSDF, addressing critical gaps in performance-aware modeling and manufacturable design generation. The proposed method leverages a continuous signed distance function (SDF) representation to reconstruct and generate smooth, watertight geometries with quantified accuracy. It establishes an interpretable, near-Gaussian latent space that aligns with blade-relevant parameters, such as taper and chord ratios, enabling controlled exploration and unconditional synthesis through interpolation and Gaussian sampling. In addition, a compact neural network maps engineering descriptors, such as maximum directional strains, to latent codes, facilitating the generation of performance-informed geometry. The framework achieves high reconstruction fidelity, with surface distance errors concentrated within $1\%$ of the maximum blade dimension, and demonstrates robust generalization to unseen designs. By integrating constraints, objectives, and performance metrics, this approach advances beyond traditional 2D-guided or unconstrained 3D pipelines, offering a practical and interpretable solution for data-driven turbine blade modeling and concept generation.

Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectives and constraints. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning prediction, and the prohibitive cost of quantum chemical calculation. Here we present LUMOS, a data-and-physics driven framework for inverse design of fluorescent molecules. LUMOS couples generator and predictor within a shared latent representation, enabling direct specification-to-molecule design and efficient exploration. Moreover, LUMOS combines neural networks with a fast time-dependent density functional theory (TD-DFT) calculation workflow to build a suite of complementary predictors spanning different trade-offs in speed, accuracy, and generalizability, enabling reliable property prediction across diverse scenarios. Finally, LUMOS employs a property-guided diffusion model integrated with multi-objective evolutionary algorithms, enabling de novo design and molecular optimization under multiple objectives and constraints. Across comprehensive benchmarks, LUMOS consistently outperforms baseline models in terms of accuracy, generalizability and physical plausibility for fluorescence property prediction, and demonstrates superior performance in multi-objective scaffold- and fragment-level molecular optimization. Further validation using TD-DFT and molecular dynamics (MD) simulations demonstrates that LUMOS can generate valid fluorophores that meet various target specifications. Overall, these results establish LUMOS as a data-physics dual-driven framework for general fluorophore inverse design.

Accurate and efficient rumor detection is critical for information governance, particularly in the context of the rapid spread of misinformation on social networks. Traditional rumor detection relied primarily on manual analysis. With the continuous advancement of technology, machine learning and deep learning approaches for rumor identification have gradually emerged and gained prominence. However, previous approaches often struggle to simultaneously capture both the sequential and the global structural relationships among topological nodes within a social network. To tackle this issue, we introduce a hybrid model for detecting rumors that integrates a Graph Convolutional Network (GCN) with a Transformer architecture, aiming to leverage the complementary strengths of structural and semantic feature extraction. Positional encoding helps preserve the sequential order of these nodes within the propagation structure. The use of Multi-head attention mechanisms enables the model to capture features across diverse representational subspaces, thereby enhancing both the richness and depth of text comprehension. This integration allows the framework to concurrently identify the key propagation network of rumors, the textual content, the long-range dependencies, and the sequence among propagation nodes. Experimental evaluations on publicly available datasets, including Twitter 15 and Twitter 16, demonstrate that our proposed fusion model significantly outperforms both standalone models and existing mainstream methods in terms of accuracy. These results validate the effectiveness and superiority of our approach for the rumor detection task.

China's Tianwen-2 mission, launched on 29 May 2025, targets the near-Earth object (469219) Kamo`oalewa, an Earth quasi-satellite trapped in a 1:1 mean-motion resonance with our planet. Determining the origin of Kamo`oalewa is central to understanding the formation pathways and dynamical evolution of Earth's quasi-satellite population. Here we show a strong possibility of main-belt origin for Kamo`oalewa using long-term dynamical simulations. We examine three candidate source regions: the $\nu_6$ secular resonance ($\nu_6$), the 3:1 mean-motion resonance with Jupiter (3:1J MMR), and the Flora family. A total of 42,825 test particles were integrated over 100 Myr. We find that asteroids from all three regions can be transported onto Kamo`oalewa-like orbits, albeit with markedly different efficiencies. Particles originating near the $\nu_6$ show the highest transfer probability (3.31%), followed by the Flora family (2.54%) and the 3:1J MMR (0.39%). We further identify representative dynamical pathways linking these source regions to Earth quasi-satellite orbits. The Tianwen-2 spacecraft is expected to rendezvous with Kamo`oalewa in 2026, performing close-proximity operations and returning samples. The mission will provide decisive observational constraints on the asteroid's composition and physical properties, offering a critical test of its proposed origin.

Quantum entanglement offers an incredible resource for enhancing the sensing and spectroscopic probes. Here we develop a microscopic theory for the stimulated Raman scattering (SRS) using entangled photons. We demonstrate that the time-energy correlation of the photon pairs can optimize the signal for polyatomic molecules. Our results show that the spectral-line intensity of the entangled-photon SRS (ESRS) is of the same order of magnitude as the one for the entangled two-photon absorption (ETPA); the parameter window is thus identified to do so. Moreover, the vibrational coherence is found to play an important role for enhancing the ESRS against the ETPA intensity. Our work paves a firm road for extending the schemes of molecular spectroscopy with quantum light, based on the observation of the ETPA in experiments.

We investigate Hamiltonian fluid reductions of the one-dimensional Vlasov-Poisson equation. Our approach utilizes the hydrodynamic Poisson bracket framework, which allows us to systematically identify fundamental normal variables derived from the analysis of the Casimir invariants of the resulting Poisson bracket. This framework is then applied to analyze several well-established Hamiltonian closures of the onedimensional Vlasov equation, including the multi-delta distribution and the waterbag models. Our key finding is that all of these seemingly distinct closures consistently lead to the formulation of a unified form of parametric closures: When expressed in terms of the identified normal variables, the parameterization across all these closures is revealed to be polynomial and of the same degree. All these parametric closures are uniquely generated from one of the moments, called $\mu$2, a cubic polynomial in the normal variables. This result establishes a structural connection between these different physical models, offering a path toward a more unified and simplified description of the one-dimensional Vlasov-Poisson dynamics through its reduced hydrodynamic forms with an arbitrary number of fluid variables.

We theoretically investigate a quartet superfluid state in fermionic matter by using the quartet Bardeen-Cooper-Schrieffer (BCS) variational theory and the Green's function method. We demonstrate that the quartet BCS theory with the multiple-infinite-product ansatz successfully reproduces an exact four-body result in a one-dimensional four-component Fermi gas at the dilute limit, in contrast to the single-infinite-product ansatz. To see the validity of the quartet BCS state, we derive the self-consistent equation for the quartet superfluid order parameter within the generalized imaginary-time Nambu-Gor'kov formalism, which is found to be consistent with the quartet BCS variational equation. Moreover, by numerically computing the momentum-resolved single-particle spectral function in a one-dimensional system, we discuss how the single-particle spectra evolve with increasing the strength of the four-body cluster formation. We show that a coherent BCS-like quasiparticle branch on the weak-coupling side evolves into a strongly damped, continuum-dominated spectrum in the strong-coupling side, while nonzero quartet superfluid order parameter persists throughout the crossover regime. Our results would be useful for understanding beyond-BCS pairing effects and four-body cluster formations in fermionic systems in an interdisciplinary way.

Improved local numerical solution for the ADER-DG numerical method with a local DG predictor for solving the initial value problem for a first-order ODE system is proposed. The improved local numerical solution demonstrates convergence orders of one higher than the convergence order of the local numerical solution of the original ADER-DG numerical method and has the property of continuity at grid nodes. Rigorous proofs of the approximation orders of the local numerical solution and the improved local numerical solution are presented. Obtaining the proposed improved local numerical solution does not require significant changes to the structure of the ADER-DG numerical method. Therefore, all conclusions regarding the convergence orders of the numerical solution at grid nodes, the resulting superconvergence, and the high stability of the ADER-DG numerical method remain unchanged. A wide range of applications of the ADER-DG numerical method is presented for solving specific initial value problems for ODE systems for a wide range of polynomial degrees. The obtained results provide strong confirmation for the developed rigorous theory. The improved local numerical solution is shown to exhibit both higher accuracy and improved smoothness and point-wise comparability. Empirical convergence orders of all individual numerical solutions were calculated for a wide range of error norms, which well agree with the expected convergence orders. The rigorous proof, based on the $\epsilon$-embedding method, of the applicability of the ADER-DG numerical method with a local DG predictor to solving DAE systems is presents.

Superradiance is a hallmark of cooperative quantum emission, where radiative decay is collectively enhanced by coherence among emitters. Here, extending superradiant effects to photon pair generation from multi-level atoms, two-photon process offers a pathway to novel quantum light sources and a useful case for practical superradiance. We report bright heralded single-photon superradiance via spontaneous four-wave mixing in a 1-mm-long, high-density cesium vapor cell. By reducing the average distance between atoms in the atomic vapor to 0.29 times the idler photon wavelength, we observe a dramatic narrowing of the temporal two-photon wavefunction. This compression of temporal two-photon wavefunction evidences the superradiance of heralded photons in the collective two-photon emission dynamics. Furthermore, our heralded single-photon superradiance is accompanied by a coincidence-to-accidental ratio of 200 and the detected photon-pair counting exceeding 10^6 pairs/s. These findings establish dense thin atomic vapors as a practical, robust medium for realizing superradiant photon sources, with immediate relevance for quantum optics and the development of efficient photonic quantum technologies.

We present a systematic method for constructing universal composite phase gates with a continuously tunable target phase. Using a general Cayley--Klein parametrization of the single-pulse propagator, we design gates from an even number of nominal $\pi$ pulses and derive analytic phase families by canceling, order by order in a small deviation parameter, the leading contributions to the undesired off-diagonal element of the composite propagator, independently of the dynamical phase. The resulting sequences provide intrinsic robustness against generic control imperfections and parameter fluctuations and remain valid for arbitrary pulse shapes. Numerical simulations in a standard two-level model confirm high-order error suppression and demonstrate broad, flat high-fidelity plateaus over wide ranges of simultaneous pulse-area and detuning errors, highlighting the efficiency of the proposed universal composite phase gates for resilient phase control in quantum information processing.

Quantum-correlated states of light, such as squeezed states, constitute a fundamental resource for quantum technologies, enabling enhanced performance in quantum metrology, quantum information processing, and quantum communications. The practical deployment of such technologies requires squeezed-light sources that are compact, efficient, low-cost, and robust. Here we report a compact narrowband source of two-mode squeezed light at 795 nm based on four-wave mixing in hot 85Rb atomic vapor. The source is implemented in a small, modular architecture featuring a single fiber-coupled input, an electro-optic phase modulator combined with a single Fabry-Perot etalon for probe generation, and two free-space output modes corresponding to the signal and conjugate fields. Optimized for low pump power, the system achieves up to -8 dB of intensity-difference squeezing at an analysis frequency of 0.8 MHz with a pump power of only 300 mW. The intrinsic narrowband character of the generated quantum states makes this source particularly well suited for atomic-based quantum sensing and quantum networking, including interfaces with atomic quantum memories. Our results establish a versatile and portable platform for low-SWaP squeezed-light generation, paving the way toward deployable quantum-enhanced technologies.

Bacteria regulate their motility through a variety of mechanisms, including quorum sensing (QS) and other density-dependent responses mediated by diffusible signals. While nonlinear density-dependent motility is well known in active-matter theory to generate nonequilibrium spatial patterns, its consequences for the coexistence of growing, interacting species remain less explored. Here we develop a minimal spatially structured model for two strongly competing species in which local demographic interactions are coupled to an escape response: each species increases its motility nonlinearly (sigmoidal) with the local abundance of its competitor. We show that this sigmoidal motility regulation promotes optimal spatial self-organization and can sustain long term coexistence via segregation, even in parameter regimes that yield competitive exclusion in well-mixed Lotka-Volterra dynamics. On two-dimensional lattices, the interplay between demographic competition and density-dependent motility generates a range of emergent patterns, including regimes in which the weaker competitor counterintuitively has higher total abundance. Overall, our results identify nonlinear, competitor-induced motility as a fundamental mechanism capable of sustaining coexistence in competing microbial populations.

Ongoing advances in scanning probe microscopy techniques are continually expanding the possibilities for nanoscale characterization and correlated studies of functional materials. Here, we demonstrate how a recent extension of piezoresponse force microscopy (PFM), known as interferometric vector PFM, can be utilized for simultaneously mapping the local crystallographic orientations and the domain structure of distributed grains in uniaxial ferroelectric polycrystals. By shifting the laser beam position on the cantilever, direction-dependent piezoresponse signals are acquired analogous to classical vector PFM, but without the need to rotate the sample. Using polycrystalline ErMnO$_{3}$ as a model system, we demonstrate that the reconstructed piezoresponse vectors correlate one-to-one with the crystallographic orientations of the micrometer-sized grains, carrying grain-orientation and domain-related information. We establish a versatile approach for rapid, multimodal characterization of polycrystalline uniaxial ferroelectrics, enabling automated, high-throughput reconstruction of polarization and grain orientations with nanoscale precision.

Spatially-resolved photon counting of a twin beam performed by an iCCD camera allows for versatile tailoring the properties of the beams formed by parts of the original twin beam. Dividing the idler beam of the twin beam into three equally-intense parts and post-selecting by detecting a given number of photocounts in the whole signal beam we arrive at the idler fields exhibiting high degrees of nonclassicality and being endowed with tripartite quantum correlations. Nonclassicality is analyzed with the help of suitable nonclassicality witnesses and their corresponding nonclassicality depths. Suitable parameters are introduced to quantify quantum correlations. These parameters are analyzed as they depend on the field intensity. The experimental photocount histograms are reconstructed by the maximum-likelihood approach and the obtained photon-number distributions are compared with a suitable model in which the original twin beam is approximated by an appropriate multi-mode Gaussian field and undergoes the corresponding beams' transformations.

A new approach for determining the binding energies of charged particles, such as ions and electrons, on dielectric surfaces in cryogenic liquids is introduced. The experimental technique outlined in this paper is employed to observe the buildup of charged particles on nonconductive surfaces using the electro-optic Kerr effect. The initial results of binding energy measurements on surfaces of deuterated tetraphenyl butadiene (dTPB)-coated and uncoated polymethyl methacrylate (PMMA) in liquid nitrogen are presented. Under these conditions, the ions or electrons displayed binding energies of less than 1 meV. Although these findings were obtained in liquid nitrogen, the methodology is not limited to cryogenic liquids and is applicable to a wide variety of fluids, with no essential dependence on temperature.

In this work, we explore the possibility of using quantum computers provided for usage in cloud by big companies (such as IBM, IonQ, IQM Quantum Computers, etc.) to run our quantum neural network (QNN) developed for data analysis in the context of LISA Space Mission, developed with the Qiskit library in Python. Our previous work demonstrated that our QNN learns patterns in gravitational wave (GW) data much faster than a classical neural network, making it suitable for fast GW signal detection in future LISA data streams. Analyzing the fees from hardware providers like IBM Quantum, Amazon Braket and Microsoft Azure, we found that the fees for running the first segment of our QNN sum up to \$2000, \$60000, and \$1000000 respectively. Using free plans, we succeed to run the 3-qubit feature map of the QNN for one random data sample on {\fontfamily{qcr} \selectfont ibm\_kyoto} and {\fontfamily{qcr}\selectfont IQM Quantum Computers\_Garnet} quantum computers, obtaining a fidelity of 99\%; we could also run the first prediction segment of our QNN on {\fontfamily{qcr} \selectfont ibm\_kyoto}, implemented for 4 qubits, and obtained a prediction accuracy of 20\%. We queried providers such as IBM Quantum, Amazon Braket, Pasqal, and Munich Quantum Valley to obtain access to their plans, but, with the exception of Amazon Braket, our applications remain unanswered to this day. Other major setbacks in using the quantum computers we had access to included Qiskit library version issues (as in the cases of IBM Quantum and IQM Quantum Computers) and the frequent unavailability of the devices, as was the case with the Microsoft Azure provider. All the results presented in this paper were accumulated in 2024.

When two molecular species with mutual affinity are mixed together, various self-assembled phases can arise at low temperature, depending on the shape of like and unlike interactions. Among them, stripes -- where layers of one type are regularly alternated with layers of another type -- hold a prominent place in materials science, occurring e.g. in the structure of superconductive doped antiferromagnets. Stripe patterns are relevant for the design of functional materials, with applications in optoelectronics, sensing, and biomedicine. In a purely classical setting, an open question pertains to the features that spherically-symmetric particle interactions must have to foster stripe order. Here we address this challenge for a lattice-gas mixture of two particle species, whose equilibrium properties are exactly determined by Monte Carlo simulations with Wang-Landau sampling, in both planar and spherical geometry, and for equal chemical potentials of the species. Somewhat surprisingly, stripes can emerge from largely different off-core interactions, featuring various combinations of repulsive like interactions with a predominantly attractive unlike interaction. In addition to stripes, our survey also unveils crystals and crystal-like structures, cluster crystals, and networks, which considerably broaden the catalog of possible patterns. Overall, our study demonstrates that stripes are more widespread than generally thought, as they can be generated by several distinct mechanisms, thereby explaining why stripe patterns are observed in systems as diverse as cuprate materials, biomaterials, and nanoparticle films.

Natural philosophy integrates scientific observation with abstract frameworks, often using a mathematical Ansatz to hypothesise about physical phenomena. Exploring the possibility of other universes, however, challenges assumptions that physical laws, like spacetime geometry, extend beyond our reality. This paper argues that mathematical abstractions, serving as a telescope beyond physical constraints, enable such reasoning. Through a projective algebra formalism (Section 4), we model the mechanism of Ansatz, abstractly describing physical objects. This yields a resolution to Wigner's unreasonable effectiveness via cardinality equivalence (Section 5) and clarifies terms like 'evidence' and 'existence' (Section 6) to align with the conventions used in physics. A Cantor-inspired paradox shows no universe can contain all mathematical abstractions (e.g., sets, numbers), as its power set exceeds it, necessitating an external abstract realm (Section 6.4). This logical necessity, which holds even in the context of alternative set theories like New Foundations, provides evidence for a minimal external universe as an abstract realm, supporting Mathematical Realism. This result is not specific to the formalism, as long as we accept that the principles of set theory are mathematically valid. As abstract entities elude empirical detection, logical evidence is apt, guiding future science and philosophy research and fostering interdisciplinary inquiry.

We return to our study \cite{BEH} of invariant spin fields and spin tunes for polarized beams in storage rings but in contrast to the continuous-time treatment in \cite{BEH}, we now employ a discrete-time formalism, beginning with the $\rm{Poincar\acute{e}}$ maps of the continuous time formalism. We then substantially extend our toolset and generalize the notions of invariant spin field and invariant frame field. We revisit some old theorems and prove several theorems believed to be new. In particular we study two transformation rules, one of them known and the other new, where the former turns out to be an $SO(3)$-gauge transformation rule. We then apply the theory to the dynamics of spin-$1/2$ and spin-$1$ particle bunches and their density matrix functions, describing semiclassically the particle-spin content of bunches. Our approach thus unifies the spin-vector dynamics from the T-BMT equation with the spin-tensor dynamics and other dynamics. This unifying aspect of our approach relates the examples elegantly and uncovers relations between the various underlying dynamical systems in a transparent way. As in \cite{BEH}, the particle motion is integrable but we now allow for nonlinear particle motion on each torus. Since this work is inspired by notions from the theory of bundles, we also provide insight into the underlying bundle-theoretic aspects of the well-established concepts of invariant spin field, spin tune and invariant frame field. Since we neglect, as is usual, the Stern-Gerlach force, the underlying principal bundle is of product formso that we can present the theory in a fashion which does not use bundle theory. Nevertheless we occasionally mention the bundle-theoretic meaningof our concepts and we also mention the similarities with the geometrical approach to Yang-Mills Theory.

This paper investigates the reactive power and spin of instantaneous Poynting vector in the bulk of gyromagnetic medium. It is shown that the gyromagnetic medium introduces a spin in the Poynting vector. The spin of the instantaneous Poynting vector and the relative strengths of the real and reactive power components are quantified using the Stokes plot method. Using this technique we investigate the presence of backward power propagation in a ferrite-filled waveguide. We present an analytical expression to locate the crossover point separating the forward and backward power propagation, where the real power propagation has a null. We further show that this backward power propagation leads to corresponding opposing surface currents on a waveguide plate of the ferrite-filled waveguide, while the potential difference between the two plates remain symmetric.

We compare the optical science & engineering graduate-level educational environments at two universities in two countries: Utsunomiya University in Japan, and the University of Arizona in the United States. Because the university education systems in the two countries are so different, we also explain how financial resources drive many of these differences and discuss how these impact student and faculty life.

We consider the problem of setting confidence intervals on a parameter of interest from the maximum-likelihood fit of a physics model to a binned data set with a large number of bins, large event-counts per bin, and in the presence of systematic uncertainties modeled as nuisance parameters. We use the profile-likelihood ratio for statistical inference and focus on the case in which the model is determined from Monte Carlo simulated samples of finite size. We start by presenting a toy model in which the properties of widely used approximations of the profile-likelihood ratio in the asymptotic limit, which are commonly expected to hold in the high-statistics regime, are manifestly broken even if the numbers of events per bin in both the data and simulated samples are seemingly large enough to warrant their validity. We then move to the general setting to show how statistical uncertainties in the Monte Carlo predictions can affect the coverage of confidence intervals constructed in the asymptotic approximation always in the same direction, namely they lead to systematic under-coverage.

Achieving strong light-matter interaction is important for studying and exploiting several physics phenomena. The light-matter interaction strength depends on the optical field intensity in the interaction region, often measured by the Purcell factor, which for a single emitter is proportional to the spectral confinement, quantified by the cavity quality factor $Q$, and inversely proportional to the spatial localization of light, quantified by the optical model volume $V$, $F \propto \frac{Q}{V}$. While plasmonic (metallic) devices can support extreme spatial light confinement, ohmic losses reduce the cavity lifetime, thereby limiting the achievable spectral confinement. It is therefore of both practical and fundamental interest to explore the potential for achieving extreme spatial light confinement in (near) loss-less dielectric environments. Employing topology optimization we explore the limits of spatial light confinement in dielectric environments when allowing for three-dimensional sculpted dielectric nanostructures. Here we discover structures supporting optical modes that are concentrated in material (air) with mode volumes that are three (four) orders of magnitude below the so-called diffraction limit, $V_{\textbf{r}_0} \approx 4 \cdot 10^{-4} \left[\lambda/(2 n)\right]^3 \left( V_{\textbf{r}_0} \approx 3 \cdot 10^{-5} \left[\lambda/2\right]^3\right)$. Remarkably, we further discover that encapsulating the nanostructure by ellipsoidal shells enables seemingly unbounded enhancement of the mode quality factor ($Q > 10^8$ demonstrated numerically) leading to theoretical Purcell factor enhancement above $10^{11}$. It is established how $V_{\textbf{r}_0}$ and $Q$ depend on the choice of material platform, device volume, minimum feature size and the number of shells. Finally a study of sensitivity towards geometric variations is presented, revealing robust behaviour.

We numerically study the nonlinear interactions of high-frequency circularly polarized electromagnetic (EM) waves and low-frequency electron-acoustic (EA) density perturbations driven by the EM wave ponderomotive force in relativistic plasmas {(moderate, strong, and ultra-relativistic)} with two groups of electrons--the population of relativistic degenerate dense electrons (bulk plasma) and the sparse relativistic nondegenerate (classical) electrons, and immobile singly charged positive ions. By pattern selection, we show that many solitary patterns can be generated and drenched through modulational instability of EM waves at different spatial length scales and that the EM wave radiation spectra emanating from compact astrophysical objects may not settle into stable envelope solitons but into different incoherent states, including the emergence of temporal and spatiotemporal chaos due to collisions and fusions among the patterns with strong EA wave emission. The appearance of these states is confirmed by analyzing the Lyapunov exponent spectra, correlation function, and mutual information {as quantitative evidence}. As a result, the redistribution of wave energy from initially exciting many solitary patterns at large scales to a few new incoherent patterns with small wavelengths in the system occurs, leading to the onset of turbulence in astrophysical plasmas.

We present a comprehensive method for determining {both exact and approximate} dispersion {relations} for one-dimensional {resonant phononic} crystals, applicable to a wide range of structures, regardless of their specific characteristics. This general framework employs a unified mathematical model, referred to as generalized {one-dimensional (1D) phononic crystal}, in which {different} types of {waves} and scatterers{/resonators} {can be} {considered} by adjusting certain parameters. The generalized {1D phononic crystal} consists of both a host {one-dimensional} homogeneous elastic {material} with physical properties represented in matrix form and an arbitrary set of scatterers {within the unit cell,} including resonators (discrete and continuous), small material inclusions, or variations in cross-sectional area. Based on general assumptions, {and imposing the periodicity and Bloch solutions} we develop a matrix-based algorithm utilizing the plane wave expansion method to derive the solution. Additionally, we propose an iterative procedure that provides analytical expressions for the first- and second-order terms, particularly useful in the context of weak scattering. The convergence conditions of the method are rigorously defined. The {efficiency of the} approach is demonstrated through several numerical examples, highlighting its versatility in different waveguide configurations and scattering scenarios.

Numerical simulations of turbulent flows present significant challenges in fluid dynamics due to their complexity and high computational cost. High resolution techniques such as Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) are generally not computationally affordable, particularly for technologically relevant problems. Recent advances in machine learning, specifically in generative probabilistic models, offer promising alternatives as surrogates for turbulence. This paper investigates the application of three generative models - Variational Autoencoders (VAE), Deep Convolutional Generative Adversarial Networks (DCGAN), and Denoising Diffusion Probabilistic Models (DDPM) - in simulating a von Kármán vortex street around a fixed cylinder projected into 2D, as well as a real-world experimental dataset of the wake flow of a cylinder array. Training data was obtained by means of LES in the simulated case and Particle Image Velocimetry (PIV) in the experimental case. We evaluate each model's ability to capture the statistical properties and spatial structures of the turbulent flow. Our results demonstrate that DDPM and DCGAN effectively replicate all flow distributions, highlighting their potential as efficient and accurate tools for turbulence surrogacy. We find a strong argument for DCGAN, as although they are more difficult to train (due to problems such as mode collapse), they show the fastest inference and training time, require less data to train compared to VAE and DDPM, and provide the results most closely aligned with the input stream. In contrast, VAE train quickly (and can generate samples quickly) but do not produce adequate results, and DDPM, whilst effective, are significantly slower at both, inference and training time.

A general equation for drift waves is derived incorporating both nonlinear electron density perturbation and ion vorticity effects. It is emphasized that the well-known Hasegawa-Mima (HM) equation for drift waves [A. Hasegawa and K. Mima, Phys. Fluids 21, 87 (1978)] includes only the ion vorticity term and neglects nonlinear electron density contribution that naturally arises from the electrons Boltzmann response. If ion vorticity term is ignored, then the general nonlinear equation reduces to an equation which can give two-dimensional soliton solution under an appropriate coordinate transformation. Furthermore, under the assumption that the normalized electrostatic potential depends only on one spatial coordinate along the predominant propagation direction, i.e. $\Phi = \Phi(y)$, the equation reduces to one-dimensional KdV equation [H. Saleem, Phys. Plasmas 31, 112102 (2024)]. Conversely, if the nonlinear electron density term is artificially suppressed and a two-dimensional potential $\Phi = \Phi(x, y)$ is considered, the equation reduces to Hasegawa-Mima equation supporting dipolar vortex solution. Because the HM equation ignores nonlinear electron density term, it cannot support one- or two-dimensional soliton solutions. Finally, the limiting forms of the general nonlinear equation are also briefly discussed using the reductive perturbation method (RPM).

$^{210}Po$ $\alpha$-decay driven neutron background is a concern for many rare event search experiments. It is a difficult to control background because its radiogenic component depends on the air exposure history of parts. In this study, we demonstrate that about half of the radon progeny $^{210}Po$ can be removed from copper and silicon surfaces relatively easily by wiping a copper sample with acetone wetted tissue and a silicon detector with acetone soaked cotton balls. For a copper sample we demonstrate that long-lived $^{210}Pb$ is removed with similar effectiveness. For copper, allocated the longest counting time, additional wiping was found to be largely ineffective. For silicon, the removal effectiveness has large uncertainties. Additional cleaning showed a small but statistically significant effect. Capitalizing on this trivial cleaning step will allow experiments to relax their requirements on the allowable air exposure time during construction, leading to cost and time savings.

Hierarchically structured cellular solids have attracted increasing attention for their superior mass-specific mechanical properties. Using a remeshing-based continuum finite element (FE) framework, we reveal that two-scale metallic hierarchical solids exhibit a distinct, localized deformation mode that involves necking and fracture of microscale tension members even at small global compressive strains (3-5%). The tensile failure is always preceded by plastic buckling of a complementary compression member. This combined necking-buckling (NB) mode critically underlies the collapse of hexagon-triangle (HTH) hierarchical lattices over a wide range of relative densities and length-scale ratios and is also seen in diamond-triangle (DTH) lattices. In lattices with very slender microscale members, necking is prevented by a competing failure mode that involves coordinated buckling (CB) of multiple members. Our custom remeshing FE framework is critical to resolve the localized large plastic strains, ductile failure, and complex local modes of deformation (including cusp formation) that are characteristic of the NB mode. A theoretical buckling analysis supports the inevitability of the NB and CB modes in HTH lattices. The occurrence of the NB mode has consequences for energy absorption by two-scale hierarchical solids, and hence influences their design.

Urban space is highly heterogeneous, with population and human activities concentrating in localized centers. However, the global organization of such intra-urban centers remains poorly understood due to the lack of consistent, comparable data. Here we develop a scalable geospatial framework to identify intra-urban activity centers worldwide using nighttime light observations. Applying this approach to more than 9,500 cities, we construct a high-resolution global dataset of over 15,000 centers. We uncover a striking regularity: despite vast differences in city size, regional development, and population density, the built-up area associated with individual centers remains remarkably consistent. Across cities, total urban area scales proportionally with the number of centers, yielding a stable mean spatial footprint. This regularity holds at the micro-scale, where Voronoi-based service areas exhibit a characteristic size that is persistent across countries and independent of local population concentration. As a geometric consequence, this polycentric multiplication maintains stable average distances to the nearest center as cities expand, preventing the accessibility decay inherent in monocentric growth. These findings reveal a universal organizing principle whereby urban expansion is accommodated through the replication of activity centers with a consistent spatial extent, providing a new empirical foundation for understanding the nature of urban growth.

There will be a paradigm shift in chemical and biological research, to be enabled by autonomous, closed-loop, real-time self-directed decision-making experimentation. Spectrum-to-structure correlation, which is to elucidate molecular structures with spectral information, is the core step in understanding the experimental results and to close the loop. However, current approaches usually divide the task into either database-dependent retrieval and database-independent generation and neglect the inherent complementarity between them. In this study, we proposed Vib2Mol, a unified deep learning framework designed to flexibly handle diverse spectrum-to-structure tasks according to the available prior knowledge by bridging the retrieval and generation. Empowered by our coarse-to-fine retrieval and generate-then-rerank strategies, Vib2Mol not only achieves state-of-the-art performance in analyzing theoretical Infrared and Raman spectra, but also outperform previous models on experimental data. Moreover, our model demonstrates promising capabilities in predicting reaction products and sequencing peptides, enabling vibrational spectroscopy a potential guide for autonomous scientific discovery workflows.

This study presents a numerical investigation of a 3D micropolar magnetohydrodynamic (MHD) blood flow through stenosis, with and without the effects of micromagnetorotation (MMR). MMR refers to the magnetic torque caused by the misalignment of the magnetization of magnetic particles in the fluid with the magnetic field, which affects the internal rotation (microrotation) of these particles. Blood can be modeled as a micropolar fluid with magnetic particles due to the magnetization of erythrocytes. In this manner, this study analyzes important flow features, i.e., streamlines, vorticity, velocity, microrotation, wall shear stress, and pressure drop under varying stenosis, hematocrit levels, and magnetic fields, using two newly developed transient OpenFOAM solvers epotMicropolarFoam and epotMMRFoam. Results indicate that micropolar effects become more pronounced at severe stenosis due to the significant reduction in artery size, resulting also in higher wall shear stress and pressure drop. Furthermore, when MMR is disregarded, the magnetic field does not significantly alter blood flow, regardless of its intensity, due to the minimal impact of the Lorentz force on blood. Conversely, MMR substantially affects blood flow, particularly at higher hematocrit levels and severe stenoses, leading to reductions of up to 30% in velocity and vorticity and up to 99.9% in microrotation and higher wall shear stress and pressure drop. Simultaneously, any vortices or disturbances are dampened. These findings underscore the critical role of MMR (which was ignored so far) in altering flow behavior in stenosed arteries, suggesting that it should be considered in future MHD micropolar blood flow studies.

Any micropolar fluid containing magnetic particles, such as blood or ferrofluids, subjected to an external magnetic field experiences a magnetic torque due to the misalignment between particle magnetization and the magnetic field. This effect, known as micromagnetorotation (MMR), remains underexplored in blood flows where erythrocyte magnetization is often neglected. To investigate this, two transient OpenFOAM solvers were developed: epotMicropolarFoam, for incompressible, laminar magnetohydrodynamic (MHD) micropolar flows, and epotMMRFoam, which extends it by incorporating MMR. Both solvers use the PISO algorithm for pressure-velocity coupling and adopt the low magnetic Reynolds number approximation. Micropolar effects are modeled by including the microrotation-vorticity difference in the momentum equation and solving the internal angular momentum equation. In epotMMRFoam, the MMR term is added to this equation, and a constitutive equation for magnetization is also solved. Validation against analytical MHD micropolar Poiseuille flow showed excellent accuracy (error less than 2 percent). Including MMR led to notable reductions in velocity (up to 40 percent) and microrotation (up to 99.9 percent), especially under strong magnetic fields and high hematocrit. Without MMR, magnetic effects were minimal due to the low electrical conductivity of blood. Simulations of 3D MHD artery and 2D MHD aneurysm flows confirmed these findings. In aneurysm geometries, MMR suppressed vortex cores, indicating strong stabilizing and shear-dampening effects. These solvers show high potential for biomedical applications such as magnetic hyperthermia and targeted drug delivery.

The coalescence and breakup of drops are classic examples of flows that feature singularities. The behavior of viscoelastic fluids near these singularities is particularly intriguing - not only because of their added complexity, but also due to the unexpected responses they often exhibit. In particular, experiments have shown that the coalescence of viscoelastic sessile drops can differ significantly from their Newtonian counterparts, sometimes resulting in a sharply defined interface. However, the mechanisms driving these differences in dynamics, as well as the potential influence of the contact angle are not fully known. Here, we study two different flow regimes effectively induced by varying the contact angle and demonstrate how that leads to markedly different coalescence behaviors. We show that the coalescence dynamics is effectively unaltered by viscoelasticity at small contact angles. The Deborah number, which is the ratio of the relaxation time of the polymer to the timescale of the background flow, scales as $\theta^3$ for $\theta \ll 1$, thus rationalizing the near-Newtonian response. On the other hand, it has been shown previously that viscoelasticity dramatically alters the shape of the interface during coalescence at large contact angles. We study this large contact angle limit using experiments and 2D numerical simulations of the equation of motion. We show that the departure of the coalescence dynamics from the Newtonian case is a function of the Deborah number and the elastocapillary number, which is the ratio between the shear modulus of the polymer solution and the characteristic stress in the fluid.

We provide an analytical framework for analyzing the quality of stochastic Verlet-type integrators for simulating the Langevin equation. Focusing only on basic objective measures, we consider the ability of an integrator to correctly simulate two characteristic configurational quantities of transport, a) diffusion on a flat surface and b) drift on a tilted planar surface, as well as c) statistical sampling of a harmonic potential. For any stochastic Verlet-type integrator expressed in its configurational form, we develop closed form expressions to directly assess these three most basic quantities as a function of the applied time step. The applicability of the analysis is exemplified through twelve representative integrators developed over the past five decades, and algorithm performance is conveniently visualized through the three characteristic measures for each integrator. The GJ set of integrators stands out as the only option for correctly simulating diffusion, drift, and Boltzmann distribution in linear systems, and we therefore suggest that this general method is the one best suited for high quality thermodynamic simulations of nonlinear and complex systems, including for relatively high time steps compared to simulations with other integrators.

Turbulent thermals emerge in a wide variety of geophysical and industrial flows, such as atmospheric cumulus convection and pollutant dispersal in oceans and lakes. When a buoyant fluid mass rises, or sinks, heat and mass transfers occur by the engulfment of the fresh surrounding fluid inside the thermal - a process that spans over multiple scales from macroscopic entrainment of ambient fluid to microscopic diffusive processes. Turbulent thermals are typically investigated through their integral properties (radius, depth, entrainment rate). However, mixing processes depend on the internal distribution of concentration or temperature inside a thermal, which remains poorly constrained. Here, we use laboratory fluid dynamics experiments and direct numerical simulations to investigate the mixing of a passive scalar in turbulent thermals with large Reynolds numbers. We track the evolution of the concentration field, computing its moments and the probability density function. The concentration distribution exhibits self-similarity over time, except at high concentrations, possibly because of the presence of undiluted cores. These distributions are well approximated by an exponential probability density function. Although diffusion has a strong effect on the spatial structure of the concentration field, we observe no significant effect of diffusivity on the concentration distributions in the investigated range of Peclet numbers.

Numerical simulations of turbulent fluids are paramount to real-life applications, from predicting and modeling flows to diagnostic purposes in engineering. However, they are also computationally challenging due to their intrinsically non-linear dynamics, which require a very high spatial resolution to accurately describe them. A promising idea is to represent flows on a discrete mesh using tensor trains (TTs), featuring a convenient scaling of the number of parameters with the mesh size. However, it is unclear how the compression power of TTs is affected by the complexity of the flows, as measured by the Reynolds number. In fact, no comprehensive analysis of how the TT representation affects the turbulent properties has yet been carried out. We fill this gap by analyzing TTs as an Ansatz to compress, simulate, and generate 3D snapshots with turbulent-like features. Specifically, we first investigate the effect of TT compression on key turbulence signatures, such as the energy spectrum, the PDF of velocity increments, and flatness. Second, we extend the 2D TT-solver introduced in [1] to a 3D cubic domain with periodic boundary conditions. We use it to simulate the incompressible Navier-Stokes dynamics at $Re_{\lambda}=315$ for a total of 9-10 Kolmogorov turnover times, showcasing the numerical stability of the TT-solver in fully developed turbulent regimes. Third, we develop a TT algorithm to synthesize artificial snapshots that exhibit turbulent-like features, with a logarithmic cost in the mesh size. Our analysis demonstrates the ability of the TT representation to capture the characteristic features of turbulence. This offers a powerful quantum-inspired toolkit for the computational treatment of turbulent flows.

Attosecond X-ray pulses are a critical tool for tracking ultrafast electron dynamics in condensed matter, molecular systems, and strongly correlated materials. Recent breakthroughs have pushed X-ray free electron lasers (XFELs) into the attosecond domain, significantly surpassing their previous femtosecond capabilities. Building on these advancements, this work investigates the potential of the Shanghai High Repetition Rate XFEL and Extreme Light Facility (SHINE), China's first continuous-wave (CW) XFEL, to generate intense attosecond X-ray pulses, thereby offering transformative capabilities for X-ray science. Through comprehensive start-to-end simulations, we show that SHINE is capable of producing hard X-ray pulses with peak powers reaching the terawatt-scale and average pulse durations of approximately 300 as. This is achieved using a self-chirping scheme within the existing machine configuration, requiring no additional hardware. Our findings demonstrate that CW XFELs can generate intense attosecond X-ray pulses at megahertz repetition rates, opening new opportunities for real-time studies of electronic dynamics in complex systems.

Impedance eduction methods are the current standard approach to measure the impedance of acoustic liner under sheared grazing flow. The dedicated facilities for these methods consists on a waveguide with rectangular cross-section, which implies a sheared grazing flow. A current debate in the literature is the effect of this sheared flow in the impedance eduction methods. We assess the impact of the flow profile shape on acoustic propagation in a two-dimensional duct within the typical operating range of impedance eduction facilities. Firstly, a numerical experiment is proposed in which the Pridmore--Brown equation is assumed to represent the true physical behaviour, and is used with both simplified flow profiles commonly used in the literature and a realistic representation of a turbulent boundary layer using a van Driest universal law of the wall model. The data from these numerical experiments are then used with a traditional impedance eduction process, and the resulting variation in obtained impedances are investigated. Secondly, we apply a less-traditional impedance eduction method that incorporates the sheared velocity profile to data obtained from real-world experiments. The results suggest that the Ingard--Myers boundary condition remains a good approximation to a realistic boundary layer profile, such as the universal law of the wall, at least in the two-dimensional case. However, it is also shown that the simplified flow profiles often used in the literature can lead to significant deviations from the results obtained using a realistic velocity distribution.

Estimating background-error covariances remains a core challenge in variational data assimilation (DA). Operational systems typically approximate these covariances by transformations that separate geostrophically balanced components from unbalanced inertio-gravity modes - an approach well-suited for the midlatitudes but less applicable in the tropics, where different physical balances prevail. This study estimates background-error covariances in a reduced-dimension latent space learned by a neural-network autoencoder (AE). The AE was trained using 40 years of ERA5 reanalysis data, enabling it to capture flow-dependent atmospheric balances from a diverse set of weather states. We demonstrate that performing DA in the latent space yields analysis increments that preserve multivariate horizontal and vertical physical balances in both tropical and midlatitude atmosphere. Assimilating a single 500 hPa geopotential height observation in the midlatitudes produces increments consistent with geostrophic and thermal wind balance, while assimilating a total column water vapor observation with a positive departure in the nearly-saturated tropical atmosphere generates an increment resembling the tropical response to (latent) heat-induced perturbations. The resulting increments are localized and flow-dependent, and shaped by orography and land-sea contrasts. Forecasts initialized from these analyses exhibit realistic weather evolution, including the excitation of an eastward-propagating Kelvin wave in the tropics. Finally, we explore the transition from using synthetic ensembles and a climatology-based background error covariance matrix to an operational ensemble of data assimilations. Despite significant compression-induced variance loss in some variables, latent-space assimilation produces balanced, flow-dependent increments - highlighting its potential for ensemble-based latent-space 4D-Var.

In this paper, we present a finite element method (FEM) framework enhanced by an operator-adapted wavelet decomposition algorithm designed for the efficient analysis of multiscale electromagnetic problems. Usual adaptive FEM approaches, while capable of achieving the desired accuracy without requiring a complete re-meshing of the computational domain, inherently couple different resolution levels. This coupling requires recomputation of coarser-level solutions whenever finer details are added to improve accuracy, resulting in substantial computational overhead. Our proposed method addresses this issue by decoupling resolution levels. This feature enables independent computations at each scale that can be incorporated into the solutions to improve accuracy whenever needed, without requiring re-computation of coarser-level solutions. The main algorithm is hierarchical, constructing solutions from finest to coarser levels through a series of sparse matrix-vector multiplications. Due to its sparse nature, the overall computational complexity of the algorithm is nearly linear. Moreover, Krylov subspace iterative solvers are employed to solve the final linear equations, with ILU preconditioners that enhance solver convergence and maintain overall computational efficiency. The numerical experiments presented in this article verify the high precision and nearly linear computational complexity of the proposed algorithm.

Clock atom interferometry is an emerging technique in precision measurements that is particularly well suited for sensitivity enhancement through large momentum transfer (LMT). While current systems have demonstrated momentum separations of several hundreds of photon momenta, next-generation quantum sensors are targeting an LMT enhancement factor beyond $10^4$. However, the viability of LMT clock interferometers has recently come into question due to the potential impact of laser frequency noise. Here, we resolve this concern by analyzing the cumulative fidelity of sequential state inversions in an LMT atom interferometer. We show that the population error from $n$ pulses applied from alternating directions scales linearly with $n$. This is a significant advantage over the $n^2$ scaling that occurs when probing a two-level system $n$ times from the same direction. We further show that contributions to the interferometer signal from parasitic paths generated by imperfect pulses are negligible, for any loss mechanism. These results establish that laser frequency noise is not a practical limitation for the development of high-fidelity LMT clock atom interferometers.

Significance: Cervical cancer progresses through cervical intraepithelial neoplasia (CIN), which are precursor lesions of cervical cancer. In low-grade CIN, atypical cells generate inside the squamous epithelium, which causes the accuracy of cytodiagnosis for cervical cancer not to be very high. The grade of CIN can be estimated by the depth of atypical cell infiltration from the basal layer to the surface, rather than the abnormality of cells. Therefore, a non-invasive method is required to evaluate the depths of abnormal cells hidden at depths. Aim: Cancerous tissues beneath healthy tissues were experimentally identified by using circularly polarized light scattering (CiPLS). This method enabled the changes in the size of the cell nuclei within the penetration depth in tissue to be investigated. Approach: Artificial unexposed cancerous tissues were prepared that consisted of healthy/cancerous/healthy layers with various thicknesses of the topmost healthy layer and the cancerous layer. A polarization imaging camera with a quarter-wave plate was used to create distribution images of the circular polarization of the scattered light. Results: CiPLS images indicated that the thickness variation of the top healthy layer (the depth of the cancerous layer) caused significant changes in the degree of circular polarization. Conclusions: The depth of unexposed cancer lying within the optical penetration depth can be evaluated using a circular polarization imaging system based on the CiPLS method. These findings will lead to the development of a non-invasive optical diagnostic method for early-stage cervical cancer, potentially improving early detection and treatment outcomes.

Coupling an emitter to a Fabry-Pérot optical cavity can provide a coherent and strong light-matter interface whose performance in a variety of applications depends critically on the emitter-photon coupling strength. Altering the typically spherical profiles of the cavity mirrors can improve this coupling strength, but directly optimising the mirror shape is numerically challenging as the multidimensional parameter space features many local optima. Here, we develop a two-step method to optimise mirror surface profiles while avoiding these issues. First, we optimise the target cavity eigenmode for the chosen application directly, and second, we construct the mirror surfaces to retroreflect this optimised target mode at both ends of the cavity. We apply our procedure to different emitter-cavity coupling scenarios. We show that mirror shaping can increase the cooperativity of coupling to a central emitter by a factor of approximately 3 across a wide range of geometries, and that, for coupling two or more emitters to a single cavity mode, the improvement factors can far exceed an order of magnitude.

Foundation models trained as autoregressive PDE surrogates hold significant promise for accelerating scientific discovery through their capacity to both extrapolate beyond training regimes and efficiently adapt to downstream tasks despite a paucity of examples for fine-tuning. However, reliably achieving genuine generalization - a necessary capability for producing novel scientific insights and robustly performing during deployment - remains a critical challenge. Establishing whether or not these requirements are met demands evaluation metrics capable of clearly distinguishing genuine model generalization from mere memorization. We apply the influence function formalism to systematically characterize how autoregressive PDE surrogates assimilate and propagate information derived from diverse physical scenarios, revealing fundamental limitations of standard models and training routines in addition to providing actionable insights regarding the design of improved surrogates.

In this paper, we propose a microflow velocimetry based on particle tracking with the aid of optical trapping of tracers, namely, optically-trapped particle tracking velocimetry (ot-PTV). The ot-PTV has two phases: a trap phase, in which individual tracers are trapped by an optical force and held at a measurement position; a release phase, in which the tracer is released and advected by the fluid flow, without interference from the optical force. The released tracer is subsequently trapped again by the optical force. By repeating the set of trap and release phases, we can accumulate the sequential images of the tracer that have the same initial position. Although the data acquisition rate of ot-PTV is lower than that of standard micro-resolution particle image velocimetry ($\mu$PIV) due to the nature of pointwise measurement, an advantage of ot-PTV is that the measurement positions can be chosen by the experimenter. That is, even when tracers are scarce in a test section because of some external effects and/or the small size of the test section -- ill-suited cases for $\mu$PIV since the experimenter has to wait for tracers to diffuse into the test section (i.e., diffusion-limited situation) -- ot-PTV remains efficient. The concept of ot-PTV is validated using a benchmark experiment, i.e., a pressure-driven flow in a straight microchannel with a square cross-section. An application to thermally-induced microflows is also demonstrated, where tracers can be scarce in a test section due to thermophoresis.

This work describes a self-mixing-assisted optomechanical platform for transferring information between near- and mid-infrared radiation. In particular, the self-mixing signal of a mid-infrared quantum cascade laser is used to detect the oscillation of a membrane driven by light-induced forces exerted by a near-infrared excitation beam, which is amplitude-modulated at the membrane resonance frequency. This technique benefits from spectral broadness and, therefore, can link different spectral regions from both the excitation and probe sides. This versatility can pave the way for future applications of this self-mixing-assisted optomechanical platform in communication and advanced sensing systems.

The search for light dark matter and cosmic primordial neutrinos necessitates detectors with sub-millielectronvolt (sub-meV) energy thresholds. While superconducting quantum sensors have approached this sensitivity, they often face significant challenges regarding readout complexity and scalability. To address these limitations, we propose a hybrid Superconductor-Insulator-P-N (S-I-P-N) detector architecture. This concept combines the high sensitivity of superconducting Cooper pair breaking with the massive intrinsic gain of semiconductor electron avalanches. A critical prerequisite for this scheme is operation at millikelvin (mK) temperatures, raising the critical fundamental question of whether silicon PN junctions can sustain avalanche multiplication in a regime where carrier freeze-out is severe. Here, we experimentally validate the critical semiconductor amplification stage of the proposed detector. We demonstrate that Silicon Photomultipliers (SiPMs) retain robust Geiger-mode avalanche capabilities at 10 mK. We report a single-photoelectron gain of order 10$^6$ and a dark count rate as low as 5~mHz/mm$^2$, 7 orders of magnitude lower than at room temperature. These results confirm the viability of high-gain semiconductor readout in the deep cryogenic regime, clearing the primary obstacle regarding the semiconductor component for the realization of scalable, sub-meV threshold S-I-P-N detectors.

X-ray free-electron lasers (XFELs) of high brightness have opened new opportunities for exploring ultrafast dynamical processes in matter, enabling imaging and movies of single molecules and particles at atomic resolution. In this paper, we present a straightforward method for multi-frame diffraction imaging, using the same electron beam to generate four-color XFEL pulses with adjustable wavelength separation and time delay. The optical klystron scheme is introduced to enhance FEL intensity and reduce the total length of undulators. The time delay is tuned via a magnetic chicane between the undulators with various colors. Using parameters of SHINE, start-to-end simulations demonstrate the effectiveness and tunability of our method, achieving representative results such as time delays of hundreds of femtoseconds and four-color XFEL pulses spanning 1.8 to 2.7 nm with 0.3 nm intervals. The proposed scheme enables the recording of multi-frame diffraction images in a single exposure, providing a new perspective for ultrafast molecular and atomic dynamics studies.

The nature of turbulence at sub-electron scales has remained an open question, central to understanding how electrons are heated in the solar wind. This is primarily because spacecraft measurements have been limited to magnetic field fluctuations alone. We resolve this by deriving new high-resolution density fluctuations from spacecraft potential measurements of Parker Solar Probe resolving scales smaller than the electron gyro-radius ($\rho_e$). A systematic comparison of the density and magnetic spectra shows that both steepen near the electron scales. Notably, the density spectrum exhibits slopes close to $-10/3$, while the magnetic spectrum becomes consistently steeper than the density spectrum at scales smaller than $\rho_e$, indicating that the turbulence becomes electrostatic. These results are consistent with theoretical predictions of an electron entropy cascade, which may explain the irreversible dissipation of turbulent energy at sub-$\rho_e$ scales. The magnetic spectrum, however, is not as steep as expected for the electron entropy cascade, which may be due to limited signal-to-noise ratio and the presence of weakly damped electromagnetic fluctuations near $\rho_e$.

We demonstrate that in pair plasma weakly nonlinear electromagnetic waves, $a_0 \leq 1$, experience Anderson self-localization. The beat between the driver and a back-scattered wave creates charge-neutral, large random density fluctuations $\delta n/n_0 \gg 1$, and corresponding fluctuations of the dielectric permittivity $\epsilon$ (random plasma density grating). Propagating in quasi-1D, waves in a medium with spatially random self-created fluctuations of dielectric permeability experience localization. {In the linear regime, the instability can be classified as Induced Brillouin Scattering; it is described by the parameter $\rho _L = \left( a_0 { \omega_{p}/ }{\omega}\right)^{2/3} \leq 1 $, related to the Pierce parameter of Free Electron Lasers. In the cold case, the growth rate is $\Gamma \approx \rho _{L} \omega$ ($a_0 $ is laser nonlinearity parameter, $\omega_p$ is plasma frequency, $\omega$ is the laser frequency). } Anderson self-localization of light leads to (i) reflection of EM waves by the under-dense pair plasma; (ii) a wave already present inside the plasma separates into bright trapped pockets and dark regions. Mild initial thermal spread with $\Theta \equiv k_B T/(m_e c^2) \approx a_0^2$, restores wave propagation by suppressing the seeds of parametrically unstable density fluctuations. A circularly polarized driver produces linearly polarized structures, with position angle varying randomly between the bright pulses. Time-variability of the resulting density structures does not suppress localization due to remaining corrections (not white noise). We discuss possible applications to astrophysical Fast Radio Bursts.

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor characterization that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

High-pressure transcritical fluid flows are central to modern energy and propulsion systems. A key challenge arises in confined configurations, where optimizing performance requires a detailed understanding of the coupled hydrodynamic and thermodynamic nonlinearities governing these flows. In this context, low-order decomposition techniques, particularly resolvent analysis, provide an interpretable linear input-output framework to identify and quantify dominant amplification mechanisms of coherent flow structures. This work pursues two main objectives: (i) to establish a resolvent-based framework tailored to high-pressure transcritical fluid flows, and (ii) to characterize the spatio-temporal sensitivity of the resolvent operator using data-driven turbulent base flows. These analyses identify flow responses and forcings that optimally enhance mixing and heat transfer, along with their characteristic scales. Results show that amplification is dominated by streamwise-elongated structures with spanwise periodicity, associated with peak singular values at normalized spanwise wavenumbers of order unity. Unlike ideal-gas or incompressible flows, the dominant forcings originate from thermodynamic fluctuations in the pseudo-boiling region. Linearization about the turbulent mean flow yields intensified responses in the form of coherent counter-rotating vortex pairs. Energetic-scale motions are constrained by the low-Reynolds-number and non-isothermal conditions considered, with a dominant spectral mode reaching streamwise lengths comparable to instantaneous structures. Data-driven analyses further reveal coherent motions propagating at phase speeds absent from classical incompressible wall-bounded turbulence, intensified near the pseudo-boiling region and constrained toward the hot wall.

A consistent kinetic modeling and discretization strategy for compressible flows across all Prandtl numbers and specific heat ratios is developed using the quasi-equilibrium approach within two of the most widely used double-distribution frameworks. The methodology ensures accurate recovery of the Navier-Stokes-Fourier equations, including all macroscopic moments and dissipation rates, through detailed hydrodynamic limit analysis and careful construction of equilibrium and quasi-equilibrium attractors. Discretization is performed using high-order velocity lattices with a static reference frame in a discrete velocity Boltzmann context to isolate key modeling aspects such as the necessary requirements on expansion and quadrature orders. The proposed models demonstrate high accuracy, numerical stability and Galilean invariance across a wide range of Mach numbers and temperature ratios. Separate tests for strict conservation and measurements of all dissipation rates confirm these insights for all Prandtl numbers and specific heat ratios. Simulations of a thermal Couette flow and a sensitive two-dimensional shock-vortex interaction excellently reproduce viscous Navier-Stokes-Fourier-level physics. The proposed models establish an accurate, efficient and scalable framework for kinetic simulations of compressible flows with moderate supersonic speeds and discontinuities at arbitrary Prandtl numbers and specific heat ratios, offering a valuable tool for studying complex problems in fluid dynamics and paving the way for future extensions to the lattice Boltzmann context, by application of correction terms, as well as high-Mach and hypersonic regimes, employing target-designed reference frames.

Quantum optimal control is central to designing spin manipulation pulses. Gradient-based pulse optimization can be facilitated by either accelerating gradient evaluation or enhancing the convergence rate. In this work, we accelerated single-spin optimal control by combining the finite element method with the method of moving asymptotes. By treating discretized time as spatial coordinates, the Liouville - von Neumann equation was reformulated as a linear system, efficiently yielding a joint solution of the spin trajectory and control gradient. The method of moving asymptotes, relying on the ensemble fidelities and gradients, achieves rapid convergence for a target fidelity of 0.995.

Dirac vortices, originally studied in quantum field theories to predict localized zero-energy modes, were recently realized in photonics, leading to Dirac vortex cavities. With topological protection, Dirac vortex cavities offer robust single-mode large-area localized modes appealing for high-performance micro-lasers and other applications. As a spectrally-isolated single mode, the radiation of a Dirac vortex cavity mode was believed as having vanishing orbital angular momentum due to time-reversal symmetry. Here, we report the direct observation of orbital angular momentum radiation of a Dirac vortex cavity through spin-resolved measurements. Remarkably, we confirm the spin-orbital angular momentum locking in such radiation due to the spin-valley locking and inter-valley couplings. We demonstrate that the spin-orbital angular momentum locking is controlled by the chirality of the Kekulé modulation and propose design schemes for arbitrary-order single-mode OAM radiation.

A referential example of a physical system that supports bound states in the continuum (BICs) with an infinite quality factor ($Q$ factor) is a metasurface of discrete scatterers (resonators), whose response can be significantly modified by exploiting lattice interactions. In this work, we explore the multipole-interference mechanism for realizing accidental acoustic BICs (trapped modes) at $\Gamma$-point (in-plane Bloch wave vector $\mathbf{k}_{\parallel} = \mathbf{0}$) of biperiodic metasurfaces of acoustic resonators with one resonator per unit cell. To do so, we expand the pressure field from the metasurface into a series of scalar zonal ($m = 0$) spherical multipoles, carried by a normally incident plane wave, and formulate analytical conditions on the resonator multipole moments under which an eigenmode becomes a BIC. The conditions enable us to determine the lattice constant and frequency values that facilitate the formation of an axisymmetric BIC with a specific parity, resulting from destructive interference between zonal multipoles of the same parity, despite each moment radiating individually. By employing the T-matrix method for acoustic metasurfaces, we numerically investigate the BIC resonance in various structures, including finite arrays, and also the transformation of such resonances into high-$Q$ quasi-BIC regimes, which can be excited by a plane wave at normal incidence.

We investigate the combined effects of carrier envelope phase and laser pulse shape on electron-positron pair production in the presence of an external asymmetric super-Gaussian electric field by solving the quantum Vlasov equation. By varying the field asymmetry, the pulse shape from Gaussian to super-Gaussian, and the carrier envelope phase, we show the momentum distribution and the number density of created pairs to exhibit extreme sensitivity to these field characteristics. The effects are also qualitatively explained by analyzing the turning-point structures within the WKB formalism. We observed that multiphoton pair production dominates in the case of long falling-pulse asymmetry. For a short falling pulse with a flat-top super-Gaussian laser profile, pair production is further facilitated. For certain field parameters, we demonstrate that the number density can be enhanced by two to three orders of magnitude.

Following recent work (Gonzalez and Taha 2022; Peters and Ormiston 2025), this manuscript clarifies what the Gauss-Appell principle determines in incompressible, inviscid flow and how it connects to classical projection methods. At a fixed time, freezing the velocity and varying only the material acceleration leads to minimization of a quadratic subject to acceleration-level constraints. First-order conditions yield a Poisson-Neumann problem for a reaction pressure whose gradient removes the non-solenoidal and wall-normal content of the provisional residual, i.e. the Leray-Hodge projection. Thus, Gauss-Appell enforces the instantaneous kinematic constraints and recovers Euler at the instant. In steady flows, this principle -- on its own -- cannot select circulation or stagnation points because these are properties of the velocity state, not the instantaneous acceleration correction. The principle only determines the reaction pressure for an already-specified velocity field. The impressed/reaction pressure bookkeeping can be supplemented with orthogonality conventions that separate prescribed conservative forcing (if any) from the reaction enforcing the constraints. This variational viewpoint also yields a simple computational diagnostic: the minimized Appellian equals a L-2 norm of the reaction-pressure gradient which vanishes for constraint-compatible updates and grows with the magnitude of divergence and wall-flux mismatch. The goal of this note is simply to lend more clarity to the application of the Gauss principle, and to connect it concretely to well known concepts including potential flow theory, recent variational approaches and projection algorithms.

A simplification of the VV10 van der Waals density functional [J. Chem. Phys. 133, 244103 (2010)] is made by an approximation of the integrand of the six-dimentional integral in terms of a few products of three-dimensional density-like distributions and potential-like functions of the interelectronic distance only, opening the way for its straightforward computation by fast multipole methods. An even faster computational scheme for molecular systems is implemented where the density-like distributions are fitted by linear combinations of usual atom-centered basis functions of Gaussian type and the six-dimensional integral is then computed analytically, at a fraction of the overall cost of a typical calculation. The simplicity of the new approximation is commensurate with that of the original VV10 functional, and the same level of accuracy is seen in tests on molecules.

The use of Oxygen in gas mixtures for drift chambers is highly discouraged because Oxygen, being strongly electronegative, is generally believed to lead, even in very small quantities, to extremely reduced drift electron survival probability, thus preventing the detector's this http URL drift chamber of the MEG II experiment at PSI has been operating for several years with a gas mixture that mainly contains He:Isobutane in relative proportions of 90:10% by molar concentration, in addition to 1.5% Isopropanol and 0.5% Oxygen. Oxygen and Isopropanol are essential for the proper functioning of the chamber. The electron attachment in the mixture used has proven negligible for the proper operation of the chamber and agrees well with the Garfield++ simulation after correctly accounting for the three-body attachment simulation.

In anticipation of the completion of the High-Luminosity Large Hadron Collider (HL-LHC) programme by the end of 2041, CERN is preparing to launch a new major facility in the mid-2040s. According to the 2020 update of the European Strategy for Particle Physics (ESPP), the highest-priority next collider is an electron-positron Higgs factory, followed in the longer term by a hadron-hadron collider at the highest achievable energy. The CERN directorate established a Future Colliders Comparative Evaluation working group in June 2023. This group brings together project leaders and domain experts to conduct a consistent evaluation of the Future Circular Collider (FCC) and alternative scenarios based on shared assumptions and standardized criteria. This report presents a comparative evaluation of proposed future collider projects submitted as input for the Update of the European Strategy for Particle Physics. These proposals are compared considering main performance parameters, environmental impact and sustainability, technical maturity, cost of construction and operation, required human resources, and realistic implementation timelines. An overview of the international collider projects within a similar timeframe, notably the CEPC in China and the ILC in Japan is also presented, as well as a short review of the status and prospects of new accelerator techniques.

This article is founded on two fundamental principles: the principle field equations introduced in Refs. \cite{S, S1, S2} and the Fock-Ivanenko covariant derivatives \cite{FI, F}. The former yields the equations of motion for free fields of arbitrary spin and helicity. In the massless case, it also dictates that Lorentz transformations for tensor fields acquire an additional term, which takes the form of a gauge transformation \cite{W, S1}. The latter principle, the Fock-Ivanenko derivative, introduces interactions based on the intrinsic and Poincare groups. This framework allows us to recover a complete Yang-Mills theory, as well as general relativity in the connection-based formulations of Palatini and Ashtekar, both of which are theories with local gauge symmetries. While the standard approach begins with the symmetries of a matter action, we will instead derive dynamics directly from Poincare invariance. This perspective reveals that for free fields, Lorentz invariance induces the gauge symmetry of massless tensors. A proper definition of these gauge transformations, in turn, requires the covariant derivatives provided by the Fock-Ivanenko approach. Considering matter fields, we derive the interacting Dirac equation in the presence of Yang-Mills and gravitational fields from its free counterpart.

The link between information and energy introduces the observer and their knowledge into the understanding of a fundamental quantity of physics. Two approaches compete to account for this link, Brillouin's negentropy law of information and Landauer's law on data erasure, which are often confused. The first, based on the Clausius' inequality and Shannon's mathematical results is very robust, while the second, based on the simple idea that information needs a material embodiment (data-bits) is today perceived as more physical and prevails. In this paper, we show that Landauer's idea results from a confusion between information (a global emergent concept) and data (a local material object). This confusion leads to many inconsistencies and is incompatible with thermodynamics and information theory. The reason it prevails is interpreted to be due to a frequent tendency of materialism towards reductionism, neglecting emergence and seeking to eliminate the role of the observer. A paradoxical trend given that it is often accompanied by the materialist idea that all scientific knowledge nevertheless originates from observation. Information and entropy are actually emergent quantities introduced in the theory by convention.

The capacity to resist pathogens is recognized as a fundamental property of the immune system, yet the capacity itself remains a phenomenological concept and lacks a defined physical basis. Its fundamental entity, definition, and quantification are thus unresolved. Here, we address these questions by introducing a theoretical framework based on Lagrangian analytical mechanics, which recasts immune recognition as a dynamical system in an immunological state space. Generalized coordinates are used to describe the conformational states of immune receptors, and their evolution is governed by Euler-Lagrange equations constructed from the antigen-receptor interaction. Central to our theory is the identification of a continuous symmetry: the action remains invariant under specific translations within the antigenic structure space or time. From this symmetry, Noether's theorem dictates a conserved quantity, $I$. We propose that $I$ is the physical embodiment of host immunity, a quantifiable measure that integrates the system's protective sensitivity (with dimensions of action) and response intensity (with dimensions of energy). Furthermore, this framework unifies key immunological phenomena as dynamical consequences of the same underlying conservation law, including vaccination, immune memory, tolerance, original antigenic sin, and T cell exhaustion. The consistency of this model with established clinical observations (e.g., conserved symptom profiles across distinct influenza strains) and published experimental data provides its initial validation. By transforming immune capacity from a phenomenological concept into a quantifiable physical entity defined by a conservation law, this work establishes a foundational framework for a unified, predictive immunology.

After a brief review of the modified causal Lorentz-Abraham (LA) classical equation of motion for an extended charged sphere and its limit to the mass-renormalized modified causal Lorentz-Abraham-Dirac (LAD) equation of motion as the radius of the charged sphere approaches zero, a concise derivation is given for the conditions on the velocity and external force required for these modified equations of motion to satisfy conservation of momentum and energy. The effects of mass renomalization on the radiated momentum-energy is clarified. The solutions to the unmodified and modified LAD equations of motion as well as the Landau-Lifshitz approximate solution to the unmodified LAD equation of motion are obtained for a charge traveling through a parallel-plate capacitor.

The incompressible three-dimensional Euler equations develop very thin pancake-like regions of exponentially increasing vorticity. The characteristic thickness of such regions decreases exponentially with time, while the other two dimensions do not change considerably, making the flow near each pancake strongly anisotropic. The pancakes emerge in increasing number with time, which may enhance the anisotropy of the flow, especially if they orient similarly in space. In the present paper, we study numerically the anisotropy by analyzing the evolution of the so-called isotropy markers [Phys. Rev. Fluids 10, L022602 (2025)]. We show that these functions drift slowly towards unity, indicating the process of slow isotropization, which takes place without the viscous scales getting exited and despite the similar orientation of the emerging pancakes.

The PICOSEC-Micromegas (PICOSEC-MM) detector is a fast gaseous detector that achieves picosecond-level timing by coupling a Cherenkov radiator, typically an MgF2 crystal, to a Micromegas-based photodetector with a photocathode. This configuration allows the fast photoelectron-induced signal to suppress the intrinsic time jitter of gaseous detectors, enabling sub-20 ps timing precision while preserving the robustness and scalability of micro-pattern gaseous detector technologies. The 96-pad PICOSEC-MM detector is a large-area demonstrator optimized for precision timing in high-energy physics, building on research and development insights from earlier 7-pad resistive prototypes to validate scalability, uniformity, and robustness for the ENUBET project. It employs a 2.5 nm diamond-like carbon photocathode and a Micromegas board with a surface resistivity of 10 megaohms per square, and was characterized using 150 GeV/c muons at the CERN SPS beamline, with one-third of the active area instrumented per run. A dedicated alignment procedure for multi-pad PICOSEC-MM systems was used to reconstruct pad centers and merge measurements across regions, yielding a timing resolution of 43 ps and uniform signal arrival time distributions over the tested area. Mechanical flatness was identified as a key factor, with planarity tolerances within 10 micrometers required to maintain good timing resolution, and the successful operation of the 96-pad demonstrator confirms the scalability of the PICOSEC-MM concept toward robust, high-granularity, picosecond-level gaseous timing detectors for monitored neutrino beam experiments such as ENUBET.

We report measurements of zero- to ultra-low-field nuclear magnetic resonance (ZULF NMR) signals at frequencies of a few hertz with a diamond-based magnetic sensor. The sensing diamond is a truncated pyramid with 0.18 mm height and a 0.5 mm x 0.5mm base. The minimum stand-off distance is < 1 mm, and the sensor sensitivity is 13 pT/(Hz)^(1/2) at frequencies f above 5 Hz with 1/f-like behavior at lower frequencies. NMR signals were generated via signal amplification by reversible exchange (SABRE) parahydrogen-based hyperpolarization resulting in zero-field signals at 1.7 Hz and 3.4 Hz corresponding to the expected hetero-nuclear J-coupling pattern of acetonitrile. This work demonstrates a magnet-free platform for detecting chemically specific NMR signals at ultra-low frequencies paving the way for portable noninvasive diagnostics in microscopic sample volumes for biomedicine, industrial sensing through metal enclosures, and field-deployable quantum analytical devices.

We develop an axiomatic reconstruction of thermodynamics based entirely on two primitive components: a description of what aspects of a system are observed and a reference measure that encodes the underlying descriptive convention. These ingredients define an "information volume" for each observational cell. By incorporating the logarithm of this volume as an additional constraint in a minimum-relative-entropy inference scheme, temperature, chemical potential, and pressure arise as conjugate variables of a single information-theoretic functional. This leads to a Legendre-type structure and a first-law-like relation in which pressure corresponds to information volume rather than geometric volume. For nonequilibrium dynamics, entropy production is characterized through the relative-entropy asymmetry between forward and time-reversed stochastic evolutions. A decomposition using observational entropy then separates total dissipation into system and environment contributions. Heat is defined as the part of dissipation not accounted for by the system-entropy change, yielding a representation that does not rely on local detailed balance or a specific bath model. We further show that the difference between joint and partially observed dissipation equals the average of conditional relative entropies, providing a unified interpretation of hidden dissipation and information-flow terms as projection-induced gaps.

Artificial intelligence, an emerging, powerful and efficient computational tool, has played a crucial role in the design of optical devices. For the design of holographic metasurfaces, traditional algorithms require multiple iterations between the metasurface and target planes, resulting in excessive computation time. Here, a physics-informed neural network (PINN) is proposed for the fast design of terahertz three-dimensional (3D) holographic metasurfaces. Trained in a self-supervised manner, the PINN eliminates the need for paired input-label datasets. After training, the PINN enables end-to-end mapping between the target holographic patterns and metasurface structures. Both simulation and experimental results of single-plane and 3D multi-plane holography demonstrate that metasurfaces designed by PINN offer higher imaging quality than traditional iterative algorithms. Moreover, the PINN can find approximate solutions of metasurface structures even in physically counterintuitive scenarios, where the holographic patterns of two parallel imaging planes are completely distinct. Furthermore, the inference process of PINN typically takes less than 1 second, much faster than the traditional algorithms requiring iterative computation. Notably, the PINN simultaneously accounts for both phase and amplitude modulation, thereby outperforming traditional phase-only modulation algorithms in handling complex physical scenarios and offering superior imaging quality. This end-to-end design approach has the potential to pave the way for the realization of high-quality, real-time, and large-scale terahertz 3D holographic display technology.

We have developed an analytical framework for magnetically induced transparency-absorption (MITA) and normal-anomalous dispersion (MINAD) in a weakly driven ${}^{87}\text{Rb}$ vapor, or any J-type three-level system, under a vector magnetic field. By solving the Bloch equations in the stationary, quasi-stationary, and short-pulse regimes, we obtained closed-form expressions for the atomic populations and coherences and identified a bifurcation in the oscillatory dynamics at zero longitudinal Zeeman splitting. The Fourier-domain analysis reveals alternating transparency/absorption and normal/anomalous dispersion with frequency-dependent sign reversals, enabling spectrally selective filtering and group-delay effects. Slow oscillatory behavior in the radio-frequency range makes the system suitable for weak magnetic-field sensing, while fast oscillations at optical frequencies suggest applications in spectral filtering and frequency-comb-like signal shaping. The results provide a theoretical basis for experimental observation of MITA/MINAD and for optimizing atomic-vapor platforms for precision magnetometry and related photonic functionalities.

Motivated by breakthroughs in inertial confinement fusion (ICF), first achieving ignition conditions in National Ignition Facility (NIF) shot N210808 and then laser energy breakeven in N221204, modeling efforts here investigate the effect of imposed magnetic fields on integrated hohlraum simulations of igniting systems. Previous NIF experiments have shown yield and hotspot temperature to increase in magnetized, gas-filled capsules in line with scalings. In this work, we use the 2D radiation-magnetohydrodynamics code Lasnex with a Livermore ICF common model. Simulations are tuned to closely approximate data from unmagnetized experiments. Investigated here is the effect of imposed axial fields of up to 100 T on the fusion output of high-performing ICF shots, specifically the record BigFoot shot N180128, and HYBRID-E shots N210808 and N221204. The main observed effect is an increase in the hotspot temperature due to magnetic insulation. Namely, electron heat flow is constrained perpendicular to the magnetic field and alpha trajectories transition to gyro-orbits, enhancing energy deposition. In addition, we investigate the impact of applied magnetic fields to future NIF designs, specifically an example Enhanced Yield Capability design with 3 MJ of laser energy as well as a high-\r{ho}R, low implosion velocity "Pushered Single Shell" design. In conclusion, magnetization with field strengths of 5-75 T is found to increase the burn-averaged ion temperature by 50% and the neutron yield by 2-12. Specifically, we see yield enhancement of at least 50% with only a 5-10 T applied magnetic field for N221204, while a 65 T field on N210808 with symmetrization gives an 8 increase in yield. This is all without further design optimization to best take advantage of an applied B field, which promises even greater improvements for designs tailored specifically towards magnetization.

Understanding how soft matter systems, including biological ones, can develop collective electromagnetic phenomena under external fields at ambient conditions remains a central challenge, as thermal fluctuations are generally expected to suppress long-range organization. Here, we report that hydrated DNA exhibits a reproducible magnetic-field-induced transition characterized by a sharp transverse-voltage threshold, followed by a regime of regular, phase-stable oscillations in the transverse polarization signal. These features emerge only beyond the threshold and display a pronounced temperature dependence, consistent with the formation of a collective mode within the hydrogen-bond network of the DNA-water interface. Motivated by recent studies of Hall-like responses carried by neutral excitations, including phonons, magnons, and excitons, we interpret the observed transverse signal in terms of coherent polarization dynamics of proton - proton-hole dipoles confined to a quasi-two-dimensional hydrated layer. Within this framework, the transverse response is attributed to a field-organized polarization mode; the measured transverse voltage arises from collective dipolar dynamics rather than steady carrier transport. These results identify hydrated DNA as a soft-matter system in which magnetic field and temperature jointly modulate collective polarization dynamics, providing a biologically relevant platform for exploring coherence and transverse phenomena in hydrogen-bonded media.

Ultrathin multimode fibers (MMFs) promise endoscopes with hair-scale diameters for accessing sub-millimeter anatomy, but in MMF far-field imaging the required small collection aperture drives speckle-dominated measurements that rapidly degrade image fidelity. Here we present Speckle Clean Network (SCNet), a physics-guided foundation model for universal speckle removal that makes photon-limited, single-fiber collection compatible with high-fidelity reconstruction across diverse scattering conditions without target-specific retraining. SCNet combines a Mixture of Experts (MoE) architecture with material-aware routing, wavelet-based frequency decomposition to separate structure from speckle across sub-bands, and a curriculum-style optimization that progressively enforces spectral consistency before spatial fidelity. Using an ultrathin dual-fiber holographic probe, we deliver wavefront-shaped illumination through one MMF and collect backscattered photons through a parallel MMF. We validate SCNet on 3D plastic objects over varying working distances, resolve 5.66 lp/mm on a paper USAF target, and restore fine structures on leaves and metal surfaces. On rabbit heart and kidney tissues, SCNet improves recovery of low-contrast anatomical texture under the same ultrathin collection constraint. We further compress SCNet through multi-teacher distillation to reduce computation while preserving reconstruction quality, enabling inference at 60 FPS. This work effectively decouples image quality from probe size, establishing a speckle-free ultrathin endoscopy for stand-off imaging in confined spaces.

We identify steerable exponentially localized in-gap mode in a quasiperiodic non-Hermitian Aubry-André-Harper chain with a spatially fluctuating, zero-mean imaginary gauge field. Under open boundary conditions, the system is exactly related to the Hermitian AAH model by a nonunitary gauge transformation: the OBC spectrum and Lyapunov exponents are unchanged, while eigenstates acquire a gauge-dependent envelope. In a parameter region with spectrally isolated in-gap boundary modes, we find two exponentially localized in-gap modes with sharply different responses to the imaginary gauge field. One remains boundary pinned, but the other is gauge-steerable: it stays exponentially localized while its probability maximum shifts as the gauge field is changed, with its eigenenergy unchanged. We further show that weak on-site gain, applied at a single site chosen once and then kept fixed, can dynamically prepare this steerable mode from a generic bulk wave packet. Changing the gauge field then yields exponentially localized states at different locations.

This paper develops a comprehensive two-dimensional generalisation of the recently introduced Friction with Bristle Dynamics (FrBD) framework for rolling contact problems. The proposed formulation extends the one-dimensional FrBD model to accommodate simultaneous longitudinal and lateral slips, spin, and arbitrary transport kinematics over a finite contact region. The derivation combines a rheological representation of the bristle element with an analytical local sliding-friction law. By relying on an application of the Implicit Function Theorem, the notion of sliding velocity is then eliminated, and a fully dynamic friction model, driven solely by the rigid relative velocity, is obtained. Building upon this local model, three distributed formulations of increasing complexity are introduced, covering standard linear rolling contact, as well as linear and semilinear rolling in the presence of large spin slips. For the linear formulations, well-posedness, stability, and passivity properties are investigated under standard assumptions. In particular, the analysis reveals that the model preserves passivity under almost any parametrisation of practical interest. Numerical simulations illustrate steady-state action surfaces, transient relaxation phenomena, and the effect of time-varying normal loads. The results provide a unified and mathematically tractable friction model applicable to a broad class of rolling contact systems.

A hidden variables (HVs) model is reported, which reproduces quantum predictions for Bell-Clauser-Horne-Shimony-Holt (Bell-CHSH) tests. The existence of such a model poses some limitations to quantum certifications that rely on Bell-CHSH inequality violations. The reported model does not prove wrong Bell's theorem. The latter assumes the factorability of the probability density $p_{AB}$, which rules the stochastic behavior of the HVs. The reported HVs model is based on an extended form of $p_{AB}$, which is suggested by Lebesgue's decomposition theorem for bounded functions. The considered $p_{AB}$ complies with locality and realism, and also with measurement independence, parameter independence and outcome independence.

We report the discovery of a universal geometric constraint on the detection of kinetic energy release (KER) in three-dimensional quantum fragmentation. By investigating the dissociation of localized Slater-type orbitals, we demonstrate that the $4\pi r^2$ radial volume element inherently masks the majority of a system's energy budget, imposing a fundamental peak-to-mean energy bound of $R_E < 0.5$. We introduce a dimensionless scaling law, $M = \alpha \zeta / Q$, and prove that the resulting energy detection ratio is scale-invariant across twelve orders of magnitude, bridging the gap between atomic and subatomic physics. Remarkably, we identify a \textbf{``Geometric Zero-Point''} at $R_E \approx 0.33$, which precisely replicates the 7~eV ``missing energy'' anomaly observed in attosecond $H_2^+$ fragmentation benchmarks. Furthermore, we demonstrate that this $1/3$ ratio provides a robust geometric baseline for the historical average-to-endpoint discrepancy in beta decay. Our results suggest that a significant portion of what is historically categorized as ``missing energy'' may be a topological artifact of 3D quantum geometry rather than an exclusive signature of undetected particles. This work establishes a universal master curve for energy reconstruction and identifies a \textbf{``detection crisis''} in highly localized systems, where the true interaction energy becomes effectively invisible to peak-centric experimental calorimetry.

The spin polarization of Rb atoms undergoing light-induced desorption from a spin-polarized Co (110) surface was investigated. Desorption induced by pulsed UV-light irradiation was driven by a non-thermal mechanism and the spins of the desorbed Rb atoms were polarized. This implies spin transfer between the surface and the adsorbate during desorption.

RNA polymerase (RNAP) elongates RNA by walking along a DNA template and selectively incorporating ribonucleoside triphosphates (rNTPs). Rather than mechanically replicating the base sequence, RNAP conditions binding and chemistry on the currently read template nucleotide, converting sequence dependence into a bias in its stochastic motion. Thermal fluctuations generate forward/backward translocation attempts; cognate rNTP binding and incorporation stabilize the forward register and suppress backward return, yielding net advance via a Brownian-ratchet mechanism. We formulate the DNA--RNAP complex as a bipartite stochastic system, separating template-side degrees of freedom $X$ from RNAP-side response degrees of freedom $Y$. Irreversibility is quantified by a Kullback--Leibler divergence between forward and time-reversed path measures, yielding joint and marginal dissipations. From these we define an exchange-invariant interface dissipation $\Sigma_{\mathrm{int}}$ that isolates time-reversal asymmetry generated specifically by coupling across the DNA--RNAP interface. We prove an exchange-symmetric second law, $\Sigma_{\mathrm{int}}\ge 0$, and show that this interface measure is well defined without invoking local detailed balance. To connect the framework to data analysis, we present a minimal continuous-time Markov jump model implementing the Brownian-ratchet logic and a likelihood-ratio protocol to estimate dissipation rates from discretely sampled trajectories. Finite-sample convergence is assessed via Markov-order diagnostics, clarifying bias--variance tradeoffs under coarse-graining. The interface-centered measure provides a consistent basis for comparing energetic cost across regimes (sequence-dependent kinetics, misincorporation, backtracking, proofreading) and can be combined with hidden-state inference when internal states are partially observed.

We propose the integral-equation formalism of population dynamics (IEPDYN) to describe the population dynamics of distinct configurational states. According to classical reaction dynamics theory, the probability density associated with a given state obeys the Liouville equation, including influx from and efflux to neighboring states. By introducing a Markov approximation for the crossing of boundaries separating the states, tractable integral equations governing the state populations are derived. Once the time-dependent quantities appearing in these equations are evaluated, the population dynamics on long timescales can be obtained. Because these quantities depend only on a few states in the local neighborhood of a given state, they can be computed using a set of short-timescale molecular dynamics (MD) simulations. The IEPDYN method is formulated in continuous time and therefore does not rely on a coarse-grained timescale (lag time). Consequently, kinetic quantities obtained from IEPDYN are free from lag-time dependence, which has been discussed as a limitation in other approaches. We apply the IEPDYN method to the binding and unbinding kinetics of CH$_4$/CH$_4$, Na$^+$/Cl$^-$, and 18-crown-6-ether (crown ether)/K$^+$ in water. For both kinetics, the time constants estimated from the IEPDYN method are almost comparable to those obtained from brute-force MD simulations. The required timescale of each MD trajectory in the IEPDYN method is approximately two orders of magnitude shorter than that in the brute-force MD approach in the crown ether/K$^+$ system. This reduction in the trajectory timescale enables applications to complex binding and unbinding systems whose characteristic timescales are far beyond those directly accessible by brute-force MD simulations.

A new interferometer topology for kilohertz gravitational-wave detection was recently proposed in [Zhang et al. Phys. Rev. X 13, 021019 (2023)]. The design is based on an L-shaped optical cavity pumped through a Sagnac-like vortex. We report a tabletop experiment that characterizes the interferometer's optical response near DC. When the laser frequency is locked to the resonance of the L-shaped cavity, we observe that the cavity input coupler becomes effectively transparent, yielding a simple Michelson-like response. Moreover, the Sagnac vortex separates into upper and lower paths, which behave as two independent pumping paths driving the cavity. These observations are in agreement with theoretical predictions. Our results provide an intuitive physical picture of this interferometer topology and offer insight into its lock acquisition strategy.

We review and study the correspondence between discrete lattice/chain models of interacting particles and their continuous counterparts represented by partial differential equations. We study the correspondence problem for nearest neighbour interaction lattice models as well as for multiple-neighbour interaction lattice models, and we gradually proceed from infinite lattices to periodic lattices and finally to finite lattices with fixed ends/zero Dirichlet boundary conditions. The whole study is framed as systematic specialisation of Fourier analysis tools from the continuous to the discrete setting and vice versa, and the correspondence between the discrete and continuous models is examined primarily with regard to the dispersion relation.

In this paper we present a compact test facility for the measurement of optical properties of liquid argon as scintillation detector. The setup is under preparation at Roma Tre and it has a volume of 40 L liquid argon, which is liquefied from argon gas with a purity of $\ge 99.9999\%$ vol. To readout the scintillation photons from liquid argon with the highest intensity near 127 nm, we use the vacuum ultraviolet silicon photomultipliers from Hamamatsu. By submerging the photon detectors directly inside the liquid argon, we can eliminate the systematics from the wave length shifter and light guides which have been commonly used to detect the scintillation photons of liquid argon.

We propose a nested Wolter-I mirror design for a neutron condenser, which is based on established X-ray telescope technology. We demonstrate through simulations that it can increase the flux density at the ESS imaging instrument ODIN by up to two orders of magnitude. Experimental measurements of reflectivity and figure errors on a prototype mirror element confirm the technical feasibility of the approach. Then, we discuss design strategies for an imaging objective to fully exploit the condenser specifications while achieving spatial resolutions comparable to those of X-ray micro-CT instruments. Analytically, we show that for monochromatic beams suitable solutions exist employing arrays of hundreds of identical objectives, realized either as compound refractive lenses (CRLs) or Fresnel zone plates (FZPs). To mitigate the inherent chromatic aberration of these optics, each individual objective could be replaced by an achromatic FZP/CRL combination. Key optical properties of the resulting microscope are estimated. This novel full-field microscopy concept for highly divergent, polychromatic neutron beams has the potential to improve temporal and spatial resolution for large samples and sample environments and to enable the simultaneous acquisition of hundreds of projections in neutron tomography.

We present matrix-product state (MPS) based band Lanczos method as solver for quantum cluster methods such as the variational cluster approximation. While a naïve implementation of MPS as cluster solver would barely improve its range of applicability, we show that our approach makes it possible to treat cluster geometries well beyond the reach of exact diagonalization methods. The key modifications we introduce are a continuous energy truncation combined with a convergence criterion that is more robust against approximation errors introduced by the MPS representation and provides a bound to deviations in the resulting Green's function. The potential of the resulting cluster solver is demonstrated by computing the self-energy functional for the single-band Hubbard model at half filling in the strongly correlated regime, on different cluster geometries. Here, we find that only when treating large cluster sizes, observables can be extrapolated to the thermodynamic limit, which we demonstrate at the example of the staggered magnetization. Treating clusters sizes with up to $6\times 6$ sites we obtain significant improvement over the extrapolation accessible with exact diagonalization solvers when comparing to quantum Monte Carlo results. Finally, we illustrate the applicability of the MPS cluster solver to more complex models by calculating spectral properties as relevant for the electron-doped cuprate CaCuO$_2$.

It is well known that a nontrivial Chern number results in quantized Hall conductance. What is less known is that, generically, the Hall response can be dramatically different from its quantized value in materials with broken inversion symmetry. This stems from the leading Hall contribution beyond the linear order, known as the Berry curvature dipole (BCD). While the BCD is in principle always present, it is typically very small outside of a narrow window close to a topological transition and is thus experimentally elusive without careful tuning of external fields, temperature, or impurities. In this work, we transcend this challenge by devising optical driving and quench protocols that enable practical and direct access to large BCD and nonlinear Hall responses. Varying the amplitude of an incident circularly polarized laser drives a topological transition between normal and Chern insulator phases, and importantly allows the precise unlocking of nonlinear Hall currents comparable to or larger than the linear Hall contributions. This strong BCD engineering is even more versatile with our two-parameter quench protocol, as demonstrated in our experimental proposal. Our predictions are expected to hold qualitatively across a broad range of Hall materials, thereby paving the way for the controlled engineering of nonlinear electronic properties in diverse media.

Estimating the eigenstate properties of quantum systems is a long-standing, challenging problem for both classical and quantum computing. Existing universal quantum algorithms typically rely on ideal and efficient query models (e.g. time evolution operator or block encoding of the Hamiltonian), which, however, become suboptimal for actual implementation at the quantum circuit level. Here, we present a full-stack design of quantum algorithms for estimating the eigenenergy and eigenstate properties, which can achieve high precision and good scaling with system size. The gate complexity per circuit for estimating generic Hamiltonians' eigenstate properties is $\tilde{O} (\log \varepsilon^{-1})$, which has a logarithmic dependence on the inverse precision $\varepsilon$. For lattice Hamiltonians, the circuit depth of our design achieves near-optimal system-size scaling, even with local qubit connectivity. Our full-stack algorithm has low overhead in circuit compilation, which thus results in a small actual gate count (CNOT and non-Clifford gates) for lattice and molecular problems compared to advanced eigenstate algorithms. The algorithm is implemented on IBM quantum devices using up to 2,000 two-qubit gates and 20,000 single-qubit gates, and achieves high-precision eigenenergy estimation for Heisenberg-type Hamiltonians, demonstrating its noise robustness.

It has recently been theoretically predicted and experimentally observed that a soliton resulting from nonlinearity can be pumped across an integer or fractional number of unit cells as a system parameter is slowly varied over a pump period. Nonlinear Thouless pumping is now understood as the flow of instantaneous Wannier functions, ruling out the possibility of pumping a soliton across a nonzero number of unit cells over one cycle when a corresponding Wannier function does not exhibit any flow, i.e., when the corresponding Bloch band that the soliton bifurcates from is topologically trivial. Here we surprisingly find an anomalous nonlinear Thouless pump where the displacement of a soliton over one cycle differs from the Chern number of the Bloch band from which the soliton comes. We develop a general theory showing that this anomalous behavior arises from a transition of a soliton between different Wannier functions by passing through an intersite-soliton (or dipole-soliton) state. Furthermore, we find a nonlinearity-induced integer quantized Thouless pump of a soliton, allowing a soliton to travel across one unit cell during a pump period, even when the corresponding band is topologically trivial. Our results open the door to studying nonlinearity-induced Thouless pumping of solitons.

We establish three impossibility results regarding our knowledge of the quantum state of the universe. Suppose the universal quantum state is a typical unit vector in a high-dimensional subspace $\mathscr{H}_0$ of Hilbert space $\mathscr{H}$, such as the low-entropy subspace defined by the Past Hypothesis. We show that: (1) Any particular observation is incapable of identifying the universal state vector in $\mathscr{H}_0$ or substantially reducing the set of possibilities. In other words, the overwhelming majority of possible state vectors are observationally indistinguishable from each other. (2) For any reasonably probable measurement outcome and for most pairs of vectors in $\mathscr{H}_0$, that outcome will not appreciably favor one vector over the other. (3) Bayesian updating on any measurement result, unless it is extraordinarily improbable, has a negligible effect on the initial uniform probability distribution over the states in $\mathscr{H}_0$. These findings represent the most stringent epistemic constraints known for a quantum universe and are derived from a typicality theorem in quantum statistical mechanics. We close by considering how theoretical considerations beyond empirical evidence might inform our understanding of this fact and our knowledge of the universal quantum state.

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

We introduce a framework for molecular structure optimization using denoising model on a physics-informed Riemannian manifold (R-DM). Unlike conventional approaches operating in Euclidean space, our method leverages a Riemannian metric that better aligns with molecular energy change, enabling more robust modeling of potential energy surfaces. By incorporating internal coordinates reflective of energetic properties, R-DM achieves chemical accuracy with an energy error below 1 kcal/mol. Comparative evaluations on QM9, QM7-X, and GEOM datasets demonstrate improvements in both structural and energetic accuracy, surpassing conventional Euclidean-based denoising models. This approach highlights the potential of physics-informed coordinates for tackling complex molecular optimization problems, with implications for tasks in computational chemistry and materials science.

Quantum Monte Carlo is one of the most promising approaches for dealing with large-scale quantum many-body systems. It has played an extremely important role in understanding strongly correlated physics. However, two fundamental problems, namely the sign problem and general measurement issues, have seriously hampered its scope of application. We propose a universal scheme to tackle the problems of general measurement. The target observables are expressed as the ratio of two types of partition functions $\langle \mathrm{O} \rangle=\bar{Z}/Z$, where $\bar{Z}=\mathrm{tr} (\mathrm{Oe^{-\beta H}})$ and $Z=\mathrm{tr} (\mathrm{e^{-\beta H}})$. These two partition functions can be estimated separately within the reweight-annealing frame, and then be connected by an easily solvable reference point. We have successfully applied this scheme to XXZ model and transverse field Ising model, from 1D to 2D systems, from two-body to multi-body correlations and even non-local disorder operators, and from equal-time to imaginary-time correlations. The reweighting path is not limited to physical parameters, but also works for space and time. Essentially, this scheme solves the long-standing problem of calculating the overlap between different distribution functions in mathematical statistics, which can be widely used in statistical problems, such as quantum many-body computation, big data and machine learning.

Recurrent neural networks (RNNs) have recently been extensively applied to model the time-evolution in fluid dynamics, weather predictions, and even chaotic systems thanks to their ability to capture temporal dependencies and sequential patterns in data. Here we present a RNN model based on convolution neural networks for modeling the nonlinear non-adiabatic dynamics of hybrid quantum-classical systems. The dynamical evolution of the hybrid systems is governed by equations of motion for classical degrees of freedom and von Neumann equation for electrons. The physics-aware recurrent convolution (PARC) neural network structure incorporates a differentiator-integrator architecture that inductively models the spatiotemporal dynamics of generic physical systems. We apply our RNN approach to learn the space-time evolution of a one-dimensional semi-classical Holstein model after an interaction quench. For shallow quenches (small changes in electron-lattice coupling), the deterministic dynamics can be accurately captured using a single-CNN-based recurrent network. In contrast, deep quenches induce chaotic evolution, making long-term trajectory prediction significantly more challenging. Nonetheless, we demonstrate that the PARC-CNN architecture can effectively learn the statistical climate of the Holstein model under deep-quench conditions.

We propose a data-driven Machine-Learning Symmetry Discovery (MLSD) framework for identifying continuous symmetry generators and their Lie-algebraic structure directly from phase-space trajectory data expressed in canonical coordinates. MLSD parameterizes candidate conserved quantities with neural networks and learns antisymmetric structure coefficients by enforcing Poisson-bracket closure, supplemented by a weak independence regularizer. We validate MLSD on two integrable benchmark systems -- the three-dimensional Kepler problem and the three-dimensional isotropic harmonic oscillator -- recovering the expected non-Abelian algebras (respectively $\mathfrak{so}(4)$ and $\mathfrak{su}(3)$) up to basis transformations. This work focuses on integrable benchmark dynamics, where global conserved quantities are well-defined and admit compact representations learnable from canonical-coordinate trajectories. Extending symmetry discovery to mixed or chaotic phase-space regimes is an important direction for future work.

We present a tensorization algorithm for constructing tensor train/matrix product state (MPS) representations of functions, drawing on sketching and cross interpolation ideas. The method only requires black-box access to the target function and a small set of sample points defining the domain of interest. Thus, it is particularly well-suited for machine learning models, where the domain of interest is naturally defined by the training dataset. We show that this approach can be used to enhance the privacy and interpretability of neural network models. Specifically, we apply our decomposition to (i) obfuscate neural networks whose parameters encode patterns tied to the training data distribution, and (ii) estimate topological phases of matter that are easily accessible from the MPS representation. Additionally, we show that this tensorization can serve as an efficient initialization method for optimizing MPS in general settings, and that, for model compression, our algorithm achieves a superior trade-off between memory and time complexity compared to conventional tensorization methods of neural networks.

We challenge the standard picture of decohering Schrödinger cat states as an ensemble average obeying a Lindblad master equation, brought about locally from an irreversible interaction with an environment. We generate self-consistent collections of pure system states correlated with specific environmental records, corresponding to the function of the wave-particle correlator first introduced in Carmichael et al. [Phys. Rev. Lett. 85, 1855 (2000)]. In the spirit of Carmichael et al. [Coherent States: Past, Present and Future, pp. 75-91, World Scientific (1994)], we find that the complementary unravelings evince a pronounced disparity when the ``position'' and ``momentum'' of the damped cavity mode - an explicitly open quantum system - are measured. Intensity-field correlations may largely deviate from a monotonic decay, while Wigner functions of the cavity state display contrasting manifestations of quantum interference when conditioned on photon counts sampling a continuous photocurrent. In turn, the conditional photodetection events mark the contextual diffusion of both the net charge generated at the homodyne detector, and the electromagnetic field amplitude in the resonator.

Exceptional points (EPs), arising in non-Hermitian systems, have garnered significant attention in recent years, enabling advancements in sensing, wave manipulation, and mode selectivity. However, their role in quantum systems, particularly in influencing quantum correlations, remains underexplored. In this work, we investigate how EPs control multimode entanglement in bosonic chains. Using a Bogoliubov-de Gennes (BdG) framework to describe the Heisenberg equations, we identify EPs of varying orders and uncover spectral transitions between purely real, purely imaginary, and mixed eigenvalue spectra. These spectral regions, divided by EPs, correspond to three distinct entanglement dynamics: oscillatory, exponential, and hybrid. Remarkably, we demonstrate that higher-order EPs, realized by non-integer-pi hopping phases or nonuniform interaction strengths, significantly enhance the degree of multimode entanglement compared to second-order EPs. Our findings provide a pathway to leveraging EPs for entanglement control and exhibit the potential of non-Hermitian physics in advancing quantum technologies.

In this work, we present a mathematical and computational framework to model the dynamics of open lipid bilayer membranes interacting with ambient Stokes flow. The model explicitly couples the three-dimensional viscous fluid, the two-dimensional membrane surface, and its one-dimensional free edge. We develop an axisymmetric hybrid BEM-FEM method that solves the problem with an effective one-dimensional formulation. A key component is a local mesh refinement strategy designed to accurately resolve singularities and boundary layers originating at the membrane edge. Several numerical examples are provided to showcase its ability to capture intricate edge dynamics and multiscale fluid-membrane coupling.

If axion simultaneously has the scalar couplings to the nucleons and pseudo-scalar couplings to the electrons, it may mediate a $\mathcal{P}$, $\mathcal{T}$-violating interaction between the electronic shell and nuclei in the molecules. The polyatomic molecule RaOH, which is considered as a promising platform for the $\mathcal{P}$, $\mathcal{T}$ violation searches, is studied for its sensitivity to such interactions. Due to the long-range nature (on molecular scales) of the axion-mediated interaction, it is important whether the enhancement parameter would be sensitive to the vibration of the molecule. Our results imply that the impact of the vibrations on the axion-mediated electron-nucleon interaction in the molecule is similar to the impact on the short-range electron-nucleon scalar-pseudoscalar interaction studied earlier.

Long-term, high-fidelity simulation of slow-changing physical systems, such as the ocean and climate, presents a fundamental challenge in scientific computing. Traditional autoregressive machine learning models often fail in these tasks as minor errors accumulate and lead to rapid forecast degradation. To address this problem, we propose NeuralOM, a general neural operator framework designed for simulating complex, slow-changing dynamics. NeuralOM's core consists of two key innovations: (1) a Progressive Residual Correction Framework that decomposes the forecasting task into a series of fine-grained refinement steps, effectively suppressing long-term error accumulation; and (2) a Physics-Guided Graph Network whose built-in adaptive messaging mechanism explicitly models multi-scale physical interactions, such as gradient-driven flows and multiplicative couplings, thereby enhancing physical consistency while maintaining computational efficiency. We validate NeuralOM on the challenging task of global Subseasonal-to-Seasonal (S2S) ocean simulation. Extensive experiments demonstrate that NeuralOM not only surpasses state-of-the-art models in forecast accuracy and long-term stability, but also excels in simulating extreme events. For instance, at a 60-day lead time, NeuralOM achieves a 13.3% lower RMSE compared to the best-performing baseline, offering a stable, efficient, and physically-aware paradigm for data-driven scientific computing. Code link: this https URL.

Chiral structures that produce asymmetric spin-phonon coupling can theoretically generate spin-phonon polarons -- quasiparticles exhibiting non-degenerate spin states with phonon displacements. These quasiparticles are speculated to be the origin of chirality-induced spin selectivity and presumably can display exotic dynamic behaviors. However, direct experimental evidence of spin-phonon polarons has been lacking. Using a chiral molecular qubit framework embedding stable semiquinone-like radicals, we report spin dynamic signatures that indicate the formation of spin-phonon polarons for the first time. Our non-adiabatic model reveals that these quasiparticles introduce an active spin relaxation channel when polaron reorganization energy approaches Zeeman splitting. This new channel manifests itself as anomalous, temperature-independent spin relaxation, which can be suppressed by high magnetic fields or pore-filling solvents (e.g. CH2Cl2, CS2). Such field- and guest-tunable relaxation is unattainable in conventional spin systems. Harnessing this mechanism could boost repetition rates in spin-based quantum information technologies without compromising coherence or quantum sensing performance.

Laguerre-Gaussian (LG) wave packets, known for their vortex structure and nonzero orbital angular momentum (OAM), are of great interest in various scientific fields. Here we study the nonrelativistic dynamics of a spatially-localized electron LG wave packet interacting with an inhomogeneous external electric field that violates the axial symmetry of the initial wave function. We focus on the analysis of the electron density and demonstrate how it is affected by the external field. Within the first order of perturbation theory, we calculate the electron wave function and reveal that the electric field may significantly alter the wave packet's structure and distort its qualitative form. We demonstrate that due to the interaction with the external field, the degenerate zeros of the initial wave function located on the $z$ axis split into multiple nondegenerate nodes in the transverse plane representing separate single-charge vortices. This mechanism resembles the analogous effects known in topological optics. These findings provide new insights into controlling and manipulating twisted matter beams and into their possible instabilities.

Machine learning interatomic potentials (MLIPs) are inherently limited by the accuracy of the training data, usually consisting of energies and forces obtained from quantum mechanical calculations, such as density functional theory (DFT). Since DFT itself is based on several approximations, MLIPs may inherit systematic errors that lead to discrepancies with experimental data. In this paper, we use a trajectory re-weighting technique to refine DFT pre-trained MLIPs to match the target experimental Extended X-ray Absorption Fine Structure (EXAFS) spectra. EXAFS spectra are sensitive to the local structural environment around an absorbing atom. Thus, refining an MLIP to improve agreement with experimental EXAFS spectra also improves the MLIP prediction of other structural properties that are not directly involved in the refinement process. We combine this re-weighting technique with transfer learning and a minimal number of training epochs to avoid overfitting to the limited experimental data. The refinement approach demonstrates significant improvement for two MLIPs reported in previous work, one for an established nuclear fuel: uranium dioxide (UO2) and second one for a nuclear fuel candidate: uranium mononitride (UN). We validate the effectiveness of our approach by comparing the results obtained from the original (unrefined) DFT-based MLIP and the EXAFS-refined MLIP across various properties, such as lattice parameters, bulk modulus, heat capacity, point defect energies, elastic constants, phonon dispersion spectra, and diffusion coefficients. An accurate MLIP for nuclear fuels is extremely beneficial as it enables reliable atomistic simulation, which greatly reduces the need for large number of expensive and inherently dangerous experimental nuclear integral tests, traditionally required for the qualification of efficient and resilient fuel candidates.

This review exhaustively evaluates the role of nanomaterials across the synthesis, characterization and application stages of biofuel systems. Common types of nanomaterials that are used for biofuel applications include metal oxides, carbon-based structures, and hybrids, which are evaluated for their effectiveness in efficient biofuel production. The properties of such nanomaterials are being utilized as an aid to produce biofuels through improved catalysis, enzyme immobilization and thermal stability. Common synthesis methods, such as sol-gel, coprecipitation, and green synthesis, are compared, alongside characterization tools, such as TEM, SEM, FTIR, and BET. This study focuses on transesterification, biomass pretreatment, and fermentation processes, where nanomaterials significantly improve yield and reusability. There are several challenges, despite the merits of using nanomaterials, and the trade-offs include cost, scalability, and environmental impact, which further expand into evaluating the life cycle of such materials. This review outlines the practical potential of nanomaterials in enabling efficient and sustainable biofuel production.

We present a decomposition of classical potentials into a conservative (gradient) component and a non-conservative component. The latter generalizes the curl component of the force in the three-dimensional case. The force is transformed into a differential $1$-form, known as the work form. This work form is decomposed into an exact (gradient) component and an antiexact component, which in turn generalizes the curl part of the force. The antiexact component is subsequently decomposed using the Frobenius theorem. This local decomposition is a useful tool for identifying the specific components of classical potentials.

A shell model can be considered as a chain of triads, where each triad can be interpreted as a nonlinear oscillator that can be mapped to a spinning top. Investigating the relation between phase dynamics and intermittency in a such a chain of nonlinear oscillators, it is found that synchronization is linked to increased energy transfer. In particular, the results provide evidence that the observed systematic increase of intermittency, as the shell spacing is decreased, is associated with strong phase alignment among consecutive triadic phases, facilitating the energy cascade. It is shown that while the overall level of synchronization can be quantified using a Kuramoto order parameter for the global phase coherence in the inertial range, a local, weighted Kuramoto parameter can be used for the detection of burst-like events propagating across shells in the inertial range. This novel analysis reveals how partially phase-locked states are associated with the passage of extreme events of energy flux. Applying this method to helical shell models, reveals that for a particular class of helical interactions, a reduction in phase coherence correlates with suppression of intermittency. When inverse cascade scenarios are considered using two different shell models including a non local helical shell model, and a local standard shell model with a modified conservation law, it was shown that a particular phase organization is needed in order to sustain the inverse energy cascade. It was also observed that the PDFs of the triadic phases were peaked in accordance with the basic considerations of the form of the flux, which suggests that a triadic phase of \pi/2 and -\pi/2 maximizes the forward and the inverse energy cascades respectively.

Sapphire has mechanical and electrical properties that are advantageous for the construction of internal components of radiation detectors such as time projection chambers and bolometers. However, it has proved difficult to assess its $\rm ^{232}Th$ and $\rm ^{238}U$ content down to the picogram per gram level. This work reports an experimental verification of a computational study that demonstrates $\gamma\gamma$ coincidence counting, coupled with neutron activation analysis (NAA), can reach ppt sensitivities. Combining results from $\gamma\gamma$ coincidence counting with those of earlier single-$\gamma$ counting based NAA shows that a sample of Saint Gobain sapphire has $\rm ^{232}Th$ and $\rm ^{238}U$ concentrations of $<0.26$ ppt and $<2.3$ ppt, respectively; the best constraints on the radiopurity of sapphire.

Quantum devices comprised of elementary components with more than two stable levels - so-called qudits - enrich the accessible Hilbert space, enabling applications ranging from fault-tolerant quantum computing to simulating complex many-body models. While several quantum platforms are built from local elements that are equipped with a rich spectrum of stable energy levels, schemes for the efficient control and entanglement of qudits are scarce. Importantly, no experimental demonstration of multi-qudit control has been achieved to date in neutral atom arrays. Here, we propose a general scheme for controlling and entangling qudits and perform a full analysis for the case of qutrits, encoded in ground and metastable states of alkaline earth atoms. We find an efficient implementation of single-qudit gates via the simultaneous driving of multiple transition frequencies. For entangling operations, we provide a concrete and intuitive recipe for the controlled-Z (CZ) gate for any local dimension d, realized through alternating single qudit and entangling pulses that simultaneously drive up to two Rydberg transitions. We further prove that two simultaneous Rydberg tones are, in general, the minimum necessary for implementing the CZ gate with a global drive. The pulses we use are optimally-controlled, smooth, and robust to realistic experimental imperfections, as we demonstrate using extensive noise simulations. This amounts to a minimal, resource-efficient, and practical protocol for realizing a universal set of gates. Our scheme for the native control of qudits in a neutral atom array provides a high-fidelity route toward qudit-based quantum computation, ready for implementation on near-term devices.

We investigate the formation of moire quasicrystal patterns in Bose gasses confined in twisted bilayer optical lattices via Floquet-engineered intralayer atomic interactions. Dynamical evolutions of the total density wave amplitude exhibit the stage for the emergence of moire quasicrystal patterns, where the pattern formation is closely associated with the momenta of collective modes excited by the weak periodic drive. Through analyzing the radial and angular density wave amplitude, we find that these new collective modes are only coupled radially and cannot be decoupled eventually. The symmetry of quasicrystal patterns can be easily manipulated by the modulation frequencies and amplitudes. Reducing the frequencies and increasing the amplitudes can both facilitate lattice symmetry breaking and the subsequent emergence of rotational symmetry. Notably, a twelve-fold quasicrystal pattern emerges under specific parameters, closely resembling the moire quasicrystal in twisted bilayer graphene. The momentum-space distributions also exhibit high rotational symmetry, which is consistent with the real-space patterns at specific evolution times. Our findings establish a new quantum platform for exploring quasicrystals and their symmetry properties in ultracold bosonic systems.

Coherent two-state oscillations are observed in numerical simulations of the one-dimensional transverse-longitudinal-field Ising model (TLFIM) within false vacuum decay regimes. Starting from the false vacuum (a nearly fully polarized ferromagnetic state), we show that in moderate-sized systems, at resonances $h\approx 2J/n$ (with longitudinal field $h$, transverse field $J$, and an integer $n$), the expected decay can give way to coherent oscillations between the false vacuum and a symmetric resonant state. The oscillation frequency, i.e., the tunneling splitting, is observed notably to exhibit a superradiant-like $\sqrt{L}$ enhancement, as confirmed by a Schrieffer-Wolff analysis. In large chains, coherence remains for $n\gtrsim L/2$ due to bubble-size blockade and is robust against stronger transverse fields; for small $n$, long-range interactions can stabilize the oscillations by lifting multi-bubble degeneracies, establishing a robust many-body coherence mechanism beyond perturbative and finite-size limits.

We use the transfer tensor method to analyze localization and transport in simple disordered systems, specifically the Anderson and Aubry-André-Harper models. Emphasis is placed on the memory effects that emerge when ensemble-averaging over disorder, even when individual trajectories are strictly Markovian. We find that transfer tensor memory effects arise to remove fictitious terms that would correspond to redrawing static disorder at each time step, which would create a temporally uncorrelated dynamic disorder. Our results show that while eternal memory is a necessary condition for localization, it is not sufficient. We determine that signatures of localization and transport can be found within the transfer tensors themselves by defining a metric called "outgoing-pseudoflux". This work establishes connections between theoretical research on dynamical maps and Markovianity and localization phenomena in physically realizable model systems.

We review inadequacy of existing nematodynamic theories and suggest a novel way of establishing relations between nematic orientation and flow based on the \emph{local} symmetry between simultaneous rotation of nematic alignment and flow, which establishes energy exchange between the the two without reducing the problem to near-equilibrium conditions and invoking Onsager's relations. This approach, applied in the framework of the vector-based theory with a variable modulus, involves antisymmetric interactions between nematic alignment and flow and avoids spurious instabilities in the absence of an active inputs.

Twin-wire laser directed energy deposition (TW-LDED) provides a promising route for alloying and fabrication of compositionally graded structures. However, inherent multiparameter coupling in twin-wire systems critically exacerbates both process instabilities and compositional inhomogeneity. This unresolved issue escalates into a fundamental technological bottleneck, as the underlying physical mechanisms remain poorly understood. This study developed a high-fidelity multi-physics and multiphase simulation framework coupled with experimental validation to reveal thermal-fluid behavior and heat-mass transfer mechanisms in TW-LDED using Inconel 718 and SS316L fine wires. Three distinct transition modes were identified: twin-wire melt droplet, twin-wire liquid bridge, and droplet-bridge mixed transitions, with the twin-wire liquid bridge regime delivering optimal stability and uniform mixing. Parametric analysis demonstrates that increasing wire feeding speed or decreasing wire initial height promotes stable liquid bridge formation, while small laser spots at low feeding speeds induce excessive volumetric energy density and bridge instability. Simulation and single-track experiments confirm that liquid bridge transitions reduce dimensional fluctuations by 85% while enhancing compositional homogeneity. Conversely, the melt droplet-bridge transition mode creates periodic flow switching and compositional discontinuities along the scan direction. Finally, a 60 mm functionally graded ring was successfully fabricated using optimized parameters, achieving uniform elemental distribution in the transition zone without significant segregation, validating the feasibility of TW-LDED for functionally graded components.

We introduce the Constraint-Enhanced Quantum Approximate Optimization Algorithm (CE-QAOA), a shallow, constraint-aware ansatz that operates inside the one-hot product space [n]^m, where m is the number of blocks and each block is initialized in an n-qubit W_n state. We give an ancilla-free, depth-optimal encoder that prepares W_n using n-1 two-qubit rotations per block, and a two-local block-XY mixer that preserves the one-hot manifold and has a constant spectral gap on the one-excitation sector. At the level of expressivity, we establish per-block controllability, implying approximate universality per block. At the level of distributional behavior, we show that, after natural block and symbol permutation twirls, shallow CE-QAOA realizes an encoded unitary 1-design and supports approximate second-moment (2-design) behavior; combined with a Paley-Zygmund argument, this yields finite-shot anticoncentration guarantees. Algorithmically, we wrap constant-depth sampling with a deterministic feasibility checker to obtain a polynomial-time hybrid quantum-classical solver (PHQC) that returns the best observed feasible solution in O(S n^2) time, where S is a polynomial shot budget. We obtain two advantages. First, when CE-QAOA fixes r >= 1 locations different from the start city, we achieve a Theta(n^r) reduction in shot complexity even against a classical sampler that draws uniformly from the feasible set. Second, against a classical baseline restricted to raw bitstring sampling, we show an exp(Theta(n^2)) minimax separation. In noiseless circuit simulations of traveling salesman problem instances with n in {4,...,10} locations from the QOPTLib benchmark library, we recover the global optimum at depth p = 1 using polynomial shot budgets and coarse parameter grids defined by the problem size.

Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of real-world networks for various applications. Graph Generative Models (GGMs) have emerged as a promising solution to this problem, leveraging deep learning techniques to learn the underlying distribution of real-world graphs and generate new samples that closely resemble them. Examples include approaches based on Variational Auto-Encoders, Recurrent Neural Networks, and more recently, diffusion-based models. However, the main limitation often lies in the evaluation process, which typically relies on Maximum Mean Discrepancy (MMD) as a metric to assess the distribution of graph properties in the generated ensemble. This paper introduces a novel methodology for evaluating GGMs that overcomes the limitations of MMD, which we call RGM (Representation-aware Graph-generation Model evaluation). As a practical demonstration of our methodology, we present a comprehensive evaluation of two state-of-the-art Graph Generative Models: Graph Recurrent Attention Networks (GRAN) and Efficient and Degree-guided graph GEnerative model (EDGE). We investigate their performance in generating realistic graphs and compare them using a Geometric Deep Learning model trained on a custom dataset of synthetic and real-world graphs, specifically designed for graph classification tasks. Our findings reveal that while both models can generate graphs with certain topological properties, they exhibit significant limitations in preserving the structural characteristics that distinguish different graph domains. We also highlight the inadequacy of Maximum Mean Discrepancy as an evaluation metric for GGMs and suggest alternative approaches for future research.