Search Results (90,971)

A highly sensitive surface plasmon resonance (SPR) sensor is theoretically presented, including Silver (Ag), Tin Diselenide (SnSe₂), Zirconium Diselenide (ZrSe₂) and a sensing layer using the Kretschmann configuration. At the optimized thickness of the Ag layer, the sensitivity was measured using the angular interrogation method with a refractive index (RI) of 1.33–1.35. The sensitivity of the sensor was found to be 337.98°/RIU for a 2 nm SnSe₂ layer thickness at RI of 1.34 and 320.94°/RIU for a 1 nm SnSe₂ layer thickness at RI of 1.35 throughout with remarkable figure of merit (FoM) of 60.78/RIU and 64.57/RIU at 633 nm wavelength. The maximum sensitivity was achieved with 1 nm thickness of the SnSe₂ layer. By systematically optimizing the Ag thickness, significant improvements in sensitivity, minimum reflectance (R_min), detection accuracy (DA), and figure of merit (FoM) were achieved compared with the conventional Ag-only configuration. These additional SnSe₂ layers increase the confinement of the electromagnetic field, increase the number of adsorption sites for biomolecules, and increase the effective change in the RI, resulting in larger shifts in the resonance angles. The proposed multilayer sensor provides a promising platform for high-performance, stable, and repeatable biosensing applications in chemical detection, environmental monitoring, and medical diagnostics, according to the results obtained. Full article

Background: price dynamics in financial markets exhibit scale-invariant volatility, quantized liquidity and collective behaviour that resist single-paradigm models; Multiscale Relativistic Quantum Finance (MRQF) reconciles these facets on an energy–entropy $(E,S)$ plane, but its translation into a deployable decision system has remained open. Methods: we propose MRQF-MAS, a cooperative multi-agent system (MAS) in which institutional, commercial and retail operators become first-class agents, each decomposed into signal, energy, entropy, risk and execution sub-agents that share beliefs through a horizontal cooperation layer and a shared knowledge base (SKB) of $(E,S)$ trajectories. The framework is benchmarked as a high-volatility regime classifier on 6978 daily EUR/USD reference rates published by the European Central Bank (ECB) over 1999–2026 against four baselines including Generalized Autoregressive Conditional Heteroscedasticity (GARCH)(1,1). Results: on the full official ECB EUR/USD series, MRQF-MAS attains 83.0% accuracy, precision 0.552 and Matthews correlation coefficient (MCC) 0.479 with 95% bootstrap CI [0.46, 0.51] and a one-day median detection latency, improving slightly on a rolling-volatility baseline while remaining below a GARCH(1,1) reference. Conclusions: MRQF-MAS delivers a structurally interpretable, agent-traceable regime decomposition complementary to scalar volatility estimators. Full article

A primary challenge in successfully manufacturing reactive materials for laser metal deposition (LMD) is to prevent oxidization. To address the oxidation susceptibility of titanium alloys in open environments, a local atmosphere protection model was developed. Using Ansys Fluent 2023R1 software, the effective protective atmosphere range provided by the local shielding device under varying shielding gas flow rates was investigated in detail, and a mathematical model for the effective protection area was obtained through quadratic polynomial fitting. A quadratic regression model linking process parameters with the length of high-temperature zones was established using central composite design (CCD). By integrating these two models, a local shielding gas model for LMD titanium alloys in an open environment was formulated. Validation experiments demonstrated excellent morphology of the single layers with shiny silver. The local shielding atmosphere can effectively protect the high-temperature region. These findings provide a basis for the deposition of active materials in an open environment and the selection of appropriate shielding gas flow rates. Full article

Traffic signal control increasingly faces disturbed operating conditions such as incidents, abrupt demand surges, sensing degradation, and abnormal driving patterns. Under these nonstationary regimes, classical fixed-time and actuated strategies may exhibit slow recovery, while purely data-driven controllers can be brittle when disturbance characteristics shift. This paper proposes an LLM-in-the-loop architecture for disturbed traffic signal control that integrates (i) deep learning for disturbance detection and short-horizon traffic forecasting, (ii) a disturbance-aware candidate generation and scoring layer (template/retrieval-based), and (iii) a constrained large language model (LLM) that selects or minimally repairs signal plans only within constraint-screened action templates. A deterministic validator enforces safety and operational constraints, including minimum/maximum greens, cycle feasibility, and clearance rules, by checking action feasibility before execution. The method is formulated as constrained decision making under uncertainty, where disturbance estimates and predictive confidence shape both retrieval/scoring and LLM supervision. The originally reported SUMO evaluation considered multiple disturbance categories, including capacity drops, demand shocks, and sensing dropouts as well as reported network delay, queue spillback, recovery time, and switching stability. Within the originally reported SUMO scenarios, descriptive results suggest that among the selected baselines, the proposed DL + LLM framework reported lower mean values of delay, spillback frequency, and recovery time than the fixed-time, actuated, and retrieval-only baselines. The reported validator-detected action-feasibility violations were zero; this result concerns timing-action feasibility rather than an absence of traffic-state risks such as spillback. Full article

Coronary in-stent restenosis (ISR) remains a major contributor to repeat revascularisation despite advances in drug-eluting stent (DES) technology. Its persistence reflects a complex and heterogeneous interplay among mechanical, biological, and procedural factors, and understanding the dominant mechanism in each case is fundamental to effective treatment selection. This narrative review provides a contemporary, mechanism-guided approach to the practical management of coronary ISR. We summarise the definition, incidence, and classification of ISR—including the Mehran, Waksman, and SCAI 2023 time-based frameworks—and outline patient-related, procedural, anatomical, and stent-related risk factors. The pathophysiology of neointimal hyperplasia and neoatherosclerosis is discussed with reference to its clinical implications. Intracoronary imaging with intravascular ultrasound (IVUS) or optical coherence tomography (OCT) is central to ISR characterisation and treatment planning. Current international guidelines support imaging use in ISR management, though it is important to recognise that this recommendation is based largely on observational and surrogate-endpoint data rather than ISR-specific randomised trials demonstrating reductions in hard clinical outcomes, and practical barriers including cost, availability, and operator expertise must be acknowledged. Evidence-based treatment strategies—including drug-coated balloons (DCB), repeat DES implantation, lesion-modifying therapies, vascular brachytherapy, and coronary artery bypass grafting—are reviewed critically with reference to contemporary trial data and their specific clinical applicability. The choice between DCB and repeat DES is addressed with greater nuance, accounting for ISR type (BMS-ISR versus DES-ISR), lesion pattern, stent layering, and bleeding risk. Management considerations in complex subsets—chronic total occlusion ISR, left main ISR, saphenous vein graft ISR, and recurrent ISR—are also addressed. We propose a practical, substrate-driven management framework aligned with the 2024 ESC, 2021 ACC/AHA/SCAI, and 2018 JCS/JSCVS guidelines. Future research priorities include ISR-specific randomised trials with hard clinical endpoints, prospective validation of imaging-guided treatment algorithms, head-to-head comparisons of DCB platforms, and investigation of pharmacological strategies targeting neoatherosclerosis progression. Full article

Wildfires in plateau and mountainous regions are increasingly destructive, often disrupting ground transportation networks and severely constraining emergency logistics, while unmanned aerial vehicles (UAVs) remain limited by payload capacity. To address this challenge, this study proposes an air–ground collaborative emergency material dispatch method for dynamic, time-varying wildfire environments. A multi-layer spatiotemporal network model is developed by incorporating key uncertainties, including fire spread and meteorological fluctuations, into dynamic parameters, and a multi-objective mixed-integer programming framework is established to jointly optimize emergency response time, total dispatch cost, and rescue fairness. To solve the resulting high-dimensional dynamic rescheduling problem, a Fast Ant Colony Optimization-Genetic Algorithm (FACO-GA) integrated with a rolling horizon mechanism is designed. Simulation results under Level 1–10 dynamic perturbations show that, compared with conventional standalone algorithms (GA and ACO), the proposed method demonstrates markedly better robustness and computational efficiency, reducing the extreme average rescheduling response time to 6.80 s, while maintaining a Hypervolume (Hv) retention rate of 96.30% and limiting the Spacing (Sp) change rate to 4.15%. These findings indicate that the proposed approach can effectively overcome computational bottlenecks and provide an adaptive decision-support framework for emergency logistics dispatch in complex wildfire scenarios. Furthermore, comprehensive ablation studies and sensitivity analyses validate the structural necessity of the rolling horizon and ACO modules, ensuring the algorithm’s parameter robustness under extreme stochastic perturbations. Full article

The present study investigates the presence, sources, and impacts of microplastics (MPs) in different soil types, including agricultural, urban, and forest areas, through a synthesis of results of published scientific papers. MPs originate from a variety of human activities, such as the widespread use of plastic mulch in agriculture and the application of organic fertilizers and treated sewage sludge, as well as from vehicle tire wear, industrial processes, and the gradual degradation of plastic products in the environment. In urban soils, the main sources of MPs are related to road traffic, industrial activity, and landfills, while in forest soils, concentrations are generally lower. However, MPs in forest areas are thought to be carried there by the air, by runoff, or from nearby areas with human activity. Available data show that larger MP particles tend to remain in the surface layers of the soil, while smaller particles can penetrate deeper soil layers, increasing their bioavailability and the likelihood of interaction with microorganisms and plant root systems. In terms of their chemical composition, polyethylene (PE) and polypropylene (PP) polymers dominate in agricultural soils, which is directly linked to agricultural practices, while polystyrene (PS) and polyvinyl chloride (PVC) are more frequently detected in urban soils. The morphological types of MPs include fragments, fibers, and films, while their color characteristics provide clues to possible sources of origin, such as plastic ground covers, tire wear, and packaging materials. Overall, the study’s results underscore the growing environmental significance of MP soil pollution and highlight the need for more effective management and recycling of plastic materials, as well as for further interdisciplinary research aimed at understanding the mechanisms of transport, accumulation, and long-term ecological effects of microplastics in terrestrial ecosystems. Full article

In the intelligent transportation system of smart cities, object detection from UAV aerial imagery serves as the core technical support for traffic flow monitoring, violation detection, and emergency response. However, traffic objects captured from UAV perspectives typically exhibit extremely low pixel occupancy and are embedded in complex backgrounds, leading to three fundamental limitations in existing detection methods: insufficient utilization of global context information, inaccurate weak feature enhancement, and severe feature scale confusion. To address these challenges, this paper proposes ContextTiny-Net, an ultra-tiny object detection network built upon multi-dimensional symmetry design principles for urban UAV scenarios. Specifically, we first construct a global–local perception symmetric MetaFormer backbone and a hierarchical scale symmetric four-layer detection head, which achieves full-coverage detection from ultra-tiny to regular traffic objects with minimal computational overhead. Second, we design an information-theoretic and spatial-distribution-complementary symmetric-weak feature enhancement module, which accurately locates and strengthens weakly activated regions of small objects from two mutually complementary and symmetric dimensions. Finally, we propose a cross-scale decoupling symmetric feature fusion module and a symmetric Gaussian distribution-based normalized Wasserstein distance loss, which effectively eliminate scale confusion and significantly improve the robustness of small object bounding box regression. Extensive experiments on three mainstream benchmarks (AI-TOD, VisDrone, and COCO) demonstrate that ContextTiny-Net outperforms state-of-the-art methods in both overall detection accuracy and ultra-tiny object detection performance, verifying the effectiveness of the proposed symmetry-enhanced design paradigm. Full article

This paper develops a two-variable truncated-exponential-based Hahn–Appell polynomial family of order r in quantum q-calculus, generated by $\mathcal{A}\left(t\right){(1-\eta t^r)}^{-1}{e}_q^{wt}\left(\zeta t\right)$, where $\mathcal{A}\left(t\right)$ is a normalized determining function and $e_q^{wt}$ denotes the Hahn-type q-exponential. From this relation, we derive connections with the underlying Hahn–Appell basis, the finite expansion induced by the truncation factor, the Hahn lowering property, operational resolvents, step-r recurrences, determinant formulas, and Hahn-factorial representations. We also obtain origin values, normalized monicity, parameter-connection and parameter-differentiation identities, quasi-monomial operators, and the associated Hahn-grid difference equation. Limiting and special cases recover known q-truncated-exponential and classical truncated-exponential Appell constructions. For the unit-Appell specialization $\mathcal{A}\left(t\right)=1$, numerical computations for $q=1/2$, $r=2$, $\eta =1$, and $w=1$ provide explicit polynomials, zero tables, diagnostics, and plots that illustrate parameter sensitivity, Hahn-grid deformation, conjugate symmetry of non-real zeros, origin zeros in odd degrees, and the observed layered rightward spread of zero clouds. Full article

Cell migration is a fundamental biological process regulated by interactions between cells and extracellular matrix. Although topographical cues are known to influence cell behaviors, directional migration across three-dimensional (3D) sloped steps remains poorly understood. Here, 3D sloped steps with patterned TiO_x surfaces were fabricated to investigate topography-guided cell migration in complex 3D microenvironments. The ultrathin TiO_x layers were patterned along the bottom, sidewall, and top regions of the steps, providing continuous guidance during cell migration up or down the steps. MC3T3-E1 cells were confined to the patterned regions and exhibited contact-guided migration along the asymmetrical arrowhead patterns. Forward and reverse arrowheads were introduced to evaluate the effect of geometrical asymmetry on cell migration directionality. Forward arrowheads preferentially guided cells from the bottom to the top of steps, whereas reverse arrowheads promoted migration down the steps, demonstrating reversible control of cell migration direction through arrowhead orientation. Analysis of cell morphology revealed that ultrathin TiO_x topographies influenced lamellipodia orientation and cell adhesion, providing mechanistic insights into geometry-mediated control of cell migration direction. These findings demonstrate that guiding pattern asymmetry can be used to regulate the speed and directionality of cell migration across sloped steps, which can be applied to control cell migration behaviors on engineered 3D platforms. Full article

Non-uniform sound velocity profiles (SVPs) cause sound-ray refraction and propagation-path bending. The straight-line mapping among propagation time, propagation distance, and target position is, therefore, disrupted, leading to systematic errors in constant-sound-speed localization. To improve the consistency between propagation correction and geometric localization, an iterative ray-path correction method based on propagation-time consistency is proposed. The method contains three coupled steps. First, a path-dependent local layered SVP model is constructed for each target-to-base-station path, rather than using a global or fixed sound-speed model. Second, the ray parameter is inverted under the constraint of measured time-of-arrival (TOA), so that the corrected ray path remains consistent with the observed propagation time. Third, the corrected slant range obtained by layered ray tracing is fed back into a known-depth weighted least squares (WLS) localization model, forming a closed-loop position update. The method is evaluated through long-baseline (LBL) simulations with multiple SVPs and propagation geometries and is validated using measured TOA data and an observation-derived SVP. The simulation results show that sub-meter accuracy can be achieved under the tested TOA-noise conditions. In measured-data validation, the planar localization error is reduced from 4.6866 m to 0.1923 m. No divergence is observed in the tested small SVP-perturbation cases. Full article

Cloud contamination obscures ground objects, interferes with surface reflectance, and disrupts spatial continuity. In thick-cloud regions, surface structures and spectral information are often extensively missing. CNN-based cloud removal methods can recover local textures, but they are less effective at modeling global structures and color consistency over large cloud-covered areas. Transformer-based methods capture long-range dependencies; however, standard self-attention introduces high computational and memory costs for high-resolution remote sensing images. Efficient attention reduces this cost but may weaken edge and texture discriminability. SAR imagery can penetrate clouds and provide surface structural information, yet repeated SAR injections may propagate speckle noise, cross-modal misalignment, and imaging discrepancies through deep restoration layers. To address these issues, this paper proposes DST-SARNet, a dual-stage SAR structural guidance network for optical remote sensing image cloud removal. In this framework, dual-stage refers to two explicit SAR-guidance positions: early structural skeleton guidance at the input side and late high-frequency modulation near the output. The Texture-Aware Asymmetric Retrieval module is placed between these two stages as a bottleneck memory retrieval operation rather than as a third dense SAR injection stage. With this design, SAR provides structural skeletons, readable texture memory, and terminal detail compensation, while the optical branch remains responsible for color, semantics, and spectral appearance recovery. Experiments on the SMILE-CR and SEN12MS-CR datasets show that DST-SARNet effectively restores cloud-contaminated imagery with a compact model scale, demonstrating its potential for efficient SAR-assisted optical cloud removal. Full article

Indium-addition synthesis of a misfit layered compound (SnSe)_1.16(NbSe₂) was found to obtain a novel sample (In-sample) with another stacking structure in (SnSe)_1.16(NbSe₂), causing the increase in the superconducting transition temperature T_c and the in-plane upper critical field μ₀$H_{\mathrm{c}2}^{\mathrm{in}-\mathrm{plane}}$ (0). Crystal structure analysis using single crystals revealed that the In-sample has a significantly elongated lattice constant along the c axis due to thickening of the layer other than the NbSe₂, while retaining the original misfit layered structure. The T_c increased from 3.6 K to 5.4 K in the In-sample. Furthermore, the in-plane upper critical fields μ₀$H_{\mathrm{c}2}^{\mathrm{in}-\mathrm{plane}}$ (0) exceeded the Pauli limit μ₀H_p, reaching 29.4 T (μ₀H_p ~ 10 T). The coherence length along the c axis was reduced in the In-sample, indicating enhanced two-dimensionality. These results suggest that the In-sample not only has another stacking structure but also exhibits higher T_c and μ₀H_c2. Full article

This study investigated density-driven mixing and stratified flow dynamics in Paldang Reservoir, a river-type reservoir formed at the confluence of the South Han River, North Han River, and Gyeongan Stream in South Korea. High-resolution field observations were conducted under varying hydrologic and hydraulic conditions using an Acoustic Doppler Current Profiler (ADCP) and multi-parameter water quality sensors (EXO2). Spatial distributions of flow velocity, water temperature, and electrical conductivity (EC) were analyzed to evaluate tributary interaction and mixing behavior within the reservoir. Distinct spatial mixing structures associated with tributary inflow heterogeneity and hydraulic operation conditions were identified. During flood-season conditions, highly turbid and high-conductivity inflow from the South Han River propagated beneath the North Han River inflow, generating density-driven lower-layer intrusion near the confluence region. Under intermittent discharge conditions at the Cheongpyeong Dam, unstable upper- and lower-layer separation structures and localized reverse-flow behavior developed. In contrast, continuous discharge conditions promoted stable tributary propagation and persistent stratified mixing structures. Case-based Richardson number (Ri) estimates further indicated localized shear-driven mixing at low-Ri inflow sections and relatively stable stratification at high-Ri sections, providing quantitative support for the observed spatial heterogeneity in density-driven mixing. Overall, spatial mixing in Paldang Reservoir was governed by tributary density contrasts and further shaped by hydraulic operation conditions. These findings improve understanding of density-driven mixing processes in river-type reservoirs under varying hydraulic conditions. Full article

This article systematically reviews the structural characteristics of layered double hydroxides and biochar (LDHs/biochar) composites, summarizes the features and optimization strategies of preparation methods such as coprecipitation, hydrothermal synthesis, ball milling, and calcination–reconstruction, analyzes their adsorption performance and mechanisms in controlling various water pollutants including organic contaminants, heavy metals, and nutrients, and provides insights into future research trends and practical applications, aiming to offer references for improving material performance and promoting practical use. The existing research results show that LDHs/biochar composites exhibit good application potential for various pollutants, such as dyes, antibiotics, heavy metal ions, and phosphates. The coprecipitation method is simple and easy to operate, and the LDHs/biochar composites prepared by this method exhibit favorable adsorption performance, with potential for industrial-scale production. The mechanisms of pollutant removal by LDHs/biochar composites primarily include electrostatic attraction, ion exchange, hydrogen bonding, complexation, and π–π electron interactions. Both the biomass type and the LDH type influence the adsorption performance of the composites. Therefore, designing LDHs/biochar composites based on pollutant characteristics and adsorption mechanisms is key to achieving effective pollution control. Currently, research on target pollutant-oriented material design and material regeneration remains underdeveloped and requires further breakthroughs. Full article