Search Results (258,922)

Background/Objectives: Catheter-appropriateness audits report a single rate without a measure of its reliability. We measured how far an emergency department (ED) estimate depends on the clinician applying the checklist, and which items carry that dependence. Methods: We enrolled 117 consecutive adults catheterized in a tertiary ED between 21 January and 21 May 2026. Three emergency physicians independently classified every patient against a ten-item checklist drawn from the 2009 CDC/HICPAC guideline and the Ann Arbor criteria. A designated senior rater served as comparator, a clinical judgment rather than a criterion standard. We computed standalone rates, per-item Fleiss’ κ and Gwet’s AC1, patient-level agreement, and a post hoc counterfactual item-replacement analysis. Results: Ten patients (8.5%, 95% CI 4.7–15.0) had no valid indication according to the senior rater. Auditing alone, the two index raters would have reported 0.9% and 25.6%. Pooled agreement over 1170 patient-by-indication decisions was substantial by κ (0.630 and 0.707) and almost perfect by AC1 (0.885 and 0.925); excluding the complementary item changed neither. Patient-level agreement was lower (three-rater κ 0.199, AC1 0.792). Disagreement concentrated in hourly urine-output monitoring (AC1 0.617) and neurogenic bladder, endorsed in 44 patients by one rater and none by another (AC1 0.668). That item accounted for 48% of one rater’s excess endorsements; replacing that rater’s item-5 ratings with the senior rater’s narrowed the between-rater range from 30-fold to 6-fold. Conclusions: In this cohort the audit rate was rater-dependent, and the dependence concentrated in identifiable checklist items. Item-level operational definitions and prospective calibration should be evaluated as strategies for improving audit reliability, and per-item agreement should be reported with a prevalence-robust coefficient alongside κ. Full article

Poly(vinyl alcohol) (PVA) has been widely investigated as a matrix for anti-corrosion coatings because of its continuous film-forming ability, relatively low cost, and the versatility of its hydroxyl groups for chemical modification. However, its high affinity for water remains a major limitation for long-term corrosion protection. Consequently, recent research has increasingly focused on cross-linking, nanostructuring, and functional modification of PVA to improve its resistance to moisture and aggressive environments. This review critically examines recent advances in PVA-based anti-corrosion coatings by integrating coating composition, cross-linking chemistry, micro- and nanostructural features, and electrochemical corrosion-protection mechanisms. Particular attention is given to the effects of glutaraldehyde, boric acid, citric acid, epichlorohydrin, and other multifunctional cross-linkers on network structure, mechanical behavior, water resistance, and barrier properties. The roles of urea, glycerol, inorganic fillers, nanomaterials, corrosion inhibitors, and inhibitor-containing nanocontainers are also evaluated in relation to coating durability and corrosion-protection performance. In addition to describing reported approaches, the review critically compares the influence of substrate, PVA characteristics, cross-linking conditions, nanofiller loading, coating architecture, electrolyte, exposure duration, and electrochemical parameters on the reported performance. Particular emphasis is placed on the relationships between cross-linked network structure, nanofiller dispersion, interfacial adhesion, electrolyte transport, and electrochemical response. The review further considers active inhibition, self-healing strategies, and environmental aspects of PVA-based coatings, while identifying limitations related to moisture sensitivity, long-term durability, and inconsistent reporting of experimental parameters. By integrating composition, cross-linking chemistry, structure–property relationships, and electrochemical evidence, this review provides a critical framework for understanding and designing more durable and environmentally compatible PVA-based anti-corrosion coatings. Full article

This paper addresses the set stabilization problem of probabilistic Boolean control networks (PBCNs) via a self-triggered control strategy. Firstly, the algebraic representation of the considered PBCNs is established by employing the semi-tensor product (STP) of matrices. Secondly, Lyapunov functions (LFs) are introduced for the set stabilization analysis, and a constructive algorithm for deriving such LFs is provided. On this basis, a necessary and sufficient condition in terms of the LF is obtained to determine whether a PBCN can achieve stabilization to a prescribed target set with probability one. Furthermore, a design method for self-triggered controls (STCs) is developed. Finally, the Escherichia coli lactose operon is presented as an example to validate the theoretical results of this paper. Full article

Marine and naval materials are exposed to complex degradation resulting from the combined effects of chloride-induced corrosion, biofouling, mechanical wear, ultraviolet radiation, and microbiologically influenced corrosion. This review critically examines recent advances in protective coating technologies for improving the durability and sustainability of materials operating in aggressive marine environments. Attention is given to polymer-based and nanocomposite coatings, smart and self-healing systems, sustainable formulations, and advanced deposition and performance-assessment strategies. The reviewed literature shows a clear transition from conventional passive barrier coatings toward multifunctional systems that combine barrier protection with active corrosion inhibition, self-healing, antifouling behavior, and enhanced mechanical resistance. Nanostructured fillers can improve coating performance by restricting the transport of water, oxygen, and chloride ions, while inhibitor-loaded carriers and stimulus-responsive systems provide localized protection after coating damage. Sustainable polymers, bio-based additives, and environmentally compatible inhibitors are also emerging as alternatives to conventional formulations with greater environmental impact. However, long-term durability, scalability, reproducibility, and performance under real marine exposure remain major limitations to industrial implementation. Future progress therefore requires the development of multifunctional and environmentally compatible coating systems supported by standardized multifactor testing, long-term field validation, and scalable industrial processing. Full article

The Loess Plateau features complex geological conditions. Large-scale coal-mining activities further trigger severe and spatially heterogeneous surface deformation, which poses substantial challenges for high-precision deformation monitoring. Conventional single-orbit, medium-resolution InSAR suffers from topographic distortion and cannot support full-coverage, high-precision regional monitoring. Taking Lingshi County as the study area, this study establishes a refined full-coverage monitoring framework using ascending and descending L-band (24 cm wavelength) LuTan-1 (LT-1) time-series InSAR (Interferometric Synthetic Aperture Radar). An optimized SBAS-InSAR workflow, which combines small-baseline interferogram with the StaMPS spatial correlation-based distributed scatterer inversion, is implemented. Integrating strip mosaicking, azimuth resampling, and deformation inversion, the proposed approach processes dual-orbit LT-1 SAR data spanning July 2023 to September 2025. We extract spatiotemporal deformation evolution, exploit the geometric complementarity of dual-line-of-sight observations, and interpret mining-dominated deformation mechanisms coupled with geological hazards. L-band LuTan-1 SAR data are adopted with temporal and spatial baseline thresholds set to 96 days and 300 m for small-baseline interferogram construction. A total of 223 deformation zones covering an area of 66.64 km² are identified, which are concentrated across three towns. The descending-orbit dataset yields a monitoring coverage of 88.69%, compared to 79.20% for ascending-orbit data. Combined dual-orbit observations increase the full-area monitoring coverage to 98.91% and eliminate layover and shadow induced blind zones. Over 90% of deformed areas are correlated with underground coal mining. Field investigations and unmanned aerial vehicle (UAV) surveys achieve a 95.96% success rate for deformation-anomaly identification (qualitative detection of deformation zones rather than quantitative velocity accuracy), which validates the reliability of the proposed workflow. This study proposes an innovative L-band SAR monitoring strategy for loess coal-mining zones and provides support for mining disturbance evaluation, ecological restoration and geological hazards detection across the Loess Plateau. Full article

Thermal infrared (TIR) whole-sky imaging enables continuous cloud observations during both daytime and nighttime, making it an important tool for ground-based cloud monitoring. However, existing infrared cloud detection methods mainly rely on manually selected thresholds or handcrafted image features, which typically require instrument-specific calibration and exhibit limited generalization capability. Moreover, wide-field imaging covers not only the zenith region but also the peripheral areas at large zenith angles, which are susceptible to the combined effects of strong atmospheric background emission, thermal radiation contamination from the surface and surrounding environment, and imaging geometric distortion, resulting in reduced cloud–background contrast and making cloud segmentation considerably more challenging than in conventional infrared sky imagery. To address these limitations, this study proposes a deep-learning framework for automatic cloud detection and cloud cover retrieval from wide-field TIR whole-sky images. The framework adopts a two-stage strategy in which infrared images are first classified as clear-sky or cloudy, followed by semantic segmentation of cloudy images for cloud cover estimation. To support model training and evaluation, a dataset containing 3000 wide-field TIR whole-sky images was established using a semi-automatic labeling procedure. Experimental results demonstrate that the proposed method effectively identifies cloud structures, including those located near the edge of wide-field whole-sky images. The classifier achieved an overall accuracy of 98.44%, the segmentation network attained a mean pixel accuracy of 90.44%, and the derived cloud cover showed excellent agreement with the manually annotated reference masks with a correlation coefficient of 0.92. Ablation studies further validate the effectiveness of the proposed deep-learning framework in improving the segmentation of complex cloud structures. The truncated EfficientNet-B0 encoder yields the most pronounced contribution, while the ASPP module improves the delineation of complex sky and cloud structures. When applied to the observations from the ground-based TIR all-sky camera, the retrieved cloud cover agreed well with the FY4B/AGRI cloud mask product, with a correlation coefficient ranging from 0.81 to 0.89 across the four seasonal months in 2023. These results prove that the proposed framework provides an effective and robust solution for long-term ground-based cloud monitoring using wide-field TIR whole-sky imagery. It also offers a practical foundation for applications such as cloud climatology, solar radiation assessment, and atmospheric observation. Full article

Energy management (EnM) is crucial for reducing GHG emissions, but there is still a large potential to improve the energy efficiency of manufacturing companies. One approach that could be leveraged is to reduce Scope 3 emissions by extending EnM towards suppliers and integrating it into supply chain management (SCM); however, research linking these perspectives remains limited. This paper aims to investigate drivers and barriers associated with extending EnM towards suppliers and how EnM practices are considered in supplier selection. Based on a nationwide online survey sent to Swedish manufacturing companies, this paper provides new insights for practitioners and policymakers to develop strategies to integrate EnM into SCM, contributing to closing the energy efficiency gap. The results show that organizational and economic drivers and barriers related to prioritization and structure are individually perceived as most important for extending EnM towards suppliers, but supply chain-related factors play an important role when factor groups are considered. Further, the study highlights that economic criteria are still perceived as more important than energy-related criteria when it comes to supplier selection, which is initially conceptualized as a potential supply chain EnM gap. Full article

Automated fibre placement (AFP) is a key process for manufacturing advanced polymer–matrix composite aerostructures; however, an uneven compaction-pressure distribution of tows during lay-up reduces the inter-laminar bonding strength and induces processing defects such as bridging and wrinkles. In this study, a pressure regulation strategy coordinating simulation-based feed-forward planning with closed-loop feedback control is proposed as a modified processing route for improving the manufacturing quality of polymer composites. The strategy adopts a two-layer “planning–execution” architecture. In the planning layer, process parameters and placement trajectories are optimized in advance based on a compaction-roller pressure simulation model and a compaction-uniformity index. In the execution layer, closed-loop pressure control suppresses on-site disturbances in real time to ensure that the simulation objectives are realized, thereby achieving uniform control of the compaction-pressure field. Finally, multi-angle lay-up experiments were carried out on a convex-surface mould, and the pressure field was measured using pressure-sensitive films. The results show that the proposed regulation strategy improves pressure distribution uniformity by more than 6%, effectively enhancing curved-surface lay-up quality and the consolidation quality of advanced polymer composite components. This work provides an intelligent design-and-manufacturing route that links process simulation with real-time control for high-quality automated composite processing. Full article

Jobs–housing balance is an important goal of urban sustainable development, and Transit-Oriented Development (TOD) is widely regarded as an effective pathway to optimize the jobs–housing relationship. However, station-area-scale jobs–housing studies remain limited, and existing jobs–housing typologies have not been linked to regulable TOD characteristics, leaving jobs–housing optimization without a basis for differentiated regulation. Using 72 built subway station areas in Shenzhen’s Density Zone 1 as samples, this study draws on three dimensions, namely transport supply (Node), land use (Place), and jobs–housing, and applies hierarchical clustering, multiple linear regression, and Lasso regression to examine jobs–housing typologies, land use and building distribution characteristics, and the effects of transport supply and land use on the jobs–housing relationship in TOD station areas. Regression analysis uses jobs–housing value, a weighted score derived from the employment–residential area ratio (JH1) and the employment–residential population ratio (JH2), as the dependent variable. The main findings are as follows. (1) TOD station areas can be classified into four types, which differ significantly in land use and building distribution; planning strategies should therefore be differentiated according to the characteristics of each type. (2) The four types exhibit a concentric spatial structure comprising an employment-oriented core, a jobs–housing balanced middle ring, and a residential-oriented periphery, with the mean betweenness centrality increasing gradually, indicating that TOD intensity rises in tandem with subway network hub status. (3) Across the full sample, the interaction term between transport supply and land use is significantly and positively associated with jobs–housing value, a result supported by spatial econometric robustness checks; the main effects of Node and Place are significantly negative only in ordinary least squares. (4) Subway line direction, bus stop density, floor area ratio, and maximum planned floor area ratio are positively associated with jobs–housing value, whereas shared bike density, POI density, total road length, and building mixing entropy are negatively associated with it. This study provides a quantitative basis and actionable planning pathways for the differentiated regulation of the jobs–housing relationship in TOD station areas. Full article

Parasitic infections compromise livestock health, productivity, and profitability, while rising anthelmintic resistance restricts the effectiveness of conventional parasite control strategies. Bee pollen (BP) contains bioactive compounds with immunomodulatory and antimicrobial potential. This study investigated whether dietary BP supplementation reduces parasite burden and influences liver function, hematological and metabolic profiles, and circulating metabolomic signatures in goats. Thirty crossbred Boer × Spanish does were randomly assigned to either a BP-supplemented group (T-BP; n = 15) or a control group (T-CTL; n = 15). Over 49 days, T-BP goats received 10 g/day BP mixed with 50 g/day pelleted feed, while controls received the same feed without BP. Body weight, weekly blood samples, FAMACHA scores, and biweekly fecal egg counts (FEC) were collected. We assessed metabolomic profiles on days −8, 21, 42, and 56. Data were analyzed using mixed models and repeated measures. We identified strongyle-type eggs, presumably Haemonchus contortus, as the predominant parasite. No significant differences were observed between treatments for body weight, liver enzymes, metabolic biomarkers, or hematological profiles (p > 0.05). FEC varied over time (p < 0.001), with significantly lower FEC in BP-supplemented goats only on Day 42 (p < 0.05). Metabolomic differences were significant on Days 21 and 42 but not on Day 56. These differences involved metabolites annotated to lipid signaling, inflammatory processes, and cellular energy metabolism. In summary, BP supplementation demonstrated a potential antiparasitic effect against H. contortus but did not significantly affect liver, metabolic, or hematological parameters. Metabolomic findings should be considered exploratory and hypothesis-generating. Full article

Oral mucosal diseases often lead to structural damage and functional impairment due to high recurrence and malignant progression. Current therapeutic outcomes remain unsatisfactory owing to the complex oral microenvironment. Recent reviews have covered individual aspects of oral mucosal drug delivery, such as bioactive materials, mucoadhesive systems, and nanocarriers, but few have considered all four interrelated dimensions, including saliva-mediated adhesive barriers, epithelial permeability, biomechanical adaptability, and translational bottlenecks, in an integrated manner. The present review offers an attempt to consolidate these dimensions into one framework, aiming to provide readers with a more comprehensive perspective on the challenges inherent in this field. To underpin this framework, we conducted a structured literature search covering Web of Science (2015–2026), with predefined inclusion/exclusion criteria, followed by thematic synthesis. We first summarize the clinicopathological features and current treatment landscape of major oral mucosal diseases. We then dissect three critical barriers to transmucosal delivery: adhesive interference by salivary flow, epithelial tight junction resistance, and dynamic biomechanical forces (e.g., chewing, tongue movement). Drawing on saliva and mucosa interfacial physics, we rationalize the need for wet-adhesive material design and survey diverse mucoadhesive polymers and formulations, detailing their binding mechanisms. For epithelial penetration, we highlight design principles of microneedles and nanocarriers while critically discussing their dual-edged safety–efficacy profiles. To cope with the highly deformable oral biomechanical environment, we propose mechanical property matching and asymmetric structural designs. Finally, we outline key translational bottlenecks and future breakthroughs. This review offers an integrated, barrier-informed guide for next-generation oromucosal formulations. Full article

Food adulteration poses severe threats to the integrity and safety of global food supply chains. Innovations in analytical techniques, especially the rapid development of LAMP-based detection methods, are indispensable for addressing these risks. However, the dominant research paradigm, which has long prioritized accelerated amplification kinetics and ultralow detection limits, largely obscures a critical limitation: insufficient detection reliability when deployed in complex field environments. This review consolidates state-of-the-art advances targeting reliability optimization and delineates key technical strategies to construct robust, improved-reliability LAMP platforms for real-food testing. Specifically, we summarize critical factors at the reaction and readout levels that compromise the reliability of LAMP under realistic scenarios, such as inexperienced operators, degraded samples, matrices containing inhibitory substances, and uncontrolled environments. We further elaborate on effective solutions to bridge the reliability gap between laboratory validation and on-site deployment, which requires coordinated technical improvements throughout the full LAMP testing pipeline. Ultimately, we propose a forward-looking developmental principle for next-generation LAMP assays: analytical reliability and operational robustness should be elevated to equal priority alongside the traditionally pursued ultra-rapid amplification and high sensitivity. This balanced technical framework may help pave the way toward large-scale standardization of LAMP-enabled food authentication, although its presumptive results must be confirmed by an independent validated method before they can support regulatory enforcement or other formal decisions. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the most economically important pathogens affecting the global swine industry. This review summarizes current knowledge of PRRSV-induced suppression of adaptive immune responses, with particular emphasis on impaired T cell activity, dysregulated B cell responses, delayed and insufficient neutralizing antibody production, and viral strategies that limit effective immune clearance. In addition, recent advances and limitations in vaccine development and antiviral strategies are presented. By integrating insights from viral pathogenesis and host immune responses, this review provides a comprehensive perspective on current challenges and emerging strategies for effective PRRSV control. Full article

Travel Reservation Strategy (TRS) provides a capacity-constrained approach for managing recurrent congestion by regulating access to selected urban road links. This study develops a Multidimensional Traffic Split and Assignment (MDTSA) model to evaluate TRS within a heterogeneous multimodal transportation system. The model integrates traveler heterogeneity in value of time, five travel alternatives, non-separable car–bus road impedance, reservation-access constraints, and joint mode–route choice under stochastic user equilibrium. A variational inequality formulation is solved using the Method of Successive Weighted Averages. Numerical experiments on the Sioux Falls network show that TRS increases average network speed by 8.1%, reduces average road saturation by 12.5%, decreases total generalized travel cost by 2.3%, and lowers vehicle-hours traveled by 6.5%. The strategy also shifts travel demand away from private cars, whose modal share decreases by 2.48 percentage points, while bus and metro shares increase by 1.91 percentage points in total. Model-comparison results indicate that neglecting traveler heterogeneity or combined travel modes weakens the estimated effects of TRS, while sensitivity analysis shows that the main performance improvements remain stable across the tested reservation-capacity ratios and demand levels. These findings demonstrate the value of multidimensional equilibrium modeling for evaluating reservation-based urban traffic management. Full article

Short-term power load forecasting plays a crucial role in the operation scheduling, energy management, and security assessment of smart grids, as its accuracy directly affects the economic efficiency and reliability of power systems. However, power load series are characterized by strong nonlinearity, non-stationarity, and multi-scale temporal dependencies, which make it difficult for traditional forecasting models to achieve both high accuracy and strong generalization under complex load scenarios. In recent years, deep learning models have demonstrated promising performance in load forecasting; nevertheless, their effectiveness is highly dependent on hyperparameter configurations. Manual hyperparameter tuning is not only time-consuming but also prone to premature convergence to suboptimal solutions. To overcome these challenges, this study develops an AMS-GRO-based short-term power load forecasting framework. By improving the original Gold Rush Optimizer (GRO), three adaptive strategies are integrated into the proposed optimizer, namely the current-to-pbest/1 mutation strategy, adaptive elite-guided search strategy, and success-rate-based adaptive strategy selection mechanism. These improvements jointly enhance the global exploration capability, local exploitation ability, and strategy adaptability of the optimizer. Subsequently, the proposed AMS-GRO is employed to perform global hyperparameter optimization for a Dilated BiGRU-Attention model, forming the AMS-GRO–Dilated BiGRU forecasting framework. Key hyperparameters, including the learning rate, number of hidden units, attention dimension, and regularization coefficient, are adaptively optimized. Extensive numerical experiments were conducted on the 30-dimensional CEC2017 suite and the 10- and 20-dimensional CEC2022 suites using 30 independent runs. According to the Friedman test, AMS-GRO achieved mean ranks of 1.37, 1.33, and 1.25, respectively, ranking first in all three experimental settings. In the short-term power load forecasting experiment, AMS-GRO–Dilated BiGRU achieved an MAE of 21.09, MAPE of 0.0177, MSE of 791.10, and R² of 0.9746. Compared with the unoptimized Dilated BiGRU model, these results correspond to reductions of 13.0%, 13.7%, and 21.5% in MAE, MAPE, and MSE, respectively, together with a 0.70-percentage-point improvement in R². From a symmetry perspective, the proposed framework balances global exploration and local exploitation through adaptive strategy selection, while the Dilated BiGRU exploits bidirectional temporal symmetry to capture multi-scale load patterns. These results demonstrate that the proposed framework provides competitive optimization performance and improves forecasting accuracy on the investigated load dataset. Full article