Search Results (2,787)

Excessive fluoride intake from drinking water remains a public health concern in geogenic high-fluoride regions, yet direct evidence linking environmental fluoride levels to internal exposure in remote high-altitude areas is limited. This study integrated environmental monitoring with human biomonitoring to assess fluoride exposure and health risks in the Taruo Lake region of the Tibetan Plateau. Surface water (n = 45 for Taruo Lake; n = 8 for its tributaries) and groundwater samples (n = 4) were collected and analyzed for fluoride concentrations, and blood ionic fluoride (BIF) levels were measured in 122 local residents (47 adults, 75 children). The results showed that fluoride concentrations in most surface water tributaries of Taruo Lake and groundwater sources were below China’s drinking water standard, whereas those in Taruo Lake exceeded this limit (routine monitoring mean 2.54 mg/L; multi-site mean 2.79 mg/L). BIF levels were significantly higher in adults (0.126 ± 0.041 mg/L) than in children (0.075 ± 0.032 mg/L) and showed a positive correlation with age (r = 0.533, p < 0.001). Notably, 23.4% of adults and 1.3% of children exceeded 0.15 mg/L, an empirical threshold typical for healthy populations in non-endemic areas. Based on the hazard quotient (HQ) model recommended by the US EPA, most drinking water sources posed acceptable non-carcinogenic risks (HQ < 1). In contrast, Taruo Lake water presented an elevated risk (HQ > 1) in 2024 primarily due to the regional geological background, and although not used for daily drinking, this finding offers an indicative reference for local water management and risk prevention. This preliminary monitoring and biomonitoring assessment provides baseline data for future studies and underscores the necessity of continuous surveillance and evaluation of total dietary fluoride intake to protect the health of this vulnerable high-altitude population. Full article

Against the coordinated advancement of building a maritime power, high-quality development of marine tourism and ecological civilization construction, realizing positive interaction between marine resource conservation and tourism industrial development has emerged as a pivotal issue for high-quality growth in coastal regions. Taking 11 coastal provincial-level administrative regions in China spanning 2008 to 2024 as the research sample, this paper first establishes an evaluation indicator system covering marine resources and the tourism industry. It further adopts an integrated empirical framework encompassing the coupling coordination degree model, spatial Markov chain model, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model to systematically unpack the spatiotemporal differentiation characteristics, internal restrictive obstacle factors and external driving determinants of the two-system coupling coordination. On this basis, a marine resource compensation mechanism for tourist destinations is formulated. Empirical results demonstrate four core findings: (1) In terms of temporal evolution, the overall coupling coordination level keeps rising and goes through three phases: initial development, rapid improvement and post-shock recovery. After a short-term decline triggered by the pandemic, the index rebounds markedly after 2023, showing that the two systems can recover and stabilize. (2) In terms of spatial layout, a persistent stratified spatial pattern featuring “higher coordination in southern coast versus lower coordination in northern coast with three-tier hierarchical differentiation” is identified; high-level neighboring regions exert prominent positive spatial spillover effects, whereas low-level adjacent areas are prone to fall into development lock-in traps. (3) For internal constraint obstacles, the marine resource subsystem is persistently restricted by resource exploitation limits and coastal spatial scarcity, while the dominant bottleneck of the tourism industrial subsystem shifts from insufficient market scale to inadequate human capital supply. (4) Regarding external driving forces, the proportion of tertiary industry and the digital infrastructure constitute core driving contributors, whereas marketization progress and opening-up degree act as primary restrictive factors, with pronounced spatial heterogeneity existing across all driving indicators. Finally, in line with the quasi-public-good attribute and ecological externality of marine resources, this study constructs a differentiated and synergistic marine resource compensation mechanism from three dimensions: stakeholder identification, compensation implementation pathways and institutional guarantee systems. The proposed framework provides theoretical references and practical policy options to facilitate high-level coupling and coordinated development between marine resource preservation and the coastal tourism industry. The marginal contribution of this research lies in integrating coupling coordination measurement, obstacle factor diagnosis, driving mechanism identification and compensation mechanism design into an integrated analytical framework, which delivers theoretical foundations and operable policy solutions for coastal marine resource protection, tourism industrial upgrading and differentiated compensation system construction. Full article

As the automotive industry undergoes a green transformation, digital upgrading, and increasingly intensive supply chain collaboration, the supply chain finance credit risks faced by small and medium-sized enterprises (SMEs) in the sector exhibit characteristics such as multi-source interaction, nonlinear transmission, and class imbalance. This study uses 210 SMEs in China’s A-share automotive sector from 2020 to 2024 and constructs a credit risk evaluation system covering 56 indicators across the macro environment, financing enterprises, supply chain characteristics, and core enterprise credit support. Methodologically, DE-LightGBM is employed for feature selection to reduce redundancy and noise, while TabPFGen is introduced to generate synthetic risk-class samples. Business logic constraints and a Nearest Neighbor Distance Ratio filtering mechanism are further applied to improve the plausibility and fidelity of generated samples. Empirical results show that the TabPFN model achieves superior predictive performance after feature selection and data augmentation, and the Wilcoxon signed-rank test confirms the effectiveness and stability of sample augmentation. In addition, the ablation experiment demonstrates that green-related features provide significant incremental predictive value for supply chain finance credit risk identification. The proposed framework provides a useful reference for SME credit assessment, risk early warning, and green financial resource allocation in the automotive industry. Full article

Crack detection in mobile inspection scenarios is constrained by both the extremely slender geometry of crack targets and the real-time inference requirements on edge devices, which expose systematic limitations of general-purpose object detectors. This paper proposes YOLO-Crack, a closed-loop solution that couples geometry-statistics-driven module design with end-to-end edge deployment validation. On the algorithmic side, we first quantify crack geometric properties and then introduce (i) a crack-aware cross-dimensional fusion attention (CFCA) module to strengthen feature representations, (ii) a dual-path feature enhancement module (DFEM) to preserve fine details during upsampling, and (iii) an empirical smooth quality window adjustment with shape consistency regularization to stabilize bounding-box regression for slender cracks. Experiments on the Crack500 dataset show that YOLO-Crack achieves 78.8% precision, 51.4% recall, and 65.7% mAP@0.5, improving over the YOLOv11n baseline by 4.2, 1.7, and 2.9 percentage points, respectively. On the engineering side, we deploy YOLO-Crack on a Jetson Orin NX mobile robot platform and evaluate it in a real ROS pipeline; the measured end-to-end throughput reaches 25.5 FPS, meeting real-time video processing requirements. The proposed framework provides a practical reference workflow for edge vision tasks, from geometry analysis to engineering verification. Full article

While double-layer insulation structures are widely adopted, their thermal performance is critically dependent on the thermophysical behavior of the interstitial air cavity, a variable often oversimplified in current design practices. This article moves beyond generic material descriptions to investigate the specific mechanism of heat transfer transition within sealed air gaps sandwiched between graphite polystyrene boards. The innovation of this experiment lies in the rigorous isolation of air gap thickness as the primary independent variable within a 1 × 1 × 1 m closed building model, instrumented with high-precision GPRS temperature and humidity sensors to capture real-time thermal gradients under the authentic climate conditions of Anyang, Henan. The results demonstrate a non-monotonic relationship between gap thickness and effective thermal resistance, governed by the competition between molecular conduction and buoyancy-driven natural convection. Specifically, the data validates that a 20 mm air gap represents the statistically significant optimum, thereby maximizing insulation efficiency while minimizing radiative heat loss. Using this optimized structure reduces steady-state heat flux compared to monolithic equivalents and aligns with the energy conservation target. Unlike previous studies limited by simulation assumptions or short-term testing, this research provides empirically verified, long-term field data that bridges the gap between theoretical fluid dynamics and practical building envelope engineering. These findings offer a robust, physics-based reference for optimizing double-layer insulation systems in the Central Plains, directly supporting the low-carbon retrofitting of existing building stocks. Full article

Background: Culturally and Racially Marginalised (CaRM) communities in Australia encounter subtle and covert forms of prejudice, commonly referred to as “new racism”. Within healthcare settings, these experiences can shape trust, engagement, and patterns of help-seeking. Mental health nurses are often the first point of contact in care delivery, and their ability to recognise, respond to, and mitigate the impacts of new racism is critical for fostering therapeutic relationships and supporting equitable access. Understanding how CaRM communities perceive the conditions that influence their mental health service use is fundamental for informing more equitable and culturally responsive care. Objective: This study explored the viewpoints of CaRM community members regarding the factors they consider important for addressing new racism in healthcare systems and supporting engagement with mental health services. Design: Q methodology was used to identify statistically derived viewpoints that reflect shared viewpoints about the conditions perceived as critical for addressing the impacts of new racism on mental health service use. Setting: Participants were recruited from culturally and linguistically diverse communities across Australia through community settings, social media, and professional networks. Participants: Thirty-five individuals from CaRM backgrounds completed the Q-sort. Methods: This Q methodology consisted of five steps: (1) set up of the Q-sorting instrument, (2) selection of participants, (3) data collection, (4) factor analysis, and (5) factor interpretation. Results: Three distinct viewpoints were identified: (1) raising awareness of mental health issues within CaRM communities (community-focused), (2) providing visible anti-racism and culturally safe services (service-focused), and (3) recognising and formally addressing new racism within healthcare systems (policy-focused). Conclusions: This study offers the first empirically derived, community-informed set of viewpoints on addressing new racism in Australian mental healthcare. While exploratory, the findings highlight multi-level considerations that are potentially relevant to mental health nursing practice, and may be useful to inform future research, policy development, and service redesign aimed at strengthening cultural responsiveness and equity in mental health systems. Full article

This paper examines how inflow-state discretisation affects wake-loss and annual energy production (AEP) estimates for offshore wind farms. A reproducible workflow is presented for constructing weighted inflow-state ensembles from long-term offshore wind datasets using empirical wind-speed–direction occurrence frequencies. Hub-height wind speeds are reconstructed from multi-level wind data using a time-varying power–law shear exponent, after which the wind climatology is discretised using configurable directional sectors and wind-speed bins. The methodology was evaluated using both a controlled synthetic wind dataset and offshore climatological datasets processed through the same inflow-state and wake-modelling workflow. The analysis quantified how directional resolution, wind-speed bin width, and sector-mean inflow representations affect predicted turbine power, wake loss, and AEP relative to empirical reference cases. For the synthetic dataset, replacing the within-sector wind-speed distribution with a single sector-mean wind speed produced an annual power difference of 12.58%, with seasonal differences ranging from 6.66% in JJA to 13.91% in DJF. Offshore wake-model calculations showed the same overall behaviour. Reducing the empirical inflow-state ensemble from 1593 to 416 retained states changed annual AEP by only 0.03% and wake loss by $-0.03$ percentage points, whereas the sector-mean inflow representation increased predicted AEP by 18.40% and wake loss by 5.13 percentage points relative to the empirical reference case. The results show that preserving the within-sector wind-speed distribution has a larger influence on predicted wake loss and AEP than moderate reductions in retained state count or directional resolution for the datasets and layouts considered here. Empirical inflow-state ensembles using 36 directional sectors together with 1 $\mathrm{m}$${\mathrm{s}}^{-1}$ or 2 $\mathrm{m}$${\mathrm{s}}^{-1}$ wind-speed bins remained within 0.03% of the higher-resolution annual AEP reference while reducing the number of retained inflow states by approximately 74%, with a corresponding reduction in the number of wake-model evaluations required. Full article

Housner’s classical rocking model assumes a rigid base, which often leads to inaccurate seismic assessments under real–world soil conditions. This study quantitatively establishes the applicability limits of the rigid–base assumption and defines a reference range for its validity. To address these limitations, a novel soil–structure interaction (SSI) rocking model was developed using Lagrange’s formulation, incorporating an event–driven spring–dashpot mechanism to characterize contact forces. Validation against LS–DYNA simulations and existing compliant base models confirms high predictive accuracy across diverse geometries and ground motions. Crucially, an empirical formulation for the interface stiffness of rocking structures was derived to ensure the alignment of the proposed analytical model with numerical observations, thereby enhancing its practical utility in industrial design. Our findings reveal that rocking behavior depends not only on soil stiffness but also on the inherent stiffness of the structure. Specifically, soft soils significantly alter rocking initiation thresholds and amplify peak angles. The proposed SSI–rocking model provides a computationally efficient and FE–compatible tool for optimizing the seismic stability of unanchored structures on flexible foundations. Full article

This study uses data of A-share listed companies in Shanghai and Shenzhen from 2020 to 2024. We manually collect green procurement lists from official government procurement websites and match them with firm samples. Employing the two-way fixed effects model and the Bootstrap method, this paper empirically examines the impact of green public procurement on corporate carbon reduction. The results show that green public procurement significantly improves firms’ carbon reduction performance. Mechanism analysis indicates that AI adoption and government green subsidies further strengthen this effect. Heterogeneity tests reveal that the impact is more pronounced for state-owned enterprises, high-tech firms and enterprises in regions with advanced digital economies. Accordingly, we propose suggestions including strengthening the driving role of green procurement, promoting coordination between green procurement and digital technology, optimising the allocation of green funds, and implementing targeted differentiated incentives. This research helps clarify the internal mechanism of green public procurement on carbon emission reduction performance and provides references for improving relevant practices in carbon emission reduction. Full article

This study presents a Systematic Literature Review (SLR) of gamified platforms for cybersecurity education and training, with a particular focus on environments that incorporate attack–defence simulation. The review examines how the literature addresses gamification, User eXperience (UX), didactics, platform design, and sustainability-related deployment conditions across higher education as the primary reference context, while also considering adjacent applied training contexts when they provide transferable evidence for platform design, deployment, or evaluation, including online learning. We analysed 172 studies published between 1 January 2015 and 8 March 2026. The findings show that the field combines authentic hands-on practice with challenge-based learning, feedback-rich progression, and diverse technical formats, including cyber range environments, serious games, CTF-oriented platforms, cloud-based infrastructures, and modular training systems. The review also indicates that the educational value of these approaches depends on the alignment between pedagogical structure, interaction design, and technical architecture, as well as on safe experimentation, adaptability, governance, and long-term maintainability. The review contributes an evidence-based taxonomy and configurational synthesis of recurrent design patterns across UX, didactics, gamification, architecture, and sustainability, and it identifies implications for future empirical research and for the design of sustainable, learner-centred cybersecurity teaching platforms. Full article

TiVNbZrCr high-entropy intermetallic alloy was investigated as a deuterium storage material using gravimetric sorption measurements, thermogravimetric analysis, and temperature-resolved synchrotron X-ray diffraction during deuterium desorption. The as-prepared alloy had an experimentally determined composition of Ti₁₇V₁₉Zr₁₉Nb₂₂Cr₂₃ and a density of 6.59 g·cm⁻³. Empirical alloy-design parameters indicate that the alloy is not a single-phase bcc solid solution, but rather a compositionally complex intermetallic alloy. The calculated hydrogen-affinity descriptors suggest a strong thermodynamic driving force for deuteride formation. Under 5 MPa D₂, the alloy absorbed 3.28 wt.% D, corresponding to D/M = 1.1. After ex situ deuteration, additional diffraction reflections were indexed using tetragonal deuteride reference structures corresponding to ZrV₂D_2.35 and TiD₂, while the Cr-rich bcc phase remained comparatively stable. Thermal desorption released 2.28 wt.% D up to 600 °C in three partially overlapping steps. These results demonstrate that deuterium storage in TiVNbZrCr is governed by phase-selective deuteride formation and decomposition rather than by homogeneous bcc lattice expansion. Full article

This study presents a phenomenological estimation framework for a membraneless alkaline water electrolyzer (MAWE), developed primarily from experimentally measured current signals and end-of-test mass-loss data. Thirteen KOH concentrations (5–35 g in 1 L deionized water) were investigated under a constant 12 V DC supply for 7200 s. The time-varying current was continuously recorded throughout each experiment, while the total gas production was determined from the net mass loss measured at the end of the electrolysis process. A time-resolved hydrogen-production representation was subsequently reconstructed from the measured current signal using Faraday’s law and constrained to be stoichiometrically consistent with the experimentally observed total mass loss. The term “charge-consistent” used throughout this study does not imply a new electrochemical principle, but rather refers to maintaining physical consistency between the experimentally measured current signal, Faraday-based charge transfer, and the experimentally observed end-of-test mass loss within the proposed phenomenological framework. Experimental results indicate that both the current response and the cumulative gas production exhibit a strong and distinctly nonlinear dependence on the KOH concentration. Two phenomenological modeling approaches were examined. The first is a static polynomial formulation describing the nonlinear relationship between the measured current signal and the reconstructed production rate. The second is a semi-empirical grey-box formulation in which the Faraday-based theoretical production term is corrected using an experimentally identified efficiency coefficient. Model performance was assessed using train/test data partitioning, residual analysis, autocorrelation functions, and Ljung–Box tests, demonstrating a high degree of internal charge consistency and macroscopic agreement with the reconstructed experimental representation. The proposed framework provides a reduced-order and experimentally accessible approach for representing reconstructed production behavior in MAWE systems without resorting to detailed multi-physics modeling or EIS-based characterization and offers a physically consistent baseline for comparison with more complex data-driven or control-oriented modeling strategies. Full article

Background/Objectives: Single-case studies represent a sophisticated and rigorous methodological framework, widely established in clinical research for providing high-resolution data on individual functional responses. This study evaluates the clinical utility of integrating immersive Virtual Reality (VR) gaming as a novel “functional ingredient” within the Video Game Therapy (VGT) protocol. Given the exploratory single-case nature of this intervention, clinical state-modulations cannot be rigorously validated using standard aggregated group statistics. Therefore, the core objective of this paper is to investigate the therapeutic potential of the VR session on psychological state-modulation, introducing the Single-Case Normative Analytics (SCNA) framework as the mandatory statistical vehicle required to validate individual longitudinal shifts against normative data. Methods: The study treats individual VR exposures as independent, short-term clinical probes embedded within a real-world clinical journey. The SCNA framework was deployed by integrating Crawford’s modified t-tests with longitudinal percentile tracking against an empirical normative reference group (n = 20). Acute state-anxiety variations (STAI-Y1), psychological well-being (PGWBI), and flow dynamics were tracked across three distinct sessions to monitor the patient’s relative repositioning within the normative distribution. Results: The inferential analysis indicates that the immersive 20-min environment facilitated reliable, statistically significant changes in acute state anxiety and flow dimensions, systematically exceeding standard measurement error boundaries and successfully moving the patient’s psychometric profile toward healthy normative ranges. Conclusions: While these findings focus on individual, idiographic reactivity, they demonstrate the utility of the SCNA framework in providing clinicians with objective, evidence-based feedback on the clinical viability of specific VR-based functional units. This approach allows for a rigorous evaluation of standalone digital tools independently of a full, holistic VGT protocol, offering a structured alternative to traditional designs focused on identifying general patterns across groups. Full article

New quality productive forces (NQPF), characterized by innovation-driven growth, high operational efficiency, resource conservation and inherent green attributes, share an inherent coupling and coordinated development logic with vocational skill formation systems. Based on panel data covering 31 provincial-level administrative regions in the Chinese mainland from 2010 to 2022, this study measures the coupling coordination degree between NQPF and vocational skill formation systems, and systematically explores its driving factors and spatial evolution patterns. The main empirical findings are as follows: (1) The overall coupling coordination degree across the full sample presents a steady upward trend. At the regional subgroup level, the eastern sample maintains the highest coordination level, followed by the central sample, while the western sample remains relatively lower. (2) The spatial agglomeration intensity of the coupling coordination degree follows an inverted U-shaped trajectory, rising first and then weakening over the study period. (3) Core driving factors show remarkable regional heterogeneity. For the eastern and central subgroups, improving workforce quality and production efficiency are the key pathways to boost coupling coordination; by contrast, the western subgroup relies more on workforce quality upgrading and increased investment in material labor resources. (4) Estimation results of the Spatial Durbin Model indicate that educational foundation, educational investment and educational operation process are the core drivers of coupling coordination. Educational foundation exerts a significant positive spatial spillover effect; the ecological environment not only promotes local coordination but also generates positive spillovers to adjacent regions; while material labor resources benefit local development but produce negative spatial spillovers to neighboring areas. This study provides empirical evidence and practical references for advancing the coordinated development of NQPF and vocational skill formation systems in multi-regional transitional economies. Full article

Deterministic radio wave propagation models provide high accuracy in complex outdoor environments but remain computationally impractical for large-scale network planning and spectrum management. In contrast, empirical and hybrid models offer low complexity at the expense of reduced accuracy, systematic bias, and limited terrain sensitivity. This paper proposes a unified delta learning framework that enhances fast baseline propagation models by learning a data-driven correction toward a deterministic Parabolic Equation Modeling (PEM) reference. A key novelty lies in a compact, physics-informed feature representation that replaces the full terrain profile with an 18-dimensional vector combining local geometric descriptors, global terrain characteristics, and baseline responses, enabling accurate correction with low-dimensional input. The study also provides the first systematic investigation of delta-based correction across multiple widely used propagation models. The framework is evaluated for free-space propagation, ITU-R P.1546, ITU-R P.1812, and ITU-R P.452 using ridge regression, kernel ridge regression, gradient boosting regression trees, and a neural network model. Model performance is assessed in terms of error reduction, bias mitigation, robustness across learning algorithms, and profile-level generalization to previously unseen propagation paths within the considered terrain categories. Results show substantial error reduction, with up to twofold improvement for simpler baseline models and consistent gains for hybrid models, while preserving computational efficiency. Full article