Search Results (1,758)

Background and Objectives: Sarcopenia is highly prevalent among maintenance hemodialysis (HD) patients, particularly in the presence of diabetes mellitus (DM). Dietary glycemic and insulinemic characteristics may contribute to metabolic disturbances associated with muscle deterioration, although evidence in HD populations remains limited. This study aimed to investigate the associations between dietary indices, sarcopenia, nutritional status, and clinical outcomes in diabetic (DM+) and non-diabetic (DM−) HD patients. Materials and Methods: This cross-sectional study included 92 maintenance HD patients (43 DM+ and 49 DM−). Dietary intake was assessed using three-day food records, and dietary insulin index (DII), dietary insulin load (DIL), dietary glycemic index (DGI), and dietary glycemic load (DGL) were calculated. Sarcopenia was evaluated using handgrip strength, bioelectrical impedance analysis, gait speed, and SARC-F. Anthropometric, biochemical, nutritional, and sarcopenia-related parameters were compared across tertiles of dietary indices. Results: Sarcopenia was identified in 32.6% of patients with diabetes and 36.7% of those without diabetes. Diabetic patients exhibited significantly lower handgrip strength, slower walking speed, longer walking time, and higher SARC-F scores (p < 0.01). Across DGL tertiles in DM+ patients, significant progressive increases were observed in body weight (p < 0.05), body mass index (p < 0.05), lean mass (p < 0.05), mid-upper arm circumference (p < 0.01), and triceps skinfold thickness (p < 0.01). Higher DIL and DGL tertiles were also associated with elevated serum phosphorus, LDL cholesterol, triglycerides, and total cholesterol levels (p < 0.05). DIL and DGL showed stronger associations with overall energy and nutrient intake compared with DII and DGI. However, no significant associations were identified between dietary indices and sarcopenia diagnosis or sarcopenia-related risk indicators after adjustment for age and sex. Conclusions: Dietary indices were associated with various anthropometric, biochemical, and nutritional parameters in HD patients, with more pronounced associations observed among patients with DM, suggesting a potential role of dietary quality in the nutritional and metabolic profile of this population. Full article

Background/Objectives: The accuracy and reliability of automated clinical analyzers are fundamental to patient safety and effective medical decision-making. Traditional quality control (QC) methods, which rely on periodic manual calibration and reactive maintenance, are inherently limited by high latency and labor costs; furthermore, they fail to provide continuous, real-time monitoring. This paper introduces a novel hybrid ensemble learning framework for the automated quality inspection of medical devices through the analysis of time-series reaction curves. Methods: Our system integrates three heterogeneous anomaly detection paradigms: an Enhanced Dynamic Time Warping (DTW) detector for robust non-linear pattern matching, a Shape Template Matching (STM) detector that mimics expert clinical logic by analyzing morphological features in a normalized shape space, and a specialized Time-series Variational Autoencoder (TimeVAE) for deep representation learning. The outputs of these detectors are fused using a weighted ensemble strategy, which is specifically designed to prioritize the minimization of false negatives—a critical requirement in medical diagnostics. Results: We evaluate our framework on a comprehensive, multi-center real-world dataset comprising seven distinct biochemical assays. Experimental results demonstrate that our proposed method achieves superior performance, attaining a $0\%$ false negative rate on CRE and DBIL assays and outperforming all baseline methods on the other five datasets. An ablation study confirms the model’s robustness even with limited training data, and a comparative analysis against eight state-of-the-art baseline methods further validates the effectiveness of our domain-optimized ensemble approach. Conclusions: The system provides a robust, interpretable, and highly automated solution for transitioning from reactive maintenance to proactive, real-time quality assurance in clinical laboratories. Full article

Ship self-sinking accidents threaten maritime safety, human life, property, and the marine environment, and understanding their causal-factor associations is essential for developing effective preventive measures. This study aims to identify the multi-level factors, recurrent association patterns, and critical structural nodes involved in ship self-sinking accidents. A hybrid framework integrating grounded theory, the Human Factors Analysis and Classification System (HFACS), FP-growth association rule mining, and complex network analysis was applied to 150 accident investigation reports released by the China Maritime Safety Administration between 2014 and 2024. Findings suggest that adverse weather and sea conditions, inadequate ship safety management, and crew incompetence are the most frequent factors. Thirty causal factors were identified and classified into four HFACS levels, and 229 association rules were generated to construct a directed weighted causal-factor association network with 19 nodes and 229 edges. Network results indicate that inadequate ship safety management, crew incompetence, ship unseaworthiness, insufficient maintenance of hull weathertight integrity, and improper or untimely emergency measures occupy critical positions in the association structure. This research offers insight into ship self-sinking accidents and identifies priority intervention points for more targeted maritime supervision, safety management and accident prevention. Full article

Introduction: Fish skin is the first line of defense against the aquatic environment, acting as a physical, chemical, and immunological barrier. In addition to preventing pathogen entry, the skin and its mucus contribute to osmoregulation, innate immunity, and redox balance. Skin lesions—caused by mechanical damage, parasites, environmental stress, or handling—disrupt this barrier, increasing susceptibility to infections, inflammation, and production losses. Thus, efficient skin regeneration is essential for fish welfare and performance. Nutrition plays a key role in this process by providing substrates for epithelial repair, immune function, and antioxidant defense. Among dietary factors, zinc (Zn) is particularly important due to its involvement in cell proliferation, enzymatic activity, and maintenance of skin integrity. Objective: Our objective is to assess the effectiveness of image-based analysis in quantifying the skin healing process in Atlantic salmon fed diets supplemented with zinc. Methodology: The trial comprised three dietary treatments: a control diet with 42 mg Zn per kg (D1), and two diets supplemented up to 120 mg/kg of zinc, derived from inorganic (D2) or organic (D3) forms. Pit-tagged fish with an initial body weight (78 ± 0.1 g) were fed the diets for 75 days. After 15 days of experimental feeding, a standardized wound lesion (2.5 mm diameter × 0.5 mm depth) was inflicted in deeply anesthetized fish, with a disposable biopsy punch, in the dorsal area. After wound infliction, the fish resumed their normal feeding regime for the rest of the trial days. The progression of skin wound healing was assessed using standardized digital image analysis. High-resolution photographs of individual wounds were collected 8, 16, 24 and 32 days post-wounding. All images were acquired under standardized conditions with the inclusion of ArUco identifiers to enable a subsequent computer-assisted comparison. Morphometric parameters (wound width, diameter, perimeter and area) were used to assess wound contraction and closure over time. In parallel, a semi-quantitative visual scoring system was applied to each wound image to capture qualitative aspects of healing that are not fully described by morphometric data alone. Results: Full data analysis is currently underway, but the first results show beneficial effects of dietary zinc supplementation on the skin regenerative process. Conclusions: The combined use of objective digital measurements and standardized visual scoring enabled a comprehensive evaluation of wound healing progress, bridging quantitative tissue remodeling with biologically relevant phenotypic outcomes. This image-based framework provides a sensitive and reproducible approach for assessing dietary interventions targeting skin regeneration and barrier restoration in Atlantic salmon. Full article

Inverters are widely applied in aviation, distributed power grids, and vehicles, where their health status directly impacts the stable operation of entire systems. Existing health assessment methods suffer from poor real-time performance, require additional measurement circuits, and are prone to misjudgment, while failing to adequately address slow degradation behaviors during inverter operation. To address these challenges, this study proposes an inverter health assessment method based on an improved D-S evidence theory. First, based on the practical requirements of subway auxiliary inverters, 13 key evaluation indicators were selected. Subjective weights were obtained using the Analytic Hierarchy Process (AHP), while objective weights were derived through the Critic method, credibility, and falsity weighting. These were then fused using game theory to obtain composite weights. Next, after data normalization, a ridge-type membership function was employed to describe health state uncertainty. Finally, the improved D-S evidence theory integrates multi-source information to achieve online health status assessment. Experimental validation demonstrates that this method effectively evaluates the impact of IGBT failures, sensor malfunctions, and capacitor–inductor degradation on the inverter. It exhibits strong robustness under DC voltage fluctuations and load variations, enabling real-time output of health scores and grades to provide a reliable basis for maintenance decisions. Full article

Flooding poses a significant threat to commercial real estate investment, disrupting business operations, escalating maintenance costs, and heightening investment uncertainty, particularly in coastal and low-lying urban environments. This study examines the social dimensions of climate vulnerability by investigating how flood risk shapes stakeholders’ decisions to invest in commercial properties within flood-prone urban areas, with a focus on Lekki Phase 1, Lagos, Nigeria. A quantitative survey design was adopted. Data were collected from 87 commercial property investors through a structured questionnaire (FIIFRZQ) measured on a four-point Likert-type scale. The instrument demonstrated acceptable overall internal consistency (Cronbach’s α = 0.72), with subscale α values ranging from 0.62 to 0.81. Multiple regression analysis was used to assess the joint and individual contributions of seven factor categories (environmental, legal, economic, neighbourhood, structural, locational and behavioural) to investors’ willingness to invest in commercial property that is at risk of flooding. The seven predictors collectively explained 61.2% of the variance in investment willingness (R² = 0.612; F(7, 79) = 17.91; p < 0.001). Five factors, namely legal, environmental, structural, economic, and locational, were statistically significant contributors to investment willingness, while neighbourhood and behavioural factors were not. Johnson’s relative weights analysis confirmed legal and environmental considerations as the dominant drivers. The findings illuminate the interplay between climate vulnerability and investor behaviour in urban real estate markets, with actionable implications for policymakers, real estate practitioners, and investors navigating decision-making in flood-exposed urban environments. Full article

Partial discharge (PD) source-type classification is essential for condition-based maintenance of high-voltage apparatus. Existing approaches based on grid discretizations of phase-resolved partial discharge (PRPD) patterns suffer from performance degradation under stochastic interference and multi-source conditions. This paper proposes a graph-based framework that integrates the morphological characterization of raw high-frequency PD waveforms with the phase-amplitude position of individual discharge events to enable multi-label classification, identifying multiple PD sources coexisting within a single test. The framework operates through three stages: a multi-task neural network extracts per-pulse embeddings and confidence scores; a construction procedure establishes selective graph connectivity based on spatial proximity and morphological similarity; and an edge-conditioned graph neural network performs classification via message passing weighted by multimodal edge attributes. Experimental evaluation on PD measurements acquired in accordance with IEC 60270 shows that the proposed framework achieves a Matthews correlation coefficient (MCC) of 0.98 and an exact match ratio of 0.97 across single-source, noisy, and multi-source conditions, substantially outperforming histogram- and set-based baselines. The framework maintains an MCC of 0.97 in multi-source scenarios, where its advantage over existing methods is most pronounced. Cross-domain evaluation on an independent dataset acquired with different laboratory equipment confirms the approach’s robustness, achieving an MCC of 0.93 without retraining. Finally, an ablation study demonstrates that the joint removal of morphological similarity filtering and confidence-based node filtering and edge gating reduces the MCC by 0.25, confirming the critical role of the waveform-informed relational structure. Full article

Equipment fault prediction in maglev train systems poses substantial challenges: fault events are inherently rare, class distributions are severely imbalanced, and individual equipment units are subject to complex spatial and functional couplings that single-device statistical approaches fundamentally cannot capture. To address these challenges, this study proposes a Knowledge Graph-driven Graph Neural Network (KG-GNN) framework. A fault knowledge graph encompassing equipment, fault, temporal, and environmental entities is constructed to unify multi-source maintenance data. Graph connectivity is established via three spatial relation types (co-location, co-zone, and co-level), with edge weights derived from Laplacian-smoothed Lift scores quantifying fault co-occurrence strength. A two-layer GATv2Conv-based graph attention network is designed: the first layer employs four-head attention with explicit edge-weight integration to capture heterogeneous neighborhood influences, while the second layer produces compact node embeddings via single-head attention. A Top-20 sparsification strategy suppresses weak-association noise, and training under severe class imbalance is stabilized through Focal Loss and F2-Score-guided early stopping. On the test set, the proposed method achieves an F2-Score of 0.5703, Recall of 0.6825, and AUC-ROC of 0.9329 (single-run evaluation); multi-seed evaluation (5 seeds) yields F2 = 0.5645 ± 0.0035, Recall = 0.6789 ± 0.0095, and AUC-ROC = 0.9298 ± 0.0026, outperforming the MLP baseline by 18.3% in F2-Score and substantially exceeding GCN (F2 = 0.1476 ± 0.0176) and GATConv (F2 = 0.4284 ± 0.0097). Ablation studies confirm the individual contributions of authentic graph topology, precise edge weighting, and graph sparsification to overall performance. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

Background/Objectives: Optimal vascular access remains a critical determinant of outcomes in patients undergoing maintenance hemodialysis. While an arteriovenous fistula (AVF) is generally preferred over an arteriovenous graft (AVG), the impact of obesity and antithrombotic therapy on access-related complications remains incompletely defined. This study evaluated the association between vascular access type, obesity status, and adverse outcomes in a large real-world cohort. Methods: We conducted a retrospective cohort study using de-identified electronic health record data from the TriNetX Global Collaborative Network. Adult patients (≥18 years) receiving maintenance hemodialysis were stratified by vascular access type (AVF vs. AVG), body mass index (normal: 18.5–24.9 kg/m², obese: ≥30 kg/m²), and antithrombotic medication exposure. Propensity score matching (1:1) was performed within BMI strata. Primary outcomes included vascular access thrombosis, AVG failure, and AVF failure. Time-to-event analyses used Kaplan–Meier and Cox proportional hazards models. Results: AVG was associated with significantly higher rates of thrombosis and access failure compared with AVF in both obese and normal-weight cohorts (all p < 0.0001). In patients with obesity, thrombosis rates increased from 10.47% (AVF) to 17.54% (AVG) at 3 months to 34.32% versus 42.24% at 5 years. Kaplan–Meier analysis demonstrated early and persistent separation of thrombosis-free survival curves, with AVG associated with increased risk (HR 1.23; 95% CI, 1.07–1.41; log-rank p = 0.0001). Antithrombotic therapy reduced absolute risks but did not eliminate the relative disadvantage of AVG. Conclusions: In this large real-world cohort, AVG was consistently associated with higher risks of thrombosis and access failure compared with AVF, regardless of obesity status or medication exposure. These findings support preferential use of AVF and highlight the need for individualized vascular access strategies in patients undergoing hemodialysis. Full article

Offshore wind turbine blades are chronically exposed to complex marine environments with high humidity, salt spray, strong wind, waves, and intense radiation. Under such conditions, blade defects often exhibit small sizes, weak visual features, and heterogeneous visible infrared manifestations. Conventional single-sensor monitoring and empirically weighted fusion methods are insufficient for reliable defect diagnosis and risk grading. To address this problem, this paper proposes a cross-sensor consistency-guided dual-spectrum fusion framework, termed CG-DSF, for offshore wind turbine blade defect diagnosis and risk assessment. First, visible-light images and infrared thermal images are acquired by UAV-mounted imaging sensors, and sensor-specific branches are constructed to extract surface structural features and thermal anomaly responses. Second, visible and infrared features are aligned at the feature token level, and cross-sensor evidence is evaluated for spatial consistency, diagnostic semantic consistency, and anomaly consistency. A reliability-aware fusion strategy is then used to suppress low-quality or conflicting observations and construct a unified defect representation. Finally, a series of representative simulation case studies are carried out to comprehensively assess the overall performance and practical applicability of the constructed model. Experimental results reveal that the proposed framework possesses evident advantages in blade defect identification for offshore wind turbines, offering a feasible solution for advancing proactive and intelligent condition-based operation and maintenance of offshore wind assets in complex marine environments. Full article

Artificial shelterbelts in arid and semi-arid regions play a key role in controlling land degradation, regulating wind erosion, and maintaining ecological security. However, their long-term protective effectiveness increasingly depends on accurate degradation diagnosis and targeted management of aging and degraded stands. This study developed a comprehensive health assessment and degradation grading framework for poplar shelterbelts in the Kubuqi Desert, northern China, using an indicator system covering stand structure, community structure, soil conditions, health risks, and external disturbances. Indicator weights were determined using the entropy weight method, and degradation grades were classified by combining the technique for order preference by similarity to ideal solution (TOPSIS) model with the rank-sum ratio (RSR)–Probit method. The results showed that soil conditions and stand structure were the dominant dimensions distinguishing degradation status, with weights of 50.98% and 25.30%, respectively. Grade I, Grade II, Grade III, and Grade IV stands accounted for 21.88%, 25.00%, 34.38%, and 18.75% of the plots, respectively, indicating that lightly and moderately degraded stands were predominant. Degradation grades were also associated with changes in understory cover and surface soil nutrients, especially decreases in soil organic matter and alkali-hydrolyzable nitrogen. Based on these results, grade-specific management strategies were proposed, including conservation and maintenance, density regulation, assisted restoration, and near-natural transformation. This framework provides a practical basis for diagnosing degradation status and guiding the renewal and management of aging shelterbelts in arid sandy regions. Full article

Choosing the most suitable tractor is a complex and high-stakes decision where technical performance, financial capability, and sustainability considerations must be balanced. However, tractor selection in existing studies lacks objective, sustainability-oriented evaluation frameworks, leaving farmers vulnerable to potentially poor investments with long-term economic, operational, and environmental impacts. Therefore, this research proposes a software-based Decision Support System (DSS) that incorporates objective multi-criteria decision-making (MCDM) models within a management control perspective focused on sustainability and provides a clear, data-driven method for tractor selection for small farmers. Four popular tractor models in Egypt were selected for evaluation based on three criteria related to sustainability: power (C1), purchase price (C2), and availability of maintenance and spare parts (C3). Subsequently, a DSS was implemented using Python, and five MCDM methods—CRITIC, MEREC, Entropy, Standard Deviation (SD), and TOPSIS—were used to select the tractor that best meets sustainability objectives. The findings indicate that tractor T2, which had the lowest purchase price (USD 12,390) and enough power (60 HP), was the best-rated tractor. The impact of each criterion varied by method: C1 was the most important in the Entropy method (0.3657), while C2 was the most important in the CRITIC (0.5552), MEREC (0.3432), and SD (0.5938) weightings. The proposed DSS improves transparency and supports more informed, evidence-based decisions in agricultural mechanization. Overall, the system offers a practical and scalable tool that helps smallholder farmers and policymakers make sustainable tractor choices, contributing to progress toward SDGs 2, 7, 12, and 13. Full article

The article investigates damage in textile-reinforced rubber conveyor belts caused by impact loading. The study aims to evaluate how impact conditions and belt structural properties affect severe damage formation and to develop predictive models for identifying critical operating conditions. Damage assessment was performed using visual inspection and computed tomography (CT), with CT serving as a reference method due to its ability to detect internal defects in the load-bearing carcass. CT identified more severe damage cases than visual inspection, confirming its higher sensitivity. Experimental tests were carried out with impact heights between 0.8 and 2.6 m and impact weights from 50 to 100 kg. The results showed that impact energy is the dominant factor influencing damage formation, as higher impact heights and weights significantly increased the probability of severe damage. Belt structural characteristics also affected damage resistance, especially the thickness of the top cover, which reduced the risk of failure. To predict severe damage, Logistic Regression, Random Forest, and XGBoost models were applied, all achieving excellent performance (AUC > 0.95). Logistic Regression (AUC = 0.994) additionally enabled the estimation of damage probability and the identification of critical impact conditions. The proposed approach supports safer operating limits, risk assessment, and predictive maintenance in conveyor systems. Full article

Pavement damages along the Qinghai–Xizang Highway (QXH) in permafrost regions are jointly controlled by geographical and engineering factors, leading to higher damage rates than in non-permafrost regions. However, the overall development trend of these damages and the spatial–temporal patterns have not been systematically quantified. To analyze the spatial distribution of different pavement damages, reveal the spatial–temporal associations, and analyze the spatial heterogeneity of the driving factors, three field surveys were conducted in 2014, 2019 and 2024, with records of seven major pavement damages. Statistical analyses were used to examine the relationships among single and co-occurring damages. Then, a novel geographical model, combining a graph convolutional network with multi-scale geographically weighted regression (GCN-MGWR), was further developed to treat the QXH as a linear geographic unit and to assess the spatial heterogeneity and relative contribution of different influencing factors. The results show that the mean pavement damage ratios in permafrost regions during the three surveys are 4.21%, 6.82%, and 4.74%, respectively, with crack-type damages (transverse, longitudinal, and block cracking) exhibiting the highest occurrence rates. The three strongest pairs of correlations are transverse and longitudinal cracking (0.584), transverse and block cracking (0.570), and waving and rutting (0.622). The primary factors influencing crack-type damages are embankment thickness, mean annual ground surface temperature (MAGST), elevation and existing damages. Transverse and longitudinal cracking show a pronounced increase with rising MAGST, and embankment thickness below 1 m or above 4 m significantly contribute to the development of both crack types (SHAP > 0.5). Overall, the evolution of crack-type damages has shifted from being primarily controlled by geographical factors to being controlled by the combined influence of engineering and geographical factors during 2014–2024. The factor contributions identified by the GCN-MGWR model provide quantitative support for the regional adaptive design and specific maintenance of roadway in permafrost regions. Full article