Search Results (844)

Sustainable coastal environmental management relies on continuous assessment of sediment quality. In the Saudi Arabian Red Sea, such sediments function as both sinks and potential secondary sources of heavy metals (HMs) originating from natural processes and anthropogenic activities. The present study investigated the concentrations of HMs in surface sediments from Jeddah and Rabigh. Contamination levels, as well as possible natural and anthropogenic inputs and potential risks to human health through ingestion, dermal contact, and inhalation pathways, were evaluated. The results indicated that Fe is the most abundant metal at both sites (1570.40 mg/kg at Jeddah and 1804.11 mg/kg at Rabigh). Cu, Ni, and Zn are present in the sediments at concentrations ranging between 3.12 and 4.49 mg/kg, while Cd and Pb remain below the detection limits in all samples. Generally, the contamination index values indicated low levels in both regions. The evaluation of human health risks identified ingestion as the primary exposure pathway. Non-carcinogenic risk levels were within safe limits for both adults and children. However, the carcinogenic risk assessment of nickel (Ni) indicated that all values fall within the acceptable range (10⁻⁶–10⁻⁴), although children consistently showed higher risks than adults. Despite the overall low levels of pollution, the moderate enrichment of Cu, Ni, and Zn in Jeddah and Cu in Rabigh, along with the higher non-carcinogenic risks to children at the Rabigh site, highlights the need for continuous environmental monitoring and further investigation to support the sustainable management of coastal ecosystems. Full article

The transportation sector faces growing supply chain disruptions driven by workforce shortages, digital skills gaps, and weak cultural readiness for risk. Human Resource Management (HRM) offers a credible path toward stronger supply chain resilience (SCR), yet existing decision support tools lack the formal reasoning and auditability that systematic HR risk assessment requires. This paper proposes a Task-Typed Reasoning Module-Enhanced Retrieval-Augmented Generation (RAG) Expert System framework for HR-driven risk assessment in transportation supply chains. The framework employs a six-layer architecture integrating four core components: a Task-Aware RAG layer for knowledge extraction from heterogeneous HR documents, a Task-Routed Evidence Extractor for risk factor identification and source reliability scoring, a Task-Based Reasoning Core applying weighted Mamdani fuzzy inference, and a Task-Guided Synthesis Module using Dempster–Shafer evidential reasoning for risk profiling. Task-Typed Reasoning Modules (TTRMs) organise reasoning into reusable, adaptable units covering Likelihood, Impact, Vulnerability, Mitigation, and Prioritisation, refined through an expert-gated feedback loop. Applied to an illustrative UK transportation logistics case study, the framework ranked HR-driven workforce risks, quantified uncertainty through belief-plausibility intervals, and generated traceable recommendations linked to four HRM-SCR dimensions. As a proof-of-concept demonstration, the framework provides a conceptual foundation for HR risk management in transportation, extending expert system reasoning through adaptive AI to offer mathematically grounded, traceable outputs for practitioners and researchers; empirical validation in live organisational settings remains a necessary next step. By formalising the human dimension of resilience, the framework contributes toward socially, economically, and environmentally sustainable transportation supply chains, aligning HR risk management with the sustainable development objectives examined in this study. Full article

Background: Cardiometabolic risk is shaped not only by the amount of food intake and physical activity but also by their timing and regularity across the 24 h cycle. We propose rhythm coherence as a hypothesis-generating framework describing the temporal stability and alignment of eating, physical activity, and sleep. Its vascular and glycemic relevance has not yet been prospectively validated. Methods: This semi-structured narrative review was supported by a transparent, staged evidence-identification process using PubMed, Scopus, and Web of Science Core Collection. Main searches used a 2020–2026 window, with a broader 2018–2026 window for targeted athlete-focused searches. After cross-database deduplication, 1081 unique records entered broad screening; 694 underwent strict screening, yielding 257 core candidate records. A targeted athlete-focused search contributed 46 additional records, producing a 303-record candidate evidence pool for topic mapping. From this pool, 131 high-priority records underwent full-text narrative synthesis, and 92 sources were ultimately cited directly in the manuscript. These counts represent successive review stages rather than a PRISMA-defined systematic-review inclusion set. Results: Earlier or more regular eating patterns, postprandial physical activity, and stable sleep–wake schedules were associated with more favorable cardiometabolic profiles in selected studies. However, the evidence was heterogeneous and included mechanistic studies, observational analyses, and intervention trials. These findings support further investigation but do not establish an effect of integrated eating–activity–sleep coherence. Numerical effects reported for individual interventions should not be attributed to the proposed integrated framework or to the Rhythm Coherence Index (RCI). Conclusions: Coordinated assessment of eating, activity, and sleep timing may offer a useful research direction. The RCI is an author-proposed candidate composite, not an existing validated instrument or clinical decision rule. Its formula, component weights, missing-data procedures, thresholds, reliability, responsiveness, predictive value, and external validity require prospective evaluation before clinical application can be considered. Full article

China is the world’s largest producer and consumer of mineral resources. While large-scale development of metal mineral resources has supported rapid national economic development, it has also caused serious heavy metal pollution in soils in mining areas. This paper systematically reviews the latest research progress on heavy metal pollution in soils in mining areas, focusing on Chinese metal mining areas, covering pollution characteristics, source identification, risk assessment, and sustainable remediation technologies. Meta-analysis shows that smelting sources contribute more than 50% of soil Cd in southwestern Pb-Zn mining areas, making them priority targets for risk management. This review aims to provide theoretical support and decision-making references for precise source tracing, scientific assessment, and green remediation of heavy metal pollution affecting soils in Chinese metal mining areas. Full article

High-resolution precipitation fields are essential for flash-flood forecasting and hydrological risk management, especially in small and medium-sized basins, yet single-source precipitation products often show limited accuracy over complex terrain. This study develops a two-stage machine-learning framework for 1 km/1 h multi-source precipitation fusion over Shaoxing, China, during the 2025 flood season. In the first stage, a machine-learning classifier identifies precipitation occurrence and reduces zero-inflated noise; in the second stage, an optimized tree-based residual-regression model corrects precipitation estimates for rainy samples. A 61-dimensional feature set was constructed by integrating satellite precipitation estimates, weather-radar precipitation estimates from the Zhejiang radar network, temporal-lag and accumulation statistics, neighborhood descriptors, cyclic time variables, and terrain-derived interaction features, with gauge observations used as the training target. After quality control, the dataset comprised 41,458 hourly station samples from 72 rain gauges. The stations were divided at the station level into a 57-station development set and a fixed 15-station held-out spatial test set containing 8637 hourly samples. Station-blocked fivefold cross-validation within the development set was used for model selection, hyperparameter tuning, and probability-threshold selection, whereas the held-out stations were used only for final performance evaluation. On the fixed held-out test set, the occurrence classifier achieved an overall accuracy of 0.947, with a probability of detection of 0.806, a false alarm ratio of 0.158, a critical success index of 0.700, and an F1 score of 0.823. For quantitative estimation, the two-stage fusion product reduced root mean square error from 2.342 mm for satellite precipitation estimates to 1.189 mm, corresponding to a 49.22% reduction, and decreased mean absolute error from 0.712 mm to 0.262 mm, while increasing the coefficient of determination to 0.685. The fused precipitation product also improved the detection of intense rainfall events, with probability of detection and critical success index reaching 0.511 and 0.442, respectively, for events exceeding 10 mm/h, while reducing false weak precipitation and showing closer agreement with observed station-level spatial variability. By separating precipitation-occurrence identification from rainfall-intensity correction, the framework reduces zero-inflated bias, improves heavy-rainfall representation, and demonstrates predictive skill at gauges excluded from model development during the 2025 flood season. Full article

Contemporary logistics systems operate in increasingly volatile and uncertain environments, where disruptions and digital transformation jointly shape system performance and continuity. In this context, resilience has emerged as a critical capability; however, existing approaches often remain fragmented, representing resilience either as isolated capabilities or aggregated indicators, while lacking an integrated conceptual architecture for resilience assessment. This study introduces the House of Resilience (HoR) as a hierarchical conceptual architecture that represents resilience as an emergent multi-layer system property and operationalizes this concept through the Fuzzy House of Resilience Assessment Model (FHOR-RAM). The model applies fuzzy logic with linguistic variables, triangular fuzzy numbers, and a hierarchical Mamdani-type fuzzy inference system to capture uncertainty in expert assessments and aggregate resilience measures at both layer and system levels. The proposed framework is illustrated through a real-world procurement system case study conducted in a manufacturing company operating within a global supply network. The results indicate a moderate overall resilience level (R_HoR = 0.402), with the weakest performance observed in the structural (R₃ = 0.31), strategic (R₄ = 0.33), and technological and risk assessment (R₁ = 0.34) layers, while the operational and adaptive layer achieved a moderate resilience level (R₂ = 0.51). The diagnostic analysis identified supplier dependency, lead-time instability, limited resilience governance, and insufficient digital capabilities as the principal resilience bottlenecks. Scenario analysis demonstrated that improvements targeting individual resilience dimensions provide only limited system-level gains, while coordinated development across multiple resilience layers enables a transition toward higher resilience levels (R_HoR = 0.647). The findings confirm that resilience should be addressed as a multidimensional and multi-layer system property rather than as an isolated technological capability. The study demonstrates that fuzzy inference provides an effective operational mechanism for translating the House of Resilience conceptual architecture into an interpretable and decision-oriented resilience assessment framework under uncertainty. The study demonstrates that fuzzy inference provides an effective operational mechanism for translating the House of Resilience conceptual architecture into an interpretable and decision-oriented resilience assessment framework under uncertainty. The case study illustrates how the framework can support structured resilience diagnosis and identification of potential improvement priorities within a procurement system. However, broader empirical validation, including multi-company studies and integration with organizational data sources, remains necessary to further evaluate its transferability and practical deployment potential. Full article

With the rapid development of artificial intelligence, UAV remote sensing, and agricultural Internet of Things technologies, crop growth monitoring is evolving from manual inspection and single-source analysis toward intelligent decision-making based on multisource perception. However, existing methods still suffer from limited robustness under environmental variations, insufficient integration between UAV imagery and sparse ground sensor observations, and weak capability for transforming predictions into practical agricultural management recommendations. This study proposes a UAV–ground sensor collaborative lightweight framework for crop growth assessment and agricultural decision support. The proposed framework integrates UAV RGB and multispectral imagery with ground sensor observations through a region-level aerial–ground alignment mechanism and a sensor-guided attention fusion module, enabling environmental conditions to enhance visual feature interpretation. Furthermore, a fact-constrained decision module is developed to generate management recommendations based on crop status, environmental risks, and field information. Experimental results demonstrate that the proposed method achieves superior performance in crop growth classification and yield-trend prediction, reaching Accuracy, Precision, Recall, and F1-score values of $92.47\%$, $91.86\%$, $91.39\%$, and $91.62\%$, respectively, with an RMSE of $0.381$ and an $R^2$ of $0.902$. The lightweight framework requires only $6.18$M parameters and $0.91$G FLOPs, achieving $39.56$ ms inference latency and $25.28$ FPS on edge devices. The proposed framework also improves decision reliability, achieving an expert agreement rate of $89.34\%$ and a risk identification accuracy of $90.18\%$. Economic analysis indicates that the proposed framework reduces labor cost, water consumption, and fertilizer input by $49.7\%$, $26.7\%$, and $23.0\%$, respectively, while increasing net benefit by $46.1\%$ compared with conventional field management practices. These results demonstrate that the proposed method provides an accurate, interpretable, and deployable AI-driven solution for intelligent crop management in smallholder and medium-sized farming systems. Full article

With the increasing integration of photovoltaic (PV) and energy storage systems into power grids, stability issues caused by converter dynamics, grid impedance interactions, and AC/DC coupling have become increasingly significant. Conventional uniform frequency-scanning methods often fail to provide sufficient resolution in stability-critical frequency regions, limiting the accurate characterization of resonance phenomena. This paper proposes an adaptive two-port admittance modeling and stability assessment method for grid-connected PV-storage systems considering AC/DC coupling characteristics. A two-port frequency-domain model incorporating AC-side admittance, DC-side admittance, and transfer admittance is first established to describe dynamic interactions between the AC and DC subsystems. An adaptive frequency-domain modeling framework with physical constraints is then developed to improve modeling accuracy in critical frequency bands. Furthermore, an improved generalized impedance-ratio criterion considering AC/DC transfer effects and DC-side participation factors is proposed for resonance risk assessment and source identification. Simulation studies and hardware-in-the-loop experiments based on MATLAB 2024b and RT-LAB 2024.1.1 are conducted to validate the proposed method. Results demonstrate that the proposed approach effectively enhances frequency-domain resolution, improves the characterization of AC/DC-coupled resonance behavior, and accurately identifies dominant resonance sources under different operating conditions. Full article

IgA nephropathy (IgAN) is a primary glomerular disease with various clinical features, disease progression, and therapeutic responses that affects people worldwide. Currently, risk stratification depends primarily on clinical variables, together with the Oxford MEST-C classification, which indicates structural damage. However, these approaches only provide a limited explanation of the molecular mechanisms responsible for the disease. Recent proteomic discoveries have enabled researchers to characterize proteins not only in blood plasma and urine but also in kidney tissues, opening new avenues for understanding the underlying biology of IgAN. Our review addresses existing findings in plasma proteomic studies and, at the same time, brings into the picture developments in urinary and tissue proteomic profiling, thereby demonstrating that molecular profiling has been essential for further understanding of IgAN pathogenesis. Several research studies highlight complement system dysregulation, immune system overactivity, extracellular matrix remodeling, and metabolic disturbances as the leading factors linked to disease activity and progression. Even after many years in biomarker discovery, the development and clinical application of single plasma-based molecules as markers for disease detection remain challenging. Proteomic signatures based on the various processes involved in a single disease consistently outperform single protein identification in describing complexity and distinguishing molecular endotypes. We also present the current status of proteomic-based profiling methods, cross-linking proteomics with other data sources, and the remaining clinical application barriers. Through the development of this technology, proteomics has not only enabled the discovery of new biomarkers but also provided a framework for viewing IgAN as a heterogeneous disease comprising distinct molecular endotypes. Overall, current evidence indicates that proteomics is evolving from biomarker discovery toward molecular disease classification, with the potential to improve prognostication, guide mechanism-based therapeutic selection, and advance precision nephrology in IgAN. Full article

In the literature, there is a limited number of case reports of Paenibacillus urinalis in adults. However, there is no information available regarding P. urinalis infection in children and infants. Paenibacillus growth in blood and sterile site cultures is generally considered contamination; it is unclear whether P. urinalis is the true cause of the infection. In this retrospective study, we aimed to clinically evaluate P. urinalis growth in the blood cultures of children at a tertiary referral hospital over a 5.5-year period, with identification performed using the MALDI-TOF MS method. A total of 170 hospitalized children showed P. urinalis growth in sterile site cultures (blood: 162; catheter: 7; CSF: 1), with forty percent of all growth occurring in only three wards (neonatal intensive care unit, hematology, oncology). Of 170 cultures, 5 (2.9%) showed further P. urinalis growth in control cultures, which could be considered significant. In cases with a second culture, the average growth time (time to positivity) was relatively short, at 24 + 7 h (mean + SD, min-max: 17–33 h). No mortality was observed in any case. Evaluations were conducted by the Hospital Infection Control Committee to determine the source of P. urinalis contamination. P. urinalis growth was detected in 33% of samples taken from clean, packaged linens before use; 53% of ambient air cultures; and 65% of hospitalized patient skin swabs. In conclusion, P. urinalis blood culture growths are primarily considered contamination; however, these growths should be carefully evaluated in risk groups (especially those admitted in neonatal intensive care units and young infants less than 3 months old). In hospitalized patients, clustered growths may be influenced by environmental (such as clean packaged linens and ambient air) and skin colonization. Full article

Heavy metals in surface waters pose significant risks to aquatic ecosystems and human health. In this study, a comprehensive analysis of multiple heavy metals (V, Cr, Mn, Co, Ni, Cu, Zn, As and Cd) was performed using extensive surface water samples from the Meili Snow Mountains glacier basin, southeastern Tibetan Plateau. Results showed that heavy metal concentrations were higher in glacier meltwater than in downstream rivers, with significant variability observed in both the glacier basin (0.00300–33.4 μg/L) and downstream rivers (0.00500–2.85 μg/L), attributable to local geological processes and regional atmospheric deposition of transported particulate pollutants. Elevated total heavy metal concentrations at Sinong River and Qunatong River were primarily driven by uneven distribution of specific metals, likely from both anthropogenic and natural sources. Principal component analysis extracted three components that grouped heavy metals into Mn–Co–Ni–Cd, Cu–Zn, and V–Cr associations, indicating mixed geogenic and anthropogenic sources, with long-range atmospheric transport from surrounding polluted regions being a notable contributor. Risk assessments indicated low ecological risks (ERI < 150) and safe non–carcinogenic risks (HI < 1), with children exhibiting higher susceptibility than adults and As being the primary contributor. Although the carcinogenic risks (TCR: 10⁻⁶–10⁻⁴) were acceptable, ingestion of As and Ni posed higher risks, especially in the Yubeng River basin for adults. These findings provide valuable insights for water resource management and health protection in the southeastern Tibetan Plateau. Full article

To improve the practicality of water-inrush hazard identification in underground metal mines under complex hydrogeological conditions, this study develops a multi-source information fusion evaluation framework. An index system is established that encompasses water-source conditions, water-conducting pathway characteristics, mining-induced disturbance, and goaf-related hazards. Subjective and objective information are integrated through combined weighting based on the intuitionistic fuzzy analytic hierarchy process (IFAHP) and the entropy weight method (EWM). To characterize uncertainty and support hazard classification, a normal cloud model is introduced. The proposed method is applied to three stopes, namely 150701, 200-6-1, and 400-27-3, in a copper–iron mine in Anhui Province. The evaluation results classify the three stopes as Level III, Level II, and Level IV, respectively, which are consistent with the observed water inflows of approximately 28 m³/h, 17 m³/h, and 35 m³/h. These results indicate that the proposed method shows applicability in stope-scale water-inrush hazard assessment in the case mine and can provide a quantitative reference for risk identification and prevention under complex hydrogeological conditions. Full article

Early detection of autism spectrum disorder (ASD) in young children is essential for timely referral, developmental monitoring, and access to early intervention. However, conventional screening and diagnostic pathways often depend on parent-report instruments, episodic clinical observation, and specialist-administered assessments, which may delay identification during the first years of life. This scoping review maps the methodological landscape of early ASD detection from traditional clinical screening to multimodal artificial intelligence (AI). A structured literature search was conducted across major biomedical, psychological, and engineering databases for studies published between January 2010 and May 2026. After screening and eligibility assessment, 65 evidence sources were included in the qualitative synthesis, with additional methodological guidelines used to support reporting and appraisal. The reviewed evidence shows that early ASD detection is increasingly shifting from single-session clinical assessment toward multidimensional risk characterization. Clinical and behavioral screening tools remain the foundation of early identification, while eye tracking, video-based motor analysis, acoustic and vocal biomarkers, electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and molecular or genomic indicators provide complementary information across different developmental windows. AI-based methods, including machine learning, deep learning, Transformer architectures, multimodal fusion strategies, and foundation-model-based representation learning, may improve the objective quantification of gaze, movement, vocalization, neural activity, and biological risk. Nevertheless, most AI-assisted systems remain limited by small and heterogeneous datasets, insufficient external validation, population bias, privacy concerns, computational burden, and limited interpretability. This review argues that future early ASD detection systems should be developed as clinician-supervised decision-support tools rather than autonomous diagnostic instruments. Clinically meaningful progress will require robust external validation, privacy-preserving deployment, age-appropriate risk stratification, and intrinsically interpretable architectures that align model outputs with developmental and clinical knowledge. Full article

Multimorbidity risk identification in elderly health consultation scenarios is challenging because chronic disease history and medication exposure may interact through complex causal pathways. Existing LLM-, RAG-, and graph-based retrieval methods often rely on semantic relevance or graph connectivity, which may be insufficient for identifying user-specific risk mechanisms. This study proposes RACPR, a generative AI-enhanced risk-aware causal path re-ranking framework for interpretable multimorbidity risk identification. RACPR ranks candidate causal paths by integrating user-entity alignment, causal coherence, risk contribution, and path length control and uses generative AI to transform selected paths into readable, path-grounded explanations. To support controlled algorithmic evaluation, we constructed a normalized benchmark of 1002 elderly multimorbidity consultation cases covering diabetes, hypertension, and chronic kidney disease. The benchmark and supporting knowledge graph were derived from publicly available biomedical and health information resources, including PubMed abstracts, guideline and review sources, DrugBank medication-safety evidence, and MedlinePlus-based terminology. Under a leakage-controlled setting, only age, diagnosed diseases, and medication exposures were used as model-accessible inputs, while abnormal indicators, support paths, and rationales were reserved for evaluation. On the test set, RACPR achieved an Accuracy of 0.659, a Precision of 0.602, a Recall of 0.938, an F1-score of 0.733, and an AUC of 0.777. Ablation analysis showed that removing the risk-aware component reduced AUC from 0.777 to 0.428. Benchmark-level explanation evaluation further showed improvements in path consistency, path hit rate, and health-oriented plausibility. These findings indicate that risk-aware causal path re-ranking improved risk ranking and explanation grounding relative to the evaluated baselines under the controlled benchmark setting. Full article

Background: Diabetic ketoacidosis (DKA) remains a preventable yet frequent complication at the onset of type 1 diabetes (T1D) in children, driven by delayed symptom recognition, socioeconomic disparities, and inconsistent access to care. Early identification of presymptomatic T1D through autoantibody-based screening, together with emerging disease-modifying therapies, may reduce the incidence of DKA. This review synthesizes evidence on epidemiology, risk determinants, screening strategies, and immunological interventions relevant to DKA prevention. Methods: A narrative review was conducted using PubMed, EMBASE, Scopus, Web of Science, and Google Scholar (2011–2026). Eligible sources included clinical studies, guidelines, systematic reviews, meta-analyses, and prevention trials addressing staging, screening, epidemiology, and disease-modifying treatments in pediatric T1D. Landmark publications outside this timeframe were included when essential. Evidence was integrated to identify determinants of DKA and strategies to reduce its occurrence. Results: DKA risk is influenced by younger age, socioeconomic disadvantage, rural residence, misdiagnosis, and limited access to specialized care. Sustained public awareness and community-based education reduce DKA incidence, whereas short-term campaigns show transient effects. Structured screening programs, including TrialNet and TEDDY, demonstrate near-elimination of DKA among monitored children. Teplizumab delayed progression from stage 2 to stage 3 T1D by a median of approximately 24 months in the original TN-10 trial, with extended follow-up demonstrating a median delay of 32.5 months. It is approved for individuals with stage 2 T1D aged ≥ 1 year and has recently gained approval for selected patients with newly diagnosed T1D, expanding opportunities for early disease modification. Global networks such as INNODIA strengthen prevention through coordinated biomarker-driven research. Conclusions: Reducing DKA at T1D onset requires integrated, sustained strategies combining public awareness, systematic autoantibody screening, structured follow-up, and access to emerging immunotherapies. Coordinated care across primary providers, pediatric endocrinologists, and research networks is essential to advance a proactive, prevention-oriented model of pediatric T1D care. Full article