Search Results (819)

Concurrent with the rising consumption of ultra-processed, high-calorie diets and the decline in physical activity, obesity and related cardiovascular conditions among young adults have continued to increase, becoming an important global public health concern. This study integrates non-invasive Internet of Things (IoT) sensing devices, including the TERUMO ES-P2000 blood pressure monitor (Terumo Corp., Tokyo, Japan) and the PhysioFlow PF07 Enduro cardiac hemodynamic analyzer (Manatec Biomedical, Poissy, France), with an artificial neural network (ANN) for cardiac index (CI) prediction. Through appropriate data preprocessing and model training strategies, the generalization ability and stability of the proposed CI prediction model were significantly enhanced. Experimental results demonstrate that, when using three physiological parameters as input, the ANN achieved a classification accuracy of 97.78%, substantially outperforming traditional approaches. Even under two-parameter input conditions, the model maintained strong predictive performance. These findings confirm the effectiveness and practical potential of the proposed framework for real-time, non-invasive CI assessment. Moreover, this research has received rigorous assessment and approval from the Institutional Review Board (IRB) under application number 202501987B0. Full article

As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability. Full article

Background/Objectives: Cardiovascular disease is the leading cause of adult deaths globally and has recently been reported to be on the rise in younger adult women. The present study examined the impact of physical and lifestyle predictors of vascular health in 125 apparently healthy premenopausal East Asian volunteers. Methods: Vascular health outcomes included carotid–femoral pulse wave velocity (cfPWV), central augmentation index (cAIx), and mean arterial pressure (MAP). Body composition/anthropometric predictors included total adiposity, visceral adipose tissue (VAT) and skeletal muscle mass (SMM), as well as body mass index (BMI) and waist circumference (WC). Lifestyle predictors included the International Physical Activity Questionnaire (IPAQ) and dietary recall. Multivariate linear regression was used to identify independent predictors of combined vascular health and individual vascular outcome variables. The analysis for independent vascular outcomes was repeated after age stratification (<35 years versus $\ge$35 years). Results: VAT showed a significant multivariate effect on combined vascular health outcomes (p = 0.002) and independently contributed to cfPWV (p = 0.013). WC positively predicted cAIx (p = 0.010) while SMM was inversely related to cAIx (p = 0.024). BMI positively predicted MAP (p = 0.039) in the multivariate analysis. After age adjustment however, only BMI emerged as a significant independent predictor of both cfPWV (p = 0.040) and MAP (p = 0.024). Furthermore, WC remained positively associated with cAIx (p = 0.042) while SMM remained inversely related to cAIx (p = 0.038). After age stratification, IPAQ was inversely related to cfPWV while BMI was positively associated with MAP (p = 0.035) in women < 35 years only. However, in older women, total adiposity (p = 0.040) and total cholesterol (p = 0.011) were both positively, while SMM (p = 0.046) was negatively associated with cAIx. Conclusions: With the exception of age, VAT was the single best predictor of general vascular health in East Asian women. Independent of age, however, BMI, WC, and SMM significantly contributed to independent vascular outcome measures and in combination with age, substantially add to the prediction of vascular risk. Furthermore, stratifying younger versus older premenopausal women resulted in different associations with independent vascular outcome measures demonstrating that across a large age range of premenopausal women, it is important to consider age in the evaluation of vascular health. Full article

Background: Alcohol-related liver disease is frequently associated with systemic vascular dysfunction and increased arterial stiffness. This may contribute to adverse clinical outcomes. Modulation of the gut microbiota through fecal microbiota transplantation (FMT) has emerged as a potential therapeutic strategy in liver cirrhosis, but its influence on vascular stiffness in humans remains insufficiently characterized. Methods: This prospective study evaluated arterial stiffness in patients with alcohol-related liver cirrhosis undergoing FMT. A control group received standard care. Vascular stiffness was assessed non-invasively using an oscillometric arteriograph based on pulse wave analysis. Measurements were performed at baseline and at one and three months after FMT under standardized conditions. The main indices assessed included aortic pulse wave velocity, augmentation index, ejection duration and return time. Direct microbiome sequencing and metabolomic profiling were not performed. Results: At baseline, the study and control groups had comparable vascular stiffness profiles. Only minor differences in selected hemodynamic parameters were observed. At one month after intervention, no statistically significant differences in arterial stiffness indices were observed between groups. Longitudinal analysis within the FMT group also showed no significant changes in direct markers of arterial stiffness across the three-month follow-up period. A non-significant tendency toward reduced ejection duration was noted. Conclusions: In patients with advanced alcohol-related liver cirrhosis, FMT did not produce measurable short-term improvements in arterial stiffness. These findings suggest that short-term vascular effects of microbiota modulation may be difficult to detect in patients with advanced alcohol-related liver cirrhosis. Larger studies including earlier-stage patients, longer follow-up and direct microbiome and metabolomic assessment are needed to clarify potential vascular effects of FMT. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Background/Objectives: Ulcerative colitis (UC) involves inflammatory response, oxidative stress, changes in metabolites, and the gut microbiota. Jingangteng capsule (JGTC) has been utilized clinically for the treatment of inflammatory diseases for many years. However, the efficacy of JGTC in ameliorating UC remains unclear, and the underlying mechanisms have not yet been elucidated. This study aims to investigate the effect and mechanism of JGTC on UC. Methods: The chemical compositions of JGTC were examined using ultra-high-performance liquid chromatography with quadrupole time-of-fight mass spectrometry. The anti-UC effect of JGTC was evaluated by assessing the disease activity index (DAI), colon length, intestinal barrier recovery, and inflammatory factors in a dextran sulfate sodium (DSS)-induced colitis model. Mechanisms were investigated through fecal 16S rDNA sequencing, metabolomics analysis, enzyme-linked immunosorbent assay (ELISA), Western blotting, and network pharmacology analysis. Results: JGTC significantly reduced the DAI scores in UC mice, increased their body weight and colon length (p < 0.001), repairing damaged intestinal tissue. It decreased the levels of inflammatory cytokines TNF-α, IL-6, IL-1β, and LPS (p < 0.01, p < 0.001), alleviating intestinal inflammation. It also raised the expression of tight junction proteins ZO-1, Claudin-1, and Occludin (p < 0.05, p < 0.001), thereby enhancing intestinal barrier function. Fecal metabolomic analysis revealed that the favorable alterations in amino acid and lipid metabolites were more pronounced. Heat maps showed strong correlations between pharmacological indicators and gut microbiota, as well as between the main differential metabolites and gut microbial communities. UPLC-QTOF-MS detection yielded 33 components of JGTC, and network pharmacology analysis based on these components predicted pathways of action of JGTC in UC. Functional pathways closely associated with significantly differential metabolites and metabolic pathways were also investigated. The PI3K-AKT-mTOR pathway was one of them, which is consistent with the conclusions drawn from network pharmacology. JGTC significantly modulated key factors in this pathway, inhibiting the expression of PI3K, Akt, PDK1, and mTOR, while augmenting the expression of PTEN (p < 0.05, p < 0.01, p < 0.001). It also mitigated the levels of related oxidative stress factors MDA, MPO, and D-LA, and raised SOD levels (p < 0.01, p < 0.001). Conclusions: JGTC improved the excessive inflammatory response in UC by regulating intestinal flora and metabolic disorders, affecting the PI3K-AKT-mTOR signaling pathway, restoring intestinal tissue damage and intestinal barrier, and inhibiting inflammatory and oxidative stress factors. Full article

Geohazards such as landslides, earthquakes, debris flows, and floods are governed by complex interactions among geological, hydrological, and human processes. Traditional data-driven models have improved hazard prediction but often lack interpretability and adaptability. This review examines the evolution of knowledge-guided approaches in geohazard research, highlighting how knowledge representation and artificial intelligence have progressively converged to enhance understanding, reasoning, and model transparency. A bibliometric analysis of 1410 publications indexed in the Web of Science reveals an evolution from early ontology-based knowledge engineering for expert reasoning to knowledge graphs (KG), frameworks enabling multi-source data integration and relational inference, and more recently, to large language model (LLM), augmented systems for automated knowledge extraction and cognitive geoscience. This review synthesizes advances in knowledge representation, knowledge graphs, and LLM-based reasoning, demonstrating how hybrid models that embed physical laws and expert knowledge can improve the interpretability and generalization of machine learning. These developments enable new forms of knowledge-driven geohazard intelligence and support applications in hazard monitoring, early warning, and risk communication. There are challenges we still face, including semantic fragmentation, limited causal reasoning, and sparse data for extreme events. Future directions require unified knowledge–data–mechanism architectures, causality-aware modeling, and interoperable standards to advance trustworthy and explainable geohazard intelligence. Full article

Enterprise decision making in marketing, finance, and audit remains fragmented, leading to inefficient budget allocation and incomplete risk assessment. This study proposes an integrated, Big Data-driven decision-support framework that unifies Large Language Models (LLMs), attention-based marketing mix modeling, and multi-agent, game-theoretic optimization to coordinate cross-functional decisions. The architecture combines five modules: LLM-enhanced customer segmentation and customer lifetime value prediction, attention-weighted marketing mix modeling, multi-agent LLM systems for hierarchical budget optimization, attention-informed Markov multi-touch attribution, and LLM-augmented audit quality assessment. Empirical validation on a large-scale e-commerce dataset with 2.8 million customers and USD 156 million in marketing expenditure shows that marketing return on investment increases from 4.2 to 6.78 (61.4% relative improvement), financial forecasting error (MAPE) decreases from 12.8% to 4.7% (63.3% reduction), fraud detection accuracy improves by 29.8%, the Audit Quality Index reaches 0.951, and customer lifetime value prediction accuracy improves from 76.4% to 91.3%. By operationalizing the convergence of LLMs, attention mechanisms, and game-theoretic reasoning within a unified and empirically validated framework, the study delivers both theoretical advances and practically deployable tools for integrated business intelligence in digital economies. Full article

Sustainable groundwater management in hyper-arid regions requires accurate water quality assessments, yet remote desert environments present major challenges due to data scarcity, high sampling costs, and limited laboratory infrastructure. This study proposes a framework integrating the Water Quality Index (WQI) with Inverse Distance Weighting (IDW)-based spatial data augmentation and machine learning classification for groundwater quality assessment in the Tabelbala region, southwestern Algeria. Three classifiers were evaluated, Random Forest (RF), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), and trained on an augmented dataset generated from 178 original groundwater samples using IDW interpolation with a sensitivity-optimized 150 m radius, producing 2779 augmented training points. RF achieved the highest predictive accuracy (85.9%), followed by ANNs (84.7%) and SVMs (83.1%), with all models demonstrating excellent discriminative performances (area under the receiver operating characteristic curve > 0.96). Permutation Feature Importance analysis identified total dissolved solids (TDS), sulfates (SO₄²⁻), total hardness (TH), and chlorides (Cl⁻) as the most influential parameters, consistent with World Health Organization (WHO) guidelines. Spatial distribution maps revealed that the majority of groundwater sources exhibited poor to very poor quality, highlighting the urgent need for local water management interventions. The proposed framework offers a replicable decision-support tool for water resource managers in data-scarce arid environments, supporting SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action). Full article

Computed tomography (CT)-guided procedures require close proximity to the CT gantry or patient, increasing occupational exposure to scattered radiation. Even though radiation-protective equipment is commonly used, the optimization of CT fluoroscopic techniques remains important. Partial-angle CT (PACT) employs a limited exposure angle, producing cumulative scattered radiation distributions that vary with the selected angle and are difficult to estimate in advance. I aimed to develop an augmented reality (AR)-based visualization method for cumulative scattered radiation distributions during PACT and to evaluate its ergonomic feasibility as a proof of concept for occupational exposure reduction. An AR display system was developed to overlay cumulative scattered radiation distributions onto physical space using AR glasses. Workload was assessed using the NASA Task Load Index (NASA-TLX), and usability was assessed using the System Usability Scale (SUS). Compared with non-virtual conditions using radiation-protective glasses alone, AR-assisted visualization was associated with increased perceived workload, and usability scores were lower than those reported in previous AR studies. These findings indicate that, for AR display systems to support occupational exposure reduction, perceived task demands must be comparable to conventional protection strategies. Further improvements in visualization methods, user familiarity with AR environments, and ergonomic optimization are required to facilitate clinical implementation. Full article

Retrieval-augmented generation (RAG) has shown strong potential for knowledge-intensive tasks, yet its performance degrades sharply when applied to structured long-context documents in power systems, where dense entity–relation dependencies, cross-document references, and strict traceability requirements exist. To address this Structured Long-Context RAG (SLCRAG) challenge, this paper proposes a hierarchical graph-enhanced RAG (HG-RAG) framework tailored for power system question answering. HG-RAG constructs a globally consistent knowledge graph via sliding-window entity–relation extraction to mitigate semantic fragmentation, and employs multi-granularity structured indexing for precise entity/relation retrieval. A hierarchical structured retrieval mechanism with multi-hop expansion and semantic distillation maximizes recall while minimizing redundancy. Furthermore, a regex-enhanced retrieval module records authoritative file_path provenance and constrains downstream retrieval to the same source documents, effectively eliminating cross-document interference—especially in cases where different documents contain similar entities and relations. Combined with version control and deduplication-merging, HG-RAG supports incremental knowledge updates with minimal forgetting and negligible token overhead. Experimental results on a domain-authentic power system QA dataset demonstrate that HG-RAG outperforms LightRAG and GraphRAG, achieving up to 85.47% accuracy in short-answer tasks with significantly lower token consumption. Ablation studies confirm that semantic distillation primarily improves precision and efficiency, while regex-enhanced retrieval safeguards recall in edge cases. Full article

Lung cancer is one of the most common causes of death from cancer around the world, and catching it early through computed tomography (CT) scans can drastically improve survival. However, automated classification of pulmonary nodule candidates is hard because images do not all have the same intensity across scanners and protocols, resulting in inconsistent performance, more false positives (FP), and a ceiling on how much deep learning models work in an average clinic. In this work, we tackle this by introducing a preprocessing step that corrects intensity differences before feeding images into classification models. We use Contrast-Limited Adaptive Histogram Equalization (CLAHE), but with its key parameters tuned automatically via a modified version of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). This helps to boost local contrast adaptively, keeps important anatomical details intact, and cuts down on noise. We tested the approach on the public LUNA16 dataset, first checking image quality (Peak Signal-to-Noise Ratio (PSNR) around 53 dB and Structural Similarity Index (SSIM) of 0.9, better than standard methods), then training three popular deep models—namely, ResNet-50, EfficientNet-B0, and InceptionV3—with CutMix augmentation for better generalization. On the enhanced images, ResNet-50 achieved up to 99.0% classification accuracy with substantially less FP than when using the raw scans. Taken together, these results demonstrate that intelligent and optimized preprocessing can effectively mitigate intensity variations via deep learning for lung nodule detection, thus coming closer to realizing the practical toolbox of computer-aided diagnosis in routine clinical practice. Full article

Background: Children with β-thalassemia major (β-TM) survive longer due to advances in transfusion and chelation therapy; however, cardiovascular complications have emerged as a leading cause of long-term morbidity. Chronic hemolysis, oxidative stress, and iron overload may promote early endothelial dysfunction and premature vascular aging, yet their impact on myocardial deformation in pediatric patients remains incompletely characterized. Objectives: To evaluate subclinical myocardial dysfunction and arterial stiffness in children with β-TM and to investigate hemolysis-related changes in asymmetric dimethylarginine (ADMA) and L-arginine as biomarkers of endothelial dysfunction in relation to cardiovascular involvement. Methods: Twenty-four children with β-TM and 20 age-matched healthy controls were included. Cardiac structure and myocardial deformation were assessed by conventional echocardiography, tissue Doppler imaging, and speckle-tracking strain analysis. Arterial stiffness was evaluated using oscillometric pulse wave analysis and bilateral carotid intima–media thickness (CIMT). Serum ADMA and L-arginine levels were measured, and hemoglobin, reticulocyte count, and ferritin levels were recorded. Results: Children with β-thalassemia major demonstrated significantly increased arterial stiffness compared with controls, including higher PWV (4.61 ± 0.37 vs. 4.38 ± 0.31), AIx@75 (augmentation index at 75 bpm) (28.5 ± 8.34 vs. 22.8 ± 6.51), left CIMT [0.45 (0.39–0.51) vs. 0.41 (0.38–0.46)], and right CIMT [0.43 (0.39–0.54) vs. 0.40 (0.34–0.46)]. In addition, patients exhibited reduced global longitudinal strain (−19.3 ± 2.91 vs. −21.84 ± 1.91), prolonged isovolumetric relaxation time [53 (37–71) vs. 45 (37–55)], and elevated E/Em (8.44 ± 2.19 vs. 6.92 ± 1.10). ADMA levels were significantly higher in patients (0.54 ± 0.19 vs. 0.39 ± 0.22) and were positively associated with reticulocyte counts and inversely correlated with hemoglobin levels. In addition, both ADMA and ferritin levels were positively correlated with arterial stiffness indices and left ventricular filling pressures. Conclusions: Children with β-thalassemia major exhibit features suggestive of early cardiovascular aging, including impaired myocardial deformation, diastolic involvement, and increased arterial stiffness. The observed association between ADMA levels and markers of hemolysis, vascular stiffness, and myocardial deformation highlights the potential involvement of endothelial dysfunction in premature myocardial–vascular remodeling. These findings suggest that ADMA may serve as a promising biomarker for early cardiovascular risk in pediatric β-thalassemia major; however, further longitudinal and multi-center studies are needed to confirm its clinical utility for risk stratification. Full article

We present a Retrieval-Augmented Generation (RAG)-based question-answering system for nuclear energy science communication, characterizing retrieval quality in generated responses. The system introduces a dual-similarity analysis that jointly measures (i) question-to-context (Q→C) and (ii) answer-to-context (A→C) semantic consistency, serving as “retrieval-side semantic alignment signal” and “post-generation semantic alignment indicator” respectively. Built with LangChain, FAISS retrieval, and a large language model, our pipeline separates offline indexing from online inference and is grounded on authoritative Taiwanese Nuclear Safety Commission documents. We evaluate two settings: (a) in-domain prompts derived from the corpus and (b) out-of-domain, randomly generated nuclear energy questions. Results show that generated answers are, on average, more semantically similar to retrieved contexts than the original questions under the present setup, while the overall association between retrieval-side and answer-side signals remains stronger in the in-domain setting. Out-of-domain questions show weaker but still observable answer-to-context alignment patterns, contingent on corpus overlap. These findings suggest that combining RAG with dual-similarity analysis offers a practical and audit-oriented approach for educational Q&A, and we discuss potential improvements in versioned regulations, re-ranking, and abstention strategies. In this study, the RAG technique and dual-similarity analysis are combined together to promote nuclear energy knowledge. The research flow chat of this study can be applied to many other fields of scientific knowledge. Full article

The volatility of oil prices has a considerable impact on the economies of oil-exporting countries, making it critical to understand how price variations affect labour markets and unemployment. This study investigates the distinct role of labour market institutions in moderating the effects of oil price volatility on unemployment. Using the Cross-Sectionally Augmented Autoregressive Distributed Lag Model (CS-ARDL) on a panel dataset of nine African oil-exporting countries from 1994 to 2024, the study establishes a strong negative link between oil price changes and unemployment. Furthermore, the results show that real GDP growth leads to a reduction in unemployment in the long run, while the labour market institutional index has a negative impact on unemployment. Interacting the oil price with the labour market institutional index causes a further reduction in unemployment. These results suggest that good labour market institutions and macroeconomic stability are essential for reducing unemployment. While increases in oil prices directly stimulate a reduction in unemployment in African oil-exporting countries, this impact is reinforced by the presence of good labour market institutions in an economy. Therefore, the results suggest that countries with strong labour market institutions are more resilient in reducing the negative impact of oil price volatility on employment. As such, policymakers must prioritise labour market institutional reforms to enhance countries’ capacity to absorb oil price shocks and reduce unemployment during periods of oil prosperity and shield against employment declines when oil prices drop. Furthermore, the creation of oil stabilisation funds in these countries may serve a similar purpose. Contribution/originality: Against a background of inconclusive empirical evidence in the literature and a dearth of research on African countries, this study investigates the role of labour market institutions (LMIs) in the oil price–unemployment nexus in African oil-exporting countries. While highly dependent on oil revenue, these countries record persistent structural unemployment. Therefore, the study provides critical evidence to guide the formulation of policies necessary to deal with external shocks and facilitate structural shifts required for employment growth. Existing studies consider general institutional variables such as democratic accountability and the rule of law and do not assess the effect of labour market institutions. The current study fills in this gap by assessing the distinct role of labour market institutions that are specifically designed to regulate only work-related activities, such as quality of labour regulations, adequacy of social protection and unemployment benefits. Furthermore, this study employed the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) for econometric estimations. Compared to previous studies, this is a more appropriate method that accounts for unobserved common factors such as oil price shocks affecting all oil-exporting countries simultaneously. Full article