Search Results (13,503)

Fine particulate matter (PM_2.5) exposure in public transport microenvironments has important implications for commuter health, yet concentration-based assessments may not adequately reflect the dose actually inhaled by passengers. This study quantified dynamic PM_2.5 inhaled doses in Taiyuan, China, using 1 Hz portable monitoring and matched travel surveys across 19 bus and metro routes during summer and winter 2025. After data screening, 1103 valid commuter samples were retained. We combined dose estimation with DML, XGBoost-SHAP, SEM, and Random Forest analysis to examine adjusted associations, explore potential nonlinear patterns, and characterize behavioral responses. Trip-averaged PM_2.5 concentrations exceeded the WHO 24 h guideline on most monitored routes when interpreted as a health-based reference benchmark for short commuting exposures rather than as a direct regulatory exceedance metric. More importantly, a clear concentration-dose decoupling pattern was observed: 6.6% of trips fell into a low-concentration but high-dose category, indicating that prolonged in-vehicle exposure could substantially elevate inhaled dose even when PM_2.5 concentrations remained below the sample median. The mean inhaled dose in the longer observed-duration group (top 20% by observed in-vehicle duration) reached 612.26 ± 412.21 μg, which was 7.2 times that of the remaining trips (84.87 ± 115.71 μg). DML results showed that inhaled dose, rather than PM_2.5 concentration alone, was significantly associated with psychological distress. SHAP analysis suggested an exploratory threshold-like pattern at approximately 300 μg per trip, above which health-risk attribution increased rapidly. SEM results indicated that inhaled dose was associated with higher self-reported somatic burden, whereas PM_2.5 concentration mainly influenced health indirectly through risk perception. These findings suggest that public transport exposure assessment should move beyond static concentration metrics and incorporate dynamic inhaled dose to better identify high-risk commuting scenarios and support more targeted health-oriented transit management. Full article

Accurate short-term prediction of urban waterlogging depth is essential for real-time flood risk management in rapidly urbanizing areas under climate variability. Departures from quasi-stationary operating conditions, caused by changes in drainage efficiency, inflow patterns, or measurement quality, weaken historical rainfall–water depth relationships, making purely data-driven models prone to error accumulation. In this study, a GeoAI-based, physics-enhanced machine learning framework is proposed, which translates the water balance principle into Physical Violation Scores (PVSs) and incorporates them as additional input features. PVSs remain zero under expected rainfall–water depth behavior and become positive only under departure scenarios, providing sparse and lightweight diagnostic signals without modifying model structures or loss functions. The framework is implemented on five algorithms (Support Vector Machine, Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and XGBoost) to construct physics-enhanced models (PEMs). These are evaluated against original feature models (OFMs) across 1 h and 2 h forecasting horizons. Results show that most PEMs improve prediction performance compared with their corresponding OFMs, with more pronounced gains at the 2 h horizon. Bootstrap analysis and RMSE-based error amplification factor further indicate comparable or lower R² variability and reduced recursive error amplification for most PEMs. Interpretability analyses show that rainfall forcing and water-depth persistence remain dominant predictors, whereas PVSs act as auxiliary diagnostic signals. Overall, the proposed framework provides a lightweight, reliable, interpretable, and scalable GeoAI approach for incorporating water balance knowledge into short-term urban waterlogging prediction, supporting climate resilience and smart urban water management. Full article

Breast cancer (BC) remains the most prevalent malignancy among women. Prolonged exposure to chemical pollutants has been identified as one of its potential risk factors. Despite increased access to health information, important informational gaps persist regarding chemical exposure. This concern is particularly pronounced among female BC survivors due to their greater vulnerability. This study investigated unmet information needs related to chemical exposure among female BC survivors from Granada (Spain). The data were collected through 150 semi-structured interviews and focus groups between March 2023 and April 2024 and analyzed using the Framework Method. Most participants demonstrated limited awareness regarding BC and its potential association with chemical exposure. Nevertheless, 78.67% of them expressed concerns regarding the issue after diagnosis and anxiety surrounding the lack of reliable information from trusted sources on recurrence prevention. These findings highlight the need for public health communication that informs and empowers protective behavior. Full article

Sexually transmitted infections (STIs) caused by C. trachomatis and HPV are the most prevalent worldwide. College students are characterized by being young women of reproductive age who may have risky sexual behavior. To describe the prevalence and factors associated with endocervical infection by C. trachomatis and HPV in college women in the Brazilian Amazon. Endocervical secretions were collected. The ompA gene of C. trachomatis and the L1 gene of HPV were detected. The Chi-square test, Fisher’s exact test, G test, Odds Ratio, and Multiple Logistic Regression were used with 95% confidence interval and p ≤ 0.05. The overall prevalence of endocervical infection by C. trachomatis was 8.3% (25/302) and by HPV was 28.9% (87/302). Low income was associated with sexually transmitted infection by C. trachomatis (14.8%, p = 0.0336). Those under 25 years old had twice the chance of HPV infection [39.3%, (OR: 2.6989), 95% CI: 1.6054–4.5371, p = 0.0002], as did women without children [31.8%, (OR: 2.333), CI: 1.1235–4.8461, p = 0.0307]. Women who did not study in a public school had 63% reduced risk of acquiring HPV infection [45.8% (OR: 0.3713), CI: 0.1951–0.7064, p = 0.0035]. C. trachomatis and HPV infections were present in low-income, childless young women who attended public schools, requiring the intensification of STI prevention policies in the Amazon region. Full article

Elderly individuals in rural China are highly vulnerable to extreme weather events and temperature fluctuations due to inadequate infrastructure in the built environment and constrained economic conditions, thereby increasing their health risks. Outdoor spaces represent one of the primary daily activity settings for rural older adults. However, existing research rarely links spatiotemporal patterns of outdoor activities to evidence-based thermal environment optimization, leaving a critical knowledge gap for age-friendly and sustainable rural design. This study focuses on the spatiotemporal differentiation patterns of daily outdoor activities among elderly people aged 60 years and above in rural Xi’an, as well as the optimization of spatial variations in thermal environments. Using on-site interviews, thermal environment measurements, thermal comfort questionnaires, continuous thermal environment monitoring, and machine learning based on random forest, this study drew the following conclusions: (1) outdoor activities in winter were concentrated between 9:00–11:00 and 13:00–17:00, while in summer, they shifted to the morning and evening periods, namely 6:00–9:00 and 17:00–21:00. (2) Models for outdoor clothing adjustment, thermal sensation, and thermal acceptability among elderly residents were established. The calculated neutral temperature was 10.19 °C, with a 90% outdoor thermal acceptability range of 9.6–27.2 °C and an 80% outdoor thermal acceptability range of 6.2–30.6 °C. These findings differ from those documented in regions with distinct climate zones and geographical settings. This discrepancy stems from regional climatic features, lifestyle variations between urban and rural older adults, and differences in the thermal environment quality of elderly-oriented outdoor activity spaces. (3) In winter, the acceptable period of the Universal Thermal Climate Index (UTCI) at south-facing entrances (10:30–16:30) was significantly longer than that in the courtyard (13:30–14:00). In summer, the comfortable period in the courtyard (before 10:00 and after 20:00) was longer than that at north-facing entrances (before 09:00). A random forest model for thermal sensation was established, and the relative importance of each parameter influencing thermal sensation was analyzed. On this basis, priority improvement pathways and strategies for the thermal environment, as well as suggestions for the subjective adaptive behaviors of elderly residents, were proposed. The research results of this study can provide technical solutions for age-friendly thermal environment design in rural areas, thereby safeguarding the comfort, health, and social well-being of the elderly population in rural areas. Full article

In recent years, the widespread availability of wearable devices and smartphones has enabled the large-scale collection of human activity data, fostering new opportunities for automatic workout recognition and personalized fitness monitoring. However, the centralized storage of video recordings raises critical privacy concerns, particularly when raw data contain identifiable individuals. Federated Machine Learning provides a paradigm designed with the aim of reducing privacy risks; here, models are collaboratively trained across distributed clients without sharing their sensitive data. In this paper, we propose an approach for workout video classification with Federated Machine Learning, enhanced by explainability through Gradient-weighted Class-Activation Mapping. The proposed method is evaluated on a real-world multi-class exercise video dataset, organized into eight biomechanically coherent macro-classes. In the experimental analysis, we consider several federated configurations in terms of the number of clients, the chosen aggregation strategy, and global communication rounds. The obtained results demonstrate that different aggregation strategies achieve comparable overall accuracy, while explainability effectively highlights the discriminative regions associated with exercise execution, revealing meaningful differences in model behavior between aggregation strategies and uncovering misclassifications driven by contextual biases, demonstrating the trustworthiness of the proposed approach for explainable workout video classification. Full article

CRC remains a major cause of cancer-related morbidity and mortality worldwide. In recent years, the gut microbiota has gained increasing attention in CRC research. Intestinal microbes are not passive bystanders in tumor development. They may promote persistent inflammation, disrupt epithelial barrier integrity, alter microbial metabolites, and affect host immune and signaling pathways. Emerging evidence also suggests that microbiota-related metabolites and microbial functional alterations may influence host epigenetic regulation, including DNA methylation and chromatin-associated signaling, thereby further shaping colorectal carcinogenesis. Together, these changes can create a microenvironment that favors tumor initiation and progression. Several bacterial species, including Fusobacterium nucleatum, Parvimonas micra, and Peptostreptococcus anaerobius, have been repeatedly associated with CRC. In contrast, beneficial commensal microbes and their metabolites, especially short-chain fatty acids, may help maintain intestinal homeostasis and limit tumor-promoting processes. Because the gut microbiota is strongly shaped by diet, lifestyle, and environmental exposure, regional differences are also relevant. This is particularly important in Sichuan, China, where distinctive dietary habits and environmental features may influence microbial patterns associated with CRC risk and disease behavior. This review summarizes the main mechanisms linking the gut microbiota to CRC, examines the regional context of Sichuan, China, and discusses current and emerging clinical strategies. These include dietary intervention, probiotics, fecal microbiota transplantation, and microbiome-informed approaches to prevention, diagnosis, and treatment. Full article

Objectives: The aim of this study was to analyze current trends in screen exposure and to provide a deeper understanding of the relationships between temperament, screen exposure, and psychological adjustment in preschoolers. Methods: The study was conducted in kindergartens and one health center in the city of Zagreb, using a convenience sample of 115 mothers who assessed their preschool children’s screen exposure, temperament, and psychological adjustment. Results: Descriptive data analysis indicated that children’s screen time generally fell within the American Academy of Child and Adolescent Psychiatry’s recommended guidelines. Correlation analysis indicated that externalizing problems were significantly positively correlated with impulsivity, activity, emotionality, and weekend screen time. Conversely, prosocial behavior was negatively correlated with impulsivity and weekend screen exposure. Moderation analyses revealed that weekend screen time significantly altered the associations between temperament and externalizing problems. Specifically, longer weekend screen exposure weakened the relationships between Impulsivity and externalizing problem and between Activity and externalizing problems, suggesting that screen time may buffer the impact of high-risk temperament profiles on behavioral difficulties. Weekend screen time did not moderate the relationship between Emotionality and externalizing problems. Similarly, longer screen exposure weakened the negative association between Impulsivity and prosocial behavior, indicating that screen time may reduce the extent to which impulsive temperament undermines prosocial functioning in preschool children. Conclusions: These findings provide deeper insight into the role of temperament and screen time exposure in predicting both maladaptive and prosocial behaviors among preschool-aged children. Full article

Explainable Artificial Intelligence (XAI) has become a critical requirement for the responsible deployment of deep learning systems in safety-critical and regulated domains, particularly in medical imaging. In computer vision, gradient-based explanation methods such as Saliency Maps and Gradient-weighted Class Activation Mapping (Grad-CAM) are widely used for interpreting convolutional neural networks (CNNs). However, the increasing adoption of Vision Transformers (ViTs) introduces structural differences in internal representations that challenge the direct transfer of convolutional explainability mechanisms. This study presents a systematic, quantitative, and statistically validated evaluation of gradient-based visual explainability across CNN architectures (VGG16 and ResNet50) and a Vision Transformer (ViT-B/16), using both a domain-specific medical imaging dataset (brain MRI, tumor vs. non-tumor classification). Beyond qualitative heatmap inspection, we conduct deletion-based faithfulness analysis, sensitivity-to-noise evaluation, feature masking validation, and statistical hypothesis testing over 30 independent runs. All models achieve strong predictive performance on the domain dataset (mean accuracy ≈ 0.99), enabling a fair and meaningful comparison of explanation methods across architectures. Results demonstrate that explanation reliability is highly method- and architecture-dependent. Sensitivity differences are consistently statistically significant, whereas deletion-based faithfulness does not always yield equally strong separation under the adopted masking protocol. Masking-based analysis reveals substantial false-positive rates in certain configurations, indicating that visually plausible heatmaps do not necessarily isolate decision-necessary evidence. These findings underscore the importance of coupling visual explanations with behavioral validation metrics, particularly in high-risk domains governed by emerging regulatory frameworks such as the EU AI Act. Overall, the study advocates for empirically validated, architecture-aware, and statistically grounded approaches to medical XAI. Full article

Accurate prediction of natural gas dispersion from buried pipelines is critical for risk assessment and emergency response. However, conventional numerical simulations often simplify soil as a homogeneous isotropic porous medium, which deviates significantly from real-world conditions characterized by stratification, anisotropy, and spatial heterogeneity. This study systematically investigates the effects of these non-ideal soil characteristics on methane diffusion behavior using computational fluid dynamics (CFD). Four distinct soil models—a baseline homogeneous model, a layered model, an anisotropic model, and a spatially heterogeneous model—were constructed and compared under identical leakage scenarios. Key risk indicators, including First Danger Time (FDT), Farthest Danger Range (FDR), Ground Danger Range (GDR), and leakage mass flow rate, were quantitatively evaluated. Results indicate that soil layering enhances vertical migration and expands horizontal hazard ranges, reducing FDT by approximately 8%. Anisotropy introduces a pronounced directional dependence in gas migration, with horizontal-preferred permeability leading to severe underestimation of lateral risk by homogeneous assumptions. The spatially heterogeneous model exhibits reduced hazard ranges compared to the homogeneous case but accelerates early breakthrough. Comprehensive evaluation reveals that the homogeneous model systematically underestimates lateral diffusion distances and delays alarm times. This study provides a quantitative basis for selecting appropriate soil modeling strategies, emphasizing that incorporating soil heterogeneity is essential for reliable safety assessments of buried gas pipelines. Full article

Background: Uterine sarcomas are rare, heterogeneous malignancies with distinct pathological behaviors. This study aimed to identify clinicopathological characteristics, prognostic risk factors, and potential therapeutic targets to enhance clinical management. Methods: A retrospective analysis was conducted on 148 patients with uterine sarcoma treated at Peking University People’s Hospital between 1996 and 2025. Clinical outcomes, pathological subtypes, and immunohistochemical profiles were assessed. Additionally, bioinformatics analyses from RNA bulk sequencing of GEO datasets (GSE87581, GSE85383, GSE222045 and GSE64763) were performed to elucidate molecular characteristics across subtypes. Results: The most prevalent subtypes were uterine leiomyosarcoma (uLMS; 38.5%) and low-grade endometrial stromal sarcoma (LG-ESS; 29.7%). The 5-year recurrence rate was 50.5%, with frequent metastases to the pelvis and lungs. LG-ESS demonstrated the most favorable 5-year survival rate (90.3%), significantly higher than that of uLMS (61.8%) and undifferentiated uterine sarcoma (50.0%). Multivariate analysis identified histological subtype, stage, and coagulative necrosis as independent prognostic factors for overall and progression-free survival. Transcriptomic profiling revealed immunosuppression (CSF1R/CSF3R expression) in high-grade ESS, while uLMS exhibited activation of cell cycle and homologous recombination pathways. Conclusions: Histological subtype, stage, and coagulative necrosis were critical prognostic factors in uterine sarcoma. The findings suggest that vigilant pulmonary surveillance and further investigation into tailored therapeutic strategies may be warranted-including endocrine therapy for hormone-receptor-positive tumors, immunotherapy for high-grade ESS, and PARP inhibitors for uLMS. However, these hypotheses require thorough preclinical and clinical validation. Additionally, caution should be exercised to avoid overtreatment of chemotherapy in early-stage uLMS. Full article

The increasing deployment of distributed energy storage systems (ESSs) presents new opportunities to enhance power system flexibility and enable innovative market participation models. However, many small-scale energy storage system assets remain underutilized due to fragmented ownership, uncertainty in market prices and revenue opportunities, as well as regulatory and operational constraints, and heterogeneous decision making behaviors. To address these challenges, this paper proposes an enhanced energy storage as a service (ESaaS) framework that enables distributed ESS owners to lease idle storage capacity to a centralized platform for coordinated participation in multiple grid support services. The proposed platform aggregates the distributed ESS capacity and allocates it across several value streams. Unlike conventional approaches that assume fully rational agents, this work incorporates behavioral decision making dynamics using prospect theory (PT), which captures loss aversion, asymmetric risk perception, and the subjective valuation of uncertain outcomes. The interaction between the ESaaS operator and ESS owners is formulated as a bi-level optimization problem, where the upper level determines leasing prices and operational strategies across multiple services while the lower-level models ESS owner participation decisions. Prospect theory is integrated at both decision layers to capture the behavioral preferences of the ESaaS operator and ESS owners under uncertainty. The resulting mixed-integer bi-level model is solved using a modified reformulation-and-decomposition approach that incorporates a nested column-and-constraint generation (NC&CG) method to ensure computational tractability. Numerical studies demonstrate that behavioral decision modeling significantly influences pricing strategies and the overall profitability of both the ESaaS platform and the participating energy storage system owners. Full article

Background/Objectives: Atrial fibrillation (AF) is a complex polygenic disorder; its genetic architecture remains challenging to fully elucidate. Methods: In this study, we leveraged the extensive genetic overlap between AF and a spectrum of cardiometabolic and behavioral factors—collectively defined by Life’s Essential 8 (LE8)—to advance our understanding of its etiology. Results: We first estimated significant genetic correlations between AF and all LE8 components (r_g: −0.11 to 0.19) using LD score regression. We then applied conditional false discovery rate analysis and detected 970 pleiotropic loci associated with AF and at least one LE8 trait. Subsequent colocalization analysis identified 179 loci harboring shared causal variants between AF and one or more LE8 components, which were further refined into 137 distinct colocalized regions. Through region-based annotation and functional predictors, we finally prioritized 164 candidate genes from these colocalized loci, including 40 novel genes. These candidate genes were enriched in pathways related to heart development and regulation of cardiac contraction, and were also enriched among molecular targets of otological agents. Among all LE8 components, blood pressure demonstrated the most extensive shared genetic architecture with AF, supported by the strongest genetic correlation, highest pleiotropic enrichment, and the greatest number of colocalized loci with AF. Polygenic risk scores constructed from these colocalized loci demonstrated significant associations not only for AF but also for arrhythmia and heart failure. Conclusions: Our findings establish a genetic pleiotropy-informed framework that enhances the discovery of novel risk loci of AF and advances our understanding of the shared genetic architecture and potential biological mechanisms between AF and LE8 components. Full article

Background: Stunting remains a major public health problem among ethnic minority children in mountainous areas of Vietnam. Picky eating has been suggested as a potential behavioral risk factor for poor child growth, but evidence from vulnerable rural populations remains limited. This study examined the association between picky eating and stunting among ethnic minority children aged 12–35 months in Vietnam. Methods: This cross-sectional study was conducted from October to November 2025 in two communes of Phu Tho province, formerly part of Lac Son District, Hoa Binh Province, Vietnam. A total of 341 children aged 12–35 months and their caregivers were included. Data were collected using structured interviewer-administered questionnaires on feeding practices and child characteristics. Picky eating was assessed based on caregiver-reported behaviors. Anthropometric measurements were performed according to standard procedures, and height-for-age Z-scores were calculated using the WHO Child Growth Standards. Zinc status was assessed in a subsample of children. Bivariate and multivariable logistic regression analyses were conducted to identify factors associated with stunting. Adjusted odds ratios (AORs) with 95% confidence intervals (95% CIs) were reported. Results: The prevalence of picky eating was 39.6%, while 24.9% of children were stunted. Zinc deficiency was identified in 41.9% of children with available blood samples. In multivariable analysis, picky eating was significantly associated with increased odds of stunting (AOR = 3.63; 95% CI: 1.71–7.70). Snacking before main meals was also independently associated with stunting (AOR = 1.81; 95% CI: 1.01–3.24). In contrast, zinc deficiency was associated with stunting in crude analysis but was not statistically significant after adjustment. Other factors, including child age, sex, caregiver identity, and timing of complementary feeding, were not independently associated with stunting. Conclusions: Picky eating was common and was independently associated with stunting among ethnic minority children in this mountainous setting. These findings suggest that behavioral feeding practices, particularly picky eating and pre-meal snacking, warrant attention in nutritional programs targeting this population; however, longitudinal studies are needed to confirm the direction of this relationship. Full article

Non-nutritive sweeteners (NNSs) are consumed to reduce intake by providing a sweet taste with little to no energy. Despite regulatory approval and extensive use, uncertainty remains about their long-term role in weight management and health, and about whether exposure to sweet taste itself, independent of energy, influences these outcomes. This narrative review synthesizes evidence from three recent European consortia: SWEET, SWITCH and Sweet Tooth, which together provide complementary data from acute, short- and long-term randomized controlled trials. The studies examined the effects of NNSs and dietary sweet taste exposure on body weight, health-related biomarkers, sweet taste preference, and eating behavior. Across studies, replacing sugars with NNSs appeared to support weight loss maintenance, while NNS consumption and dietary sweet taste exposure showed no adverse changes in body weight, glucoregulatory and endocrine biomarkers, cardiometabolic risk factors, gut microbiota, or liver enzymes. Likewise, neither NNS use nor different dietary sweet taste exposure altered sweet taste liking, appetite sensation, energy intake, or food choice. However, interpretation should consider the characteristics of the included studies, including selected populations, intervention context, outcome heterogeneity, and the fact that several behavioral and biomarker outcomes were secondary or exploratory. Overall, the reviewed evidence suggests that replacing sugar intake with NNSs may support weight management strategies, while differences in habitual dietary sweet taste exposure per se appear largely neutral with respect to health-related biomarkers and sweet taste preferences. Full article