Search Results (157,941)

A vital region for China’s water resource storage and ecological balance maintenance, the Yangtze River Basin is strategically significant for maintaining regional water security and promoting long-term social and economic development. Precipitation is the main driver of the hydrological cycle. In order to address current problems with the basin’s ecological environment and water supplies, comprehensive analyses of multi-source precipitation data are necessary. They provide an essential scientific basis for evaluating the sustainability of water resources in the Yangtze River Basin in the context of climate change. Most existing precipitation fusion studies utilize only a limited number of datasets and do not fully consider the independence among different data sources, which leads to less-than-ideal fusion accuracy and assessment metrics. This paper employs the Triple Collocation (TC) method to evaluate and fuse multiple precipitation datasets over a 19-year period from 2003 to 2021, with the aim of enhancing precipitation accuracy in the Yangtze River Basin. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation data were found to have the highest accuracy among seven datasets, with a Correlation Coefficient (CC), Relative Bias (Rbias), and Root Mean Square Error (RMSE) of 0.907, −0.027, and 25.930 mm, respectively. The “MSWEP–PERSIANN–NOAH (MPN)” fusion was shown to be the best using the Multiplicative Triple Collocation (MTC) method in conjunction with cross-error analysis. Compared to MSWEP alone, it improved CC by 0.8% and decreased RMSE by 3.8%, with matching spatial-grid CC and RMSE improvements of 1.2% and 1.8%, respectively. Further spatiotemporal analysis of the fused data increase detection capabilities for short-term flood and waterlogging occurrences and provide better knowledge of basin water-resource status. Full article

Neurotoxicity from drugs and environmental pollutants poses serious risks to brain function, yet existing computational models mainly target general neurotoxicity and lack specialized tools for brain-specific assessment. This study aimed to develop and validate a high-performance, brain-focused neurotoxicity prediction framework to improve drug safety evaluation and toxicity screening. We systematically analyzed molecular features, clustering patterns, and target predictions of brain-toxic compounds. Multiple feature representations were compared, including traditional molecular fingerprints, knowledge-guided pre-trained graph Transformer (KPGT) embeddings, and transformer-based MolFormer embeddings, combined with machine learning classifiers. Model performance was evaluated using multiple metrics, and SHAP analysis was conducted to identify influential molecular substructures. Toxic molecules showed physicochemical properties favoring central nervous system (CNS) penetration, including lower molecular weight, lower LogP, fewer hydrogen bond donors/acceptors, fewer rotatable bonds, and lower polar surface area (PSA). The KPGT-MLP model achieved the best balanced performance, with an accuracy (ACC) of 0.8928 and an ROC-AUC of 0.9459, clearly outperforming traditional fingerprint-based models, MolFormer-based models, and general prediction tools such as DI-NeuroT and ADMETlab 3.0. Overall, this study establishes a robust framework for brain-specific neurotoxicity prediction, with the KPGT-MLP model demonstrating strong accuracy and robustness. The proposed approach provides an effective strategy for early neurotoxicity screening and risk assessment, offering valuable insights for safer drug design and advancing computational toxicology and drug discovery. Full article

Tropical forests are major contributors to the global carbon budget but are affected by disturbances such as hurricanes, which cause extensive yet spatially variable tree damage and mortality. High-resolution maps of forest aboveground biomass (AGB) and its temporal change aid in quantifying disturbance impacts, assessing resilience, and supporting forest management. This study presents wall-to-wall, high-resolution mapping of pre- and post-hurricane AGB and AGB change across Puerto Rico. The maps represent forest AGB measured 0–2 years before and after two major hurricanes (Irma and Maria), as well as longer-term conditions up to four years post-disturbance. AGB was modeled using Random Forest (RF) algorithms that integrated Forest Inventory and Analysis (FIA) plot data with canopy height and cover derived from discrete-return LiDAR, multi-temporal satellite imagery, and additional geospatial predictors. Model performance was evaluated using a 10% holdout dataset. Predicted versus observed regressions yielded, at 10 m and 90 m spatial resolutions, respectively, r = 0.75 and 0.79 with model residual mean standard deviation (RMSD) = 87.7 and 39.2 Mg ha⁻¹ for pre-hurricane AGB, and r = 0.77 and 0.74 with RMSD = 69.7 and 58.1 Mg ha⁻¹ for post-hurricane AGB. AGB change models at 10 m and 90 m resolutions yielded r = 0.58 and 0.73 with RMSD = 17.0 and 18.7 Mg ha⁻¹, respectively. Ten-fold cross-validation produced stronger correlations and reduced RMSD values. Frequency distributions of mapped pixels of forest AGB and AGB change, in comparison with previously published maps and island-wide field-based estimates, indicate that, although hurricane-driven biomass reductions of up to 20% were recorded in field data, patterns consistent with longer-term recovery from historical deforestation are evident within four years after the hurricanes. The 10 m maps capture fine-scale heterogeneity in canopy damage and regrowth, whereas the 90 m maps emphasize broader regional patterns. This integrated framework provides a transferable approach for monitoring forest structure and biomass dynamics in disturbance-prone tropical ecosystems. Full article

Food safety is a major global public health concern and a key contributor to the burden of foodborne diseases. This scoping review examined the knowledge, attitudes, and practices (KAP) related to food safety among urban consumers in low- and lower-middle-income countries (LLMICs). A systematic search was conducted across seven electronic databases: Medline (PubMed), Web of Science (Social Science Citation Index), Embase (Ovid), Global Health (Ovid), PsycINFO (Ovid), Econlit (EBSCOhost), and Scopus to identify studies published in English between 2000 and 2025. Data extraction and quality appraisal were conducted independently by two reviewers, and findings were synthesized in a narrative analysis. Twenty-six studies from 14 LLMICs met the inclusion criteria. Of the 25 studies assessing knowledge and awareness, the majority reported that consumers had some understanding of food safety, although 10 (40%) highlighted limited awareness. Fifteen studies examined practices, with several noting appropriate behaviours; however, nine (56.2%) reported poor practices. Seven studies assessed attitudes, with most reflecting positive perceptions, while one (16.7%) identified negative views. Only four studies examined the full KAP triad. Across studies, factors such as age, education, gender, marital status, training, employment status, income, field of study, and residential status were found to influence food safety KAP. Overall, the evidence suggests that while consumers in urban LLMIC settings generally demonstrate some knowledge and positive attitudes towards food safety, there remain significant gaps in practices that could compromise public health. Future research should prioritise underrepresented regions, employ more rigorous study designs, and incorporate longitudinal and qualitative approaches to gain deeper insights and inform targeted interventions. Full article

Identifying noise sources in exceedance-triggered audio is essential for targeted source tracing and sustainable urban social noise governance. While accurate models require massive labeled data, the acoustic complexity, high redundancy, and imbalanced class distributions of real-world recordings incur prohibitive manual annotation costs, hindering their widespread application in IoT networks. To tackle this bottleneck, we present a label-efficient active learning framework designed to minimize annotation costs by dynamically selecting the most valuable audio samples. Specifically, rather than treating uncertainty, class balance, and diversity as separate query criteria, it encodes uncertainty and dynamic class-aware learning needs into a weighted acoustic feature space, so that diversity-based selection can be performed in a unified manner. Experiments on the UrbanSound8K benchmark and a realistic exceedance-triggered monitoring dataset demonstrate consistent label-efficiency advantages over mainstream methods. Notably, our approach reaches 98% of the fully supervised upper bound on the real-world dataset while reducing the training annotation workload by 85.0% compared to random sampling. On the real-world dataset, the proposed framework yields higher F1-scores for several challenging under-represented categories and reduces the misclassification of dominant sound events relevant to social noise source tracing. Furthermore, cross-site generalization experiments reveal rapid localized adaptation to new monitoring environments, reaching the fully supervised upper bound with only 13% of the target-domain training data. Overall, this study provides a scalable and cost-effective classification framework for urban noise monitoring, offering practical support for noise regulatory authorities and city managers in more targeted noise source tracing and governance. Full article

Background/Objectives: This study aimed to examine the association between 24-h movement behavior composition and estimate cardiorespiratory fitness in school-aged children using compositional data analysis, and to model the theoretical differences in estimated cardiorespiratory fitness associated with isotemporal reallocations of time between movement behaviors. Methods: A cross-sectional study was conducted in 222 schoolchildren aged 8 to 12 years (mean age 9.94 ± 0.69 years), with most participants aged 10 years. Twenty-four-hour movement behaviors were assessed objectively using wrist-worn accelerometers, and cardiorespiratory fitness was estimated from the 20 m shuttle run test using the Léger equation. Daily time-use composition was analyzed using isometric log-ratio coordinates and adjusted linear regression models were fitted. Estimated differences in cardiorespiratory fitness associated with 30-min isotemporal reallocations between behaviors were then modeled. Results: The 24-h movement behavior composition was significantly associated with estimated cardiorespiratory fitness. In isotemporal models, reallocating 30 min from sedentary behavior to sleep was associated with the largest modeled difference in estimated cardiorespiratory fitness, whereas other reallocations showed smaller estimated differences depending on the behavior displaced. Age was positively associated with estimated cardiorespiratory fitness, while sex showed a limited association. Bivariate analyses revealed weak or inconsistent associations, supporting the value of the compositional approach for capturing the interdependent nature of daily time use. Conclusions: Twenty-four-hour movement behavior composition was associated with estimated cardiorespiratory fitness in school-aged children. These findings support the use of compositional approaches to examine sleep, sedentary behavior, and physical activity jointly. However, given the cross-sectional design and the modeled nature of the reallocations, the estimated differences should be interpreted cautiously and not as direct causal or physiological effects. Full article

Air pollution remains a major global environmental risk, and exposure to fine particulate matter (PM_2.5) is associated with adverse health outcomes even at low concentrations. Meteorological conditions influence PM_2.5 variability, and precipitation is often expected to reduce particle loads through wet removal. However, humid and wet conditions may coincide with elevated PM_2.5 under specific atmospheric and compositional conditions. Here, we investigate long-term relationships between precipitation regimes and PM_2.5 concentrations in the Metropolitan Region of Belém (Eastern Amazonia) over the period 1980–2024. We combined PM_2.5 from the MERRA-2 reanalysis (including a bias-corrected product) with in situ precipitation records, and classified precipitation conditions using the Standardized Precipitation Index (SPI). We find statistically significant positive long-term tendencies in both precipitation and PM_2.5. Stratified analyses show that PM_2.5 concentrations are significantly higher under wet conditions, with a weak but significant positive relationship between SPI and PM_2.5 (r = 0.23 for the full period; r = 0.24 for the wet class, p-value < 0.01). These findings indicate that increased precipitation in a strong humid tropical urban environment does not necessarily lead to improved air quality. Instead, wet conditions may favor processes such as hygroscopic growth and secondary aerosol formation, contributing to higher PM_2.5 concentrations on a monthly scale. Overall, this study highlights the importance of considering precipitation regimes and associated atmospheric processes when assessing air quality in tropical urban environments. Full article

Indonesia, particularly Central Java, generates substantial amounts of agricultural biomass residues, including melinjo shells, tobacco stalks, and cacao shells, which remain underutilized for energy applications. This study addresses the limited scientific evidence on the fuel properties and environmental performance of these residues by systematically evaluating their suitability as briquette feedstocks. A factorial experimental design was applied using three biomass types and two binders (tapioca starch and clay). The produced briquettes were characterized for moisture content, ash content, volatile matter, and higher heating value according to the Indonesian National Standard (SNI 01-6235-2000), and their environmental performance was assessed using a Life Cycle Assessment (LCA) approach to estimate associated environmental costs. The results indicate that briquettes made from melinjo shells with tapioca starch binder exhibited the most favorable performance, achieving a moisture content of 7.01%, ash content of 13.58%, volatile matter of 47.15%, and a calorific value of 5453.43 cal g⁻¹. However, the ash and volatile matter contents exceeded the recommended limits for solid biofuels. These findings demonstrate that melinjo shells are a promising feedstock for briquette production due to their relatively high energy content, while further improvements in carbonization conditions and reductions in binder proportion are required to enhance fuel quality and environmental performance. Full article

Personalized Medicine has been a central aspiration of medical practice and has guided the direction of medical advances from ancient times to the present. This narrative review highlights some of the most significant past advances and present practices, discusses issues currently limiting Personalized Medicine and proposes activities necessary for Personalized Medicine to have a promising future. Throughout history, Personalized Medicine has developed along with the evolution of science and societal concepts. Notable advances paralleled the growth in what an individual person is and how experimental science can apply to medical practice. In the twentieth century, the study of inborn errors of metabolism and pharmacogenetics broadened the horizons of what Personalized Medicine could be. Presently, Personalized Medicine is challenged by different perspectives on its scope, by the various clinical scientific activities which can inadvertently or by misinterpretation serve to depersonalize medicine, and by the difficulties involved in integrating the massive amount of available scientific data to optimize medical practice centered on the individual. The conditions necessary for Personalized Medicine to have a promising future include developing broader, deeper, and more dynamic knowledge of disease processes, new methods to identify anomalous, singular disease-contributing characteristics in individuals, and improving data quality in research and medical practice. Advancing Personalized Medicine requires developing new perspectives for research, healthcare education, medical practice, and healthcare governance, as well as deploying medical advances at scale across populations. Full article

Economic inequities in healthcare access persist globally, yet the impact of income on palliative care (PC) utilization in Middle Eastern contexts remains empirically understudied. This cross-sectional study of 200 cancer patients in Riyadh, Saudi Arabia, employed a socioeconomic stratification analysis to examine income-stratified differences in PC awareness and access. Using chi-square and linear-by-linear association tests, results revealed pronounced income gradients; awareness increased from 41.9% in the low-income group to 71.9% in the high-income group (p = 0.001), demonstrating a significant dose–response trend. Access disparities were even more striking, with only 35.5% of low-income patients utilizing services compared to 76.1% of high-income patients (p < 0.001), representing a 40.6 percentage-point gap. After multivariable adjustment, after controlling for age, gender, education, and geographic living region, the results of logistic regression analysis showed that cancer patients with high income were more than three times as likely to access PC services compared with lower-income cancer patients (OR = 3.32; 95% CI: 1.83–6.02; p < 0.001). Geographic stratification further indicated that income disparities were significantly amplified in peripheral regions compared to the Central region (p = 0.072 for interaction), where service scarcity exacerbates economic barriers. These findings underscore that economic barriers operate through awareness gaps and structural obstacles like transportation and opportunity costs. Addressing these inequities requires multifaceted strategies, including financial support and geographic service expansion, to ensure equitable PC distribution under the Vision 2030 framework. Full article

Harmonic reducers exhibit non-stationary and phase-dependent degradation behavior during long-term service, challenging the ability of classical stochastic degradation models to accurately assess reliability. To address phase-dependent differences in degradation behavior, this paper proposes a reliability assessment model based on a two-phase hybrid stochastic degradation process. In the proposed framework, the Wiener process is employed to characterize early-phase gradual degradation dominated by stochastic fluctuations, while the Inverse Gaussian process is used to describe later-phase monotonically accelerated degradation driven by cumulative damage. The framework allows for sample-level variability in transition times to more realistically capture individual degradation behavior. The Schwarz Information Criterion is also adopted to detect change points. Maximum likelihood estimation is performed for model parameter inference, and analytical expressions for the reliability function, cumulative distribution function, and probability density function are derived. Numerical results indicate that a change point exists for each tested product and that the proposed model achieves the best goodness of fit among the considered candidates, demonstrating its superiority in capturing phase-dependent characteristics of harmonic reducer degradation. In terms of reliability assessment bias, the proposed model (0.06%) significantly outperforms the Wiener degradation model (32%) and the IG degradation model (9.9%). These results further confirm that, under an identical failure threshold, the proposed approach yields more accurate and realistic reliability assessment outcomes. Full article

Background: The use of contraceptive methods is a key component of public health and reproductive health, contributing to family planning, maternal well-being, and social stability. However, contraceptive use is often influenced by the availability and continuity of medical counseling. Limited evidence exists on how regular specialist counseling affects informed contraceptive use in real-world community settings. Methods: A cross-sectional study was conducted between January 2025 and January 2026 using a structured questionnaire. A total of 2400 participants aged 18–55 years were included. The study population was divided into two groups: 1000 women who had been regular patients or receiving consultation for at least one year at the Gynecology and Endocrinology Department of the General Hospital in Ferizaj, and 1400 community participants who had not received regular medical counseling related to reproductive health during the previous year. Data were analyzed using descriptive statistics, chi-square tests, and multivariable logistic regression. Results: The prevalence of current contraceptive use was significantly higher among women receiving regular medical counseling compared with those without regular consultations (72.4% vs. 41.8%; p < 0.001). Modern contraceptive methods were more frequently used in the counseled group, including oral hormonal contraceptives (38.5%), intrauterine devices (21.4%), and implants (7.8%), whereas condom use (49.3%) and traditional methods (18.4%) predominated among participants without counseling (p < 0.001). Use of contraceptives based on medical recommendation was reported by 81.2% of counseled women compared to 29.6% in the non-counseled group (p < 0.001). Long-term contraceptive use (≥12 months) was significantly more common among counseled participants (64.9% vs. 33.5%; p < 0.001). After adjustment for age, education, and marital status, regular medical counseling was independently associated with higher odds of modern contraceptive use (OR = 3.62; 95% CI: 3.01–4.35; p < 0.001). Conclusions: Regular medical counseling by gynecologists and endocrinologists is strongly associated with informed, consistent, and modern contraceptive use among adults aged 18–55 years. These findings underscore the importance of strengthening structured counseling services as an integral component of public health strategies aimed at improving reproductive health outcomes. Full article

This study presents a systematic review and an integrative interpretive synthesis of the architectural literature addressing sensory–interactive design strategies in early childhood learning environments that support children with Autism Spectrum Disorder (ASD), Down Syndrome (DS), and Attention Deficit Hyperactivity Disorder (ADHD). Following a systematic review conducted in accordance with PRISMA 2020 guidelines, twenty-nine peer-reviewed studies were analyzed to examine how environmental design variables may influence sensory load, cognitive processing, emotional stability, and behavioral engagement across neurodevelopmental profiles. Rather than remaining within conventional descriptive approaches, architectural variables—including lighting, color, acoustics, materials, spatial configuration, and environmental controllability—are reconceptualized as regulatory dimensions shaping child–environment interactions. The synthesis suggests that identical environmental variables may elicit divergent, and at times conflicting, sensory–emotional and behavioral responses among children with ASD, DS, and ADHD, highlighting the limitations of standardized design solutions. Accordingly, the study proposes the Sensory–Interactive Architecture Framework (SIAF), an analytical framework that links neurodevelopmental response patterns with sensory–emotional regulation mechanisms and environmental design variables as regulatory dimensions. The findings indicate that effective inclusive design does not rely on generalized sensory interventions but rather on the deliberate regulation of sensory variability through more legible, graded, and controllable spatial systems, thereby promoting learning engagement, emotional stability, and adaptive behavior in neurodiverse children. Full article