Search Results (109)

Background: This cross-sectional study examined the association between body mass index (BMI) and executive function (EF) in older adults, with a focus on the moderating role of attitudes toward own aging (ATOA). Method: A total of 431 community-dwelling elderly individuals from Yakumo Town and Kyoto City, Japan, participated between 2023 and 2024. EF was assessed using the Digit Cancellation Test (D-CAT), and ATOA was measured via a validated subscale of the Philadelphia Geriatric Center Morale Scale. Results: Multiple linear regression analyses adjusted for demographic and health covariates revealed a significant interaction between BMI and ATOA in the younger-old cohort. Specifically, higher BMI was associated with lower executive function only in individuals with lower ATOA scores. No such association was observed in those with more positive views on aging. Conclusions: These results indicate that positive psychological constructs, particularly favorable self-perceptions of aging, may serve as protective factors against the detrimental cognitive consequences of increased body mass index in younger-old populations. Full article

Volatile Organic Compounds (VOCs) are important contributors to indoor and occupational air pollution, such as environments involving the extensive use of paints and solvents. The routine measurement of VOCs is often limited by resource constraints, creating a need for indirect estimation techniques. This work presents the need for a predictive framework that offers a practical, interpretable alternative to a full-spectrum chemical analysis and supports early exposure detection in resource-limited settings, contributing to environmental health monitoring and occupational risk assessment. This study explores the capability of machine learning to simultaneously predict the concentrations of five paint-related VOCs using other co-emitted VOCs along with demographic variables. Three models—Multi-Output Gaussian Process Regression (MOGP), CatBoost Multi-Output Regressor, and Multi-Output Neural Networks—were calibrated and each achieved a high predictive performance. Further, a feature importance analysis is conducted and showed that certain VOCs and some demographic variables consistently influenced the predictions across all models, pointing to common exposure determinants for individuals, regardless of their specific exposure setting. Additionally, a subgroup analysis identified the exposure disparities across demographic groups, supporting targeted risk mitigation efforts. Full article

Against the backdrop of global climate change and increasing ecological pressure, the refined monitoring of forest resources and accurate tree species identification have become essential tasks for sustainable forest management. Hyperspectral remote sensing, with its high spectral resolution, shows great promise in tree species classification. However, traditional methods face limitations in extracting joint spatial–spectral features, particularly in complex forest environments, due to the “curse of dimensionality” and the scarcity of labeled samples. To address these challenges, this study proposes a synergistic classification approach that combines the spatial feature extraction capabilities of deep learning with the generalization advantages of machine learning. Specifically, a 2D convolutional neural network (2DCNN) is integrated with a support vector machine (SVM) classifier to enhance classification accuracy and model robustness under limited sample conditions. Using UAV-based hyperspectral imagery collected from a typical plantation area in Fuzhou City, Jiangxi Province, and ground-truth data for labeling, a highly imbalanced sample split strategy (1:99) is adopted. The 2DCNN is further evaluated in conjunction with six classifiers—CatBoost, decision tree (DT), k-nearest neighbors (KNN), LightGBM, random forest (RF), and SVM—for comparison. The 2DCNN-SVM combination is identified as the optimal model. In the classification of Masson pine, Chinese fir, and eucalyptus, this method achieves an overall accuracy (OA) of 97.56%, average accuracy (AA) of 97.47%, and a Kappa coefficient of 0.9665, significantly outperforming traditional approaches. The results demonstrate that the 2DCNN-SVM model offers superior feature representation and generalization capabilities in high-dimensional, small-sample scenarios, markedly improving tree species classification accuracy in complex forest settings. This study validates the model’s potential for application in small-sample forest remote sensing and provides theoretical support and technical guidance for high-precision tree species identification and dynamic forest monitoring. Full article

Effectively managing carbon monoxide (CO) pollution in complex industrial cities like Jubail remains challenging due to the diversity of emission sources and local environmental dynamics. This study analyzes spatiotemporal CO patterns and builds accurate predictive models using five years (2018–2022) of data from ten monitoring stations, combined with meteorological variables. Exploratory analysis revealed distinct diurnal and moderate weekly CO cycles, with prevailing northwesterly winds shaping dispersion. Spatial correlation of CO was low (average 0.14), suggesting strong local sources, unlike temperature (0.92) and wind (0.5–0.6), which showed higher spatial coherence. Seasonal Trend decomposition (STL) confirmed stronger seasonality in meteorological factors than in CO levels. Low wind speeds were associated with elevated CO concentrations. Key predictive features, such as 3-h rolling mean and median values of CO, dominated feature importance. Spatiotemporal analysis highlighted persistent hotspots in industrial areas and unexpectedly high levels in some residential zones. A range of models was tested, with ensemble methods (Extreme Gradient Boosting (XGBoost) and Categorical Boosting (CatBoost)) achieving the best performance ($R^2>0.95$) and XGBoost producing the lowest Root Mean Squared Error (RMSE) of 0.0371 ppm. This work enhances understanding of CO dynamics in complex urban–industrial areas, providing accurate predictive models ($R^2>0.95$) and highlighting the importance of local sources and temporal patterns for improving air quality forecasts. Full article

Feline leukemia (FeLV) is a viral disease that primarily affects domestic cats (Felis catus), with high mortality rates because it mainly targets the immune system and is also associated with lymphomas. The objective of this study is to retrospectively analyze the prevalence of FeLV in cats treated at veterinary centers in the city of Quito, between September 2021 and December 2024. Data were obtained from diagnostic test results conducted at the Laboratory of Biology and Molecular Genetics (LABIGEN) using RT-qPCR. A total of 850 samples met the inclusion criteria. FeLV was detected in 28.59% (243/850) of samples, with a slightly higher prevalence in males (53.50%) than females (46.50%). Regarding age, 54.32% (132/243) were between 1 and 5 years, 22.22% (54/243) were between 1 and 11 months, 18.52% (45/243) were between 5 and 10 years, and 4.94% (12/243) were between 10 and 19 years. In conclusion, the prevalence of FeLV in Quito, Ecuador, is high, requiring greater efforts to prevent and control this disease, in pursuit of animal health and well-being. Full article

This study investigates the effectiveness of the Lagos Recycle Initiative (LRI) on landfill diversion (LFD) in Lagos, Nigeria, where evidence-based assessments of such initiatives are lacking. It evaluates the recycling diversion rate (RDR) of household recyclables (HSRs) across local government areas using field surveys and population data. Machine learning algorithms (logistic regression, random forest, XGBoost, and CatBoost) refined with Bayesian optimisation were employed to predict household recycling motivation. The findings reveal a low RDR of 0.37%, indicating that only approximately 2.47% (31,554.25 metric tonnes) of recyclables are recovered annually compared to a targeted 50% (638,750 metric tonnes). The optimised CatBoost model (accuracy and F1 score of 0.79) identified collection time and the absence of overflowing HSR bins as key motivators for household recycling via the SHapley Additive exPlanations (SHAP) framework. This study concludes that current LRI efforts are insufficient to meet recycling targets. It recommends expanding recovery efforts and addressing operational challenges faced by registered recyclers to improve recycling outcomes. The policy implications of this study suggest the need for stricter enforcement of recycling regulations, coupled with targeted financial incentives for both recyclers and households to boost recycling participation, thereby enhancing the overall effectiveness of waste diversion efforts under the LRI. This research provides a benchmark for assessing urban recycling initiatives (RIs) in rapidly growing African cities. Full article

This study aims to explore the methodology for assessing landslide susceptibility by using machine learning techniques based on a geographic information system (GIS) in an effort to develop landslide susceptibility maps and assess landslide risk in the Yiyang region. A landslide dataset in Yiyang was constructed after 16 landslide predisposing factors were identified across four categories, topography, geology, environment, and hydrometeorology, through factor state determination and multicollinearity analysis. A Blending ensemble model was created and achieved higher prediction accuracy by fusing predictions from Random Forest, CatBoost, and XGBoost with logistic regression used as the meta-learner, thus deriving the importance coefficients of the landslide predisposing factors and their contribution rates. The Blending ensemble model achieved high predictive accuracy with an AUC value of 0.8784, demonstrating balanced and stable performance characteristics. With the addition of the rainfall factor, the AUC value of the Blending ensemble model has increased by 0.1199. In combination with the information value method, this model was applied to assess landslide susceptibility and rainfall-induced landslide risks in Yiyang City, demonstrating its validity. In addition, experimental validation confirmed the prediction and evaluation accuracy of the GIS-based Blending ensemble model. Results showed that the frequency ratio (FR) of historical landslide occurrences in high-susceptibility and extremely high-susceptibility zones in Yiyang City exceeded 1, indicating strong consistency between the landslide risk classification and actual distribution of historical landslides. The landslide susceptibility maps created for Anhua County, Heshan District, and Taojiang County in Yiyang City may provide support for the early warning and prevention of landslides and land-use planning in this region. The proposed methodology may be of reference value for improving natural disaster prevention and risk management. Full article

With the acceleration of climate change and the increase of extreme rainfall, the risk of flooding has intensified in the Huang-Huai region, which is often hit by floods. Urban water accumulation is a complicated process, and the hydrological simulation analysis is highly accurate, but it is time-consuming and laborious. Machine learning is becoming an important new method because of its ability to analyze large areas with high precision. In this paper, a simulation analysis method based on machine learning is constructed by selecting 13 disaster factors, and the waterlogging point in Xuzhou city is predicted successfully. The following conclusions are found: (1) Among the five machine learning models, CatBoost has the highest accuracy rate, reaching 81.67%. (2) Temperature, elevation, and rainfall are relatively important influencing factors of waterlogging. (3) Machine learning can discover water accumulation areas that are easily overlooked except for the built-up areas. (4) The results of the coupling analysis show that the exposure risk of the population exposed to rainwater in the old urban area, the southern area, and the northwestern area is relatively high. This research is of great significance for reducing the risk of exposure to rain and flooding and promoting the safety and sustainable development of cities. Full article

Unmanned aerial vehicles (UAVs) have emerged as effective tools for monitoring air pollution across varying altitudes, including assessing atmospheric mercury (Hg) levels. However, studies on the vertical distribution of atmospheric Hg (i.e., total gaseous mercury–TGM) concentrations remain limited, particularly in Southeast Asia. This study utilized a UAV equipped with a TGM sampling device to measure concentrations at different altitudes in Ben Cat City, an industrial area in Southern Vietnam. The purpose of this study is to examine the applicability of UAV in investigating the altitudinal distribution of TGM and to analyze specific case studies related to Hg emissions from stack. A total of 36 flight experiments were conducted (including 36 concurrently ground level measurements), including 50 m (20 flights), 200 m (7 flights), and 500 m (9 flights). TGM concentrations increase noticeably with altitude under stack emission conditions, while they remain relatively consistent at all altitudes during non-emission conditions. Under the emission conditions, three vertical distribution patterns were observed: (1) elevated TGM concentrations at higher altitudes compared to ground level; (2) lower TGM concentrations at higher altitudes relative to ground level; and (3) nearly equivalent TGM concentrations between ground level and higher altitudes, with differences less than 0.4 ng m⁻³. The observed distributions imply the important role of atmospheric dynamics in understanding the dispersion of pollutants and the impact of emissions. This study pioneers the use of UAVs in Vietnam for simultaneous TGM measurements across altitudes, highlights their potential for atmospheric Hg monitoring, and improves stack emission management. Full article

Urban free-roaming cats present challenges like noise, urination, defecation, property damage, public health risks, and wildlife predation. Traditional enforcement methods, such as containment laws and impounding, are ineffective, especially in low-income areas, where many free-roaming cats live. These cats are often cared for by “semi-owners”, who feed them without formal ownership. Financial barriers to sterilization for owned and semi-owned cats in these areas result in unplanned litters, sustaining the free-roaming population and burdening local authorities and animal welfare organizations. Cats causing complaints are frequently impounded and euthanized, affecting the mental health of veterinary, shelter, and council staff. This paper critiques punitive, compliance-driven strategies and highlights the success of assistive Community Cat Programs offering free sterilization, microchipping, and registration. In Banyule, Victoria, such a program reduced cat impoundments by 66%, euthanasia by 82%, and complaints by 36% between 2013 and 2021. Two other programs in large cities and rural towns in NSW and a rural town in Queensland have now reported similar results. Based on the One Welfare framework, these programs address the interconnectedness of animal welfare, human well-being, and environmental health. By removing financial barriers, they build trust between authorities and caregivers, improving compliance and welfare for cats, communities, and wildlife. However, following the loss of key program staff and the reintroduction of financial barriers in Banyule, cat intake rose by 140% between 2022 and 2024, demonstrating the detrimental impact of financial barriers and punitive approaches. This underscores the importance of sustained, community-based solutions and legislative reforms that prioritize humane, barrier-free strategies. Understanding the critical success factors for Community Cat Programs is essential for effective cat management. Full article

Background/Objectives: Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health. At physiological levels, it acts as an antioxidant, protecting against oxidative stress. However, excessive uric acid can lead to hyperuricemia, contributing to conditions like gout, kidney stones, and cardiovascular diseases. Emerging evidence also links elevated uric acid levels with metabolic disorders, including hypertension and insulin resistance. Understanding its regulation is crucial for preventing associated health complications. Methods: This study, part of the Tlalpan 2020 project, aimed to predict uric acid levels using advanced machine learning algorithms. The dataset included clinical, anthropometric, lifestyle, and nutritional characteristics from a cohort in Mexico City. We applied Boosted Decision Trees (Boosted DTR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Shapley Additive Explanations (SHAP) to identify the most relevant variables associated with hyperuricemia. Feature engineering techniques improved model performance, evaluated using Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), and the coefficient of determination (R²). Results: Our study showed that XGBoost had the highest accuracy for anthropometric and clinical predictors, while CatBoost was the most effective at identifying nutritional risk factors. Distinct predictive profiles were observed between men and women. In men, uric acid levels were primarily influenced by renal function markers, lipid profiles, and hereditary predisposition to hyperuricemia, particularly paternal gout and diabetes. Diets rich in processed meats, high-fructose foods, and sugary drinks showed stronger associations with elevated uric acid levels. In women, metabolic and cardiovascular markers, family history of metabolic disorders, and lifestyle factors such as passive smoking and sleep quality were the main contributors. Additionally, while carbohydrate intake was more strongly associated with uric acid levels in women, fructose and sugary beverages had a greater impact in men. To enhance model robustness, a cross-feature selection approach was applied, integrating top features from multiple models, which further improved predictive accuracy, particularly in gender-specific analyses. Conclusions: These findings provide insights into the metabolic, nutritional characteristics, and lifestyle determinants of uric acid levels, supporting targeted public health strategies for hyperuricemia prevention. Full article

Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. A dataset comprising 2700 known geological hazard locations in Yibin City was analyzed to extract key environmental and topographic features influencing hazard susceptibility. Several machine learning models were evaluated, including random forest, XGBoost, and CatBoost, with model optimization performed using the Sparrow Search Algorithm (SSA) to enhance prediction accuracy. This study produced high-resolution susceptibility maps identifying high-risk zones, revealing a distinct spatial pattern characterized by a concentration of hazards in mountainous areas such as Pingshan County, Junlian County, and Gong County, while plains exhibited a relatively lower risk. Among different hazard types, landslides were found to be the most prevalent. The results further indicate a strong spatial overlap between predicted high-risk zones and existing rural settlements, highlighting the challenges of hazard resilience in these areas. This research provides a refined methodological framework for integrating machine learning and geospatial analysis in hazard prediction. The findings offer valuable insights for rural land use planning and hazard mitigation strategies, emphasizing the necessity of adopting a “small aggregations and multi-point placement” approach to settlement planning in Southern Sichuan’s mountainous regions. Full article

Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences. Full article

Between September 2013 and 2022, the American Society for the Prevention of Cruelty to Animals (ASPCA) received 2783 suspected animal cruelty cases involving 5745 animals through a partnership with the New York City Police Department (NYPD). These cases involved dogs (2271/2783, 82%), cats (408/2783, 15%), and other species (104/2783, 4%). Dogs were most likely to be presented for suspected neglect (1424/2271, 63%), and cats for suspected non-accidental injury (233/408, 58%). Animals were most often presented by law enforcement (1018/2783, 37%), municipal shelters (383/2783, 14%), and veterinarians (311/2783, 11%). These findings contribute to understanding neglect as a commonly reported type of animal cruelty. Cruelty toward cats appears less well understood and may be under-reported or more severe, requiring further research and attention. Full article

Feline calicivirus (FCV) is an infectious pathogen in cats that mainly causes upper respiratory tract disease. Enhancing our understanding of the epidemiological characteristics of FCV can contribute to better strategies against FCV infection. To investigate the prevalence of FCV in Beijing, explore the risk factors associated with FCV infection and elucidate its genetic evolutionary characteristics. Cats (n = 402) from the China Agricultural University Veterinary Teaching Hospital (CAUVTH) were investigated from June to December in 2023. The rate of FCV-positive cats in the sample examined was 31.3%. Risk factors significantly associated with FCV infection were age, vaccination status and residential density by Logistic regression analysis. Phylogenetic analysis of completed genomes revealed a radial phylogeny, with no obvious geographical clustering. Amino acid analysis at different sites of E region of the major capsid protein revealed variable neutralizing antibody epitopes, while feline junctional adhesion molecule-A (fJAM-A) binding sites remained conserved. Additionally, the first FCV recombinant isolate was detected in Beijing, originating from two 2019 isolates collected in the city. This study elucidates the molecular epidemiology and genetic diversity of FCV in Beijing, which provides valuable insights for the development of effective measures for FCV prevention and control. Full article