Search Results (2,596)

Rainfall-induced slope failures are among the most frequent and destructive natural hazards in Japan’s mountainous regions, often causing severe loss of life and damage to infrastructure. This study presents an integrated statistical framework for regional-scale landslide hazard mapping, with a focus on 0-order basins. To enhance spatial prediction accuracy, both bivariate and multivariate statistical models are employed. Bivariate models efficiently assess the relationship between individual conditioning factors and landslide occurrences but assume variable independence. Conversely, multivariate models account for multicollinearity and the combined effects of interacting factors, although they often require more complex data processing and may lack spatial clarity. To leverage the strengths of both approaches, two hybrid models were developed and applied to a 242.94 km² area in Fukuoka Prefecture, Japan. Model validation was performed using a matrix-based evaluation supported by a threshold optimization algorithm. Among the models tested, the hybrid Frequency Ratio–Logistic Regression (FR + LR) model demonstrated the highest predictive performance, achieving a success rate of 84.30%, a false alarm rate of 17.88%, and a miss rate of 12.30%. It effectively identified critical slip surfaces within zones classified as ‘High’ to ‘Very High’ susceptibility. This integrated approach offers a statistically robust, scalable, and interpretable solution for landslide hazard assessment in geomorphologically complex terrains. It provides valuable support for regional disaster risk reduction and contributes directly to achieving the Sustainable Development Goals (SDGs). Full article

E-commerce platforms offering regional fresh produce often face a trade-off between logistics costs and product quality. Due to limited use of cold chain logistics, consumers frequently receive damaged goods, resulting in negative post-purchase experiences. This study examines how logistics service encounters, as reflected in consumer reviews, influence subsequent purchase behaviour, and how the alignment between review images and text moderates this relationship. We analyse sales and review data from 694 fruit products on Tmall between February and April 2024. Latent Dirichlet Allocation (LDA) is applied to extract logistics-related review content. At the same time, image–text similarity is assessed using the Chinese-CLIP model. Regression analysis reveals that positive logistics service encounters significantly enhance purchase intention. However, high image–text similarity weakens this positive effect, suggesting that overly repetitive content may reduce informational value for prospective buyers. These findings advance understanding of consumer behaviour in online fresh produce markets by highlighting the interactive effects of logistics experiences and user-generated content. The results offer practical implications for improving logistics services, enhancing content diversity in review systems, and increasing consumer trust in e-commerce environments. Full article

This study presents and evaluates three sampling strategies to characterize electricity demand in regions of Colombia with limited metering infrastructure. These areas lack Advanced Metering Infrastructure (AMI), relying instead on traditional monthly consumption records. The objective of the research is to obtain user samples that are representative of the original population and logistically efficient, in order to support energy planning and decision-making. The analysis draws on five years of historical data from 2020 to 2024. It includes monthly energy consumption, geographic coordinates, customer classification, and population type, covering over 500,000 users across four subregions of operation determined by the region grid operator: North, South, Center, and East. The proposed methodologies are based on Shannon entropy, consumption-based probabilistic sampling, and Kullback–Leibler divergence minimization. Each method is assessed for its ability to capture demand variability, ensure representativeness, and optimize field deployment. Representativeness is evaluated by comparing the differences in class proportions between the sample and the original population, complemented by the Pearson correlation coefficient between their distributions. Results indicate that entropy-based sampling excels in logistical simplicity and preserves categorical diversity, while KL divergence offers the best statistical fit to population characteristics. The findings demonstrate how combining information theory and statistical optimization enables flexible, scalable sampling solutions for demand characterization in under-instrumented electricity grids. Full article

The complex topography of China’s Tibetan Plateau mountainous roads, characterized by diverse curve types and frequent traffic accidents, significantly impacts the safety and sustainability of the transportation system. To enhance driving safety on these mountain roads and promote low-carbon, resilient transportation development, this study investigates the mechanisms through which different curve types affect driving safety and proposes optimization strategies based on interpretable machine learning methods. Focusing on three typical curve types in plateau regions, drone high-altitude photography was employed to capture footage of three specific curves along China’s National Highway G318. Oblique photography was utilized to acquire road environment information, from which 11 data indicators were extracted. Subsequently, 8 indicators, including cornering preference and vehicle type, were designated as explanatory variables, the curve type indicator was set as the dependent variable, and the remaining indicators were established as safety assessment indicators. Linear models (logistic regression, ridge regression) and non-linear models (Random Forest, LightGBM, XGBoost) were used to conduct model comparison and factor analysis. Ultimately, three non-linear models were selected, employing an explainability-oriented dynamic ensemble learning strategy (X-DEL) to evaluate the three curve types. The results indicate that non-linear models outperform linear models in terms of accuracy and scene adaptability. The explainability-oriented dynamic ensemble learning strategy (X-DEL) is beneficial for the construction of driving safety models and factor analysis on Tibetan Plateau mountainous roads. Furthermore, the contribution of indicators to driving safety varies across different curve types. This research not only deepens the scientific understanding of safety issues on plateau mountainous roads but, more importantly, its proposed solutions directly contribute to building safer, more efficient, and environmentally friendly transportation systems, thereby providing crucial impetus for sustainable transportation and high-quality regional development in the Tibetan Plateau. Full article

As a key component of new infrastructure, smart logistics is becoming an essential driver for reducing foreign trade costs and risks and promoting the sustainable development of foreign trade. Using panel data from 286 prefecture level and above cities from 2014 to 2023, this article attempts to refine the measurement of smart logistics level from provincial to municipal levels, construct a two-way fixed effect model and a mediation effect model, and deeply explore the inherent relationship between smart logistics, industrial structure upgrading, and sustainable development of foreign trade. The results reveal that: (1) smart logistics significantly promotes the sustainable development of foreign trade. (2) Rationalization and advancement of industrial structure play an intermediary role between the two. (3) Market integration has a positive moderating effect on the path of “smart logistics—industrial structure rationalization”, but the moderating effect is not significant in other paths. It has been confirmed that there is a “siphon effect” in the advantageous regions. (4) Heterogeneity analysis shows that the effect of smart logistics on foreign trade promotion is more significant in the central and inland regions. This study provides a theoretical basis and practical inspiration for optimizing regional smart logistics layout and deepening industrial structure adjustment. Full article

Background and Objectives: Aortic aneurysms and dissections are life-threatening vascular disorders with high morbidity and mortality. Enhancing diagnostic and therapeutic strategies requires a precise characterization of their anatomical and clinical features. This study aimed to detail the demographic, clinical, and imaging-based anatomical characteristics of aortic aneurysms and dissections in a cohort of Colombian patients. Materials and Methods: We conducted a retrospective, descriptive, observational study at a tertiary hospital in Bogotá, Colombia. Adult patients (≥18 years) with radiologically confirmed aortic aneurysm or dissection on computed tomography angiography (CTA) between 2010 and 2021 were included. Demographic, clinical, and morphological data were extracted. Associations were explored using chi-squared and Mann–Whitney U tests. Multivariate logistic regression was applied to identify independent predictors of in-hospital mortality. Results: This study included 539 patients (mean age: 69.3 ± 11.5 years; 53.6% male). Infrarenal abdominal aortic aneurysms were the most frequent (63.7%), with fusiform morphology observed in 92% of cases. Saccular aneurysms were significantly more common in females (p = 0.0267) and in non-infrarenal segments (p = 0.0162). Among patients with aneurysms, aortic diameter was an independent predictor of mortality (OR = 1.03; 95% CI: 1.01–1.05; p < 0.001). No individual variable significantly predicted mortality in dissection cases. Conclusions: In this cohort, infrarenal location and fusiform shape predominate. Morphological patterns were associated with sex and anatomical distribution. Although trends were observed, no statistically significant predictors of death were identified in dissection cases. These findings highlight the value of early detection and tailored management and reinforce the importance of generating region-specific data to inform clinical decision making in Latin American settings. Full article

Discrimination and harassment (DH) against women are topics of broad concern to gender equality advocates. This study aimed to investigate the prevalence of DH against women in Nigeria, based on seven specific forms of DH captured in the 2021 Nigeria Multiple Indicator Cluster Survey (MICS), and to identify key socio-demographic factors associated with an aggregated DH outcome variable. Drawing upon data from 38,806 women aged 15–49, we used descriptive statistics to summarize the prevalence of DH across seven reasons and the socio-demographic characteristics of respondents, followed by chi-square analysis to test bivariate associations and binary logistic regression to identify predictors. Results showed that the prevalence of DH against Nigerian women (18.9%) was significantly associated with socio-demographic factors such as age, education level, wealth index, marital status, and ethnicity. At the individual level, women who felt very unhappy had higher odds of experiencing DH (OR = 3.101, 95% CI: 2.393–4.018, p < 0.001) compared to those who felt very happy. In contrast, women with higher/tertiary education (OR = 0.686, 95% CI: 0.560–0.842, p < 0.001) were 31.4% less likely to face DH than those with no education. Regionally, respondents living in Zamfara (OR = 5.045, 95% CI: 3.072–8.288, p < 0.001) were over five times more likely to experience DH than those in Kano state. The findings underscore the need for policy interventions and support systems to address DH against women in Nigeria. Full article

Human toxocariasis is a neglected tropical disease with a potentially major impact on public health. Our aim was to assess the seroprevalence and risk factors associated with Toxocara seroprevalence in blood donors from Romania. Serum samples were obtained from 1347 Romanian blood donors and serologically tested for anti-Toxocara antibodies. An epidemiological questionnaire was used to determine the risk factors associated with Toxocara infection. The overall prevalence of Toxocara antibodies was 29.6%, with a significant age-associated increase (p < 0.001). A higher rate was observed in individuals from rural areas compared to urban areas (p = 0.002) and in males compared to females (p = 0.001). In univariate statistical analysis, seropositivity was significantly associated with household ownership (p < 0.001), contact with soil (p < 0.001), owning dogs (p < 0.001), cats (p = 0.003), and consumption of undercooked poultry (p = 0.002). In a stepwise multivariate logistic regression model, only a lower level of education, age, male gender, consumption of undercooked or raw poultry, and contact with soil were associated with higher Toxocara seroprevalence. Our findings suggest a significant prevalence of Toxocara infection in this region. The identified risk factors highlight the necessity of health education programs that focus on public awareness and promote preventive behaviors, especially among at-risk populations. Full article

The betel palm is a critical economic crop on Hainan Island. Accurate and timely maps of betel palms are fundamental for the industry’s management and ecological environment evaluation. To date, mapping the spatial distribution of betel palms across a large regional scale remains a significant challenge. In this study, we propose an integrated framework that combines knowledge-based and machine learning approaches to produce a map of betel palms at 10 m spatial resolution based on Sentinel-1/2 data and Google Earth Engine (GEE) for 2023 on Hainan Island, which accounts for 95% of betel nut acreage in China. The forest map was initially delineated based on signature information and the Green Normalized Difference Vegetation Index (GNDVI) acquired from Sentinel-1 and Sentinel-2 data, respectively. Subsequently, patches of betel palms were extracted from the forest map using a random forest classifier and feature selection method via logistic regression (LR). The resultant 10 m betel palm map achieved user’s, producer’s, and overall accuracy of 86.89%, 88.81%, and 97.51%, respectively. According to the betel palm map in 2023, the total planted area was 189,805 hectares (ha), exhibiting high consistency with statistical data (R² = 0.74). The spatial distribution was primarily concentrated in eastern Hainan, reflecting favorable climatic and topographic conditions. The results demonstrate the significant potential of Sentinel-1/2 data for identifying betel palms in complex tropical regions characterized by diverse land cover types, fragmented cultivated land, and frequent cloud and rain interference. This study provides a reference framework for mapping tropical crops, and the findings are crucial for tropical agricultural management and optimization. Full article

Global food security is increasingly challenged by climate change and the availability of arable land. This situation calls for improved crop monitoring and management strategies. Rice is a staple food for nearly half of the world’s population and a significant source of calories. Accurately identifying rice varieties is crucial for maintaining varietal purity, planning agricultural activities, and enhancing genetic improvement strategies. This study evaluates the effectiveness of machine learning algorithms to identify the most effective approach to predicting rice varieties, using multitemporal Sentinel-2 images in the Marismas del Guadalquivir of Sevilla, Spain. Spectral reflectance data were collected from ten Sentinel-2 bands, which include visible, red-edge, near-infrared, and shortwave infrared regions, at two key phenological stages: tillering and reproduction. The models were trained on pixel-level data from the growing seasons of 2021 and 2024, and they were evaluated using a test set from 2022. Four classifiers were compared: random forest, XGBoost, K-nearest neighbors, and logistic regression. Performance was assessed based on accuracy, precision, recall, specificity and F1 score. Non-linear models outperformed linear ones. The highest performance was achieved with the Random Forest classifier during the reproduction phase, reaching an exceptional accuracy of 0.94 using all bands or only the most informative subset (red edge, NIR, and SWIR). This classifier also maintained excellent accuracy (0.93 and 0.92) during the initial tillering phase. This fact demonstrates that it is possible to perform reliable varietal mapping in the early stages of the growing season. Full article

Background/Objectives: High-risk human papillomavirus (HPV) is the principal etiological agent of cervical cancer, with distinct genotype-specific oncogenic potential. While HPV type 16 is most frequently implicated in carcinogenesis, the role of other genotypes and their interaction with sexually transmitted infections and cervico-vaginal dysbiosis is gaining recognition. This study aimed to assess the genotype-specific distribution of high-risk HPV among HPV-positive women from Southern Croatia and examine associations with age and co-infections with selected microbial pathogens. Methods: We conducted a retrospective cross-sectional study on 1211 HPV-positive women (out of 3098 tested) from Split and Dalmatia County between 2023 and 2024. Cervico-vaginal swabs were tested using molecular and culture-based methods for 14 high-risk HPV genotypes and several pathogens, including Chlamydia trachomatis, Mycoplasma genitalium, Mycoplasma hominis, Ureaplasma urealyticum, Gardnerella vaginalis, and other microorganisms. In the analysis, each detected HPV genotype was also treated as a distinct line-level observation. Genotypes were grouped by phylogenetic and carcinogenic profiles, and statistical analyses—including chi-square tests and multinomial logistic regression—were performed to evaluate associations with age and co-infections. Results: Among high-risk HPV-positive women, the most frequently detected high-risk HPV genotypes were HPV 16 (23.3%), HPV 31 (22.4%), and HPV 51 (13.5%). Notably, HPV 18 was less prevalent (8.1%) and occurred at a similar frequency to HPV 58 and 68. Although younger age was significantly associated with high-risk HPV positivity (p < 0.001), no significant differences in HPV genotype group distribution were observed between age groups; however, C. trachomatis and Streptococcus agalactiae were significantly more prevalent in women aged ≤29 years (p < 0.001 and p = 0.029, respectively). Multinomial regression revealed that C. trachomatis was negatively associated with 16-related and lower-risk genotypes, while G. vaginalis showed a positive association with 16-related types. Conclusions: There is a complex interplay between high-risk HPV genotypes and microbial co-infections, which means the broader cervico-vaginal microbiome has to be considered in HPV risk assessment. The findings highlight the need for extended genotyping and microbial screening to inform regional prevention strategies. Full article

Background/Objectives: This study examines individual perceptions of national healthcare system efficiency before and after the COVID-19 pandemic across 18 countries grouped into three clusters (the Anglo-world, Europe, East Asia). This paper aims to identify the demographic, socioeconomic, health-related, and macroeconomic healthcare drivers of public assessments, and explain changes in attitudes between 2011–2013 and 2021–2023. Methods: Using individual-level data from the International Social Survey Programme (ISSP) for 2011–2013 and 2021–2023, logistic regression models of perceived healthcare inefficiency are estimated. In addition, the Oaxaca–Blinder decomposition model is adopted in order to decompose the assessment gap between the two periods. Models include a range of individual demographic and socioeconomic characteristics and national healthcare controls (healthcare expenditure, potential years of life lost). Results: Health-related factors, especially self-assessed health and trust in doctors, consistently emerge as predictors of more favourable evaluations across regions and periods. Higher national healthcare expenditure is associated with more positive public views and is the single largest contributor to the improved assessments in 2021–2023. Demographic and socioeconomic variables show smaller regionally and temporally heterogeneous effects. Decomposition indicates that both changes in observed characteristics (notably, expenditure and trust) and unobserved behavioural, cultural, or institutional shifts account for the gap in public healthcare assessments between the two time periods. Conclusions: Public assessments of healthcare systems are primarily shaped by individual health status, trust in providers, and national spending rather than differential demographic and socioeconomic traits. Therefore, policymakers should couple targeted investments in the healthcare sector in order to address adequately public healthcare needs, and strengthen doctor–patient relationships in order to sustain public support. Future research should focus on disentangling the cultural and behavioural pathways influencing healthcare attitudes. Full article

Background: Although several epidemiological studies have suggested a potential association between infections and Parkinson’s disease (PD), relatively few have specifically examined the relationship between upper respiratory tract infections (URIs) and PD, apart from coronavirus disease 2019 (COVID-19). Objectives: We investigated whether a history of URI was associated with the diagnosis of PD among Korean individuals aged ≥40 years, using data from the Korean National Health Insurance Service–Health Screening Cohort. Methods: A total of 5844 patients newly diagnosed with PD were identified and matched with 23,376 control participants at a 1:4 ratio based on age, sex, income, and geographical region. Conditional logistic regression analyses were performed to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for PD, adjusting for potential confounders including smoking, alcohol consumption, body mass index, blood pressure, comorbidity scores, blood glucose, and serum cholesterol levels. Results: Overall, no significant association was found between a history of URI and PD when considering a two-year exposure window. However, in the one-year window analysis, individuals with a history of URI had a modestly reduced odds of PD (≥1, ≥2, or ≥3 episodes: (adjusted OR: 0.93, 95% CI: 0.88–0.97, aOR: 0.91, 95% CI: 0.87–0.96 and aOR: 0.92, 95% CI: 0.87–0.98, respectively). Subgroup analyses revealed that the inverse association was more pronounced among women, older adults (≥65 years), and those with higher comorbidity scores. No clear dose–response trend was observed across increasing frequencies of URI diagnoses. Conclusions: Our findings suggest that the apparent protective association between recent URI history and PD is unlikely to be causal and may instead reflect confounding by medication use or reverse causation related to the prodromal phase of PD. These results should therefore be interpreted with caution and regarded as hypothesis-generating. Further prospective studies incorporating detailed prescription data and long-term follow-up are warranted to clarify the role of infections and anti-inflammatory medications in the pathogenesis of PD. Full article

Background: Several studies have examined the prevalence of musculoskeletal pain (MSP) among university students internationally. We aimed to assess the prevalence, pattern, and potential risk factors for MSP among Majmaah University students in Saudi Arabia. Methods: A cross-sectional questionnaire was administered to students from different faculties at Majmaah University. We collected data via the validated Arabic versions of the Standardized Nordic Musculoskeletal Questionnaire, the International Physical Activity Questionnaire, and the Perceived Stress Scale. Bivariate and multivariate logistic regression analyses were performed to explore associations between MSP and demographic, ergonomic, lifestyle, and psychosocial variables. Results: A total of 257 students (n = 195, 75.9% female; n = 62, 24.1% male) were included in this study. The 12-month prevalence of MSP was 225 (87.5%), with the lower back (n = 119, 46.3%) and neck (n = 113, 44.0%) regions being the most affected. Compared with male students, female students reported a higher MSP prevalence (90.3% vs. 79.0%, p = 0.035). The multivariable model was significant (likelihood-ratio χ² = 26.042, df = 7, p < 0.001), accounted for Nagelkerke R² = 0.182 of variance, and showed good calibration (Hosmer–Lemeshow χ² = 8.505, df = 8, p = 0.386). Perceived stress was the only independent predictor of 12-month MSP (β = 0.084, adjusted OR = 1.087, 95% CI 1.034–1.143, Wald χ² = 10.732, p = 0.001), while sex, smoking, academic workload, and sleep duration were non-significant (all p > 0.127). Conclusions: MSP appears to be prevalent among Majmaah University students, with psychological stress emerging as a key independent risk factor. Preventive strategies should include stress management prioritization and ergonomic and physical activity education to support university student well-being. Full article

Background: Flood disasters, alongside prolonged conflict and socioeconomic hardship in Maiduguri, Borno State, Nigeria, have heightened the psychological vulnerability of children. This study examined the prevalence of psychological distress and explored the coping mechanisms employed by children affected by flooding in the region. Method: Children aged 7–17 years from flood-affected areas in Maiduguri were included in the study. Psychological distress was measured using the parent version of the Strengths and Difficulties Questionnaire (SDQ-13), and coping mechanisms were assessed using the KidCOPE parent version. Multivariate and ordinal logistic regression examined factors associated with psychological distress and coping mechanisms. Results: A total of 374 children participated in the study. A total of 63.6% experienced abnormal psychological distress. Moderate and high levels of maladaptive coping were significantly associated with greater odds of psychological distress (odds ratio [OR] = 1.72, 95% CI: 1.25–2.36; OR = 2.43, 95% CI: 1.46–4.04). Similarly, moderate adaptive coping was associated with higher odds of distress compared to poor coping (OR = 1.90, 95% CI: 1.38–2.61). In unadjusted models, age, female gender, higher education, Christian religion, and higher household income were associated with increased psychological distress. However, these were not significant in the adjusted model. Ordinal logistic regression showed no significant predictors of either maladaptive or adaptive coping levels. Conclusions: A high proportion of flood-affected children in Maiduguri experience psychological distress, with maladaptive coping playing a key role. The findings indicate the need for targeted psychosocial interventions to improve adaptive coping skills in flood-affected children. Full article