Search Results (1,188)

Parks serve as vital components of green infrastructure within urban ecosystems, providing recreational opportunities that not only enhance human well-being but also support bird diversity. However, the shared use of park spaces by both humans and birds inevitably leads to spatial overlap and natural competition between the two groups. Consequently, addressing the diverse needs of both groups and balancing the ecosystem services provided to each has become an urgent and critical issue. In this study, we conducted bird and social surveys in an urban park and employed the SolVES and MaxEnt models to investigate the spatial patterns of cultural ecosystem services (CES), supporting ecosystem services (SES), and bird plumage color CES in the park. We then analyzed the trade-offs and synergies between different ecosystem service relationship pairs, as well as the factors influencing them, using bivariate spatial autocorrelation and geographical detectors analyses. Our results indicated a synergistic relationship between the recreational value of park CES and both park SES and bird plumage color CES. High-coverage vegetation areas along main roads promoted synergy, benefiting visitors’ appreciation of cultural services, bird roosting, and the supply of plumage color CES. Meanwhile, trade-offs were observed between the aesthetic value of park CES, park SES, and bird plumage color CES, primarily in fitness plazas where noise levels exceeded 70 dB. In contrast, visitors reacted more strongly to disturbances than birds. Furthermore, the colonization of colorful insectivorous birds enhanced the visual aesthetic value while simultaneously increasing the number of bird-feeding guilds and strengthening ecosystem stability. Our study suggests that planting tall trees, especially along park boundaries, expanding the perimeter green separation zone, and incorporating micro-water landscapes will help improve both avian CES and provide a more pleasant environment for visitors in parks. Full article

This study leverages multi-source remote sensing data—Nighttime Light (NTL) imagery and POI (Point of Interest) datasets—to quantify the spatiotemporal interaction between urban spatial restructuring and logistics industry evolution in Zhengzhou, China. Using calibrated NPP/VIIRS NTL data (2012–2022) and fine-grained POI data, we (1) identified urban functional spaces through kernel density-based spatial grids weighted by public awareness parameters; (2) extracted built-up areas via the dynamic adaptive threshold segmentation of NTL gradients; (3) analyzed logistics agglomeration dynamics using emerging spatiotemporal hotspot analysis (ESTH) and space–time cube models. The results show that Zhengzhou’s urban form transitioned from a monocentric to a polycentric structure, with NTL trajectories revealing logistics hotspots expanding along air–rail multimodal corridors. POI-derived functional spaces shifted from single-dominant to composite patterns, while ESTH detected policy-driven clusters in Airport Economic Zones and market-driven suburban cold chain hubs. Bivariate LISA confirmed the spatial synergy between logistics growth and urban expansion, validating the “policy–space–industry” interaction framework. This research demonstrates how integrated NTL-POI remote sensing techniques can monitor policy impacts on urban systems, providing a replicable methodology for sustainable logistics planning. Full article

Air pollution, particularly particulate matter (PM₁, PM_2.5, and PM₁₀), poses a significant environmental health risk globally. This study evaluates the predictive performance of three machine learning algorithms, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), for forecasting particulate matter concentrations in four Brazilian cities (Porto Alegre, Recife, Goiânia, and Belém), which share similar demographic and urbanization characteristics but differ in geographic and climatic conditions. Using data from the Copernicus Atmosphere Monitoring Service, daily concentrations of PM₁, PM_2.5, and PM₁₀ were modeled based on meteorological variables, including air temperature, relative humidity, wind speed, atmospheric pressure, and accumulated precipitation. The models were tested under two climate change scenarios (+2 °C and +4 °C temperature increases). The results indicate that RF consistently outperformed the other models, achieving low RMSE values, around 0.3 µg/m³, across all cities, regardless of their geographic and climatic differences. KNN showed stable performance under moderate temperature increases (+2 °C) but exhibited higher errors under more extreme warming, while SVM demonstrated higher sensitivity to temperature changes, leading to greater variability in bivariate contexts. However, in multivariate contexts, SVM adjusted better, improving its predictive performance by accounting for the combined influence of multiple meteorological variables. These findings underscore the importance of selecting suitable machine learning models, with RF proving to be the most robust approach for particulate matter prediction across diverse environmental contexts. This study contributes valuable insights for the development of region-specific air quality management strategies in the face of climate change. Full article

Background: Peritonitis in peritoneal dialysis and cirrhosis remains common and leads to morbidity. Neutrophil gelatinase-associated lipocalin (NGAL) has been evaluated as a rapid adjunctive biomarker. Methods: Following PRISMA-DTA and PROSPERO registration (CRD420251105563), we searched MEDLINE, Embase, Cochrane Library, LILACS, Scopus, and Web of Science from inception to 31 December 2024, and ran an update on 30 June 2025 (no additional eligible studies). Diagnostic accuracy studies measuring NGAL in peritoneal/ascitic fluid against guideline reference standards were included. When 2 × 2 data were not reported, we reconstructed cell counts from published metrics using a prespecified, tolerance-bounded algorithm (two studies). Accuracy was synthesized with a bivariate random effects (Reitsma) model; 95% prediction intervals (PIs) were used to express heterogeneity; small-study effects were assessed by Deeks’ test. Results: Thirteen studies were included qualitatively and ten were entered into a meta-analysis (573 cases; 833 controls). The pooled sensitivity was 0.95 (95% CI, 0.90–0.97) and specificity was 0.86 (0.70–0.94); likelihood ratios were LR+ ≈7.0 and LR− 0.06. Between-study variability was concentrated on specificity: the PI for a new setting was 0.75–0.98 for sensitivity and 0.23–0.99 for specificity. Deeks’ test showed evidence of small-study effects in the primary analysis; assay/platform and thresholding contributed materially to heterogeneity. Conclusions: NGAL in peritoneal/ascitic fluid demonstrates high pooled sensitivity but variable specificity across settings. Given the wide prediction intervals and the signal for small-study effects, NGAL should be interpreted as an adjunct to guideline-based criteria—not as a stand-alone rule-out test. Standardization of pre-analytics and assay-specific, locally verified thresholds, together with prospective multicenter validations and impact/economic evaluations, are needed to define its clinical role. Full article

Background/Objectives: Maternal depression, anxiety, perceived stress, and resilience are recognized determinants of perinatal health, yet routine psychological screening is still uncommon in Romanian obstetric practice. This study examined how these four psychological factors relate to preterm birth, gestational hypertension, intra-uterine growth restriction (IUGR), and low birth weight in primiparous women. Methods: In a cross-sectional study at a tertiary maternity center in Timișoara (February 2024–February 2025), 240 women at 20–28 weeks’ gestation completed the Edinburgh Postnatal Depression Scale (EPDS), Generalized Anxiety Disorder-7 (GAD-7), Perceived Stress Scale-10 (PSS-10), and Connor–Davidson Resilience Scale-25 (CD-RISC-25). Obstetric outcomes were abstracted from medical records. Pearson correlations described bivariate associations; multivariate logistic regression assessed independent effects after mutual adjustment. Results: Preterm birth occurred in 21% of pregnancies, gestational hypertension in 17%, IUGR in 15%, and low birth weight in 21%. Higher EPDS, GAD-7, and PSS-10 scores correlated positively with each complication (r = 0.19–0.36; p < 0.02), whereas CD-RISC-25 scores showed inverse correlations (r = −0.22 to −0.29; p ≤ 0.012). In the fully adjusted model, GAD-7 remained the only independent psychological predictor of the composite obstetric outcome (β = 0.047; 95% CI 0.010–0.083; p = 0.013). Perceived stress approached significance; depression and resilience were no longer significant after adjustment. Conclusions: Generalized anxiety was the most robust psychological determinant of adverse obstetric outcomes, with perceived stress, depression, and lower resilience showing contributory roles at the unadjusted level. Incorporating brief instruments such as the GAD-7, PSS-10, and CD-RISC-25 into routine prenatal care could facilitate early identification of at-risk pregnancies and inform targeted preventive interventions. Full article

Introduction/Background: Medical treatments received by transgender and/or gender diverse (TGD) people can impact fertility, yet the literature lacks data on factors that influence fertility decisions among TGD people. Specific Aim(s): This study aimed to identify predictors of interest in fertility preservation (IFP). Materials and Methods: This retrospective observational study utilized data from 2021–2023 from an adult gender registry for patients receiving care at academic medical center (n = 206). Patient demographic data and survey responses to questions about fertility were queried and analyzed. Bivariate and multivariate analyses were conducted using logistic regression. Results: Most patients (73.8%, n = 152) were not interested in fertility preservation (FP) and 16.5% (n = 34) were unsure. Reasons most often cited were not wanting biological children (55.9%, n = 104), preferring adoption (20.4%, n = 38), cost (19.9%, n = 37), and dysphoria (19.4%, n = 36). Bivariate analyses showed that increasing age, being married, and already having children were significantly inversely associated with IFP (p = 0.03, 0.01, 0.02, respectively). Non-Hispanic Black race/ethnicity (OR (95% CI): 3.43 (1.19, 9.84)) and disability or unemployment (OR (95% CI): 4.19 (1.42, 13.00)) were significantly associated with IFP vs. Non-Hispanic White race/ethnicity and full-time employment, respectively. In multivariate models, being married was significantly inversely associated with IFP, e.g., OR (95% CI): 0.30, (0.07, 0.99), when accounting for age and already having children. Race/ethnicity and employment comparisons remained significant after adjusting for other factors. Conclusions: Most patients did not desire FP. Among those IFP, potential predictors include age, marital status, already having children, race and ethnicity, and employment and disability status. Full article

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

As industrial and urban growth advances, the challenge of rural population shrinkage has grown more pronounced, impacting rural functions. Northeast China is an example in this study, and a rural function evaluation index system is constructed based on four dimensions: agricultural production, economic development, social security, and ecological conservation. The spatio-temporal heterogeneity of the impact of rural population shrinkage on rural functions is quantified in this study using bivariate spatial autocorrelation and geographically and temporally weighted regression (GTWR). The results show that from 2000 to 2020, the rural population in most counties in Northeast China declined, while agricultural production, economic development, social security, and ecological conservation functions generally trended upwards. According to the GTWR model, the positive effect of rural population density on agricultural production weakened over time, slightly promoting social security and continuing to inhibit ecological conservation. In contrast, the supporting effect of average rural population size on economic development strengthened, its inhibitory effect on ecology decreased, and it slightly inhibited social security. While rural population shrinkage generally promoted agricultural development, economic growth, social security, and ecological improvements, its positive impact on agricultural development declined over time, and the promotion effects on social security and ecological conservation partially turned into inhibition after 2020. Policy recommendations are presented in this paper, providing a solid scientific foundation for the sustainable development of rural areas in Northeast China. Full article

Background: Telepsychiatry expanded rapidly during the COVID-19 pandemic, yet patient experience data from mixed urban and rural areas in Saudi Arabia remain scarce. Objective: We aimed to quantify the perception of telepsychiatry among adults with major depressive disorder [MDD] in Madinah City, the KSA, and to identify associated demographic and clinical factors. Methods: A cross-sectional survey was conducted at Madinah Mental Health Hospital between December 2024 and March 2025. Eligible participants were Arabic-speaking adults [≥18 years] with a clinician-confirmed diagnosis of major depressive disorder [MDD] according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5], following a scheduled video- or audio-based telepsychiatry consultation. Perception of telepsychiatry was assessed using the validated 10-item Arabic version of the Telehealth Satisfaction Scale [TeSS], which evaluates audiovisual quality, communication, and support. Variables associated with perception at p < 0.20 in the bivariable analyses were entered into a multiple linear regression model to identify independent predictors. Results: Of the 218 eligible patients, 207 participated [response rate = 95%], with similarly high participation rates being reported in comparable telepsychiatry surveys [e.g., 90–91%]. The majority were male [59%], with a mean [SD] age of 38.4 [11.2] years. The mean satisfaction score was 32.3 ± 6.3, and 36% of participants were classified as highly satisfied. In the multivariable analysis, higher satisfaction was independently associated with male gender [B = 3.0, 95% CI: 1.3–4.7], intermediate versus elementary education [B = 4.3, 95% CI: 1.1–7.6], and the presence of a chronic illness [B = 2.1, 95% CI: 0.3–3.8]. Conclusions: Telepsychiatry is generally well-accepted among adults with depression in Madinah. However, women and individuals with lower educational attainment report lower satisfaction. Targeted interventions such as improving privacy, offering digital literacy support, and tailoring communication may help improve the telepsychiatry experience for underserved groups. Full article

Background/Objectives: The location and distribution of excess fat, rather than overall adiposity, are stronger predictors of cardiometabolic risk and are commonly assessed using the waist-to-hip ratio (WHR). Fat distribution in women has a heritable component, yet the genetic factors that influence it remain poorly understood. We aim to assess the association between obesity-related polymorphisms with WHR and cardiometabolic risk in overweight and obese women. Methods: A cohort study was conducted in 512 women (56.1 ± 6.4 years; body mass index (BMI) ≥ 25 kg/m²). WHR was calculated, and participants were classified into android (WHR > 0.85) or gynoid (WHR ≤ 0.85) obesity groups. We genotyped 15 SNPs previously associated with obesity using TaqMan real-time PCR. Different genetic models (dominant, recessive, and allelic) were analysed, and bivariate and multivariate analyses were performed to compare the fat distribution groups. Results: Of the 15 SNPs studied, only 3 presented a significant association with WHR > 0.85. PSRC1 rs599839 in a dominant model (AA + GA vs. GG) with OR = 3.18 (p = 0.041), SLC30A8 rs1326634 in a recessive model (CC vs. TC + TT) (OR = 2.38; p = 0.004), both showing increased susceptibility to central obesity. KIF6 rs20455 offers protection in a recessive model (CC vs. TC + TT) with an OR of 0.47 (p = 0.043). After adjusted multivariate analysis, only SLC30A8 and diabetes remained independently associated with an increased risk of android obesity (OR = 2.50; p = 0.003 and OR = 3.63; p = 0.004, respectively). Conclusions: The SLC30A8 variant was significantly associated with android fat distribution and high cardiometabolic risk in overweight/obese women. Identifying genetic factors that influence fat distribution may help specify targeted lifestyle changes or pharmacological interventions to reduce risk. 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

Accurate forecasting of potential collision risk in dense traffic is addressed by a framework grounded in multi-vehicle trajectory prediction. A spatio-temporal fusion architecture, STGAT-EDGRU, is proposed. A Transformer encoder learns temporal motion patterns from each vehicle’s history; a boundary-aware graph (GAT) attention network models inter-vehicle interactions; and a Gated Multimodal Unit (GMU) adaptively fuses the temporal and spatial streams. Future positions are parameterized as bivariate Gaussians and decoded by a two-layer GRU. Using probabilistic trajectory forecasts for the main vehicle and its surrounding vehicles, collision probability and collision intensity are computed at each prediction instant and integrated via a weighted scheme into a Collision Risk Index (CRI) that characterizes risk over the entire horizon. On HighD, for 3–5 s horizons, average RMSE reductions of 0.02 m, 0.12 m, and 0.26 m over a GAT-Transformer baseline are achieved. In high-risk lane-change scenarios, CRI issues warnings 0.4–0.6 s earlier and maintains a stable response across the high-risk interval. These findings substantiate improved long-horizon accuracy together with earlier and more reliable risk perception, and indicate practical utility for lane-change assistance, where CRI can trigger early deceleration or abort decisions, and for risk-aware motion planning in intelligent driving. Full article

Objectives: Personalized sepsis care requires understanding how pre-existing health status can influence outcomes. The aim of this study is to evaluate its impact on in-hospital and 12-month mortality in patients with sepsis, taking into account age, comorbidities, the Charlson Comorbidity Index, frailty, anemia, and the Sequential Organ Failure Score Assessment (SOFA) in the first 24 h. Methods: An observational and retrospective study was conducted using data from the Sepsis Code program at the Hospital Universitario de La Princesa. The relationship between risk factors and mortality, as well as Intensive Care Unit (ICU) admission, was analyzed for the period 2016–2018 using bivariate and multivariate logistic regression. Results: A total of 547 patients were included. In the multivariate analysis, the risk factors independently associated with mortality were Rockwood Clinical Frailty Scale ≥ 5 (OR 2.45, p < 0.05); SOFA ≥ 4 (OR 2.13, p < 0.05); age (OR 1.98, p < 0.05); anemia (OR 1.85, p < 0.05); and specific comorbidities such as ischemic heart disease (OR 2.34, p < 0.05), severe liver disease (OR 3.62, p < 0.05), and metastatic cancer (OR 3.14, p < 0.05). Patients who were frail, had dementia, or heart failure were less likely to be admitted to the ICU. Conclusions: Frailty, comorbidities, age, and anemia are associated with outcomes in patients with sepsis and could be incorporated into mortality prediction models to guide tailored treatment strategies. Full article

Background/Objectives: Recent data shows increasing diabetes prevalence among African Americans. Youth with a family history of diabetes are at high risk for diabetes. This study explores the multilevel risk factors associated with a family history of diabetes among African American youth in public housing. Methods: This study used a cross-sectional, quantitative, and community-based participatory research (CBPR) approach. The research team, comprising community stakeholders and academic researchers, employed respondent-driven sampling (RDS) for data collection (survey) and used univariate and bivariate analyses to examine variable relationships. A sequential logistic regression highlighted factors influencing the likelihood of having a family history of diabetes. Results: The final sample (n = 190, mean age 18.5 years, 58% female) included 35% of youth with a family history of diabetes. Forty-six percent reported medium to severe household hardships. Results suggest that reporting a family history of diabetes is correlated with maternal substance use (tau-b = 0.27 **) and alcohol problems (tau-b = 0.16 ***), paternal substance use (tau-b = 0.17 *), and eating fewer fruits (tau-b = 0.17 *). With an odds ratio (OR) of 1.70 [0.68, 4.13] and attributable fraction among the exposed at 41.3%, the final model (3) was not significant [χ² = 11.19(8)]. Thus, we fail to reject the null hypothesis that the model fits the data well. Fewer vegetable consumption (OR = 15.08, p < 0.001), higher soda consumption (OR = 0.06, p < 0.001), severe household hardships (OR = 5.82, p < 0.01), and maternal substance use problems (OR = 6.81, p < 0.05) predicted a higher likelihood of a history of diabetes. Conclusions: Our study calls attention to the need to reevaluate interventions for hardships and substance use in diabetes management, particularly in poor neighborhoods and among minority families. Full article

Land use change significantly affects regional carbon emissions and ecosystem service value (ESV). Under China’s Dual Carbon Goals, this study takes Beijing-Tianjin-Hebei, experiencing rapid land use change, as the study area and counties as the study unit. This study employs a combination of methods, including carbon emission coefficients, equivalent-factor methods, bivariate spatial autocorrelation, and a multinomial logit model. These were used to explore the spatial relationship between land use carbon emissions and ESV, and to identify their key driving factors. These insights are essential for promoting sustainable regional development. Results indicate the following: (1) Total land use carbon emissions increased from 2000 to 2015, then declined until 2020; emissions were high in municipal centers; carbon sinks were in northwestern ecological zones. Construction land was the primary contributor. (2) ESV declined from 2000 to 2010 but increased from 2010 to 2020, driven by forest land and water bodies. High-ESV clusters appeared in northwestern and eastern coastal zones. (3) A significant negative spatial correlation was found between carbon emissions and ESV, with dominant Low-High clustering in the north and Low-Low clustering in central and southern regions. Over time, clustering dispersed, suggesting improved spatial balance. (4) Population density and cultivated land reclamation rate were core drivers of carbon–ESV clustering patterns, while average precipitation, average temperature, NDVI, and per capita GDP showed varied effects. To promote low-carbon and ecological development, this study puts forward several policy recommendations. These include implementing differentiated land use governance and enhancing regional compensation mechanisms. In addition, optimizing demographic and industrial structures is essential to reduce emissions and improve ESV across the study area. Full article