Search Results (286)

Intensive human interference has severely disrupted the natural and ecological environments of coastal areas, threatening ecosystem services (ESs). Meanwhile, the relationships between ESs exhibit certain variations across different spatial scales. Therefore, identifying the scale effects of interrelationships among ESs and their underlying driving mechanisms will better support scientific decision-making for the hierarchical and sustainable management of coastal ecosystems. Therefore, employing the Integrated Valuation of ESs and Tradeoffs (InVEST) model combined with GIS spatial visualization techniques, this investigation systematically examined the spatiotemporal distribution of four ESs across three scales (grid, county, and city) during 2000–2020. Complementary statistical approaches (Spearman’s correlation analysis and bivariate Moran’s I) were integrated to systematically quantify evolving ES trade-off/synergy patterns and reveal their spatial self-correlation characteristics. The geographical detector model (GeoDetector) was used to identify the main driving factors affecting ESs at different scales, and combined with bivariate Moran’s I to further visualize the spatial differentiation patterns of these key drivers. The results indicated that: (1) ESs (except for Water yield) generally increased from coastal regions to inland areas, and their spatial distribution tended to become more clustered as the scale increased. (2) Relationships between ESs became stronger at larger scales across all three study levels. These ESs connections showed stronger links at the middle scale (county). (3) Natural factors had the greatest impact on ESs than anthropogenic factors, with both demonstrating increased explanatory power as the scale enlarges. The interactions between factors of the same type generally yield stronger explanatory power than any single factor alone. (4) The spatial aggregation patterns of ESs with different driving factors varied significantly, while the spatial aggregation patterns of ESs with the same driving factor were highly similar across different spatial scales. These findings confirm that natural and social factors exhibit scale dependency and spatial heterogeneity, emphasizing the need for policies to be tailored to specific scales and adapted to local conditions. It provides a basis for future research on multi-scale and region-specific precision regulation of ecosystems. Full article

This study focuses on quantifying wind–temperature load combination coefficients for long-span hybrid cable-stayed suspension bridges (HCSSBs) to overcome limitations of traditional methods in ignoring load correlation and geometry nonlinearity. A probabilistic framework is proposed to use site-specific load data to determine load combination coefficients, focusing on load correlation and geometric nonlinearity while assuming that stress reflects load effects and that 100-year samples are statistically representative. Long-sequence meteorological data, including wind and temperature measurements, were used to construct marginal and bivariate joint distributions, which characterize the randomness and correlation of wind and temperature loads. Load samples covering the design reference period were generated and validated via convergence tests. Four load scenarios (individual temperature, individual wind, linear superposition, and nonlinear coupling) were designed, and key control points are screened using indicators reflecting the comprehensive load effect $\stackrel{-}{\mathit{EII}}$, combined load proportion $\zeta$, and nonlinear influence $\eta$. Based on stress responses of key control points, load combination coefficients were derived with probability modeling. A case study for a bridge with span length of 2300 m shows that the load combination coefficients for the main girder are 0.60 (east wind) and 0.59 (west wind), while they are 0.51 (east wind) and 0.58 (west wind) for the main tower. These results demonstrate that the proposed method enables the provision of rational load combination coefficients. Full article

Background and Objectives: Urban–rural health disparities reflect differences in health outcomes, healthcare access, and socio-economic conditions between populations. In Serbia, limited research has quantified how socio-demographic and socio-economic characteristics influence settlement type and healthcare utilization. The aim of this study was to examine the relationship between settlement type and socio-demographic/socio-economic factors, and to assess whether these differences are reflected in patterns of healthcare utilization. Materials and Methods: Data were drawn from the 2019 Serbian National Health Survey, a nationally representative, stratified, two-stage random sample including 12,439 adults aged ≥20 years. Settlement type (urban vs. rural) was the primary dependent variable. Descriptive statistics, Chi-square and t-tests, and bivariate and multivariate logistic regression models were used to assess associations. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated, with significance set at p < 0.05. Results: Urban residence was more likely among unmarried individuals, those living in Šumadija/Central Serbia, and those with higher education. Primary or lower education reduced the odds of urban residence, and middle-income groups were less likely to live in urban areas compared to the richest. Settlement type was not significantly associated with hospital or day hospital use. However, rural residents had lower use of prescribed medicines, higher use of non-prescribed medicines, and more frequent physiotherapy visits. Private practice use was over twice as likely in urban settlements. Conclusions: To address urban–rural healthcare disparities in Serbia, targeted strategies could include enhancing health literacy in rural areas, incentivizing physicians to work in underserved regions, expanding telemedicine and mobile health services, improving access to prescribed medications, and strengthening public–private healthcare integration to ensure equitable access across all settlement types. Full article

Background: Evidence on how everyday walking and sleep relate to mood in health profession students from Central–Eastern Europe remains limited. Methods: We conducted a cross-sectional study among 277 Romanian medical students. Data were collected using validated instruments for physical activity (IPAQ-SF), sleep quality (PSQI), and depressive/anxiety symptoms (HADS). Associations were examined using bivariate and multivariable regression models, including sex-stratified analyses. Results: In bivariate analysis, total physical activity was inversely correlated with depressive symptoms (ρ = −0.19, p < 0.001). However, in the multivariable model, this effect was not statistically significant after controlling for other factors. Poor sleep quality emerged as the dominant independent predictor of both depression (β = 0.37, p < 0.001) and anxiety (β = 0.40, p < 0.001). Walking time and frequency were specifically protective against depressive symptoms. Sex-stratified analyses revealed distinct patterns: female students benefited more from walking, whereas male students showed stronger associations between overall physical activity and lower depressive symptoms. Conclusions: Within the constraints of a cross-sectional design, this study provides novel evidence from Eastern Europe that sleep quality and physical activity are central to student mental health. Psychological benefits of walking appear sex-specific, and the null mediation finding suggests benefits operate via direct or unmodelled pathways. Sleep is a critical independent target for tailored, lifestyle-based strategies. Full article

Background/Objectives: The U.S. Centers for Disease Control and Prevention recommends some vaccinations using shared clinical decision-making (SCDM). SCDM recommendations are made when not every individual within a given age or risk group would benefit from vaccination, requiring collaborative discussions between patients and providers to assess risks and benefits. Pharmacists play a key role in implementing this recommendation and have frequent opportunities to engage with patients who may be eligible for SCDM-based vaccines. Because SCDM requires provider discussions to assess each patient’s eligibility for the vaccines under SCDM, trust may play a central role in the process. Trust has been suggested to affect patient’s participation in their care and their decision making preferences; however, the nature of this relationship in the context of SCDM vaccines and willingness to engage with pharmacists has yet to be investigated. As the CDC continues to expand the SCDM vaccine category, there is need to assess these. This study aimed to examine relationships between patient characteristics, trust in pharmacists, willingness to engage in SCDM, and vaccination decision control preference. Methods: Using quota sampling, cross-sectional data were collected from Alabama residents aged 18+ between February and March 2024 via a validated online questionnaire. Bivariate and multivariable logistic regression analyses were used to determine the association between trust, patient characteristics and willingness. Structural equation modeling was used to assess the direct and indirect relationships between trust, willingness and vaccination decision control preference. Statistical significance was set at p < 0.05. Results: A substantial portion (45.8%) of participants were unaware that certain vaccinations were based on SCDM. Multivariable logistic regression showed that race (Black vs. White, p = 0.001), age (25–34 vs. 18–24, p = 0.029), highest degree obtained (high school diploma or graduate equivalency degree vs. less than high school, p = 0.001; associate degree or vocational certificate vs. less than high school, p = 0.000; bachelor’s degree or higher vs. less than high school, p = 0.001), political affiliation (Democrat vs. Republican, p = 0.002), confidence in understanding health-related information (high vs. low, p =.029); moderate vs. low, p = 0.002), and patients’ trust in community pharmacists’ communication skills (p = 0.045) and benevolence (p = 0.001) towards their patients were significantly associated with patients’ willingness to engage in SCDM. Trust had a significant direct (p = 0.001) and indirect relationship (p = 0.000) with decision control preference through the willingness variable. Conclusions: Educational interventions are recommended to improve awareness and knowledge of SCDM vaccines among patients. Given their trusted role, pharmacists should actively build and maintain trust with patients, as this may help foster collaborative environments for discussion, encourage patient engagement in SCDM, and support more informed vaccination choices. Full article

Background: Rapid-eye-movement (REM) sleep disinhibition—shorter REM latency and a larger nightly REM fraction—is a well-described laboratory correlate of major depression. Whether the same pattern can be captured efficiently with consumer wearables in everyday settings remains unclear. We therefore quantified REM latency and proportion of REM sleep out of total sleep duration (labeled “REM sleep coefficient”) from Apple Watch recordings and examined their association with depressive symptoms. Methods: 191 adults wore an Apple Watch for 15 consecutive nights while a custom iOS app streamed raw accelerometry and heart-rate data. Sleep stages were scored with a neural-network model previously validated against polysomnography. REM latency and REM sleep coefficient were averaged per participant. Depressive severity was assessed twice with the Beck Depression Inventory and averaged. Descriptive statistics, normality tests, Spearman correlations, and ordinary-least-squares regressions were performed. Results: Mean ± SD values were BDI 13.52 ± 6.79, REM sleep coefficient 24.05 ± 6.52, and REM latency 103.63 ± 15.44 min. REM latency correlated negatively with BDI (Spearman ρ = −0.673, p < 0.001), whereas REM sleep coefficient correlated positively (ρ = 0.678, p < 0.001). Combined in a bivariate model, the two REM metrics explained 62% of variance in depressive severity. Conclusions: Wearable-derived REM latency and REM proportion jointly capture a large share of depressive-symptom variability, indicating their potential utility as accessible digital biomarkers. Larger longitudinal and interventional studies are needed to determine whether modifying REM architecture can alter the course of depression. Full article

Background: This study is particularly relevant to the Romanian context, where relatively few empirical investigations have examined post-pandemic health perceptions and levels of trust in public institutions. The purpose of this study is to investigate the long-term impact of the COVID-19 pandemic on health perceptions and trust in the healthcare system by examining key socioeconomic and epidemiological factors. Methods: A cross-sectional online survey was conducted among Romanian adults (N = 423), between March and April 2025. Demographic data, lifestyle habits, mental health, and access to healthcare were assessed. Statistical analyses included both bivariate (chi-square test) and multivariable logistic regression models to identify independent associations. Results: 31.9% of participants reported increased stress and anxiety during the pandemic. Decreased trust in the healthcare system (75.6%) and a perceived reduction in life expectancy (74.3%) were also noted as a consequence of the COVID-19 pandemic. Perceived life expectancy decline was linked to lower education and inconsistent healthcare behavior. Conclusion: In our sample, the perception of decreased life expectancy reflects not only epidemiological realities but also emotional and social responses to crises. Individuals’ trust, behavior, and shared vision of the future have also been challenged during the COVID-19 pandemic. Full article

Nomophobia, or the fear of not being able to use a smartphone and/or the services, has gained increasing attention due to its growing prevalence. This study aimed to examine the prevalence of nomophobia and of potential variables associated with the phenomenon. Additionally, it sought to determine if the average of total nomophobia and the four second-order factors differed across gender. Finally, it analyzed the associations between nomophobia (overall and second-order factors) and psychological variables (self-esteem, loneliness, life satisfaction, and phubbing behavior), internet use, and sociodemographic characteristics. A cross-sectional survey was conducted with 306 participants (68.6% women), aged between 18 and 79 years (M = 38.0, SD = 16.3), using an online questionnaire. Descriptive statistics, independent samples t-tests comparing groups by gender, and bivariate correlations were computed. After, multiple linear regression analyses were performed to obtain parsimonious models with the most relevant variables (psychological variables, internet use, and sociodemographic characteristics) associated with overall nomophobia and its four dimensions. The results were generally consistent with the previous literature. Notably, gender and phubbing behavior were significantly associated with nomophobia. These findings contribute to a better understanding of the nomophobia phenomenon and may inform future interventions aimed at mitigating its potential impact on well-being. Full article

We develop and evaluate a copula-based multistate model for illness–death processes with dependent transition times. The framework couples Cox proportional hazards models for the marginal transition intensities with Archimedean copulas to capture dependence, and it is estimated via the Inference Functions for Margins (IFM) approach under right censoring. A Monte Carlo study shows that assuming independence between transitions can severely underestimate joint survival, yielding coverage as low as $40\%$ under strong dependence, compared with $92\%$ to $97\%$ when copulas are used. We apply the method to a large Colombian cohort of COVID-19 patients (2021 to 2022) that includes sociodemographic, clinical, and vaccination data. The Gumbel copula best captures the strong positive dependence between hospitalization and death, producing more accurate joint survival estimates than independence-based models. Model diagnostics, including proportional hazards tests, Kaplan-Meier comparisons, hazard rate functions, and TTT plots, support the adequacy of the Cox margins. We also discuss limitations and avenues for extension, such as parametric or cure-fraction margins, nested or vine copulas, and full-likelihood estimation. Overall, the results underscore the methodological and applied value of integrating copulas into multistate models, offering a robust framework for analyzing dependent event times in epidemiology and biomedicine. Full article

The dynamic environment of coastal regions subjects infrastructure to multiple interacting natural hazards, with the simultaneous occurrence of windstorms and earthquakes posing a particularly critical challenge. Unlike inland hazards, these coastal threats frequently exhibit irregular statistical behavior and terrain-induced anomalies. This study proposes a novel probabilistic framework to assess compound hazard effects, advancing beyond traditional single-hazard analyses. By integrating maximum entropy theory with bivariate Copula models, a unified return period analysis is developed to capture the joint probability structure of seismic and wind events. The model is calibrated using long-term observational data collected from a representative coastal zone since 2000. For the PGA marginal distribution, our sixth-moment maximum-entropy model achieved an R² of 0.90, compared with 0.57 for a conventional GEV fit—reflecting a 58% increase in explained variance. Analysis shows the progressive evolution of damage from slight damaged through moderate damaged and severe damaged to collapse for an 18-story reinforced concrete frame structure, and shows that the combined effect of seismic and wind loads results in risk probabilities of aforementioned damage state of approximately 2 × 10⁻³, 6 × 10⁻⁴, 2 × 10⁻⁴, and 3 × 10⁻⁵, respectively, under a 0.4 g ground motion and a concurrent wind speed of 15 m/s. Furthermore, when both the uncertainty of loss ratios and structural parameters are incorporated, the standard deviation of the economic loss ratio reaches up to 0.015 in the transition region (PGA 0.2–0.4 g), highlighting considerable variability in economic loss assessment, whereas the mean economic loss ratio rapidly saturates above 0.8 with increasing PGA. These findings demonstrate that uncertainty in economic loss is most pronounced within the transition region, while remaining much lower outside this zone. Overall, this study provides a robust framework and quantitative basis for comprehensive risk assessment and resilient design of coastal infrastructure under compound wind and seismic hazards. Full article

Background: Caregivers of individuals with cognitive impairment face heightened emotional and psychological burdens, yet the interaction between caregiver well-being, patient characteristics, and socio-demographic factors still requires investigation. This study aimed to examine the psychological well-being of family caregivers in an urban Romanian context, focusing on the role of depressive and anxiety symptoms, education, and care-recipient cognition function. Methods: A cross-sectional study was conducted among family caregivers recruited from a neurology-psychiatry service in Bucharest. Caregivers completed Ryff’s Psychological Well-Being Scales, the Patient Health Questionnaire-9 (PHQ-9), and the COVI Scale. Cognitive status of care recipients was obtained from medical records (Mini-Mental State Examination, MMSE). Descriptive statistics, correlation analyses, and separate linear regression models were performed for each well-being dimension. Results: Caregivers reported moderate to high well-being scores, with Environmental Mastery highest (M = 38.01, SD = 8.70) and Purpose in Life lowest (M = 33.14, SD = 6.72). Depression scores averaged 18.49 (SD = 6.55), indicating moderate depressive symptoms, and anxiety scores averaged 12.14 (SD = 2.23), consistent with severe anxiety. Cognitive impairment in care recipients was marked (MMSE M = 11.47, SD = 6.99). Bivariate analyses showed that lower MMSE scores were associated with higher caregiver anxiety (ρ = −0.287, p = 0.014). Regression models (R² = 0.08–0.25) indicated that higher education was positively associated with autonomy, personal growth, positive relations, and environmental mastery, whereas older age and female gender were linked to lower well-being in several domains. Depressive symptoms were unexpectedly associated with higher autonomy and self-acceptance. Conclusions: Caregiver psychological well-being was modestly associated with depressive symptoms, education, gender, and age, while care-recipient cognitive status showed only weak links to anxiety. Education emerged as a consistent protective factor, whereas female gender and older age were associated with lower well-being. Although the Bonferroni correction eliminated significance in separate models, a complementary multivariate multiple regression confirmed global effects of education, caregiver gender, and depression across well-being domains. These findings emphasize the need for systematic psychological support for caregivers and call for larger, longitudinal studies to clarify causal mechanisms and additional protective factors. 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

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

In many practical applications, data collected over time often exhibit autocorrelation, which, if unaccounted for, can lead to biased or misleading statistical inferences. To address this issue, we propose a varying-coefficient additive model for density-valued responses, incorporating a functional auto-regressive (FAR) error process to capture serial dependence. Our estimation procedure consists of three main steps, utilizing spline-based methods after mapping density functions into a linear space via the log-quantile density transformation. First, we obtain initial estimates of the bivariate varying-coefficient functions using a B-spline series approximation. Second, we estimate the error process from the residuals using spline smoothing techniques. Finally, we refine the estimates of the additive components by adjusting for the estimated error process. We establish theoretical properties of the proposed method, including convergence rates and asymptotic behavior. The effectiveness of our approach is further demonstrated through simulation studies and applications to real-world data. Full article

In many complex applications, both data heterogeneity and homogeneity are present simultaneously. Overlooking either aspect can lead to misleading statistical inferences. Moreover, the increasing prevalence of complex, non-Euclidean data calls for more sophisticated modeling techniques. To address these challenges, we propose a density data response additive model, where the response variable is represented by a distributional density function. In this framework, individual effect curves are assumed to be homogeneous within groups but heterogeneous across groups, while covariates that explain variation share common additive bivariate functions. We begin by applying a transformation to map density functions into a linear space. To estimate the unknown subject-specific functions and the additive bivariate components, we adopt a B-spline series approximation method. Latent group structures are uncovered using a hierarchical agglomerative clustering algorithm, which allows our method to recover the true underlying groupings with high probability. To further improve estimation efficiency, we develop refined spline-backfitted local linear estimators for both the grouped structures and the additive bivariate functions in the post-grouping model. We also establish the asymptotic properties of the proposed estimators, including their convergence rates, asymptotic distributions, and post-grouping oracle efficiency. The effectiveness of our method is demonstrated through extensive simulation studies and real-world data analysis, both of which show promising and robust performance. Full article