Search Results (316)

Background/Objectives: Orthorexia nervosa (ON) is an emerging condition marked by a preoccupation with healthy eating that is linked to diminished well-being and social functioning. While research on ON extends across countries, no studies about ON have been found in Lithuania. This study aimed to investigate dietary patterns, socio-demographic correlates, and the prevalence of orthorexic tendencies in a Lithuanian adult sample. Methods: A cross-sectional online survey was conducted using the ORTO-R 6-item scale, a Food Frequency Questionnaire (FFQ), and socio-demographic and dietary behavior measures. Principal component analysis (PCA) was applied to the FFQ to identify dietary patterns, and stepwise multiple linear regression was used to examine predictors of orthorexic tendencies. Results: Approximately 15% of the Lithuanian adult sample exhibited elevated orthorexic tendencies, while three dietary factors were extracted, including Balanced-Traditional, Processed-Dense and Protein-Rich patterns. Both Balanced-Traditional and Protein-Rich dietary patterns were positively associated with orthorexic tendencies, although only the Balanced-Traditional pattern remained a significant predictor in the fully adjusted regression model, which explained 16.2% of the variance in ORTO-R scores (F(7,468) = 12.97, p < 0.001). Higher orthorexic tendencies were associated with following a dietary plan, adherence to the Healthy-Traditional pattern, being female, younger age, higher meal frequency, employment status, and being married. Conclusions: Orthorexic behaviors were more prevalent among younger women, individuals following structured diets, and those adhering to health-oriented eating patterns. These findings highlight the interplay between demographic and dietary factors in shaping orthorexic tendencies in the Lithuanian population. Full article

A well-functioning talent ecosystem serves as a crucial foundation for promoting high-quality development of Hainan Free Trade Port (HFTP) in China, holding strategic significance for enhancing the competitiveness and sustainable development of its industrial parks. This study aims to evaluate the talent ecosystem within key industrial parks of HFTP and identify its key influencing factors. Data were collected through questionnaire surveys, with respondents who fully completed relevant measurement items selected as research subjects. Multiple linear stepwise regression analysis and robustness tests were comprehensively employed for data analysis. The findings reveal that: (1) gender, age, and political affiliation exert significant influences on talent ecosystem evaluations; (2) social capital demonstrates a significant positive impact on ecosystem assessments; (3) economic capital shows no statistically significant effect; and (4) cultural capital exhibits a significant negative influence. Based on these results, governors should embrace an ecological governance mindset. This approach involves establishing an innovative “Talent Ecosystem Health Index” monitoring system, with periodic evaluation and public reporting of its findings. A multi-stakeholder “Talent Ecosystem Governance Committee” should be formed to coordinate strategic planning and policy alignment. Additionally, “policy mix experiments” should be conducted to explore the optimal integrated conditions for talent policies. Ultimately, these initiatives aim to establish a self-adaptive regulatory mechanism based on dynamic monitoring and feedback, thereby enhancing the adaptability and long-term resilience of the talent ecosystem. Full article

Detecting trace metals in soil across geologically diverse terrains remains challenging due to complex mineral–metal interactions and the limited spatial coverage of traditional geochemical tests. This study develops a scalable VIS–NIR–SWIR spectroscopy and machine learning (ML) framework to predict and map soil concentrations of Cr, As, Cu, and Cd in the Aligudarz District, located within the geotectonically complex Sanandaj–Sirjan Zone of western Iran. Laboratory reflectance spectra (~350–2500 nm) obtained from 110 soil samples were pre-processed using derivative filtering, scatter-correction techniques, and genetic algorithm (GA)-based wavelength optimisation to enhance diagnostic absorption features linked to Fe-oxides, clay minerals, and carbonates. Multiple ML-based approaches, including artificial neural networks (ANNs), support vector regression (SVR), and partial least squares regression (PLSR), as well as stepwise multiple linear regression (SMLR), were compared using nested, spatial, and external validation. Nonlinear models, particularly ANNs, exhibited the highest predictive accuracy, with strong generalisation confirmed via an independent test set. GA-selected wavelengths and derivative-enhanced spectra revealed mineralogical controls on metal retention, confirming that spectral predictions reflect underlying geological processes. Ordinary kriging of spectral-ML residuals generated spatially consistent metal-distribution maps that aligned well with local and regional geological features. The integrated framework demonstrates high predictive accuracy and operational scalability, providing a reproducible, field-ready method for rapid geochemical assessment. The findings highlight the potential of VIS–NIR–SWIR spectroscopy, combined with advanced modelling and geostatistics, to support environmental monitoring, mineral exploration, and risk assessment in geologically complex terrains. Full article

This study compares and evaluates the performance of a statistical model, Multiple Linear Regression (MLR), and a deep learning model, Long Short-Term Memory (LSTM), for predicting monthly mean temperature in the Han River Basin, South Korea. Predictor variables were dynamically selected based on lagged correlation analysis between climate indices and temperature over the past 40 years, identifying the top ten variables with the highest correlations for lag times ranging from 1 to 18 months. The MLR model was developed through stepwise regression with cross-validation, while the LSTM model was constructed using an 18-month input sequence to capture temporal dependencies in the data. Model performance was evaluated using percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficient (r), and tercile-based probability metrics. Both models reproduced the seasonal variability of monthly temperature with high accuracy (NSE > 0.97, r > 0.98). The LSTM model showed slightly higher predictive skill in several periods but also exhibited larger prediction variance, reflecting the sensitivity of nonlinear architectures to variations in predictor–response relationships. In contrast, the MLR model demonstrated more stable predictive behavior with narrower uncertainty bounds, particularly under low signal-to-noise conditions, owing to its structural simplicity. These findings indicate that the two approaches are complementary; the LSTM model better captures nonlinear temporal dynamics, while the MLR model provides interpretability and robustness. Future work will explore advanced hybrid architectures such as CNN–LSTM and Transformer-based models, as well as multi-model ensemble methods, to further enhance the accuracy and reliability of medium-range temperature prediction. Full article

Making advancements in Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for predicting biological activities in new compounds. Traditional 2D-QSAR and 3D-QSAR methods often face challenges in terms of computational efficiency and predictive accuracy. This study introduces a machine learning approach using 3D Discrete Tchebichef Moments (3D-DTM) to address these issues. The 3D-DTM method offers efficient computation, robust descriptor generation, and improved interpretability, making it a promising alternative to conventional QSAR techniques. By capturing global 3D shape information, this method provides better representation of molecular interactions essential for biological activities. We applied the 3D-DTM model to a dataset of 46 molecules derived from the Dihydrophenanthrene scaffold, screened against the enzymatic activity of 3-chymotrypsin-like protease, a key antiviral target. Principal Component Analysis and k-means clustering refined descriptors, followed by stepwise Multiple Linear Regression (step-MLR), Partial Least Squares Regression (PLS-R), and Feed-Forward Neural Network (FFNN) techniques for 3DTMs-QSAR model development. The results showed high correlation and predictive accuracy, with significant validation from internal and external tests. The step-MLR model emerged as the optimal method due to its balance of predictive power and simplicity. Validation through y-Randomization and applicability domain analysis confirmed the model’s robustness. Virtual screening of 100 novel compounds identified 32 with improved pIC₅₀ values. This study highlights the potential of 3D-DTMs in QSAR modeling, providing a scalable and reliable tool for computational chemistry and drug discovery. A user-friendly software tool was also developed to facilitate 3D-DTM extraction from input 3D molecular images. Full article

This study investigates the factors influencing stakeholders’ willingness to contribute to the Bucak Model Forest (BMF) in Türkiye, a participatory governance initiative aimed at promoting sustainable forest management. Based on a survey of 1134 local residents and stakeholders, the research employs both descriptive statistics and multivariate analyses, including stepwise multiple linear regression and Chi-square tests. The regression analysis revealed that variables such as awareness of the BMF, positive attitudes toward ecotourism, trust in forestry institutions, and willingness to engage in forest-related activities without financial gain positively affect the intention to contribute, while gender showed a weak negative relationship. The overall explanatory power of the regression model was 23%, indicating the need to consider additional variables for a deeper understanding. Chi-square analyses demonstrated weak but significant associations between demographic characteristics and perceptions of forest use, conservation, and organizational trust. The findings underscore the necessity of refining participatory strategies in Model Forests by enhancing outreach, adjusting strategic planning based on local dynamics, and strengthening institutional capacities. The study contributes to the literature on collaborative forest governance and provides practical insights for improving stakeholder engagement in similar landscape-scale sustainability initiatives. Full article

Background and objectives: This study examines how sociodemographic, clinical, and psychological factors within the patient–caregiver dyad affect caregiver burden and health-related quality of life (HRQoL) in cognitive impairment. By comparing baseline data with a 1-year follow-up, the research aims to identify key predictors of caregiver burden and well-being. Methods: A longitudinal observational study was conducted in an Italian rehabilitation hospital, recruiting 132 outpatients and their caregivers at baseline, categorized as (a) Mild Cognitive Impairment (MCI, n = 33); (b) dementia (DEM, n = 58); (c) healthy subjects (No-CI, n = 41). One year after baseline assessment (T0), patients were contacted and invited for an in-person follow-up re-evaluation (T1). Most attrition was related to the COVID-19 pandemic. Statistical analyses included non-parametric tests for group comparisons and stepwise multiple linear regression to identify predictors of burden, adjusting for confounders (e.g., age, gender, education, employment, co-residence). Results: A total of 51 subjects (age: 80.0 ± 6.1) and 34 caregivers (age: 58.8 ± 15.9) were evaluated. Patients were balanced by gender (53% males); most were retired (96%), married (62.7%), and cared for by sons (47%) or wife–husband (47%). Caregivers (females: 85%) were married (68.3%) and active workers (46.4%). Over one year, 17 No-CI subjects developed MCI or DEM; 15 MCI patients progressed to DEM. Caregiver HRQoL negatively correlated with distress and burden in MCI and DEM groups. Patient cognitive status, functional abilities, neuropsychiatric symptoms, and gender predicted caregiver burden, emphasizing the interplay between clinical and demographic factors. Conclusions: It is essential to monitor psychosocial factors in both the patient and the caregiver to develop effective prevention and support strategies. Full article

Background/Objectives: Language impairments are highly prevalent in children with Autism Spectrum Disorder (ASD). In preschoolers (3–6 years), language development predicts future social outcomes. Despite the availability of standardized tests for typically developing children, few studies have specifically examined language impairments in preschool-aged children with ASD using these tools. This study aimed to comprehensively assess receptive and expressive lexicon, receptive comprehension, phonology and articulation using standardized tools, and to evaluate their feasibility. A secondary goal was to compare the results obtained with standardized tests to those from developmental batteries and hetero-assessments (caregiver reports). Methods: Forty-seven children with ASD aged 4 to 6 years participated. Assessments included standardized language tests, developmental batteries and hetero-assessments. The dichotomous Rasch model evaluated feasibility and item performance of standardized tests. Concordance across methods was analyzed via Pearson correlations and stepwise linear regression. Results: Standardized assessments were feasible for most participants despite wide variability in language abilities. Partial but non-equivalent concordance was found among assessment methods, each providing complementary insights into language profiles. Conclusions: Combining multiple assessment methods is crucial to capture the complexity of language development in children with ASD. Standardized tests can be adapted and provide more precise profiles than developmental batteries or hetero-assessments alone. A multimodal approach is essential to accurately identify language strengths and therapeutic targets in preschool-aged children with ASD. Full article

Background: Extracorporeal membrane oxygenation (ECMO) can affect the disposition of drugs, notably by sequestering them in a circuit. This review aimed to provide a comprehensive summary of existing ex vivo studies investigating the impact of contemporary ECMO circuits on drug sequestration, and to examine the associations between the physicochemical properties of drugs, the features and settings of ECMO devices, and the extent of drug sequestration. Method: A comprehensive search was conducted to identify ex vivo studies that determined drug concentrations in ECMO circuits. Studies that did not allow for the proper assessment of drug loss by degradation were excluded. Drug characteristics and experimental conditions were recorded. Drug sequestration in the circuit was calculated as the difference between the drug loss measured in the ECMO circuit and the drug loss due to spontaneous degradation measured under control conditions. To identify predictors of drug sequestration, a stepwise multiple linear meta-regression was applied by testing the physicochemical properties of drugs and ECMO device features/settings. Results: A total of 40 studies were identified, of which 21 were included in the analysis, covering 41 drugs. The Maquet membrane oxygenator was the most used brand (73%). About half of the circuits were adult and half were pediatric. Our final regression model retained lipophilicity, and to a lesser extent ionization at a physiological pH, as significant predictors of drug sequestration (R² 0.44, relative standard error 23%). Protein binding had no additional effect. Anti-infectives were the most studied class of drugs (n = 28). Antibiotics were overall not significantly sequestered, while lipophilic drugs such as posaconazole, voriconazole, paracetamol, fentanyl, sufentanil, propofol, thiopental, dexmedetomidine and amiodarone were highly sequestered (≥50%). However, this sequestration occurred mainly within the first few hours of the experiments, possibly reflecting a saturation effect. Conclusions: Lipophilic drugs are significantly sequestered in ex vivo ECMO circuits, although this effect may be limited by early saturation. Full article

Particulate matter (PM), particularly PM_2.5, is a major urban air pollution concern globally. While temporary mitigation measures are generally implemented during high-pollution periods, sustainable solutions focusing on forest landscape management are crucial. This study examines the effects of forest landscape types and environmental variables on PM_2.5 concentrations during the high-pollution period (January–March 2022) in South Korea, using data from 40 national air quality monitoring stations. GIS and Fragstats were used to construct spatial variables and landscape indices. Stepwise multiple linear regression analyses were then conducted to identify significant factors affecting PM_2.5 concentrations. The aggregated forest model (i.e., without distinguishing between forest types) explained 72.9% of the variance in PM_2.5 concentrations. Forest percent cover (within 5000 m) and distance from the China national border were found to negatively affect PM_2.5 levels, while population size (within 5000 m) and urbanized area patch density (within 5000 m) had positive effects (p < 0.05). By incorporating forest types as variables, the forest type model improved explanatory power to 83.4%. Specifically, mixed forest percent cover (within 5000 m), mixed forest patch density (within 3000 m), and broad-leaved forest percent cover (within 1000 m) were negatively correlated with PM_2.5, while population size and urbanized area patch density (within 5000 m) showed positive effects (p < 0.05). These results highlight the importance of considering forest types, along with anthropogenic environmental variables, when assessing the mitigating effects of forests on PM_2.5, as both showed scale-dependent relationships with pollution levels. This study informs urban planning and long-term environmental management strategies for reducing PM_2.5 pollution. Full article

The stages of change have been identified as a valuable framework for understanding the transition toward a healthy lifestyle. It is also important to recognize change through other psychosocial variables, such as motivation and self-efficacy. The objective of this study was to explore weight control over the course of an academic year (nine months) through three behaviors: the stage of change toward weight control (pre-contemplation, contemplation, preparation, action, maintenance), healthy and unhealthy eating behaviors for weight control, and the frequency of physical activity (PA). Furthermore, we wanted to ascertain whether the three distinct types of motivation (autonomous, controlled, and amotivation) and self-efficacy could account for fluctuations in weight control over time. The sample consisted of 303 adolescents (205 female and 98 male) between the ages of 15 and 23 (M = 17.26; SD = 1.65). Chi-square, t-test, and multiple linear stepwise regression analysis were employed. The results indicated that a higher proportion of adolescents were in the precontemplation and action stages at Time 2. Concurrently, an increase in the frequency of moderate-to-vigorous PA and an increase in healthy and unhealthy behaviors were observed during the school period. The present study posits that autonomous motivation, controlled motivation, and self-efficacy can explain healthy eating behaviors for weight control and the frequency of moderate-to-vigorous PA, while only controlled motivation explains unhealthy eating behaviors for weight control. The conclusion of the study points out that healthy behaviors can change over time due to individual regulation of motivation and increased self-perception of efficacy in one’s own abilities to perform a specific action to control weight. Full article

Background/Objectives: The dual burden of cancer and pain during chemotherapy can negatively impact individuals’ personal integrity, or the “self”. Yet, coping strategies addressing these dual challenges are rarely employed in cancer-related pain management. Recent findings from evidence-based behavioral models, such as psychological flexibility in pain, highlight the potential role of self-as-context (SAC) as a central coping strategy for adjustment. The aim of this study was to examine the network structure of “conventional” coping strategies, such as active coping, behavioral disengagement, substance use, seeking support, religion, humor, and avoidance (Brief-COPE-8 coping strategies), in relation to “self-based” coping strategies. Methods: Individuals diagnosed with cancer, mostly in advanced stages (i.e., II and III), experiencing cancer-related pain (n = 135), completed a cross-sectional online study. Participants filled out self-reported questionnaires, including the Brief-COPE, the Psychological Inflexibility in Pain Scale—Greek Version (G-PIPS-II), and the Self-as-Context Scale (SACS) scale, which included two subfactors: centering and transcending. The study employed a stepwise analysis plan. We first conducted a series of traditional correlations, analysis of variance (ANOVA), and hierarchical multiple linear regressions, to examine the predictive role of demographics/clinical characteristics, psychological inflexibility, and SAC (independent variables) on the eight coping strategies (dependent variables). We then selected the highest predictors of coping in cancer-related pain and included them in a network analysis model. In the network analysis, we estimated the LASSO network regularization and examined network stability. We also assessed the centrality and stability of the network model, focusing on the associations between SAC items, the most predictive coping strategies (Brief-COPE), and psychological inflexibility (G-PIPS-II). Results: SAC correlated positively with effective coping (active coping and humor) and negatively with substance use. There were no correlations between demographics, type, stage of cancer, and coping strategies for pain. Multiple linear regressions identified psychological inflexibility and SAC as the main contributors to pain adjustment, with SAC explaining substantially more variance in active coping. The partial correlation network included 12 nodes. Active coping, centering, and three of the six transcending items were the most influential in the network. Active coping demonstrated the highest centrality, exerting positive links with SAC items that reflected calm reactions and invariant perspective-taking in response to the pain experience. Conclusions: SAC might be considered as a tailored, self-based coping strategy for managing cancer-related pain. Future analog studies should explore the role of integrating self-based perspective-taking strategies to momentarily address cancer-related pain. Full article

Fast and precise monitoring of soil organic matter (SOM) during maize growth periods is crucial for real-time assessment of soil quality. However, the big challenge we usually face is that many agricultural soils are covered by crops or snow, and the bare soil period is short, which makes reliable SOM prediction complex and difficult. In this study, an unmanned aerial vehicle (UAV) was utilized to acquire multi-temporal hyperspectral images of maize across the key growth stages at the field scale. The auxiliary predictors, such as spectral indices (I), field management (F), plant characteristics (V), and soil properties (S), were also introduced. We used stepwise multiple linear regression, partial least squares regression (PLSR), random forest (RF) regression, and XGBoost regression models for SOM prediction, and the results show the following: (1) Multi-temporal remote sensing information combined with multi-source predictors and their combinations can accurately estimate SOM content across the key growth periods. The best-fitting model depended on the types of models and predictors selected. With the I + F + V + S predictor combination, the best SOM prediction was achieved by using the XGBoost model (R² = 0.72, RMSE = 0.27%, nRMSE = 0.16%) in the R3 stage. (2) The relative importance of soil properties, spectral indices, plant characteristics, and field management was 55.36%, 26.09%, 9.69%, and 8.86%, respectively, for the multiple periods combination. Here, this approach can overcome the impact of the crop cover condition by using multi-temporal UAV hyperspectral images combined with valuable auxiliary variables. This study can also improve the field-scale farmland soil properties assessment and mapping accuracy, which will aid in soil carbon sequestration and soil management. Full article

In the face of the challenge of urban water resource degradation, green infrastructure construction has become a core strategy in modern urban water resource management. Urban aquatic horticulture (UAH), as an important component of this strategy, possesses the dual value of ecological purification and landscape aesthetics. However, its practical implementation is often constrained by public awareness and acceptance. This study aims to address the mismatch between the dual values of urban aquatic horticulture and public perception, and to develop an optimised plant selection strategy that integrates purification functions with public perception. Based on literature reviews, 18 images of aquatic plant landscapes showcasing different ornamental forms, species richness, and life types were created. A questionnaire survey was conducted on 320 participants to assess their perceptions of landscape aesthetic appeal and visual preferences, and a quantitative relationship model was established using multiple stepwise linear regression analysis. The public’s aesthetic perception of aquatic plant landscapes with different ornamental forms and species richness varies significantly, with flowering plant landscapes more likely to evoke aesthetic perception than non-flowering landscapes. The public’s visual preferences for landscape attributes significantly influence their aesthetic perception of aquatic plant landscapes. A multiple stepwise linear regression equation was established to model the relationship between the aesthetic perception of aquatic plant community landscapes and the public’s visual preferences for landscape attributes. There is no significant association between species richness and perceived landscape aesthetic appeal. The study developed an optimised selection strategy for aquatic plants that integrates purification functions with public perception, providing theoretical basis and practical guidance for the scientific configuration of aquatic horticultural systems in urban green infrastructure. In landscape design, flowering plants with ornamental value should be prioritised, with emphasis on landscape layers, colour, and spatial shaping to enhance public acceptance and promote the sustainable development of urban water resource management. Full article

Background/Objectives: Empathy is a core competency in nursing, contributing to patient care quality and professional resilience. This study investigated empathy levels among Greek undergraduate nursing students at the University of Peloponnese and examined the personal and educational factors that contribute to empathic development. Methods: A cross-sectional survey was conducted with 144 students from all academic years using the Jefferson Scale of Physician Empathy—Health Professions (JSPE-HP) and the SF-12 Health Survey. Data were analyzed using ANOVA and stepwise multiple linear regression. Results: Mean empathy scores were relatively high (M = 110.31, SD = 10.52). Empathy increased significantly with academic progression (p < 0.001), and higher scores were associated with parental status (p = 0.030) and better mental health (p = 0.044). Conversely, students with a chronically ill close contact reported lower empathy (p = 0.018). Regression analysis identified having children and exposure to chronic illness as significant predictors. Conclusions: Educational progression, life experience, and well-being are key contributors to empathy development. These insights support strategies to enhance empathy through curriculum design, student support, and wellness programs. Integrating empathy training into management policy can foster professional growth, reduce burnout, and improve patient care and workforce sustainability. Full article