Search Results (5,100)

Vegetation dynamics in the Hulun Lake Basin (HLB), a vulnerable grassland–wetland–forest transition zone in Northeastern Inner Mongolia, North China, are sensitive to climate change, but traditional greenness metrics like the normalized difference vegetation index (NDVI) lack process-level insights. Using the GIMMS NDVI3g dataset (1982–2015) and meteorological data, this study analyzed the spatiotemporal dynamics of the NDVI and vegetation NDVI change rate (VNDVI)—a metric quantifying greening and browning speeds via NDVI temporal variation—employing linear regression and partial correlation analyses. The NDVI exhibited an overall significant upward trend of +0.0028 yr⁻¹ (p < 0.05) across more than 70% of the basin, indicating a persistent greening tendency. The VNDVI revealed an accelerated spring greening rate of +0.8% yr⁻¹ (p < 0.05) and a slowed autumn browning rate of −0.6% yr⁻¹ (p < 0.05), reflecting an extended growing season. Spatial correlation analysis showed that the temperature dominated spring greening (r = 0.52), precipitation governed summer growth (r = 0.64), and solar radiation modulated autumn senescence (r = 0.38). Compared with the NDVI, the VNDVI was more sensitive to both climatic fluctuations and anthropogenic disturbances, highlighting its utility in capturing process-level vegetation dynamics. These findings provide quantitative insights into the mechanisms of vegetation change in the HLB and offer scientific support for ecological conservation in North China’s grassland–forest ecotone. Full article

The tourism industry is currently experiencing a substantial digital transformation, with Online Travel Agencies (OTAs) committed to integrating innovations such as artificial intelligence (AI) in order to enhance service delivery and personalize user experiences. This study investigates the relationship between the utilization of AI and the technical quality scores of tourism websites, aiming to identify significant associations and variances in the critical conversion phase. An exploratory research design is employed to evaluate the technical quality of three prominent international tourism websites, i.e., Booking.com, Airbnb.com, and Tripadvisor.com. The investigation uses Google Lighthouse, with Performance, Accessibility, Best Practices, and Search Engine Optimization (SEO) as variables analyzed across both desktop and mobile versions, as well as on pages with and without AI functionality. Data analysis is performed using JASP (version 0.19.3), including linear regression analysis to quantify the predictive relationship. The analysis confirms that the Performance variable is the most sensitive to the influence of AI. AI integration demonstrates a significant positive influence on the Performance score of travel websites. The regression model indicates that AI usage explains 78.9% of the variation in the Performance score (${\mathrm{R}}^2$ = 0.789, p < 0.001), indicating a substantial correlation with technical sustainability. Nevertheless, there remains an ongoing necessity for optimization, particularly with regard to the enhancement of overall performance and improvement of scores for mobile devices. The study acknowledges certain limitations related to the sample size of AI applications and the accessibility of specific AI versions in particular geographic regions. Full article

Background: Muscle and bone show reciprocal interactions and are associated in a muscle-bone unit. The muscle-bone unit has been investigated to a very limited extent in soccer players. The objective of this work was to investigate in detail the muscle-bone unit in male youth elite soccer players. Methods: Bone mineral and lean mass were measured with dual-energy X-ray absorptiometry (DXA). The functional muscle-bone unit (fMBU) and the muscle-to-bone ratio (MBR) were calculated from the DXA output in a sample of players aged 14–19 (n = 193) playing in the youth squads of an Italian Serie A team. Results: Statistically significant (p < 0.05) correlations were found between lean mass variables and bone mineral content and density, also after adjusting for age, body mass, stature, maturity, and ethnicity (White/Black). fMBU and MBR were statistically significantly associated with age, body mass, stature, maturity, and ethnicity. Linear regression showed that body lean mass was the strongest predictor for bone mineral content and density. Age was a statistically significant predictor for fMBU and MBR. Playing position did not show any statistically significant relationship with bone mineral content and density, as well as fMBU or MBR. Centiles for fMBU and MBR were calculated as a reference. Conclusions: This work is the first detailed characterization of the muscle-to-bone relationship in soccer players. It is expected to be of use for sport scientists and the wide community of sportsmen and professionals involved in soccer. Full article

Rapid and reliable detection of pesticide residues in vegetables is essential for food safety and sustainable agriculture. This work presents a four-port closed-loop ring resonator (CLRR) sensor for quantitative detection of carbamate residues in leafy vegetables. Operating through the S₃₁ transmission path, the sensor enhances electric-field coupling and sensing resolution in the high-field region. Four resonance modes were identified at 1.05, 2.10, 3.12, and 4.11 GHz, with the third mode (3.12 GHz) showing the most stable and linear response. Vegetable extracts of Chinese kale and Choy sum were prepared with carbamate concentrations of 0–8% (w/v). Increasing concentration caused a red-shift in resonance frequency corresponding to a reduction in dielectric constant. Regression analysis revealed a strong linear correlation between frequency shift and concentration (R² = 0.9855–0.9988). The CLRR achieved average normalized sensitivities of 6.39% and 6.54% per unit dielectric variation, outperforming most planar and metamaterial sensors. Fabricated on a single-layer FR-4 substrate, the sensor combines high sensitivity, low cost, and excellent repeatability, offering a practical, label-free, non-destructive tool for on-site monitoring of pesticide contamination in leafy vegetables. Full article

Purpose: The purpose of this study is to assess whether AI-based models improve reproducibility of breast density (BD) and background parenchymal enhancement (BPE) classification and to explore whether contrast-enhanced mammography (CEM) can serve as a proof-of-concept platform for systemic risk surrogates. Materials and Methods: In this retrospective single-center study, 213 women (mean age 58.3 years; range 28–80) underwent CEM in 2022–2023. Histology was obtained when lesions were present (BI-RADS 4/5). Five radiologists independently graded BD and BPE; consensus served as the ground truth. Linear regression and a deep neural network (DNN) were compared with a simple linear baseline. Inter-reader agreement was measured with Fleiss’ κ. External validation was performed on 500 BI-RADS C/D cases from VinDr-Mammo targeted density endpoints. A secondary exploratory analysis tested a multi-output DNN to predict BD/BPE together with bone mineral density and systolic blood pressure surrogates. Results: Baseline inter-reader agreement was κ = 0.68 (BD) and κ = 0.54 (BPE). With AI support, agreement improved to κ = 0.82. Linear regression reduced the prediction error by 26% versus the baseline (MSE 0.641 vs. 0.864), while DNN achieved similar performance (MSE 0.638). AI assistance decreased false positives in C/D by 22% and shortened the reading time by 35% (6.3→4.1 min). Validation confirmed stability (MSE ~0.65; AUC 0.74–0.75). In exploratory analysis, surrogates correlated with DXA (r = 0.82) and sphygmomanometry (r = 0.76). Conclusions: AI significantly improves reproducibility and efficiency of BD/BPE assessments in CEM and supports feasibility of systemic risk profiling. Full article

Projects continue to fail approximately half the time, both before and after the COVID-19 pandemic. While prior studies highlight the influence of project leadership and individual competencies, little is known about whether team members’ willingness to disclose past performance can improve team allocation decisions and enhance business process success. However, we do not know if team members’ willingness to disclose their past performance may improve teamwork allocation in projects, thereby increasing business process success while reducing the likelihood of the project failing. We applied a rigorous post-positivist research design using correlation, conditioned correlation, t-tests, and ordinary least squares (OLS) linear regression to test the hypotheses. Controlling established predictors including budget, end user community size, and certification, we found that team members’ willingness to share their past performance evaluations significantly improved project success, increasing explained variance from 9.6% to 18.8%. The results indicate that transparency factors—specifically, willingness to share past performance—outweigh traditional resource allocation variables in predicting Fintech project outcomes, explaining an additional 19% of the variance in project success. Full article

Background: This study aims to investigate the association between the presence and severity of non-motor symptoms (constipation, REM sleep behavior disorder [RBD], hyposmia, and depression) and the severity of motor impairment in Parkinson’s disease (PD). Methods: We used data from Parkinson’s Progression Markers Initiative (PPMI), comprising patients with established PD, prodromal PD, and healthy controls. Motor disability was evaluated with the MDS-UPDRS part III. Non-motor symptoms were assessed with standardized scales for constipation (MDS-UPDRS part I sub-item), depression (15-item GDS), RBD questionnaire (RBDQ), and hyposmia (UPSIT). The relationships between non-motor symptoms and motor severity were explored using linear regression models (adjusted for age/sex). Results: Constipation was significantly more prevalent in PD and prodromal PD and independently associated with greater motor severity in both groups (p < 0.001). Constipation also correlated with increased freezing and falls. Depressive symptoms were similar across groups, but in prodromal PD, higher GDS scores were associated with worse UPDRS III scores (p = 0.02), as well as higher freezing and fall scores. Hyposmia was strongly reduced in PD and prodromal PD compared with controls but was not independently associated with motor severity. Higher RBDQ scores were associated with worse motor impairment in PD, but not in prodromal PD after adjustment. Conclusions: Constipation and REM sleep behavioral disorder were independent correlates of worse motor severity in prodromal and established PD, whereas depressive symptoms predicted more severe parkinsonism only within the prodromal phase. Full article

Wild Physalis angulata L. has promising medicinal potential due to its rich flavonoids. However, a green analytical approach for these compounds from this plant has not yet been thoroughly optimized. Therefore, this study optimized ultrasound-assisted extraction using the response surface method for the UV-VIS spectroscopic determination of the total flavonoid content in P. angulata in Vietnam. Notably, the greenness of the whole procedure was evaluated by AGREE, Eco-Scale, GAPI, BAGI methodologies. The Box–Behnken model was applied to design the experiments with four variables: ethanol concentration, solid-to-liquid ratio, extraction temperature, and time. The UV-Vis spectrophotometric method was validated at 510 nm according to AOAC guidelines and met all the requirements, including specificity, linearity (R² = 0.9996) in the working range of 15–120 µg/mL, repeatability (RSD = 1.89%), intermediate precision (RSD = 2.21%), and accuracy (recoveries from 99.52 to 104.06%). The limits of detection (LOD) and quantification (LOQ) were 2.48 µg/mL and 7.52 µg/mL, respectively; however, to avoid noise signal at lower concentrations, the validated lower limit of quantification (LLOQ) was set at 15 µg/mL. Data were analyzed using second-order regression. The R² = 0.9726 shows a close correlation between variables and the experimental data. The optimal extraction conditions were 31.66% ethanol, 30:1 mL/g ratio, 80 °C and 48.73 min. The predicted values (38.09 ± 1.70 mg RU/g) were not statistically different from the experimental values (34.58 ± 0.87 mg RU/g), confirming the model’s accuracy and applicability in optimizing the extraction process. The ultrasound-assisted extraction was optimized to enhance the flavonoid extraction yield from P. angulata, providing a solid scientific foundation for further pharmacological research. Full article

Recognizing a dearth of understanding on the experiences of foster parents of children with disabilities, this study aimed to compare well-being, psychological flexibility, and social support among foster parents and biological parents of children with disabilities, as well as parents of children without disabilities, while exploring the impact of service use and social support on psychological flexibility and well-being. A convenience sample comprised 135 parents: 36 biological parents of children with disabilities, 32 foster parents of children with disabilities, and 67 biological parents of children without disabilities. Statistical analyses included one-way ANOVA, Pearson correlation, simple linear regression, and multiple linear regression. Results showed that foster parents of children with disabilities exhibited significantly higher psychological flexibility, well-being, and social support compared to biological parents of children with disabilities. Among biological parents of children with disabilities, psychological flexibility and well-being showed significant correlations with service satisfaction and social support, with social support explaining 62% of variance in psychological flexibility and 51% in well-being. Among foster parents of children with disabilities, neither service use nor social support significantly predicted psychological flexibility or well-being, suggesting different adaptive mechanisms. Among parents of children without disabilities, social support significantly predicted both psychological flexibility and well-being. The findings, which should be interpreted cautiously given the small sample size, highlight the need for targeted support interventions for biological parents of children with disabilities and further research into foster care families’ unique experiences. Full article

Background/Objectives: Chronic kidney disease (CKD) is commonly complicated by anemia resulting from impaired erythropoietin (EPO) production, iron dysregulation, and chronic inflammation. Erythroferrone (ERFE) and hepcidin are key regulators of erythropoiesis and iron metabolism, but their interaction in CKD remains incompletely understood. This study aimed to examine the associations among ERFE, hepcidin, EPO, and hemoglobin, and to determine whether these markers independently relate to anemia severity in CKD. Methods: This cross-sectional case–control study included 126 patients with CKD (stages 2–5) and 33 age- and sex-matched healthy controls. Laboratory parameters, including hemoglobin, ferritin, transferrin saturation (TSAT), EPO, ERFE, hepcidin, and renal indices (eGFR, BUN, creatinine), were analyzed. Group differences were assessed using ANOVA or Kruskal–Wallis tests with post hoc analyses, and trends were evaluated using the Jonckheere–Terpstra test. Age- and sex-adjusted correlations and multivariable linear regression identified independent associations with hemoglobin. Results: Patients with CKD were older (61.2 ± 14.8 vs. 33.4 ± 10.7 years, p < 0.001) and had lower hemoglobin (11.8 ± 1.9 vs. 13.5 ± 1.4 g/dL, p < 0.001) and higher ferritin levels (245 (110–470) vs. 105 (40–240) ng/mL, p = 0.002) compared with controls. eGFR declined progressively across CKD stages (median (IQR): 73 (64–86) to 12 (7–17) mL/min/1.73 m², p-trend < 0.001). ERFE and hepcidin showed increasing trends with advancing CKD (p-trend = 0.031 and 0.047, respectively). Hemoglobin correlated negatively with ERFE (r = −0.40, 95% CI: −0.53 to −0.26, p < 0.001) and positively with eGFR (r = 0.42, 95% CI: 0.28–0.54, p < 0.001). In adjusted regression analysis, ERFE (β = −0.29, 95% CI: −0.41 to −0.18, p < 0.001) and eGFR (β = 0.25, 95% CI: 0.13–0.37, p < 0.001) remained independently associated variables of hemoglobin (adjusted R² = 0.47). Conclusions: Anemia severity in CKD is independently associated with both renal dysfunction and higher ERFE concentrations, suggesting a disrupted ERFE–hepcidin regulatory balance. These findings provide hypothesis-generating insights into the complex interplay between iron metabolism and erythropoiesis in CKD. Validation in larger, multi-center longitudinal studies that include inflammatory markers is warranted. Full article

Background: Depression, anxiety, and stress are increasingly prevalent among university students, raising concerns about the role of video gaming behaviors, social support, and academic factors in mental health. Internet Gaming Disorder (IGD), recognized in international classifications, has been linked to psychological distress but remains underexplored in Portuguese higher education students. Objective: This study aimed to examine the relationships between IGD, social support, academic performance, and mental health outcomes. Methods: A cross-sectional survey was conducted with Portuguese university students, collecting sociodemographic information, gaming habits, academic performance, social support, and mental health indicators. Data analyses included non-parametric tests, Spearman correlations, and multiple linear regression models to explore group differences, associations, and predictors of mental health outcomes. Results: No significant gender or age differences were observed in social support or mental health. Students living with parents, engaging in multiplayer gaming, and exercising regularly reported higher social support. Social support correlated negatively with depression, anxiety, and stress, whereas IGD correlated positively with these symptoms. Regression analyses identified academic performance, IGD, and intimacy as predictors of depression; family satisfaction as a predictor of anxiety; and family satisfaction and academic performance as protective factors against stress. Conclusions: Findings highlight the interplay of behavioral, social, and academic factors influencing students’ mental health. Effective interventions should reinforce family support and students’ academic self-efficacy, alongside fostering responsible gaming practices. Limitations include cross-sectional design and self-reported measures, indicating the need for longitudinal studies. Full article

We compared oxidative markers and their links to inflammation in diabetic nephropathy and hemodialysis to identify independent determinants. We studied 180 adults, 90 patients with type 2 diabetes and diabetic nephropathy and 90 patients on hemodialysis. We measured serum advanced oxidation protein products (AOPP) and thiobarbituric acid reactive substances (TBARS) by enzyme-linked immunosorbent assay (ELISA). Partial correlations were adjusted for age, sex, and albumin with false discovery rate (FDR) control. Principal component analysis (PCA) summarized inflammatory indices and linear models tested predictors of AOPP and TBARS. Oxidative damage was higher in hemodialysis, with AOPP median 25.80 versus 5.06 and TBARS 8.49 versus 1.89, p less than 0.0001. C reactive protein (CRP) and mean corpuscular volume-to-lymphocyte ratio (MCVL) were higher in patients ongoing hemodialysis; systemic immune-inflammation index (SII) was higher in diabetic nephropathy. PCA yielded a dominant inflammation axis in both cohorts, 74.73 percent in hemodialysis and 85.20 percent in diabetic nephropathy. In regression, creatinine (β = 2.47, p = 0.026) predicted AOPP in hemodialysis. Dialysis vintage inversely predicted TBARS, β = −0.2305, p = 0.0209. In diabetic nephropathy, the PCA inflammation score predicted AOPP, β = 1.134, p = 0.0003. Protein oxidation tracked systemic inflammation in diabetic nephropathy, but not in hemodialysis. AOPP outperformed TBARS as an inflammatory partner and a practical monitoring candidate in diabetic kidney disease. Prospective studies should test for prognostic value and therapy sensitivity. Full article

In order to conveniently and efficiently determine the Permissible Exposure Limits (PELs) of organic chemicals in the workplace, this study employed Quantitative Structure–Activity Relationship (QSAR) modeling to predict properties related to occupational health and safety. The predictive study was conducted by correlating the PELs of 75 hydrocarbons and their oxygen-containing derivatives with the molecular structures of the organic compounds. Meanwhile, this study conducted a comprehensive and in-depth comparative analysis of the four developed predictive models. The sample set was partitioned using the Affinity Propagation (AP) clustering algorithm. Four characteristic molecular descriptors were selected by integrating the Genetic Algorithm (GA) with the variance inflation factor (VIF) value. Subsequently, the Multiple Linear Regression (MLR) model and two nonlinear models, namely the Support Vector Machine (SVM) and the Extreme Gradient Boosting (XGBoost), were developed and used for predictive comparison. Furthermore, the performance of the models was evaluated through both internal and external validation methods, and the Williams plots were constructed to define the model’s applicability domain. The results indicated that the XGBoost model achieved high performance, with a coefficient of determination (R²) of 0.9962 on the training set and 0.8892 on the testing set. The corresponding root mean square errors (RMSE) were 0.1012 and 0.6623 for the training and testing sets, respectively. The internal validation coefficient (Q²loo) was 0.8975, while the external validation coefficient (Q²ext) was 0.832. Moreover, the majority of the sample data (approximately 96%) fell within the application domain defined by ±3 times the standard residue-to-critical arm ratio, where h* = 0.2. This demonstrates that the XGBoost model exhibits excellent fitting capability, stability, and predictive power, thereby uncovering a significant nonlinear relationship between the molecular structure of compounds and the PELs. As outlined above, the utilization of the QSAR method for predicting the PELs of hydrocarbons and their oxygen-containing derivatives constitutes a highly effective approach. Full article

Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms. Full article

This paper assesses the correlates of perceived well-being among young women aged 18 to 30 in five Balkan cities: Athens, Greece; Plovdiv, Bulgaria; Bucharest, Romania; Nis, Serbia; and Shtip, North Macedonia, by integrating urban, travel behavioural, and socio-economic features. A cross-sectional survey was employed using standard questionnaires including the Warwick–Edinburgh Mental Well-being Scale (WEMWBS), the short version of the International Physical Activity Questionnaire (IPAQ), and the adapted ALPHA environmental questionnaire. To answer research questions, linear regression models were developed to analyse predictors of well-being at both regional and national levels. Results show that neighbourhood and mobility features play a significant role in shaping mental well-being. Access to walkable sidewalks, green spaces, mixed land-use structure, and attractive local facilities (e.g., shops, recreational centres in the neighbourhood) were consistently associated with higher levels of well-being. Conversely, perceived insecurity, especially at night or regarding bicycle theft, significantly reduced well-being. Physical activity levels, particularly days of walking and vigorous activity, showed strong positive associations, underscoring the role of active lifestyles in promoting mental health. Socio-economic variables, including financial status, relationship status, and work status, were also found to be linked to perceived well-being. Cycling-related variables may affect Greek well-being up to 16.5 times. Perception of crime during the night may negatively affect both Bulgarian and Serbian well-being (up to 10 times), while Romanian well-being is mostly affected by the existence of shopping facilities. Finally, the most impactful factors for well-being in North Macedonia refer to cycling safety and scooter accessibility. Full article