Search Results (4,186)

Background: Mental illness stigma among healthcare professionals can adversely affect patient care and recovery. While attitudes are shifting globally, limited data exist on stigma among non-psychiatrist doctors (NPDs) in India. This study aimed to assess the attitudes of NPDs toward mental illness and psychiatry using the Mental Illness Clinicians’ Attitudes Scale (MICA-4), and to explore associated sociodemographic and clinical factors. Methods: A cross-sectional online survey was conducted across India over six months in 2022, following ethics approval. The survey link was distributed via professional social media platforms using convenience and snowball sampling. Non-psychiatrist doctors with at least an MBBS degree were eligible. The MICA-4 scale assessed stigma across five domains. Descriptive statistics, correlation analyses, and multiple regression analysis were conducted. Results: A total of 102 responses were analysed. The mean MICA-4 score was 48.37, indicating moderately positive attitudes. Domain-wise analysis revealed higher stigma in knowledge/misconception and self-disclosure domains, while attitudes towards ethics and patient care were more favourable. No significant differences were found by gender, specialty, or practice setting. Weekly psychiatric caseload was not associated with reduced stigma. Internal consistency of the scale was low (Cronbach’s α = 0.46), raising concerns about cultural fit. The regression model was statistically significant F (5, 96) = 661.95, p < 0.001, explaining 97.18% of the variance in overall attitudes toward mental illness. Among the five domains, Respect for Psychiatry and Knowledge and Misconceptions emerged as the strongest predictors, highlighting their critical role in shaping positive professional attitudes in the public sector. Conclusions: Stigma toward mental illness persists among NPDs, particularly around misconceptions and help-seeking attitudes. These biases are culturally embedded and may not be significantly influenced by clinical exposure alone. While stigma was generally moderate, persistent misconceptions and self-stigma point to the importance of further developing culturally adapted tools and systemic interventions to promote reflective practice and ethical parity in clinical settings. Full article

This study investigates the technological and chemical causes of early zinc-coating degradation on cold-formed steel sections produced from DX51D+Z140 galvanized coils. Commercially manufactured products exhibiting early corrosion symptoms were used in this study. The entire processing route, which included strip preparation, cold rolling, hot-dip galvanizing, passivation, multi-roll forming, storage, and transportation to customers, was analyzed with respect to the residual surface chemistry and process-related deviations that affect the coating integrity. Thirty-three specimens were examined using electromagnetic measurements of coating thickness. Statistical analysis based on the Cochran’s and Fisher’s criteria confirmed that the increased variability in zinc coating thickness is associated with a higher susceptibility to localized corrosion. Surface and chemical analysis revealed chloride contamination on the outer surface, absence of detectable Cr(VI) residues indicative of insufficient passivation, iron oxide inclusions beneath the zinc coating originating from the strip preparation, traces of organic emulsion residues impairing wetting and adhesion, and micro-defects related to deformation during roll forming. Early zinc coating degradation was shown to result from the cumulative action of multiple technological (surface damage during rolling, variation in the coating thickness) and environmental (moisture during storage and transportation) parameters. On the basis of the obtained results, a methodology was proposed to prevent steel product corrosion in industrial conditions. Full article

Water quality monitoring faces increasing challenges due to accelerating industrialization and urbanization, demanding accurate, real-time, and reliable prediction technologies. This study presents a novel ensemble learning framework integrating Gaussian Process Regression, Support Vector Regression, and Random Forest algorithms for high-precision water quality pH prediction. The research utilized a comprehensive spatiotemporal dataset, comprising 11 water quality parameters from 37 monitoring stations across Georgia, USA, spanning 705 days from January 2016 to January 2018. The ensemble model employed a dynamic weight allocation strategy based on cross-validation error performance, assigning optimal weights of 34.27% to Random Forest, 33.26% to Support Vector Regression, and 32.47% to Gaussian Process Regression. The integrated approach achieved superior predictive performance, with a mean absolute error of 0.0062 and coefficient of determination of 0.8533, outperforming individual base learners across multiple evaluation metrics. Statistical significance testing using Wilcoxon signed-rank tests with a Bonferroni correction confirmed that the ensemble significantly outperforms all individual models (p < 0.001). Comparison with state-of-the-art models (LightGBM, XGBoost, TabNet) demonstrated competitive or superior ensemble performance. Comprehensive ablation experiments revealed that Random Forest removal causes the largest performance degradation (+4.43% MAE increase). Feature importance analysis revealed the dissolved oxygen maximum and conductance mean as the most influential predictors, contributing 22.1% and 17.5%, respectively. Cross-validation results demonstrated robust model stability with a mean absolute error of 0.0053 ± 0.0002, while bootstrap confidence intervals confirmed narrow uncertainty bounds of 0.0060 to 0.0066. Spatiotemporal analysis identified station-specific performance variations ranging from 0.0036 to 0.0150 MAE. High-error stations (12, 29, 33) were analyzed to distinguish characteristics, including higher pH variability and potential upstream pollution influences. An integrated software platform was developed featuring intuitive interface, real-time prediction, and comprehensive visualization tools for environmental monitoring applications. Full article

Background: Catechol-O-methyltransferase (COMT) catalyzes catecholamine O-methylation and contributes to dopamine turnover, potentially influencing levodopa requirements in Parkinson’s disease (PD). We evaluated whether the Gothelf COMT haplotype—and its constituent variants rs2075507, rs4680 (Val158Met), and rs165599—differ in frequency between PD cases and controls. We then tested associations between these variants and clinical phenotypes, with a prespecified focus on levodopa equivalent daily dose (LEDD). Finally, we examined whether haplotype structure and allele-specific context (e.g., background-dependent effects) help explain observed genotype–phenotype relationships in the PD cohort. Aim: Analysis of the rs2075507, rs4680 and rs165599 at individual and haplotype level between control and diseased groups. Furthermore, analysis of association of individual SNPs or haplotype level with clinical outcomes. Subjects and methods: Fifty-five individuals with Parkinson’s disease (PD) and fifty-three neurologically healthy controls were enrolled at a single center. Genomic DNA was isolated from peripheral blood, and three COMT variants—rs2075507 (promoter), rs4680/Val158Met (coding), and rs165599 (3′UTR)—were genotyped by Sanger sequencing. Allele, genotype, and tri-marker haplotype frequencies were estimated, and case–control differences were evaluated. Within the PD cohort, associations with clinical outcomes—primarily levodopa equivalent daily dose (LEDD)—were analyzed using multivariable linear models. Statistical tests were two-sided, with multiplicity control as specified in the corresponding tables. Results: The rs2075507 polymorphism showed a robust additive association with LEDD; each A allele predicted higher dose (LEDD ≈ +1331 mg/day, p = 0.001) after adjusting for age and sex. The tri-haplotype test did not show significant association with LEDD. Nevertheless, rs2075507 SNP strongly marked downstream backgrounds: in AA carriers, rs4680–rs165599 haplotypes were enriched for Val (G) and rs165599-G; in GG carriers, for rs165599-A with mixed Val/Met; and GA was A-loaded at both loci. Exact tests confirmed that AA and GG differed in rs4680–rs165599 composition, whereas GA vs. GG was not significant. Conclusions: The promoter variation at rs2075507 may represent the genetic contributor to levodopa dose requirements when modeled with SNP–SNP interactions, with its effect is modified mostly by rs165599 polymorphism. Tri-haplotypes do not independently predict LEDD. The rs4680 (coding) and rs165599 (3′UTR) context appears to fine-tune rather than determine dosing needs, mainly via interaction with rs2075507 SNP. Full article

With the increasing competition in elite sports, pre-competition anxiety has become increasingly prevalent among high-level competitive athletes, and high levels of such anxiety may impair sports performance and threaten athletes’ psychological health. Traditional psychological interventions (e.g., cognitive-behavioral therapy) are often poorly accepted and costly; however, pre-competition anxiety in these athletes may be alleviated through multiple pathways of traditional mind–body exercises like Tai Chi. Yet, the psychological mechanism by which mind–body exercises such as Tai Chi training influence pre-competition anxiety remains insufficiently explored, particularly the chain-mediating effect of the “flow experience → mental toughness” pathway. This study thus aimed to investigate the impact of Tai Chi training on pre-competition anxiety in high-level competitive athletes and verify the chain-mediating role of the “flow experience → mental toughness” pathway, thereby providing a theoretical basis and practical reference for sports psychology interventions. Using a randomized controlled experimental design, 86 high-level competitive athletes were randomly divided into an experimental group (n = 43) and a control group (n = 43). The experimental group received standardized Tai Chi training for 8 weeks, while the control group maintained their regular training regimen. Data were collected at baseline, week 4, and week 8 of the intervention using the Competition State Anxiety Inventory-2 (CSAI-2), Flow State Scale-2 (FSS-2), and Sport Mental Toughness Questionnaire (SMTQ), and chain-mediating effects were tested via hierarchical regression analysis and the bootstrap method with 5000 resamples. The results indicated that Tai Chi training could reduce pre-competition anxiety levels (β = −0.30, p < 0.5), and both flow experience (β = 0.38, p < 0.5) and mental toughness (β = 0.21, p < 0.5) exerted significant mediating effects. The chain mediation model further revealed that Tai Chi training alleviated pre-competition anxiety by enhancing flow experience and improving mental toughness sequentially (β = 0.01, 95% CI [0.00, 0.03]), accounting for 78.9% of the total mediated effect. In conclusion, Tai Chi training is associated with reduced pre-competition anxiety in high-level competitive athletes, and this relationship is statistically mediated by the sequential pathway of flow experience and mental toughness. These findings offer a new theoretical basis and practical direction for mind–body interventions in sports psychology. It should be noted that future research could further optimize and refine the intervention protocol, and explore the underlying mechanism of mind–body interventions at the neurobiological level. Full article

Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care. Full article

Unsafe behaviors at construction sites often originate from cognitive failures such as lapses in memory and attention. This study proposes a novel, hybrid framework to systematically identify and predict the key contributors of cognitive failures among construction workers. First, a detailed literature review was conducted to identify 30 candidate factors related to cognitive failures and unsafe behaviors at construction sites. Thereafter, 10 construction safety experts ranked these factors to prioritize the most influential variables. A questionnaire was then developed and field surveys were conducted across various construction sites. A total of 500 valid responses were collected from construction workers involved in residential, highway, and dam projects in Pakistan. The collected data was first analyzed using conventional statistical analysis techniques like correlation analysis followed by multiple linear and binary logistic regression to estimate factor effects on cognitive failure outcomes. Thereafter, machine-learning models (including support vector machine, random forest, and gradient boosting) were implemented to enable a more robust prediction of cognitive failures. The findings consistently identified fatigue and stress as the strongest predictors of cognitive failures. These results extend unsafe behavior frameworks by highlighting the significant factors influencing cognitive failures. Moreover, the findings also imply the importance of targeted interventions, including fatigue management, structured training, and evidence-based stress reduction, to improve safety conditions at construction sites. Full article

We statistically examine the global distribution of free content websites (FCWs) by analyzing their hosting network scale, cloud service provider, and country-level presence, both in aggregate and across specific content categories. These measurements are contrasted with those of premium content websites (PCWs) and with general websites sampled from the Alexa top-1M. We further evaluate their security characteristics using multiple security indicators. Our findings show that FCWs and PCWs are predominantly hosted in medium-scale networks, which are strongly associated with a high concentration of malicious websites. At the cloud and country level, FCW distributions follow heavy-tailed patterns that differ from those of PCWs. Beyond static distributions, our analysis also uncovers dynamic trends, where PCWs demonstrate improving security postures over time while FCWs reveal increasing maliciousness in several categories and hosting regions. This study contributes to understanding the FCW ecosystem through comprehensive quantitative analysis. The results suggest that the harm posed by malicious FCWs can potentially be contained through effective isolation and filtering, given their concentration at the network, cloud, and country levels, and that longitudinal monitoring is essential to capture their evolving risks. Full article

This study systematically verified the applicability and accuracy of the Probability Density Evolution Method (PDEM) in the probabilistic modeling of the dynamic response of anchored rock slopes under random seismic action through large-scale shaking table model tests. Across 144 sets of non-stationary random ground motions and 7 sets of white noise excitations, key response data such as acceleration, displacement, and changes in anchor axial force were collected. The PDEM was used to model the instantaneous probability density function (PDF) and cumulative distribution function (CDF), which were then compared with the results of normal distribution, Gumbel distribution, and direct sample statistics from multiple dimensions. The results show that the PDEM does not require a preset distribution form and can accurately reproduce the non-Gaussian, multi-modal, and time evolution characteristics of the response; in the reliability assessment of peak responses, its prediction deviation is much smaller than that of traditional parametric models; the three-dimensional probability density evolution cloud map further reveals the law governing the entire process of the response PDF from “narrow and high” in the early stage of the earthquake, “wide and flat” in the main shock stage, to “re-convergence” after the earthquake. The study confirms that the PDEM has significant advantages and engineering application value in the analysis of random seismic responses and the dynamic reliability assessment of anchored slopes. Full article

Effective signaling in instructional materials—through cues such as highlights, arrows, and annotations—can guide learner attention, reduce cognitive load, and enhance comprehension in multimedia-rich online courses. While the benefits of signaling are well documented, little is known about how combinations of signaling strategies influence both the average performance and the consistency of student outcomes. In this study, we propose a data-driven approach to evaluate and optimize signaling strategies in online teaching. Using lecture materials from three semesters of introductory and intermediate statistics courses, we extracted multiple features of textual and visual signaling, including highlighted words, annotated formulas, arrows, and notes. Principal Component Analysis identified four distinct signaling strategies employed by the instructor. We then applied a heteroscedastic beta regression model to link these strategies to topic-level exam performance, allowing simultaneous assessment of mean learning outcomes and their variability. Results show that strategies combining formula highlighting with arrows and detailed notes improve both the average proportion of successful learners and the stability of outcomes, while relying solely on formula highlighting increases variability. Our findings provide actionable guidance for instructors to design effective signaling strategies, and demonstrate a flexible framework for data-driven evaluation of teaching practices in online learning environments. Full article

Background/Objectives: Cellulite is a common aesthetic condition in women, traditionally assessed using visual inspection and palpation-based scales that are inherently subjective. Therefore, image-based methods that may support standardized severity grading are of growing interest. To evaluate infrared thermography as an imaging-based method for grading cellulite severity and to perform methodological validation of a newly developed thermographic classification scale by comparing it with clinical palpation and anthropometric parameters. Methods: This retrospective, non-interventional study analyzed anonymized clinical and thermographic data from 81 women with clinically assessed cellulite. Cellulite severity was evaluated using the Nürnberger–Müller palpation scale and a newly developed five-point thermographic scale based on skin surface temperature differentials and histogram pattern analysis. The associations between the assessment methods were evaluated using ordinal statistical measures, and agreement was assessed using weighted Cohen’s kappa statistics. Results: Thermographic grading demonstrated high agreement with palpation-based assessment, with a percentage agreement of 93.8% and an almost perfect agreement based on weighted Cohen’s κ. A strong ordinal association was observed between the methods. Thermography consistently classified a subset of cases as one grade higher compared with palpation. No statistically significant associations were observed between thermographic grade and body mass index or waist-to-hip ratio. Conclusions: Infrared thermography enables image-based grading of cellulite severity and shows a strong concordance with established palpation scales. The proposed thermographic classification provides preliminary methodological validation of an imaging-based grading approach. Further multicenter studies involving multiple assessors and diverse populations are required to assess reproducibility, specificity, and potential clinical applicability. Full article

Background and Objectives: Keratoconus is a bilateral but often asymmetric ectatic corneal disease characterized by progressive thinning, increased curvature, and conical shape of the cornea. Previous studies have reported that the use of swimming goggles in patients with keratoconus can lead to increased intraocular pressure (IOP) and a transient reduction in anterior chamber volume (ACV), potentially affecting anterior segment morphology. This study aimed to evaluate the short-term effects of periorbital pressure induced by swimming goggles on corneal parameters in keratoconic eyes. Materials and Methods: A total of 44 eyes of 44 patients (mean age: 26.1 ± 5.1 years) diagnosed with keratoconus Stage 1–4 according to the Amsler–Krumeich classification were included. Measurements were taken using a Pentacam^® Scheimpflug camera before swimming goggle application and immediately after 20 min of wear. The parameters assessed included keratometry values (K1, K2, Km, Kmax), central and thinnest corneal thickness, corneal volume within the 10 mm zone (CV10), ACV, anterior chamber depth (ACD), iridocorneal angle (ICA), and pupil diameter (PD). Results: No statistically significant changes were observed in keratometric values, central and thinnest corneal thickness, ACV, ACD, ICA, PD, or IOP (all p > 0.05). CV10 showed a small reduction following goggle wear (Δ = −0.18 mm³, corresponding to a 0.3% decrease), which was statistically significant in the unadjusted analysis (p = 0.008) but did not remain significant after correction for multiple comparisons (p for false discovery rate [FDR] = 0.10). Conclusions: Short-term swimming goggle use may induce subtle reductions in CV10 in keratoconic eyes, suggesting a potential biomechanical sensitivity to transient periocular pressure. Although the observed change in CV10 did not retain statistical significance after multiple-comparison correction, it may reflect an early physiological response in structurally compromised corneas. CV measurements could serve as exploratory indicators of mechanical responsiveness in keratoconus, warranting further investigation in larger controlled studies. Full article

Reliable models for predicting the uniaxial compressive strength (UCS) of rocks are crucial for mining operations and rock engineering design. Empirical methods, including statistical methods, are often faced with many limitations when generalizing in a wide range of lithological types. To address this limitation, this study investigates the capability of grey wolf optimization (GWO)-optimized ensemble machine learning models, including decision tree (DT), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost) for predicting UCS using a small dataset of easily measurable and non-destructive rock index properties. The study’s objective is to evaluate whether metaheuristic-based hyperparameter optimization can enhance model robustness and generalization performance under small-sample conditions. A unified experimental framework incorporating GWO-based optimization, three-fold cross-validation, sensitivity analysis, and multiple statistical performance indicators was implemented. The findings of this study confirm that although the GWO-XGBoost model achieves the highest training accuracy, it exhibits signs of mild overfitting. In contrast, the GWO-AdaBoost model outpaced with significant improvement in terms of coefficient of determination (R²) = 0.993, root mean square error (RMSE) = 2.2830, mean absolute error (MAE) = 1.6853, and mean absolute percentage error (MAPE) = 4.6974. Therefore, the GWO-AdaBoost has proven to be the most effective in terms of its prediction potential of UCS, with significant potential for adaptation due to its effectively learned parameters. From a theoretical perspective, this study highlights the non-equivalence between training accuracy and predictive reliability in UCS modeling. Practically, the findings support the use of GWO-AdaBoost as a reliable decision-support tool for preliminary rock strength assessment in mining and geotechnical engineering, particularly when comprehensive laboratory testing is not feasible. Full article

In regions with limited meteorological monitoring systems, spatial drought modeling is of importance for efficient water resource management. This study recommends an alternative drought modeling strategy for Standardized Precipitation Evapotranspiration Index (SPEI) prediction at multiple target stations using data from neighboring stations. The Multi-Output Random Forest (MORF) model is implemented in this study to consider the spatial correlations among stations for the simultaneous prediction of SPEI for multiple stations instead of training independent models for each station. The efficiency of MORF is further compared to Multi-Output Support Vector Regression (MOSVR) and three baselines; a single-output RF, a monthly climatology model, and a persistence model. In addition to statistical performance criteria, drought characteristics are evaluated using intensity–duration–frequency analysis for three temporal scales (SPEI-3, SPEI-6, and SPEI-12). Results demonstrate that MORF outperformed MOSVR and RF in approximating observed drought intensity, duration, and frequency under moderate, severe, and extreme drought scenarios. Furthermore, spatial analysis reveals that MORF accurately captured the seasonal evolution of drought conditions including onset and recovery phases. The remarkable success of MORF in contrast to MOSVR and three traditional baselines can be explained by its ability to detect nonlinear and complex interactions of drought condition among various neighboring stations. This study emphasizes the promise of multi-output machine learning algorithms for drought monitoring in water resource management and climate adaptation planning in data-scarce regions. Full article

Effective communication skills are widely recognized as essential for entrepreneurial success, yet limited empirical research has explored their direct relationship with individual entrepreneurship perceptions, particularly among women entrepreneurs. This study addresses this gap by investigating how communication competencies correlate with entrepreneurial self-perception, while also examining whether these variables vary according to demographic and professional characteristics such as age, occupational field, business ownership, and job position. Employing a quantitative research design with a descriptive survey model, data were collected from 145 women entrepreneurs. Statistical analyses, including ANOVA, multiple regression analysis and correlation tests, were applied to explore differences and relationships among variables. Findings indicate that certain demographic factors, notably age and job position, significantly influence both communication skills and entrepreneurship perceptions. Furthermore, a strong positive correlation emerged between the communication skills and individual entrepreneurship perceptions of women entrepreneurs. In addition, the fact that the communication skills and entrepreneurship perceptions of branch managers were higher than those of other work statuses showed that the “manager position” served as an important node affecting both variables within the system. These results underscore the importance of enhancing communication capabilities as a strategic component in fostering entrepreneurial identity and potential among women in diverse professional contexts. Full article