Search Results (7,942)

Despite growing recognition that organizational culture shapes nursing practice, the linking of ethical climate to sustainable environmental health literacy (SEHL) remains poorly understood. This study examined whether moral courage statistically mediates the relationship between perceived ethical climate and self-reported environmental health literacy among Egyptian nurses, rather than observed competencies or clinical actions. A cross-sectional correlational design was employed with 743 nurses recruited from government, private, and university-affiliated hospitals. Participants completed the Hospital Ethical Climate Survey, the Nurses’ Moral Courage Scale, and the Environmental Health Literacy Scale. Mediation analysis used Hayes’ PROCESS macro with 5000 bootstrap samples. Ethical climate demonstrated strong positive associations with moral courage (r = 0.81) and SEHL (r = 0.86), while moral courage and SEHL were also strongly correlated (r = 0.82). Ethical climate explained 74% of variance in SEHL and 66% of variance in moral courage. Moral courage was associated with partial statistical mediation of the ethical climate–SEHL relationship, accounting for 33.4% of the total effect (β = 0.31, 95% CI [0.26, 0.37]), while the direct effect remained substantial (66.6%). These findings suggest that supportive ethical climates may be associated with nurses’ environmental health literacy via two statistical pathways: one directly linked to environmental learning and another indirectly linked to moral courage. Healthcare organizations should prioritize ethical climate development alongside moral courage training as potentially promising approaches to advance climate-responsive nursing practice. Full article

Micro-expressions, subtle and often asymmetric facial movements, play a pivotal role in nonverbal emotional communication. Addressing the core challenges of temporal misalignment, fragmented feature extraction, and slow real-time detection in micro-expression recognition (MER), we propose a novel dual-branch spatiotemporal model for dynamic sequence MER. Leveraging MediaPipe for 3D facial feature extraction and Dynamic Time Warping (DTW) for sequence alignment, our method nonlinearly maps variable-length sequences to a fixed length. A hybrid data augmentation technique enhances model robustness, while the dual-branch network simultaneously captures local spatial features and global temporal dynamics. Experimental results on the CASMEII dataset demonstrate state-of-the-art performance with 99.22% accuracy, along with a significant improvement in real-time detection speed. This approach holds substantial practical value for applications in deception detection, mental health assessment, and human–computer interaction. Full article

This study explores the sustainability of pedagogical innovation in higher education by examining a faculty-collaborative, ADDIE-based selective flipped learning framework in an engineering education context. It addresses a persistent challenge in engineering classrooms, namely the mismatch between traditional teaching approaches and the diverse learning needs of students, while also considering whether such innovations can be realistically sustained over time. A mixed-methods sequential explanatory design was implemented in a foundational Circuits I course at Princess Sumaya University for Technology (PSUT), involving 110 undergraduate students and eight faculty members. The ADDIE model guided the design and implementation of selectively flipped instructional materials. Quantitative data were analyzed using independent samples t-tests, while qualitative data from surveys, interviews, and focus groups were examined through thematic analysis to better understand faculty experiences and implementation processes. The findings show statistically significant improvements in student performance. Independent samples t-tests indicated significant differences in overall scores and final exam performance (p < 0.001), while additional analyses of formative assessment components also demonstrated statistically significant improvements. At the same time, the results reveal important implementation challenges. Although the course was collaboratively designed, implementation varied across instructors, and faculty were divided in their willingness to continue using the approach. This highlights a clear gap between instructional design and classroom practice, shaped by both human and institutional factors. Overall, the study suggests that well-structured instructional design models such as ADDIE can support improved learning outcomes. However, the findings do not provide conclusive evidence of long-term sustainability; rather, they highlight the conditions under which pedagogical innovations may be sustained, including institutional support, faculty engagement, and alignment with teaching realities. Full article

Emotional intelligence and teaching self-efficacy are essential competencies for teachers’ professional and personal development. The aim of this study was to evaluate the effectiveness of a psychoeducational intervention to enhance both areas in future teachers. A quasi-experimental design with pretest and posttest measures was implemented, including an experiment. An eight-week program was conducted using active, reflective, and participatory methodologies to promote emotional awareness and confidence in teaching abilities. The OSTES instrument was used to measure teaching self-efficacy, the TMMS-24 to assess perceived emotional intelligence, and the EQ-i to evaluate socioemotional competencies, and. Results revealed significant improvements in the experimental group in emotional attention, clarity, and repair; in instructional strategies, classroom management, and student engagement; as well as in adaptability, interpersonal skills, stress management, and overall emotional intelligence. These effects ranged from moderate to large in magnitude and contrasted with the stable scores in the control group. The findings confirm that psychoeducational interventions focused on emotional competencies can be effective in strengthening emotional intelligence and self-efficacy in pre-service teachers. These outcomes suggest that such programs may contribute to the promotion of well-being and teaching effectiveness during initial teacher education, offering implications for future curricular development in teacher training programs. Full article

Objective: To analyze the relationship between weekly accumulated external load and pre-match neuromuscular performance assessed through the countermovement jump (CMJ), in under-21 (U21) football players across 10 competitive microcycles. Methods: Sixteen U21 football players (age: 18.9 ± 0.42 years; height: 180 ± 6.3 cm; body mass: 78.5 ± 8.5 kg) from a Chilean professional club were monitored over 10 consecutive weeks. In each microcycle, the relationship between changes in neuromuscular performance estimated from CMJ-derived variables and two components of external load was analyzed: (1) weekly accumulated external load and (2) the acute–chronic workload ratio (ACWR). External load variables included total distance (TD), high-speed running distance (HSR), accelerations (ACC), decelerations (DC), and PlayerLoad (PL). CMJ variables included jump height (JH), modified reactive strength index (RSI-mod), and peak eccentric velocity (PEV). Performance changes were calculated as the percentage change (Δ%) between MD + 2 (start of the microcycle) and MD − 1 (pre-match). Pearson or Spearman correlation coefficients were applied depending on data distribution. Results: Significant negative associations were observed between weekly accumulated external load and changes in CMJ performance. Reductions in JH were associated with TD, HSR, ACC, and PL. Similar patterns were found for RSI-mod, while PEV showed a particularly strong association with ACC. Additionally, ACWR demonstrated significant negative relationships with CMJ changes, especially for HSR, ACC, and PL. Conclusions: Higher weekly accumulated external loads and elevated ACWR, particularly in high-intensity metrics such as high-speed running and accelerations, are associated with impaired pre-match neuromuscular performance. Consequently, monitoring CMJ-derived variables alongside external load data is recommended to manage fatigue and optimize match readiness in young football players. Full article

The rapid institutional and technological transformations of the 2020–2025 period have had a significant impact on teacher professional identity. Drawing on Rosa’s social acceleration thesis and Harvey’s concept of time–space compression, this scoping review examined the dimensions of professional identity emerging in the literature published between 2020 and 2025 among in-service pre-kindergarten through 12th grade (PK-12) teachers, the educational contexts in which these dimensions were addressed, and how they interrelate. Following the PRISMA-ScR guidelines, 45 peer-reviewed articles retrieved from the Scopus and Web of Science databases were analyzed through inductive thematic coding and a dimension–context interaction matrix. Six analytically distinct yet interrelated identity dimensions were identified: Biographical and Personal, Professional and Pedagogical, Emotional and Psychological, Social and Relational, Political and Agentic, and Prospective and Imagined. These dimensions were organized within a dialogical space model distinguishing internal/individual and external/structural domains. The Emotional and Psychological dimension achieved near-universal representation, while the Prospective and Imagined dimension remained the least studied. Six convergence, five divergence, and six gap patterns were identified across seven educational contexts. The findings reveal that, in this period, teacher professional identity is not a fixed attribute carried by the individual but rather a dynamic process continuously negotiated under structural pressures. Full article

This research focuses on creating a real-time facial emotion recognition system for smart glasses designed for visually impaired users. By adapting a contextually adaptive attention mechanism (CBAM) in a lightweight classification header with a pre-trained deep learning model, we obtain a model capable of successfully predicting emotions from facial features. The model will be complemented by a comprehensive preprocessing pipeline that includes face detection and alignment, standard normalization, and data augmentation for underrepresented classes. The model was trained on a merged benchmark dataset (FER24, RAF-DB, CK+) and evaluated across basic emotion classes: surprise, happiness, disgust, fear, sadness, neutral, and anger. Two models were compared: FaceNet–CBAM and EmoFormer–CBAM (a ViT-Base model enhanced with a 1D-CBAM attention module). EmoFormer–CBAM achieved 98% and ~72% test accuracy on new dataset (CleanFER25_RAF_CK) and AffectNet, respectively. In addition, a small set of external real-world images is used as a pilot qualitative evaluation to assess robustness under unconstrained conditions. A detailed analytical study of both models was performed to determine the impact of their structural components on overall performance using the available data. Based on the results, the most successful model under all conditions, EmoFormer–CBAM, was selected as the prototype for the smart glasses for the visually impaired. The necessary mechanisms for future deployment and implementation of the smart glasses prototype for the target users were also studied, in accordance with the ethical approvals previously obtained from Gazi University in Türkiye. Full article

Background and Objective: Previous studies have shown that muscle mass and visceral fat are interrelated and affect metabolic health. However, there is limited research exploring machine learning (ML) models that can help us understand the relationship between muscle mass and the risk of adiposity in the adult population. The objective of this study was to identify predictors of obesity on the basis of data from 13,663 adults assessed via body composition analysis via optimal and interpretable ML algorithms. Methods: A cross-sectional design was used to analyze data from 13,663 adults, comprising men (n = 6877) and women (n = 6786). The variables were obtained via 8-point multifrequency BIA under standardized clinical protocols with an Inbody^® Model 770 device validated for the adult population. To illustrate the interaction between body composition components, a probability heatmap was generated on the basis of the values predicted from the logistic model. The decision boundary was defined via the metabolic risk probability gradient, allowing visualization of the two-dimensional transition between low- and high-risk states. Statistical processing and figure generation were performed via Python software v.3.10. Results: The evaluation of the 10 algorithms demonstrated exceptional predictive performance, with the multilayer perceptron (MLP) standing out as the superior model in both sexes. The AUC-ROC was 0.981 for men and 0.993 for women, with F1 scores of 0.912 and 0.969, respectively. Overall, systematically higher accuracy was observed in the female cohort, exceeding 95% accuracy in most models. Conclusions: Muscle mass has been shown to act as a metabolic mediator, modulating and reducing the risk associated with visceral adiposity. It also concludes that the use of ML algorithms, specifically neural networks, is a good model for analyzing the risk associated with excess visceral fat. Full article

The rapid proliferation of generative artificial intelligence (GenAI) tools has created an urgent need for instruments to evaluate their educational value as teachers, faculty, administrators, and instructional designers consider adopting them. While rubrics exist to assess mobile applications and virtual reality tools, no comparable instrument has been developed specifically for large language models (LLMs) and AI media generators. The authors reviewed existing evaluation rubrics for edtech and GenAI tools, with edtech meaning digital tools that support ethical teaching to improve student learning and GenAI referring to neural networks that simulate human interactions by contextualizing relevant content based on learning needs. Grounded in Waks’ framework, the resulting Edu-GenAI Rubric comprises multiple dimensions organized into five domains: the Instrumental, Technical, Hedonic, Use, and Beneficial values. Dimensions include accuracy, productivity, personalization, citation, user interface, user experience, sharing, storage, and ethical dimensions encompassing data privacy, data transparency, guardrails, fair use, and algorithmic discrimination. The Edu-GenAI Rubric offers decision-makers with a preliminary, theory-informed instrument for evaluating GenAI tools in educational contexts that can be applied to institutional adoption decisions, developer benchmarking, and future research. Full article

Background: Regulatory restrictions and mandates typically offer short-term behaviour guidance, whereas interventions to improve knowledge and attitudes could result in more sustainable behavioural changes. Health authorities implemented awareness campaigns to enhance public knowledge and attitudes regarding COVID-19. This study explored the interplay between knowledge, attitudes and behaviours related to COVID-19 among university undergraduate students in Jordan, aiming to inform public health initiatives and educational programmes. Methods: A cross-sectional survey targeting undergraduate students enrolled at Yarmouk University in Jordan was conducted between January and May 2021. Participants consented to complete an anonymised validated self-administered questionnaire to evaluate their understanding of COVID-19 symptoms, treatment and transmission and attitudes and behaviours towards preventive measures. Data were analysed using descriptive and inferential statistics and structural equation modelling to investigate the associations between knowledge, attitudes and behaviours. Results: A total of 1375 undergraduate students participated in the survey. Knowledge of COVID-19 was low among most participants, with only 1.3% demonstrating high knowledge. Conversely, 58.5% exhibited good behaviour, and 31.4% reported full compliance with recommended behaviours. Significant differences were found in knowledge, attitudes and behaviours across different faculty clusters, with health faculties showing superior knowledge and more positive attitudes. Female participants (66.3%) were more likely to engage in positive behaviours than males (p-value = 0.02). Structural equation model (SEM) analysis showed that knowledge significantly influenced attitudes, which affected behaviours, confirming the model’s validity. Conclusions: The study highlights the critical role of knowledge and attitudes in shaping COVID-19-related behaviours among university students. Significant variations in knowledge and attitudes across different academic disciplines highlight the need for tailored educational interventions. The analysis supports the theoretical model linking knowledge, attitudes and behaviours, emphasising the importance of improving knowledge and attitudes to drive behaviour change. The findings suggest that comprehensive health education programmes targeting cognitive and affective aspects are essential for effective public health responses during pandemics. Full article

In the Mediterranean region, for over 150 species belonging to the subtribe Orchidinae, chromosome number has been documented and found to range from 2n = 32 to 2n = 42. This work renews and updates chromosome numbers and reports a karyomorphological comparison between species with 32 or 36 chromosomes and species with 40 or 42 chromosomes. Notably, in the 32,36-chromosome group, species within the genus Anacamptis s.l. show substantially similar karyomorphology, although some species exhibit differences in chromosome structure and heterochromatin distribution. In contrast, in the 40,42-chromosome group, the chromosomes are comparatively smaller and therefore difficult to sort into karyotypes when standard staining techniques are used. However, the two groups display distinct heterochromatin patterns, particularly in centromeric and telomeric regions. Given the growing body of information in this field, a review of current cytogenetic knowledge is warranted. In this report, the authors present classical and molecular cytogenetic data and highlight important aspects of karyotypic evolution in Orchidinae. Traditional karyotypic analysis, based on stained mitotic chromosomes, can be used to distinguish and identify taxonomic groups. Karyomorphometric studies in particular reveal subtle differences between closely related chromosome sets within Orchidinae. Furthermore, wide variation among genera in terms of heterochromatin content was observed. Further comparative data between the two above-mentioned groups are summarized. Full article

The reorganization of academic work following the COVID-19 pandemic has intensified debate regarding the effects of work modality on faculty well-being, particularly in Latin American contexts characterized by structural inequalities and digital divides. This study examines the influence of work modality on the integrated well-being of university faculty in Peru. A quantitative, non-experimental explanatory design was employed with a sample of 448 faculty members from public and private universities. Occupational well-being and quality of life were assessed using validated instruments and subsequently integrated into a higher-order construct due to the absence of discriminant validity. Structural relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping (5000 resamples). The results indicate that work modality significantly predicts integrated well-being (β = 0.823; p < 0.001), although the model explains a limited proportion of variance (R² = 0.168). Comparative analysis revealed that faculty in in-person modality report significantly higher levels of well-being and quality of life than those in virtual modality. These findings suggest that work modality operates as a structural condition shaping faculty well-being and highlight the need for institutional policies that promote balanced and human-centered work designs in higher education. Full article

In this quantitative study, we investigated the conditional mediating role of students’ mathematical language self-efficacy in the relationship between self-regulation and mathematics anxiety. The study employed a relational research design and included survey data from 706 middle school students attending public schools in Turkey. Findings indicated that both self-regulation and perceived self-efficacy in mathematical language use were significantly associated with mathematics anxiety. Moreover, the effect of self-regulation on mathematics anxiety was significantly mediated by students’ perceptions of their ability to understand and use mathematical language self-efficacy. The indirect effect was negative while the direct effect was positive, indicating a suppression (competitive mediation) effect, whereby self-regulation exerts both anxiety-reducing and potentially anxiety-inducing influences through different pathways. Conditional mediation analysis further revealed that this mediating effect varied as a function of students’ perceived academic support, with the indirect effect being non-significant for students who did not receive support. Measurement invariance across gender and grade level was examined to ensure that the constructs were measured equivalently across groups. These findings highlight the importance of fostering both self-regulation skills and mathematical language proficiency, particularly in contexts where students may lack sufficient support. These findings provide a theoretically grounded and practically relevant framework for understanding mathematics anxiety within inclusive mathematics education contexts. Full article

This study develops an effective numerical method for addressing the time-fractional gas dynamics equation formulated with the Caputo time-fractional derivative. Novel basis functions are utilized, formulated as particular generalized Fibonacci polynomials contingent on a free parameter. This family generalizes the second kind of Chebyshev family. For the proposed polynomials, we establish basic analytical properties, including closed-form series expansion, inverse relation, moment and linearization formulas, and operational matrices for both integer-order and Caputo fractional derivatives. Using these tools, the fractional model, together with its underlying conditions, can be transformed into a finite system of nonlinear algebraic equations via a collocation strategy. Using Newton’s iterative method, the resulting system can be treated. A full convergence analysis of the double generalized Chebyshev expansion is provided. We demonstrate the accuracy and reliability of the presented method through several numerical simulations. Comparisons with existing numerical methods show that this approach achieves higher accuracy and faster execution. Full article

The rapid expansion of Generative Artificial Intelligence (GAI) is transforming higher education systems, particularly public institutions seeking to advance toward smart governance models and digital transformation. In this context, digital teaching competence emerges as a strategic factor for the effective, ethical, and pedagogically sound adoption of these technologies. This study assesses the level of digital competence among public higher education faculty in Paraguay and examines its predictive capacity regarding the adoption of GAI tools using machine learning models. A nationwide quantitative study was conducted with a sample of 800 faculty members from public universities across Paraguay. Data were collected through a structured questionnaire based on international digital competence frameworks, incorporating additional variables such as attitudes toward GAI, technological experience, institutional infrastructure, and perceived organizational support. Data analysis involved the application of machine learning techniques, including Logistic Regression, Random Forest, and Gradient Boosting, to identify the variables with the strongest predictive power regarding faculty readiness and willingness to integrate GAI into teaching practices. Model performance was evaluated using metrics such as accuracy, F1-scores, and the AUC-ROC. The findings identify key predictors of technological readiness and structural gaps within Paraguay’s public higher education system. This research provides empirical evidence from Latin America on the factors influencing GAI adoption in public sector educational contexts and contributes to the design of educational policies aimed at fostering smart universities and digitally sustainable academic ecosystems. Full article