Search Results (7,111)

Course selection presents a persistent challenge for students who often have difficulty articulating clear goals, integrating multiple considerations, and aligning academic choices with personal and professional aspirations. This study investigates whether concept mapping, as a metacognitive scaffolding tool, may shape how students interact with Generative AI (GenAI) systems during academic decision-making. In a randomized controlled experiment, 180 undergraduates at a polytechnic university in China were assigned to either a GenAI-only condition or a GenAI + Concept Map condition. After excluding 3 outlier participants, 177 students were included in the final analysis. Controlling for prior academic performance via ANCOVA, students with concept-map support showed different interaction patterns: they had a longer maximum consecutive-question chain within a session (GPA-adjusted means: 11.92 vs. 9.07 questions), formulated longer questions (15.27 vs. 11.93 words), and spent more time per conversation session on average (8.05 vs. 6.77 min). An analysis of conversation content showed that the concept-map group discussed a wider range of course selection factors (covering 4.46 vs. 3.66 main dimensions and 8.70 vs. 6.36 detailed factors). Epistemic Network Analysis further suggested that concept-map users linked different factors more frequently in their conversations, connecting academic requirements with career development, intrinsic interests, and external recognition in their discourse. Notably, these group differences remained after controlling for GPA in the ANCOVA models. These findings suggest that metacognitive scaffolding may reshape the way students engage with GenAI, with concept-map users shifting from brief exchanges to extended conversations covering multiple integrated factors related to their academic choices. Full article

Despite the growing participation of women in higher education worldwide, they continue to face persistent challenges in their career advancement, including limited promotion opportunities, underrepresentation in leadership positions, lower research productivity, and unequal access to institutional resources. These challenges are shaped by a range of structural, institutional, and socio-cultural constraints within academia. Understanding these influencing factors is essential for promoting gender equity within universities. This study investigates the factors influencing the career advancement-related challenges experienced by women academics in Jordanian higher education institutions, focusing on career experience, family responsibilities, and organisational support. Grounded in Gendered Organisations Theory, Work-Family Conflict Theory, and Social Support Theory, the study develops and empirically tests an integrated conceptual model. Data were collected through a questionnaire survey of women academics across Jordanian universities. The quantitative data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), while qualitative responses on strategies for overcoming challenges were examined using directed qualitative content analysis. The findings indicate that family responsibilities represent the most influential driver of perceived challenges, highlighting the continuing tension between professional and domestic roles. Career experience is found to reduce perceived challenges, suggesting that accumulated professional capital and institutional familiarity enhance women’s ability to navigate academic environments. Organisational and social support not only directly reduce perceived challenges but also buffer the impact of family responsibilities. Multi-group analysis further reveals differences in the strength of these relationships between teaching-research academics and those occupying leadership roles. The qualitative results identify key strategies for addressing these challenges, including mentoring systems, flexible institutional policies, professional networking, and leadership development initiatives. By integrating structural modelling with qualitative insights, this study advances understanding of the complex dynamics shaping women’s academic careers and provides evidence-based recommendations for fostering more inclusive and supportive higher education environments. Full article

Hyperspectral image classification remains challenging because robust recognition requires preserving spatial–spectral coupling, extracting complementary spectral and spatial cues, and fusing heterogeneous features without excessive redundancy. To address this issue, a dual-branch convolutional neural network (CNN) with depthwise separable fusion, termed DSFA-CNN, is developed. The network combines a 3D convolution branch for coupled spatial–spectral representation learning with a 1D+2D branch for efficient spectral and spatial modeling. A convolutional block attention module (CBAM) is introduced in the decomposed branch to emphasize informative spectral responses and salient spatial regions, and a depthwise separable fusion module is used to improve cross-branch integration while limiting fusion-stage redundancy and the risk of overfitting. Experiments on Indian Pines, University of Pavia, Salinas, and Houston2013 yield overall accuracies of 95.62 ± 0.13%, 99.25 ± 0.13%, 99.89 ± 0.11%, and 97.62 ± 0.23%, respectively. The gains are most evident on the more challenging Indian Pines and Houston2013 scenes. Ablation results show that the dual-branch design provides complementary information, whereas CBAM and the fusion module further improve representation selectivity and feature integration. Computational cost analysis further indicates that DSFA-CNN achieves a more favorable trade-off between classification accuracy and computational efficiency than several recent competitive baselines. These results demonstrate the effectiveness of parallel coupled–decomposed modeling with efficient feature fusion for robust hyperspectral image classification. Full article

High-precision satellite clock offset prediction is a core prerequisite for the BeiDou-3 Global Navigation Satellite System to achieve precise single-point positioning and timing. However, because of space radiation and the physical aging of the clock itself, the operational state of onboard atomic clocks exhibits a high degree of physical heterogeneity and time-varying drift characteristics. Traditional physical models struggle to capture complex nonlinear residuals, while existing deep learning methods often face boundary discontinuities caused by baseline separation when handling long-sequence forecasts. Furthermore, channel crosstalk in multivariate prediction and insufficient sensitivity to dynamic multiscale features limit the robustness of long-term predictions. To address these issues, this paper proposes a clock offset prediction architecture that integrates physically motivated statistical constraints with dynamic adaptive feature learning. Extensive experiments conducted using real BDS-3 precise clock difference products provided by Wuhan University demonstrate that the proposed method effectively mitigates the performance degradation often observed in existing models on heterogeneous satellites during the evaluated period. In the 24-h extrapolation task, the architecture achieved an average root-mean-square error as low as 0.507 ns, significantly improving prediction accuracy. It outperformed mainstream physical models and advanced deep learning baseline algorithms, providing a promising framework with good interpretability for high-precision clock error forecasting under dynamic space weather conditions. Full article

This study aimed to describe the profile of professional collaboration among students in teacher-education programmes and to examine whether readiness for further collaboration is associated with prior experience. The work was conducted within a university grant at XXXXXXXXX and implemented as a descriptive–comparative study based on a posttest cross-sectional snapshot with no baseline measurement. Data were collected via Google Forms from 91 students of the Department of Primary Education (Years 1–4). The questionnaire covered collaboration experience (yes/no), frequency of participation in joint projects, perceived value of collaboration (1–5), perceived impact on the learning experience, readiness to participate in joint research/projects in the future (1–5), and open-ended questions on motivations, barriers, and expected university support. Prior collaboration experience was reported by 40.7% of respondents; participation was predominantly irregular (48.4% “never”, 44.0% “rarely”). Perceived value was high (M = 4.05, SD = 1.15; Me = 4), and most respondents reported a positive contribution to their learning experience (75.8%). Readiness for future participation was moderately high (levels 4–5: 52.7%). A Mann–Whitney test indicated higher readiness among students with prior collaboration experience (U = 1288, p = 0.016, r = 0.29). Thematic grouping of open-ended responses showed that knowledge sharing and mutual support were the dominant motivations, while organisational/time and communication barriers were most frequently mentioned; the most commonly requested support measures included regular joint events and support for student communities. Findings are interpreted as a descriptive snapshot rather than causal evidence. The results may inform the design of facilitated collaboration formats and subsequent monitoring of student readiness. Full article

Background: Dental caries remain a major public health issue among Italian children, with prevalence exceeding 60% in specific subgroups and marked socioeconomic gradients. Objectives: This multicenter study aimed to describe baseline caries experience, malocclusions, and oral hygiene status in pediatric populations residing in three Italian regions and to develop and preliminarily evaluate the feasibility of an integrated care pathway for the prevention and management of caries and malocclusions. Materials and Methods: Within the CCM 2024 program (ID 10), a cross-sectional baseline assessment was conducted on 795 children aged 6–11 years, examined in school settings and via mobile dental units. Caries experience was assessed using the dmft/DMFT indices and International Caries Detection and Assessment System (ICDAS) criteria. Malocclusions were evaluated using the Index of Orthodontic Treatment Need (IOTN). Oral hygiene was assessed through standardized clinical indices. The proposed care pathway comprises three tiers: (1) universal, school-based oral health education; (2) targeted clinical preventive and interceptive interventions; and (3) telemedicine/AI-supported follow-up for high-risk children. Descriptive and multivariable statistical analyses were performed. Results: At baseline, overall caries burden was low. No statistically significant differences in dmft/DMFT were observed between males and females. A non-significant trend toward higher caries indices was found among children with a positive breastfeeding history. By contrast, oral hygiene level was strongly associated with caries indices: children with insufficient hygiene had the highest dmft/DMFT, those with moderate hygiene showed intermediate values, and those with optimal hygiene presented the lowest caries experience. In multivariable models, oral hygiene emerged as the main independent predictor of dmft/DMFT. Conclusions: In this low-caries cohort, oral hygiene was confirmed as the principal modifiable determinant of caries risk. A tiered, school- and community-based care pathway focused on hygiene promotion, early screening, and minimally invasive clinical interventions appears feasible at baseline and may be scalable, with the aim of reducing the burden of caries and malocclusions and improving equity in pediatric oral health. Full article

While the use of artificial intelligence (AI) in higher education is widespread, students’ experiences with AI-supported learning in their regular courses remain underexplored. Objective: This research examines the relationships among perceived usefulness, perceived ease of use, and academic engagement among undergraduate students enrolled in AI-supported courses at a Taiwan university. It adopts the Technology Acceptance Model, where learning desire indicates perceived usefulness, and technology self-efficacy indicates perceived ease of use. Methods: The study takes a questionnaire with six dimensions of technology self-efficacy, learning desire, learning methods, learning planning, learning habits, and learning process to evaluate students’ attitudes toward AI-supported learning and their academic engagement. Results: Students’ attitudes toward AI-supported learning were moderate to positive. Multiple regression analysis showed that perceived usefulness was significantly and positively associated with academic engagement, whereas perceived ease of use showed a positive but non-significant association. Implications: Students’ academic engagement is influenced more by how useful AI tools are perceived for learning, rather than by their confidence in using AI tools. This paper enriches the literature on student-centered AI in higher education and gives insights for designing AI-supported courses that integrate AI tools with meaningful learning tasks. Future research can examine larger and more diverse samples and use longitudinal or experimental designs to test how students’ perceptions of AI tools develop over time. Full article

Background/Objectives: Mental health problems are common among university students and are more consistently associated with dissatisfaction with academic performance than with low grades alone. This study examined the prevalence and determinants of perceived stress, depressive symptoms, and low psychological well-being among university students in Punjab, Pakistan, and assessed their association with academic performance. Methods: A cross-sectional survey was conducted among students aged 15 to 29 years at three public universities in Punjab, Pakistan. A total of 1308 questionnaires were completed, yielding a response rate of 91.4%. This study uses data collected in 2015 as a pre-COVID historical baseline, providing valuable insights into student mental health before the global pandemic. This temporal context offers a benchmark for future comparative studies, especially when assessing the mental health impact of COVID-19 on university students. Data were analyzed using SPSS with descriptive statistics, chi-square tests, binary logistic regression, and multinomial logistic regression. Results: The findings revealed that perceived stress and depressive symptoms were prevalent, with 54.9% of students reporting high levels of stress (mean PSS score = 27.6, SD = 8.3), and 44.2% experiencing depressive symptoms (mean M-BDI score = 33.8, SD = 16.2). Female students exhibited higher stress and depressive symptoms compared to male students. Year of study was also a factor, with second- and third-year students experiencing more stress than their final-year counterparts (p < 0.05). Financial strain was associated with poorer mental health outcomes; 62% of students who reported inadequate financial support also reported higher stress levels (p < 0.05). In contrast, students with sufficient financial resources had lower odds of experiencing stress and depressive symptoms (AOR = 0.55, p < 0.05). Additionally, students living in university or private hostels reported better psychological well-being than those living at home (AOR = 0.47, p < 0.01). Mental health issues, particularly high stress and depression, were more strongly linked with academic dissatisfaction than low grades alone, with students in the “low grades and unsatisfied” group exhibiting higher odds of mental health problems (AOR = 2.30, p < 0.05). Conclusions: Mental health problems were common among university students and were associated with poorer academic experiences, particularly dissatisfaction with academic performance. Universities should strengthen accessible mental health support through counseling services, stress-management programs, and stigma-reduction initiatives. Full article

Period poverty, defined as difficulty affording menstrual products, is increasingly recognized as a basic needs issue among students in the United States. However, evidence on the prevalence and awareness of this phenomenon among both undergraduate and graduate populations remains limited. Therefore, the aim of this descriptive cross-sectional study is to describe period poverty experiences and awareness levels among menstruating college students at a public university in South Florida. An online survey was administered to menstruating undergraduate and graduate students (n = 151). Period poverty was assessed using a past-year affordability question, while awareness of period poverty was measured descriptively through seven items derived from a previous study on period poverty in U.S. college students. Overall, 13.9% of respondents reported past-year period poverty. Awareness of period poverty was limited, despite high support for policies providing free menstrual products. Only 16.67% perceived period poverty to be highly prevalent in developed countries, and only 8% believed that it existed in their local area. Three fourths (75.00%) of the sample strongly supported policies to provide free menstrual products. Finally, over half of the respondents felt “not at all embarrassed” (55.07%) towards buying menstrual products, while just over one fourth reported being “fairly embarrassed” (28.26%). The discrepancy between the number of students experiencing period poverty and the levels of awareness of the issue shows a clear need for evidence-based educational interventions and menstrual resources on college campuses to improve overall menstrual well-being. Full article

(1) Background: Problematic social media use has increasingly been conceptualized as a non-clinical addictive-like behavior characterized by impaired control and negative functional consequences. Despite the rapid growth of TikTok and its algorithm-driven content delivery, the contribution of individual psychological factors and users’ awareness of algorithmic processes to addictive symptoms remains insufficiently understood, particularly in Latin American contexts. This study examined the associations between personality traits, algorithmic awareness, and addictive symptoms of TikTok use among university students. (2) Methods: A quantitative, cross-sectional design was conducted with a convenience sample of 238 university students from Ecuador. Participants completed self-report measures of social media addiction, algorithmic media content awareness, and Big Five personality traits. Spearman correlations and hierarchical multiple regression analyses were performed, controlling for age and sex. (3) Results: Algorithmic awareness dimensions were not significant predictors of addictive symptoms. Demographic variables explained minimal variance, whereas personality traits accounted for the largest increase in explained variance in the final model. Neuroticism and Extraversion were positively associated with addictive symptoms, while Conscientiousness and Openness to Experience were negatively associated. (4) Conclusions: Personality traits were more informative than algorithmic awareness in explaining addictive-like TikTok use among university students, underscoring the relevance of self-regulatory and affective dispositions for prevention and intervention strategies. Full article

Quadrupedal wheel-legged robots possess exceptional mobility in complex terrains, but their robust locomotion control is severely hindered by the difficulty of accurate state estimation without external sensors. Existing reinforcement learning methods relying on two-stage imitation often suffer from representation collapse and information loss during sim-to-real transfer. To address these challenges, this paper proposes a novel end-to-end reinforcement learning framework for implicit state estimation, incorporating terrain and external force features. Inspired by internal model control, the proposed method leverages a history of purely proprioceptive observations to extract explicit kinematic responses, as well as implicit environmental and external force representations via prototypical contrastive learning, completely circumventing explicit terrain regression and the need for physical force sensors. Furthermore, a tailored composite reward function and a progressive curriculum training strategy with large-scale domain randomization are integrated to ensure dynamic stability and hardware safety. Extensive cross-simulator validations and real-world deployments demonstrate that the approach achieves highly agile and robust locomotion, including adaptive traversal over diverse terrains. Experiments show that the method significantly enhances robustness under external disturbances, notably reducing the lateral linear velocity tracking error from 0.2421 m/s to 0.1319 m/s. The proposed method realizes zero-shot sim-to-real transfer with superior sample efficiency, providing a reliable and universal control paradigm for wheel-legged robots in unstructured environments. Full article

Using attribution-based priors to improve the temporal interpretability and robustness of dynamic neural networks provides a computationally efficient method that does not alter the model structure during inference. We explore explanation-guided training for timeseries classification through the introduction of attribution-sensitive loss terms that serve as regularizers for the evolution of input relevance over time. The main contributions are the Temporal Relevance Smoothness Index (TRSI) and a ratio-based loss that reduces irregular step-to-step changes in channel-aggregated absolute relevance. TRSI is compared against temporal total-variation penalties computed using Layer-wise Relevance Propagation Total Variation (LRP-TV) and Integrated Gradients Total Variation (IG-TV). Experiments on a controlled three-class subset of the Korean University Human Activity Recognition (KU-HAR) dataset using a finite impulse response neural network (FIRNN) show that TRSI yields the strongest smoothness improvement, reducing the total variation of the aggregated relevance signal from 0.768 to 0.447 (41.8%), compared with 0.667 (LRP-TV) and 0.677 (IG-TV). Robustness tests indicate a clear advantage for TRSI under impulsive and white Gaussian test-time noise. Full article

Membrane roofs with saddle geometry are widely used in stadiums and public facilities that are highly exposed to rainfall. However, current design practice typically considers rainfall only in terms of seepage effects, drainage requirements, or static stability checks, while the influence of extreme rainfall on dynamic behavior and prestress loss has not been comprehensively quantified. In this study, the behavior of a restored engineering-scale saddle-shaped membrane roof under three representative rainfall intensities (50, 300, and 550 mm/h) is investigated through combined laboratory experiments (span L = 2.52 m) and numerical simulations, with particular emphasis on how supporting conditions and pretension levels affect vertical displacement, vibration propagation, and rainfall-induced edge-cable pretension loss. The findings are intended to reveal response mechanisms and trends, while quantitative extrapolation to full-size roofs should be conducted with scaling considerations. The numerical model is validated against the experimental results through comparisons of cable forces and vertical displacements. The results indicate that while the maximum vertical displacement induced by heavy rainfall is small (millimeter-level) and does not cause immediate failure, the rainfall event induces a significant permanent loss of pretension (a maximum observed relaxation of 10.4% in the edge cables for the tested specimen) in the edge cables. This relaxation degrades the structural stiffness, potentially compromising aerodynamic stability under subsequent wind events. Consequently, for the tested configuration, post-rainfall pretension inspection is recommended for events exceeding 300 mm/h, with retensioning suggested if significant tension loss is detected. This recommendation should be interpreted as an indicative engineering reference for the present specimen rather than a universal criterion for all saddle membrane roofs. Full article

Background: A significant percentage of patients with major depressive disorder (MDD) fail to achieve remission with antidepressant monotherapy and frequently experience residual mood and cognitive symptoms that impair their functional recovery. Thus, an augmentation with vortioxetine, a multimodal antidepressant with reported cognitive benefits, might be a useful strategy for such patients. Methods: We conducted a 12-week naturalistic, prospective observational study in a Malaysian university hospital; 40 adults with MDD and inadequate response to at least eight weeks of antidepressant therapy received either adjunctive vortioxetine or optimization of their existing antidepressant as part of treatment-as-usual care. Depressive symptoms were assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS), cognitive symptoms using the Perceived Deficits Questionnaire-5 (PDQ-D5), and global improvement using the Clinical Global Impressions—Improvement (CGI-I) scale. Results: Both groups demonstrated significant improvements in MADRS and PDQ-D5 scores over 12 weeks (p < 0.001). Remission rates at Week 12 were high in both groups (93.8% adjunctive vortioxetine vs. 86.7% control). Both groups demonstrated significant improvements in depressive and cognitive symptoms over 12 weeks. Although between-group differences were not statistically significant, descriptive trends toward earlier symptomatic improvement were observed in the adjunctive vortioxetine group in several core depressive symptoms, including apparent sadness, suicidal ideation, and appetite disturbance. Greater clinician-rated global improvement was observed in the vortioxetine group at Week 12 (87.5% vs. 40.0%, p < 0.001). Conclusions: In this outpatient clinical setting, adjunctive vortioxetine was associated with earlier improvement of core depressive symptoms and greater global clinical improvement compared with optimization of existing antidepressant therapy. Collectively, these findings suggest adjunctive vortioxetine as a clinically relevant option for patients with MDD who show an inadequate response to antidepressant monotherapy; however, findings are exploratory and not causal, and thus larger RCTs are needed for affirmation. Full article

Poor seed germination severely limits the propagation and conservation of Zelkova schneideriana (Chinese zelkova). However, the comparative effects of different exogenous phytohormones on seed germination of this species and the associated molecular responses remain insufficiently understood. To evaluate the effects of exogenous phytohormones on seed germination and to explore the underlying molecular basis, a germination experiment was conducted from January to March 2024 at Central South University of Forestry and Technology, Changsha, Hunan, China, in which seeds were treated with different concentrations of 6-benzylaminopurine (6-BA; 20, 40, and 80 mg/L), gibberellic acid (GA₃; 125, 250, and 500 mg/L), indole-3-acetic acid (IAA; 100, 200, and 300 mg/L), brassinolide (BR; 10, 20, and 30 mg/L), and abscisic acid (ABA; 50, 100, and 150 mg/L). Germination traits were assessed, and transcriptome sequencing was performed for the BR treatment showing the strongest promotive effect. The results demonstrate that exogenous hormones exerted distinct regulatory effects on seed germination, among which BR at 10 mg/L showed the strongest promotive effect, increasing the final germination rate at 40 d from 50% in the control to 68%, whereas higher concentrations caused inhibitory effects. Transcriptome analysis identified 169 differentially expressed genes between BR-treated seeds and the control, mainly associated with reactive oxygen species (ROS) metabolism, redox regulation, energy and carbohydrate metabolism, and plant hormone- and MAPK-related signaling pathways. Antioxidant enzyme assays showed that BR10 increased POD activity but decreased SOD, CAT, APX, and GR activities. Endogenous hormone-related analysis further revealed marked BL accumulation and significant decreases in ACC, GA₃, GA₄, IAA, JA, and SA. Overall, exogenous BR promotes seed germination of Z. schneideriana through coordinated physiological and molecular regulation, providing a useful basis for seed pretreatment and seedling propagation. Full article