Search Results (5,094)

Artificial intelligence (AI) has become increasingly embedded in technology-enhanced learning environments, where peer assessment now serves both instructional and analytic purposes. Beyond allocating feedback and grades, it also produces data that is later interpreted through learning analytics systems. In practice, visible indicators such as students’ fairness perceptions and the degree of agreement among peer raters are often treated as signs that the assessment process is functioning effectively. However, these indicators do not necessarily correspond to grading validity. Students may regard a peer assessment process as fair even when peer-generated ratings remain weakly aligned with expert judgement. This study, therefore, examines whether the socio-technical configurations associated with high perceived fairness in a digitally mediated peer assessment environment also correspond to criterion-referenced grading validity. Data were collected from 215 undergraduate students enrolled in an Artificial Intelligence Foundations course over two consecutive semesters at a university in Taiwan, with instructor ratings serving as an external expert reference within the course context, rather than as a universal ground truth. Because anonymity conditions and semester were fully confounded in the study design, differences linked to anonymity should not be interpreted as isolated causal effects. A two-stage fuzzy-set Qualitative Comparative Analysis (fsQCA) was used. In the first stage, three equifinal configurations associated with high perceived fairness were identified. In the second stage, these configurations were examined against four grading objectivity outcomes: peer–instructor alignment, peer convergence, familiarity bias, and leniency bias. The findings show that fairness perception and grading validity are only partially aligned. Configurations anchored in explicit criterion transparency consistently supported both experiential legitimacy and evaluative accuracy. By contrast, one configuration was associated with high peer convergence while remaining weakly aligned with instructor standards, a pattern described here as false objectivity; this context-dependent configurational finding warrants further investigation across other settings. The study contributes to research on digitally enhanced assessment and learning analytics by showing that fairness perception, peer convergence, and grading validity should be treated as analytically distinct dimensions of assessment quality. Full article

The purpose of this study was to examine the multivariate relationship between college students’ Big Five personality traits and their perceptions of generative artificial intelligence (AI). Guided by sustainable digital education and expectancy-value theory, this study investigated whether personality profiles were associated with students’ knowledge of AI, attainment value, intrinsic value, utility value, perceived cost, and intention to use AI. Using a cross-sectional survey design, data were collected from 375 students enrolled at a Southwestern doctoral-granting public university. Participants completed an adapted measure of generative AI perceptions and the Big Five Inventory, and canonical correlation analysis (CCA) was conducted to examine the multivariate relationship between the two variable sets. The results indicated that the full canonical model was statistically significant and that three interpretable canonical functions were retained. The first and strongest function showed that higher openness, agreeableness, and conscientiousness were associated primarily with greater AI knowledge and, to a lesser extent, with higher perceived cost. The second function indicated that higher neuroticism was associated with greater perceived cost and lower utility and attainment value. The third function showed that lower neuroticism, together with higher openness and conscientiousness, was associated with a stronger attainment value, greater intention to use AI, and lower perceived cost. Our findings suggest that students differ meaningfully in how they understand and value generative AI. These results have important implications for higher education because they highlight the potential value of differentiated, human-centered AI literacy efforts in supporting more equitable and responsible AI integration. Full article

Against the backdrop of global industrial sustainable transition and the advancement of UN Sustainable Development Goals (SDGs), higher education―a core carrier of sustainable human capital supply―plays a pivotal role in adjusting majors to meet labor market demands, resolving education–industry structural mismatch, and boosting regional sustainable development. From the perspective of “higher education supporting industrial sustainable transition,” this study explores how government Policy Mix Intensity enhances universities’ Major–Industry Alignment and its transmission mechanism, aiming to reveal higher education governance’s sustainable development path. Using panel data from 30 Chinese provinces (2012–2023), we constructed a PMI quantitative index and conducted empirical analysis via a two-way fixed-effects model. The results show the following: (1) high-intensity policy mixes significantly improve alignment, overcoming university organizational inertia and laying an institutional foundation for sustainable education–industry synergy; (2) Policy Mix Intensity acts through three pathways―optimizing capital allocation, deepening industry–education integration, and enhancing dynamic responsiveness―forming a “sustainable factor allocation—sustainable industry-education alignment” logic; (3) policy efficacy is more pronounced in highly marketized Eastern regions and via regulatory tools, reflecting the moderating effect of regional sustainable endowments and policy tool types. This study provides empirical evidence for the “policy mix intensity–sustainable efficacy” transformation mechanism, offers theoretical references and empirical insights from China for the global collaborative realization of SDG4, SDG8, and SDG9 through higher education policy optimization, and proposes that policy design should shift toward factor integration-based sustainable comprehensive governance. Full article

Disinformation is a global challenge that affects areas such as politics, health, economics, and science and is spread rapidly by social media (SM), suggesting the necessity of advancing educational proposals to strengthen critical thinking (CT) and digital literacy (DL). This quantitative, non-experimental, descriptive study identified the self-perception that master’s students (n = 72; at three Spanish universities; October 2024–March 2025) have regarding their DL, along with their CT, in post-truth and fake news settings. A self-administered online questionnaire (53 items) was conducted, covering aspects such as the responsible use of information and platforms, algorithmic perceptions, actions taken to verify this information, and concepts of CT, post-truth, and fake news. The results show that Instagram (97%) and WhatsApp (96%) predominated, with a notable proportion of users (86%) reporting that algorithms influenced them ‘highly’ or ‘moderately’. Despite being aware of disinformation they find on social media (65%) as well as its close link to hate speech (90% who ‘strongly’ or ‘somewhat’ agreed), this knowledge does not fully translate into taking measures to counter it. In fact 61% of respondents report sharing news on at least some occasions, while only 25% are able to identify a professional fact-checking organization. In conclusion, these findings suggest the merit of assessing the prevalence of skills such as Critical Thinking (CT) and strategies like fact-checking among students in other postgraduate education systems. Such assessments could inform the potential promotion of media and digital literacy as cross-curricular skills in education. This approach would help bridge the gap between theoretical knowledge and the active verification needed to counter disinformation. Full article

Feature selection is a core step in data analysis and is referred to as attribute reduction in rough set theory. Granular ball computing has emerged as a novel data analysis paradigm characterized by high computational efficiency, robustness, and scalability. However, in previous attribute reduction methods for interval numbers, the construction of tolerance classes and the reduction iteration process suffer from inefficiency. To address these limitations, this paper proposes an efficient attribute reduction method based on fuzzy interval-valued granular balls. This method integrates fuzzy interval-valued granular balls with an acceleration strategy based on the positive region. Specifically, we first construct tolerance classes efficiently using fuzzy interval-valued granular balls, thereby enabling a reasonable partition of the universe. We then remove redundant objects in the positive region during the reduction iteration to avoid unnecessary computations. On this basis, we further propose a conditional entropy-based algorithm for attribute reduction. Experimental results show that this algorithm substantially improves computational efficiency while maintaining high classification accuracy. Full article

Deep neural networks (DNNs) are traditionally analyzed as black-box function approximators, yet their internal structure exhibits phase transitions characteristic of complex physical systems. In this study, we investigate topological–functional decoupling—the phenomenon whereby a network retains full graph connectivity while losing computational function—in trained neural networks through the lens of percolation theory. By subjecting three distinct architectures (Shallow, Deep, and Wide MLPs) to a unified edge-pruning analysis on Fashion-MNIST, we uncover a fundamental divergence between structural integrity and computational capacity in this experimental setting. We report three key phenomena observed in these experiments: (1) the zombie network state under stochastic pruning, where the system retains global connectivity ($P_{\infty }\approx 1.0$) yet suffers a catastrophic functional collapse (accuracy falls below 50% of baseline at prunning ratio $p_f\approx 0.35$–$0.68$ depending on depth), proves that graph reachability does not imply computational capability; (2) depth fragility, where increased network depth triggers multiplicative signal decay (the avalanche effect), rendering deep architectures exponentially more vulnerable to random edge removal than shallow ones ($p_f^{\mathrm{deep}}\approx 0.35$ vs. $p_f^{\mathrm{shallow}}\approx 0.68$); and (3) scale-free universality, observed under magnitude-based pruning, where a robust functional skeleton maintains accuracy near the baseline (∼89%) up to extreme sparsity ($p_f\approx 0.85$–$0.95$) across all three architectures. Robustness stems not from holographic redundancy in the overall connection count but from the emergent heavy-tailed rich-club organization of weight magnitudes—a sparse set of high-magnitude synapses that form the functional backbone of the network, decoupled from the redundant topological mass. These findings offer new physical constraints for the design of resilient neuromorphic hardware. Full article

The Pearl River Estuary (PRE) is highly vulnerable to alien species invasion due to intense anthropogenic activities in southern China. However, the invasion risk of alien aquatic vertebrates in the PRE remains unclear. In this study, 12 environmental DNA (eDNA) samples were collected from the PRE to reveal the composition and distribution of alien aquatic vertebrates using a vertebrate-universal primer set, and to assess their invasion risks using the Aquatic Species Invasiveness Screening Kit (AS-ISK). We identified a total of nine alien aquatic vertebrate species, including one amphibian (Aquarana catesbeiana) and eight fish species (Coptodon zillii, Oreochromis niloticus, Gambusia affinis, Cirrhinus mrigala, Labeo rohita, Pterygoplichthys pardalis, Ictalurus punctatus, and Neosalanx taihuensis). Notably, six of the nine alien species were detected at eight or more sampling sites, indicating their wide distribution in the PRE. Moreover, all nine alien species were classified as high risk based on AS-ISK, suggesting potential damage to local ecosystems and the aquaculture industry. Our study can help inform policy decisions for the prevention and control of alien aquatic vertebrates in the PRE. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

Background: Suicide prevention strategies in university settings largely rely on detecting explicit suicidal ideation. However, students experiencing severe psychiatric distress may not endorse suicidal thoughts and therefore remain unidentified by ideation-centered screening models. This study aimed to identify and clinically characterize university students with high depressive symptoms, poor sleep quality, and elevated impulsivity who deny suicidal ideation in order to examine whether they represent a vulnerable yet overlooked subgroup. Methods: This cross-sectional secondary analysis included 814 undergraduate students from the National Sleep Research Resource (ANSWERS dataset). Participants were classified into three groups based on median splits of depressive symptoms (CES-D), sleep quality (PSQI), impulsivity (UPPS-P), and the presence or absence of suicidal ideation in the past three months: Invisible (high symptoms without ideation), Visible (high symptoms with ideation), and Healthy (low symptoms without ideation). Group differences were examined using ANOVA and chi-square tests. Multivariate logistic regression was conducted to assess independent predictors of suicidal ideation. Results: The Invisible group comprised 11.8% of the sample. Compared with Healthy participants, these individuals showed poorer sleep quality and higher levels of thwarted belongingness and perceived burdensomeness (all p < 0.001). Cannabis use was most prevalent in the Invisible group (54.2%), exceeding both Visible and Healthy groups (p < 0.001). In adjusted analyses, depressive symptoms (OR = 1.10, 95% CI: 1.08–1.12) and sleep disturbance (OR = 1.06, 95% CI: 1.01–1.12) independently predicted suicidal ideation, whereas impulsivity did not. Conclusions: A clinically meaningful subgroup of students experience severe psychological distress without endorsing suicidal ideation yet show behavioral and interpersonal vulnerability. These findings highlight a limitation of ideation-focused screening and support broader, symptom-informed mental health assessment strategies in university populations. Full article

Background: Measles remains a significant occupational hazard in healthcare settings. In the context of the 2017–2018 measles outbreak in Greece and amid an outbreak at the study hospital, this seroprevalence study aimed to identify gaps in measles serologic status among healthcare workers in a tertiary hospital in central Greece. Methods: We conducted a seroprevalence study among hospital employees between February and December 2017. Blood samples and data on sociodemographic and work-related characteristics were collected from a convenience sample of participants. Measles IgG and IgM antibodies were measured using the ELISA method to determine seropositivity. The 95% CIs for measles IgG seronegativity proportions were calculated using the Clopper–Pearson exact method. Associations between participant characteristics and measles antibody status were assessed using Firth’s penalized logistic regression models. Results: A total of 336 healthcare workers participated in the study (response rate: 24.9%). Overall, 5.4% (95% CI: 3.2–8.3) tested negative for measles IgG antibodies. No significant associations were observed between participants’ characteristics and measles IgG antibody status. Male participants had 15.8 times higher adjusted odds of testing positive for measles IgM antibodies compared with female participants (aOR: 15.8; 95% CI: 2.33–107.54; p = 0.005). Conclusions: Our results indicate a low—but not negligible—proportion of IgG measles seronegativity among participants. The detection of seronegative individuals born prior to 1970 challenges the assumption of universal natural immunity based solely on year of birth. Given the recent rise in measles outbreaks and the limited seroprevalence data among healthcare workers in Greece, these findings provide valuable data to support ongoing efforts to achieve full vaccination coverage in this group. Further research is warranted to investigate the observed sex differences in susceptibility to measles infection. Full article

Motor fault diagnosis under small-sample conditions remains challenging because limited labeled data often cause deep models to overfit and generalize poorly. To address this problem, we propose STR-DDPM, a fault data augmentation framework that combines moving-average-based seasonal–trend–residual decomposition with a denoising diffusion probabilistic model. Specifically, multichannel signals are decomposed into trend, seasonal, and residual components, and class-conditional diffusion modeling is performed only in the residual domain. This design emphasizes fault-related stochastic variations while reducing interference from deterministic structures. To improve generation stability, we adopt velocity prediction and develop an enhanced one-dimensional U-Net with multi-scale convolutions, channel attention, self-attention, and feature-wise linear modulation for controllable conditional generation. Experiments on the University of Ottawa and Paderborn motor fault datasets demonstrate that the proposed method generates samples that are highly consistent with real data and improves diagnostic performance under multiple synthetic-data-assisted settings. These results indicate that STR-DDPM provides an effective and practical solution for data augmentation in data-limited motor fault diagnosis. Full article

Background: Cultural competence is essential in nursing, enabling the delivery of ethical, patient-centered, and respectful care that respects diverse cultural backgrounds in an increasingly diverse healthcare setting. Improving cultural competence can substantially reduce stereotyping, time pressure, and distress among nurses. Objective: This study aimed to examine the relationship between cultural competence and loneliness among nurses working at a university medical city in Saudi Arabia and to identify associated demographic and psychological factors. Methods: A descriptive cross-sectional study was conducted using a convenience sample of 184 nurses. Data were collected via an online questionnaire that included the Cultural Capacity Scale, the Revised UCLA Loneliness Scale, and the Patient Health Questionnaire-4 between April and May 2024. Descriptive statistics, Spearman’s correlation, and multiple linear regression were used in the data analysis. Result: Findings indicate high cultural competence (mean score: 78.82) but moderate loneliness (mean score: 11.9). Notably, a strong negative correlation exists between cultural competence and feelings of loneliness (r = −0.777) and anxiety/depression (r = −0.818), suggesting that increased cultural competence is associated with lower loneliness and mental health issues. Conclusions: Both cultural knowledge and sensitivity emerged as significant predictors of lower anxiety and depression levels. These findings highlight the association between cultural competence and reduced loneliness and psychological distress among nurses, suggesting the need for targeted training interventions to improve nurses’ well-being and the quality of patient-centered care in culturally diverse healthcare settings. Full article

Gender-based violence in dating relationships is a multifaceted issue that encompasses diverse forms. In university settings, high prevalence rates have been reported, with psychological violence being the most common. New forms of digital violence, such as cyberbullying, control through social media, and digital aesthetic pressure, further complicate the phenomenon. Purpose: This study aimed to explore Health Sciences students’ perceptions of gender-based violence in dating relationships to identify key dimensions for understanding and intervention. Methods: A qualitative design was employed using focus groups with ten participants, analyzed through Interpretative Phenomenological Analysis (IPA). Results: Four main themes emerged: characteristics of gender-based violence in dating relationships, types of violence identified, aesthetic pressure within affective relationships, and strategies for detecting and responding to violence. Conclusions: Findings emphasize the importance of incorporating students’ voices into prevention strategies and propose educational interventions that address both offline and online dynamics of gender-based violence in dating relationships. Full article

Background/Objectives: Tunisia lacks recent national data on hepatitis B virus (HBV) prevalence, particularly following the introduction of universal HBV vaccination in 1995. A national HBV seroprevalence study is essential to guide prevention strategies. This study aimed to estimate the national seroprevalence of HBV infection and identify its determinants 20 years after vaccine introduction. Methods: We conducted a nationwide, household-based, cross-sectional sero-epidemiological survey among a representative sample of the Tunisian general population using a two-stage cluster sampling method. The study was conducted by the National Observatory of New and Emerging Diseases (ONMNE) between December 2014 and June 2015. Data were collected using standardized questionnaires, and blood samples were tested using electrochemiluminescence (ECLIA) to detect HBV biomarkers (HBsAg, anti-HBc, anti-HBs). HBV infection was defined as the presence of HBsAg and/or anti-HBc with the absence of anti-HBs. Associations between HBV infection and explanatory variables (socio-demographics, vaccination status, intrafamilial transmission, and hospital exposures) were assessed using multivariate logistic regression, reporting adjusted odds ratios (aORs) with 95% confidence intervals (CIs). Results: Among 21,720 participants, 19,155 (88.2%) were tested. The national prevalence of HBsAg was 1.7% (95% CI: 1.55–1.85%), higher among males (2.1%; 95% CI: 1.9–2.4%) than females (1.4%; 95% CI: 1.3–1.6%) (p < 0.001; M/F ratio = 1.48). The mean age of HBsAg-positive participants was 48 ± 15.7 years. Prevalence was highest in the Central (2.3%; 95% CI: 2.0–2.7%) and Southern regions (2.2%; 95% CI: 1.8–2.8%) (p < 0.001). In multivariate analysis, independent risk factors for HBV infection included age >20 years (aOR = 15.10; 95% CI: 4.79–47.64; p < 0.001), having a family member with HBV infection (aOR = 2.82; 95% CI: 2.09–3.79; p < 0.001), residing in the Southern (aOR = 2.51; 95% CI: 1.76–2.71; p < 0.001) or Central region (aOR = 2.18; 95% CI: 1.76–2.71; p < 0.001), male gender (aOR = 1.69; 95% CI: 1.39–2.05; p < 0.001), and hospital follow-up (aOR = 1.23; 95% CI: 1.01–1.51; p = 0.039). HBV vaccination was strongly protective (aOR = 0.36; 95% CI: 0.20–0.62; p < 0.001). Conclusions: The national HBsAg seroprevalence in Tunisia was 1.7%, reflecting a low-endemic status. Vaccination programs should prioritize high-risk groups, including males, adults over 20 years, household contacts of HBV carriers, and residents of the Central and Southern regions. Strengthening infection prevention and control in healthcare settings and adopting intrafamilial precautions among high-risk populations are essential for long-term HBV control. Full article

The research landscape in tourism and hospitality often feels like a house divided. On one side, there is the quantitative camp searching for broad, linear patterns; on the other side, there are qualitative scholars who prefer deep, contextual dives. This division suggests that scholars may overlook valuable insights in the middle. Therefore, this study examines how Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Necessary Condition Analysis (NCA) are transforming the landscape and bridging the methodological and analytical divide. For this purpose, authors analyzed 91 peer-reviewed articles using PRISMA 2020 systematic review principles from six databases. The findings highlight that this multi-methodological triangulation addresses causal asymmetry, acknowledging that the drivers of success are not necessarily mirror images of those of failure. The study implies that, in theory, it bridges the gap between qualitative nuance and quantitative rigor, moving from universal linear assumptions to complexity theory. Methodologically, it allows for a prioritized roadmap in which NCA pinpoints exact operational thresholds and fsQCA provides strategic flexibility. In practice, the findings offer a two-tiered decision-making framework for industry managers: first, addressing non-negotiable bottlenecks, and second, selecting the strategic configuration that best aligns with their unique resource base. The review concludes that, while challenges such as data calibration and interpretative complexity remain, integrating these paradigms offers a more authentic and comprehensive understanding of the volatile landscape of tourism and hospitality. Full article