Search Results (1,041)

With deepening global higher education reforms, outcome-based education has emerged as the core paradigm for teaching model innovation. This study investigates the structural dependencies and teaching effectiveness of the Environmental Design curriculum at Hubei Engineering University in medium-sized cities, China, addressing challenges of enrollment decline and market contraction critical for urban sustainability. Using network analysis, we construct curriculum support and contribution networks and course temporal networks to assess structural dependencies and teaching effectiveness, revealing structural patterns and optimizing the OBE-based Environmental Design curriculum to enhance educational quality and student competencies. Analysis reveals computer basic courses as knowledge transmission hubs, creating a course network with a distinct core–periphery structure. Technical course reforms significantly outperform theoretical course reforms in improving student performance metrics, such as higher average scores, better grade distributions, and reduced performance gaps, while innovative practice courses show peripheral isolation patterns, indicating limited connectivity with core curriculum modules, which reduces their educational impact. These findings provide empirical insights for curriculum optimization, supporting urban sustainable development through enhanced professional talent cultivation equipped to address environmental challenges like sustainable design practices and resource-efficient urban planning. Network analysis applications introduce innovative frameworks for curriculum reform strategies. Future research expansion through larger sample validation will support urban sustainable development goals and enhance professional talent cultivation outcomes. Full article

Higher education and science (HES) is one of the key drivers of a country’s economic growth. In this study, we examine national projects and research capacity in HES in Kazakhstan from 2014 to 2024. We conducted a content review and scientometric analysis with network and temporal visualizations. Our data sources included policy documents, statistical reports, and the Scopus database. Our findings suggest that, while Kazakhstan aligns with global trends in the field (e.g., digitalization, scientometrics monitoring, and internationalization), these are achieved through a state-led, policy-driven approach shaped by its post-Soviet context. Additionally, we note a dual structure in Kazakhstan’s HES sector, characterized by a strong top-down direction and increasing institutional engagement. In terms of the thematic trends from the temporal analysis, the country experienced a three-staged evolution: foundational reforms and system modernization (2014–2017), capacity building and evaluation (2018–2021), and, most recently, strategic expansion, inclusivity, and globalization (2022–2024). Throughout the analyzed period, low R&D intensity, disciplinary imbalances, and structural barriers still undermine desired development efforts in HES. The analyzed case of Kazakhstan can serve as “lessons learned” for policymakers and researchers working in the science evaluation and scholarly communication area in similar emerging or transition countries. Full article

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

Against the backdrop of the rapid advancement of intelligent speech interaction and holographic display technologies, this paper introduces an interactive holographic display system. This paper applies 2D-to-3D technology to acquisition work and uses a Complex-valued Convolutional Neural Network Point Cloud Gridding (CCNN-PCG) algorithm to generate a computer-generated hologram (CGH) with depth information for application in point cloud data. During digital human hologram building, 2D-to-3D conversion yields high-precision point cloud data. The system uses ChatGLM for natural language processing and emotion-adaptive responses, enabling multi-turn voice dialogs and text-driven model generation. The CCNN-PCG algorithm reduces computational complexity and improves display quality. Simulations and experiments show that CCNN-PCG enhances reconstruction quality and speeds up computation by over 2.2 times. This research provides a theoretical framework and practical technology for holographic interactive systems, applicable in virtual assistants, educational displays, and other fields. Full article

In higher education, the employability and entrepreneurship services play an essential role in the labor market insertion of graduates. The management of public relations promotes institutional projection, the creation of networks with companies, and the dissemination of initiatives to strengthen professional skills. This research analyzes how the communication strategies of Andalusian public universities improve employability and encourage entrepreneurship. A methodological triangulation is used: literature review, analysis of social networks with Fanpage Karma, and study of corporate websites. The results show a focus on counseling, training, workshops, job fairs, and networking events, strengthening the brand of universities and their commitment to the professional development of students. Full article

Despite extensive prevention strategies in Spain since the 1980s, gender-based violence, including among adolescents, remains prevalent, as observed in the Romance SUCC-ED Project (R&D&I Operating Programme ERDF Andalusia 2014–2020). This research study investigates the dimensions, meanings, relationships, and practices shaping the culture of romance in digital Andalusian adolescence (12–16 years) and its potential impact on school trajectories in Compulsory Secondary Education. Based on the premise that equality-focused relationship education is key to preventing gender violence, the study employs an ethnographic methodology with 12 Andalusian school case studies (4 out of them are located in rural areas) and 220 in-depth interviews (126 girls, 57.3%; 94 boys, 42.7%). This article aims to empirically explain gender violence in early adolescence by analysing the culture of romance as an explanatory factor. Findings reveal an interconnected model where dimensions (love, couple, sexuality, pornography, social networks, and cultural references), meanings (constructed by adolescents within each of them), relationships (partner), and practices (control and jealousy) reinforce romanticised femininity and dominant masculinity, thus explaining the high incidence of gender-based violence among students in the study. Full article

This research aims to determine the utilization levels of local products and the challenges and opportunities of creating a recognizable food-centered cultural identity based on collaborative networks developed between agriculture and tourism. This has the potential to strengthen collaborative entrepreneurship. It uniquely contributes to the existing literature by exploring the connections between agri-food and tourism, while proposing strategies to maximize business opportunities centered on food culture. Descriptive and inferential statistics are conducted based on primary data collected by distributing a questionnaire to 59 public and private organizations in the Peloponnese region in Greece, which has significant agricultural production but limited tourist flows. The results indicate a lack of collective action and business recognition of the value of regional food culture among participants. The human resources employed in tourism lack the skills to highlight traditional food heritage. The presence of structural and operational barriers undermines efforts to facilitate communication, manage suppliers, and enhance the visibility of products designated with Geographical Indications. This paper offers preliminary results; however, extensive future studies are needed to validate the findings fully. The study highlights key implications: Improved communication between stakeholders could enhance the management of the local food network. Agri-food and tourism businesses can develop educational programs and food-focused tourism packages that promote social cohesion and preserve cultural heritage. Full article

This study suggests a method for analyzing the risk of methane explosions using the N-K model and Social Network Analysis (SNA) to understand how different risk factors related to coal-mine methane explosions are connected and change over time, aiming to prevent these accidents effectively. We identified 41 secondary risk factors and four fundamental risk factors—human, equipment, environment, and management—based on the 4M accident causation theory. The SNA model was utilized to determine the main risk factors and their evolutionary routes, while the N-K model was utilized to quantify the degree of risk coupling. The findings show that the number of risk variables engaged in the methane-explosion risk system closely correlates with the number of accidents that occur and the maximum coupling level among the four elements. The primary control factors in the methane-explosion risk system are poor equipment management, broken safety monitoring and control systems, inadequate safety education and training, safety regulation violations, and poor safety production responsibility system implementation. We utilized the primary evolution paths and key elements to propose risk control approaches. A reference for ensuring safety in coal-mine operations can be found in the research findings. Full article

Entrepreneurship serves as a vital engine of economic development, yet the mechanisms translating entrepreneurial intention into behavior have gradually emerged. This study employs bibliometric analysis of 61 SSCI-indexed articles (2014–2024) using CiteSpace to examine co-authorship networks, co-citation patterns, and research hotspots. Our findings demonstrate that individual-level factors (personality traits, entrepreneurial self-efficacy, and entrepreneurship education) drive both entrepreneurial intention and entrepreneurial behavior. More importantly, environmental factors (university milieu, regional social legitimacy, and national cultural dimensions) moderate the relationship between entrepreneurial intention and behavior. The study also identifies a temporal pattern in the entrepreneurial intention–behavior correlation. These results advance theoretical understanding of the intention–behavior transition and offer practical insights for entrepreneurship education and policy design. Full article

Emotion detection using computer vision has advanced significantly in recent years, achieving remarkable performance that, in some cases, surpasses that of humans. Convolutional neural networks (CNNs) excel in this task by capturing facial features that allow for effective emotion classification. However, most research focuses on basic emotions, such as happiness, anger, or sadness, neglecting more complex emotions, like frustration. People set expectations or goals to meet; if they do not happen, frustration arises, generating reactions such as annoyance, anger, and disappointment, which can harm confidence and motivation. These aspects make it especially relevant in mental health and educational contexts, where detecting it could help mitigate its adverse effects. In this research, we developed a CNN-based approach to detect frustration through facial expressions. The scarcity of specific datasets for this task led us to create an experimental protocol to generate our dataset. This classification task presents a high degree of difficulty due to the variability in facial expressions among different participants when feeling frustrated. Despite this, our new model achieved an F1-score of $0.8080$, thus obtaining an adequate baseline model. Full article

Assessing teacher candidates’ self-efficacy in using reading strategies is essential for understanding their academic development. This study developed and validated the Teacher Candidates’ Self-Efficacy Scale for Informational Reading Strategies (TCSES-IRS) using a mixed-methods sequential exploratory design. Initial qualitative data from interviews with 33 candidates and a literature review guided item generation. Lawshe’s method confirmed content validity. The scale was administered to 1176 teacher candidates. Exploratory (n = 496) and confirmatory factor analyses (n = 388) supported a five-factor structure—cognitive, note-taking, exploration and preparation, physical and process-based, and reflective and analytical strategies—explaining 63.71% of total variance, with acceptable fit indices (χ²/df = 2.64, CFI = 0.912, TLI = 0.900, RMSEA = 0.069). Internal consistency was high (α = 0.899 total; subscales α = 0.708–0.906). An additional sample of 294 participants was used for nomological network validation. Convergent validity was demonstrated by significant item-total correlations and strong factor loadings. Discriminant validity was evidenced by moderate inter-factor correlations. Criterion-related validity was confirmed via significant group differences and meaningful correlations with an external self-efficacy measure. The TCSES-IRS emerges as a psychometrically sound tool for assessing informational reading self-efficacy, supporting research and practice in educational psychology. Full article

Background: Alopecia areata (AA), an autoimmune condition causing non-scarring hair loss, often coexists with atopic dermatitis (AD) due to shared T-helper cell type 2 (Th2)-mediated pathways. Dupilumab, a monoclonal antibody inhibiting IL-4 and IL-13 signaling, is a cornerstone treatment for AD but has conflicting reports regarding its impact on AA, with some suggesting therapeutic benefits and others indicating AA induction. Methods: This retrospective study, utilizing the TriNetX Research Network’s de-identified data from over 300 million patient records, investigates the association between dupilumab use and AA risk in AD patients. Results: After propensity score matching, 23,782 dupilumab users were compared with an equal number of controls. Results revealed a statistically significant increased AA risk in dupilumab users (odds ratio: 1.436, 95% CI: 1.066–1.935, p = 0.0167) after 16 weeks. Cases occurring within 16 weeks were excluded. Conclusions: Potential mechanisms include immune rebalancing, with Th2 suppression possibly upregulating Th1/Th17 pathways or unmasking latent AA in predisposed individuals. These findings challenge dupilumab’s potential as an AA treatment and highlight the need for vigilant monitoring, including routine scalp examinations and patient education. Future research should focus on mechanistic pathways, risk stratification, and comparative studies with other biologics to optimize personalized treatment strategies for AD and AA. Full article

Over a three-year R&D project, the perception of mobile phone use in Spanish secondary schools shifted from initial tolerance to increasingly prohibitive policies. Drawing on the Actor–Network Theory, this study examines how mobile phones—alongside institutional discourses and school and family concerns—acted as dynamic actants, shaping public and political responses. The research adopted a qualitative design combining policy and media document analysis, nine semi-structured interviews with key stakeholders, ten regional case studies, and twelve focus groups. The study concluded with a public multiplier event that engaged the broader educational community. The Madrid region, among the first to adopt a restrictive stance, contributed two school-based case studies and three focus groups with teachers, students, and families. Findings suggest that the turn toward prohibition was motivated less by pedagogical evidence than by cultural anxieties, consistent with what it conceptualizes as a techno-pessimistic shock. This shift mirrors the historical patterns of societal reaction to disruption and technological saturation. Rather than reinforcing binary framings of promotion versus prohibition, such moments invite critical reflection. The study argues for nuanced, evidence-based, and multilevel governance strategies to address the complex role of mobile technologies in education. Full article

Background/Objectives: Repetitive behaviors such as hand flapping, body rocking, and head shaking characterize Autism Spectrum Disorder (ASD) while functioning as early signs of neurodevelopmental variations. Traditional diagnostic procedures require extensive manual observation, which takes significant time, produces subjective results, and remains unavailable to many regions. The research introduces a real-time system for the detection of ASD-typical behaviors by analyzing body movements through the You Only Look Once (YOLOv11) deep learning model. Methods: The system’s multi-layered design integrates monitoring, network, cloud, and typical ASD behavior detection layers to facilitate real-time video acquisition, wireless data transfer, and cloud analysis along with ASD-typical behavior classification. We gathered and annotated our own dataset comprising 72 videos, yielding a total of 13,640 images representing four behavior classes that include hand flapping, body rocking, head shaking, and non_autistic. Results: YOLOv11 demonstrates superior performance compared to baseline models like the sub-sampling (CNN) (MobileNet-SSD) and Long Short-Term Memory (LSTM) by achieving 99% accuracy along with 96% precision and 97% in recall and the F1-score. Conclusions: The results indicate that our system provides a scalable solution for real-time ASD screening, which might help clinicians, educators, and caregivers with early intervention, as well as ongoing behavioral monitoring. Full article

Ubiquitous blended learning, leveraging mobile devices, has democratized education by enabling autonomous and readily accessible knowledge acquisition. However, its reliance on traditional interfaces often limits learner immersion and meaningful interaction. The emergence of the wearable metaverse offers a compelling solution, promising enhanced multisensory experiences and adaptable learning environments that transcend the constraints of conventional ubiquitous learning. This research proposes a novel framework for ubiquitous blended learning in the wearable metaverse, aiming to address critical challenges, such as multi-source data fusion, effective human–computer collaboration, and efficient rendering on resource-constrained wearable devices, through the integration of embodied interaction and multi-agent collaboration. This framework leverages a real-time multi-modal data analysis architecture, powered by the MobileNetV4 and xLSTM neural networks, to facilitate the dynamic understanding of the learner’s context and environment. Furthermore, we introduced a multi-agent interaction model, utilizing CrewAI and spatio-temporal graph neural networks, to orchestrate collaborative learning experiences and provide personalized guidance. Finally, we incorporated lightweight SLAM algorithms, augmented using visual perception techniques, to enable accurate spatial awareness and seamless navigation within the metaverse environment. This innovative framework aims to create immersive, scalable, and cost-effective learning spaces within the wearable metaverse. Full article