Search Results (311)

For a considerable period, China’s eastern and western regions have grappled with imbalances in industrial development, with industrial leapfrogging emerging as a pivotal solution. This study examines the impact of artificial intelligence technology spillovers and sustainable innovation on industrial leapfrogging between eastern and western regions. Empirical analysis is conducted using panel data from 22 provinces and municipalities across eastern and western China spanning 2014–2024, employing both a spatial difference-in-differences model and a dual machine learning model. Findings reveal that both AI technology spillovers and sustainable innovation significantly enhance the efficiency of industrial leapfrogging across regions. Their synergistic effects are pronounced, generating positive spatial spillovers. Institutional environments exert a significant influence on leapfrog industrial development. By regulating AI technology environments and sustainable innovation environments, institutional frameworks enhance leapfrogging efficiency, though this mediation exhibits a dual-threshold effect: most western provinces have yet to cross the first threshold. Industrial and economic heterogeneity weaken the efficiency of AI technology spillovers and sustainable innovation in facilitating industrial leapfrogging between eastern and western regions. This research provides robust empirical support for addressing industrial development imbalances and enhancing industrial resilience between eastern and western regions. Full article

Teaching in a globalised world offers opportunities to reduce barriers, enhance understanding, and expand access for diverse learners. Blended approaches, combining in-person and online delivery, can encourage learning communities across geographical boundaries. However, disparities in access to technology, internet reliability, and conducive study environments highlight inequalities and varied learner experiences. While digital networks may support identity and belonging, some students report feeling distracted or disengaged in online settings. This study explores the experiences of first-year doctoral candidates completing the final taught module of their Doctorate in Business Administration (DBA) at the University of Gloucestershire, UK. Participants, mostly international students now based in the UK, are engaged in both in-person classes and online staff-led webinars. Data was gathered through four in-person focus groups. Engeström’s Cultural-Historical Activity Theory was applied as an analytical framework, conceptualising teaching and learning as an activity system mediated by Tools, Rules, Community, and Division of Labour. This enabled a comparison of students’ experiences in online and in-person contexts. The findings revealed contradictions within the system, identifying barriers to engagement and adaptation, and offering insights into the evolving pedagogical demands of blended doctoral education. Full article

As conversational artificial intelligence (CAI), including smart speakers, social robots, and dialogic learning apps, becomes increasingly present in children’s lives, its potential to support early mathematical thinking warrants closer attention. While existing research largely concentrates on literacy and language development, the role of CAI in early numeracy remains underexplored. This paper examines how voice-based CAI might contribute to informal mathematical thinking in early childhood. Adopting a conceptual lens, this paper synthesises existing theory and research to examine the potential roles of CAI in early mathematical learning. Grounded in sociocultural theory and dialogic pedagogy, this paper positions CAI as a potential mediator of early mathematical thinking through responsive dialogue. Four interrelated dimensions (child agency, cognitive scaffolding, mathematical language quality, and responsiveness and timing) are identified as a conceptual lens for evaluating how dialogue-based interactions with CAI may support or constrain young children’s mathematical thinking. Rather than framing CAI as a direct teaching tool, this paper explores its potential role as a dialogic partner in play-based numeracy and inquiry. The framework contributes to early mathematics education by highlighting both the promise and the limitations of CAI, offering guidance for research, technology design, and educational practice. It underscores the need for intentional, ethically informed integration of CAI that approximates the qualities of human dialogue while acknowledging current constraints in real-world use. Full article

Since the beginning of the 21st century, major epidemics and pandemics such as SARS, H1N1pdm09, Ebola, and COVID-19 have repeatedly challenged global systems of disease diagnostics and control. These crises exposed the weaknesses of traditional diagnostic models, including long turnaround times, uneven resource distribution, and supply chain bottlenecks. As a result, there is an urgent need for more advanced diagnostic technologies and integrated diagnostics strategies. Our review summarizes key lessons learned from four recent major outbreaks and highlights advances in diagnostic technologies. Among these, molecular techniques such as loop-mediated isothermal amplification (LAMP), transcription-mediated amplification (TMA), recombinase polymerase amplification (RPA), and droplet digital polymerase chain reaction (ddPCR) have demonstrated significant advantages and are increasingly becoming core components of the detection framework. Antigen testing plays a critical role in rapid screening, particularly in settings such as schools, workplaces, and communities. Serological assays provide unique value for retrospective outbreak analysis and assessing population immunity. Next-generation sequencing (NGS) has become a powerful tool for identifying novel pathogens and monitoring viral mutations. Furthermore, point-of-care testing (POCT), enhanced by miniaturization, biosensing, and artificial intelligence (AI), has extended diagnostic capacity to the front lines of epidemic control. In summary, the future of epidemic and pandemic response will not depend on a single technology, but rather on a multi-layered and complementary system. By combining laboratory diagnostics, distributed screening, and real-time monitoring, this system will form a global diagnostic network capable of rapid response, ensuring preparedness for the next global health crisis. Full article

Introduction: Artificial intelligence (AI) tools have emerged in cardiovascular clinical practice. Regarding transcatheter aortic valve replacement/implantation (TAVR/TAVI) procedures, their utilization optimizes procedural planning, aids physicians with decision making, and predicts possible post-procedural complications. Moreover, machine-learning (ML) models, compared with traditional mortality risk scores, show promising results considering predicted mortality in TAVI patients. However, further validation is required. As the implementation of cardiovascular procedures can be challenging, AI technology broadens the armamentarium of tools that a clinician is able to use for a more comprehensive evaluation of patients, minimizing complications and resulting in optimum clinical outcomes. Methods: A comprehensive literature search was conducted through the PubMed and Google Scholar databases from inception to 20 September 2025, to identify relevant studies. The search strategy included the following keywords: [“TAVI” OR “TAVR”] AND [“AI”, Artificial Intelligence]. Results: According to our database research, 7177 articles were initially screened, and 2145 duplicate articles were excluded. Eventually, 189 articles were evaluated by our reviewers and 51 articles of studies published between 2014 and 2025 were included in our review. Conclusions: AI algorithms could revolutionize the Heart Team decision making process, being not only a tool for patient evaluation but an active member of the team with applications to analyze and optimize all stages of the TAVI procedure, guide decision making and predict outcomes, and, with the contribution and evaluation of information from all human members of the team, enhance even more the patient-mediated medicine/interventions. Full article

Background: The COVID-19 pandemic uniquely challenged non-formal education (NFE), a sector reliant on interpersonal engagement, by forcing a rapid shift to remote work. This study examines how managerial leadership styles, technological self-efficacy (TSE), and attitudes toward remote work intersect among NFE coordinators in Israel’s Arab society, a minority community facing distinct cultural and systemic challenges. Aim: Focusing on school-based social-community education coordinators (SCECs) and community-based non-formal education coordinators (NFECs), the study investigates how leadership and organizational context shaped their adaptation to crisis. Method: The study employed a cross-sectional survey design, with data collected from 132 coordinators and 47 youth department directors between June and October 2021 using validated questionnaires. Pearson correlations, moderated mediation analysis, and ANOVA were used to analyze the data. Findings: The results revealed positive correlations between transformational leadership style (TLS), TSE, job satisfaction, and positive attitudes toward remote work. Critically, the analysis uncovered a context-dependent mechanism: TSE fully mediated the relationship between TLS and attitudes toward remote work, but this effect was significant only for community-based NFECs, not for school-based SCECs. Additionally, SCECs reported higher satisfaction and TSE than NFECs, who perceived more laissez-faire leadership. Contributions: Drawing on Bronfenbrenner’s ecological systems theory, the findings underscore that leadership’s effectiveness in crises is not one-size-fits-all; its impact is channeled through different mechanisms depending on the organizational ecosystem. The study highlights the pivotal roles of adaptive leadership and TSE in sustaining resilient NFE in minority communities. Theoretical and practical implications point to the need for culturally responsive, context-sensitive leadership development and targeted technology training to foster equitable learning environments. Full article

The growing integration of generative artificial intelligence (GenAI) tools in higher education has potential to transform learning experiences. However, empirical research comparing GenAI-supported learning with traditional instruction lags behind these developments. This study addresses this gap through a controlled experiment involving 96 undergraduate computer science students in a Database Management course. Participants experienced either GenAI-supported or traditional instructions while learning the same concept. Data were collected through questionnaires, quizzes, and interviews. Analyses were grounded in self-determination theory (SDT), which posits that effective learning environments support autonomy, competence, and relatedness. Quantitative findings revealed significantly more positive learning experiences with GenAI tools, particularly enhancing autonomy through personalized pacing and increased accessibility. Competence was supported, reflected in shorter study times with no significant achievement differences between approaches. Students performed better on moderately difficult questions using GenAI, indicating that GenAI may bolster conceptual understanding. However, interviews with 11 participants revealed limitations in supporting relatedness. While students appreciated GenAI’s efficiency and availability, they preferred instructor-led sessions for emotional engagement and support with complex problems. This study contributes to the theoretical extension of SDT in technology-mediated learning contexts and offers practical guidance for optimal GenAI integration. Full article

Educational strategies based on technological models that integrate the dimensions of prevention, screening, and treatment of diabetic foot ulcers are emerging as promising methods to improve nurses’ knowledge, skills, and clinical competencies in primary care. In this investigation, we evaluated the effectiveness of a digital education program, mediated by a virtual learning environment, in enhancing nurses’ clinical knowledge about diabetic foot ulcers. This quasi-experimental intervention study was conducted with 114 nurses, selected for convenience, from the five health districts that make up primary care in the municipality of Teresina, Brazil. Two stages, separated by the educational intervention, allowed us to measure their knowledge levels before and after the implementation of the digital technology. A characterization form and the Nurse Knowledge Assessment Questionnaire on Diabetic Foot were used to evaluate the outcomes. The McNemar test compared the pre- and post-intervention knowledge levels, while accuracy rate-based parameters allowed for the classification of results into performance categories. The intervention effect size was estimated using Cohen’s d test. Results showed substantial improvements in knowledge, particularly in domains related to definition (p = 0.002), risk factors (p < 0.001), associated complications (p < 0.001), signs and symptoms of neuropathies (p < 0.001), application of tests to assess protective sensation (p < 0.001) and foot biomechanics (p < 0.001), risk classification (p < 0.001), and prevention strategies (p < 0.001), with performance ratings predominantly “good” or “excellent” after the intervention. The effect size for paired samples was large (Cohen’s dz = 1.82), based on the total knowledge scores. Findings support the effectiveness signal of the virtual learning environment for knowledge improvement; however, without a control group, we cannot rule out testing effects. Controlled or stepped-wedge trials should confirm causality. Full article

The integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, into higher education has introduced new opportunities and challenges for students and lecturers alike. This study investigates the psychological, ethical, and institutional factors that shape the adoption of GenAI tools in Saudi Arabian universities, drawing on an extended Technology Acceptance Model (TAM) that incorporates constructs from Self-Determination Theory (SDT) and ethical decision-making. A cross-sectional survey was administered to 578 undergraduate students and 309 university lecturers across three major institutions in Southern Saudi Arabia. Quantitative analysis using Structural Equation Modelling (SmartPLS 4) revealed that perceived usefulness, intrinsic motivation, and ethical trust significantly predicted students’ intention to use GenAI. Perceived ease of use influenced intention both directly and indirectly through usefulness, while institutional support positively shaped perceptions of GenAI’s value. Academic integrity and trust-related concerns emerged as key mediators of motivation, highlighting the ethical tensions in AI-assisted learning. Lecturer data revealed a parallel set of concerns, including fear of overreliance, diminished student effort, and erosion of assessment credibility. Although many faculty members had adapted their assessments in response to GenAI, institutional guidance was often perceived as lacking. Overall, the study offers a validated, context-sensitive model for understanding GenAI adoption in education and emphasises the importance of ethical frameworks, motivation-building, and institutional readiness. These findings offer actionable insights for policy-makers, curriculum designers, and academic leaders seeking to responsibly integrate GenAI into teaching and learning environments. Full article

Digital literacy (DL) among farmers serves as a vital link between digital technology and green sustainable development, significantly enhancing agricultural green total factor productivity (AGTFP). This study employs panel data from the China Family Panel Studies (CFPS) covering 2014–2020, applying a two-way fixed effects model and machine learning techniques to examine the influence of farmers’ digital literacy on AGTFP. The results indicate that DL positively contributes to AGTFP. Further heterogeneity analysis shows stronger effects among male farmers, households with low trust, and those within the working-age population. Mechanism analysis indicates that social capital accumulation mediates the relationship, whereas agricultural socialization services strengthen the positive impact of DL on AGTFP. Additional analysis using machine learning models reveals that the impact of farmers’ digital literacy on AGTFP changes over time. Specifically, entertainment and learning-oriented network use enhances AGTFP, whereas work-related, social, and lifestyle-related use suppresses it. This study offers a more nuanced understanding by shifting from traditional macro-level frameworks to a micro-level perspective focused on farmers’ digital literacy. Moreover, the innovative application of explainable machine learning provides empirical evidence for the underlying drivers of AGTFP. Full article

This study investigates the impact of Machiavellian leadership on team creativity through the mediating role of cross-understanding and the moderating effect of task interdependence. While prior research has emphasized the negative consequences of Machiavellian tendencies, we argue that in highly interdependent team settings—such as project-based groups in technology, manufacturing, and financial enterprises—such leaders may foster constructive processes that enhance innovation. Drawing on social learning and trait activation theories, we conducted a multi-source survey of 86 teams (379 employees) in Chinese organizations. Team members assessed task interdependence and cross-understanding, while leaders reported their own Machiavellian tendencies and rated team creativity. Results show that Machiavellian leadership predicts team creativity indirectly through cross-understanding, with task interdependence strengthening this pathway. Theoretically, this study enriches leadership and creativity research by providing a nuanced view of how dark traits can stimulate team-level creativity through cognitive interaction mechanisms and by identifying task interdependence as a boundary condition. Practically, the findings suggest that organizations should recognize the creative potential of Machiavellian leaders in high-interdependence contexts, channel their ambition toward innovation goals, and design workflows that promote cross-understanding and collaboration. Full article

The rapid digital transformation of higher education creates opportunities and challenges, particularly in professional programmes where students must balance academic learning with preparation for operational duties, such as in medicine, engineering, and defence. While digital technologies are widely used in higher education, their sustainable integration into professional contexts, especially security and defence education, remains underexplored. This study investigates the determinants of perceived e-learning usefulness among undergraduates (cadets) at the Lithuanian Military Academy, applying an adapted technology acceptance model framework. A structured questionnaire was used to measure constructs related to distance learning effectiveness, classroom innovation, security, sustainability of digital systems, and individual learning preferences, with hypotheses tested through mediation and moderated mediation models. The results indicate that the effectiveness of distance learning is the strongest factor influencing intention to use it, supported by the roles of classroom innovation and system security. Perceived usefulness further emerges as both a direct predictor of adoption and a conditional factor shaping the impact of pedagogical and infrastructural design on acceptance. These findings extend traditional technology acceptance frameworks and provide new insights into how sustainable digital teaching can be fostered in higher professional education, where academic quality and operational readiness must be aligned. Full article

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article

This study explored how AI affects the sustainability of competitive advantage in the tourism and hospitality sector, with a particular focus on the mediating roles of digital culture and digital skills in the lens of the Technology Acceptance Model (TAM). Data were collected via a structured questionnaire distributed to a purposive sample of 488 managers and supervisors working in five-star hotels, travel agencies, and DMCs across Saudi Arabia. The findings revealed that AI has a significant direct effect on sustainable competitive advantage and also exerts strong positive effects on both digital culture and digital skills. In turn, both of these internal enablers significantly contribute to sustaining a competitive advantage. Mediation analysis further showed that both digital culture and digital skills partially mediate the relationship between AI and sustainable competitiveness. The study addresses a notable gap in tourism research by providing localized evidence from a market undergoing rapid transformation under Vision 2030, and, taken together, extends TAM to an organizational lens by demonstrating AI’s role in shaping culture and skills that underpin a durable advantage while pointing to actionable priorities—targeting high-value AI use cases, conducting capability audits, institutionalizing continuous learning through visible leadership and role-based upskilling, and embedding culture- and skills-oriented KPIs within AI governance. Full article

The methylation of CpG sites with 5mC mark is a dynamic epigenetic modification. However, the relationship between the methylation and the surrounding genomic sequence context remains poorly explored. Investigation of the allele methylation provides an opportunity to decipher the interplay between differences in the primary DNA sequence and epigenetic variation. Here, we performed high-coverage long-read whole-genome direct DNA sequencing of one individual using Oxford Nanopore technology. We also used Illumina whole-genome sequencing of the parental genomes in order to identify allele-specific methylation sites with a trio-binning approach. We have compared the results of the haplotype-specific methylation detection and revealed that trio binning outperformed other approaches that do not take into account parental information. Also, we analysed the cis-regulatory effects of the genomic variations for influence on CpG methylation. To this end, we have used available Deep Learning models trained on the primary DNA sequence to score the cis-regulatory potential of the genomic loci. We evaluated the functional role of the allele-specific epigenetic changes with respect to gene expression using long-read Nanopore RNA sequencing. Our analysis revealed that the frequency of SNVs near allele-specific methylation positions is approximately four times higher compared to the biallelic methylation positions. In addition, we identified that allele-specific methylation sites are more conserved and enriched at the chromatin states corresponding to bivalent promoters and enhancers. Together, these findings suggest that significant impact on methylation can be encoded in the DNA sequence context. In order to elucidate the effect of the SNVs around sites of allele-specific methylation, we applied the Deep Learning model for detection of the cis-regulatory modules and estimated the impact that a genomic variant brings with respect to changes to the regulatory activity of a DNA loci. We revealed higher cis-regulatory impact variants near differentially methylated sites that we further coupled with transcriptomic long-read sequencing results. Our investigation also highlights technical aspects of allele methylation analysis and the impact of sequencing coverage on the accuracy of genomic phasing. In particular, increasing coverage above 30X does not lead to a significant improvement in allele-specific methylation discovery, and only the addition of trio binning information significantly improves phasing. We investigated genomic variation in a single human individual and coupled computational discovery of cis-regulatory modules with allele-specific methylation (ASM) profiling. In this proof-of-concept analysis, we observed that SNPs located near methylated CpG sites on the same haplotype were enriched for sequence features suggestive of high-impact regulatory potential. This finding—derived from one deeply sequenced genome—illustrates how phased genetic and epigenetic data analyses can jointly put forward a hypotheses about the involvement of regulatory protein machinery in shaping allele-specific epigenetic states. Our investigation provides a methodological framework and candidate loci for future studies of genomic imprinting and cis-mediated epigenetic regulation in humans. Full article