Search Results (3,703)

The rapid adoption of Generative Artificial Intelligence (GenAI) is transforming higher education by reshaping learning experiences, student engagement, and creativity. However, little is known about the mechanisms through which GenAI enhances creativity, particularly within tourism and hospitality education. Unlike prior studies that primarily examine students’ intention to adopt GenAI, this study explains how GenAI adoption translates into creativity through enhanced learning and learning engagement while accounting for ethical risks. Drawing upon contemporary learning and creativity literature, this study examined how students’ adoption of GenAI influences creativity through the mediating roles of enhanced learning and learning engagement, while also investigating the moderating effect of perceived ethical risks associated with GenAI use. Data were collected from 420 students enrolled in tourism and hospitality faculties and institutes across Egypt. The proposed research model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that enhanced learning and learning engagement are the principal mechanisms through which GenAI adoption improves students’ creativity, whereas perceived ethical risks significantly weaken these positive relationships. This study contributed to the emerging GenAI literature by moving beyond technology adoption perspectives to explain how GenAI shapes creativity outcomes in higher education. It further extends tourism and hospitality education research by providing one of the first empirical examinations of the mechanisms through which GenAI enhances student creativity while accounting for ethical concerns. The findings provide practical guidance for higher education institutions seeking to integrate GenAI responsibly while fostering creativity, engagement, and innovative learning environments. Full article

The rapid diffusion of generative artificial intelligence (GenAI) tools such as ChatGPT has unsettled established academic practices related to assessment, authorship, integrity, and disciplinary knowledge production. This qualitative repeated cross-sectional study examines how faculty and staff at a large public research university understood these changes in 2023 and 2024. The analysis draws on responses to the same open-ended survey question collected from independent respondent groups in 2023 (n = 104) and 2024 (n = 313). Responses were analyzed inductively through thematic analysis and subsequently interpreted using Disruptive Innovation Theory and Complex Adaptive Systems Theory. The analysis identified both continuity and change across the two datasets. Responses in 2023 emphasized uncertainty, threats to academic integrity, and defensive assessment redesign. Responses in 2024 more frequently described pedagogical experimentation, process-oriented assessment, and AI literacy as emerging academic and professional competencies. Concerns about authorship, equity, reliability, and inconsistent institutional guidance persisted across both years. The findings suggest that faculty and staff discourse shifted from primarily containing GenAI-related risks toward selectively integrating the technology into teaching and professional practice. However, because the study used independent cross-sectional samples, it does not establish individual change over time. The study contributes a theoretically informed account of institutional sensemaking during the first two years following ChatGPT’s public release and identifies strategies for balancing innovation, integrity, equity, and the human purposes of higher education. Full article

N-acetylcysteine (NAC) is a thiol-containing molecule with antioxidant, anti-inflammatory, antimicrobial, and cytoprotective properties that has increasingly been incorporated into hydrogel-based biomaterials. This systematic review evaluates the strategies used to integrate NAC into hydrogels and examines their effects on material properties, controlled release, biocompatibility, and therapeutic activity. The review was conducted according to the PRISMA guidelines using Scopus, PubMed, Web of Science, and SciFinder to identify English-language articles published between 2000 and 2025. Seventy-three studies met the eligibility criteria of this review. NAC has been employed as a physically loaded therapeutic agent, covalently conjugated polymer modifier, contributor to hydrogel crosslinking, metal-coordination ligand, and compound incorporated into nano- and microparticulate carriers dispersed in hydrogel. These approaches enable the modulation of gelation, swelling, adhesion, degradation, and drug-release kinetics. NAC-containing hydrogels have demonstrated robust antioxidant, antimicrobial, antibiofilm, anti-inflammatory, angiogenic, and tissue-regenerative properties in various in vitro and in vivo models, underscoring their potential for advanced biomaterial applications. Release profiles varied from rapid stimulus-responsive delivery to sustained release over several days, depending on the network architecture and the NAC–matrix interactions. However, comparisons among studies were limited by the heterogeneous formulations, release conditions, biological models, and outcome measures. Standardized physicochemical characterization, NAC stability assessment, dose–response evaluation, and rigorous preclinical validation are required to support the translation of NAC-based hydrogels into biomedical applications. We hope that this review will help scientists and innovation centers understand the potential of the NAC-containing hydrogel biomaterials discussed in this study, as well as the opportunities and demands for additional research in this field. Full article

Rapid innovation-driven transitions have, across successive eras, been associated with increased psychological distress, substance use, and suicide when social and economic change outpaces the adaptive capacity of individuals, communities, institutions, and governments. The early artificial intelligence (AI) economy is emerging at an unprecedented pace, reshaping education, employment, and social organization in ways that may intensify these risks, particularly among young people. This perspective argues that school-based mental health nursing, working collaboratively with school psychologists, counsellors, physicians, and other interdisciplinary professionals, represents a practical upstream response to AI-era disruption. Drawing on historical evidence from the Industrial Revolution, population mental health research, and the contemporary mental health nursing literature, the paper examines the mechanisms through which rapid socio-economic change may contribute to psychological distress and substance-use problems. It further distinguishes AI as both a source of socio-economic disruption and a clinical tool that can support—but not replace—relationship-based nursing practice. Strengthening school-based mental health nursing within interdisciplinary systems offers a practical, prevention-oriented strategy to promote resilience, facilitate early intervention, and mitigate downstream mental health and substance-use harms during the AI transition. Full article

Small- and medium-sized enterprises (SMEs) increasingly use artificial intelligence (AI)-based customer tools and social media marketing to compete in digital markets. However, prior research has not fully explained how customer-facing digital interaction and strategic customer sensing are converted into internal organisational capabilities or how alternative combinations of capabilities lead to business sustainability. In this study, we develop and test a sequential mixed-method framework for Thai SMEs. In Phase I, we applied the Interval Type-2 Fuzzy Delphi Method (IT2FDM) with 21 experts to validate 43 observed variables. In Phase II, we analysed 659 Thai SME responses using Partial Least Squares Structural Equation Modelling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). The PLS-SEM measurement assessment showed that service innovation and internal learning formed a consolidated service innovation–internal learning capability (SILC) construct, with 42 indicators retained in the final measurement model. The structural model supported all hypothesised paths: AI chatbot usage, social media marketing, and customer value anticipation were positively associated with SILC; SILC was positively associated with external learning, business performance, and business sustainability; external learning was positively associated with business performance; and business performance was positively associated with business sustainability. The fsQCA results showed that no single present or absent/low condition was necessary for business sustainability and identified three sufficient pathways, with SILC and business performance present across all primary configurations. One pathway further showed that strong SILC, external learning, and business performance could support business sustainability even when AI chatbot usage, social media marketing, and customer value anticipation were weak or absent. The findings advance SME digital transformation and sustainability research by demonstrating capability conversion, integrated innovation–learning transformation, and multiple compensatory pathways to business sustainability. Full article

Portland cement (PC) is responsible for about 7–9% of the CO₂ emissions worldwide. Alkali-activated materials (AAMs) are alternatives that have been intensely researched in recent decades, but some of their characteristics have hindered their extensive use in construction. Hybrid cements (HCs) and limestone calcined clay cements (LC³-50) are other, more innovative alternatives to PC, which seem easier to implement. Nowadays, there is active research about them, and pioneering studies about the durability of carbon steel reinforcements with these two types of binders are beginning to be published. In this review, the properties of HC and LC³-50 are briefly summarized and related with those of PC and AAMs. The uncertainties implied by using carbonation and chloride diffusion tests designed for PC to compare materials with different compositions are discussed. Using electrochemical methods seems logical to obtain full information about the durability of reinforcements in the innovative binders. However, these alternative binders have special features that can sometimes make the traditional electrochemical approach used yield misleading results. Factors such as the high resistivity some alternative mortars can exhibit, or the possible development of redox processes in the binder due to the nature of the precursors, must be borne in mind. Full article

As artificial intelligence (AI) technologies advance, traditional craftsmen face new challenges: innovating using digital tools while preserving cultural authenticity and heritage knowledge. The “hybrid artisan,” who strategically integrates AI-based design tools with traditional craft, emerges as a response to this tension. This article addresses research questions regarding how integrating generative AI technologies into design processes influences: (1) artisans’ productivity and product quality; (2) cultural authenticity and heritage preservation; (3) sustainable business models in creative entrepreneurship. The research methodology employs a convergent design with mixed methods, combining: (a) a systematic literature review (SLR) guided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA 2020, n = 33 articles, 2022–2025); and (b) a qualitative survey (n = 13 artisans, Romania; semi-structured questionnaire, 34 items). The literature review identifies three dominant human–AI collaboration models: task-level cooperation, process-level coordination, and system-level co-creation. Diffusion models fine-tuned with low-rank adaptation (LoRA) and generative adversarial networks (GANs) achieve cultural authenticity scores of 73–95% while reducing design time by 30–70%. Empirical data reveal paradoxes: artisans value authentic creativity and sustainability (4 of 13 respondents (31%) rate sustainability as “extremely important”) but adopt AI cautiously (6 of 13 respondents (46%) report that they were not familiar with AI tools). Those using AI report 15–40% productivity gains without a proportional increase in sales, suggesting that market recognition of AI-assisted crafts remains uneven and that sustainability benefits are not yet clearly linked to AI use in practice. The successful “hybrid artisan” model relies on collaborative rather than autonomous AI positioning, explicit cultural safeguards in system design, and transparent communication with consumers about AI involvement. This research provides a conceptual heuristic, points to new research directions, and outlines policy implications for understanding when and how AI-assisted craft practices may support cultural integrity while also accepting that such benefits are context-dependent and not universally validated. Full article

As an integrated educational model that combines inquiry-based learning with travel experiences, study travel plays a significant role in fostering students’ social responsibility, innovative spirit, and practical abilities, and has attracted growing attention worldwide. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study conducts a systematic review of the literature published between 1950 and 2025. It identifies six major themes corresponding to the three stages of study travel. Through bibliometric analysis, the research delineates publication trends, core journal distributions, and keyword co-occurrence networks in the field. The findings indicate that experiential learning, constructivism, and transformative learning theory constitute the dominant theoretical foundations in current scholarship. Moreover, a notable “learning–travel imbalance” is observed, whereby educational processes and learning outcomes receive substantially more scholarly attention than travel attributes, industry logic, and managerial dimensions. This paper offers a systematic mapping of the knowledge terrain and theoretical architecture in international study travel research, clarifies existing gaps, and suggests directions for future interdisciplinary integration and for deeper synthesis of “learning” and “travel” in both research and practice. Full article

The aorta is a single arterial organ, yet the clinical response to its diseases is fragmented across specialties, institutions, and nations. Acute aortic syndromes remain lethal at every stage of the care continuum: a large share of patients with type A dissection die before reaching any hospital, roughly half die before reaching a specialist centre, a substantial proportion are misdiagnosed at first medical contact, and untreated mortality has historically been estimated at approximately one to two percent per hour. Reported 30-day mortality differs across surgical registry cohorts—for example, 7.6% in the Japan Cardiovascular Surgery Database and 16.9% in the cited GERAADA cohort—but such ecological comparisons are descriptive and cannot identify the contribution of organisational, biological, clinical, or ascertainment differences. This review synthesises registry, population, and health-services data from Europe, Japan, North America, and low- and middle-income settings to characterise where the pathway fails and what each system has already demonstrated. We argue that the next material advance in aortic care will come not from any single innovation in isolation but from the architecture that connects prevention, diagnosis, treatment, and follow-up and that system redesign and technological progress are complementary rather than competing; we propose a five-pillar framework (Prevention, Presentation, Pathway, Person, and Learning) mapped throughout to published exemplars and future research priorities. Full article

Generative Artificial Intelligence (AI) has become a rapidly expanding area of scientific research, generating a growing body of literature across technical and applied domains. This study provides a comprehensive bibliometric analysis of global generative AI research to characterize its publication growth, disciplinary and geographical distribution, institutional participation, funding patterns, citation performance, and thematic development. The analysis covers 22,758 Scopus-indexed journal articles and conference papers published between 2020 and 2025, identified using the phrase “generative artificial intelligence” enclosed in double quotation marks in TITLE-ABS-KEY fields. A reproducible computational workflow was used to examine publication output, document types, subject areas, countries, institutions, funding sponsors, citation patterns, and keyword co-occurrence. Citation analysis incorporated annualized citation rates and cohort-normalized annual citation rates to improve comparisons across publication years. Results show a pronounced acceleration in publication output after 2022, with an approximate 105% compound annual growth rate over 2020–2025. Computer Science remained the largest subject area, while substantial representation extended across Engineering, Social Sciences, Medicine, Mathematics, and other domains. Publication activity was concentrated among leading countries and institutions, with the United States and China recording the highest output. Funding analysis identified major national and international sponsors through publication–sponsor associations. Citation performance varied substantially across cohorts, with the 2023 cohort exhibiting the highest cohort-normalized annual citation rate (1.58). Keyword analysis revealed a thematic shift from early AI and GAN-related research toward generative AI and large language model themes, alongside education, innovation, human–AI interaction, and responsible AI. The findings provide an evidence-based, multidimensional characterization of the rapidly evolving generative AI research landscape. Full article

Carbonate formations host abundant hydrocarbon resources and constitute a key target for current and future petroleum explorations. A massive Paleocene-Eocene carbonate platform system overlies Late Cretaceous Deccan volcanic rocks within the Indus Fan Offshore Basin. Low geophysical exploration maturity and insufficient targeted research on carbonate reef identification and platform evolution hinder hydrocarbon discoveries in this region. Using 2D seismic data, this study employs multiple seismic attribute methods to characterize carbonate reef-related anomalies. An innovative reef identification method based on a weighted superposition of spectrally decomposed amplitude spectra is proposed to delineate reef-prone sedimentary facies and characterize the multi-stage evolutionary patterns of the carbonate platforms. Based on quantitative constraints from seismic reflection and amplitude differences, platform evolution is classified into four clear phases: initial development, platform expansion, gradual decline and platform drowning. The initial development phase shows prominent high-amplitude and low-frequency seismic responses, and intervals with such high-amplitude responses may indicate favorable reservoir potential. Platform margin zones show the most prominent amplitude anomalies and are considered priority exploration targets, suggesting excellent hydrocarbon potential within strata formed during the initial platform stage. This study provides quantitative geophysical support for the stage classification of Paleocene–Eocene carbonate platforms in the Indus Fan Offshore Basin, and offers practical guidance for carbonate reservoir exploration and the prospect evaluation of analogous basins worldwide. Full article

The quality and safety supervision of agricultural products at the grassroots level has long faced the triple superposition dilemma of small-sample sampling, multi-source evidence conflict, and risk chain evolution. Although existing data-driven models have considerable accuracy, they are difficult to leverage for intervention decisions, and the simple serial connection of traditional Bayesian networks and evidence theory cannot respond to dynamic scenarios. Aiming at this research gap, this paper constructs a dynamic risk assessment model, CIBE-DR, that deeply couples Bayesian networks with evidential reasoning. It contains three core innovations. First, the structure learning method of the causally identifiable Bayesian network embeds a graded do-calculus identifiability score covering both back-door and front-door criteria into the BDeu scoring function and combines this reward with an expert-prior divergence penalty that breaks Markov equivalence so as to realize the transition from relevance modeling to intervention decision modeling. Second, the conflict-aware adaptive evidence synthesis rule orthogonally decomposes multi-source conflict into an epistemic component and an ontological component, which are modeled respectively by Tsallis belief entropy and abductive inference over a discrete twenty-seven-point heterogeneity hypothesis space and are then fused under a reparameterized Dempster–Yager interpolation in which the two endpoints recover the two named rules under a single consistent interpretation. Third, the bidirectional closed-loop coupling mechanism between BN and ER realizes the mutual calibration between the conditional probability table and the evidence credibility prior under a Lyapunov monotone descent argument with the explicit Lipschitz bound $L_{\theta }$ ≤ 0.028 < 1, endowing the model with time-varying self-correction ability. Based on experiments on 156,847 sampling samples from counties and townships in East China, Central China, and Southwest China from 2021 to 2024, the proposed method achieved the best value in six of the seven evaluation indicators, with a minority recall of 0.864 ± 0.014, an intervention effect estimation error of 0.063 ± 0.005, and a dynamic response delay of 2.8 ± 0.3 days, significantly ahead of eleven mainstream baselines under the McNemar test on classification ($p$ < 0.001) and the Wilcoxon signed-rank test on intervention-effect estimation ($p$ < 0.001). The only indicator on which CIBE-DR does not lead is overall accuracy, which is 0.002 lower than that of Transformer; this difference does not reach statistical significance under the McNemar test ($p$ = 0.32) and does not weaken the value of grassroots supervision in the strong-imbalance scenario where the positive rate is only 1.04%. The robustness advantage of the model is particularly prominent in the scenarios of sparse data, adversarial perturbation, and prior-graph incompleteness, and the intervention-effect estimates were additionally validated against two post-2022 policy interventions with absolute deviations of 1.4 and 1.2 percentage points respectively. These results verify the product gain and grassroots deployability of the three mechanisms. Full article

The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). Full article

Lycium barbarum polysaccharides (LBPs), the primary bioactive components extracted from the traditional medicinal and edible plant Lycium barbarum L., have attracted extensive attention in biomedical research due to their superior anti-inflammatory, immunomodulatory and biological safety properties. Inflammation is a key pathological basis of various chronic metabolic and immune diseases, and LBPs can exert targeted regulatory effects on inflammatory responses through multiple molecular pathways. This article systematically reviews the latest advances in extraction technologies and structural characterization of LBPs, and illustrates their regulatory mechanisms against inflammation in different organs. Relevant toxicological studies are also summarized to confirm their low toxicity and safety. Additionally, it comprehensively concludes the current progress of LBPs in healthcare products, pharmaceutical development, and related patent innovations. Future perspectives highlight green efficient extraction, structural modification, targeted delivery, and in-depth mechanism exploration, aiming to provide a comprehensive theoretical basis for the further development and industrial application of LBPs. Full article

This review paper systematically investigates the surface modification technology of wooden table tennis blades, with a particular focus on the inherent conflict between coating-induced protection and the preservation of acoustic performance—a critical yet underexplored aspect of blade design. While protective coatings are essential for enhancing durability against moisture, wear, and impact, they inevitably alter the blade’s vibrational characteristics and acoustic feedback, compromising the tactile–auditory perception that elite players rely upon. The current literature predominantly treats protection and acoustics as separate design objectives, lacking an integrated framework to resolve their inherent trade-off. To address this gap, this review establishes a material–structure–function integrated design paradigm that elucidates the synergistic optimization of coating protection and acoustic response. We systematically analyze the regulatory mechanisms of key coating parameters—specifically elastic modulus, density, and damping coefficient—on blade vibration modes and impact sound characteristics, demonstrating that conventional singular optimization inevitably leads to undesirable frequency shifts and diminished tactile feedback. Our synthesis of materials science, acoustic analysis, and biomechanics reveals that the key to synergy lies in constructing a mechanical impedance-matching transition system through material selection and thickness gradient design. Notably, we show that a multi-layer gradient coating architecture, guided by finite element simulation, can enhance protective performance by 40% while restricting acoustic deviation to within 5%, validating a rational “design–simulation–verification” closed-loop methodology. Furthermore, this review identifies critical research frontiers, including smart adaptive coatings and sustainable bio-based materials, and proposes a multi-objective optimization framework to bridge the gap between laboratory innovation and manufacturable, high-performance sporting equipment. This work provides a foundational theoretical roadmap for the next-generation design of competition-grade table tennis blades, balancing durability with the nuanced sensory demands of elite athletes. Full article