Search Results (5,214)

Fake news has caused significant harm and disruption across various sectors of society. With the rapid advancement of the Internet and social media platforms, both academic and industrial communities have shown growing interest in multimodal fake news detection. In this work, we propose MERF (Multimodal Evidence Retrieval and Forensics with LVLM), a unified framework for multimodal fake news detection that leverages the reasoning capabilities of Large Vision-Language Models (LVLMs). While LVLMs outperform traditional Large Language Models (LLMs) in processing multimodal content, our study reveals that their reasoning abilities remain limited in the absence of sufficient supporting evidence. MERF addresses this challenge by integrating web-based content retrieval, reverse image search, and image manipulation detection into a coherent pipeline, enabling the model to generate informed and explainable veracity judgments. Specifically, our approach performs cross-modal consistency checking, retrieves corroborative information for both textual and visual content, and applies forensic analysis to detect potential visual forgeries. The aggregated evidence is then fed into the LVLM, facilitating comprehensive reasoning and evidence-based decision-making. Experimental results on two public benchmark datasets—Weibo and Twitter—demonstrate that MERF consistently outperforms state-of-the-art baselines across all major evaluation metrics, achieving substantial improvements in accuracy, robustness, and interpretability. Full article

Background/Objectives: Nomophobia, the irrational fear of being without a mobile phone, is increasingly prevalent among university students and has emerged as a concerning form of digital dependence. Among nursing students, this condition is particularly relevant due to the emotional demands and cognitive challenges of healthcare education. Nomophobia has been linked with adverse psychological outcomes, sleep disturbances, and impaired academic and clinical performance. However, existing evidence remains fragmented and lacks an integrated conceptual synthesis. This review aimed to synthesize current evidence on the prevalence, correlates, and outcomes of nomophobia among nursing students. Methods: An integrative review was conducted following Whittemore and Knafl’s methodology and PRISMA guidelines. A systematic search was performed in PubMed, CINAHL, PsycINFO, PsycArticles, and Medline (between 2015 and 2025), supplemented by Google Scholar. Cross-sectional studies and literature focusing on nomophobia in nursing students were included. The primary studies and selected review articles were considered when no overlap with the included primary evidence was identified. Methodological quality appraisal was assessed using validated tools (QuADS and JBI). Results: Twenty-two studies were included (19 cross-sectional and 3 reviews). Four thematic areas emerged: prevalence and severity (50–90% moderate to severe); psychological correlates (anxiety, depression, stress, insomnia, alexithymia, fear of missing out); academic and cognitive outcomes (impaired performance, procrastination, reduced decision-making); and behavioural predictors (excessive smartphone use and emotional dysregulation). The Nomophobia Questionnaire (NMP-Q) was the most frequently used instrument. Conclusions: Nomophobia represents a relevant dimension of the mind–technology relationship in nursing education, with implications for students’ mental health, academic engagement, and clinical readiness. Addressing nomophobia may support healthier learning environments and contribute to the development of emotionally competent and safe future healthcare professionals. However, significant gaps remain, particularly regarding longitudinal evidence and intervention-based approaches. Full article

Background/Objectives: Simulation-based education often lacks personalization, focusing on technical competence rather than individual student profiles. This protocol describes a study designed to evaluate whether adapting gamified narratives to nursing students’ personality profiles has the potential to support academic performance in obstetrics. This study aims to validate the integration of psychometric profiling and AI as a sustainable strategy for personalized clinical training. Methods: A cluster-randomized controlled longitudinal pilot trial will be conducted at the University of Atlántico Medio. The protocol has been submitted for registration at ClinicalTrials.gov (Registration Pending). Thirty-eight second-year nursing students meeting inclusion criteria (excluding repeaters or those with prior specialized training) will be assigned by natural practice to either a control group (generic gamification) or an experimental group (gamification adapted according to Player Personality and Dynamics Scale profiles using AI-generated content). The intervention comprises four clinical simulation sessions focusing on pregnancy and childbirth, which are managed via the Wix platform. The primary outcome is academic performance, measured as “Learning Gain” (post-test scores minus pre-test scores). Secondary outcomes include student satisfaction measured via the Gameful Experience Scale. Data will be analyzed using Mann–Whitney U tests to compare overall efficacy and intragroup evolution. To minimize observer bias, knowledge assessments will utilize automated, objective scoring, and participants will be blinded to the study hypothesis. Expected Outcomes: The study aims to establish the technical and pedagogical feasibility of integrating AI-adapted narratives into nursing curricula. It is anticipated that the personalized approach will show positive trends in learning gains and engagement patterns, providing a baseline for larger multicenter trials. Conclusions: This protocol presents a framework for “Precision Education” in nursing, shifting from “one-size-fits-all” simulations to student-centered adaptive training. The use of Generative AI makes such personalization sustainable and cost-effective for health science faculties. Full article

Early and reliable fault detection in Brushless Direct Current (BLDC) motors is essential for improving system reliability and reducing unplanned industrial downtime. This study presents a controlled experimental investigation of data-driven machine learning approaches for the classification of multiple common BLDC motor faults. Four representative fault-related indicators were obtained under systematically designed operating conditions, and a consistent feature extraction procedure was applied prior to model development. A comparative evaluation was conducted using Multi-Layer Perceptron (MLP), Support Vector Machines (SVM), k-Nearest Neighbour (kNN), and decision tree-based classifiers. All models were trained and tested on the same dataset using an identical validation protocol to ensure methodological fairness and reproducibility. Performance was assessed through standard classification metrics, enabling a transparent comparison of predictive capability and stability. The results show that the MLP model achieved the highest overall classification accuracy (91.6%), closely followed by SVM (91.4%) and kNN (90.2%). Although the performance differences are moderate, the neural network demonstrated more consistent behaviour in scenarios where fault signatures exhibited overlapping characteristics. These findings suggest that non-linear feature interactions play a significant role in BLDC fault discrimination and can be effectively captured by multi-layer architectures. The study provides a reproducible experimental framework and a balanced performance assessment that may support both academic research and the practical development of intelligent condition monitoring systems for BLDC-driven applications. Full article

Self-regulated learning (SRL) is a fundamental competence for academic transition and success in higher education, especially in health sciences, where autonomy and learning management are essential. This systematic review analyzed the relationship between SRL, academic performance, and student well-being among undergraduate health sciences students. Following the PRISMA protocol, 39 articles published between 2015 and 2025 on Web of Science, Scopus, and PubMed databases were selected. The consolidated sample consisted of 24,835 participants. The methodological quality of the selected studies was assessed using the Newcastle–Ottawa scale (NOS). A predominantly positive association was found between high levels of SRL and academic performance (GPA) (with correlation coefficients ranging from r = 0.11 to r = 0.55 in the primary studies). Furthermore, evidence from standardized self-report questionnaires in the reviewed literature indicates that several studies report female students showed higher levels of organization and planning, but these findings were not consistently observed across all studies. SRL acts as a key protective factor against stress, anxiety, and academic burnout. However, a “stagnation paradox” was identified: SRL skills do not always evolve linearly, often showing regression or stagnation in advanced clinical years due to the high cognitive load and insufficient support structure in those environments. Regarding sociodemographic variables, female students reported higher levels of planning and responsibility. SRL does not develop spontaneously with academic progress. Therefore, higher-education institutions must implement systematic and intentional pedagogical strategies from the early years of training to foster student well-being and the development of resilient professionals. Full article

Designing long-range aircraft for 2050 is a complex, multi-disciplinary challenge requiring integration of technical performance with sustainability objectives, including environmental responsibility, economic viability, circularity, and social acceptance. Existing studies on stakeholder needs in aviation are limited, focusing on specific groups, technical requirements, or individual aircraft concepts, resulting in a fragmented understanding of sustainability-driven needs. This study addresses this gap by systematically identifying stakeholders who influence long-range aircraft development and deriving 191 stakeholder needs, organized into coherent categories spanning manufacturers, operators, passengers, regulators, communities, and energy suppliers. Needs were classified across technical, environmental, economic, circular, and social dimensions, based on a comprehensive review of academic and grey literature, regulatory documents, and industry sources. The resulting framework provides a structured, reproducible approach to support conceptual aircraft design and requirement definition within the European EXAELIA project. By integrating multi-dimensional stakeholder expectations early in the design process, this approach facilitates aircraft development that is technically robust, environmentally sustainable, economically viable, circular, and socially inclusive, demonstrating the value of a stakeholder-driven method for sustainable systems engineering. Full article

One of the main problems faced by database administrators for optimizing analytic workloads is fragmentation. Therefore, in recent decades, several fragmentation methods for analytical platforms have been proposed because this technique is able to improve the performance of OLAP (Online Analytical Processing) queries. In this study, we conducted an exploratory review of horizontal fragmentation methods for analytical repositories such as data warehouses, data lakes, and data lakehouses. This study presents a scoping review conducted using Arksey and O’Malley’s methodological framework and reported according to the PRISMA guidelines, covering 58 primary studies on horizontal fragmentation published from 2015 to 2025. Our analysis focuses on five aspects: (1) determining the main techniques used in horizontal fragmentation works for analytical repositories, (2) the classification of these studies, (3) the performance metrics considered when evaluating the horizontal fragmentation scheme, (4) the information type indexed by the repositories, and (5) the technologies most used by the approaches. Our findings suggest that horizontal fragmentation is a good opportunity to improve the performance of analytical workloads in most cases. The results of this scoping review will provide important guidelines for future research on horizontal fragmentation methods. In addition, the results will provide clues about the use of OLAP technologies for professionals and academics considering future directions. Full article

This study explores how various factors—including motivation, emotion, demographics, and institutional characteristics—interrelate to shape academic success among Greek university students. Based on Self-Determination Theory (SDT) and Tinto’s model of integration, it fills a gap in research by addressing the specific characteristics of the Greek higher education system. While prior research emphasizes the importance of motivation and integration, few studies have combined these with factors like program alignment, student type, and gender in a structural model. A sample of 284 students, aged 18–28, completed validated Greek versions of the AMS, PASS, and SACQ. Structural Equation Modeling (SEM) was used to assess both the direct and indirect effects on academic success. Key variables included gender, traditional vs. non-traditional student status, first-choice program enrollment, intrinsic and extrinsic motivation, academic and social integration, emotional adjustment, institutional attachment, and procrastination. Gender (female) was the strongest predictor of academic success (β = 0.819), affecting outcomes through intrinsic motivation, emotional adjustment, and procrastination. Academic integration (β = 0.424) and traditional student status (β = 0.300) also significantly predicted GPA. Social integration had an indirect effect through academic engagement. Procrastination (β = −0.228) and emotional maladjustment (β = −0.143) were major obstacles. While selecting a first-choice program affected institutional attachment, it did not directly impact academic performance. Conclusion: Academic success in Greek universities is influenced by a range of personal, motivational, and contextual factors. Improving integration, reducing procrastination, and fostering intrinsic motivation can boost academic outcomes. Interventions should consider gender and student pathways to be more effective. Full article

The hospitality industry is characterised by high levels of complexity, uncertainty, and interpersonal intensity. While emotional intelligence (EI) has dominated both academic and practitioner debates on effective hospitality leadership, considerably less attention has been paid to cognitive intelligence (CI) as a foundational managerial competency. Drawing on interdisciplinary research from management, psychology, and hospitality studies, this paper argues that cognitive intelligence constitutes a critical yet under-theorised capability for innovation management and organisational performance in hospitality contexts. Building on established distinctions between cognitive and emotional intelligence, and synthesising evidence from hospitality and general management research, this paper develops a conceptual framework positioning CI as a core meta-competency that enables sensemaking, judgement, problem-solving, and adaptive decision-making in complex service environments. This conceptual paper contributes to the literature on innovation and hospitality management by reframing managerial intelligence as a performance-enabling capability that underpins learning, adaptability, and long-term organisational effectiveness in hospitality organisations. Full article

Background/Objectives: This study investigates the predictive role of pre-operative sonographic assessment of ovarian mobility in determining intra-operative ovarian resectability among patients undergoing vaginal hysterectomy for pelvic organ prolapse. Methods: This prospective study was conducted in a tertiary academic urogynecology center. Women with pelvic organ prolapse scheduled for vaginal hysterectomy were consecutively recruited after providing informed consent. Pre-operatively, all patients had a detailed history, pelvic examination (POP-Q), and pelvic floor ultrasound (including assessment of the mobility of both ovaries and sonographic determination of ovarian descent in relation to the pelvic ischial spines). Patients were planned for vaginal hysterectomy, anterior and posterior colporrhaphy, McCall culdoplasty, and bilateral salpingo-oophorectomy (SO), where feasible. During surgery, the location and mobility of the ovaries, as well as the presence of peri-ovarian adhesions, were recorded. Pair-to-pair comparisons between sonographic and clinical findings were performed. Results: From February 2023 to January 2024, 50 Caucasian adult women underwent reconstructive vaginal surgery for prolapse. Thirty-five patients underwent concomitant bilateral SO via vaginal route, seven underwent unilateral SO, and three under went salpingectomy only. ROC analysis indicated that pre-operative ultrasound assessment of ovarian mobility predicts: (1) intra-operative ovarian mobility (sensitivity 95.6%, specificity 77.8%); (2) the presence of peri-ovarian adhesions (sensitivity 46.1%, specificity 94.2%); and (3) resectability, i.e., the ability to perform SO via the vaginal route (sensitivity 96.4%, specificity 50.0%). The absence of ovarian mobility was not associated with an increased risk of intra-operative and post-operative complications. Conclusions: Pre-operative sonographic assessment of ovarian location and mobility can predict ovarian location and resectability during vaginal surgery with high diagnostic accuracy. Full article

The rapid transition to Industry 4.0/5.0 has widened the gap between graduates’ skill sets and labor market expectations; this study aimed to profile student competencies and align academic pathways with inclusive and adaptive AI-driven learning. A quantitative design was applied: an online survey of n = 126 students (engineering and economics, February–March 2025), expert evaluations from 5 faculty and 5 employers on a 5-point scale, framed by T-shaped competencies, 4C skills, and Bloom’s taxonomy. Analysis was performed in Python 3.11; future demand until 2035 was forecasted using ARIMA and Prophet models trained on publicly available labor market data (OECD, WEF, Eurostat 2015–2024); competency prioritization employed K-Means clustering and Random Forest models. Strengths included cooperation 4.2, critical thinking 3.9, communication 3.8, and creativity 3.6. Deficits were programming 2.8, project management 3.2, and solution development 3.2; employers rated programming at 2.5 (−0.7 compared to faculty). Forecast 2025–2035 showed growth in demand for programming +56% (3.2 → 5.0), data analytics +39% (3.6 → 5.0), project management +34% (3.2 → 4.3), digital literacy +30% (3.7 → 4.8), and critical thinking +15% (3.9 → 4.5). Clustering identified critical (programming, analytics, project management), supporting (creativity, communication, teamwork), and optional (narrow theoretical depth) competencies. Curriculum adjustment with practice-oriented modules, AI-enabled adaptive learning, and systematic university–employer feedback is essential; the proposed AI-supported profiling model is scalable and enhances inclusiveness. Full article

This study examines the intercorrelations that exist between brand equity, expectation confirmation, and sustainable repurchase intentions within Saudi Arabia’s burgeoning e-commerce sector, emphasizing its cultural and digital transformation context aligned with Vision 2030. The main objectives are to identify how brand perceptions influence customer satisfaction, and to explore the applicability of integrated theoretical frameworks, namely Brand Equity Theory and Expectation-Confirmation Theory in explaining sustainable consumer behavior in an emerging market. Utilizing a quantitative research approach, data was collected through an online self-reported questionnaire distributed via social media platforms targeted at active e-commerce consumers in the Hail region. Convenience sampling combined with snowballing yielded a sample size of 361 respondents, ensuring broader demographic representation. Data analysis was conducted using structural equation modeling with partial least squares (SEM-PLS), a technique suited for theory exploration and handling complex variable relationships. The findings demonstrate that brand awareness and brand image significantly positively influence customer satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. Similarly, expectations and perceived performance also have significant positive effects on satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. All hypotheses were supported, with significant relationships observed between the variables, with the model demonstrating robust validity and fit, evidenced by acceptable SRMR, d_ULS, and d_G values. The study’s originality lies in its culturally contextualized application of these theories to a less studied yet vital emerging market, providing novel insights into how cultural nuances influence digital consumer loyalty. These outcomes contribute to both academic theory and practical strategies for e-commerce firms aiming to build sustainable, trust-based relationships within culturally diverse digital environments, offering a valuable blueprint for similar markets undergoing digital transformation. Full article

This study maps European countries’ resilience to cognitive warfare by developing a cross-national composite measure. The framework integrates three pillars: information ecology, institutional-digital capacity, and socioeconomic context—drawing on a systemic perspective linking social structures to societal functions. Publicly available secondary indicators are compiled from online sources for EU (European Union) and EEA (European Economics Area) states. The dataset is examined through descriptive analysis, association testing, multivariate modelling, dimensionality reduction to derive a composite resilience score, and unsupervised clustering to produce a country typology. Indicators capture governance effectiveness, e-government maturity, public-sector AI (Artificial Intelligence) readiness, digital connectivity and infrastructure, media freedom and broader media-ecosystem quality, academic freedom, and socioeconomic vulnerabilities such as youth labour market exclusion. Results show that resilience aligns most strongly with institutional capacity and governance performance; a healthy ecology acts as a reinforcing layer. Digital infrastructure appears necessary but insufficient without capable, credible institutions and coherent public policy. Socioeconomic vulnerabilities tend to erode resilience and heighten susceptibility to hostile cognitive influence. The study concludes that policy efforts should prioritise governance integrity and effectiveness, end-to-end digital government, responsible public-sector AI capability, and safeguards for media and academic autonomy, alongside measures that improve youth inclusion. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Academia has undergone significant changes recently, such as financial cuts, restructuring, new management policies, precarious employment, and rapid technological advancement. We argue that these shifts can lead to organizational anomie, characterized by deregulation and a breakdown of academia’s normative structure, impacting teaching, learning, and research. In Norway, we conducted qualitative individual interviews with academics (n = 12) and two group interviews with students (n = 13) to explore whether they perceive their academic environment in terms of organizational anomie. Staff participants see the academic environment as transformative but increasingly shaped by economic rationality. They also see a conflict between academic ideals and current work designs and practices, which are highly gamified, reliant on quantified performance measures, and dependent on external funding. They view these changes negatively, casting doubt on whether universities can still fulfil their mission in pursuit of independent critical inquiry. Students report a mismatch between expectations and reality, with some viewing academia instrumentally as a platform to the labor market, reflecting governmental policies to promote employability as a key goal of higher education. Others regard academia as a space for critical inquiry. Although the focus group discussions ultimately converged on the university’s norms and values being a space for critical inquiry, both groups expressed dissatisfaction that the current system fails to fully meet either of these goals. These findings are discussed in light of our understanding of organizational anomie in academia. Full article