Search Results (30)

This article presents an academic literature analysis of 75 Industry 4.0 (I4.0) and Industry 5.0 (I5.0) maturity models published between 2020 and 2024, examining their empirical validation, sectoral scope, geographical origin, and stated applicability to developing-country contexts. The study combines descriptive profiling, contingency-table analyses with exact tests and effect sizes, and a large-scale synthesis of 562 research gaps reported by model authors. Knowledge production is highly concentrated in single-country studies (77.3%) and in developed economies, while most models do not explicitly or implicitly document applicability to developing-country settings (approximately 83%). Empirical validation practices are uneven, with multiple-case studies (33.3%) and surveys (24.0%) dominating, and sectoral coverage is strongly skewed toward manufacturing, limiting transferability to other sectors relevant for emerging economies. A statistically detectable association is observed between the development level of the model’s country of origin and the presence of applicability statements (${\chi }^2$ = 17.13, $p<0.05$, moderate effect size), whereas authorship configuration shows no substantive association. Thematic analysis of reported gaps highlights persistent deficits in empirical rigor, sectoral breadth, SME orientation, operationalization of human-centric and sustainability dimensions associated with Industry 5.0, availability of implementation tools, and longitudinal or predictive evidence. The article concludes by outlining a research agenda focused on context-aware validation designs, broader sectoral grounding, and greater transparency and reproducibility, supported by open access to all underlying data, codebooks, and taxonomies. Full article

The aim of this study is to systematically capture, synthesize, and evaluate current research trends related to Automated Multiple-Choice Question Generation as they emerge within the broader landscape of natural language processing (NLP) and large language model (LLM)-based educational and assessment research. A systematic search and selection process was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, using predefined inclusion and exclusion criteria. A total of 240 eligible publications indexed in the Scopus database were identified and analyzed. To provide a comprehensive overview of this evolving research landscape, a bibliometric analysis was performed utilizing performance analysis and scientific mapping methods, supported by the Bibliometrix (version 4.2.2) R package and VOSviewer (version 1.6.19) software. The findings of the performance analysis indicate a steady upward trend in publications and citations, with significant contributions from leading academic institutions—primarily from the United States—and a strong presence in high quality academic journals. Scientific mapping through co-authorship analysis reveals that, despite the increasing research activity, there remains a need for enhanced collaborative efforts. Bibliographic coupling organizes the analyzed literature into seven thematic clusters, highlighting the main research axes and their diachronic evolution. Furthermore, co-word analysis identifies emerging research trends and underexplored directions, indicating substantial opportunities for future investigation. To the best of our knowledge, this study represents the first systematic bibliometric analysis that examines Automated Multiple-Choice Question Generation research within the context of the broader LLM-driven educational assessment literature. By mapping the relevant scientific production and identifying research gaps and future directions, this work contributes to a more coherent understanding of the field and supports the ongoing development of research at the intersection of generative AI and educational assessment. Full article

The aim of this study was to map global research on community-based strategies to reduce risky alcohol consumption and promote health, aiming to clarify growth, leading contributors, thematic structure, and integration with public-health frameworks. Using a PubMed corpus, we analyzed production, authorship, and collaboration indicators, built a thematic map (centrality/density) to identify core topics, and applied Multiple Correspondence Analysis to assess conceptual proximity between alcohol-specific and broader prevention domains. The dataset comprised 2607 documents across 916 sources, with output led by the USA, with substantial contributions from Australia, Canada, the UK, and rising activity in sub-Saharan Africa. The thematic map showed a mature core centered on adolescents and pregnancy, cross-cutting foundations in health education and sexual behavior with substance-related disorders, measurement-oriented niches at the periphery, and emerging work linking family planning. The Multiple Correspondence Analysis positioned alcohol-prevention terms close to health promotion, primary prevention, and epidemiology, with maternal–child health bridging community programs and clinical prevention. Overall, community-based alcohol prevention is expanding, globally distributed, and embedded in mainstream public-health practice. Limitations include the absence of citation data in PubMed, and future work should integrate citation-enabled databases and compare patterns across income groups. Full article

This study presents a novel framework for digital copyright management that integrates AI-enhanced perceptual hashing, blockchain technology, and digital watermarking to address critical challenges in content protection and verification. Traditional watermarking approaches typically employ content-independent metadata and rely on centralized authorities, introducing risks of tampering and operational inefficiencies. The proposed system utilizes a pre-trained convolutional neural network (CNN) to generate a robust, content-based perceptual hash value, which serves as an unforgeable watermark intrinsically linked to the image content. This hash is embedded as a QR code in the frequency domain and registered on a blockchain, ensuring tamper-proof timestamping and comprehensive traceability. The blockchain infrastructure further enables verification of multiple watermark sequences, thereby clarifying authorship attribution and modification history. Experimental results demonstrate high robustness against common image modifications, strong discriminative capabilities, and effective watermark recovery, supported by decentralized storage via the InterPlanetary File System (IPFS). The framework provides a transparent, secure, and efficient solution for digital rights management, with potential future enhancements including post-quantum cryptography integration. Full article

Background/Objectives: The internet is a major source of health information, including prostate cancer, but the quality of such content is inconsistent and may influence patient decision-making. This study aimed to evaluate the quality of online prostate cancer information by language, location, and user mode (“Logged off” vs. “Anonymous”) using the Google search engine. Methods: We conducted a cross-sectional, observational study between 5 and 11 December 2022, evaluating Google search results for prostate cancer information across three European cities (Basel, Munich, and Paris) and three languages (English, German, and French) in both “Logged off” and “Anonymous” user modes. A total of 900 websites (450 per mode) were retrieved and classified as: (1) university, (2) hospital, (3) governmental/medical societies, (4) industrial/commercial/NGOs, or (5) other. Website quality was assessed using the validated QUEST, which evaluates authorship, attribution, conflicts of interest, currency, and evidence. Inclusion rates and QUEST scores were compared across languages, locations, and categories using Kruskal-Wallis tests with multiple comparison adjustments. A total of 900 websites (450 per mode) were retrieved in English, German, and French from searches conducted in Basel, Munich, and Paris. Websites were classified as: (1) university, (2) hospital, (3) governmental/medical societies, (4) industrial/commercial/NGOs, or (5) other. Quality was assessed using the QUEST, which evaluates authorship, attribution, conflicts of interest, currency, and evidence. Inclusion rates and QUEST scores were compared across languages, locations, and categories using Kruskal-Wallis tests with multiple comparison adjustments. Results: Inclusion rates were high for both modes (Logged off: 86%; Anonymous: 85%). Location-based differences were significant for Basel (p = 0.04) and Paris (p = 0.02), while language-based differences were not significant. In “Logged off” mode, Category 1 achieved the highest median QUEST score (18.3), followed by 3 (17.8), while Category 2 scored lowest (14.2). Differences were significant (χ² = 50, p < 0.001), particularly between Categories 2 vs. 3 and 2 vs. 4 (p < 0.001). Similar patterns were observed in the “Anonymous” mode. Conclusions: Online prostate cancer information varies substantially in quality. French-language sites, despite high inclusion rates, were of lower quality, while English and German content more frequently met high-quality standards. University websites were the most reliable, hospital websites the least. Language, location, and site type influence the accessibility and reliability of online prostate cancer information. Full article

Today, healthcare systems face many challenges due to the increasing number of elderly people and the complex needs of patients with multiple diseases. Previous research has shown that assistive technologies (ATs), like wearable devices, mobile health (mHealth) apps, and smart monitoring systems, can help improve patient care and make healthcare services more efficient. However, many of these studies do not focus so much on hospitals and do not clearly show the effects on clinical outcomes. In this study, the authors conducted a bibliometric analysis using the Scopus database to determine how much research has been carried out on assistive technologies in hospitals, especially for patient profiling and treatment. The authors chose articles from the last 20 years using specific inclusion and exclusion criteria, and VOSviewer software (version 1.6.20) was used to study keywords, co-authorship, and citation networks to find research trends and missing areas. The results show that even if assistive technologies are growing fast, there are not many studies that focus on hospitals or on important outcomes like quality of care and treatment results. Most of the research is in computer science and engineering, and many keywords for hospital use are not common. This study discusses how assistive technologies can help change healthcare and also shows the current problems, like system integration, data privacy, cost, and whether users accept the technologies. The authors suggest that future research must look at personal solutions, international standards, and better cooperation between doctors, engineers, and policymakers. Full article

This study conducts a comprehensive bibliometric analysis of research applying data envelopment analysis (DEA) to the evaluation of sustainability and performance in complex socioeconomic systems between 2010 and mid-2025. DEA has become an increasingly valuable tool for measuring efficiency, benchmarking practices, and supporting decision-making in contexts where sustainability challenges intersect with economic, environmental, and governance dimensions. To capture global research dynamics, we extracted and merged bibliographic data from Web of Science and Scopus, analyzing publication trends, thematic clusters, co-authorship networks, citation structures, and keyword co-occurrences using bibliometric tools such as VOSviewer and Bibliometrix. Our findings reveal a consistent growth trajectory of the field, with research outputs peaking in 2020 and subsequently diversifying across multiple thematic areas. Conceptual mapping highlights two dominant domains: (i) policy, governance, and planning and (ii) environmental, ecological, and management applications, both linked through the overarching theme of sustainable development. The analysis further underscores the geographic diversity of contributions, the concentration of knowledge in key publication outlets, and the increasing connectivity of international collaboration networks. By identifying thematic gaps and underexplored intersections, this study emphasizes the need for more interdisciplinary approaches that integrate bibliometric insights with practical sustainability outcomes. The results provide a structured overview of the field’s evolution, offering researchers and policymakers a valuable reference point for advancing DEA applications in sustainability research. Full article

Background: Cognitive and neuropsychological effects of cancer and its treatments have gained increasing attention over the past decade, with growing evidence of persistent deficits across multiple cancer types. While numerous studies have examined these effects, the literature remains fragmented, and no comprehensive bibliometric synthesis has been conducted to map the field’s intellectual structure and emerging trends. Methods: A bibliometric and science mapping analysis was performed using the Scopus database to identify peer-reviewed articles published between 2015 and 2025 on neuropsychological or cognitive outcomes in adult cancer populations. Data from 179 eligible publications were analyzed with VOSviewer and Microsoft Power BI, applying performance metrics and network mapping techniques, including co-authorship, bibliographic coupling, co-citation, and keyword co-occurrence analyses. Results: Publication output increased steadily over the decade, with leading contributions from the Journal of Neuro-Oncology, Psycho-Oncology, and Brain Imaging and Behavior. Co-citation analysis identified three core intellectual pillars: (i) clinical characterization of cancer-related cognitive impairment, (ii) mechanistic and neuroimaging-based investigations, and (iii) neurosurgical and neuropathological research in brain tumors. Keyword mapping revealed emerging themes in sleep and circadian rhythm research, biological contributors to cognitive decline, and scalable rehabilitation strategies such as web-based cognitive training. Collaborative networks, while showing dense local clusters, remained moderately fragmented across disciplines. Conclusions: This review provides the first quantitative, decade-spanning map of cognitive oncology research, highlighting both consolidated knowledge areas and underexplored domains. Future efforts should prioritize methodological standardization, cross-disciplinary collaboration, and integration of cognitive endpoints into survivorship care, with the ultimate aim of improving functional outcomes and quality of life for cancer survivors. Full article

The volume of scientific publications has increased exponentially over the past decades across virtually all academic disciplines. In this landscape of information overload, objective criteria are needed to identify high-impact research. Citation counts have traditionally served as a primary indicator of scientific relevance; however, questions remain as to whether they truly reflect the intrinsic quality of a publication. This study investigates the relationship between citation frequency and a wide range of editorial, authorship, and contextual variables. A dataset of 339,609 articles indexed in Scopus was analyzed, retrieved using the search query TITLE-ABS-KEY (management) AND LIMIT-TO (subarea, “Busi”). The research employed a descriptive analysis followed by two predictive modeling approaches: a Random Forest algorithm to assess variable importance, and a binary logistic regression to estimate the probability of a paper being cited. Results indicate that factors such as journal quartile, country of affiliation, number of authors, open access availability, and keyword usage significantly influence citation outcomes. The Random Forest model explained 94.9% of the variance, while the logistic model achieved an AUC of 0.669, allowing the formulation of a predictive citation equation. Findings suggest that multiple determinants beyond content quality drive citation behavior, and that citation probability can be predicted with reasonable accuracy, though inherent model limitations must be acknowledged. Full article

In the present article, we illustrate and analyze the co-authorship network of Paul G. Mezey, focusing only on his collaborations on chirality-related papers. We consider scientific works from the Web of Science database as of 10 April 2024. Unlike previous studies on co-authorship networks, this network allows parallel edges, indicating multiple collaborations between the scientists involved. We also present a co-authorship network based on articles citing Mezey’s chirality-related papers (excluding self-citations), examining its main communities detected. Publications on the development of the theoretical and mathematical background of the new ideas on chirality are also considered. Full article

This study systematically reviews the academic literature on unpaid care work during and after COVID-19, emphasizing gender dimensions. Using Web of Science (WOS) and SCOPUS, it analyzes 75 empirical articles published between 2020 and 2024 in English and Spanish. The selection focused on studies addressing unpaid care from multiple perspectives, particularly family dynamics. Quantitative analysis examined frequencies and percentages, while qualitative analysis explored content depth. Results reveal a dominant biomedical perspective on care, often neglecting emotional well-being and broader socioeconomic impacts. The present study also identifies a lack of critical reflection on care’s gendered nature and unequal caregiving responsibilities. Women, historically burdened with care duties, faced increased domestic demands during the pandemic, due to school closures and limited services, exacerbating gender inequality and reducing workforce participation. A bibliometric analysis of research on COVID-19, gender, and social care highlights limited collaboration, with studies fragmented across research groups and lacking international co-authorship. This study calls for governmental and international initiatives to foster cross-border collaboration, enabling a more comprehensive understanding of care that integrates emotional and socioeconomic aspects alongside health concerns. This would promote a more inclusive and reflective approach to unpaid caregiving research. Full article

A company’s “value” is increasingly influenced by three criteria: the way it acts to protect the environment, its attitude towards society and the principles of corporate governance it has adopted. That is the Environmental, Social and Governance (ESG) acronym, and it has substantial impact on company value. To further understand the ESG landscape in business research, this article aims to analyze the existing literature and present the current state of knowledge, main trends, and future perspectives. Through the Scopus database, the authors examine a sample of 1034 articles spanning from 2006 to 2022. VOSviewer and Biblioshiny packages are used for performance analysis and visualization of the publication trends, the conceptual structure of the field and the research collaborations. The results suggest that the publication and citation trends of ESG register an upward trend over time. In terms of research institutions, most of the influential ones emanate from the US, while a significant percentage of articles were published in top-tier financial journals. Science mapping via co-authorship analysis bifurcates the sample into six clusters and reveals the major themes and their evolution. Keyword analysis unfolds emerging trends that could be further explored. Given the breadth of the sustainability field and the ever-changing business environment, this paper is of great practical importance in motivating companies to engage in ESG activities. To the authors’ knowledge, no other study has attempted a comprehensive and detailed BA covering multiple aspects and dimensions of ESG in the corporate research field. The theoretical framework of this paper fills this gap and offers an in-depth synthesis of all published papers, providing invaluable insights to scholars, the business community and regulatory authorities, and creating alternative research paths for aspiring researchers. Full article

In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students’ unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. Full article

Automated text generation (ATG) technology has evolved rapidly in the last several years, enabling the spread of content produced by artificial intelligence (AI). In addition, with the release of ChatGPT, virtually everyone can now create naturally sounding text on any topic. To optimize future use and understand how humans interact with these technologies, it is essential to capture people’s attitudes and beliefs. However, research on ATG perception is lacking. Based on two representative surveys (March 2022: n₁ = 1028; July 2023: n₂ = 1013), we aimed to examine the German population’s concepts of and attitudes toward AI authorship. The results revealed a preference for human authorship across a wide range of topics and a lack of knowledge concerning the function, data sources, and responsibilities of ATG. Using multiple regression analysis with k-fold cross-validation, we identified people’s attitude toward using ATG, performance expectancy, general attitudes toward AI, and lay attitude toward ChatGPT and ATG as significant predictors of the intention to read AI-written texts in the future. Despite the release of ChatGPT, we observed stability across most variables and minor differences between the two survey points regarding concepts about ATG. We discuss the findings against the backdrop of the ever-increasing availability of automated content and the need for an intensive societal debate about its chances and limitations. Full article