With the increasing complexity of supply chain management, supply chain concentration (SCC) has become a prominent research topic in academia and practice. To clarify the developmental context and research trends within this field, this study utilizes the Web of Science core collection as the data source, selecting 362 English-language publications from 1975 to 2025. CiteSpace 6.2 was employed to conduct a visual bibliometric analysis, systematically examining the social structure, conceptual structure, and intellectual structure of SCC research through co-authorship, co-word, and co-citation analyses. The results indicate rapid growth in SCC research since 2020, with China and the United States being the major contributing countries, and collaborations exhibiting regional characteristics. High-frequency keywords prominently include "customer concentration," "supplier concentration," and "performance," with research themes progressively extending toward frontier topics such as "digital transformation," "green innovation," and "corporate social responsibility." Co-citation analysis identified representative works by authors such as Panos Patatoukas, Dan Dhaliwal, and Murillo Campello, highlighting a shift in research focus from traditional performance perspectives to governance mechanisms and sustainable strategies within a digital context. This study summarizes core literature clusters, evolutionary paths of clusters, and significant citation bursts, revealing interdisciplinary integration and paradigm shifts in SCC research. The paper provides a systematic review of future directions in SCC studies.

We present a scientometric analysis of the research landscape about the application of Total Quality Management (TQM) rules within surgical and broader healthcare contexts. The study utilizes a dataset of 200 highly cited articles extracted from Scopus and maps the intellectual structure, key themes, and evolving priorities in this critical field. The study discloses a mature yet dynamically evolving survey domain characterized by a distinct shift from theoretical process frameworks to patient-centric and data-driven methodologies. Key study clusters determined include Patient Safety Culture and Adverse Event Reduction, Specific Surgical Procedure Optimization, Methodological Frameworks for Quality Improvement (QI), and Ethical & Inclusive Care Considerations. Highly cited articles and authors as well as influential institutions are determined, representing a global collaboration network with strong representation from the United States and Northern Europe. The most effective publications, as stated by citation frequency, are studied in detail, briefing their contributions to building safety protocols, validating QI methodologies like DMAIC, and expanding the discourse on patient engagement and health equity. The present review summarizes that the field is advancing towards more predictive, equitable, and technologically integrated models of care, with future research poised to leverage artificial intelligence and federated learning to personalize and enhance surgical quality improvement on a global scale.

The COVID-19 pandemic has created an exponential trend in global research mobilization, with vaccines as a primary objective. This present survey gives a scientometric study on the landscape of research concerning the COVID-19 pandemic and vaccines, using a dataset of 19,749 records from the Scopus database where only 200 records are chosen for the review. The analysis maps the conceptual structure and dynamics of this area by looking at the publication trends, key contributors, core research themes, and impact of the published papers. The results have disclosed an exponential trend in publications, peaking in 2021-2022, driven by the quick requirement for development, evaluating, and deploying vaccines. The study was investigated by large-scale international collaborations, with prolific contributions from universities as well as vaccine makers in the United States, China, and Europe. High-impact journals like The Lancet and The New England Journal of Medicine considered critical dissemination channels. Thematic clusters are dominated by vaccine development and immunology, real-world effectiveness, SARS-CoV-2 variants and immune evasion, vaccine safety, and public acceptance. The evolution of study is concentrated on from initial clinical trials to real-world evidence, variant-specific challenges, and eventually, long-term impact and systemic lessons. The survey gives a comprehensive review of a defining scientific effort, showing the collaborative and rapid-response nature of research during a global health crisis.

Bidirectional Encoder Representations from Transformers (BERT) has become a paradigm shift in natural language processing (NLP). This scientometric study analyzes a curated dataset of 200 highly-cited Scopus publications to map the intellectual landscape and research trajectories catalyzed by BERT and other transformer models. The study discloses a quick evolution from foundational architectural innovations and pre-training paradigms to widespread domain adaptation, rigorous model optimization for efficiency, and critical examination of model capabilities and societal effects. The literature shows BERT's role as a foundational model, successfully implemented in diverse fields such as biomedicine, education, and software engineering, while simultaneously spurring substantial research into compression, quantization, and efficient inference. A parallel and influential strand of studies emerged concentrated on “BERTology”, giving the linguistic knowledge and biases encoded within these models, and addressing ethical concerns regarding their deployment. The study synthesizes these developments, presenting how BERT not only set new performance benchmarks but also built a new paradigm for transfer learning and spurred a self-critical research community, ultimately paving the way for the present era of large language models.

The emergence of large language models (LLMs), like ChatGPT, has caused a major shift in all academic and professional fields; education being the one impacted the most deeply. The first 200 most cited articles from Scopus on the topic of "large language models" and "education" were selected for a scientometric analysis of this survey. The aim is to map the intellectual landscape, specifying dominant research themes, key contributors, methodological trends, and the emergent challenges that researchers faced during this initial, explosive phase of research. This review systematically analyzes the publication trends, authorship patterns, geographical and institutional contributions, and the conceptual structure of the literature to provide a quantitative and qualitative snapshot of a field that is rapidly changing. The analysis uncovers a domain where exploratory studies, conceptual debates, and early empirical validations dominate, and where the focus of research is mainly on higher education and medical training. The major issues being discussed are academic integrity, assessment redesign, and the ethical integration of AI, while promising applications are personalized learning, automated feedback, and content generation. This survey is a reference for a better understanding of the roots and forefronts of LLM-related educational research as it passes from the initial reaction to systematic integration.

The introduction of Large Language Models (LLMs) in the healthcare sector is a major step, which can for sure transform all aspects of medicine including practice, research, and education. This review of publications presents the first 200 most cited articles which were obtained from a Scopus search on the terms "large language models" and "healthcare". The purpose was to elucidate the knowledge and trends in this fast-growing area. The analysis looks at the yearly publications, the main journals, the most prominent authors and institutions, the leading research areas, the methodological approaches used, and the ethical and regulatory issues that are the most talked about. The results show an increase in the number of scholars interested in this field, especially in 2023 and 2024. Besides that, it was found that there is a lot of high-impact publications going on in the leading multidisciplinary and specialized medical journals areas. The largest research areas that were found are: clinical trials and their outcomes, ethical and governance frameworks, educational integration, and technological advancements and surveys. The research is mainly focused on testing the performance of LLMs like ChatGPT in particular medical tasks but in the background, there are profound concerns about their accuracy, bias, and safety. This review presents the current knowledge state, points out the most active and leading research fronts, and recognizes the gaps with future directions hence it offers the most basic reference for researchers, doctors, and policymakers who will be dealing with the LLMs incorporation into the healthcare system.

The worldwide quest for sources of energy that are both sustainable and renewable has made algal biofuel a strong candidate to take over fossil fuels. Here, for the first time, a scientific study offers an in-depth analysis based on citation of the top 100 articles in the field of algae and biofuel and the Scopus database has been used for drawing the citation map of this fast-developing research area. Various quantitative bibliometric indicators are used in this paper to show and explain the development of the field through publication growth, citation analysis, geographical distribution, author and institutional productivity, journal performance, and keyword co-occurrence networks. It was 2010 when there was an exploding point of research output and citation impact and all this was due to the global energy crisis and climate change imposing a greater need for research believers. Also, the cutting-edge themes have kept on changing from the primary dependence of the choice of the best strain for lipid induction and isolation to complications in downstream processing, techno-economic assessment, life cycle analysis (LCA), integrated biorefinery systems, and co-products valorization. Mapping of geographical distribution clearly points out U.S., China, and India as the top three countries contributing to research along with their noteworthy international collaboration. Also, the views of Pugazhendhi Arivalagan, Wei-Hsin Chen, and Mohamed F. A. Hossain are among those that have most impacted the discourse. The keyword co-occurrence network revealed four major clusters of themes which were: "Lipid extraction and transesterification," "Biorefinery and sustainability," "Cultivation and photobioreactors," and "Hydrothermal liquefaction." In conclusion, the field is growing from the basic laboratory research to the entire, large-scale, and economically viable systems, with future trends in the direction of the application of genetic engineering, AI-driven optimization, and integration with the circular bio-economy. The results were a great help for researchers, policymakers, and industry players in determining the past, present, and future of algal biofuel research.