A scientometric analysis of climate change research in the Middle East: mapping, visualization, and emerging trends

BackgroundArtificial intelligence (AI) has been extensively applied in the individualized diagnosis and treatment of critical illness, and numerous studies have been published on this topic. Therefore, a bibliometric analysis of these publications should be performed to provide a direction of hot topics and future research trends.MethodsA bibliometric analysis was performed on the research articles to identify the hot topics and any unsolved issues regarding the use of AI in individualized diagnosis and treatment of critical illness. Articles published from January 2011 to December 2021 were retrieved from the Web of Science (WOS) core collection database for bibliometric analysis, and a cross-sectional analysis of the relevant studies that had been registered at ClinicalTrials.gov was also conducted.ResultsThe number of articles published showed an annually increasing trend, with a worldwide geographic distribution over the past decade. Ultimately, 427 research articles were included in the bibliometric analysis. The relevant articles were divided into four separate clusters that focused on AI application aspects, prediction model establishment, coronavirus disease 2019 (COVID-19) treatment and outcome assessments, respectively. “Machine learning” was the most frequent keyword (147 occurrences, 165 links, and 395 total link strengths) followed by “risk”, “models”, and “mortality”. With 205 articles, the United States of America (USA) had interacted the most with other countries (20 links, and 94 total link strength), while the domestic research institutes in China had infrequently collaborated with others. Approximately 130 trials focusing on the application of AI in the intensive care unit (ICU) and emergency department (ED) had been registered at ClinicalTrial.gov, and most of them (n=71, 54.6%) were interventional. The main research objectives of these trials were to provide decision making assistance and establish prediction models. However, only 3.8% (5 trials) of them had reached exact conclusions which favored the application of AI.ConclusionsThe application of AI has raised great interest in critical illness and has mainly been focused on decision making assistance and prediction model establishment. Cooperation between agencies engaged in AI research needs to be strengthened. An increasing number of trials have been registered at ClinicalTrial.gov, and the results of them are promising.KeywordsBibliometric analysis; artificial intelligence (AI); individualized diagnosis; critical care medicine; emergency department (ED)

Artificial intelligence (AI) can improve the health and well-being of older adults and has the potential to assist and improve nursing care. In recent years, research in this area has been increasing. Therefore, it is necessary to understand the status of development and main research hotspots and identify the main contributors and their relationships in the application of AI in geriatric care via bibliometric analysis. Using bibliometric analysis, this study aims to examine the current research hotspots and collaborative networks in the application of AI in geriatric care over the past 23 years. The Web of Science Core Collection database was used as a source. All publications from inception to August 2022 were downloaded. The external characteristics of the publications were summarized through HistCite and the Web of Science. Keywords and collaborative networks were analyzed using VOSviewers and Citespace. We obtained a total of 230 publications. The works originated in 499 institutions in 39 countries, were published in 124 journals, and were written by 1216 authors. Publications increased sharply from 2014 to 2022, accounting for 90.87% (209/230) of all publications. The United States and the International Journal of Social Robotics had the highest number of publications on this topic. The 1216 authors were divided into 5 main clusters. Among the 230 publications, 4 clusters were modeled, including Alzheimer disease, aged care, acceptance, and the surveillance and treatment of diseases. Machine learning, deep learning, and rehabilitation had also become recent research hotspots. Research on the application of AI in geriatric care has developed rapidly. The development of research and cooperation among countries/regions and institutions are limited. In the future, strengthening the cooperation and communication between different countries/regions and institutions may further drive this field's development. This study provides researchers with the information necessary to understand the current state, collaborative networks, and main research hotspots of the field. In addition, our results suggest a series of recommendations for future research.

Articles on hemorrhagic shock published between 2000 and 2021: A CiteSpace-Based bibliometric analysis

Endogenous retrovirus (ERV) research amalgamates host-retroviral coevolutionary, phylogenomic, infection, immunity, and cellular studies in various hosts ranging from fish to humans. Henceforth, a bibliometric analysis of these publications may aid in the identification of trends in ERV research. It was the foremost bibliographic study, with the key aim to conduct the bibliometric network analysis (e.g. co-authorship, co-occurrence, citation, bibliographic coupling, and co-citation analysis) to find the most prolific authors, organizations, and countries in ERV research, based on the mapping of bibliographic data. Second, the mapping based on text data comprised to chalk out the research trend over the time. The global literature about endogenous retroviruses published between 1985 and Sep 2021 was searched in the Web of Science (Core Collection) database using the “ENDOGENOUS RETROVIRUS” keyword. The bibliometric analysis of this dataset was carried out using VOSviewer version 1.6.17. According to findings, English was the de facto language of these publications, and 2157 were original articles. Among 2939 published documents, “endogenous retrovirus” was the most frequent keyword. Moreover, it revealed the United States as a core contributor to studies on the ERV. The Journal of Virology published a substantial amount of manuscripts in ERV. Robert Koch Institute and Harvard University were leading organizations for research in this field. The application of ERV research from China could be the research hotspot to follow in the coming years. Current bibliometric analysis provides a comprehensive picture of ERV research progress and has highlighted the contribution of different stakeholders.

Purpose The purpose of this study is to present a comprehensive scientometric mapping of research on Sustainable Development Goal 9 (SDG 9), with a specific focus on industry and innovation, over the past decade (2015–2024). Traditional bibliometric methods, which primarily rely on quantitative measures such as citation counts and impact factors, have undergone a paradigm shift with the emergence of modern metrics. In most cases, researchers obtain metadata from databases such as Scopus or Web of Science (WoS). The present study applies scientometric laws (Zipf’s Law and Bradford’s Law) along with advanced bibliometric techniques, including keyword co-occurrence network analysis, to delineate the evolution and thematic structure of this research field. Design/methodology/approach It was found that only a limited number of bibliometric studies combine Scopus and WoS for scientometric mapping of SDG 9, industry and innovation. The study integrated two data sets using the R package “Bibliometrix” to conduct bibliometric analyses, ensuring broader coverage of publications. The data was downloaded from Scopus as well as WoS database and filtered through preferred reporting items for systematic reviews and meta-analyses approach. Findings It depicts publication trends, citations, growth rate and doubling time, most influential authors, sources production over time, highly cited documents, most frequent words, network analysis of keyword co-occurrence, most prolific keywords, countries and authors through three field plots, key thematic focus areas with emerging topics and countries scientific production in industry, innovation and SDG 9 research. The findings reveal a significant increase in scholarly output following the adoption of the 2030 Agenda for Sustainable Development (United Nations, 2015), underscoring the influence of global policy on research. The analysis identifies seminal works and influential authors, while also highlighting robust international collaboration networks that contribute to the dynamic and interdisciplinary nature of SDG 9 research. Research limitations/implications The data set was confined to research papers from major databases such as WoS and Scopus. This focus may have excluded relevant contributions from non-indexed journals, grey literature and non-English sources. Overall, this scientometric review provides valuable insights for policymakers and research scholars into how academic research and digital innovation intersect to promote sustainable innovation and industrialization. Practical implications The scientometric mapping of SDG 9 research highlights how global policy initiatives, interdisciplinary approaches and international collaborations are driving the evolution of sustainable industrialization. As the world continues to advance toward the 2030 Agenda, it is imperative that future research not only deepens our understanding of these dynamics but also translates findings into actionable policies and innovative practices. By integrating diverse methodological approaches and fostering collaborative networks, researchers and policy-makers alike can work together to ensure that sustainable development remains at the forefront of global industrial progress. Originality/value Earlier studies have analyzed the role of governmental policies in facilitating sustainable industrialization, highlighting the intersection between regulation and innovation. However, much of the existing literature concentrates on regional case studies or isolated thematic areas. Scientometric mapping has emerged as a pivotal method for understanding rapid transformation such as sustainable development and industrial innovation. The methodological foundation for this approach was laid by early studies that emphasized the visualization of knowledge structures through bibliometric networks. No study was found that have applied scientometric laws and combined the dataset of WoS and Scopus by using R-studio and Biblioshiny.

As the urgency of addressing climate change grows, strategies such as developing zero-emission campuses to achieve carbon neutrality are becoming increasingly crucial. Yet, research in this field remains somewhat underdeveloped and fragmented. This study aims to bridge this gap, providing a scientometric analysis of the research conducted on zero-emission campuses from 1997 to 2023, using data from the Web of Science Core Collection. The study analyzed 1009 bibliographic records with the aid of CiteSpace software, focusing on identifying key co-authors, co-words, co-citations, and clusters. The findings indicate a rapid increase in research in the field of zero-emission campuses, with a significant surge in the number of publications in recent years, culminating in 174 in 2021 alone. The leading universities in terms of publication count were the University of California System, Egyptian Knowledge Bank, and the Chinese Academy of Sciences. Furthermore, the United States, China, and the United Kingdom were identified as the main contributing countries/regions to publishing in this field, indicating a broad, global collaboration. The scope of research has broadened from technical elements, such as energy, to encompass social factors that influence sustainability. Emerging research areas were identified, including education and sustainability, renewable energy and energy efficiency, campus planning and design, waste management and recycling, policy support, and pro-environmental behavior. This study provides a structured overview of the research landscape in the field of zero-emission campuses, offering valuable guidance for academics and encouraging further collaboration. The identified research clusters, notable authors, and influential institutions hold significant implications for policy decisions, industry practices, and the implementation of zero-emission strategies on campuses, aiding in the broader pursuit of sustainability.

Systematic evaluation and review of Germany renewable energy research: A bibliometric study from 2008 to 2023

Background: The provision of energy services is a vital component of the energy system. This is often considered emission-intensive and at same time, highly vulnerable to climate change conditions. This forms the fundamental objective of this thesis, poised to examine technoeconomic and environmental implications of policy intervention, targeted at cushioning impacts of climate change on the energy system. Aims: Four research queries are central to this work: (1) Review literature on impacts of CVC (2) Estimate influence of seasonal climatic and socioeconomic factors on energy demand in Australia; (3) Model dynamic interactions between energy policies and climate variability and change (CVC and (4) Identify least-cost combination of electricity generation technologies and effective emissions reduction policies under climate change conditions in Australia. Methods: A systematic scoping review method was first applied to identify consistent pattern of CV&C impacts on the energy system, while spotting research gaps in studies that met the inclusion criteria. Databases consisting of Scopus and Web of Science were searched, and snowballing references in published studies was adopted. Data was collated and summarised to identify the characteristic features of the studies, consistent pattern of CV&C impacts, and locate research gaps to be filled by this study. The second study applied an autoregressive distributed lag (ARDL) model to estimate temperature sensitive electricity demand in Australia. Estimates were used with projected temperatures from global climate models (GCMs) to simulate future electricity demand under climate change scenarios. The study further accounted for uncertainties in electricity demand forecasting under climate change conditions, in relation to energy efficiency improvement, renewable energy adoption and electricity price volatility. The estimates from the ARDL model and projections from GCMs were used for energy system simulation using the Long-range Energy Alternative and Planning (LEAP) system. It considered climate induced energy demand in the residential and commercial sector, alongside linking the non-climate sensitive sector with energy supply sector. This model was vital to justifying policy options under investigation. Further, LEAP modelling analysis was extended by identifying effective emission reduction policies considering CV&C impacts. Here, the Open Source Energy Modelling System (OSeMOSYS) was used for optimisation analysis to identify least-cost combination of electricity generation technologies and GHG emission reduction policies. Whereas, in the third and final study, cost-benefit analysis and estimation of long run marginal cost of electricity were conducted, while decomposition analysis of GHGs were analysed in the third study alone. Data used in the ARDL model included socioeconomic data which includes gross state product, as well as population and electricity prices from 1990-2016. The LEAP and OSeMOSYS model as used, was dated to 2014 as the base year, while several technological (power plant characteristics, household technologies), economic (energy prices, economic growth, carbon price) and environmental (emission factors, emission reduction target) variables were used to develop Australia's energy model. Results: The literature search generated 5,062 articles in which 176 studies met the inclusion criteria for the final literature review. Australian studies were scarce compared to other developed countries. Also, just few articles made attempt to examine decarbonisation under climate change. The ARDL model estimates and GCMs simulation of future electricity demand under CV&C show that Australia had an upward sloping climate-response functions, resulting to an increase in electricity demand. However, the researcher identified an annual increase in projected electricity demand for states and territory in Australia, which calls for the need to scale up RET. The LEAP model results showed substantial impacts on energy demand, as well as impacts on power sector efficiency. Under the BAU scenario, CV&C will result in an increase in energy demand by 72 PJ and 150 PJ in the residential and commercial sectors, respectively. Induced temperature enlarges the non-climate BAU demand, which will increase threefold before 2050. Under the non-climate BAU, there is an expansion of installed capacity to 81.8 GW generating 524.6 TWh. Due to CV&C impacts, power output declines by 59 TWh and 157 TWh in Representative Concentration Pathways (RCP) 4.5 and 8.5 climate scenarios. This leads to an increase in generation costs by 10% from the base year, but a decrease in sales revenue by 8% and 21% in RCP 4.5 and RCP 8.5, respectively. The LEAP-OSeMOSYS model suggests renewables and battery storage systems as least-cost option. However, the configuration varied across Australia. Carbon tax policy was observed to be effective in reducing Australia's emission and foster huge economic benefits when compared to the current emission reduction target policy in the country. Also, renewable energy technologies increase electricity sales and decrease fuel cost better than fossil fuel dominated scenarios. Conclusions: Data from this study reveals that seasonal electricity demand in Australia will be influenced by warmer temperatures. Also, the study identified the possibility of winter peaking which is somewhat higher than summer peak demand in some states located in the southern regions of Australia. However, winter peaking is projected to decline by mid-century across the RCPs, while summer peak load is projected to increase, thereby, causing power companies to expand their generation capacity which may become underutilised. Owing to increase in cooling requirements up to 2050, policy uncertainties analysis recommend renewables to match an increasing future electricity demand. The energy model indicates that ignoring the influence of CV&C may result in severe economic implications which range from increased demand, higher fuel cost, loss in revenue from decreased power output, as well as increased environmental externalities. The study concludes that policy options to reduce energy demand and GHG emissions under climate change may be expensive on the short-run, though, may likely secure long-run benefits in cost savings and emission reductions. It is envisaged that this could provide power sector management with initiatives that could be used to overcome cost ineffectiveness of short-term cost. The modelling results makes a case for renewable energy in Australia as lower demand for energy and increased electricity generation from renewable energy source presents a win-win case for Australia.

This study explores the evolution and significance of heutagogical learning in professional development using a bibliometric analysis of literature published between 2010 and 2024. Heutagogy, emphasizing learner autonomy and self-determined learning, has emerged as a transformative educational paradigm, particularly in the context of workforce training and lifelong learning. The research employs a bibliometric approach to analyze 473 peer-reviewed publications sourced from Scopus, highlighting trends, influential authors, collaborative networks, and key thematic areas. Findings indicate a notable rise in publications since 2016, driven by the increasing demand for adaptive and autonomous learning strategies. Prominent contributions come from multidisciplinary domains, including social sciences, medicine, and computer science, underscoring the broad applicability of heutagogical principles. Co-authorship and co-citation analyses using VOSviewer reveal robust global collaborations, with significant contributions from institutions in the US, UK, and Australia. Key thematic clusters, such as self-directed learning, digital education, and professional adaptability, were identified, reflecting the relevance of heutagogical practices in addressing 21st-century workforce challenges. In order to maximise heutagogy's potential for cultivating flexible, adaptable individuals, this study promotes inclusive research approaches and real-world applications. The results offer significant perspectives for scholars, decision-makers, and professionals who seek to include heutagogical approaches into professional development initiatives.

Background: Artificial intelligence (AI) emerged as a promising tool, including machine learning and deep learning, the Internet of Things, and blockchain technology have created new opportunities for improving healthcare delivery and outcomes for gastric cancer (GC) patients. This study aimed to analyze research development, patterns, and trends in AI applications on GC. Methods: Publications related to AI applications in GC from 1993 to 2024 were extracted from the Web of Science (WoS) database (assessed date: Sep 13, 2024). To explore the relationships among the most frequently occurring terms in titles and abstracts, we employed the text-mining functionality of VOSviewer to generate a co-occurrence network. Global collaborations, research disciplines, and frequently used terms were visualized using this software. This study applied a machine learning approach, Latent Dirichlet Allocation, and dendrogram analysis was employed to identify hidden topics and hierarchical clustering of key research domains. A linear regression model was utilized to determine research development trends. Results: The bibliometric analysis encompassed 1, 717 articles from 70 countries/regions, with China, Japan, and the United States making outstanding contributions. The study revealed an unequal distribution of publications, with low/middle-income countries (LMICs) facing the greatest burden of cancer yet receiving inadequate attention. AI has confirmed its role in addressing global challenges, especially during the most stressful period, such as the COVID-19 pandemic, leading to a dramatic increase in publications. Topic modeling identified that “Comparative Analysis of Robotic vs. Laparoscopic Gastrectomy” had the highest interest with the highest number of publications, however, it shows a consistent downward trend in the last 5 years. The six remaining topics included AI applications in Tumor Segmentation, Predictive Models for Neoadjuvant Chemotherapy Response, Global Research Trends, Dietary and Environmental GC Prevention, and Cell-Free DNA and Biomarkers were considered hot trends. Hierarchical clustering of research areas grouped Computer Science and AI-related fields, underscoring their importance in developing AI methods supporting GC research and treatment. Conclusion: Research on AI in GC is increasing attention. These findings suggest future studies should focus more on LMICs to provide a comprehensive overview of AI applications in GC. AI Application on GC highlights the interdisciplinary nature of research. Although AI has positive clear effects on the development of GC research, it still has certain limitations, there is a need to develop AI algorithms capable of supporting pre-surgical planning, establishing surgical protocols and post-operative monitoring to alleviate healthcare system burdens, enhancing surgical quality, and improve the quality of life for GC patients. Citation Format: Trong Anh Vu Dam, Thi Huyen Trang Nguyen, Jeongseon KIM. Global mapping of artificial intelligence applications in gastric cancer from 1993-2024 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7447.

Objective: This study aims to conduct a bibliometric analysis of publications on MRI-based staging of rectal cancer and the applications of artificial intelligence (AI). The study examines key trends in the literature, leading authors, and collaboration networks. Material and Methods: Data were retrieved from the Web of Science (WoS) Core Collection for the period between 2014 and 2024. Citation analysis, co-authorship mapping, bibliographic coupling, and keyword co-occurrence analysis were performed using VOSviewer software. A minimum threshold of two publications and five citations was applied in the analyses. Network, overlay, and density maps were generated. Results: A total of 104 publications were analyzed. The findings indicate that Cui, Cusumano, and Li are among the most frequently cited authors in the field of MRI and AI applications for rectal cancer. Prominent keywords include “rectal cancer,” “magnetic resonance imaging,” “deep learning,” and “machine learning,” reflecting high research activity in these areas. In contrast, terms such as “transfer learning” and “tumor budding” appeared less frequently. Strong collaboration networks were identified, particularly between China and the Netherlands, with GE Healthcare emerging as a key institution in the field. However, the integration of traditional MRI protocols with AI-driven analyses remains limited, highlighting a critical research gap that warrants further exploration. Conclusion: This bibliometric analysis identifies influential themes and leading researchers in MRI and AI applications for rectal cancer. Future research should focus on integrating traditional MRI protocols with AI algorithms and strengthening international collaboration to advance the field.

Comprehensive and Visualized Analysis of Interventional Clinical Trials of Spinal Cord Injury in the Past Two Decades: A Bibliometric Study

Global trends and collaboration networks in radiology: A bibliometric analysis of the 500 most-cited articles in web of science.

The aim of this study is to present studies on nature and environmental education in related literature from 1977 to the present time by using bibliometric method. Nature and environmental education, which has an increasing popularity especially in the last years, gain a place in so many countries’ teaching policy. Thus, increasing number of scientific studies and researches on this field in the related literature draw attention. In this study, publications in nature and environmental education have been examined both bibliometrically and in content, and their distribution by years, institutions, journals, citations and the features of co-works have been presented with descriptive and visual maps. Web of Science (WoS) Core Collection database has been used in obtaining data. “Nature education” and “Environmental education” terms were scanned with the aim of reaching highest number of studies from database. A total of 1312 publications have been included in study through various filtering processes. Bibliometric analysis techniques have been used in data analysis. VOSviewer programme has been used in determining the network analysis of those data obtained. In the study, the distribution of publications by years, the authors with the most publications, the journals, institutions and countries with the most publications have been determined. Besides, those data obtained have been discussed in the light of related literature by forming visual maps together with co-author, co-citation taken together, co-word and co-citation analyses. At the end of the study, some suggestions have been provided in the light of findings obtained.

A multi-model approach based on CARIMA-SARIMA-GPM for assessing the impacts of climate change on concentrated photovoltaic (CPV) potential