Emerging Research Trends Research Articles

Bibliometric analysis of imaging and treatment strategies for severe tricuspid regurgitation from 2015 to 2023.

Severe tricuspid regurgitation is a progressive disease with an unfavourable prognosis. In recent years there have been extraordinary gains in knowledge through both clinical and basic scientific work. We performed a bibliometric analysis on tricuspid regurgitation with a focus on imaging techniques and treatment approaches and to identify scientific milestones and emerging research trends. Publications, published between 2015 and 2023 were identified. Study characteristics, impact factors and countries of origin studies were recorded. Heat maps were created to visualise data and to identify leading centers. Most frequently cited publications were recognised as milestones. We screened 3,519 studies. 368 studies were included, of which 326 were clinical studies. Clinical studies were further subdivided into interventional (n = 138), surgical (n = 115) or studies on imaging modalities (n = 74). We detected an enormous increase in scientific output worldwide, especially in imaging and interventional studies. The United States, Germany and Poland were identified as leading countries in imaging, interventions and preclinical studies respectively. Our study reflects the global gain in knowledge over the last 9 years. We were able to identify an annually rising number of interventional studies. Imaging studies have also seen a rapid increase, especially since 2020. In recent years, we monitored a decline in surgical studies.

A bibliometric and visualization analysis of entosis research from 2007 to 2024.

In 2007, entosis was proposed as a form of programmed cell death, distinct from apoptosis. This process involves a living cell (internalized cell) actively invading a neighboring live cell of the same type (host cell), forming a cell-in-cell structure. Recently, entosis has been increasingly associated with cancer, leading to significant advancements in research. Despite this progress, a comprehensive and unbiased review of the current state of entosis research is lacking. This study aims to evaluate the developments in the field of entosis over the past decade and highlight emerging research trends. We performed a literature search for studies published since the introduction of the entosis concept, using the Web of Science Core Collection database. The bibliometric analysis was conducted using VOSviewer, CiteSpace, Microsoft Excel, and the Bibliometrix R package. A total of 196 articles from 39 countries and 346 institutions were included. Between 2007 and 2024, research on entosis has seen rapid growth, with most publications originating from China and the United States. The United States also leads in total citations, with Memorial Sloan Kettering Cancer Center emerging as the top research institution. Sun Qiang is the most prolific author in this field, while Overholtzer M has the highest number of citations. Current Molecular Medicine has published the most articles related to entosis. Frequently occurring keywords include "entosis," "cannibalism," "autophagy," and "apoptosis." In recent years, keywords such as "phagocytosis," "drug resistance," and "human cancers" have surged, indicating a growing focus on understanding the role of entosis in tumor progression and exploring its potential as a therapeutic target for cancer treatment. This study provides the first bibliometric analysis of entosis, detailing its evolution over the last decade. It highlights critical areas of interest, including the development of inhibitors targeting entosis and their potential clinical applications. This research aims to guide future investigations and serve as a valuable resource for scholars exploring entosis in cancer biology.

Unveiling Weak Signals of Emergence in Underwater Sensing Research Trends

Detecting emerging research trends is crucial as it allows for the proactive identification and monitoring of novel and influential topics in the scientific community. Monitoring research trends aids researchers, institutions, and policymakers in allocating resources, fostering innovation, and staying competitive in rapidly changing scientific landscapes. The growing significance of underwater sensing technologies in various domains has propelled research endeavours aimed at understanding the characteristics of academic discourse in this field. In this work, we comprehensively analyzed the academic research topics related to underwater sensing technologies using advanced computational methodologies. Leveraging natural language processing, topic modelling, and weak signal detection techniques, and focusing on underwater sensing as the case technology, we dissect a large corpus of scholarly articles published between 2007 and 2021 to unveil underlying thematic patterns and emergent trends within this domain while shedding light on signals of emerging technologies. Among the eighty extracted topics, six research topics were identified and recognized as emerging weak signals, and validated by experts. Notably, deep learning for underwater imaging was the only topic that transitioned from being weak to a strong signal in the final period. Received: 22 July 2024 | Revised: 9 October 2024 | Accepted: 15 October 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Ashkan Ebadi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration; Alain Auger: Conceptualization, Validation, Writing - review & editing; Yvan Gauthier: Validation, Writing - review & editing.

Research trends in nutritional interventions for stroke: a bibliometric analysis and literature review.

Over the past 23 years, there has been a thorough analysis of literature concerning nutritional interventions, nutrients, and feeding approaches related to stroke. Furthermore, a scientific knowledge map was established, elucidating the current state of research, examining its development and trends, and offering new research viewpoints for the future. This study aimed to investigate global and emerging research trends in nutritional interventions for stroke from 2000 to 2023 through bibliometric analysis. A bibliometric analysis of literature from the Core Collection of Scientific Networks for the years 2000-2022 was conducted. CiteSpace, VOSviewer, and bibliometric graphical software were used to identify major contributors to publications, including authors, countries, institutions, journals, references, and keywords. The bibliometric analysis yielded a total of 464 publications. This is a gradually increasing number in terms of the number of publications during the study period. China had the highest number of publications. Clinical Nutrition" was the journal with the highest number of relevant publications, and the most commonly used keywords were "stroke," "nutrition" and "malnutrition." These analyses reveal research trends in nutritional therapy for stroke from 2000 to 2023 and point to prospective research frontiers. This study provides a deeper understanding of what nutritional treatment of stroke entails and provides guidance and support for future research in this area.

Research Progress on Food-Derived Bioactive Peptides: An Overview of the 3rd International Symposium on Bioactive Peptides.

Interest in food-derived bioactive peptides is on the rise. In 2023, the 3rd International Symposium on Bioactive Peptides (ISBP) was held in Niagara Falls, Canada, to provide a platform for knowledge exchange, networking, and collaboration among researchers in this field. This article aims to provide a high-level overview of the key progress and emerging trends in bioactive peptides based on the 3rd ISBP. This review highlights the production of bioactive peptides from sustainable sources through the integration of artificial intelligence and wet-lab research, the emerging roles of bioactive peptides in cognitive function, and the ability of peptides to act as taste modifiers. The emerging research trend in bioactive peptides focuses on utilizing novel processing technologies, understanding peptide-receptor interactions, applying omics in mechanistic studies, conducting clinical trials, and facilitating product development and commercialization.

Finding the new potential research on diabetic kidney disease and hemodialysis in healthcare insurance databases: A bibliometric analysis.

To the best of our knowledge, bibliometric analysis has not been performed for studies related to diabetic kidney disease (DKD) and hemodialysis using healthcare big data. Herein, the aim of this bibliometric analysis was to identify emerging research trends in DKD and hemodialysis within healthcare insurance databases by exploring authors, co-author networks, and countries to discover new potential research areas. A bibliometric study was conducted, utilizing data obtained from the Scopus database. Keywords such as diabetic kidney disease, hemodialysis, insurance or big data, and prediction were employed. Inclusion criteria were original articles and review articles written in English published between 2010 and 2022. VOSviewer and the Bibliometrix package in R were used for comprehensive bibliometric analysis. VOSviewer facilitated keyword co-occurrence analysis to identify clusters and visualize relationships among keywords, emphasizing distinct research themes, keyword density, and network visualization. Meanwhile, Bibliometrix allowed exploration of key metrics such as prolific authors and institutions, publication trends, co-authorship networks, citations, document types, emerging trends through keyword analysis, and network visualizations, including co-authorship and keyword co-occurrence. Results from both tools were integrated for a thorough analysis. The present study yielded 2,199 articles, which was reduced to 1,828 after removing duplicates and applying inclusion criteria. This bibliometric analysis found that machine learning and artificial intelligence are emerging yet remain relatively under-researched in the context of hemodialysis and DKD. The prominence of topics such as diabetic nephropathy, non-insulin treatments, and lifestyle modifications highlighted ongoing research priorities in DKD and hemodialysis. Taiwan's dominance in publications suggested robust research activity in this field, while international collaboration underscored global interest and the potential for diverse research perspectives. The need for similar research development in Indonesia, leveraging big data and machine learning, indicates opportunities for advancing the understanding and management of DKD and hemodialysis within the region.

Global Knowledge Map and Emerging Research Trends in Induced Pluripotent Stem Cells and Hereditary Diseases: A CiteSpace-based Visualization and Analysis.

The rise of induced pluripotent stem cells (iPSCs) technology has ushered in a landmark shift in the study of hereditary diseases. However, there is a scarcity of reports that offer a comprehensive and objective overview of the current state of research at the intersection of iPSCs and hereditary diseases. Therefore, this study endeavors to categorize and synthesize the publications in this field over the past decade through bibliometric methods and visual knowledge mapping, aiming to visually analyze their research focus and clinical trends. The English language literature on iPSCs and hereditary diseases, published from 2014 to 2023 in the Web of Science Core Collection (WoSCC), was examined. The CiteSpace (version 6.3.R1) software was utilized to visualize and analyze country/region, institution, scholar, co-cited authors, and co-cited journals. Additionally, the co-occurrence, clustering, and bursting of co-cited references were displayed. Analysis of 347 articles that met the inclusion criteria revealed a steady increase in the number of published articles and citation frequency in the field over the past decade. With regard to the countries/regions, institutions, scholars, and journals where the articles were published, the highest numbers were found in the USA, the University of California System, Suren M. Zakian, and Stem Cell Research, respectively. The current research is focused on the construction of disease models, both before and after correction, as well as drug target testing for single-gene hereditary diseases. Chromosome transplantation genomic therapy for hereditary diseases with abnormal chromosome structures may emerge as a future research hotspot in this field.

Analysis between Ontology and Multicriteria methods: An Integrated Bibliometric Study

This systematic mapping addressed a significant research gap by integrating ontology and multi-criteria methods to facilitate decision-making. The study utilized the Scopus and Web of Science (WoS) databases to review research conducted between 2013 and the present, focusing on seminal articles supported by robust citations and recent relevance. The analysis involved using specialized software and platforms, such as VOSviewer, RStudio, and Bibliometrics, to stratify and interpret bibliometric data. This approach enabled the identification of emerging research trends, key journals driving this field of knowledge, notable authors dedicated to this area, and global interactions among various research groups. A script written in the R language facilitated the identification of forty-one duplicated documents across the analyzed databases.

Deep Reinforcement Learning for Fluid Mechanics: Control, Optimization, and Automation

A comprehensive review of recent advancements in applying deep reinforcement learning (DRL) to fluid dynamics problems is presented. Applications in flow control and shape optimization, the primary fields where DRL is currently utilized, are thoroughly examined. Moreover, the review introduces emerging research trends in automation within computational fluid dynamics, a promising field for enhancing the efficiency and reliability of numerical analysis. Emphasis is placed on strategies developed to overcome challenges in applying DRL to complex, real-world engineering problems, such as data efficiency, turbulence, and partial observability. Specifically, the implementations of transfer learning, multi-agent reinforcement learning, and the partially observable Markov decision process are discussed, illustrating how these techniques can provide solutions to such issues. Finally, future research directions that could further advance the integration of DRL in fluid dynamics research are highlighted.

Causal effects between personality and psychiatric traits and lung cancer: a bidirectional two-sample Mendelian randomization and bibliometric study.

The causality between personality and psychiatric traits and lung cancer (LC) remains unclear. Therefore, we aimed to elucidate the causality between these traits and LC. Bidirectional two-sample Mendelian randomization (MR) and bibliometric approaches were conducted to estimate the causality between personality (neuroticism, extraversion, agreeableness, conscientiousness, and openness) and psychiatric (schizophrenia, attention-deficit/hyperactivity disorder [ADHD], major depressive disorder, autism spectrum disorder, bipolar disorder, insomnia, and anxiety) traits and LC and its subtypes (lung squamous cell carcinoma, lung adenocarcinoma, and small cell LC). Summary data of these traits were extracted from large datasets (17,375-462,341 participants). Inverse variance weighting was used as the primary MR analysis, with supplementary models, including MR-Egger and weighted medians. Sensitivity analyses were conducted to detect pleiotropy. Bibliometric data were retrieved from the Web of Science Core Collection, Scopus, and PubMed. The main mapping techniques adopted were co-word, collaboration, and citation analyses. Schizophrenia was associated with an increased risk of LC (odds ratio [OR] = 1.077, 95% confidence interval [CI] = 1.030-1.126, P = 0.001). Moreover, LC increased the risk of ADHD (OR = 1.221, 95% CI = 1.096-1.362, P < 0.001). No significant bidirectional associations were observed between other mental traits and LC and its subtypes. Causality, psychiatry, and psychiatric comorbidity are emerging keywords. Research dynamics and landscapes were revealed. This study suggests that schizophrenia is a risk factor for LC and that LC is a risk factor for ADHD. Furthermore, causality, psychiatry, and psychiatric comorbidity have become emerging research trends in related fields.

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics.

Elaborating the knowledge structure and emerging research trends of physical activity for multiple sclerosis: A bibliometric analysis from 1994 to 2023

Elaborating the knowledge structure and emerging research trends of physical activity for multiple sclerosis: A bibliometric analysis from 1994 to 2023

Research on digital governance based on Web of Science—a bibliometric analysis

Exploring digital governance is essential for grasping how technology can be employed to enhance public services, boost efficiency, and foster transparency and engagement. This study aims to conduct a systematic review of digital governance research in order to explore its development, emerging research trends, network of leading researchers, countries and institutions that contribute greatly to this field. A bibliometric study was conducted on digital governance works using the following terms: “digital governance,” “E-governance,” “digital government,” and “E-government” as the keywords. VOSviewer and CiteSpace were two tools used for the bibliometric analysis. Results showed that the United States played a dominate role in digital governance studies, followed by China, the United Kingdom, India, and Spain. Brunel University, University at Albany, and University of Johannesburg were the top three research institutes for digital governance. Reddick C.G., Weerakkody V., Dwivedi, Y. K., Mensah, I. K., and Jaeger, P.T. served as the representative researchers in this field. In addition, topics including usability and reliability of digital governance system, quality assurance under the framework of digital governance, the quality of digital service, impact of digital governance on public perception, effects of digital transformation on public value perceptions were the focal points in recent years.

Mapping the Research Landscape in Malaysia: A Bibliometric Analysis of Early Childhood Education and Development Publications

This study presents a comprehensive bibliometric analysis of early childhood education and development research in Malaysia, focusing on articles published in Scopus. Publications included in this study consisted of journal articles, books and book chapters, and conference papers, all of which sampled children between four to seven years old. The resulting publications were cleaned up in Open Refine, then later loaded into Excel and Harzing’s Publish or Perish for analysis. Visualisations were generated using VOSViewer. The aim was to investigate the publication patterns, citation trends, and collaboration networks of researchers in this field. A total of 250 publications from 1988 to 2022 were analyzed, with a significant increase in publication output observed in the last five years (2017-2022). The results revealed 1576 citations, an average of 46.35 citations per year, and 6.30 citations per paper. The h-index and g-index were found to be 21 and 29, respectively, indicating a growing impact of Malaysian early childhood research in the global academic community. Annual collaboration metrics revealed a diverse range of collaborative efforts among researchers, with the Degree of Collaboration and Collaborative Coefficient showing varying degrees of research partnerships. The collaboration network visualization indicated 31 clusters and 95 items, with the largest cluster containing 11 items, highlighting the potential for further interdisciplinary collaborations in the field. Despite the limitations of using a single database (Scopus) and focusing on a specific topic within the Malaysian context, this bibliometric analysis provides valuable insights into the research landscape of early childhood education and development in Malaysia. Future studies could expand the scope to include additional databases, as well as explore emerging research trends and potential areas for collaboration. This study serves as a foundation for understanding the research dynamics in the field, which could help guide future research endeavours and inform policy and practice in early childhood education in Malaysia.

Breaking Boundaries in Spinal Surgery: GPT-4's Quest to Revolutionize Surgical Site Infection Management.

Surgical site infection (SSI) is a common and costly complication in spinal surgery. Identifying risk factors and preventive strategies is crucial for reducing SSIs. Generative Pre-trained Transformer 4 (GPT-4) has evolved from a simple text-based tool to a sophisticated multimodal data expert, invaluable for clinicians. This study explored GPT-4's applications in SSI management across various clinical scenarios. GPT-4 was employed in clinical scenarios related to SSIs in spinal surgery. Researchers designed specific questions for GPT-4 to generate tailored responses. Six evaluators assessed the responses for logic and accuracy using a 5-point Likert scale. Interrater consistency was measured with Fleiss' kappa, and radar charts visualized GPT-4's performance. Interrater consistency, measured by Fleiss' kappa, ranged from 0.62 to 0.83. The average scores for logic and accuracy were 24.27 ± 0.4 and 24.46 ± 0.25. Radar charts indicated consistently high performance across criteria. GPT-4 demonstrated proficiency in creating personalized treatment plans, improving SSI management strategies, and identified emerging research trends. GPT-4 shows a significant potential in SSI management in spinal surgery, promoting patient-centered care and precision medicine. Despite limitations in antibiotics and patient education, GPT-4's continuous learning, data privacy focus, and professional collaboration indicate its potential to revolutionize SSI management, requiring further development.

Review of material flow analysis and its application under carbon neutralization target: a bibliometric perspective

Material flow analysis (MFA) could provide methodological support for quantifying material carbon flow and carbon emissions in the pursuit of carbon neutrality. Despite the extensive publication of MFA studies in academic journals, significant challenges remain on MFA and its application in carbon emission (MFA-CE) research, including identifying emerging research trends. This paper reviews MFA and MFA-CE research based on bibliometric analysis of data from WOS (Web of Science) platform, spanning from 1991 to 2022. We find that over the last 32 years: (1) Both MFA and MFA-CE research share similar article characteristics, such as rapid and active fluctuations in trends, with high-output countries primarily being the USA, China, and others; (2) MFA and MFA-CE are multidisciplinary fields, showing the fastest growth in Environmental Studies and Economics research; (3) Highly cited papers mainly focus on global material flow, environmental impact, and recycling. Notably, high citation analysis shows that both MFA and MFA-CE research have garnered substantial attention since 2014; and (4) Combining MFA with other methods would help identify material flow, such as carbon material flow. Additionally, the future perspectives of MFA-CE research were summarized: increasing interdisciplinary cooperation; a growing emphasis on multi-scale research; and enhanced availability and application of data.

Environmentally friendly fire retardant natural fibre composites: A review

There is a growing drive in replacing conventional non-renewable fibres such as glass and carbon reinforced composites with more sustainable and renewable reinforcements such as flax, hemp, jute in biobased composites in key industry sectors such as automotive, marine, building and construction motivated by lower carbon footprint and sustainability. Despite this drive, flammability characteristics of these sustainable biobased composites are not fully understood. Through an up-to-date review, this article meticulously discusses the theme of bio-based and eco-friendly flame retardants (FRs), delving into their intricate mechanisms, flammability testing methodologies, and emerging research trends. It underscores the pivotal necessity of developing tailor-made eco-friendly FRs customised for a diverse range of materials to bolster fire safety in sustainable material applications. Furthermore, it illuminates the limitations associated with prevalent methods for assessing flammability and advocates for advancements in dynamic testing and multi-scale analyses to more accurately simulate real-world fire scenarios. This review also highlights the key characteristics of natural plant fibres and their composites critical for the development of sustainable and fire safe materials for key applications areas. Additionally, it highlights the diverse spectrum of strategies employed in the realm of flame-retardant materials research, emphasizing a pronounced shift towards eco-friendly alternatives, innovative coatings, and the ongoing exploration of synthetic biopolymers, nanocomposites, and fibres in the pursuit of heightened fire safety. Amidst the inherent challenges, this comprehensive review unequivocally underscores the pivotal role of interdisciplinary research collaboration in driving forward fire safety within the domain of sustainable materials.

Integrating Sustainability and Digitalization in Business Model Innovation. A Bibliometric Study

Abstract The aim of the study is to get a comprehensive understanding of the research landscape at the nexus between business model innovation, sustainability and/or digitalization, in order to determine key research patterns and trends. Accordingly, the following objectives have guided the development of the paper: (a) to examine the dynamics and structure of the research output (articles and citations) on the topic; (b) to explore the network of the most impactful articles; (c) to trace the origins of the research on the topic; (d) to decipher current developments in the research on the topic; and (e) to search for emerging trends in the research on the topic. An advanced search of the SCOPUS database was conducted to identify the relevant literature available; 1195 articles (published between 2014 and 2023) were selected and exported in VOSviewer for bibliometric analysis. The results suggest the emergence of a niche research area in the broad strategic management field, bringing together four related (sub)topics: (1) circular economy and sustainable business models; (2) tools, frameworks and processes for sustainable business model innovation; (3) digitalization and business model transformation; (4) business models for societal and sustainability transitions. For this research area to crystallize, the identified emerging research trends – on digital innovation and technology; strategic management and analytical methods; supply chain and manufacturing; policy, governance and sustainability – need to be further explored, both individually (in search of refinement and clarity) and collectively (in search of consistency and synergy).

A comprehensive analysis of real options in solar photovoltaic projects: A cluster-based approach

A comprehensive analysis of real options in solar photovoltaic projects: A cluster-based approach

Augmented Reality for Education: A Bibliometric Analysis Using VOSviewer

This review aims to present a comprehensive knowledge mapping and analysis of Augmented Reality in education by conducting bibliometric analysis. This study will provide insight into the scope of research, the most frequently emerging research trends, and other areas in education. The use of AR in education has become an interesting topic for researchers over the past few years, presenting significant progress and transformation in educational technology that can improve students' understanding in the learning process. The data was taken from the Scopus database which discusses the use of Augmented Reality in the field of education and found 1396 journal articles. Based on the results of VOSviewer data processing with the topic of augmented reality in education, 4,2561 keywords were found. and related keywords were limited to 9 keywords. and 103 related keywords were found. Bibliometrics can improve the understanding of augmented reality and provide many contributions by discussing keywords used in the literature and grouping research trends and providing directions for further research.