Emerging Research Trends Research Articles

Application and Development of Nanotechnology in Traditional Chinese Acupuncture in Recent 20 Years: A Comprehensive Review.

Acupuncture, rooted in Traditional Chinese Medicine (TCM), serves as a complementary and alternative therapeutic modality that involves the insertion of needles into specific body points to stimulate and treat a variety of conditions. Over time, acupuncture has garnered global acceptance, progressively addressing an increasing spectrum of medical disorders. The integration of nanotechnology holds considerable potential to improve drug delivery efficacy and broaden the scope of acupuncture's applications, providing novel opportunities for investigating the underlying principles of meridians and acupoints. A bibliometric analysis of the application of nanotechnology in acupuncture over the past two decades (2004-2024) highlights emerging research trends and focal points in the field. This article provides an overview of the history, biological mechanisms, preparation methods, and clinical research status of nanoacupuncture, with a focus on analyzing the applications of nanoacupuncture technology. Nanotechnology enables the visualization and imaging of meridians and acupoints, facilitating a deeper understanding of the physiological mechanisms behind acupuncture's therapeutic effects. Modified acupuncture needles incorporating nanotechnology can serve dual purposes: functioning as sensors for real-time monitoring of various physiological parameters, thereby supporting disease diagnosis and enhancing therapeutic efficacy through specialized interventions. Furthermore, nanotechnology-enhanced acupuncture needles can act as platforms for targeted drug delivery working in tandem with responsive nanoparticles for disease treatment. Despite its promising potential and clinical applicability, nanoacupuncture faces inherent limitations and challenges that must be addressed.

FinTech and small and medium enterprises: current state and future directions

PurposeFinancial technology (FinTech) is the result of the rapid convergence of financial services and advanced technology. FinTech has significantly transformed traditional financial institutions and the financial landscape for small and medium enterprises (SMEs). This study examined the current state and future directions of FinTech adoption and its implications for SMEs.Design/methodology/approachCitation analysis, bibliographic coupling and keyword co-occurrence analysis were employed to perform a bibliometric analysis of 125 Scopus-indexed documents published from 2016 to April 2024. The analysis synthesized existing research and identified key themes, influential research and emerging trends in FinTech and SMEs. The study used VOSviewer and Gephi for the analysis and visualization.FindingsFinTech has introduced novel approaches to credit scoring, alternative financing and digital financial inclusion. However, gaps remain in understanding the full scope of these innovations and their impact on corporate supply chains and Islamic finance. The role of artificial intelligence in revolutionizing credit assessment and the regulatory frameworks governing FinTech companies remains underexplored. The findings highlight the role of FinTech in addressing challenges that SMEs face, such as regulatory complexities and access to financing, thus fostering innovation and economic growth. This study highlights underexplored areas for future research.Originality/valueThe novelty of the study lies in its bibliometric analysis of FinTech-SME literature. It maps the existing body of research to contribute to academic discourse by identifying emerging research trends, gaps and opportunities for future research. The study also provides valuable insights for researchers, policymakers and practitioners on leveraging FinTech to benefit SMEs.

Retraction notice to "Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review" [Heliyon 10 (2024) e36743

Retraction notice to "Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review" [Heliyon 10 (2024) e36743

Marine algal polysaccharides: Multifunctional bioactive ingredients for cosmetic formulations.

Marine algal polysaccharides: Multifunctional bioactive ingredients for cosmetic formulations.

Emerging research trends in green intellectual capital: A bibliometric analysis

This article aims to provide an overview of the research trends related to green intellectual capital (GIC). A bibliometric analysis was performed using VosViewer software, examining 144 papers through keyword co-occurrence networks. The findings reveal four key research trends and suggest a research agenda focused on green intangible assets. The study emphasizes that green IC has gained considerable attention in management literature, particularly as it relates to enhancing green performance, green human resource management, firm performance, and sustainability. This study contributes to the existing literature by broadening the understanding of green IC, while outlining the key research themes explored in the literature. Also, this study offers valuable insights for managers and policymakers aiming to effectively incorporate green IC into their business models by highlighting the mechanisms through which it can support the accomplishment of their targets.

Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations

The integration of renewable energy systems into modern buildings is essential for enhancing energy efficiency, reducing carbon footprints, and advancing intelligent energy management. However, optimizing RES operations within building energy management systems introduces significant complexity, requiring advanced control strategies. One significant branch of modern control algorithms concerns reinforcement learning, a data-driven strategy capable of dynamically managing renewable energy sources and other energy subsystems under uncertainty and real-time constraints. The current review systematically examines RL-based control strategies applied in BEMS frameworks integrating RES technologies between 2015 and 2025, classifying them by algorithmic approach and evaluating the role of multi-agent and hybrid methods in improving real-time adaptability and occupant comfort. Following a thorough explanation of a rigorous selection process—which targeted the most impactful peer-reviewed publications from the last decade, the paper presents the mathematical concepts of RL and multi-agent RL, along with detailed summaries and summary tables of the integrated works to facilitate quick reference to key findings. For evaluation, the paper examines and outlines the different attributes in the field considering the following: methodologies of RL; agent types; value-action networks; reward functions; baseline control approaches; RES types; BEMS types; and building typologies. Grounded on the findings presented in the evaluation section, the paper offers a structured synthesis of emerging research trends and future directions, identifying the strengths and limitations of RL in energy management.

Bibliometric Analysis of Financial Inclusion Research

Financial inclusion has emerged as a crucial factor in promoting economic development, reducing poverty, and fostering financial stability. This study employs a bibliometric analysis to explore the intellectual landscape, key research themes, and trends in financial inclusion research using data exclusively from the Scopus database. Utilizing VOSviewer, the study examines keyword co-occurrence, co-authorship networks, and geographic distribution to identify influential scholars, institutions, and countries contributing to the field. The findings reveal an evolution from traditional microfinance approaches toward digital financial services, financial technology (FinTech), and sustainability-linked financial inclusion initiatives. The analysis highlights strong international research collaborations, with India, China, and the United Kingdom playing significant roles in shaping financial inclusion discourse. Moreover, emerging research trends suggest an increasing focus on digital finance, environmental sustainability, and gender disparities in financial access. Despite progress, persistent challenges such as financial literacy gaps, regulatory barriers, and regional inequalities remain critical concerns. This study contributes to the understanding of financial inclusion research trends and provides insights for policymakers, financial institutions, and researchers to design more inclusive and effective financial policies. Future research should integrate interdisciplinary approaches and advanced data analytics to address existing gaps and enhance financial accessibility globally.

Citation Count Modelling: The Role of Statistical Distributions

The statistical distributions develop more accurate models of research impact, such as citation counts and h-index. The statistical models play a crucial role in scientometric analysis and citation mapping. The distributions help identify typical citation pattern, outliers, anomalies.These enable comparison across fields, authors, and papers. The probability distributions aid in predicting future citation counts and emerging research trends as well as areas of research. These indicate papers with immoderately high impact and metrics of research quality, such as citation-based metrics. By applying statistical distributions, scientometric analysis can improve accuracy, reveal underlying citation behaviour and how citations accumulate over time, trends, predict research phenomena. Advance theory development helps contribute to progress of theories of research behaviour and impact. This paper intends to gain insights into research impact, quality and citation flow that support the advancement of scientific research. Main objectives of this study are to understand and describe the patterns of citation counts and to predict the number of citations a paper or author will receive.

Bibliometric Insight into Financial Sustainability Publication Trends

Objectives: This study addresses the fragmented literature on financial sustainability by reviewing research conducted over the past five years. The objective is to bridge the knowledge gap by compiling an annotated bibliography of recent financial sustainability publications, focusing on the developments from 2018 to 2023. The paper identifies key topics and emerging research trends in this field.Methodology: The research employs bibliometric analysis and VOSviewer to analyze data from the Scopus database. It aims to identify four distinct research clusters related to financial sustainability by examining recent publications. The study also assesses the research methods used in the literature, including archival, experimental, and survey methods.Findings: The bibliometric analysis reveals four distinct research clusters that have emerged in the past decade. These clusters include topics such as co-creation, digitization of cultural heritage, financial management in the digital era, international digital platforms, ethical issues, organizational forms, social innovation, work integration social enterprises (WISE), cryptocurrencies, and financial regulation. The study also highlights the predominance of archival, experimental, and survey research methods over the past six years.Conclusion: This research provides a comprehensive overview of the evolving landscape of financial sustainability, offering valuable insights for students and academics. It summarizes key trends, methodologies, and emerging topics in financial sustainability research, contributing to a better understanding of the field and guiding future studies.

Augmented Reality in Higher Education: A Bibliometric Analysis of Research Trends and Evolution from 1997 to 2024

Augmented reality (AR) is a technology that superimposes digital content onto the physical world, blending virtual components like images, videos, and 3D models with real environments. In the realm of higher education, AR-based learning presents significant benefits. This research seeks to examine and illustrate studies on AR in the context of higher education. Data were obtained from the Scopus database, focusing on AR-related publications, which were analyzed and visualized using Biblioshiny. The article selection process in bibliometric analysis followed the PRISMA diagram. The analysis examined various bibliographic factors, including countries, institutions, journals, authors, publications, and frequently co-occurring keywords. A total of 832 articles meeting the inclusion criteria were analyzed, covering the period from 1997 to 2024. Research in this field has expanded significantly, with a notable rise in publications since 2015. The article by Akçayir M, published by Elsevier in the Educational Research Review journal in 2017, has the highest total citations, with 1309 citations. The United States and China emerged as the leading contributors to this area of study. The university with the most publications is led by the University of Bologna in Italy. Key research themes in AR for higher education include education, computer science, and technology. This article also highlights emerging research trends and the evolution of AR concepts in the educational domain, with a particular focus on higher education.

Mapping Machine Learning Trends in Chemistry Research using LLM with Multi-Turn Prompting

A review of research in the field of chemistry that incorporates machine learning is essential to identify recent developments and explore its potential applications. Published research articles provide an opportunity to analyze emerging research trends. The use of natural language processing (NLP) technology not only accelerates text data analysis but also enhances accuracy in understanding the content and context of scientific articles. Previously, trend analysis in ophthalmology research had been conducted using Zero-Shot Learning. In this study, an analysis of chemistry-related articles focusing on machine learning was carried out using a multi-turn prompting technique. The process began with data collection through web scraping of abstracts containing the keywords "machine learning" and "chemistry." The retrieved data was then tabulated and analyzed using a Large Language Model (LLM) with a Multi-Turn Prompting approach, where general prompts were initially used, followed by deeper exploration based on previous responses. Additionally, statistical descriptive analysis was performed using targeted prompts. Analysis of 200 article abstracts identified seven key terms related to the use of machine learning in chemistry: chemical (138 articles), protein (119 articles), drug (107 articles), structure (100 articles), molecular (96 articles), chemistry (91 articles), and quantum (84 articles). Furthermore, three dominant research topics were found in the intersection of chemistry and machine learning: protein and molecular structure, quantum chemistry, and drug discovery. The number of articles on machine learning in chemistry began to rise in 2012 and saw a significant increase in 2019. The findings suggest that there are still many opportunities for developing machine learning applications in chemistry, particularly in quantum chemistry. This field only began to gain attention in 2013, and the number of published articles remains relatively low each year, indicating that it is still in the early stages of exploration.

Mapping the knowledge domain of Natural Capital and Sustainability: A bibliometric analysis using the Scopus database for future research direction

Mapping the knowledge domain of Natural Capital and Sustainability: A bibliometric analysis using the Scopus database for future research direction

Key contributors and trends in circulating tumor DNA research in lung cancer: A bibliometric analysis

Lung cancer is one of the leading causes of cancer-related mortality, and liquid biopsy, particularly the detection of circulating tumor DNA (ctDNA), offers a promising non-invasive alternative for diagnosis. Despite significant research on ctDNA in lung cancer, a comprehensive bibliometric analysis on this topic is lacking in the literature. This study systematically reviews ctDNA research trends in lung cancer using bibliometric methods to identify leading contributors, emerging themes, and underexplored areas for future research. We conducted a search of the Web of Science Core Collection database for ctDNA-related lung cancer publications up to 2023. The bibliometric analysis was performed using VOSviewers, CiteSpace, and the R package “bibliometrix.” The results revealed a total of 2862 publications on ctDNA in lung cancer, comprising 1998 articles and 864 reviews. Between 2021 and 2023, the number of publications stabilized, with an average of approximately 360 publications per year. The countries with the highest number of published papers were China and the United States. The University of Texas MD Anderson Cancer Center was the leading institution in terms of publication output. Among journals, Cancers published the highest number of papers, while Clinical Cancer Research had the highest citation impact. Lanman RB was the leading author by publication count, and Newman was the most co-cited author. Current research on ctDNA in lung cancer primarily focuses on areas such as minimal residual disease, prognosis and recurrence monitoring, adjuvant therapy decision-making, epidermal growth factor receptor and targeted therapy, and immunotherapy. This bibliometric analysis highlights the impact of ctDNA in lung cancer, revealing key contributors and emerging research trends.

A Comprehensive Review on Cellulose Nanofibers, Nanomaterials, and Composites: Manufacturing, Properties, and Applications.

Cellulose nanofibers (CNFs), cellulose nanomaterials (CNMs), and cellulose-based composites represent a convergence of material science, sustainability, and advanced engineering, paving the way for innovative and eco-friendly materials. This paper presents a comprehensive review of these materials, encompassing their extraction, preparation methods, properties, applications, and future directions. The manufacturing of CNFs and CNMs leverages diverse techniques-chemical, mechanical, and enzymatic-with each offering distinct advantages in tailoring material characteristics to meet specific needs. Strategies for functionalization and surface modification are detailed, highlighting their role in enhancing the properties of CNFs and composites while addressing challenges in scaling production to industrial levels. The structural, mechanical, thermal, optical, electrical, and biocompatibility properties of CNFs, CNMs, and their composites are explored, underscoring their versatility for applications across various industries. Cellulose-based composites, in particular, demonstrate exceptional tunable properties for specific uses, although achieving uniform dispersion remains a key technical hurdle. These materials have applications in packaging, automotive, aerospace, biomedical devices, energy storage, and environmental remediation. Emerging research trends emphasize the integration of CNFs and CNMs with advanced manufacturing technologies, promoting sustainable practices and life cycle considerations while advancing their commercialization potential. This rapidly evolving field holds immense promise for addressing global challenges by creating high-performance, and sustainable materials. This review is crucial in advancing the understanding of cellulose nanofibers, nanomaterials, and cellulose-based composites, providing valuable insights that will drive the development of sustainable, high-performance materials for a wide range of applications, ultimately addressing key global challenges.

Challenges and Perspectives in Lignin‐Derived Polyurethane Foam Synthesis

Abstract Polyurethane foams (PUFs) represent a significant segment of the polyurethane (PU) and cellular plastics industries, owing to their versatile applications and desirable properties. However, the production of PUFs heavily relies on petroleum‐derived chemicals, including polyols and isocyanates, raising critical environmental concerns. Lignin, an abundant aromatic macromolecule, offers a promising alternative for replacing petroleum‐based polyols because of its intrinsic hydroxyl groups. While efforts have been made to produce and apply various lignin‐based polyurethane foams (LPUFs), their commercialization remains limited by challenges such as low product consistency, poor technical performances, and high production costs. This study critically evaluates recent advances in the development of LPUFs, including innovative synthesis methods, functional applications, and emerging research trends. Moreover, potential strategies are discussed, such as lignin fractionation, modification, and co‐solvent assistance, for addressing the challenges. By resolving them, LPUFs could play a pivotal role in transitioning the PU industry to help achieve a circular bioeconomy.

Wellness tourism: a systematic literature review

ABSTRACT Wellness tourism is a proactive process where people travel to maintain or enhance their well-being. The main goal of this study is to analyse the publications on wellness tourism as a subcategory of health tourism over the last five years and contribute to a better understanding of this field. Specifically, the objectives are: (a) to identify the theoretical models and methodological frameworks involving wellness tourism, (b) to identify prominent and emerging themes, and (c) to provide directions for future investigations. A systematic literature review based on the PRISMA guidelines was carried out through Web Science and Scopus databases in December 2023. After applying the inclusion and exclusion criteria, 72 peer-reviewed empirical articles published between 2019 and 2023 in English-language academic journals were selected. The review identified and assessed the selected studies’ theoretical and methodological frameworks. Additionally, the thematic analysis revealed 66 distinct variables related to wellness tourism, highlighting emerging research trends. Finally, the study outlines potential avenues for future research. This systematic review provides theoretical insights and practical contributions to the growing field of wellness tourism.

Research trends of inflammation in autism spectrum disorders: a bibliometric analysis.

Inflammation has been recognized as a significant factor in the pathophysiology of autism spectrum disorders (ASD), which have garnered increasing scholarly attention over the past few decades. This study aims to explore research trends related to inflammation and ASD through bibliometric analysis. A comprehensive literature search was conducted in the Web of Science Core Collection (WoSCC) on August 28, 2024. This study was restricted to literature published in English. The bibliometric analysis utilized VOSviewer, CiteSpace, and the R package "bibliometrix" to visualize collaborations, keyword co-occurrences, and emerging research trends. A total of 1,752 articles addressing inflammation and ASD were published, demonstrating a consistent upward trend in research output. The United States emerged as the country with the highest volume of publications. Saleh A. Bakheet was identified as the most prolific authors, significantly contributing to the literature with 54 publications. The University of California System was recognized as the most productive institution in this area of study. The journal of Brain Behavior and Immunity was noted as a prominent venue for publication in this field, exhibiting high citation metrics that reflect its considerable influence. The keyword "children" was the most frequently occurring term, with other significant terms including "oxidative stress" and "brain." The keyword burst analysis revealed notable periods of increased research focus on topics such as "inflammatory bowel disease," "cytokine production," "neurodevelopmental disorders," and "microbiota." This bibliometric analysis highlights the growing scholarly attention devoted to the relationship between inflammation and ASD. Significant contributions and emerging trends emphasize the pivotal role of neuroinflammation in ASD, indicating a necessity for further exploration in this domain.

Emerging Trends of Artificial Intelligence and Human, Personal, or Customer Values using R Studio and Vos Viewer

The objective of the research is to visually examine the emerging research trends pertaining to the connection of artificial intelligence and human, personal, or customer value on a global scale. The methodology of the research employs bibliometric approaches from the Scopus database (2018-2022), a total of 2094 documents. The analysis of the study utilizes the VOS Viewer software and R studio software. Based on the findings of the study, it has been observed that the University of California and Li Y are the top institution and author that exhibit the highest level of activity in the domain of artificial intelligence, which mainly infused with customer value. Secondly, the domains of artificial intelligence have witnessed significant exploration in the subject fields of computer science (CS). Lastly, the research found main research themes such as artificial intelligence, big data, and internet of things, which will be useful for future researchers.

The evolution of cybersecurity in self-driving cars: insights from bibliometric research

Purpose This study aims to investigate the evolution of cybersecurity in autonomous vehicles over the past decade, focusing on influential publications, leading authors, key themes and emerging research trends. Design/methodology/approach A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach, with data extracted from The Lens database and analyzed using VOSviewer and Bibliometrix. This study provides a quantitative overview of academic trends from 2014 to 2023. The analysis reveals significant growth in scientific production, predominantly driven by the USA, China and the UK. Central themes include network security, cyberattack prevention and regulatory frameworks. Findings The findings emphasize that cybersecurity, artificial intelligence (AI) and regulation are critical for developing secure and reliable vehicular systems. Research limitations/implications Future research should focus on enhancing security in vehicle-to-everything, vehicle-to-vehicle and vehicle-to-infrastructure communications by improving protocols and integrating AI. Practical implications Key themes identified include trust in security, reliability and user experience. Social implications The analysis highlights future research directions, particularly the integration of AI with sustainable development and autonomous transportation policies. Originality/value This study provides a quantitative overview of academic trends from 2014 to 2023 regarding the theme of cybersecurity and self-driving cars.

Emerging Research Trends In Computer Science: A Bibliometric Analysis

This study explores emerging research areas in Computer Science through bibliometric analysis using data retrieved from the Scopus database. A systematic search was conducted using a predefined query, initially yielding 6,300 documents, which were refined based on publication year (2015–2025), subject area (Computer Science), document type (journal articles), language (English), and Open Access availability, resulting in 447 articles. The analysis was conducted by using the generated bibliometric maps, including term co-occurrence, and co-authorship networks. The results highlight key emerging research trends, including advancements in artificial intelligence, machine learning, cloud computing, Internet of Things (IoT), cybersecurity, and quantum computing. Additionally, global collaboration patterns reveal leading research contributors and potential areas for international cooperation. The findings provide valuable insights into the future directions of Computer Science research, offering strategic opportunities for institutions like Arusha Technical College to contribute to and benefit from these advancements.