Search Results (458)

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

The use of Internet of Things (IoT) for sports innovation has transformed the way athletes train, compete, and recover in any sports activity. This study performs a bibliometric analysis to examine research trends, collaborations, and publications in the realm of IoT and Sports. Our analysis included 780 Scopus articles and 150 WoS articles published during 2012–2025, and duplicates were removed. We analyzed and visualized the bibliometric data using R version 3.6.1, VOSviewer version 1.6.20, and the bibliometrix library. The study provides insights from a bibliometric analysis, showcasing the allocation of topics, scientific contributions, patterns of co-authorship, prominent authors and their productivity over time, notable terms, key sources, publications with citations, analysis of citations, source-specific citation analysis, yearly publication patterns, and the distribution of research papers. The results indicate that China and India have the leading scientific production in the development of IoT and Sports research, with prominent authors like Anton Umek, Anton Kos, and Emiliano Schena making significant contributions. Wearable technology and wearable sensors are the most trending topics in IoT and Sports, followed by medical sciences and artificial intelligence paradigms. The analysis also emphasizes the importance of open-access journals like ‘Journal of Physics: Conference Series’ and ‘IEEE Access’ for their contributions to IoT and Sports research. Future research directions focus on enhancing effective, lightweight, and efficient wearable devices while implementing technologies like edge computing and lightweight AI in wearable technologies. Full article

Understanding soil infiltration at the watershed level is crucial to hydrological studies, as it significantly influences surface runoff, groundwater replenishment, and ecosystem sustainability. Research in this area—particularly employing the Soil and Water Assessment Tool (SWAT)—has seen sustained scholarly interest, with an upward trend in related publications. This study analyzed 141 peer-reviewed articles from the Web of Science (WOS) Core Collection. By applying bibliometric techniques through CiteSpace visualization software, it explored the key themes and emerging directions in the use of the SWAT model for soil infiltration studies across watersheds. Findings revealed that this field integrates multiple disciplines. Notably, the Journal of Hydrology and Hydrological Processes emerged as two of the most impactful publication venues. Researchers and institutions from the United States, China, and Ethiopia were the core contributors to this area. “Land use” and “climate change” are currently the hotspots of interest in this field. There are three development trends: (1) The scale of research is continuously expanding. (2) The research subjects are diversified, ranging from initially focusing on agricultural watersheds to surrounding areas such as hillsides, grasslands, and forests. (3) The research content becomes more systematic, emphasizing regional coordination and ecological sustainability. Overall, the research on soil infiltration at the watershed scale using the SWAT model presents a promising and thriving field. This study provides researchers with a framework that objectively presents the research hotspots and trends in this area, serving as a valuable resource for advancing academic inquiry in this domain. Full article

Background: Perinatal depression refers to a major depressive episode that begins during pregnancy or within four weeks after childbirth and persists through the first year postpartum. Perinatal depression is one of the most common complications of pregnancy, with significant adverse maternal and infant outcomes. Numerous reviews and policy guidelines have emerged from Canada; however, a bibliometric analysis that focuses not only on the international sources for perinatal depression research, but also on Canadian sources, has not been undertaken. Purpose: To provide insight on perinatal depression publications conducted by researchers affiliated with Canadian institutions, within an international context. Methods: A bibliometric analysis was performed using performance analysis and science mapping techniques, with data retrieved from Scopus until 31 December 2022. The analysis focused on original peer-reviewed publications, applying no language restrictions and ensuring at least one author was affiliated with a Canadian institution. VOSviewer version 1.6.20 was used to generate visual networks for analysis. Results: In total, there were 763 publications identified in 160 different journals. Among these publications, there were 123 institutions represented. At least one author was associated with a Canadian institution per publication. The University of Toronto had the highest frequency of affiliations (n = 313). Most publications (79.55%) occurred between 2011 and 2022, with 2021 as the year with the most publications (n = 80). The journal with the most publications was Archives of Women’s Mental Health (n = 57, 35.65%). Canadian institution-affiliated authors with the largest number of publications were Dennis (n = 57), Oberlander (n = 39), Meaney (n = 38), and Letourneau (n = 37). Conclusion: This is the first study mapping publications on perinatal depression research within a Canadian context. This bibliometric analysis provides a valuable reference for future research by identifying key authors, institutions, journals, and research areas that prioritize perinatal mental health. Full article

Principal component analysis (PCA) is a widely applied multivariate statistical technique across scientific disciplines, with forestry being one of its most dynamic areas of use. Its primary strength lies in reducing data dimensionality and classifying parameters within complex ecological datasets. This study provides the first comprehensive bibliometric and literature review focused exclusively on PCA applications in forestry. A total of 96 articles published between 1993 and 2024 were analyzed using the Web of Science database and visualized using VOSviewer software, version 1.6.20. The bibliometric analysis revealed that the most active scientific fields were environmental sciences, forestry, and engineering, and the most frequently published journals were Forests and Sustainability. Contributions came from 198 authors across 44 countries, with China, Spain, and Brazil identified as leading contributors. PCA has been employed in a wide range of forestry applications, including species classification, biomass modeling, environmental impact assessment, and forest structure analysis. It is increasingly used to support decision-making in forest management, biodiversity conservation, and habitat evaluation. In recent years, emerging research has demonstrated innovative integrations of PCA with advanced technologies such as hyperspectral imaging, LiDAR, unmanned aerial vehicles (UAVs), and remote sensing platforms. These integrations have led to substantial improvements in forest fire detection, disease monitoring, and species discrimination. Furthermore, PCA has been combined with other analytical methods and machine learning models—including Lasso regression, support vector machines, and deep learning algorithms—resulting in enhanced data classification, feature extraction, and ecological modeling accuracy. These hybrid approaches underscore PCA’s adaptability and relevance in addressing contemporary challenges in forestry research. By systematically mapping the evolution, distribution, and methodological innovations associated with PCA, this study fills a critical gap in the literature. It offers a foundational reference for researchers and practitioners, highlighting both current trends and future directions for leveraging PCA in forest science and environmental monitoring. Full article

(1) Background: Physiological responses, such as heart rate and heart rate variability, have been increasingly utilized to monitor, detect, and predict mental stress. This review summarizes and synthesizes previous studies which analyzed the impact of mental stress on heart activity as well as mathematical, statistical, and visualization methods employed in such analyses. (2) Methods: A total of 119 articles were reviewed following the Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Non-English documents, studies not related to mental stress, and publications on machine learning techniques were excluded. Only peer-reviewed journals and conference proceedings were considered. (3) Results: The studies revealed that heart activities and behaviors changed during stressful events. The majority of the studies utilized descriptive statistical tests, including t-tests, analysis of variance (ANOVA), and correlation analysis, to assess the statistical significance between stress and non-stress events. However, most of them were performed in controlled laboratory settings. (4) Conclusions: Heart activity shows promise as an indicator for detecting stress events. This review highlights the application of time series techniques, such as autoregressive integrated moving average (ARIMA), detrended fluctuation analysis, and autocorrelation plots, to study heart rate rhythm or patterns associated with mental stress. These models analyze physiological data over time and may help in understanding acute and chronic cardiovascular responses to stress. Full article

Research on and the demand for hydrogen-powered vehicles have grown significantly over the past two decades as a solution for sustainable transportation. Bibliometric analysis helps to assess research trends, key contributions, and the impact of studies focused on the safety and reliability of hydrogen-powered vehicles. This study provides a novel methodology for bibliometric analysis that systematically evaluates the global research landscape on hydrogen-powered vehicle reliability using Scopus-indexed publication data (1965 to 2024). Eighteen key parameters were identified for this study that are often used by researchers for the bibliometric analysis of hydrogen-related studies. Data analytics, VOSviewer-based visualization, and research impact indicators were integrated to comprehensively assess publication trends, key contributors, and citation networks. The analysis revealed that hydrogen-powered vehicle reliability research has experienced significant growth over the past two decades, with leading contributions from high-impact journals, renowned institutions, and influential authors. The present study emphasizes the significance of greater funding as well as open-access distribution. Furthermore, while major worldwide institutions have significant institutional relationships, there are gaps in real-world hydrogen infrastructure evaluations, large-scale experimental validation, and policy-driven research. Full article

Sulfonamide drugs (SAs) are a class of emerging contaminants widely present in water environments, which has gradually attracted attention from scholars worldwide. Based on the Web of Science core collection database, this study employs bibliometric methods and visualization tools, such as CiteSpace, Bibliometrix, and VOSviewer, to systematically analyze the literature on the treatment of SAs from 2004 to 2024, exploring the research status, hotspots, and development trends in this field. The results indicate that research on SAs in the past 20 years can be categorized into three stages: initial exploration (2004–2008), slow development (2009–2016), and in-depth research (2017–2024), with an overall increasing trend in number of publications. China and the United States have published the most articles on SAs, with 2266 and 592 articles respectively, and the collaborative ties between the two countries are the strongest. The Chinese Academy of Sciences is the most prolific institution, having published 348 articles. Science of the Total Environment is the journal with the highest publication volume. Among the many SAs, sulfamethoxazole has garnered the greatest research interest, and its primary entry route into the water environment is through the discharge of sewage treatment plants. The research focus has gradually shifted from the source analysis of SAs in the environment to seeking efficient methods for removing SAs. Future research should prioritize investigations into antibiotic-resistant bacteria and antibiotic resistance genes associated with SAs. Full article

This article investigates the phenomenon of missionary landscape photography with an eye to contributing to the field of environmental history. It uses photos made by Dutch missionaries in newly independent Nigeria between about 1960 and 1968. The missionaries were focused on economic development, agricultural innovation, medical aid, and strengthening local churches. Most photos reflect these preoccupations. Even so, many of the photos also portray trees, animals, agricultural fields, and especially landscapes. We argue that missionaries, through their landscape photography, were instrumental in developing a Western gaze of tropical nature, even if their photography cannot be defined as environmental. By comparing the photos to journals of the missionaries, we can distinguish distinct visual and textual narratives that are obviously connected but also have different accents. Whereas both portray tropical wilderness as exotic or as a challenge for missionary efforts, the photos are less optimistic about opportunities for mission by emphasizing desolate, uncultivated landscape. Overall, we argue that missionary photography offers a rich resource for the study of environmental history. Full article

Dysphagia is a medical condition affecting millions globally. Traditional modified diets designed for individuals with dysphagia often focus primarily on improved swallowability, yet they typically fall short in terms of palatability, nutritional diversity, and visual appeal, leading to reduced food intake, malnutrition and reduced quality of life. Recent advancements in 3D-printing technology offer the potential to create texture-modified foods that not only facilitate swallowing but also preserve nutritional content and visual appeal. However, there is a noticeable gap in the comprehensive bibliometric analysis of studies on the use of 3D printing to address swallowing difficulties. To bridge this gap, this study systematically analyzes the literature on the development of 3D-printed foods tailored for individuals with dysphagia from the bibliometric perspective. The results highlight the top journals, leading countries, and prominent institutes/authors in this field. The study also examines the adoption of various 3D-printer brands, the key indicators used to assess the palatability of 3D-printed foods for dysphagia, and the common ingredients used for the development of 3D-printing ink. Overall, this review provides a comprehensive overview of current research trends in the development of 3D-printed food for dysphagia, offering valuable insights for future research in this area. Full article

The Three-North Shelter Forest Program (TNSFP) is a large-scale ecological restoration project that has attracted worldwide attention. It covers 4.069 million km² across 13 provinces in northern China, including northwestern, north-central, and northeastern regions. Bibliometric analysis provides a structural overview of the research in this field and offers insights into key research fronts. We conducted a literature review of the Web of Science Core Collection (WoSCC) from 1990 to 2024 using HistCite for a comprehensive literature analysis and CiteSpace for visualizing research trends and co-citation networks. Based on the literature data from the WoSCC, we performed a bibliometric visualization review of the TNSFP. We observe a rising trend in research on the TNSFP, with the number of publications steadily increasing, especially after 2011. Remote Sensing emerged as the leading journal during the study period, accounting for 8.84% of the total publications. China is the leading contributor to research in this field, comprising 99.32% of the publications, with the Chinese Academy of Sciences (CAS) being the primary research institution, accounting for 36.05%. Research on the TNSFP is interdisciplinary, with Environmental Sciences serving as its primary focus. Ecological restoration and climate change are likely to be the main trends in future research. This study provides a comprehensive overview of the TNSFP’s research landscape, offering insights that can inform policy decisions, guide future research directions, and support on-the-ground conservation and afforestation strategies. Full article

Objectives: This study utilized bibliometric and visualization analyses to explore global research trends and identify research hotspots in extracorporeal membrane oxygenation (ECMO) for circulatory support to provide references and guidance for future research. Methods: This study was based on data from the Web of Science Core Collection, covering the period from 1945 to 1 August 2024. Bibliometric tools, such as VOSviewer and CiteSpace, were used to visualize the analysis of countries/regions, institutions, journals, co-cited references, and keywords in the relevant literature. Results: A total of 14,804 valid papers were included in the study. The research interest in ECMO support for circulatory failure has increased annually, with the United States being the most active in this field. The U.S. occupies most of the top journals and institutions, leading in both the volume of publications and the intensity of international collaboration. Although China has a relatively high number of publications, it lags significantly in international collaboration and representation in top journals. Keyword and citation burst analysis indicates that research on cardiac arrest, post-cardiac surgery circulatory failure, left ventricular unloading, and prognostic factors have been the focus of recent studies and are prevalent in highly impactful literature. Conclusion: The research interest in ECMO support for circulatory failure continues to rise, particularly in cardiac arrest, post-cardiac surgery circulatory failure, left ventricular unloading, and prognostic factors. Future research should investigate these key areas and optimize techniques to enhance the clinical outcomes. Full article

This study investigated how the front-page designs of digital newspapers differ based on institutional origin—comparing platforms that were born-digital with those that have transitioned from print—focusing specifically on their appeal to Generation Z audiences. Grounded in Media Richness Theory, this research employed a mixed-methods approach that combined a quantitative visual content analysis with qualitative semi-structured interviews. In the quantitative phase, the front pages of ten Saudi digital newspapers (five digital-native and five digital-migrant) were systematically analyzed to identify differences in their layouts, typography, multimedia usage, and interactivity. The qualitative phase then explored Generation Z users’ perceptions of the design clarity, visual engagement, and interactive affordances, as well as their suggestions for improving interface usability. The results indicate that digital-native newspapers more closely adhere to contemporary digital design standards and demonstrate significantly higher levels of media richness. This study contributes to digital journalism scholarship by offering both theoretical insights into interface-centered richness and practical design recommendations for enhancing user experience and engagement among younger audiences. Full article

Osteoarthritis is a disabling condition with highly complex overall management and persistent shortcomings, contributing significantly to the global disease burden. Although research in the field has grown considerably in recent years alongside technological advancements, a cohesive and structured understanding of the evolution of the scientific literature, particularly regarding clinical management and outcome evaluation, remains insufficiently developed. To date, most bibliometric analyses in osteoarthritis have focused narrowly on specific subdomains, leaving a notable gap in comprehensive assessments of the broader clinical framework. This study addresses that gap through an integrated, structured, and visual approach using multiple bibliometric techniques targeting osteoarthritis diagnosis and management, aiming to guide future research and improve strategic development. Scientific publication in osteoarthritis has expanded exponentially, peaking in 2024 with 1234 documents. The United States led in both output and citation impact, while China showed rapid growth. Osteoarthritis and Cartilage emerged as the most influential journal. Australian institutions, especially the University of Sydney, demonstrated a remarkable ascent. Five global research clusters were identified, with the U.S. as the central node and Australia serving as a bridge between Western and Asian collaborations. Research themes evolved toward integrated models connecting biological mechanisms, therapeutic strategies, and patient-centered outcomes. This bibliometric assessment underscores exponential growth in osteoarthritis research and highlights the urgent need for more personalized, multidimensional evaluation strategies to enhance clinical translation. Full article

Considering that nowadays the economy plays a crucial role, time series forecasting has become an essential tool across various economic areas and industries. The process of predicting future trends based on historical values in a reliable and accurate manner has generated numerous benefits, such as simplified decision-making processes or strategic planning and reduced risk management. Furthermore, with the advancement made through the use of Artificial Intelligence (AI) methods, time series forecasting has quickly become more precise, adaptive, and scalable, being able to better overcome real-world challenges. In this context, the present paper analyzes the implications of artificial intelligence in time series forecasting by evaluating the scientific articles from the field indexed in Clarivate Analytics’ Web of Science Core Collection database. Through a bibliometric approach, the research identifies key journals, affiliations, authors, and countries, as well as the collaboration networks among authors and countries. It also analyzes the most frequently used keywords and authors’ keywords. The annual growth rate of 23.11% indicates sustained interest among researchers. Prominent journals such as IEEE Access, Energies, Mathematics, Applied Sciences—Basel, and Applied Energy have been the home for the most published papers in this field. Further, thanks to the Biblioshiny library in R, a variety of visualizations have been created, including thematic maps, three-field plots, and word clouds. A comprehensive review of the most cited papers has been performed to highlight the role of AI in time series forecasting. Research results and methods confirmed the versatility of the topics, which have been applied in various fields, such as, but not limited to, finance, energy, climate, and healthcare, and are further discussed. Cutting-edge methodologies and approaches that lead to the transformation of the field of time series analysis in the context of AI are uncovered and discussed through the use of thematic maps. Full article