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PurposeThis article offers a bibliometric analysis and explores the relationships among the documents on ecopreneurship by using relational techniques. The results highlight the publication trends; most cited documents, top contributing authors, countries and institutions with highest productivity and most contributing journals to the research field.Design/methodology/approachInitially, 216 documents were retrieved from the Thompson Reuters Web of Science Core Collection database with three document types: articles, review and book review. All the documents were considered for the analysis. Then VOSviewer and bibliometric analysis using R with an inbuilt utility Biblioshiny were used together for co-word analysis, co-citation network analysis, generating collaboration networks and also generating a unique three-field plot to analyze the evolution of a research field.FindingsThe results highlight the publication trends: most cited documents, top contributing authors, countries and institutions with highest productivity and most contributing journals to the research field. The network analysis of co-authorship, co-citation, keyword co-occurrence and bibliographic coupling reveals most prominent relationships between authors, documents, co-cited references, sources and countries for the available documents on the research field.Research limitations/implicationsThe study helps not only in expansion of knowledgebase on the research topic but also in understanding the evolution of the ecopreneurship to provide research support further in this area.Originality/valueEcopreneurship is an emerging field of research connecting ecology and entrepreneurship together, making it a potential research area. The contributions made to this research field from 1989 to 2019 serve as a core for conducting this analysis. The study is an effort to help in coordinating research network across countries, authors and affiliating universities.

New tools for reproducible exploratory data analysis of large datasets are important to address the rising size and complexity of genomic data. We developed the valr R package to enable flexible and efficient genomic interval analysis. valr leverages new tools available in the ”tidyverse”, including dplyr. Benchmarks of valr show it performs similar to BEDtools and can be used for interactive analyses and incorporated into existing analysis pipelines.

Mapping forest types and tree species at regional scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. Here, we assess the potential of a U‐net convolutional network, a recent deep learning algorithm, to identify and segment (1) natural forests and eucalyptus plantations, and (2) an indicator of forest disturbance, the tree species Cecropia hololeuca, in very high resolution images (0.3 m) from the WorldView‐3 satellite in the Brazilian Atlantic rainforest region. The networks for forest types and Cecropia trees were trained with 7611 and 1568 red‐green‐blue (RGB) images, respectively, and their dense labeled masks. Eighty per cent of the images were used for training and 20% for validation. The U‐net network segmented forest types with an overall accuracy >95% and an intersection over union (IoU) of 0.96. For C. hololeuca, the overall accuracy was 97% and the IoU was 0.86. The predictions were produced over a 1600 km2 region using WorldView‐3 RGB bands pan‐sharpened at 0.3 m. Natural and eucalyptus forests compose 79 and 21% of the region's total forest cover (82 250 ha). Cecropia crowns covered 1% of the natural forest canopy. An index to describe the level of disturbance of the natural forest fragments based on the spatial distribution of Cecropia trees was developed. Our work demonstrates how a deep learning algorithm can support applications such as vegetation, tree species distributions and disturbance mapping on a regional scale. In this paper, we have assessed the potential of a deep learning algorithm, the U‐net model, to identify and segment (1) natural forest and eucalyptus plantation, and (2) a tree species indicator of past forest disturbance (Cecropia hololeuca) using Red‐Green‐Blue WorldView‐3 images at 0.3 m of spatial resolution. The overall accuracies of both forest types and C. hololeuca segmentations were above 95%. The method was therefore used to map forest types and all individuals of C. hololeuca in a 1600 km² region of fragmented Atlantic Forest near São Paulo, Brazil. From the C. hololeuca occurrence and distribution in the fragments, we derived a new disturbance metric. Our results show that this method is very promising for applications such as tree species or vegetation mapping.

Purpose The current study focuses on many risk categories that have emerged in the digital ecosystem of the financial technology industry, which has dramatically changed traditional financial systems as a result of innovations in financial technology.Design/methodology/approach The Web of Science Core Collection database was used to find a data set of 719 pertinent papers on the subject encompassing the year 2015–2023. The sample procedure was carried out utilising the PRISMA approach. The keywords were first gathered relating to technological risks in banking sectors and after confirming the keywords, the authors performed the search by the “topic” which covers “title” in the search bar. On February 15, 2023, the Web of Science database was searched using the terms “Cyber security risk OR data theft OR financial crimes OR financial stability risk OR operational risk OR default risk OR money laundering OR financial terrorism AND FinTech AND banking sector”. Two-step approach is applied in this study. First, descriptive analysis is applied using RStudio to highlight prominent authors, countries and affiliations. Furthermore, relationship among authors, countries and keywords is shown by using three fields plot. Second, using VOSviewer, co-occurrence of keyword analysis is used to determine the most influential themes.Findings The findings show that 2,611 documents have been published from 2016 to 2023. Year 2021 is the most productive year in terms of number of publications. The results also show that WANG XC is tied for the position of most prolific contributing author. In a similar vein, the United States leads the world in publication output. Furthermore, Southwestern University of Finance and Economics in China is leading the list with 15 articles. The results from the co-occurrence of keywords reveal that “default risk”, “operational risk”, “money laundering”, “credit risk”, “corporate governance”, “systematic risk”, “financial stability risk”, “risk management” and “crises” are the frequently keywords.Originality/value The results of this study are beneficial to academia and industry in order to advance their current understanding of FinTech and associated concerns. This work expands the understanding of the technology hazards facing the banking industry from a broad perspective.

The aim of this study is to consolidate the state of mobile commerce consumer research from 2001 to 2022. Based on a systematic literature review employing a bibliometric technique, this study not only reports the significant contributions of authors and their affiliations but also discusses the evolution of m-commerce research over the last two decades. Examination of annual production trends revealed that publications were on the rise all along; the year 2022 clocked the highest number of publications (53 documents), which further reinforces that the research on this domain is in its blooming season. China is the most contributing country in terms of the number of publications and citations received, followed by the USA. The author Keng-Boon Ooi has been the most productive researcher; his studies continue to be the foundation on which m-commerce consumer research continues to thrive. The analysis of scientific mapping revealed that, although many studies were carried out on mobile commerce adoption intention, the focus of the researchers lately shifted towards studying continuous use intention (since 2018). Further, it was observed that the base theory, the Technology Acceptance Model, which has been widely used for determining antecedents of technology adoption intention, is losing its significance and is being overtly replaced by the Unified Theory of Acceptance and Use of Technology. While the topics “trust, loyalty, satisfaction, mobile banking, UTAUT, continuance intention, perceived enjoyment, and COVID-19” were identified as mother (engine) themes, the keywords “privacy, self-efficacy, social influence, TAM, attitude, and intention to use” became diminishing themes. The following topics have been identified as emerging themes: “Mobile social commerce, Mobile payment, Mobile marketing, Omnichannel, Fintech, and Live streaming commerce”. This study provides useful insights to potential researchers.

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field’s impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka’s law is reflected in the author’s contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford’s Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape.

This study investigates the application of advanced simulation and modeling technologies to optimize the performance and reliability of renewable energy systems. Given the urgent need to combat climate change and reduce greenhouse gas emissions, integrating renewable energy sources into existing infrastructure is essential. Using bibliometric methods, our research spans from 1979 to 2023, identifying key publications, institutions, and trends. The analysis revealed a significant annual growth rate of 16.78% in interest in simulation and modeling, with a notable surge in published articles, reaching 921 in 2023. This indicates heightened research activity and interest. Our findings highlight that optimization, policy frameworks, and energy management are central themes. Leading journals like Energies, Energy, and Applied Energy play significant roles in disseminating research. Key findings also emphasize the importance of international collaboration, with countries like China, the USA, and European nations playing significant roles. The three-field plot analysis demonstrated interconnections between keywords, revealing that terms like “renewable energy sources”, “optimization”, and “simulation” are central to the research discourse. Core funding agencies, such as the National Natural Science Foundation of China (NSFC) and the European Union, heavily support this research. This study underscores the importance of policies and sustainability indicators in promoting renewable energy technologies. These insights emphasize the need for ongoing innovation and interdisciplinary collaboration to achieve a sustainable energy future.

Colorectal cancer ranks as the third most prevalent cancer, was the second most common cause of cancer-related deaths globally. Nowadays, plenty of papers are published about Curcuma sp. as colorectal cancer; however, no bibliometric study about the topic exists. This study examines the existing papers about Curcuma sp. as colorectal cancer agent using bibliometric analysis focusing on countries, institutions, publishers, authors, documents, and keywords. Bibliographic information of relevant research articles were obtained from the Scopus database. This research presents a detailed bibliometric analysis of scientific publications from 2003 to 2023. In total, 121 documents from 95 journals, it was found that production has grown steadily, with an annual growth rate of 5.65%. The average number of citations per document is 65.02, indicating a significant impact on the scientific community. This analysis highlights the most productive authors, the most frequently cited papers, and the key geographic regions that drive scientific output. This study explains the role of Curcuma sp. in the fight against colorectal cancer. It includes descriptions of molecular mechanisms, pre-clinical testing, clinical trials, and the effectiveness of alternative therapies. The study highlights the ongoing significance of this topic over time, illustrating the transition from basic research to clinical applications.

Fintech has emerged as a force of disruption in the reconfiguration of traditional financial services through technological innovation, while research on this domain remains fragmented. The present study tries to fill this literature gap by carrying out a bibliometric analysis of 494 Fintech-related articles indexed in the Web of Science Core Collection from 2001 to 2024. Using VOSviewer and RStudio, publication trends, citation networks, and the contributions of highly productive authors, institutions, and countries are presented. The findings show a rapid growth in Fintech research, especially during the pandemic period of Covid-19, with dominant themes being adoption factors, financial impacts, shadow banking, and technological development. Three important research trends identified in this study are: the economic impact of Fintech in emerging markets, the role of Fintech in capital markets, and improvements in disclosure and rating systems. These reflect the increasing importance of Fintech in enhancing financial inclusions, regulatory adaptation, and efficiency in markets. The results reveal gaps in current research, such as the need for further exploration of Fintech’s social implications in developing economies and its regulatory challenges. This study contributes to the literature by providing a comprehensive mapping of the Fintech research landscape, offering insights that are valuable to policymakers, financial institutions, and academics. The conclusions highlight the need for innovation, regulation, and investment strategies to support the sustainable growth of Fintech. In addition, the study proposes a future research agenda aimed at fostering more inclusive and efficient financial systems, particularly in emerging markets, thereby enhancing Fintech’s role in global financial markets.