This bibliometric review examines the research state of artificial intelligence (AI) and machine learning (ML) applications in the Banking, Financial Services, and Insurance (BFSI) sector. The study focuses on Scopus-indexed articles to identify key research clusters. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, 39,498 articles were screened, resulting in 1045 articles meeting the inclusion criteria. N-gram analysis identified 177 unique terms in the article titles and abstracts. Co-occurrence analysis revealed nine distinct clusters covering fintech, risk management, anti-money laundering, and actuarial science, among others. These clusters offer a comprehensive overview of the multifaceted research landscape. The identified clusters can guide future research and inform study design. Policymakers, researchers, and practitioners in the BFSI sector can benefit from the study\'s findings, which identify research gaps and opportunities. This study contributes to the growing literature on bibliometrics, providing insights into AI and ML applications in the BFSI sector. The findings have practical implications, advancing our understanding of AI and ML\'s role in benefiting academia and industry.

OBJECTIVE: To summarize the results of adult obesity neuroimaging studies (structural, resting-state, task-based, diffusion tensor imaging) published from 2010, with a focus on the treatment of sex as an important biological variable in the analysis, and identify gaps in sex difference research.
RESULTS: Neuroimaging studies have shown obesity-related changes in brain structure, function, and connectivity. However, relevant factors such as sex are often not considered. We conducted a systematic review and keyword co-occurrence analysis. Literature searches identified 6281 articles, of which 199 met inclusion criteria. Among these, only 26 (13%) considered sex as an important variable in the analysis, directly comparing the sexes (n = 10; 5%) or providing single-sex/disaggregated data (n = 16, 8%); the remaining studies controlled for sex (n = 120, 60%) or did not consider sex in the analysis (n = 53, 27%). Synthesizing sex-based results, obesity-related parameters (e.g., body mass index, waist circumference, obese status) may be generally associated with more robust morphological alterations in men and more robust structural connectivity alterations in women. Additionally, women with obesity generally expressed increased reactivity in affect-related regions, while men with obesity generally expressed increased reactivity in motor-related regions; this was especially true under a fed state. The keyword co-occurrence analysis indicated that sex difference research was especially lacking in intervention studies. Thus, although sex differences in the brain associated with obesity are known to exist, a large proportion of the literature informing the research and treatment strategies of today has not specifically examined sex effects, which is needed to optimize treatment.

BACKGROUND: The existing selection of driving distraction recognition methods is based on a specific research perspective and does not provide comprehensive information on the entire field of view.
METHODS: We conducted a systematic review of previous studies, aiming to come up with appropriate research methods to identify the driver\'s distraction state. First, this article selects four sets of search keywords related to driving distraction discrimination from five databases (Web of Science, ScienceDirect, Springer Link, IEEE, and TRID) and identifies 1,620 peer-reviewed documents from 2000 to 2020; these 1,620 documents underwent bibliographic analysis and co-occurrence network analysis. The co-occurrence coupling relationship is analyzed from the aspects of time, country, publication, author and keywords. Second, 37 papers published were screened, and the driving distraction recognition methods proposed by these 37 papers were summarized and analyzed.
RESULTS: The results show that this field has been prevalent since 2013; countries such as the United States, Britain, Germany, Australia, China, and Canada are in the forefront of research in this field, and the cooperation between related countries is relatively close. The cooperation between authors is characterized by aggregation, and the mobile phone as the main keyword is almost connected to other keyword nodes; the recognition model of deep learning algorithm based on video surveillance data sources has become the mainstream hot spot distraction recognition method. The recognition model of machine learning algorithm based on vehicle dynamics data, driver physiology, and eye movement data sources has specific advantages and disadvantages.
CONCLUSIONS: The results can help people to understand the current situation of driving distraction comprehensively and systematically, provide better theoretical support for researchers to choose the subsequent driving distraction recognition model, and provide research direction for driving distraction recognition in the future.

This study seeks scientometric, content and co-occurrence analysis of systematic review and Meta-analysis articles in the field of gamification in education. In terms of purpose, this is an applied study and regarding type, it is a scientometric and co-occurrence analysis. The researchers conducted a search in WoS, Scopus and PubMed databases. The abstract and full text of 25 out of 71 articles were selected to be included in the study. Then, the citation and altmetrics indicators were investigated. In addition, VOSviewer software was utilized to analyze and visualize keywords and map of articles. Finally, the full texts of all articles were analyzed to be provided more information about the types of analyses in these articles. The findings showed that 25 articles were published between 2016 and 2021. Co-occurrence map of articles showed that the three variables of motivation, learning, and engagement have been considered in gamified education studies and most studies have examined gamification in the e-learning environment. Finally, the content analysis of the articles showed that 344 articles were included and analyzed in these 25 systematic reviews and meta-analyses. The types of analyzes performed on these 344 articles categorized them in 7 categories including Country/Territory, Duration of intervention, Lessons/content and the level of gamified educational course, the number of learners, platforms, the game elements and the theories. The results of the study illustrate that different dimensions of the gamification in articles in the field of Education have been considered by the researchers.

Sustainable remediation, which promotes the use of more sustainable practices during environmental clean-up activities, is an area of intense international development. While numerous indicators related to sustainable remediation assessment have been utilized and published in related academic literature, they are difficult to unify and vary in emphasis between countries. Following literature retrieval from CNKI, Springer, ScienceDirect, and Wiley Online databases, we present a systematic and bibliometric analysis of relevant national and international literature to define the most frequently considered indicators of sustainability, which play important roles in selecting remediation technologies or site management methods from a sustainability perspective. Following the application of co-occurrence analysis and social network analysis, the results indicate that 1) environmental criteria are most commonly used in evaluating remediation technologies, with significantly less emphasis on social criteria in Chinese publications in particular; 2) with an increasing number of publications in the last 20 years, sustainable remediation has gone through an initial stage, rising stage, and burst or wider adoption stage, characterized by a transformation of the research theme from a predominantly risk-based management approach to a sustainability-based one, with risk management as an underpinning principle; 3) health, resource, cost, and time are the most widely used indicators in terms of social, environmental, economic, and technical criteria, respectively; 4) clear differences exist between China and other nations, particularly in the frequency of usage of each indicator, the application of social criteria, and preferred stakeholders. Nevertheless, China has made significant progress and now makes increasing contributions to sustainable remediation at an international level.