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A quantitative analysis of scientific articles published in the Journal of Agrometeorology (JAM) between 2008 and 2022 was conducted using SCOPUS database and a variety of scientometric indicators. Various metrics were utilized to examine aspects including yearly research output, highly referenced sources, author rankings, contributions and profiles, cooperation trends, highly contributing nations, most cited papers, commonly searched keywords and worldwide collaboration mapping. This study employs biblioshiny for analysis and only looks at data that is available in Scopus database. With an h-index (17), a g-index (21) and 3238 total citations across the study period, the journal demonstrated considerable influence. With the greatest number of research publications (n=46) and the greatest number of citations (236), Pandey V stands out among other authors. In terms of the number of papers and citations, India emerged as the leading nation, with the Punjab Agricultural University in the lead with 744 publications. Four clusters were found by co-citation network analysis, with Allen RG being the most quoted author among them. The study also highlighted the fact that Indian authors worked together the most. This analysis is important for assessing the influence of the JAM and offers insightful information about noteworthy research trends and developments in the scientific community. [ABSTRACT FROM AUTHOR]

• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

Objective: In India research on health issues of transgender populations are very recent and limited though transgenders are an important sub-group of the population. Hence, this study attempts to understand the state of transgender health research in India through a systematic review of literature. Methods: A systematic literature review was conducted using bibliometric analysis. Initially, 132 studies were identified, and only 37 articles meeting selection criteria were subsequently selected for review using PRISMA 2020 guidelines. The research landscape was examined with tools such as Biblioshiny, Arc-GIS (10.1), and Vos-Viewer. Results: The review highlights that existing literature on transgender health in India mainly focuses on sexual health while neglecting their overall health status. It also emphasises the skewed geographical coverage of these studies. Based on the analysis, the interdisciplinary nature of the subject is illustrated in a three-field plot and through term co-occurrence. These indicate the need for culture-specific gender-affirmative services promoting a holistic approach to comprehend the health of transgender populations in India. Conclusion: In India research on transgender health is lopsided and at an initial stage. There is a need to develop diverse research focus on various health issues of transgenders that should also be geographically representative. Future in-depth research on this subject will enable optimizing resource allocation, developing effective gender-inclusive policies, and support holistic planning for better health status of transgender people in India, and other countries with similar socio-cultural background. [ABSTRACT FROM AUTHOR]

In recent years, there has been widespread interest in the applications of Artificial Intelligence (AI) techniques to the financial sector and in the development of new financial products and services. AI methods are widely regarded as the most important methods in the emerging market for providing not only cutting-edge financial services, but also an innovative approach to business process automation, a solution to the challenges of reducing service costs associated with managing low-income and rural customers and a method of identifying and evaluating the creditworthiness of those customers. No clear reviews are identified in the areas of AI and its contribution to Financial Innovations (FI) research in finance. To address the above gap, the present study provides a systematic literature review and bibliometric view of AI and FI research in finance. Co-citation, co-occurrence and bibliographic coupling analysis techniques are being used to make inferences about the structure of AI and FI research in finance from 1987 to 2022. The study used 237 filtered research articles from the Scopus database and processed through VOS-Viewer and Biblioshiny through "R" to justify study objectives. Through bibliometric analysis, this study unveils influential authors, journals and institutions, emphasizing top-cited research articles and unveiling six emerging thematic clusters. The novelty lies in the identification of prominent keywords linked to AI and financial innovation research, accompanied by a comprehensive analysis of globally and locally cited articles. Employing an analytical approach, the study identifies research gaps to contribute to the existing body of knowledge. [ABSTRACT FROM AUTHOR]