This study conducts a comprehensive literature review to understand the direction and trends in contemporary labor studies, emphasizing significant global issues attracting scientific attention. Employing a scientometric approach, recent research data is explored using bibliometric analysis. The research adopts a mixed-methods approach, utilizing National Labor Statistics and conducting Focus Group Discussions (FGD) for nuanced insights into labor conditions in Indonesia. A bibliometric analysis of Scientific Labor Research Articles in Scopus (2020-2022) identifies trends and classifies global labor-related topics. Results highlight challenges in the labor landscape, driven by technological advancements and globalization, impacting job security, creating skill gaps, and raising concerns about the Fourth Industrial Revolution. The informal sector, particularly pronounced in Indonesia, poses challenges related to poverty, inequality, and the gig economy. Emerging issues like informal care for the elderly, social capital, and informal learning call for nuanced policy approaches. Indonesia's aging population adds complexity, requiring sustainable support mechanisms for healthcare and social services. The digital landscape, specifically Fintech, plays a significant role, yet research gaps persist. Bridging the digital talent gap is crucial for effective digital transformation, necessitating collaboration between government, educational institutions, and industry players. Challenges in Fintech development highlight the importance of initiatives promoting digital literacy, ethical practices, and regulatory frameworks. In conclusion, a holistic and collaborative approach is essential for navigating complexities and fostering sustainable economic growth.

Banana diseases remarkably influence the worldwide production of bananas. Innumerable studies have focused on timely recognition, prediction, and management of banana plant diseases using various chemical, biological, socio-economic, and AI-based methods. The survey scrutinizes 184 articles accumulated from Scopus, Web of Science, and Google Scholar using defined keywords. These findings reveal the global distribution of the previous studies on plant disease detection, the evolution of ML techniques, and the most frequently studied diseases. The literature shows a swift progress towards machine learning, deep learning, remote sensing, and IoT systems for banana plant disease detection. However, numerous AI models lack real-world validation, datasets are fragmented, and severity quantification mechanisms are understudied. The synthesis analyzes the strong dominance of CNN-based models, which account for the highest proportion of published works and remain the foundational architecture for banana disease detection. Countries such as India, China, the Philippines, Ecuador, and Indonesia have contributed significantly to disease detection. Despite notable progress, many existing systems still rely on single-source and limited datasets, which leads to a lack of cross-source robustness. Evolution of a robust framework integrating multiple datasets, explainable AI, decision support systems and socio-economic insights can lead to more enhanced farmer-friendly banana plant disease management in future This survey provides a detailed overview of the global research studies, highlighting key research gaps that need to be addressed and outlines future directions for building more reliable, interpretable, and comprehensive decision-support pipelines, which will guide the future research work.

Climate change that is happening all around the world and is global that way poses a great threat to the productivity of agriculture and therefore the need for both resilient crops and sustainable farming systems is very strong. Climate-resilient biotechnology has become a major field that is dealing with this issue through the use of the most advanced genetic, microbial, and molecular tools. The present study is a scientometric one and it makes use of data from the Scopus database consisting of 122 scientific publications to map the research field, find the main trends and specify the intellectual structure of this area from 2013 to 2026. The research shows the existence of a very fast-growing field where the main focus of the research is on genome editing especially CRISPR-Cas9 for introducing tolerance to abiotic and biotic stress, the use of plant-growth-promoting microbes for the purpose of improving the plant's resilience, and the combination of omics for trait discovery. The major research areas are: the staples such as wheat, rice and maize, the underutilized nature-resilient crops, and the coming together of biotechnology with AI (artificial intelligence) and models of sustainable systems. The results of the study give evidence of a change in paradigm moving towards precision breeding and biological control measures, and this change is accompanied by the highlighting of the role of biotechnology as critical in securing future food supply under the conditions of volatile climate.

This scientific analysis of 18,441 solid-state battery articles in the Scopus database gives us a picture not only of the whole global research but also of the most dynamic parts of it. The issue leads to a conclusion that the Chinese researchers are the ones that produce the most papers, which is an expression of the Chinese government's great attention to energy innovation. It is also noteworthy that the two major Chinese governmental funding organizations support more than half of the research, which is an indication of the strong institutional commitment to the development of battery technologies. The distribution of disciplines shows a strong interdisciplinary base, with Material Science, Chemistry, Energy, and Engineering being the main contributors. These areas are working together to make breakthroughs in solid electrolytes, interface stability, and scalable manufacturing. The analysis points out that the solid-state battery research is not only technically involved but also geopolitically focused, with China as the key player in determining the direction and influence of the research. This research presents an all-encompassing picture of the intellectual and institutional map, giving clues to the main themes and funding trends that are crucial for the progress of the future energy storage systems.

The worldwide prevalence of type 2 diabetes (T2D) and obesity has been a driving force behind the heavy research on glucagon-like peptide-1 receptor agonists (GLP-1RAs). Orforglipron, an innovative, oral, non-peptide GLP-1RA, is a major development in the therapeutic field, and it has the potential to make the delivery of drugs easier and more patient-friendly than the usual injections. The purpose of this scientometric study is to present the scientific works on orforglipron and thus portray its research landscape, important topics, and growing evidence base. A detailed examination of 101 scientific works (from 2023 to 2025) taken from the Scopus database has been done in this study. The documents were divided into categories depending on the type (e.g., clinical trial, review, meta-analysis), the theme dealt with, and the writer's background. The synthesis included research trends in focus, drug efficacy and safety outcomes, and positioning of the drug within the larger area of pharmacotherapy. The literature on orforglipron has just begun, with the first publications coming out in 2023. The data set consists of a substantial primary research foundation comprising clinical trials of all three Phases, plus a great number of reviews, meta-analyses, and editorial commentaries. The major research themes are: (1) Efficacy in Type 2 Diabetes and obesity, showing pronounced drops in HbA1c and body weight; (2) Safety and tolerability, where the main concern is with gastrointestinal adverse events; (3) Comparative effectiveness against other GLP-1RAs and multi-agonists; (4) Pharmacological characterization as a small-molecule agonist; and (5) The potential role of the drug in cardiovascular risk reduction and other areas that are yet to be explored. The scientific literature surrounding orforglipron is growing at a rapid pace and this indicates that there is a high interest in the drug's potential to change the management of T2D and obesity. The current evidence places it as a powerful, oral option to be administered instead of GLP-1RAs that are delivered by injection. The future research initiatives will probably be directed towards the investigation of the long-term cardiovascular outcomes, real-world effectiveness, and the use in a broader range of patients with different diseases.

The swift development of artificial intelligence (AI) has shifted to a new paradigm, i.e., AI agents—independent entities that can sense, think and do different things in ever-changing surroundings. This paper, through a scientometric analysis, looks at the growing territory of AI agents’ research by sifting through data related to a wide-ranging dataset of academic articles. It maps out the key research directions, the most popular areas for application, the prominent methodological approaches, and the nascent difficulties. The results reveal that there has been a considerable increase in the research related to AI agents, which is mainly due to the progress made in the field of large language models (LLMs), multimodal AI, and agentic frameworks. The most important application fields are healthcare, education, manufacturing, finance, and smart cities. The research also points to the limitations in terms of ethics, security, and operations that would need to be worked through if AI agents are to be deployed in a responsible manner. This study not only presents an organized picture of the current situation, but also indicates new areas for researchers to explore.