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This paper provides a systematic review of research on metabolomics and peptidomics in cocoa, focusing particularly on compounds that influence flavor formation during postharvest processing. Special emphasis is placed on peptides and carbohydrates, which play a critical role in flavor quality. The systematic review involved searching the Scopus and Web of Science databases using equations, followed by merging the results into a unified dataset. A scientometrics analysis was conducted to evaluate scientific productivity across various parameters. Theoretical insights were illustrated using the Tree of Science framework to clarify core contributions. Key findings highlight that omics methodologies are pivotal for understanding the cocoa metabolome and peptidome, contributing to the establishment of robust databases of flavor precursors. Nevertheless, further research is necessary to identify specific metabolites that directly influence sensory attributes and to determine essential metabolites and optimal conditions for the development of high-quality products. Moreover, evaluating how postharvest and agro-industrial processing conditions affect quality biomarker modulation in order to maintain consistent sensory quality. This review uniquely contributes by introducing the innovative application of the Tree of Science methodology to cocoa research, offering a structured scientometric perspective on the evolution and emerging trends in cocoa metabolomics and peptidomics. Furthermore, the analysis of key flavor-related metabolites presented here serves as a valuable resource for optimizing cocoa flavor quality through targeted postharvest interventions.

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This bibliometric analysis of publications on Arenga pinnata Merr aims to evaluate research trends, institutional contributions, and themes. About 755 publications were examined using Scopus and Web of Science (WoS) database datasets. A systematic approach was used to analyse datasets using VOSviewer and ScientoPy tools. The publication trends showed an exponential increase starting in 2010. This input subtly portrayed the growing knowledge of the industrial, financial, and environmental uses of Arenga pinnata. The leading institution is Universiti Putra Malaysia (UPM), whereas Indonesia and Malaysia predominate in research output, reflecting substantial policy backing and academic interest. Additionally, the study exposed important themes related to agroforestry, sustainable agriculture, biotechnology, bio-based materials, and functional food uses. This study revealed Arenga pinnata planting difficulties, which include inadequate agronomic research, labour-intensive processing, and limited market accessibility despite significant developments. The results underline important research gaps and the necessity of investigating more industrial uses, supply chain optimisation, and technology improvements to improve commercial viability. Promoting sustainable growth and the economic potential of Arenga pinnata in worldwide agricultural and environmental settings, this study offers insightful analysis for researchers, policymakers, and industry stakeholders.

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In the ever-evolving landscape of sustainable development and social innovation, social enterprises have emerged as pivotal players. These businesses are at the forefront of cutting-edge approaches with a focus on environmental preservation, youth employment, and community revitalization. Bibliometric analysis and TCCM analysis are extremely helpful in fully grasping the body of knowledge in this field. The multifaceted nature of social enterprise and innovation research is revealed, showcasing its dynamic and connected global environment through word clouds, keyword co-occurrence, and thematic maps. We explore this expansive landscape using the SCOPUS database as our main resource, highlighting the connections between and the universal applicability of important research themes. Our analysis highlights countries and regions as well as concepts like "innovation," "entrepreneurship," "sustainability," and "social impact." It underscores the collaborative efforts of scholars worldwide to explore these multifaceted dimensions. Finally, this study offers a broad perspective on the dynamic field of social enterprise and innovation, highlighting recurrent themes and offering insightful information about this academic field.

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Although the empirical research in energy footprint have been ballooned in size, bibliometric research of energy of footprint remains limited. Using VOSviewer, we wish to contribute a bibliometric assessment of emerging research trends on energy footprint from 2001 to 2023. Publication characteristics, author keywords, authors collaboration, institutions, and countries were all examined. This analysis also recorded a total of 817 papers from the Web of Science Core Collection database. Study on energy footprint gives significant evidence on the possible linkages between energy and other threatening processes; and assists policy makers to prioritize the feasible departing idea for reducing energy footprint based on the research themes and evolutionary dynamics, hence produces some useful inferences that may be important policy implications. The finding of the bibliometric analysis, containing both the network analysis and the descriptive analysis, as well as focusing on the author keyword, reveal that science, followed by computer science dominate the number of studies in terms of author keyword, keyword cluster and author keyword co-occurrence network. Based on the findings, energy footprint research is expected to increase intensely in the near future. The USA accumulated the most documents and citations over the observed period. Journal of Cleaner Production was the most productive journal while Nature was the most cited journal in energy footprint literature. Owen A had the most publications in this subject area. Machine learning, China, water-energy-food nexus, water footprint, artificial intelligence, internet of things and edge computing have been the topic of previous research. Lastly, the outcomes of this study suggest that future studies of energy footprint could find themselves more innovative by venturing into some aspects of social science such as economic growth and institutional quality, so that the literature on energy footprint could be more diverse and comprehensive.

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This scientometric review analyzes the advances and trends in the development of novel methods for extracting bioactive compounds from coffee residues, with a particular focus on caffeine as a secondary metabolite. Despite the growing attention toward the reuse of agricultural byproducts, residues generated during coffee harvesting and processing, such as pulp, mucilage, parchment, and coffee grounds, have received limited attention in scientific literature. Between 2002 and 2024, the number of studies targeting these byproducts has remained low, despite their high content of bioactive compounds of significant relevance to the food, pharmaceutical, and cosmetic industries. This gap in the literature underscores the need to explore innovative and sustainable approaches for the integral use of coffee residues. Furthermore, investigating these methods represents an opportunity to promote a circular economy, reduce environmental pollution, and diversify industrial applications of coffee byproducts, contributing to the sustainable development of the coffee industry.

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Scientific Research collaboration is one of the strengths in the research ecosystem due to its advantages in productivity and citation. Co-authorship network is one of the methods to analyze and evaluate the emerging research collaborations. Collaboration between pair of authors for the first time plays a vital role as the key to success for their collaboration in future. In this context, a focus on SAARC is highly justified, as fostering intra-regional scientific collaboration could help address shared challenges such as public health, climate change, and sustainable development, which demand collective scientific expertise. Therefore, the objective of this paper is to build a machine learning model for predicting new potential authors within South Asian Association for Regional Cooperation (SAARC) region who never collaborated for the last 20 years (2001-2020) using data from Web of Science (WoS). The co-authorship network was analyzed between two authors using structural and semantic similarities to predict whether the collaboration will happen in future or not. A proposed Meta-Learner Binary Classifier model is applied to the link prediction predictors after data pre-processing. The result shows structural and semantic features are good features to predict potential collaborators with 0.87 AUC before sampling and 0.99 AUC after sampling.

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In today's competitive and globalized environment, higher education institutions must establish a solid and enduring reputation to influence prospective students' rational and emotional decision-making processes. This study maps the research landscape on brand equity in higher education through a bibliometric analysis of 628 publications from the Web of Science database. We identified leading authors, influential journals, and emerging trends using advanced tools such as VOSviewer and Biblioshiny. Our methodology incorporates bibliographic coupling, citation analysis, cluster analysis, keyword examination, and a four-field diagram to uncover new insights into brand awareness, brand equity, brand image, and loyalty. The study underscores the crucial role of a robust reputation as a critical factor in attracting students. Our findings provide a comprehensive understanding of the dynamics of higher education branding, ensuring robustness and generalizability across bibliometric datasets, and contribute significantly to advancing research in this field.

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This study presents the landscape of educational technology research in India from 2014 to 2023, aiming to identify publication trends, influential authors, institutions, key themes, citation impact, and international collaborations. A bibliometric analysis of 1112 was conducted using data extracted from the Scopus database, employing tools Bibliomagika, Bibliometrix and VOSviewer for comprehensive analysis and visualization. The study reveals a growing trend in educational technology research in India, with a significant increase in publications, particularly during the COVID-19 pandemic. Key themes include adaptive learning, artificial intelligence, distance education, and the impact of COVID-19 on teaching and learning. Prominent authors and institutions are identified, highlighting their contributions. International collaborations enhance research impact, especially with countries like the US, the UK, and Australia. The study is limited to publications indexed in Scopus and focuses primarily on research within India. Future research could explore collaborations across disciplines and regions and the impact of specific educational technologies on learning outcomes. This study presents an in-depth assessment of educational technology research in India, providing advantageous feedback to researchers, policymakers, and practitioners. It highlights key trends, identifies influential contributors, and suggests areas for future research that will contribute to developing effective and innovative educational practices in India.

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The purpose of this study is to map global research trends on aging and poverty using scientometric analysis and visualization techniques from 2015 to 2025. As the aging population grows, particularly in low- and middle-income countries, financial insecurity, limited access to healthcare, and weak social support systems amplify the vulnerabilities of older adults. Using data from the Scopus database, 1,729 relevant publications were analyzed using Bibliometrix R, VOSviewer, and OpenRefine to map publication trends, authorship patterns, leading sources, and collaboration networks. The study revealed moderate research output with a negative annual growth rate, although the citations indicated considerable academic influence. Lotka’s Law was applied to assess author productivity, confirming that most contributors were one-time authors of the journal. The United States, the United Kingdom, and China emerged as the most prolific and highly cited countries, whereas institutions such as Harvard University and Johns Hopkins University led in research impact. Thematic evolution and co-occurrence analyses identified “aging and poverty” and “healthcare” as central research themes. This review underscores the importance of interdisciplinary collaboration and policy-relevant evidence in addressing the complex socioeconomic challenges faced by older populations in Japan. These findings provide valuable insights for scholars, practitioners, and policymakers aiming to enhance global aging welfare.

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This study aims to map the knowledge structure and research trends in computational thinking within primary education, emphasizing its growing importance in equipping students with critical 21st-century skills. Utilizing a bibliometric analysis approach, the study systematically examines a comprehensive dataset from the Web of Science, employing co-citation, bibliographic coupling, co-occurrence analyses, and strategic diagramming to identify key themes and influential works. The findings reveal a strong emphasis on foundational concepts such as computational thinking, programming, and primary education, while also highlighting emerging areas like digital competence and teacher professional development. However, the study acknowledges limitations, including the reliance on a single database and the focus on quantitative metrics, which may overlook qualitative nuances. These findings underscore the need for further research into diverse educational contexts and the long-term impacts of computational thinking education. The study's originality lies in its comprehensive approach, offering valuable insights into the current state and future directions of computational thinking research, and highlighting its crucial role in preparing students for a technologically advanced world.

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Both researchers and the public have debated social media's effects on mental health since its inception, a concern mirrored in the literature. This bibliometric review analyses 1,415 publications retrieved from the Scopus database to explore the evolving landscape of research on social media and well-being. The results indicate a significant increase in publication volume, particularly following the COVID-19 pandemic, reflecting heightened academic interest in the psychological impacts of social media. Performance analysis also reveals a concentration of research output among leading journals, and a network of prolific authors predominantly based in the United States. Network analysis identifies collaborative patterns among authors, institutions, and countries, with a notable presence of international collaborations. The thematic analysis highlights key areas of focus, including mental health challenges, the effects of specific platforms like Facebook and Instagram, and emerging concerns such as social media addiction, and body dissatisfaction. It was also found that research areas like Fear of Missing Out (FOMO), cyberbullying, and social media fatigue remain underexplored. This study offers an overview of the current literature, and signifies the need for continued research that addresses the balance between the benefits and risks of social media use, with a focus on emergent areas of investigation.

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The integration of emojis with textual content in digital communication has transformed sentiment analysis, necessitating advanced methodologies to decode nuanced emotions in hybrid data. This study presents a comprehensive keyword and thematic analysis of 487 publications (2008-2024) on emoji-text sentiment classification, with 43.9% of research rooted in computer science. Data were systematically retrieved from IEEE, Scopus, Web of Science, and EBSCO using predefined search queries. Collaboration networks exhibit strong thematic evolution, progressing from basic sentiment analysis to specialized domains like emotion detection and socio-cultural implications of digital communication. Methodologically, the integration of keywords and R-based bibliometric tools provided granular insights into thematic structures. These results establish a strategic framework for future research, emphasizing the interdisciplinary convergence of computational techniques and socio-linguistic studies in emoji-text sentiment analysis.

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The rapid development of technology in the Internet of Things (IoT) challenges instructors/trainers in preparing workers to face this technological change. This research describes the competency needs of IoT training instructors by reviewing various competency standard documents from governments and articles. The study follows the Systematic Literature Review (SLR) regulation that consists of two main steps: planning and conducting. The analysis results revealed 3 themes of competency needs for IoT training instructors included vocational teacher’s competencies, IoT worker’s competencies, and Industrial Revolution 4.0 (IR 4.0) competencies. Based on the study, this review proposed 7 competencies for an IoT training instructor those are pedagogy-andragogy strategies, technical proficiency, technology and digital literacy, industrial business management, leadership and team management, life skills, and interdisciplinary skills. The research results carry considerable consequences for both educators and those involved in designing curricula. This research can provide valuable guidance in developing curricula and training programs to improve instructor competency so that they can provide quality and relevant education in facing the future dynamic challenges of IoT technology.

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Pathway analysis tools are essential for interpreting high-dimensional multi-omics data. However, while many tools have been developed, their practical usage in applied research pipelines remains unclear, since tools are often cited without being actually used. Here, we systematically evaluated the practical adoption of 28 publicly available software tools designed for multi-omics pathway analysis. We analyzed 18,447 open-access full-text articles published before 2025 and downloaded the corresponding full-texts from the PubMed database. Citations were classified based on their appearance in different sections of each article via the JATSdecoder package. Tools were considered “used” if cited in methodological sections. We report that ClusterProfiler and Pathview are the most frequently used tools, applied in over 80% of citing research articles. Other tools, such as ReactomeGSA and PARADIGM, have high citation counts but were used in fewer than 50% and 20% of cases, respectively. Overall, citation frequency did not reliably reflect practical usage. Our findings highlight a discrepancy between citation and practical adoption of multi-omics pathway analysis tools, emphasizing the need for usage-based evaluation metrics to inform tool selection in bioinformatics.

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India stands at a critical juncture in its research and innovation trajectory. With growing ambitions to become a global knowledge economy, the way research is assessed in Indian institutions warrants serious introspection. Globally, the research community is increasingly adopting the principles of Responsible Research Assessment (RRA) to ensure that evaluation mechanisms foster quality, equity, integrity, and societal relevance. The Indian research ecosystem, however, remains tethered to outdated metrics and bureaucratic inertia. This opinion paper explores whether India is truly ready for reform in research assessment and what systemic shifts are needed to realize a more responsible, context-sensitive, and robust framework.