Some comments on using of Web of Science for bibliometric studies [Environ. Sci. Pollut. Res. Vol. 25]

Current trends and future directions of global research on wastewater to energy: a bibliometric analysis and review

This paper presents a structured bibliometric analysis and review of the research publications recorded in the Web of Science database from 2000 to 2023 to methodically examine the landscape and development of the 'wastewater to energy' research field in relation to global trends, potential hotspots, and future research directions. The study highlights three main research themes in 'wastewater to energy', which are biogas production through anaerobic digestion of sewage sludge, methane generation from microbial wastewater treatment, and hydrogen production from biomass. The analysis reveals activated sludge, biochar, biomethane, biogas upgrading, hydrogen, and circular economy as key topics increasingly gaining momentum in recent research publications as well as representing potential future research directions. The findings also signify transformation to SDGs and circular economy practices, through the integration of on-site renewables and biogas upgrading for energy self-sufficiency, optimising energy recovery from wastewater treatment systems, and fostering research and innovation in 'wastewater to energy' supported by policy incentives. By shedding light on emerging trends, cross-cutting themes, and potential policy implications, this study contributes to informing both knowledge and practices of the 'wastewater to energy' research community.

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

The machine learning (ML) paradigm has gained much popularity today. Its algorithmic models are employed in every field, such as natural language processing, pattern recognition, object detection, image recognition, earth observation and many other research areas. In fact, machine learning technologies and their inevitable impact suffice in many technological transformation agendas currently being propagated by many nations, for which the already yielded benefits are outstanding. From a regional perspective, several studies have shown that machine learning technology can help address some of Africa's most pervasive problems, such as poverty alleviation, improving education, delivering quality healthcare services, and addressing sustainability challenges like food security and climate change. In this state-of-the-art paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 89% were articles with at least 482 citations published in 903 journals during the past three decades. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent.

Comments on: Li et al. (2019) “Bioelectrochemical Systems for Groundwater Remediation: The Development Trend and Research Front Revealed by Bibliometric Analysis” Water, 11, 1532

Li et al. (2019) used inappropriate searching words and method to publish a bibliometric paper in Water. The results show a huge difference from the results by using an appropriate method. This comment points out each of problems.