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    Carbon dioxide-assisted Torrefaction of Maize Cobs by Thermogravimetry: Product Yield and Energy Recovery Potentials
    (VASYL STEFANYK PRECARPATHIAN NATL UNIV, VUL SHEVCHENKA 57, IVANO-FRANKIVSK 76018, UKRAINE, 2022) Nyakuma, Bemgba Bevan; Ajibade, Samuel-Soma M.; Adebayo, Victor B.; Alkali, Habib; Otitolaiye, Victor Olabode; Audu, Jemilatu Omuwa; Bashir, Faizah Mohammed; Dodo, Yakubu Aminu; Mahmoud, Abubakar Sadiq; Oladokun, Olagoke
    The objective of this study is to examine the potential product yields and energy recovery of maize cobs (MC) through carbon dioxide-assisted torrefaction using thermogravimetric analysis (TGA). The CO2-assisted torrefaction of MC was performed from 240 °C to 300 °C (? 30 °C) for the residence time of 30 minutes based on the selected non-isothermal/isothermal heating programme of the TGA. Furthermore, the physicochemical, microstructure and mineral characteristics of MC were examined. The results showed that the CO2-torrefaction of MC resulted in mass loss (ML) ranging from 18.45% to 55.17%, which resulted in the mass yield (MY) ranging from 81.55% to 44.83%. The HHV of the solid product was in the range from 22.55 MJ/kg to 26 MJ/kg, which indicates the CO2-torrefaction process enhanced the energy content of MC by 40% – 60%. In conclusion, the findings showed that the CO2 torrefaction is a practical, sustainable, and cost-effective approach for the valorisation of MC into a clean solid biofuel for enhanced energy recovery.
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    Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis
    (ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2024) Ajibade, Samuel-Soma M.; Alhassan, Gloria Nnadwa; Zaidi, Abdelhamid; Oki, Olukayode Ayodele; Awotunde, Joseph Bamidele; Ogbuju, Emeka; Akintoye, Kayode A.
    This bibliometric research explores the global evolution of machine learning applications in medical and healthcare research for 3 decades (1994 to 2023). The study applies data mining techniques to a comprehensive dataset of published articles related to machine learning applications in the medical and healthcare sectors. The data extraction process includes the retrieval of relevant information from the source sources such as journals, books, and conference proceedings. An analysis of the extracted data is then conducted to identify the trends in the machine learning applications in medical and healthcare research. The Results revealed the publications published and indexed in the Scopus and PubMed database over the last 30 years. Bibliometric Analysis revealed that funding played a more significant role in publication productivity compared to collaboration (co-authorships), particularly at the country level. Hotspots analysis revealed three core research themes on MLHC research hence demonstrating the importance of machine learning applications to medical and healthcare research. Further, the study showed that the MLHC research landscape has largely focused on ML applications to tackle various issues ranging from chronic medical challenges (e.g., cardiological diseases) to patient data security. The findings of this research may be useful to policy makers and practitioners in the medical and healthcare sectors and to global research endeavours in the field. Future studies could include addressing issues such as growing ethical considerations, integration, and practical applications in wearable technology, IoT, and smart healthcare systems.
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    Fuel Characterisation of the Physicochemical, Thermal and Kinetic Properties of Corn Cob Biomass Wastes for Potential Energy Recovery
    (Slovnaft VURUP a.s, 2018) Otitolaiye, Victor O.; Dodo, Yakubu A.; Jagun, Zainab T.; Bashir, Faizah M.; Moveh, Lawrence P.; Ajibade, Samuel-Soma M.; Moveh, Samuel
    This study presents insights into the solid biofuel properties of corn cob biomass (CCB) wastes for sustainable energy recovery. The physicochemical, thermal, and kinetic properties of CCB were charac-terised through ultimate, proximate, heating value, and thermogravimetric (TGA) analyses. Results showed that CCB contains high carbon (41.88 wt.%), hydrogen (6.33 wt.%), volatile matter (68.21 wt.%), and higher heating (15.70 wt.%) values for potential energy recovery. However, the high ash (16.56 wt.%) content could pose bed agglomeration, fouling, and sintering problems during high-temperature conversion. Thermal analysis resulted in 55.84%-59.51% loss of mass and residual mass of 40.49%-44.17%. Kinetic analyses revealed that CCB is highly reactive as characterised by the average activation energy, Ea=134.58 kJ/mol and pre-exponential factor, ko=2.53×l008/min. In conclusion, CCB is a potentially practical feedstock for sustainable energy recovery through thermo-chemical conversion. © 2020. All rights reserved.
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    Machine Learning Applications in Renewable Energy (MLARE) Research: A Publication Trend and Bibliometric Analysis Study (2012-2021)
    (Mdpi, 2023) Ajibade, Samuel-Soma M.; Bekun, Festus Victor; Adedoyin, Festus Fatai; Gyamfi, Bright Akwasi; Adediran, Anthonia Oluwatosin
    This study examines the research climate on machine learning applications in renewable energy (MLARE). Therefore, the publication trends (PT) and bibliometric analysis (BA) on MLARE research published and indexed in the Elsevier Scopus database between 2012 and 2021 were examined. The PT was adopted to deduce the major stakeholders, top-cited publications, and funding organizations on MLARE, whereas BA elucidated critical insights into the research landscape, scientific developments, and technological growth. The PT revealed 1218 published documents comprising 46.9% articles, 39.7% conference papers, and 6.0% reviews on the topic. Subject area analysis revealed MLARE research spans the areas of science, technology, engineering, and mathematics among others, which indicates it is a broad, multidisciplinary, and impactful research topic. The most prolific researcher, affiliations, country, and funder are Ravinesh C. Deo, National Renewable Energy Laboratory, United States, and the National Natural Science Foundation of China, respectively. The most prominent journals on the top are Applied Energy and Energies, which indicates that journal reputation and open access are critical considerations for the author's choice of publication outlet. The high productivity of the major stakeholders in MLARE is due to collaborations and research funding support. The keyword co-occurrence analysis identified four (4) clusters or thematic areas on MLARE, which broadly describe the systems, technologies, tools/technologies, and socio-technical dynamics of MLARE research. Overall, the study showed that ML is critical to the prediction, operation, and optimization of renewable energy technologies (RET) along with the design and development of RE-related materials.
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    New Insights into the Research Landscape on the Application of Artificial Intelligence in Sustainable Smart Cities: A Bibliometric Mapping and Network Analysis Approach
    (Econjournals, 2023) Zaidi, Abdelhamid; Ajibade, Samuel-Soma M.; Musa, Majd; Bekun, Festus Victor
    Humanity’s quest for safe, resilient, and liveable cities has prompted research into the application of computational tools in the design and development of sustainable smart cities. Thus, the application of artificial intelligence in sustainable smart cities (AISC) has become an important research field with numerous publications, citations, and collaborations. However, scholarly works on publication trends and the research landscape on AISC remain lacking. Therefore, this paper examines the current status and future directions of AISC research. The PRISMA approach was selected to identify, screen, and analyse 1,982 publications on AISC from Scopus between 2011 and 2022. Results showed that the number of publications and citations rose from 2 to 470 and 157 to 1,540, respectively. Stakeholder productivity analysis showed that the most prolific author and affiliation are Tan Yigitcanlar (10 publications and 518 citations) and King Abdulaziz University (23 publications and 793 citations), respectively. Productivity was attributed to national interests, research priorities, and national or international funding. The largest funder of AISC research is the National Natural Science Foundation of China (126 publications or 6.357% of the total publications). Keyword co-occurrence and cluster analyses revealed 6 research hotspots on AISC: Digital innovation and technologies; digital infrastructure and intelligent data systems; cognitive computing; smart sustainability; smart energy efficiency; nexus among artificial intelligence, Internet of Things, data analytics and smart cities. Future research would likely focus on the socio-economic, ethical, policy, and technical aspects of the topic. It is envisaged that global scientific interest in AISC research and relevant publications, citations, products, and services will continue to rise in the future. © 2023, Econjournals. All rights reserved.
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    A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning
    (Cell Press, 2023) Ajibade, Samuel-Soma M.; Zaidi, Abdelhamid; Bekun, Festus Victor; Adediran, Anthonia Oluwatosin; Bassey, Mbiatke Anthony
    Climate change (CC) is one of the greatest threats to human health, safety, and the environment. Given its current and future impacts, numerous studies have employed computational tools (e.g., machine learning, ML) to understand, mitigate, and adapt to CC. Therefore, this paper seeks to comprehensively analyze the research/publications landscape on the MLCC research based on published documents from Scopus. The high productivity and research impact of MLCC has produced highly cited works categorized as science, technology, and engineering to the arts, humanities, and social sciences. The most prolific author is Shamsuddin Shahid (based at Universiti Teknologi Malaysia), whereas the Chinese Academy of Sciences is the most productive affiliation on MLCC research. The most influential countries are the United States and China, which is attributed to the funding activities of the National Science Foundation and the National Natural Science Foundation of China (NSFC), respectively. Collaboration through co-authorship in high -impact journals such as Remote Sensing was also identified as an important factor in the high rate of productivity among the most active stakeholders researching MLCC topics worldwide. Keyword co-occurrence analysis identified four major research hotspots/themes on MLCC research that describe the ML techniques, potential risky sectors, remote sensing, and sustainable development dynamics of CC. In conclusion, the paper finds that MLCC research has a significant socio-economic, environmental, and research impact, which points to increased discoveries, publications, and citations in the near future.