Browsing by Author "Adesina, A. D."

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Aspects of structures and depositional environment of sand bodies within tomboy field, offshore western Niger Delta, Nigeria
(2009) Nton, M. E.; Adesina, A. D.
" Sand bodies deposited across normal growth faults and associated rollover anticlines are critical reservoirs for the accumulation of oil and gas. This paper addresses aspects of structures and depositional environments of some sand bodies within the Tomboy field, offshore western Niger Delta. Structural interpretation was undertaken to identify and assign faults found in the 3-D seismic volume. Time and depth structure maps in combination with well logs were used to produce for five horizons, namely: H1 to H5 and identify the depositional environments respectively. Two major growth faults (F4 and F7 which are normal, listric concave in nature), three antithetic (F1, F3 and F6) and two synthetic faults (F2 and F5) were identified. Structural closures identified as rollover anticlines, and displayed on the time/depth structure maps; suggest probable hydrocarbon accumulation at the downthrown side of the fault F4. Point bars, distributary channel and mouth bars, barrier island and tidal channels are the depositional environments. This study shows that the Tomboy field is made up of sand bodies deposited in different environments across normal, growth faults and associated rollover anticlinal structures. "
Integration of artificial intelligence in industrial education: a review of current trends and future directions
(Stecab Publishing, 2025) Momoh, A. M.; Olajide, F. O.; Ogundipe, R. O.; Adesina, A. D.
Digital revolution and the resultant emergence of Industry 4.0 has driven the incorporation of Artificial intelligence (AI) in Industrial education to enhance skills development in the industry. However, there is a lack of adequate empirical evidence on the integration of Artificial intelligence in industrial education. To fill this gap, this study reviewed previous studies on the adoption of AI in Industrial education and examined the frequency of occurrence of variables obtained in the studies reviewed. Relevant literature was screened and reviewed to find empirical evidence to support findings. A systematic review of 14 studies provided insights into the current applications, benefit and challenges of AI integration in industrial education. The study found that the most cited applications of AI is Adaptive and personalized learning systems, which customise workers/learners’ information based on their interaction with learning content. Other applications are augmented simulators for real-time feedback, virtual mentors, and intelligent tutoring systems which replicate real-life interaction with professionals among others. Majority of the studies found increased engagement and improved learning outcomes and skills development as benefit of AI integration in Industrial education. Other benefits are promotion of early identification of learning challenges and timely intervention and feedback, improvement in administrative efficiency and support, personalisation of learning. Notable challenges were skills and capacity gaps, lack of infrastructure and AI resources, curriculum issues and difficulty in integrating AI into current curriculum, ethical and privacy concerns among others. Based on the findings of the study, it was recommended that the skill gap should be filled with training in AI applications and use, investment in AI infrastructural development should be explored, industry collaboration and partnership in the area of needs should be considered, AI marketing and literacy should be adopted in industries, all AI intervention should be a continuing and lifelong process to ensure sustainability.