Browsing by Author "Tayeb, Ahmad"

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Characterising Jurassic Carbonate Successions: Exploring Southern Outcrops of the Hanifa Formation, Saudi Arabia
(University of Aberdeen, 2024-06-17) Tayeb, Ahmad; Howell, John; Brasier, Alexander
The Jurassic successions in Saudi Arabia are significant as they are both reservoirs and source rocks for the World’s largest hydrocarbon fields. There are seven formations that make up Saudi Arabia's Jurassic system: the Marrat, Dhurma, Tuwaiq Mountain, Hanifa, Jubailah, Arab, and Hith. In central Saudi Arabia, the Jurassic carbonate strata of the Hanifa Formation play a significant role as a hydrocarbon-producing interval. The main objective of this thesis is to provide a high-resolution sedimentology and stratigraphic study of the carbonate rocks of the Hanifa Formation in a series of previously unstudied outcrops, south of the main oil fields. The goal was to evaluate the reservoir characterisation by studying the outcrops using traditional field techniques and novel virtual outcrops (VOs) methods to investigate analogues for reservoir performance on an inter well scale. The Hanifa is interpreted as a being deposited on a shallow carbonate ramp. The studied outcrops lie along a depositional dip profile and allow comparison between systems from proximal upper ramp dominated by corals and stromatoporoid reef facies to a more muddy distal outer ramp. Silica layers are also present within the carbonates the proximal settings, they are linked to periods of allocyclic inputs of silicious material to the basin. There origin and impact on reservoir performance are discussed. Carbonate systems are not straightforward to interpret in the virtual outcrop models (VOMs) and many of the layers look broadly similar, thus petrographic analysis and geochemistry were also incorporated into the study to provide a multiscale view of the reservoir potential. Where all these approaches together achieved excellent outcomes to determine the depositional environment and investigate the reservoir characterisations of the Hanifa formation.
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Investigating and Enhancing Online Software Development Resources: Automated Responses, Semantic Search, and Tagging in Video Tutorials
(Florida State University, 2024) Tayeb, Ahmad; Haiduc, Sonia
The field of software development is rapidly evolving, requiring developers to continually refine their skills and adapt to new technologies. While video tutorials have become a popular medium for learning new concepts and techniques, challenges persist in interactive engagement, search functionality, and effective tagging. This dissertation explores innovative methods to enhance software development video tutorials by addressing these challenges using advanced large language models and transformer-based models. Firstly, we explore developers' preferences in terms of online learning resources in the era of AI-driven chatbots like ChatGPT. Despite the rise of AI chatbots, which offer instant, personalized responses, video tutorials remain a preferred medium due to their visual and detailed explanations. However, our study also revealed opportunities for improving video tutorials by integrating interactive elements and leveraging AI technologies, setting the foundation for our subsequent projects. Building on these insights, we introduce VidTutorAssistant, a system that leverages Generative Pre-trained Transformer (GPT) models to automate responses to video tutorial viewers' questions, thereby increasing interactive engagement in video tutorials and enhancing the learning experience. Next, we present an improved video tutorial search method, ISM, that leverages transformer-based models to create semantically dense vectors from video data, enabling a more intuitive and efficient search experience. By capturing the contextual meaning of queries and video content, ISM surpasses traditional search methods, helping developers find the most relevant tutorials and specific content within them. Finally, we introduce BM25-BERT, a hybrid approach for refining video tagging that combines traditional BM25F methods with transformer-based models. By re-ranking candidate tags initially generated by BM25F to improve context-awareness and accuracy, this method significantly enhances tutorial discoverability. Through empirical studies and user evaluations, this dissertation advances software engineering and educational technology by offering innovative solutions to enhance video tutorials and examining the impact of AI tools on developers' learning preferences. By rigorously assessing the proposed methodologies, this research contributes to both academic research and practical applications.
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