http://repository.aust.edu.ng/xmlui/handle/123456789/348 This community contains master's Theses and Dissertations in all the courses offered in AUST, from 2009-2025. Sat, 05 Sep 2026 10:12:07 GMT 2026-09-05T10:12:07Z http://repository.aust.edu.ng/xmlui/handle/123456789/5208 Gender-Based Violence Data Governance improving reporting mechanisms for effective policy interventions in Kaduna Public Secondary Schools Saddiq, Faisal This study investigates Gender-Based Violence (GBV) data governance and its implications for improving reporting mechanisms to support effective policy interventions in Kaduna public secondary schools. GBV remains a pervasive challenge in Nigeria, with Kaduna State recording a sharp rise in reported cases, highlighting the urgency of addressing structural and systemic barriers to reporting and response. The research adopts a quantitative paradigm with a descriptive correlational design to examine the effectiveness of reporting mechanisms, data governance structures, and policy frameworks in capturing, managing, and utilizing GBV-related data for evidence-based interventions. A sample of 400 respondents comprising students, teachers, administrators, and policymakers was drawn from urban and rural schools, with 390 valid responses retrieved. Data were collected using structured questionnaires and analyzed using descriptive and inferential statistics, complemented by thematic analysis of open-ended responses. Findings reveal that while counsellor/teacher reporting remains the most accessible mechanism, underreporting persists due to fear of stigma, lack of confidentiality, and weak institutional responses. The study also identifies poor data governance practices characterized by inadequate trained personnel, fragmented reporting systems, and limited use of collected data in shaping policies. Furthermore, analysis shows that existing policies are often poorly enforced, undermining their potential to protect students and address GBV effectively. However, respondents highlighted strategies such as training and awareness creation, adoption of digital reporting platforms, improved confidentiality measures, and regular policy reviews as essential to strengthening GBV data governance. The study concludes that effective data governance frameworks, coupled with survivor-cantered reporting mechanisms, are crucial for ensuring accurate, secure, and timely GBV data that can inform robust policy responses. It recommends a comprehensive approach integrating digital innovations, institutional reforms, and stakeholder collaboration to foster safer learning environments and enhance the accountability of schools and government institutions in addressing GBV. Thu, 12 Mar 2026 00:00:00 GMT http://repository.aust.edu.ng/xmlui/handle/123456789/5208 2026-03-12T00:00:00Z http://repository.aust.edu.ng/xmlui/handle/123456789/5206 Fast and Accurate Feature-based Region Identification Maduakor, Francis There have been several improvements in object detection and semantic segmentation results in recent years. Baseline systems that drive these advances are Fast/Faster R-CNN, Fully Convolutional Network and recently Mask R-CNN and its variant that has a weight transfer function. Mask R-CNN is the state-of-art. This research extends the application of the state-of-art in object detection and semantic segmentation in drone-based datasets. Existing drone datasets was used to learn semantic segmentation on drone images using Mask R-CNN. This work is the result of my own activity. I have neither given nor received unauthorized assistance on this work. Thu, 20 Jun 2019 00:00:00 GMT http://repository.aust.edu.ng/xmlui/handle/123456789/5206 2019-06-20T00:00:00Z http://repository.aust.edu.ng/xmlui/handle/123456789/5205 Multiple String-Matching Using Wavelet Matrix and Burrows-Wheeler Transform (Bwt) Adam, Saleh Adam The problem of multiple string matching is fundamental in computer science, with applications in bioinformatics, text mining, and information retrieval. Traditional methods struggle with large datasets due to high computational and memory requirements. This research proposes a novel algorithm that combines the Burrows-Wheeler Transform (BWT) for text compression and the Wavelet Matrix (WM) for efficient pattern search. The proposed method achieves faster search times, lower memory usage, and effective compression, particularly for repetitive datasets like DNA sequences. Experimental results demonstrate that the method performs better compared to existing algorithms. This work contributes to the advancement of efficient and scalable multiple string-matching techniques, with potential applications in large-scale text processing and bioinformatics. Keywords: Algorithms, Text Compression, Wavelet Matrix, Burrows-Wheeler transform, Multiple String matching Sun, 18 Feb 2024 00:00:00 GMT http://repository.aust.edu.ng/xmlui/handle/123456789/5205 2024-02-18T00:00:00Z http://repository.aust.edu.ng/xmlui/handle/123456789/5204 Development and Characterization of Bio-Based Basalt Fiber Reinforced Polymer Composites for Automotive Structural Applications Musa, Abdulrahman Adeiza The growing demand for sustainable lightweight materials in the automotive industry has increased interest in basalt fiber-reinforced polymer (BFRP) composites as eco-friendly alternatives to conventional composites. Basalt fibers (BFs) offer excellent mechanical properties, thermal stability, and environmental benefits. However, their application is often limited by weak interfacial bonding with polymer matrices due to their smooth and chemically inert surfaces. This study presents a novel nanocellulose (NC) grafting approach as the primary contribution, where cellulose nanofiber (CNF) and cellulose nanocrystal (CNC) were directly anchored onto silane-functionalized BFs before composite fabrication. Unlike conventional direct NC dispersion in epoxy, which often suffers from agglomeration and poor dispersion, the proposed grafting strategy localizes NC at the fiber–matrix interface, significantly improving load transfer and interfacial adhesion. The NC-grafted BFRP composites exhibited significant improvements in interfacial bonding, resulting in enhanced impact resistance, interlaminar shear strength, and overall mechanical performance compared with composites produced through direct NC–epoxy mixing. In addition, the grafted composites demonstrated improved resistance to moisture absorption, despite the naturally hydrophilic nature of NCs, indicating that surface immobilization of NC effectively mitigates water uptake at the interface. Surface analyses using X-ray Photoelectron Spectroscopy (XPS) and Field Emission Scanning Electron Microscopy (FE-SEM) confirmed successful grafting and improved interfacial morphology. To assess structural applicability, composite components were further evaluated through impact crushing experiments and finite element simulations using Abaqus CAE. The strong agreement between experimental and simulation results confirmed the reliability, energy absorption capability, and crashworthiness of the developed composites for lightweight automotive structures. Overall, this work demonstrates that NC grafting onto BFs is a highly effective strategy for overcoming interfacial bonding and dispersion challenges, offering a promising route toward durable and sustainable automotive composite materials. Thu, 23 Apr 2026 00:00:00 GMT http://repository.aust.edu.ng/xmlui/handle/123456789/5204 2026-04-23T00:00:00Z