Afe Babalola University Based Journals
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Detection and Classification of Dress Code Violations in Educational Environments Using Deep Learning
Get PDFThis paper explores the utilization of deep learning techniques for the detection and classification of dress code violations in educational environments, identifying the challenges of manual enforcement and the potential for systems that are automated. This paper exhibits a model that integrates Faster R-CNN for detection and EfficientNet for classification, which provides an accurate and very efficient system to monitor students’ compliance with the dress code policies. The model was trained on a dataset of images that were collected from Federal University Dutsin-Ma and were classified into “decent” and “indecent” dressing for both male and female students. The result achieved demonstrates that the model works efficiently, reaching a training accuracy of 98% and a validation accuracy of 96%, and with overall scores for precision, recall, and F1-score exceeding 97%, thereby proving its effectiveness in different dress code categories. The Uniformity across the techniques substantiates the feature extraction performance of the model and demonstrates its generalization ability. This paper outlines the benefits of automation in alleviating bias and human error by improving transparency and fairness and enforcing the dress code. The results showed how it is effective by combining powerful deep learning models with strong frameworks to solve problems of classification
Investigation of Thermal Behaviour of Mesocarp Fibre and Bituminous Coal with their Blends
Get PDFIn this study, an investigation was carried out on the thermal behaviour of mesocarp fibre, bituminous coal and their blends through dynamic thermogravimetric analysis. The analysis was carried out using 100% percentage composition of mesocarp fibre and bituminous coal respectively, and their blends (75, 50, and 25 wt% of mesocarp fibre, which implied 25, 50 and 75 wt% of coal). Two different heating rates were employed and pyrolysis temperature was between 30 through 900 oC. Three different thermal stages were identified from the DTG curve, and the mass loss during the first stage was attributed to the release of moisture still contained in the feedstock material. The decomposition intensity of MF was higher than that of CF for 5 oC/min while for 10 oC/min, the decomposition intensity of CF was substantially higher than that of MF for 100% MF and 100% CF by weight. For 5 oC/min, as the percentage by mass of CF increased in the sample, the initial temperature increased and final temperature decreased for stage 2 of the thermal stages, which was accompanied by a gradual decrease in weight loss. Although, the blending of MF with CF was found to have significant effects on the thermal behaviour of both feedstocks, due to a better pattern in the feedstocks’ behaviour at 5 oC/min heating rate, it can be deduced that this rate helps to have a better insight into the thermal behaviour of the feedstocks studied
Development of an Open Hearth Furnace with a Mechanical Blower and Mechanized Bellow
Get PDFThis study focuses on the development of a hearth furnace with a mechanical blower and mechanized bellow intended to replace traditional open-hearth furnaces with restricted operational efficiency. The bellow system utilizes a crank-slider mechanism powered by an electric motor to generate a continuous airflow, thereby optimizing combustion within the furnace. The total force acting on the crank-slider was 22.22 N, and the calculated stoichiometric air required for complete combustion of the charcoal was 26.80 . The maximum temperature recorded during performance evaluation was 110 5 for the blower with energy consumption of 940.8 kJ and 923.9 for the bellow with energy consumption of 24.4 kJ. The air speed achieved by the bellow was 3.5 m/s each time the bellow compresses, with reduced pulsating interval and enhanced combustion of the charcoal. Compared to manual operation, the furnace reduced human exposure to heat, eliminated operational fatigue, and improved the overall efficiency and safety of the furnace operation. The results demonstrate that integrating a mechanical blower and a mechanized bellow into open-hearth furnace systems is a viable method to boost production rates and occupational safety in small-scale foundry operations
The Synergy of Minds and Machines: Rethinking the AI-HI Relationship through Dialectical Reconstruction
Get PDFToday, there is an issue regarding the superiority of intelligence regarding the nature of human intelligence and artificial intelligence. Human intelligence, which refers to the natural capacity of humans to think, reason, learn, and adapt to new situations based on experience and emotional instincts, represents the core of what it means to be human. Artificial intelligence on the other hand is the simulation of human cognitive functions by machines, especially in tasks such as problem-solving, pattern recognition, and decision-making, which often operates based on algorithms and data, both of which are unique and important in themselves. However, there is a presupposition by some commentators, which happens to be the problem of this paper. On the one hand, there is the argument that artificial intelligence; particularly generative artificial intelligence, can perform tasks better than humans, while on the other hand, is the argument that human consciousness and creativity remain irreducible, both of which have sparked renewed discussions about whether AI can rival or even surpass human cognition. However, rather than reduce the discourse to a binary conflict, this paper through a critical and dialectical method, critically engages with established perspectives, proposing a complementary view that reconciles both intelligences. It argues that artificial intelligence represents an existential evolution that targets enhancing human productivity rather than replacing humans. Contrary to fears that artificial intelligence diminishes human relevance, this paper demonstrates how it complements human intelligence by ensuring collaboration and improved productivity in advancing knowledge and innovation
Silver Nanoparticles Recovery from X-ray Films using Humic and Fulvic Acids: Synthesis and Antimicrobial Applications
Get PDFThe current study investigates the recovery and synthesis of silver nanoparticles (AgNPs) from used x-ray photographic films as a sustainable and cost-effective strategy for scalable production. Silver was extracted using an alkaline NaOH solution and stabilised with humic (HA) and fulvic (FA) acids, demonstrating a green chemistry approach that enhances nanoparticle stability and biocompatibility. FTIR analysis confirmed the incorporation of functional groups for AgNP stabilisation, SEM revealed well-dispersed nanoparticles, and XRD verified their crystalline structure. The HA- and FA-stabilised AgNPs showed superior antimicrobial activity compared to pure AgNPs, with inhibition zones of 35 and 39 mm against Escherichia coli and Streptococcus pneumonia, respectively. The antibacterial effects of the synthesised particles were found to be concentration-dependent. This dual approach of waste photographic film valorisation and in situ nanoparticle stabilisation offers an eco-friendly pathway for functional AgNP production with potential applications in medical and environmental fields
In-Silico Investigation of Chromolaena Odorata (L.) against Methicillin Resistant Staphylococcus Aureus
No full textGlobally, approximately 2 billion individuals are estimated to carry Staphylococcus aureus (S. aureus), with around 53 million harbouring the Methicillin-Resistant Staphylococcus aureus (MRSA) strain. The widespread resistance of staphylococcal infections to β-lactam antibiotics, including penicillin, poses significant challenges to the treatment of MRSA infections. The mechanism of β-lactam antibiotics involves inhibition of the transpeptidase activity of penicillinbinding proteins (PBPs), crucial for bacterial cell wall synthesis. However, MRSA strains evade this inhibition through the mecA gene, which encodes PBP2a. This protein facilitates peptidoglycan crosslinking even in the presence of β lactam antibiotics, ensuring bacterial survival. Targeting PBP2a has emerged as a promising therapeutic strategy against MRSA. The mecA gene transcription confers methicillin resistance by enabling cell wall biosynthesis despite β-lactam presence, leading to drug resistance and membrane degradation. This highlights the need for novel interventions in combating MRSA infections. Plants contain numerous phytochemical constituents, many of which are biologically active and responsible for a variety of pharmacological activities
Amplifying Women’s Voices in Environmental Justice Movements through Gendered Communication for Stability and Development
Get PDFEnvironmental justice movements have increasingly highlighted the unequal distribution of ecological harms and benefits; however, women’s voices remain significantly underrepresented, especially in the Global South. This study investigated how gendered communication amplifies women’s participation in environmental justice movements and contributes to sustainable development. Guided by Feminist Political Ecology and Participatory Communication Theory, the research employed a qualitative design using secondary data from scholarly literature, NGO reports, and documented case studies. Thematic content analysis revealed that cultural, economic, and institutional barriers persistently hinder women’s involvement in environmental governance. Nonetheless, women strategically utilised community radio, oral storytelling, protest songs, and social media to advocate for environmental rights and mobilise communities. Case studies, such as Kenya’s Green Belt Movement and women-led resistance in Nigeria’s Niger Delta, emphasised the effectiveness of gendered communication in transforming environmental discourse and fostering resilience. Based on these findings, the study recommended mandating gender-inclusive environmental policies, supporting women-led advocacy platforms, and providing communication tools and training for grassroots women. It further called for participatory media strategies and more empirical research on digital feminism and environmental activism in underrepresented regions. The study concludes that inclusive, gendered communication is pivotal for equitable environmental outcomes, community stability, and long-term development
Patronage and Power Politics in the 2027 Presidential Election in Nigeria
Get PDFNigeria's Fourth Republic has been shaped by power struggles and fierce political rivalry, even as the nation battles with complex insecurity and violence within its electoral democracy. The viability of democratic governance, however, is seriously threatened by the actions of modern political actors, who have increasingly fueled volatility. Using historical records, analysed employing a theme-based strategy, this study examines the tactics used by political actors and evaluates the consequences of these tactics for patronage politics in Nigeria. The findings reveal that the support extended by political actors to electoral candidates has fostered a volatile and fragile democratic environment characterised by inflammatory rhetoric, politically motivated violence, and instances of state-sanctioned aggression. The primary objective of this research is to develop a framework aimed at mitigating the risk of politically instigated violence in the coming 2027 general elections. Furthermore, this paper seeks to illuminate the existing insecurity divisions and violent conflicts, with the goal of facilitating strategies for managing and accommodating potential violent conflicts during the 2027 presidential election
Analysis of the Impact of Strategic Leadership on Organisational Performance: A Study of Afe Babalola University, Ado-Ekiti
Get PDFThis study examined the impact of strategic leadership on organisational performance, with a focus on strategic agility and strategic communication as key leadership dimensions. Afe Babalola University, Ado Ekiti (ABUAD) served as the case study institution, given its rapid institutional growth and reputation for strategic innovation. The study employed a quantitative, cross-sectional survey design, and data were collected from 192 academic and non-academic staff using a structured questionnaire. Descriptive statistics, Pearson correlation, and multiple regression analyses were used to analyse the data. Results showed that both strategic agility (β = 0.426, p < 0.001) and strategic communication (β = 0.403, p < 0.001) had significant positive effects on organisational performance. The combined model explained approximately 59.9% of the variance in performance outcomes. These findings underscore the importance of leadership behaviours that enhance adaptability, clarity of vision, and participatory communication in achieving institutional goals. This study is significant because it highlights the insufficiently studied yet pivotal role of specific strategic leadership practices in driving university performance. By isolating strategic agility and communication as key predictors, the study provides empirical evidence that can inform leadership development, governance reforms, and policy strategies in higher education. It concludes that strategic leadership is a critical driver of organisational success and recommends that universities invest in agility training, robust communication systems, and decentralised decision-making practices to strengthen institutional effectiveness
Deep Learning Models for Oil Spill Detection in Marine Settings: A Literature Review
Get PDFOil spills in marine settings can be identified and tracked by remote sensing. The accuracy and effectiveness of oil spill detection using faraway sensing data have shown tremendous promise for deep learning (DL) algorithms, particularly deep neural networks (DNNs). In this literature review, we summarized the key DL models that have been used in oil spill detection, including CNN, RNN, DBN, AE, and GAN. We also discussed the different components and tasks involved in DL models, such as pooling layers, forward and backpropagation, and optimization of weights. Additionally, we present several case studies that have successfully applied DL approach in oil spill recognition, including the use of DBN to differentiate oil spills from lookalikes in SAR images, and the use of spatial-spectral jointed SAE to acquire and categorize oil slicks on the ocean surface using hyperspectral data. The findings from these studies demonstrate the potential of DL models to improve the accuracy and proficiency of oil spill detection using RS data