Afe Babalola University Based Journals
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From Neoliberalism to Economic Nationalism: Implications of Deglobalisation for African Security and Development
Full text linkThe erosion of neoliberal orthodoxy alongside accelerating deglobalization and the resurgence of economic nationalism signals a profound reconfiguration of the global economic order. While these dynamics are well documented in advanced economies, their implications for Africa remain insufficiently explored. This study examines how shifts in the global political economy shape African state capacity, developmental trajectories and vulnerability to emerging economic and security risks. Drawing on dependency theory and critical political economy, the analysis employs a qualitative comparative methodology, combining case studies of Nigeria, South Africa and Kenya with discourse analysis of policy documents and scholarly literature. The findings reveal that the retreat of neoliberal globalization presents both constraints and opportunities: economic nationalism opens space for industrial policy, regional value chain development and technological upgrading, yet protectionism in the Global North intensifies Africa’s marginalization in global trade and investment. These opportunities are further constrained by weak regional coordination and limited institutional capacity, heightening exposure to external shocks, inequality and insecurity. The study argues that navigating the post-neoliberal global order requires a strategic recalibration of Africa’s development agenda through deeper regional integration, investment in domestic technological capabilities and enhanced collective bargaining in multilateral economic forums
Stratified Normalization Technique with Long Short-Term Memory-Based Autoencoder for Anomaly Detection in Heartbeat ECG Data
Full text linkDisruptions in heartbeat patterns have been identified as a critical health concern globally, often leading to serious health risks and death. Due to the subtle and infrequent nature of these irregularities, individuals may overlook early warning signs, highlighting the need for continuous monitoring and early detection systems. Traditional methods for detecting heartbeat abnormalities, while contributing significantly in the past, are often labour-intensive and lack the precision required for timely intervention. Recent advancements, particularly in wearable electrocardiogram (ECG) devices and machine learning (ML), have changed the narrative in how heartbeat data is collected and analysed. Despite the progress, existing ML models often fall short in accounting for inter-patient variability, which is essential for reliable anomaly detection across diverse populations. In addressing the limitation, this paper proposes Long Short-Term Memory implementation of Autoencoder (LSTM-AE) enhanced with Stratified Normalisation (SN), called LAESN. The LAESN model is designed to improve sensitivity to individual patient differences in ECG signals. The LAESN was trained and evaluated on the ECG5000 dataset using the ‘tanh’ activation function, a batch size of 32, and 50 epochs. It achieved an F1 score and AUC of 0.9660 and 0.9952, respectively, surpassing both the baseline AE and other state-of-the-art models. These results highlight the effectiveness of SN in strengthening the ECG anomaly detection model, enabling it to capture subtle variations in heartbeat signals and support a patient-centric anomaly detection system
Non-Hybrid Machine Learning Techniques for Classifying and Detecting Skin Disease Variants
Full text linkEczema, acne, and psoriasis are all skin diseases that must be diagnosed early on to avoid complications. To detect and classify skin diseases, many researchers have developed a variety of support vector machine (SVM)-based classification models. However, these existing models suffer from imbalanced datasets, irrelevant feature selection, and difficulty in fine-tuning the SVM's hyperparameters. As a result, this study developed “Aquila Optimiser-Support Vector Machine (AO-SVM)” and “Harris Hawk Optimiser-Support Vector Machine (HHO-SVM)” to categorise eight (8) different skin diseases, “Granuloma Annulare (GRA)”, “Haemangioma (HEM)”, “Herpes (HEP)”, “Hidradenitis Suppurativa (HSP)”, “Keratocanthoma (KEC)”, “Lupus (LUP)”, “Sebaceous Hyperplasia (SEH)”, and “Sun Damaged Skin (SDS)”, using 2,700 photos of skin disease datasets, including 250 photos of each diseased dataset class and 700 photos of normal skin from the Kaggle village datasets. The images were pre-processed, including reducing the size of the images, "digital hair removal using the Black-Hat transformation and inpainting algorithm", and eliminating noise, then the affected area was segmented using the Sobel edge detection method. The Grey Level Spatial Dependence and Colour Moment were then used to extract texture, shape, and colour features, and performance metrics such as false positive rate, specificity, accuracy, precision, and sensitivity were used to compare the efficiency of the two classification models (“AO-SVM” and “HHO-SVM”). The results show that the “AO-SVM and HHO-SVM” classification models perform at 95.99% and 96.56%, respectively. This study adds to the body of knowledge by developing two refined Multiclass Support Vector Machine classification models, “AO-SVM and HHO-SVM”, for a subset of skin diseases. These models optimise the SVM classifier parameters (penalty cost, C, and kernel function, γ) to reduce false positives and improve classification accuracy. In conclusion, these two models can be extremely useful in assisting people living in remote areas who have limited access to expert dermatologists in detecting their disease as soon as possible
Microstructural Evolution and Mechanical Performance of Martempered Medium Carbon Steel
Full text linkThe mechanical properties and microstructure of medium carbon steel (0.367 wt.% C) were studied following martempering treatments in oil and water baths maintained at 25 °C and 100 °C. Specimens were initially austenitised at 800 °C before controlled quenching. Oil-martempered samples at 100 °C showed superior impact toughness (59.21 J) and tensile strength (1.875 MPa), while water-martempered samples at 100 °C exhibited the highest hardness (180.9 BHN). Microstructural analysis via SEM revealed tempered martensite with uniformly distributed spheroidised carbides (0.3–0.5 µm) in oil-quenched samples, whereas water-quenched specimens displayed lath martensite with noticeable interlath microcracks. XRD analysis confirmed the presence of significantly higher retained austenite in oil-treated specimens (4.2 ± 0.5 vol.%) compared to water-quenched steel (0.9 ± 0.2 vol.%). These findings highlight that martempering medium carbon steel in oil at elevated temperatures provides an optimal combination of strength and ductility, emphasising the critical influence of quenchant type and temperature on final material performance
An Evaluation of the Implications of the Effect of Economic Recession on Good Governance in Nigeria under Buhari, 2015-2023
No full textDrawing on the case study of the Buhari civilian administration (29 May 2015- 28 May 2023), this study explores the implications of recession for good governance within Nigeria’s democratic system. Using mixed methods, the quantitative survey data were collected from a total of 450 respondents in three political zones (150 in each zone), while the qualitative data were derived from nine in-depth interviews with relevant political office holders, scholars, community leaders, members of opposition parties, and civil society individuals (four per zone). Findings indicate that widespread hardship was a significant worsening issue (mean: 4.25, Rank 1st). Major drivers of the recession included the weakened purchasing power of the Naira (mean: 4.29), declining domestic food production due to insurgency (mean: 4.26, Rank 2nd), and falling global oil prices (mean: 4.19, Rank 3rd). Regression analysis revealed that there is a significant effect of economic recession on governance (F (1, 148) = 113.406, p < .05). The regression model accounted for 40.6% of the variance in the governance quality (R²=0.406), with a correlation of R=0.637. However, Pearson correlations showed no significant direct linear correlation between recession and multi-dimensional measures of good governance (r = -0.157, p > 0.05). The analysis concludes that while the economic crisis significantly impacted the government’s ability to protect welfare, it did not correlate directly with structural measures of good governance. This implies that Nigerian governance issues stem from deeper institutional crises, of which periodic economic recessions are merely symptomatic
Technical Losses across Distribution Networks in Nigeria and Mitigative Measures: A Review
Full text linkThe different distribution companies (DisCOs) in Nigeria constantly battle with the issue of technical losses on their respective distribution networks. And this is one factor that heavily affects their revenue. Though, technical losses are inevitable because they cannot be totally eliminated but can be rather reduced. This paper identifies lengthy distribution lines, worn-out equipment, insufficient size of conductors of distribution lines, no growth provision of system, unequal load distribution of the three phases of low tension lines, low voltage, overloading of distribution lines, load factor effect, low power factor, abnormal operating conditions, transformer sizing and selection, location of distribution transformers, feeder length, poor workmanship, use of overrated distribution transformers, efficiency of equipment and lack of proper maintenance as some of the root causes of technical losses. In addition, practicable solutions are provided on ways the identified technical losses can be curbed on the networks of the distribution companies if implemented. The focus of this survey is to present the prevalent factors that induce technical losses on the DisCOs networks and the measures that can be taken to limit the occurrence of this class of losses. This assessment will aid industry experts, potential investors and other investigators in taking appropriate decisions on projects within this field
Design and Fabrication of a Locally Made Plastic Shredder
Full text linkPlastic waste management is a growing environmental concern due to the increasing production and disposal of plastic materials, which contribute to pollution and ecological degradation. Conventional plastic shredders are often expensive and inaccessible to small-scale recyclers, necessitating the development of cost-effective and locally fabricated alternatives. The project focuses on designing and fabricating a locally made plastic shredder that addresses plastic waste management challenges. The methodology involved a series of steps including needs assessment, literature review, conceptual and detailed design, material selection, fabrication, assembly, testing, and optimization. The shredder was designed to be cost-effective and efficient, utilizing locally available materials and expertise. Materials used included Aluminium and steel alloys for the frame, copper wire for the motor, high-quality bearings, rubber seals, and various electrical components. Tools employed in the fabrication process ranged from hand tools and power tools to welding equipment and testing instruments. Safety gear was also emphasized to protect workers during the fabrication and operation processes. The testing phase covered functional testing, load testing, efficiency testing, safety assessments, durability testing, environmental testing, and quality control inspections. Design calculations focused on parameters such as shredding capacity, torque, shear force, blade design, hopper volume, material feed rate, structural integrity, energy consumption, and shredder efficiency. Results indicated that the locally made plastic shredder effectively shredded various types of plastic waste, with a satisfactory shredding capacity of 0.21 kg/hr and a shredder efficiency of 83.12%. The torque transmitted by the shaft was 62.50 Nm, and the shear force required to cut through plastic was 2843.5 N. The blade speed was calculated at 41.89 rads/sec, with a cutting speed of 4189 m/sec. The energy consumption of the shredder was 4.48 Kwh. The project concluded that locally made plastic shredders could significantly contribute to sustainable plastic waste management, resource conservation, and environmental protection
IoT-Driven Solutions for Empowering Widows and Safeguarding Women’s Rights in Southern Nigeria
Full text linkThe empowerment of women and their autonomy in the areas of social, economic, political, and health is highly necessary. However, some categories of women, like the widows, have been neglected over time when policies that has to do with socio-economic development is being considered in any level of government. Based on this, the study examined how the Rights of women and especially widows are protected. Also, the study further examined the impact of the adoption of Internet of Things technology in ensuring adequate empowerment of women as well of their Rights protection. The findings revealed that training and evaluation of the vulnerable women in this category is important. Also, it was suggested that there must be adequate monitoring of the IoT technology to ensure that is acceptable. More so, training of the widows is very important to ensure their understanding and subsequent improvement in the area of sustainable economy
Development of Adaptive Resource Allocation and Interference Mitigation for Spectrum Sharing in D2D-Enabled 5G Heterogeneous Networks: A Case Study of Urban Microcell Environments
Full text linkDevice-to-device (D2D) communication in heterogeneous networks (HetNets) poses significant challenges in resource allocation and interference management, especially within 5G networks where spectrum sharing between cellular users (CUEs) and D2D user equipment (DUEs) is critical. This study developed an adaptive resource allocation framework using Long Short-Term Reinforcement Learning (LSRL), which integrated Long Short-Term Memory (LSTM) networks with Deep Reinforcement Learning (DRL) technique. The proposed approach addressed the dynamic nature of interference in urban microcell environments by leveraging a Hierarchical Data Format (HDF5) dataset generated from network simulations. These simulations incorporate diverse scenarios, including varying user densities, transmission power levels, and interference conditions. The LSRL-based scheme was evaluated against conventional DRL methods, demonstrating notable improvements in network performance. Specifically, the proposed framework achieved up to a 6.67% increase in sum throughput and an 8.2% enhancement in power efficiency, even under dense user conditions. Additionally, the LSRL model proved resilient to variations in D2D pair distances, maintaining robust spectral efficiency and quality of service (QoS). These findings underscore the potential of the LSRL-based adaptive approach for improving resource management in 5G HetNets, particularly in dense urban deployments, and provide valuable insights for optimizing next-generation wireless communication systems
Development of a Portable Electro-mechanical Crack Monitoring Device for Pipeline Steel Materials
Full text linkThe pipeline infrastructure, particularly in Nigeria face significant challenges arising from defects such as cracks, which could lead to unforeseen leakage of flammable materials, risks to human and aquatic lives and could result in the loss of valuable petroleum products. This paper presents the development of a portable electromechanical device powered by lithium-ion batteries for monitoring surface pipelines for cracks, facilitating timely maintenance to prevent adverse consequences. The device is equipped with two types of electronic sensors mounted on a mobile platform that transmits data to a laptop via microcontrollers (Arduino Nano) and a USB cable. The casing of the device was constructed using polyvinyl chloride modeling board. Three tests were carried out on a 0.75 cm thick test pipe with 21 cm external diameter: No-crack test, initiated crack test, and covered crack test. Operating at 15 cm/s, the device transmitted surface condition data in real-time. The results showed no significant spikes during the no-crack and covered crack tests, while the cracked pipe test revealed spikes of 0.5 cm at positions 20 cm, 29 cm, and 38 cm along the pipe. The tractive analysis of the device indicated a net tractive force of 4.11 N and a slip value of 0.03, confirming effective movement without skidding. This study demonstrated that the developed device is reliable for pipeline monitoring and can significantly contribute to the maintenance of pipeline structures