Afe Babalola University Based Journals
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African Governments’ Responses to the Covid-19 Pandemic and its Impact on Women: A Case Study of Nigeria
Get PDFThe emergence of the COVID-19 pandemic in Africa created widespread socio-economic and healthcare challenges for countries, and peoples’ lives and livelihoods. Like other public health emergencies, the COVID-19 pandemic is gendered, and affected men, women, boys and girls in complex and varying ways; exacerbating the existing deep-seated gender inequalities in African societies. Similarly, the response measures adopted by African governments, including Nigeria, in addressing the disease outbreak and preventing its spread had a disproportionate impact on women and girls. To cushion the impact of the measures adopted against the disease, the Nigerian government created socio-economic initiatives and programs for the citizenry. However, these measures did not adequately integrate a gender perspective in their design and implementation. Using a desk review of secondary sources, the paper examines the impact of the COVID-19 pandemic and the response measures adopted by the Nigerian government. It concludes that the failure by policy makers in Nigeria, and Africa generally, to effectively mainstream gender into the design and implementation of these policies, and underrepresentation of women in government taskforces limited the effectiveness of the measures
Trans-Border Land Migration and Crime in The Nigeria-Niger Border (2015-2023)
No full textThe porous nature of the Nigeria-Niger border has, over the years, fueled illegal migration. This has been discovered to be among the factors responsible for the persistence of border crimes and, which, has posed a serious challenge to border security for the two countries. This paper examined the porosity of the border and shortage of manpower as responsible for the illegal land migration. The study analysis trans-border land migration and crime, within the framework of Securitization theory which believes that the ideas of security should not be limited to military or political aspect of threat alone, but also issues which affect regional and human security. The study used secondary data and the content analysis of the information generated by exploring and examining published texts on the Nigeria-Niger land border migration, border security, border crimes, smuggling, etc. In this regards, the data were collected solely through documentation of secondary sources, including review of related literature, and newspapers. The study finds that the porous nature of the borders breed regional insecurity and human security threats, the persistence of terrorism, insurgency, armed banditry, armed robbery, theft, smuggling, among many other social and security challenges in the border areas. Furthermore, there is inadequate manpower to secure the Nigeria-Niger border, and the need for a modern technology that can simplify prompt security response by both countries. The paper recommends that both borders need to be manned by more manpower and modern technology for instant identification of illegal migrants along the route by both countries
Development and Performance Evaluation of a Heart Disease Prediction Model Using Convolutional Neural Network
No full textHeart disease is a leading cause of mortality globally and its prevalence is increasing year after year. Recent statistics from the World Health Organization show that about 17.9 million individuals are embattled with heart diseases annually and people under the age of 70 account for one-third of these deaths. Hence, there is need to intensify research on early heart disease prediction and artificial intelligence-based heart disease prediction systems. Previous heart disease prediction systems using machine learning techniques are unable to manage large amount of data, resulting in poor prediction accuracy. Hence, this research employs Convolutional Neural Networks, a deep learning approach for prediction of heart diseases. The dataset for training and testing the model was obtained from a government owned hospital in Nigeria and Kaggle. The resulting system was evaluated using precision, recall, f1-score and accuracy metrics. The results obtained are: 0.94, 0.95, 0.95 and 0.95 for precision, recall, f1-score and accuracy respectively. This show that the CNN-based model responded very well to the prediction of heart diseases for both negative and positive classes. The results obtained were also compared to some selected machine-learning models like Random Forest, Naïve Bayes, KNN and Logistic Regression and results show that the developed model achieved a significant improvement over the methods considered. Therefore, convolutional neural network is more suitable for heart disease prediction than some state-of-the-art machine-learning models. The contribution to knowledge of this research is the use of Afrocentric dataset for heart disease prediction. Future research should consider increasing the data size for model training to achieve improved accuracy
Influence of Extraction Temperature on the Quality of Neem Seed Oil: Preliminary Investigation
Get PDFThe storage of neem oil for a long time before usage comes along with challenge of quality retain ability of the oil; and the extraction methods can affect the quality of neem seed oil. This research work compared the mechanical expression method to the solvent extraction method to find a better method that will give high-grade neem oil for long-term storage. A plant with a capacity of 50 kg/day of neem seed kernel was used to extract oil from neem seed using ethanol as extraction solvent. The increase of extraction temperature from 20 oC (mechanically expressed) to 78 oC leads to decrease of iodine value from 62.70 to 60.10 gI2/100 g; increase of acid value from 3.4 to 4.2 mg KOH/g and increase of saponification value from 158.74 to 210.18 mgKOH/g. The Fames standard method was used for the GC – MS analysis and the percentage composition of the polyunsaturated components in the 20 oC (mechanically expressed), 50 oC, 55 oC, 60 oC and 78 oC oils were 21.58, 6.33, 3.09, 1.83 and 0.21% respectively. The changed of extraction temperature from 20 oC to 78 oC brings about reduction of polyunsaturated components from 21.58% to 0.21%. The increase of extraction temperature leads to conversion of unsaturated components to saturated components due to auto – oxidation process. This is clearly seen as the extraction temperature increased from 20 oC to 78 oC, the percentage composition of the saturated components increased from 22.40% to 43.70% and the polyunsaturated component decreased from 20.47% to 0%. The fatty acid composition associated with the 78 oC oil are: Oleic acid, 46.61%; Stearic acid, 11.83%; Palmitic acid, 16.54%; 11 – Octadecenoic acid, 3.58%; Cis – Vaccenic acid, 5.90%; Cyclopropaneoctanal, 11.19%; Squalene, 0.21% and Trimethylsilyl – di(timethylsiloxy) – silane, 4.14%. The functional groups identified in the 78 oC oil were C – H, C = O, C – C and C – O. Based on the lowest iodine value, lowest percentage composition of the polyunsaturated component value and high percentage composition of saturated component, the neem oil obtained at 78 oC from the miscella is considered as the high grade neem oil because it is less reactive due to lowest percentage composition of polyunsaturated and can be stored for long time before usage. Furthermore, the results from this work will assist manufacturers in selecting the extraction temperature for particular application of the neem seed oil. The extracted oil is recommended for soap production due to its high saponification value
Sparse FIR Filter Design using Double Generalized Orthogonal Matching Pursuit (DGOMP)
Get PDFIn this paper, sparse FIR filter was designed using Double Generalized Orthogonal Matching Pursuit (DGOMP) to reduce memory usage and increasing the speed thereby decreasing computational complexity of the algorithm. Mathematical models were formulated and simulations were conducted to validate the performance of the proposed method. The performance was compared with BOMP and Conventional FIR filter. The results showed that the DGOMP method achieved higher sparsity and a better approximation of an ideal filter. Additionally, the designed sparse FIR filters using DGOMP showed better performance in terms of time of execution when the signal lengths keep increasing, giving a 10% faster execution time when compared to BOMP. The passband and stopband attenuation, as well as ripple values were better, offering the flexibility of parameter adjustment. The results showed that DGOMP is a promising approach for designing sparse FIR filters
Performance Evaluation of Some Selected Classification Algorithms in a Facial Recognition System
No full textFacial Recognition (FR) has been an active area of research and has diverse applicable environment, it continues to be a challenging research topic. With the development of image processing and pattern recognition technology, there are many challenges in machine learning to select the appropriate classification algorithms, most especially in the area of classification of extracted features to have low classification time, high sensitivity and accuracy of the classification algorithms, so it is very important to explore the performance of different algorithms in image classification. The three selected supervised learning classification algorithms: Learning Vector Quantization (LVQ), Relevance Vector Machine (RVM), and Support Vector Machine (SVM) performance were evaluated so as to know the most effective out of the selected algorithms for facial images classification. The development of the system has four stages, the first stage is image acquisition and 180 images were taken by digital camera under same illumination and light colour background. The second stage is pre-processing to improve the images data by suppressing unwilling distortion; grayscale and normalization were used for image pre-processing. The third stage is feature extraction; Discrete Cosine Transform (DCT) is adopted for this purpose. While the fourth stage is face recognition classification, Receiver Operating Characteristics (ROC) was used to test the performance of each the three algorithms. However the Learning Vector Quantization algorithm, Relevance Vector Machine and Support Vector Machine performance have not been compared together to the most effective out of the three algorithms in term of False Positive Rate, Sensitivity, Specificity, Precision, Accuracy and Computation Time. Hence, this work evaluated the performance of the Learning Vector Quantization; Relevance Vector Machine and Support Vector Machine classification algorithms in facial recognition system and Support Vector Machine outwit the other two algorithms in facial recognition in term of specificity, recognition time and recognition accuracy at different threshold
Taguchi Optimization of Screw Flight Bending Operation
Get PDFThe optimizations of screw flight bending operating parameters have been successfully carried out. The optimization of the screw flight bending operation, aims at determining optimal values for key parameters using Taguchi Design and Genetic Algorithm (GA) optimization tools. The parameters investigated include bending radius, diameter of screw, flight thickness, and bending force. Through the systematic application of Taguchi methodology and GA optimization, optimal values of 79.99 mm for bending radius, 69.997 mm for diameter of screw, 5.005 mm for flight thickness, and 232.62 N for bending force were identified. The effectiveness of the optimized parameters was assessed through analysis of variance (ANOVA), revealing an R-squared value of 84.78 % and an adjusted R-squared value of 75.64 %. These results indicate that the developed model explains a significant portion of the variability in the response variable, providing confidence in the reliability and significance of the optimized solutions. Overall, the integration of Taguchi methodology with GA optimization has proven to be a powerful approach for systematically exploring parameter space and identifying optimal solutions in screw flight bending operations. The optimized parameter values offer the potential for enhanced performance, accuracy, and efficiency in the bending process, contributing to improved product quality and manufacturing productivity
Unlocking the Potential of Palm Kernel Shell and Quarry Dust: A Cost-Driven Approach to Replacing Sand and Gravel in Concrete
No full textThis research investigates the potential of palm kernel shells (PKS) and quarry dust (QD) as sustainable and cost-effective replacements for sand and gravel in concrete production. The study explores the impact of varying PKS and QD content on workability, density, water absorption, and mechanical properties. While increasing these alternative aggregates decreases workability and density, it improves water absorption and, in some cases, mechanical strength. Response Surface Methodology (RSM) identified a combination of 5% PKS and 20% QD (-1, -1) as the optimal replacement level for achieving a balance between cost and performance. This mix offers a significant cost reduction of 18.2% relative to concrete made with conventional aggregates. The study highlights the potential of PKS and QD as sustainable alternatives for conventional aggregates. Utilizing these readily available waste materials can reduce reliance on natural resources, promote waste management practices, and contribute to a more environmentally friendly construction industry. Additionally, the research suggests that quarry dust alone might be a more suitable replacement material than PKS due to its superior influence on concrete strength. This research provides valuable insights for optimizing concrete mix design with PKS and QD, promoting cost-effective and sustainable construction practices in regions with abundant palm oil production and quarrying activities
Efficiency of Ceramic Composite Filter Produced Using Nano Particulate Carbonaceous Material
No full textMillions of people have no access to safe water. This has led to continued incidence of waterborne diseases with severe cases of complications and sometimes death. Attempts to use silica sand-based materials for the production of filters by researchers are still evolving. In this study a novel hybrid clay-nanoparticulate agro-waste blend water filter with appropriate pore size and flow rate for contaminant-free water filtration was produced. The composite filters were produced using hydraulic pressing and sintering process. Four different ratios of clay, activated carbon and nanoparticle coconut shell (60:30:10, 60:20:20, 60:10:30, and 60:0:40 wt %) were mixed and fired at 700 °C, 750 °C, 800 °C, 850°C, 900 °C, 950 °C, and 1000 °C respectively at the rate of 75 °C/hr. E. Coli, Total Coliform, and turbidity tests were carried out on the influent water collected from Lagos Lagoon, and effluent water samples were filtered using the produced samples. The result of water tests revealed that all the filters produced removed between 86.81 % to 99% Escherichia Coliform (E. coli), and 81.81% to 93.31%% total coliform in the water sample. The blend of 60:10:30 fired at 850 °C and 900 °C showed improved properties with a flow rate value of 2.83 l/hr and 2.77 l/hr. In conclusion, the study established that synthesis of nanoparticle coconut shell and activated carbon is a suitable material for producing clay composite filters that can purify water to Nigerian Standards for Drinking Water Quality (NSDWQ) acceptable level
Role of Bar and Restaurant Management System: Case of London Lounge, Ubungo Dar-es-Salaam
Get PDFThis study aims at revealing the role played by Bar and Restaurant Management System (BRMS) in Tanzania, by studying its functionalities and user perceptions and later providing recommendations that will improve the Food and Beverage (F&B) services. London Lounge was selected as a case study to represent the middle-class bar within the country that makes use of BRMSs. The literature shows that the common design of BRMS include functionalities for accounting, inventory and employee management where the system users had positive perception on the advantages of system usage except for the inexperienced users. Focus group discussion was used as the method for data collection where the study team identified that the appropriate system users are Counter Attendants, Supervisors, Managers and the Director. The findings revealed that the system had enough functionality to exhaust the principal duties for each system user based on their job positions and hence played a big role in managing their operations. The study highlighted some system weaknesses hence provided the recommendations to improve the functionalities of the system and attract more users towards system usage. Future research on design and development of BRMS integrated with other systems to manage multiple businesses under the same ownership is highly recommended in this study