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In this study, f_oF2 variability diurnally, seasonally and annually was investigated during a Low Solar Epoch (LSE) over Ilorin (latitude 8.31°N, longitude 4.34°E, dip latitude 2.95^o), a low latitude station along the equatorial anomaly trough. The percentage variability index () was used for the analysis. The percentage variability index () is lowest during the day (2-17%); increases during night to (8-55%); and attained the highest magnitude during pre-sunrise phase (17-68%), for the period of LSE. Two major peaks were noticed in V_R: the pre-sunrise peak, which is higher, and the post-sunset peak. Annually,  peaks at 44% for the pre-sunrise phase, and 37% for the post-sunset phase, during the LSE. The rapid electron drift away from the equator coincides with subsequent rise in the percentage variability index immediately after sunset for all the seasons.

Olax manii stems were collected in Ikire, Osun State, Nigeria. Using the hydrodistillation process, essential oil was extracted from the stem of Olax manii. To ascertain the chemical composition of the essential oil, Gas chromatography mass spectrometry (GCMS) technique analysis was employed. Using doses ranging from 100 to 25 µg/ml, the antioxidant properties of the essential oil were examined using the 2, 2-Diphenyl-1 picryl hydroxyl (DPPH) radical scavenging activity method. The results of the Gas chromatography mass spectrometry (GC-MS) analysis indicated that the main chemical constituents were squalene (6.18 %), amyrone (4.49%), and neophytadiene (4.22 %). The minor constituents were identified as limonene (0.23 %), caryophyllene (0.31 %) and phytol (1.62 %). The essential oil radical scavenging activity at a concentration of 100 mg/ml was compared to the standard ascorbic acid at the same concentration and it revealed that the essential oil exhibited a significant percentage radical scavenging activity of 78.52 %. The percentage radical scavenging activities increased with increase in concentrations of the essential oil. The Essential oil constituents which possess antioxidant activities could be responsible for the antioxidant activities and ethnomedicinal uses of Olax manii.

The mandatory wearing of face masks orchestrated by the COVID-19 pandemic has brought some complexities to the ability of face recognition systems in identifying such faces. Thus, this study examined the performance of some selected machine learning algorithms: Convolutional Neural Network (CNN), Linear discriminant analysis (LDA), Logistic Regression (LR), K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naïve Bayes (NB) and Decision Tree (DT) for the recognition of masked and unmasked human faces; and developed a face recognition system based on the algorithm with the best performance. The dataset was composed of 11,792 masked and unmasked facial images collected from Kaggle online repository. The set was divided into 70%, 20% and 10% respectively for training, testing and validation each for the masked and the unmasked face images. The images were augmented randomly for training, with some rotated 30 degree, some zoomed 20%, some shifted horizontally by 10% in width, some shifted vertically by 10% in height and some flipped horizontally. The performance analysis of the algorithms presented accuracies of 99% and 99% for the CNN; 93% and 98% for LDA; 93% and 93% for LR; 86% and 78% for the NB; 70% and 70% for the KNN; 64% and 45% for the DT; and 92% and 98% for the SVM respectively for training and cross validation. Overall, the CNN recorded the highest recognition accuracy. Thus, a CNN user-friendly face recognition, system was developed, and tested with a number of real-life human masked and unmasked faces which showed excellent recognition performance. The implementation was carried out with appropriate Python machine libraries. The developed system could be very useful for access control and surveillance. Therefore, this study recommends the adoption of the developed systems for face recognition-based security system.