The 2017 Dadin Kowa flood had a more devastating effect in Hore Gare and Tunga areas. The flood occurred as a result of high rainfall, obstruction, siltation and encroachment into flood-prone areas. Monthly maximum and annual rainfall analyses were carried out. The month of June, 2017 received the maximum monthly rainfall of a total of 344 mm which coincided with the month the flood occurred. The analysis of data indicated that flooding in Dadin Kowa town is rainfall-induced and the river channels are expected to be on higher risks of flooding when the channel obstruction capacity coincides with high rainfall intensity. The mean value of annual and maximum rainfall is 834.93 mm and 249.08 mm with standard deviation of 131.95 mm and 49.24 mm respectively at Dadin Kowa town. The recurrence interval of 2, 5, 10, 25, 50, 100 and 200 years of monthly maximum rainfall is 250.70 mm, 292.80 mm, 313.00 mm, 332.70 mm, 344.30 mm, 353.90 mm and 362.00 mm respectively. The frequency analysis of the return period of the maximum monthly rainfall of 344.00 mm is expected to occur in 50 years from 2018 with a magnitude of 344.30 mm. Also, the recurrence interval of 2, 5, 10, 25, 50, 100 and 200 years of annual maximum rainfall is 838.78 mm, 949.40 mm, 1003.60 mm, 1057.70 mm, 1090.30 mm, 1118.10 mm and 1142.10 mm respectively. The frequency analysis of the return period of the
Sleep Apnea is an anomaly in sleeping characterized by short pause in breathing. Failure to treat sleep apnea leads to fatal complications in both psychological and physiological being of human. Electroencephalogram (EEG) performs an important task in probing for sleep apnea through identifying and recording the brain’s activities while sleeping. In this study, computer aided detection of sleep apnea from EEG signals is developed to optimize and increase the prompt recognition and diagnosis of sleep apnea in patients. The time domain, wavelets, and frequency domain of the EEG signals were computed, and features were extracted from these domains. These features are inputted into two machine learning algorithms: Support Vector Machine and K-Nearest Neighbors of different kernel functions and orders. Evaluation metrics such as specificity, accuracy, and sensitivity are computed and analyzed for the classifiers. The KNN classifier outperforms the SVM in classifying apnea from non-apnea events in patients. The KNN order 3 shows the highest performance sensitivity of 85.92%, specificity of 80% and accuracy of 82.69%.
Fifth-order Korteweg-de Vries (KdV) equations, arise in modeling waves phenomena such as the propagation of shallow water waves over a flat surface, gravity-capillary waves and sound waves in plasmas. In this work, a one-step block hybrid linear multistep method was derived using the collocation technique, to solve fifth-order KdV models via the Method of Line (MoL). The consistency, stability and convergence of the method were established. The efficiency of the method can be seen from comparison of the exact solutions of problems and other methods cited from literature.
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