No abstract

Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites.

The high-resolution aeromagnetic data over part of the Bornu basin (sheet 84) north eastern (NE) Nigeria, was processed and interpreted using spectral analysis to determine the depth to the basement. The study area is bounded between longitude 10⸰ 00’ 00’’E to 10⸰ 30’ 00’’ E and latitude 11⸰ 00’ 00’’ N to 12⸰ 00’ 00’’. Regional residual separation of the total magnetic field intensity (TMI) was performed using polynomial fitting method. The residual map was divided into nine square spectral blocks using the filtering tool of Microsoft excel software. The Microsoft excel program employed Fast Fourier Transform (FFT) technique. It transforms the magnetic field data into the radial energy spectrum for each block. Then the average radial energy spectrum is computed in MATLAB. The result shows that depth to the deeper magnetic source ranges from 4503.9m to 1948.3m. However, it can be observed that NN, NE, and NW are having less deep magnetic sources ranging from 1948m to 2459.7m. The deepest sources happened at the major towns of interest which are Bidawa , Matsango , Yakiri and Fatara areas, Katagum Bauchi state ranging from 2632.8m to 4503.9m The maximum depth of the sedimentary unit was estimated as 4.5km because the isolated value beyond this depth cannot be connected , and this depth occurs around Matsango and Bidawa. The shallow depth magnetic source map shows that SS, SW, SE are having least shallow depth, central to northern part of the study area are having shallowest depth to the magnetic source.