Performance analysis of FFNN and Kriging model on prediction of ionospheric TEC during April 2022–X 1.1 solar flare

Artificial Intelligence (AI) in data analysis has become an integral tool in recent times. In this research, Total Electron Content (TEC) prediction by the Feed Forward Neural Network (FFNN) model is compared with the Ordinary Kriging based Surrogate Model (OKSM) to verify the significance of the sample size required for FFNN and OKSM. To assess the credibility of the constructed models, the FFNN and OKSM prediction models were evaluated on 30 April 2022, during which a Solar Flare (SF) of intensity X 1.1 occurred. The TEC data is taken from Hyderabad (17.31°N and 78.55°E) from IONOLAB data servers. The solar parameters were collected from the NASA OMNIWEB data server. The surrogate model is built to predict the seventh-day TEC values by using the previous six days of TEC data, Whereas the FFNN model uses 146 days of TEC data as training data set to predict the subsequent four days of TEC. The performance of the models is evaluated using statistical parameters like Root Mean Square Error (RMSE), Correlation Coefficient (CC), Mean Absolute Error (MAE) and symmetric Mean Absolute Percentage Error (sMAPE). The results were represented as linear regression scatter plots, showing fewer residuals for the constructed prediction models.</p

Manufacturing and experimental characterization of new-developed natural fiber reinforced polymer nanocomposite

In this work, a nanocomposite polymer is developed using less utilised palm leaf stalk fiber as natural reinforcement and nano coconut shell powder blended with polyester resin as a matrix. The fibers are extracted from palm leaves. The fibers are treated with 5% potassium permanganate (KMnO4) as an alkali for 1 h. They are then thoroughly cleaned with water and dried in an oven. The fibers are chopped into short strands. The polyester matrix is pre-prepared by blending with coconut shell nanopowder. The fabrication of the composite is completed using these mats as reinforcement in the prepared blend. Mechanical tests are performed on the newly developed composites. The experimental findings are compared to similar natural fibers, and found that palm-based composite exhibits superior values. Structural electron microscopy observations reveal the presence of matrix, reinforcement and the absence of voids due to the addition of nanopowder. The developed composite may be recommended for automobile and aerospace applications