Investigation of 316L Stainless Steel by Flame Hardening Process

Austenitic stainless steel offer great imperviousness to general erosion because of the development of a detached surface film. They are broadly utilized as a part of the sustenance and concoction preparing ventures and in addition in biomaterial applications. In any case, they can experience the ill effects of setting erosion in chloride particle containing arrangements. All things considered, in the meantime they have discovered little use in mechanical building applications in view of their low hardness and poor wear resistance. In this examination work, to enhance the previously mentioned reasons, surface solidifying by Flame hardening procedure is done. It has for some time been an outstanding a warm treatment for enhancing the surface properties of austenitic stainless steel. The examples were fire solidified for 5 minutes, 10 minutes and 15 minutes separately. Wear test for every one of the examples were completed by stick on plate testing process. The outcomes were contrasted and an untreated specimen and finished up with metallographic tests like optical tiny tests and examining electron magnifying lens tests

Predicting Smartphone Vision Syndrome: A Feasible Approach using Machine Learning Algorithms

Smartphone Vision Syndrome (SVS) is an evitable problem for people who spend a great deal of time watching digital screens. It is a major concern for rapid growth in technology where the burden is significantly greater due to factors such as limited access to and use of personal protective equipment, as well as lesser break time. The objective of the model is to achieve a feasible and higher level of eye health for people who are working long hours with digital screens. The dataset is obtained through an online survey form containing metrics that contribute to the occurrence of SVS. After applying Machine Learning algorithms, namely Logistic Regression, Random Forest Classifier, Naïve Bayes and Support Vector Machine (SVM), the model’s overall performance is assessed using the test sample. Accuracies obtained by Random Forest, Support Vector Machine, Logistic Regression, Naïve Bayes, and Gaussian Naïve Bayes are 98.75%, 97.5%, 77.5%, 95% and 96.25%

Prediction of Ionospheric TEC Using RNN During the Indonesia Earthquakes Based on GPS Data and Comparison with the IRI Model

Total electron content (TEC) is a significant descriptive measure for the ionosphere of the earth. Due to either the sun’s activity like solar flare or the positive hall effect caused during earthquake (EQ), the oxygen atoms of the ionosphere split into oxygen ions and electrons increasing the electron content in the ionosphere which causes a rise in the TEC value, thus causing the delay in the signals coming from the satellite to the earth. TEC is associated with the Sun’s parameter and geomagnetic indices. In this research, parameters such as planetary K and A-index (Kp and Ap), Radio flux at 10.7 cm (F10.7), Sunspot number (SSN), and IONOLAB true TEC values were collected for the BAKO IGS network station situated in Indonesia (− 6.45° N, 106.85° E) for predicting TEC variations during EQ days occurred in the years 2004 and 2012. A total of three months of TEC data from the BAKO station during the years 2004 and 2012 were used for the developed Recurrent Neural Network (RNN) model in order to predict the TEC before and after the EQ days. For the year 2004, the model has an average Root Mean Square Error (RMSE) and Correlation Coefficient (CC) of 6.79 and 0.90. Also, for the year 2012, during April it has the average RMSE and CC of 8.90 and 0.94. For the same year in August month, the model has the average RMSE and CC of 8.70 and 0.94. The performance of the model is also evaluated using linear regression scatter plot. The Pearson’s R value calculated from the scatter plot is 0.92, shows that the model has good correlation with the true TEC.</p