Abstract

Knowing the amount of residual stresses and find technological solutions to minimize and control them during the production operation are an important task because great levels of deformation which occurs in single point incremental forming (SPIF), this induce highly non-uniform residual stresses. In this papera propose of a method for multilayer single point incremental forming with change in thickness of the top plate (0.5, 0.7, 0.9) mm and lubrication or material between two plates(polymer, grease, grease with graphite, mos₂) to knowing an effect of this method  and parameters on residual stresses for the bottom plates. Also compare these results for the

A novel analytical method is developed for the determination of azithromycin. The method utilizes continuous flow injection analysis to enhance the chemiluminescence system of luminol, H2O2, and Cr(III). The method demonstrated a linear dynamic range of 0.001–100 mmol L-1 with a high correlation coefficient (r) of 0.9978, and 0.001–150 mmol L-1 with a correlation coefficient (r) of 0.9769 for the chemiluminescence emission versus azithromycin concentration. The limit of detection (L.O.D.) of the method was found to be 18.725 ng.50 µL−1 based on the stepwise dilution method for the lowest concentration within the linear dynamic range of the calibration graph. The relative standard deviation (R.S.D. %) for n = 6 was less than 1.2%

Due to severe scouring, many bridges failed worldwide. Therefore, the safety of the existing bridge (after contrition) mainly depends on the continuous monitoring of local scour at the substructure. However, the bridge's safety before construction mainly depends on the consideration of local scour estimation at the bridge substructure. Estimating the local scour at the bridge piers is usually done using the available formulae. Almost all the formulae used in estimating local scour at the bridge piers were derived from laboratory data. It is essential to test the performance of proposed local scour formulae using field data. In this study, the performance of selected bridge scours estimation formulae was validated and sta

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod