At atmospheric pressure and at a frequency of 9.1 kHz, a constructed magnetically stabilized tornado gliding arc discharge (MSGAD) system was utilized in this study to generate a non-thermal plasma with an alternating voltage source from 2,4,6,8 to 10 kV. Argon gas was used to generate the arc plasma with an adjustable flow rate using a flow meter regulator to stabilize the gas flow rate to 2 L/min. A gliding plasma discharge is achieved by a magnetic field for the purpose of a planned investigation. The influence of the magnetically stabilized tornado gliding arc discharge parameters such as magnetic field and applied voltage on microscopic tornado plasma parameters was studied. The electron temperature1was measured using a Boltzmann plot

NiO0.99Cu0.01 films have been deposited using thermal evaporation
technique on glass substrates under vacuum 10-5mbar. The thickness
of the films was 220nm. The as -deposited films were annealed to
different annealing temperatures (373, 423, and 473) K under
vacuum 10-3mbar for 1 h. The structural properties of the films were
examined using X-ray diffraction (XRD). The results show that no
clear diffraction peaks in the range 2θ= (20-50)o for the as deposited
films. On the other hand, by annealing the films to 423K in vacuum
for 1 h, a weak reflection peak attributable to cubic NiO was
detected. On heating the films at 473K for 1 h, this peak was
observed to be stronger. The most intense peak is at 2θ = 37

In this research, hand lay- up technique is used to prepare samples from epoxy resin reinforced with multi- walled carbon nanotubes in different weight fractions (0, 2, 3, 4, 5) wt%. The immersion effect by sodium hydroxide solution (NaOH) at normality (0.3N) for a period of (15 days) on the thermal conductivity of nanocomposites was studied, and compared to natural condition (before immersion). The thermal conductivity of epoxy nanocomposites specimens were carried out using Lee’s disk method. The experimental results showed that thermal conductivity increased with increase weight fraction before and after immersion for all specimens, while the immersion effect leads to decrease thermal conductive values compared to thermal conductivi

This study was conducted to determine the effects of concentration of hydrochloric acids, temperature, and time on the hydrolysis of soya proteins (defatted soya flour) by determining the value of total protein nitrogen  concentration, and amino nitrogen concentration of protein, peptides, and amino acids, and then calculated the hydrolysis rate of proteins.

        The variables of the conditions of hydrolysis process was achieved in this study with the following range value of tests parameter:

• Concentration of HCl solution ranged between 1-7 N,
• Hydrolysis temperature ranged between 35-95 °C, and
• The time of hydroly

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

     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

Low salinity (LS) water flooding is a promising EOR method which has been examined by many experimental studies and field pilots for a variety of reservoirs and oils. This paper investigates applying LS flooding to a heavy oil. Increasing the LS water temperature improves heavy oil recovery by achieving higher sweep efficiency and improving oil mobility by lowering its viscosity. Steam flooding projects have reported many problems such as steam gravity override, but override can be lessened if the steam is is alternated with hot LS water. In this study, a series of reservoir sandstone cores were obtained from Bartlesville Sandstone (in Eastern Kansas) and aged with heavy crude oil (from the same reservoir) at 95°C for 45 days. Five reservo

Understanding of in-situ stress profiles and orientations plays a vital role in designing a successful hydraulic fracturing treatment. This paper is an attempet to examine the effect of lithology and in situ stress on geometery of hydraulic fractures. A hydraulic fracturing design simulator software called FracproPT with various capabilities for designing most of hydraulic fracture was used for  simulate and optimize the hydraulic fracturing. For studying purpose,  three different cases of stress gradient contrast between different formations  are considered in this study (0.4, 0.5 and 0.75 psi/ft). The results obtained from the simulator showed that  lithologies surrounding the pay zone have an effect on the fracture