Some new norms need to be adapted due to COVID-19 pandemic period where people need to wear masks, wash their hands frequently, maintain social distancing, and avoid going out unless necessary. Therefore, educational institutions were closed to minimize the spread of COVID-19. As a result of this, online education was adapted to substitute face-to-face learning. Therefore, this study aimed to assess the Malaysian university students’ adaptation to the new norms, knowledge and practices toward COVID-19, besides, their attitudes toward online learning. A convenient sampling technique was used to recruit 500 Malaysian university students from January to February 2021 through social media. For data collection, all students

In petroleum reservoir engineering, history matching refers to the calibration process in which a reservoir simulation model is validated through matching simulation outputs with the measurement of observed data. A traditional history matching technique is performed manually by engineering in which the most uncertain observed parameters are changed until a satisfactory match is obtained between the generated model and historical information. This study focuses on step by step and trial and error history matching of the Mishrif reservoir to constrain the appropriate simulated model. Up to 1 January 2021, Buzurgan Oilfield, which has eighty-five producers and sixteen injectors and has been under production for 45 years when it started

In this paper, the Monte-Carlo simulation method was used to compare the robust circular S estimator with the circular Least squares method in the case of no outlier data and in the case of the presence of an outlier in the data through two trends, the first is contaminant with high inflection points that represents contaminant in the circular independent variable, and the second the contaminant in the vertical variable that represents the circular dependent variable using three comparison criteria, the median standard error (Median SE), the median of the mean squares of error (Median MSE), and the median of the mean cosines of the circular residuals (Median A(k)). It was concluded that the method of least squares is better than the

This deals with estimation of Reliability function and one shape parameter (?) of two- parameters Burr – XII , when ?(shape parameter is known) (?=0.5,1,1.5) and also the initial values of (?=1), while different sample shze n= 10, 20, 30, 50) bare used. The results depend on empirical study through simulation experiments are applied to compare the four methods of estimation, as well as computing the reliability function . The results of Mean square error indicates that Jacknif estimator is better than other three estimators , for all sample size and parameter values

In this research, the focus was placed on estimating the parameters of the Hypoexponential distribution function using the maximum likelihood method and genetic algorithm. More than one standard, including MSE, has been adopted for comparison by Using the simulation method

Although renewable energy systems have become an interesting global issue, it is not continuous either daily or seasonally. Latent heat energy storage (LHES) is one of the suitable solutions for this problem. LHES becomes a basic element in renewable energy systems. LHES compensate for the energy lack when these systems are at low production conditions. The present work considered a shell and tube LHES for numerical investigation of the tube rotation influence on the melting process. The simulation and calculations were carried out using ANSYS Fluent software. Paraffin wax represents the phase change material (PCM) in this work, while water was selected to be the heat transfer fluid (HTF). The calculations were carried o

 Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat