We try through this research to show the relationship between the crescent and the month in the Koran, and then their importance and their relationship to timing, and the timing of this relationship with people, and the significance of these timings, so we gathered verses that spoke about the family and the month, and we tried to study them in a detailed objective study, so this came The research of five subjects was the first topic introductory research and the time and month, while the remaining four detectives on the verses that talked about the timing of the month, and we have explained the relationship of this timing with people and the significance of these times.

Abstract:

Objectives: The present study aims to evaluate effectiveness of educational program the nurses' knowledge towards early prediction of acquired weakness in the intensive care unit.

Methodology: A pre-experimental study design (comparison of two groups), which was achieved through the pre and post-test method for the study sample through the application of an educational program in the intensive care unit of Al-Zahra Teaching Hospital in Kut city, Wasit Governorate. The study was conducted for the period from 28^th April 2022 to 15^th August 2022 by selecting a purposive (non-probability) sample for this study. The study sample size was (52) nu

The research seeks to design a program of guidance in the form of emotional perception rational to reduce the fear of failure, to identify the effect of method of emotional perception rational in reducing the fear of failure. To achieve these objectives, the researcher adopted the null-hypotheses, which assume there are no statistically significant differences in the degree of fear of failure (for the control group) in the pre-posttest. There are no statistically significant differences in the fear of failure (for the experimental group) in the pre-posttest. There were no statistically significant differences in the fear of failure of the groups (experimental and control) after the application of the program in the post-test. In order to

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci

Imitation learning is an effective method for training an autonomous agent to accomplish a task by imitating expert behaviors in their demonstrations. However, traditional imitation learning methods require a large number of expert demonstrations in order to learn a complex behavior. Such a disadvantage has limited the potential of imitation learning in complex tasks where the expert demonstrations are not sufficient. In order to address the problem, we propose a Generative Adversarial Network-based model which is designed to learn optimal policies using only a single demonstration. The proposed model is evaluated on two simulated tasks in comparison with other methods. The results show that our proposed model is capable of completing co