Organizational learning is one of the most important means of human resource development in organizations, but most of the organizations, especially public ones do not realize the importance of organizational learning enough, and estimated his role accurately in building intellectual capital, the resource competitive importantly for organizations of the third millennium and who suffers is other end of lack of understanding of its meaning and how to prove its presence and measured in public organizations, so there is the need for this research, which aims to investigate the effect of organizational learning its processes (knowledge acquisition, Information transfer, Interpreting the information, Organizational me

This study aimed to identify the role of school administration in achieving educational and learning goals from the point of view of educational supervisors in the stage of basic education. The descriptive method was adopted. As for the sample size, it has reached (59) educational supervisors. A questionnaire consisting of 29 paragraphs divided into four areas was used. The data were statistically analyzed on the Chi-square test, the percentage, and the mono-variance analysis. The result showed that the school administration contributes to achieving educational goals. It also works to solve problems in democratic ways, and in modern methods, there are differences in the criteria for choosing the headmaster. The study recommended that sch

Accurate calculation of transient overvoltages and dielectric stresses from fast-front excitations is required to obtain an optimal dielectric design of power components subjected to these conditions, which are commonly due to switching and lightning, as well as utilization of power-electronic devices. Toroidal transformers are generally used at the low voltage level. However, recent investigations and developments have explored their use at the medium voltage level. This paper analyzes the model-based improvement of the insulation design of medium voltage toroidal transformers. Lumped and distributed parameter models are used and compared to predict the transient response and dielectric stress along the transformer winding. The parameters

Objectives: Determine the age and gender distribution of children who experience diabetes mellitus (DM) under
the age of 15 years and the presence of some associated factors that might be a predisposing factor for the
disease including obesity.
Methodology: A cross-sectional study was conducted at diabetic clinic in Children Welfare Teaching Hospital
in Baghdad City during 2006. The study sample included diabetic children less than 15 years of age. Data were
taken from the patients' record and by direct interview with the patients' parents. Information included
demographic data, as well as past history of the patient and his/her family relative to diabetes and other immune
diseases.
Results: Data analysis showed t

An essential element in English as a foreign language (EFL) learning is vocabulary. There is a big emphasis on learning the new words' meaning from the books or inside classrooms. Also, it is a major part of language teaching as well as being fundamental to the learner but there is a big challenge in vocabulary instruction due to the weak confidence by teachers in selecting the suitable practice in teaching vocabulary or they sometimes unable to specify a suitable time for it during the teaching process. The major aim of this study is to investigate the value of posters in vocabulary learning on the 2nd grade students at Halemat Alsaadia High School in Baghdad – Iraq. It hypothesized that there are no statistically significant differences

 To expedite the learning process, a group of algorithms known as parallel machine learning algorithmscan be executed simultaneously on several computers or processors. As data grows in both size andcomplexity, and as businesses seek efficient ways to mine that data for insights, algorithms like thesewill become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are justsome of the methods described in this article for speeding up machine learning algorithms. We alsocover the benefits and threats associated with parallel machine learning, such as data splitting,communication, and scalability. We compare how well various methods perform on a variety ofmachine learning tasks and datasets, and we talk abo

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