Abstract: This paper presents simulation technique for life assessment of the insulation of the power transformer. Load and ambient temperatures are two important factors that influence the hot spot of transformers. The transformer hot spot temperature is one of the most critical parameter in determining the life of transformer paper insulation. The simulation (Matlab Software Package) is based on related international standards as the IEC 60076-7:2005 loading guide method. Load and ambient temperatures of the transformer obtain from General Electrical Company and meteorology office of Libya are used as the input for the simulation together with transformer parameters from heat run test. Hottest spot temperature and loss-of-life are calculated. Lifetime of transformer is predicted. Keywords: Hot-spot temperature, power transformer, top-oil temperature, loss of life

Abstract: In light of the aspiration to achieve sustainability in the field of education, especially after the development of artificial intelligence technologies in general and artificial neural network technologies in particular, the process of improving e-learning has become one of the most important strategies for achieving sustainability. This study aims to evaluate the impact of using artificial intelligence techniques and artificial neural networks on improving cloud e-learning by extracting and mining data from the electronic cloud and analyzing it to provide a more efficient and effective educational experience. Through several methodologies, including descriptive methodology to describe the factors influencing the improvement of machine learning through the use of artificial intelligence techniques and the mechanism for using these techniques, quantitative methodology was used in collecting data and analytical scientific methodology to analyze the results of the proposed model, which is a hybrid of convolutional artificial neural networks (CNN) and recursive neural networks (RNN). Through the model, it is possible to extract and analyze user data such as interaction with content and duration of study to extract patterns and behaviors that support improving the learning experience, and personalizing individual learning paths. Predicting the academic performance of students using machine learning techniques. The results showed an improvement in the learning experience of 86% and an improvement in platform performance of 85%. Machine learning indicators, such as accuracy (94%), recall (93%) and f1 score (91%), also indicated the success of the model used in