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

In the era of digital communication, Facebook has emerged as a powerful platform influencing language use, particularly among university students. This study investigates the phenomenon of "Facebook English," a distinctive digital variety marked by informal grammar, abbreviations, emoticons, and creative lexical formations. Focusing on Libyan English department students, the research explores the relationship between Facebook usage and its impact on academic writing, grammatical accuracy, and perceptions of language formality. Using a quantitative methods approach that includes surveys and statistical analysis, the findings reveal no significant correlation between Facebook use and academic English performance. However, gender and age differences were observed in the use and perception of Facebook English. The study emphasizes the need for language educators to integrate digital literacy and critical language awareness into curricula, enabling students to navigate both informal and academic registers effectively. These findings contribute to the broader understanding of computer-mediated communication and its pedagogical implications in EFL contexts. Keywords: Facebook English, Computer-Mediated Communication (CMC), Sociolinguistics, Libyan University Students, Digital Literacy, Language Variation, Academic Writing, Informal Registers, English as a Foreign Language (EFL), Social Media Linguistics

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

Artificial Intelligence in Education (AIEd) is an emerging education field. This Research Paper explicitly investigates the use of AI technology in language learning specifically in English as a Second Language (ESL). In recent years, the integration of Artificial Intelligence (AI) into our lives is becoming stronger, which can be seen in many areas, such as education. This has led to a revolution in teaching methods across different disciplines, particularly in the teaching of English as a Foreign Language (EFL), where it provides useful tools for teachers. The intersection between AI and FL represents a paradigm shift in educational practice, due to its ability to personalize learning experiences, provide instant feedback, and tailor to individual student needs Therefore, the main goal of this report is to analyze the uses of AI tools in EFL, and to propose concrete and suitable AI tools to enhance the teaching-learning process.