الملخص

يواجه استقرار الجهد تحديات كبيرة بسبب الطلب المتزايد على الكهرباء والاعتماد على خطوط النقل الطويلة، خاصة في شبكات الجهد المتوسط بجنوب ليبيا. تركز هذه الدراسة على شبكة سبها بجهد 66 كيلو فولت، التي تعاني من عدم استقرار الجهد نتيجة الاعتماد على مصادر توليد الطاقة الشمالية وخسائر الطاقة التفاعلية العالية. باستخدام برنامج، NEPLAN تم تقييم فعالية جهازين من تقنيات، FACTS وهما المعوض المتزامن الثابت (SVC) والمعوض المتزامن الديناميكي، (STATCOM) في تحسين استقرار الجهد.

تناولت الدراسة ثلاثة سيناريوهات: بدون تعويض، باستخدام، SVC وباستخدام. STATCOM أظهرت النتائج أن كلا الجهازين يحسنان استقرار الجهد بشكل ملحوظ ويقللان من خسائر الطاقة، مع تفوق STATCOM في الاستجابة الديناميكية والكفاءة الإجمالية. تؤكد هذه النتائج أهمية تقنيات التعويض المتقدمة في ضمان استقرار وموثوقية توصيل الطاقة في الظروف التشغيلية الصعبة.

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

Abstract:- This paper introduces a technique for modeling and effectively controlling the trajectory tracking of a robotic manipulator through an LQG controller optimized by the IPSO algorithm. Extensive simulations have been conducted within the MATLAB simulation environment. The LQG, which combines a Kalman Filter (KF) and a Linear Quadratic Regulator (LQR), is developed to follow the desired input for the robotic manipulator while minimizing the impact of process and measurement noise on its performance. The parameters of the LQG controller, which include the elements of the state and control weighting matrices for the LQR and KF, are optimally adjusted using the IPSO method. The validation of the proposed hybrid LQG-IPSO controller is conducted based on the control criteria parameters. The findings demonstrate that the proposed hybrid LQGIPSO controller can achieve excellent movement performance. To fully leverage the capabilities of the IPSO algorithm, careful adjustment and determination of IPSO parameters such as inertia weight, iteration count, acceleration constants, and particle quantity are essential. Therefore, The initial focus of this study is to conduct a comparative analysis of various fitness functions; simulation results indicate that the proposed hybrid LQGIPSO control method achieves commendable fitness outcomes with minimal steady-state error (ess). Following this, a comparison of the performances of a robotic manipulator using the hybrid LQG-IPSO controller, hybrid LQR-IPSO controller, LQG controller, and LQR controller is also presented. Based on the analysis, it can be concluded that superior performance parameters are attained with the hybrid LQG-IPSO controller when compared to its counterparts—the hybrid LQR-IPSO controller, LQG controller, and LQR controller—offering enhanced performance.

Keyw Keywords: Linear Quadratic Gaussian (LQG) controller, Linear Quadratic Regulator (LQR) controller, Improved Particle Swarm Optimization (IPSO) algorithm, Particle Swarm Optimization (PSO) algorithm and MATLAB etc

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