The Dean of the Faculty received the President of Aswan University and the Head of the Permanent Scientific Committee for the Promotion of Professors and Associate Professors in the field of Electrical Power and Machines.

On Monday, July 6, 2026, Professor Khaled Salah, Dean of the Faculty, received Professor Loay Saad El-Din Nosrat, President of Aswan University and Professor of Electrical Power Engineering, on the sidelines of his participation in the examination committee for the doctoral dissertation submitted by Engineer Mahmoud Ibrahim Mohamed Saad, Assistant Lecturer in the Department of Electrical Engineering. The Dean also received Professor Al-Moataz Youssef Abdel Aziz, Professor of Electrical Power Engineering at the Faculty of Engineering, Ain Shams University, Head of the Permanent Scientific Committee for the Promotion of Professors and Associate Professors in the field of Electrical Power and Machines, and also a dissertation examiner.

The reception was attended by Professor Mohamed Safwat Abu Raya, Vice Dean for Education and Student Affairs, Professor Mohamed Abbas Abdel Rady, Head of the Department of Electrical Engineering, and Professors Ali Mohamed Youssef and Ahmed Abdel Malek, both professors in the Electrical Power Engineering Department and the dissertation supervisors. It is worth noting that the thesis, titled "Design and Analysis of Optimal Performance of an Electrical Power System Using Artificial Intelligence," focuses on improving fuel utilization to enhance operational efficiency and ensure effective management of the power system. The study also aims to reduce operating costs and improve the sustainability of the electrical grid by proposing and developing two simple and effective approaches for implementing economical operation across diverse operating conditions, particularly in scenarios of fuel shortages or supply constraints. This maximizes resource utilization while maintaining capacity balance and system reliability within defined operational limits.

The study then addressed a broader challenge faced by many optimization algorithms: random initialization, which often leads to increased search times and unstable results. To overcome this problem, a hybrid optimization framework was developed that integrates artificial intelligence and machine learning techniques to guide optimization algorithms toward an optimal starting point. This approach contributes to reducing the time and effort required to identify the best possible solution.

The research has resulted in the publication of several scientific articles in prestigious indexed journals and conferences within leading scientific institutions and databases such as Web of Science, Scopus, and IEEE.