http://dspace-su.server.ly:8080/xmlui/handle/123456789/659 2026-05-03T12:58:50Z http://dspace-su.server.ly:8080/xmlui/handle/123456789/670 Effect of variable parameters on the performance of gas turbine cycle Mahdi Algool; Nasar A. O. Ali; Salem Elokalli The gas turbine are being usually used in several application such as aircraft engine and electricity power station plants. To improve its performance many researches has been performed. In this study the thermodynamic analysis has been carried out of a gas turbine power plant located at the sea level in Rasanuf city ( north of Libya ), based on the conservation of energy to evaluate its efficiency, net power and specific fuel consumption. The analysis of thermodynamic equations of first law performed depending on the variable parameters (atmosphere air temperature, pressure ratio) at fixed turbine inlet temperature (TIT) for 1000 K, 1200 K, and 1500 K, compressor pressure ratio from 4 to 20 and ambient temperature 15 oC to 45 oC. 2023-12-02T00:00:00Z http://dspace-su.server.ly:8080/xmlui/handle/123456789/666 Calculating the Ultimate Bearing Capacity of a Strong Soil Layer over a Weak Soil Layer Maha A. A. Shanaa The ultimate bearing capacity calculations for shallow footings in two-layered soil systems hinge on the underlying failure surface pattern. his study examines scenarios observed in nature, where strong soil overlays a weaker layer within layered soils, specifically focusing on square footings and calculation of the ultimate bearing capacity value using different theoretical equations according to the approaches of the scientists Terzaghi, Hansen, Meyerhof, and Vesic. an analysis of the values of the results and compared with each other. Presented the results of the study for the ultimate bearing capacity of layered [C - Ꝋ] soils are stronger than all studied soils cases and the ultimate bearing capacity of sandy layered soils is times stronger than the ultimate bearing capacity of clayey layered soils, As that ultimate bearing capacity of clayey layered soils is weaker than all studied soils cases. 2023-12-02T00:00:00Z http://dspace-su.server.ly:8080/xmlui/handle/123456789/663 الكشف عن وتشخيص أورام المخ باستخدام الشبكات العصبية التلافيفية عبد القادر الربعي Brain tumors are one of the major health problems related to abnormalities in the human brain. An activist diagnosis of brain tumors is critical to improve patient outcomes and lives. Early detection of tumors is crucial for treatment. Magnetic Resonance Imaging (MRI) is one of the most commonly used diagnostic methods for brain tumors in the clinical area. Manual detection of brain tumors is becoming increasingly time-consuming and costly. Therefore, an automated Computer Aided Diagnosis (CAD) system is needed to help doctors and radiologists detect these deadly tumors in time, thereby saving precious lives. Convolutional Neural Networks (CNNs) are widely used in various CAD systems. CNNs play an important role in healthcare as image processing techniques for segmentation, recognition, and classification of MRI images and classification and detection of brain tumors. This paper applies the Deep Learning (DL) architectures for brain tumor detection and classification. Implement a deep learning (CNN) based computational approach that includes image pre-processing to extract regions of interest in the image itself to identify and detect tumors in the brain. The highest accuracy rate in the experiment reached 96%. Evaluation metrics used include sensitivity, precision, loss, and F1 score. The results obtained will aid in the diagnosis and detection of brain tumors. 2023-12-02T00:00:00Z