امتازت النصوص المسرحية بما تحمله من قيم تربوية ورؤى فنية في المسرحية التعليمية من مضامين فكرية واضحة، وأن المسرحية التعليمية ليست محصورة فقط في الحاضر، بل إنها تعمل على استعراض وتوثيق التاريخ والتراث، وتقديمها بشكل يجعلها ملائمة ومثيرة للاهتمام للأجيال الجديدة، ولأنها تلعب دورًا مهمًا في نقل القيم والمعارف وتعزيز التواصل الثقافي بين الماضي والحاضر والمستقبل، وهي وسيلة فعّالة لتعليم الجمهور بطريقة تف

New technologies have risen into popularity causing the Liquid membrane techniques to evolve over other separation techniques due to its high selectivity and recovery, increased fluxes, and reduced investment and operating cost. This work focuses on extracting Methylene Blue (MB), a cationic dye using a simple BLM separation technique from its aqueous phase. It combines extraction and stripping in a single unit operation. The feed phase was an aqueous solution of MB, the solvent chosen was soybean oil for the liquid/organic membrane phase, and tri-octyl amine acted as a carrier. The strip phase was a hydrochloric acid solution for this study. A two-phase equilibrium study was done to choose the correct solvent, carrier,

This study was done to investigate the impact of different nanoparticles on diesel fuel characteristics, Iraqi diesel fuel was supplied from al-Dura refinery and was treated to enhance performance by improving its characteristics. Two types of nanoparticles were mixed with Iraqi diesel fuel at various weight fractions of 30, 60, 90, and 120 ppm. The diesel engine was tested and run at a constant speed of 1600 rpm to examine and evaluate the engine's performance and determine emissions. In general, ZnO additives' performance analysis showed they are more efficient for diesel fuel engines than CeO. The performance of engine diesel fuel tests showed that the weight fraction of nanoparticles at 90 and 120 ppm give a similar performance,

This study was done to investigate the impact of different nanoparticles on diesel fuel characteristics, Iraqi diesel fuel was supplied from al-Dura refinery and was treated to enhance performance by improving its characteristics. Two types of nanoparticles were mixed with Iraqi diesel fuel at various weight fractions of 30, 60, 90, and 120 ppm. The diesel engine was tested and run at a constant speed of 1600 rpm to examine and evaluate the engine's performance and determine emissions. In general, ZnO additives' performance analysis showed they are more efficient for diesel fuel engines than CeO. The performance of engine diesel fuel tests showed that the weight fraction of nanoparticles at 90 and 120 ppm give a similar

Changing oil-wet surfaces toward higher water wettability is of key importance in subsurface engineering applications. This includes petroleum recovery from fractured limestone reservoirs, which are typically mixed or oil-wet, resulting in poor productivity as conventional waterflooding techniques are inefficient. A wettability change toward more water-wet would significantly improve oil displacement efficiency, and thus productivity. Another area where such a wettability shift would be highly beneficial is carbon geo-sequestration, where compressed CO2 is pumped underground for storage. It has recently been identified that more water-wet formations can store more CO2. We thus examined how silica based nanofluids can induce such a wettabil

Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting millions of people globally, is defined by symptoms of hyperactivity, impulsivity, and inattention that can significantly affect an individual's daily life. The diagnostic process for ADHD is complex, requiring a combination of clinical assessments and subjective evaluations. However, recent advances in artificial intelligence (AI) techniques have shown promise in predicting ADHD and providing an early diagnosis. In this study, we will explore the application of two AI techniques, K-Nearest Neighbors (KNN) and Adaptive Boosting (AdaBoost), in predicting ADHD using the Python programming language. The classification accuracies obtained w

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att