Hafidh l. AI Sadi's research while affiliated with Al al-Bayt University and other places

In this paper, a numerical investigation is carried out to study the blood flow behavior within the stenosis artery. An artery is under applying a constant heat flux on the boundary walls in this simulation. Lumen model is employed for simulation of the artery and the Sisko model is used to indicate properties of blood as non-Newtonian fluid. Also, the cone geometry of stenosis with different severities and radii are simulated. Then, effects of heat flux, different severities of stenosis, and different radii of the artery are studied on the blood flow behavior. It is reported that before stenosis, velocity is increasing and heat transfer rate is also increasing which cause temperature to be decreased in stenosis position. But after stenosis, velocity is decreased. Consequently, heat transfer rate is decreased which leads to reduction in blood temperature. Also, since the blood particles adhere to the arterial wall, with increasing radial distance from the walls, velocity is increased, which causes maximum velocity to be found in the central region. Moreover, the thermal driving force is damped in the lateral region of the artery and does not affect velocity. On the other side, as the severity increases step by step, the temperature decreases, respectively. In fact, the crosssectional area decreases with increasing severity of stenosis. Consequently, velocity increases and causes heat transfer enhancement, which leads to a reduction in blood temperature. Therefore, the highest temperatures are related to the artery with an intensity of 20%. Although the crosssection area of the artery can change blood temperature, but its role can be ignorable in temperature enhancement and body healthy in this regard

Preparation of drug nanoparticles has been studied and evaluated in this study based on supercritical-based processing as green technology. Computational works have been conducted to evaluate the possibility of manufacturing nanomedicine using this novel technology, and the results are compared with experimental measurements. Chlorothiazide, used as a diuretic and as an antihypertensive was considered as model drug in this work. For the modeling, we used a small data set consisting of two input features, namely temperature and pressure, and one output, namely solubility, in order to analyze the data. Tree ensemble models, including bagging and boosting based on decision trees, have been selected to analyze and model the data. Extremely randomized Trees (Extra Tree), Adaptive Boosting (AdaBoost), and Gradient Boosting models are specifically chosen for this modeling. The hyperparameters of the models were optimized with the help of genetic algorithm (GA) and finally the optimal models were obtained for each of the three methods. Finally, the models were evaluated with different methods. Based on the evaluations, the gradient boosting model showed the best results, and its score was 0.9820 with the coefficient of determination (R²-score) criterion. Also, the error of the final model with the MEA criterion is 1.51 × 10⁻², with the RMSE criterion equal to 2.51× 10⁻², and the MAPE error value is 1.59 × 10⁻².