The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

The dye–semiconductor interface between N749 sensitized and zinc semiconductor (ZnSe) has been investigated and studied according to quantum transition theory with focusing on the electron transfer processes from the N749 sensitized (donor) to the ZnSe semiconductor (acceptor). The electron transfer rate constant and the orientation energy were studied and evaluated depended on the polarity of solvents according to refractive index and dielectric constant coefficient of solvents and ZnSe semiconductor. Attention focusing on the influence of orientation energies on the behavior of electron transfer rate constant. Differentdata of rate constant was discussion with orientation energy and effective driving energy for N749-ZnSe system.

BACKGROUND: Sickle cell nephropathy, a heterogeneous group of renal abnormalities resulting from complex interactions of sickle cell disease (SCD)-related factors and non-SCD phenotype characteristics, is associated with an increased risk for morbidity and mortality. AIMS: The aims of this study were to determine the frequency of microalbuminuria (MA) among pediatric patients with SCD and to determine risk factors for MA among those patients. SUBJECTS AND METHODS: A case–control study was carried out on 120 patients with SCD, 2–18 years old, registered at Basrah Center for Hereditary Blood Diseases, and 132 age-and sex-matched healthy children were included as a control group. Investigations included complete blood panel, blood urea, se

This study concerns a new type of heat exchangers, which is that of shell-and-double concentric tube heat exchangers. The case studies include both design calculations and performance calculations.

       The new heat exchanger design was conducted according to Kern method. The volumetric flow rates were 3.6 m³/h and 7.63 m³/h for the hot oil and water respectively. The experimental parameters studied were: temperature, flow rate of hot oil, flow rate of cold water and pressure drop.

A comparison was made for the theoretical and experimental results and it was found that the percentage error for the hot oil outlet temperature was (- 1.6%). The percentage

The effects of solar radiation pressure at several satellite (near Earth orbit satellite, low Earth orbit satellite, medium Earth orbit satellite and high Earth orbit satellite ) have been investigated. Computer simulation of the equation of motion with perturbations using step-by-step integration (Cowell's method) designed by matlab a 7.4 where using Jacobian matrix method to increase the accuracy of result.

Friction stir welding (FSW) of Tee-joints is obtained by inserting a specially designed rotating pin into the clamped blanks, through top plate (skin) to bottom plate (stringer), and then moving it along the joint, limiting the contact between the tool shoulder and the skin. The present work aims to investigate the defects occur for Tee-joint of an Aluminum alloy (Al 5456) with dimensions (180mm x 70mm) for the skin plate, (180mm x 30mm) for stringer plate and thickness of (4mm).
The effects of welding parameters such as rotational speed, linear speed, plunging depth, tool tilting, and die radii of welding fixture on the welding quality of Aluminum Alloy will be studied. Weld defects had been summarized and studied, and then the best

The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently.  Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ r

The downhole flow profiles of the wells with single production tubes and mixed flow from more than one layer can be complicated, making it challenging to obtain the average pressure of each layer independently.  Production log data can be used to monitor the impacts of pressure depletion over time and to determine average pressure with the use of Selective Inflow Performance (SIP). The SIP technique provides a method of determining the steady state of inflow relationship for each individual layer. The well flows at different stabilized surface rates, and for each rate, a production log is run throughout the producing interval to record both downhole flow rates and flowing pressure. PVT data can be used to convert measured in-situ rates

The accurate identification of internal and external pressures in thick-walled hyperelastic vessels is a challenging inverse problem with significant implications for structural health monitoring, biomedical devices, and soft robotics. Conventional analytical and numerical approaches address the forward problem effectively but offer limited means for recovering unknown load conditions from observable deformations. In this study, we introduce a Graph-FEM/ML framework that couples high-fidelity finite element simulations with machine learning models to infer normalized internal and external pressures from measurable boundary deformations. A dataset of 1386 valid samples was generated through Latin Hypercube Sampling of geometric and l