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

In this work, an inventive photovoltaic evaporative cooling (PV/EC) hybrid system was constructed and experimentally investigated. The PV/EC hybrid system has the prosperous advantage of producing electrical energy and cooling the PV panel besides providing cooled-humid air. Two cooling techniques were utilized: backside evaporative cooling (case #1) and combined backside evaporative cooling with a front-side water spray technique (case #2). The water spraying on the front side of the PV panel is intermittent to minimize water and power consumption depending on the PV panel temperature. In addition, two pad thicknesses of 5 cm and 10 cm were investigated at three different water flow rates of 1, 2, and 3 lpm. In Case #1,

Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage

To accommodate utilities in buildings, different sizes of openings are provided in the web of reinforced concrete deep beams, which cause reductions in the beam strength and stiffness. This paper aims to investigate experimentally and numerically the effectiveness of using carbon fiber reinforced polymer (CFRP) strips, as a strengthening technique, to externally strengthen reinforced concrete continuous deep beams (RCCDBs) with large openings. The experimental work included testing three RCCDBs under five-point bending. A reference specimen was prepared without openings to explore the reductions in strength and stiffness after providing large openings. Openings were created symmetrically at the center of spans of the other specimens

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m

     Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age, characterized by anovulatory, infertility and metabolic disturbances. This study aimed to assess the diagnostic accuracy of Leucine-rich alpha-2-glycoprotein-1 (LRG1) and xanthine oxidase (XO) activity as novel biomarkers for PCOS, as well as to explore the relationship between LRG1 and XO activity across different groups. A total of 150 married women, aged 18-46 years were enrolled and divided into three groups: 50 with PCOS under treatment, 50 PCOS without treatment conditions shared by women with polycystic ovary conditions, and 50 healthy women. Serum samples were analysed to measure LRG1, xanthine oxidase (XO) activity,

This paper considers approximate solution of the hyperbolic one-dimensional wave equation with nonlocal mixed boundary conditions by improved methods based on the assumption that the solution is a double power series based on orthogonal polynomials, such as Bernstein, Legendre, and Chebyshev. The solution is ultimately compared with the original method that is based on standard polynomials by calculating the absolute error to verify the validity and accuracy of the performance.

Numerical Investigation was done for steady state laminar mixed convection and thermally and hydrodynamic fully developed flow through horizontal rectangular duct including circular core with two cases of time periodic boundary condition, first case  on the rectangular wall while keeping core wall constant and other on both the rectangular duct and core walls. The used governing equations are continuity momentum and energy equations. These equations are normalized and solved using the Vorticity-Stream function and the Body Fitted Coordinates (B.F.C.) methods. The Finite Difference approach with the Line Successive Over Relaxation (LSOR) method is used to obtain all the computational results the (B.F.C.) method is used to generate th

The mixed ligand complexes of Mn(II), Co(II), Ni(II), Cu(II), Zn(II), Cd(II) and Hg(II) with alanine and 8-hydroxyqinoline (Oxine) were synthesized and characterized by FT-IR ,spectra electronic, flam-AAS] along with conductivity measurements , solubility , melting point, magnetic susceptipibility.The synthesized complexes were tested in vitro for antimicrobial activity. The results obtained indicated that some of these complexes are more active than with others.