Glass fiber–reinforced polymer (GFRP) reinforcement provides an effective alternative to conventional steel in concrete structures due to its corrosion resistance. Nevertheless, the lower elastic modulus of GFRP necessitates careful consideration of serviceability behavior in GFRP-reinforced concrete members. This study presents a numerical sectional analysis model for predicting the flexural response and ultimate capacity of hybrid reinforced concrete beams incorporating embedded GFRP profiles in combination with either mild steel or GFRP reinforcement bars under monotonic static loading. The proposed model employs realistic nonlinear stress–strain relationships for concrete and steel, together with secant moduli of elasticity

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Platinum (Pt) supported on sulfated zirconia (SZ) and HY-zeolite as a solid acid catalyst was synthesized successfully for isomerization reaction using precipitation and impregnation method. The physicochemical properties of the catalyst were characterized using various techniques including X-ray diffraction (XRD), Fourier transformation infra-red spectroscopy (FTIR), BET Surface area and pore volume, and Field Emission Scanning Electron Microscopy (FESEM).  The prepared composite catalyst Pt/SZ-HY consisted of high Bronsted acidic sites and Lewis acidic sites. The addition of multi-walled carbon nanotubes (MWCNTs) to SZ increased the surface area and pore volume, resulting in smaller crystal sizes and a narrower particle size dist

Preparation of superposed thin film (CdTe)1-xSex / ZnS) with concentration of (x= 0.1, 0.3, 0.5) at a temperature of substrate (Ts= 80 0C) by using Thermal Vacuum Evaporation System. The measurement of X-ray diffraction shows that the compounds CdTe, ZnS, (CdTe)1-xSex and (CdTe)1-xSex / ZnS have a polycrystalline structure, the C-V characteristic shows that the capacitance degrease by increasing the concentration (x) in reverse bias, while the I-V characteristic shows the current dark (Id) increase in forward and reverse bias by increasing (x) and the photocurrent (Iph) increase in reverse bias by increasing the concentration (x), the values of photocurrent are greater than from the values of the dark current for all concentrations

In this work, ZnS thin films have been deposited by developed laser deposition technique on glass substrates at room temperature. After deposition process, the films were annealed at different temperatures (200ºC , 300 ºC and 400ºC ) using thermal furnace.The developed technique was used to obtain homogeneous thin films of ZnS depending on vaporization of this semiconductor material by continuous CO2 laser with a simple fan to ensure obtaining homogeneous films. ZnS thin films were annealed at temperature 200ºC, 300 ºC and 400ºC for (20) minute in vacuum environment. Optical properties of ZnS thin film such as absorbance, transmittance, reflectance, optical band gap, refractive index extinction coefficient and absorption coefficien

The technical of Flame Thermal Spray had been used in producing a cermet
composite based on powders of stabilized zirconium oxide containing amount of
Yatteria oxide (ZrO2- 8Y2O3) reiforced by minerals powders of bonding material
(Ni-Cr- Al- Y) in different rates of additions (25, 35, 50) on stainless steel base type
(304) after preparing it by the way of Grit Blasting.
Before heat treatment, the coated cermet layers were characterized for porosity
and electric resistivity. All samples were heat treated in vacuum furnace at different
temperature and times. The physical tests had been operated after heat treatment
and gave best results especially porosity, which found to be reduced dramatically
and producing hig