The aim of this work is to study the influence of the type of fiber glass –mat on fatigue behavior of composite material which is manufactured from polyester and E-glass (woven roving, chopped strand mat (CSM)) as a laminate with a constant fiber volume fraction (VF) of 33%. The results showed that the laminates reinforced with E-glass (woven roving) [0/90, ±45.0/90] and [0/90, CSM, 0/90] have lower fatigue strength than the laminates reinforced with E-glass [0/90]₃,[CSM]₃ and [CSM, 0/90, CSM]  although they had different tensile strength; the best laminate was [0/90]₃ .

The physicochemical behaviour of dodecyltrimethylammonium bromide (DTAB) in water and ethanol-water mixture in the presence and absence of ZnSO₄ were studied by measuring the conductivity at 298.15 K. The pre-micellar (S₁) and post-micellar slopes (S₂) were obtained and calculated the degree of dissociation (α) and the critical micelle concentration (cmc). With an increase in ethanol content, the cmc and α of DTAB increased whereas, in the presence of ZnSO₄, the cmc and α decreased. By using cmc and α, thermodynamic properties as the standard free energy of micellization ( ) were evaluated. With an increase in ethanol content, the negative values of  are decreased indicating less spont

     Titanium dioxide nanotube arrays (TiO2 NTAs) were successfully decorated with nanoclusters of cobalt by an electrochemical deposition method. This Co-TiO2 NTAs nanostructure exhibited high compatibility with aluminum chloride\ chloroacetamide (an ionic liquid) and calcium chloride dihydrate\ acetamide (a deep eutectic solvent), leading to significant improvements in the electrochemical properties of the system. Significantly, this led to a discernible augmentation in both potential and current values, concomitant with a decrease in internal resistance. The presence of cobalt facilitated a faster transfer of electric charge, enhancing the overall efficiency of the system. Moreover, the incorporation of cobalt exhibited a ben

Several schottky diodes were fabricated from polyaniline/ Carbon nanotube (single and multiwalled) composites. These composites were synthesized with different concentration and two carbon nanotubes types, Single and Multi-Walled Carbon Nanotubes (SWCNT & MWCNT). Aluminum and silver paste were chosen as schottky and ohmic contact respectively. physical and electrical were used to studied these composite by using Atomic Force Microscopy (AFM) and electrical measurements. The Root Mean Square RMS surface roughness of the composite samples was found to be around 4nm. The currentvoltage characteristic were measurements for all samples in the bias range ±15V at room temperature. The results shows the increasing in carbon nanotubes concentration

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

The present work aims to improve the flux of forward osmosis with the use of Thin Film Composite membrane by reducing the effect of polarization on draw solution (brine solution) side.This study was conducted in two parts. The first is under the effect of polarization in which the flux and the water permeability coefficient (A) were calculated. In the second part of the study the experiments were repeated using a circulating pump at various speeds to make turbulence and reduce the effect of polarization on the brine solution side.
A model capable of predicting water permeability coefficient has been derived, and this is given by the following equations:
Z=Z₀ +C.R.T/9.8(d²/D²+1) [Exp. [-9.8(d