Laser ablation of a silver target immersed in distilled water using Nd:YAG laser with a fundamental wavelength of 1064nm was carried out to fabricate silver nanoparticles (Ag NPs) with different laser energy in the presence and absence of magnetic field. UV-Visible spectrum showed that the nanoparticles are almost spherical in shape. The number of Ag NPs increased by increasing laser energy while their particle size was reduced by increasing  laser energy without magnetic field. In the presence of magnetic field, the size of Ag NPs increased slightly by increasing laser energy. According to AFM results, the presence of magnetic field did not affect the average diameter of Ag NPs. The presence of a magn

Metal corrosion is a destructive process for many industrial operations, including oil well acidizing and acid pickling. Therefore, numerous efforts made by many researchers to control the steel corrosion. In the present work, A (E)-4-(((4-(5-mercapto-1,3,4-oxadiazol-2-yl) phenyl) amino) methyl)-2-methoxyphenol (MOPM) has been synthesized and characterized as a new corrosion inhibitor for mild steel in 0.1 M hydrochloric acid. FTIR and 1 HNMR were used in the diagnosis of MOPM, while electrochemical polarization technique was employed to test the performance of inhibitor at various temperatures and inhibitor concentrations. Electrochemical studies showed that MOPM acts as a mixed-type inhibitor with a maximum inhibition efficiency of

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

<span lang="EN-GB">Transmitting the highest capacity throughput over the longest possible distance without any regeneration stage is an important goal of any long-haul optical network system. Accordingly, Polarization-Multiplexed Quadrature Phase-Shift-Keying (PM-QPSK) was introduced lately to achieve high bit-rate with relatively high spectral efficiency. Unfortunately, the required broad bandwidth of PM-QPSK increases the linear and nonlinear impairments in the physical layer of the optical fiber network. Increased attention has been spent to compensate for these impairments in the last years. In this paper, Single Mode Fiber (SMF), single channel, PM-QPSK transceiver was simulated, with a mix of optical and electrical (Digi

      TV medium derives its formal shape from the technological development taking place in all scientific fields, which are creatively fused in the image of the television, which consists mainly of various visual levels and formations. But by the new decade of the second millennium, the television medium and mainly (drama) became looking for that paradigm shift in the aesthetic formal innovative fields and the advanced expressive performative fields that enable it to develop in treating what was impossible to visualize previously. In the meantime, presenting what is new and innovative in the field of unprecedented and even the familiar objective and intellectual treatments. Thus the TV medium has sought for work

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

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

Chitosan (CH) / Poly (1-vinylpyrrolidone-co-vinyl acetate) (PVP-co-VAc) blend (1:1) and nanocomposites reinforced with CaCO3 nanoparticles were prepared by solution casting method. FTIR analysis, tensile strength, Elongation, Young modulus, Thermal conductivity, water absorption and Antibacterial properties were studied for blend and nanocomposites. The tensile results show that the tensile strength and Young’s modulus of the nanocomposites were enhanced compared with polymer blend [CH/(PVP-co-VAc)] film. The mechanical properties of the polymer blend were improved by the addition of CaCO3 with significant increases in Young’s modulus (from 1787 MPa to ~7238 MPa) and tensile strength (from 47.87 MPa to 79.75 MPa). Strong interfacial

The present study considers an influence of WS₂ nanoparticles lubricants on physical characteristics of wrought Aluminium alloys. It is investigated parameters-performance relationship via tribological pin-on-disc tests, the pin is made of Aluminium alloys and the disk is made of AISI.1045, and the humidity was 70%. Oils with WS₂ nanoparticles and without them reveal the loss rate of wear. In this study, the coefficient of friction (CoF) is reduced from 0.27 to 0.22 and the wear rate decreased from 0.128 x 10^-6 Nm^-1 to 0.107 x 10^-6 Nm^-1 at a load of 20 N. All worn surfaces were typically three types in wear mechanisms such as adhesive, abrasive, and oxidative wear. In addi

Our recent work displays the successful preparation of Schiff_bases that carried out between hexane-2,5-dione and 2 moles of (Z)-3-hydrazineylideneindolin-2-one forming in Schiff-bases-(L), Which in turn allowed combining with each of the next metal ions: (M²⁺) = Ni, Mn, Zn, Cu and Co forming complexes_ in high stability. The formation of resulting Schiff_ bases (L) is detected spectrally using LC_Mss which gave approximately matching results with theoretical incomes, ¹HNMR proves the founding of doublet signal of (2H) for 2NH, FTIR indicates the occurrence of two interfered imine bands and UV-VIS mean is also indecates the formation of ligand. On the other hand, complexes-based-Schiff were characterized using the s