Porous silicon (P-Si) has been produced in this work by photoelectrochemical (PEC) etching process. The irradiation has been achieved using diode laser of (2 W) power and 810 nm wavelength. The influence of various irradiation times on the properties of P-Si material such as P-Si layer thickness, surface aspect, pore diameter and the thickness of walls between pores as well as porosity and etching rate was investigated by depending on the scanning electron micrograph (SEM) technique and gravimetric measurements.

In this paper, chip and powder copper are used as reinforcing phase in polyester matrix to form composites. Mechanical properties such as flexural strength and impact test of polymer reinforcement copper (powder and chip) were done, the maximum flexural strength for the polymer reinforcement with copper (powder and chip) are (85.13 Mpa) and (50.08 Mpa) respectively was obtained, while the maximum observation energy of the impact test for the polymer reinforcement with copper (powder and chip) are (0.85 J) and (0.4 J) respectively  

My research tagged [the lights of the statement in the first part of the Koran] came to show that the dear book was developed according to a precise linguistic system is not increased by a word or letter or movement - Aldmh and Kira and the hole - and does not lack anything of it except with the wisdom required by the meanings of the Koranic text or Sura generally . The Koran does not come falsehood from his hands or from behind it is infallible and preserved; because it was revealed by the sage Hamid Hamid ﭽﮗ ﮘ ﮙ ﮚ ﮛ ﮜ ﮝ ﮞ ﭼ [stone]. The Qur'an is safe from any verbal or moral dominance and dominates all the heavenly books and exists at any time and place that speaks the truth ﭽ ﯛ ﯜ ﯝ ﯞ ﯟ ﯹ ﯺﭼ [The

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

Abstract: The M(II) complexes [M2(phen)2(L)(H2O)2Cl2] in (2:1:2 (M:L:phen) molar ratio, (where M(II) =Mn(II), Co(II), Cu(II), Ni(II) and Hg(II), phen = 1,10-phenanthroline; L = 2,2'-(1Z,1'Z)-(biphenyl-4,4'-diylbis(azan-1-yl-1-ylidene))bis(methan-1-yl-1- ylidene)diphenol] were synthesized. The mixed complexes have been prepared and characterized using 1H and13C NMR, UV/Visible, FTIR spectra methods and elemental microanalysis, as well as magnetic susceptibility and conductivity measurements. The metal complexes were tested in vitro against three types of pathogenic bacteria microorganisms: Staphylococcus aurous, Escherichia coli, Bacillussubtilis and Pseudomonasaeroginosa to assess their antimicrobial properties. From this study shows that a

Sulfamethoxazole (SMX) is the most significant antibiotic in the sulfonamide family. It was chosen as the representative of this category because of its widespread use. Starting with sulfamethoxazole, a new series of 2-Azetidinone (M1-M6) was synthesized, the structure of these new derivatives was confirmed using spectral methods, starting with the synthesis of Schiff’s bases by reflux of different aromatic benzaldehydes, separately, with Sulfamethoxazole in ethanol with few drops of acetic acid. The final compounds were obtained by ketene-imine synthesis of β-lactam using chloroacetyl chloride. The designed chemicals’ synthesis has been completed successfully. Physical parameters (melting points and Rf values), Fourier transfo