The behavior of AC conductivity (σ_ac), loss tangent (tan δ), and relative permittivity (ε′) for composites of PVC-P/graphite electrode waste (GEW) was investigated, and a qualitative explanation was provided as a function of PVC-P weight fractions (0, 5, 10, 15, 20, and 25) wt. percent, temperature (30-90) °C, and frequency (100Hz-2MHz). The behaviors of the composites' ac. conductivity and impedance as a frequency function and temperature have been examined. The permittivity was shown to rise with increasing temperature (Tg). The relative permittivity increased as the GEW filler concentration increased and was highest in the low-frequency range; nevertheless decreased as the frequency increased.

     In this paper, ZnO NPs were prepared using D.C high-voltage and high frequency with an output of 6 kHz at two different preparation times preparation (10,12) minutes. Transmission electron microscopy (TEM) with (FE-SEM) was used to examine the homogenous, compact, and dense surface of the zinc oxide nanoparticles created with apparent grain size determined by (XRD), XRD results explain that the increase of the preparation time from 10 minutes to 12-minute caused an increase in crystallite size. In addition, FE-SEM showed that the increase in the ZnO NPs cluster distribution with particle size increases with increasing the preparation time. AFM was also utilized to determine the degree of cooperation between the surfaces of the z

The field of Optical Character Recognition (OCR) is the process of converting an image of text into a machine-readable text format. The classification of Arabic manuscripts in general is part of this field. In recent years, the processing of Arabian image databases by deep learning architectures has experienced a remarkable development. However, this remains insufficient to satisfy the enormous wealth of Arabic manuscripts. In this research, a deep learning architecture is used to address the issue of classifying Arabic letters written by hand. The method based on a convolutional neural network (CNN) architecture as a self-extractor and classifier. Considering the nature of the dataset images (binary images), the contours of the alphabet