The increasing use of polymeric materials in the daily life, leads to challenges in the processing industry to deliver high performance materials with affordable terms. However, new processing techniques lead to high costs. In order to reduce processing costs it is necessary to understand the non-Newtonian behavior of the polymers in their molten state to be able to simulate the processes before the construction of the plants starts. Here the shear thinning behavior of the viscosity of polymeric melts is essential. Thus, this paper deals with the experimental investigation of the thermo-rheological behavior of the viscosity of one of the most used polymers (Polypropylene) over a wide range of temperatures and shear rates.  Furthermo

Zygapophyseal joints (or facet joints), are a plane synovial joint which located between the articular facet processes of the vertebral arch which is freely guided movable joints. Ten dried vertebrae were used for the lumbar region and taking (L₄) as a sample to reveal stress pathways across the joints by using ANSYS program under different loading conditions which used Finite Elements Analysis model. Results obtained from the ANSYS program are important in understanding the boundary conditions for load analysis and the points of stress concentration which explained from the anatomical point of view and linked to muscle and ligament attachments. This model used as a computational tool to joint biomechanics and to prosthetic im

Expired drug Metoclopramide was investigated as an antibacterial corrosion inhibitor for carbon steel in 0.5M H₃PO₄ solution using the electrochemical method at 30^oC and 60^oC. The results showed that this drug is an efficient inhibitor for carbon steel and the efficiency reached to 82.244 % for 175 ppm at 30^oC and 76.146% for 225 ppm at 60^oC. The adsorption of drug on carbon steel surface follows Langmuir adsorption isotherm with small values of adsorption-desorption constant. The polarization plots revealed that Metoclopramide acts as mixed-type inhibitor. Some parameters of inhibition process were calculated and discussed. The surface morphology of the carbon steel speci

الجمود المؤسسي واثره في فشل التنمية السياسية : محاولة من اجل بناء مفهوم 

The denoising of a natural image corrupted by Gaussian noise is a problem in signal or image processing.  Much work has been done in the field of wavelet thresholding but most of it was focused on statistical modeling of wavelet coefficients and the optimal choice of thresholds.  This paper describes a new method for the suppression of noise in image by fusing the stationary wavelet denoising technique with adaptive wiener filter. The wiener filter is applied to the reconstructed image for the approximation coefficients only, while the thresholding technique is applied to the details coefficients of the transform, then get the final denoised image is obtained by combining the two results. The proposed method was applied by usin

Nitrogen-comprising heterocyclic compounds and their derivatives have empirically been invaluable as therapeutic agents. Fundamentally, 4-chloro-6-nitro-2-amino-1,3-benzothiazole 1 was synthesized via bromination of 2-chloro-4-nitro aniline with ammonium thiocyanate. This new heterocyclic haloorganoamino-1,3-benzothiazole derivative, was a starting material, which condensed and tethered with three different aromatic aldehyde pendant arm in presence of ethanol and glacial acetic acid isolating an interesting sequence of tridentate Schiff bases 2-4. These compounds were used for complexation reactions in 1:1 (metal: ligand) stoichiometry to obtain heteroleptic Al(III), Ni (II) and K(I) benzothiazole chelat

The wear behavior of alumina particulate reinforced A332 aluminium alloy composites produced by a stir casting process technique were investigated. A pin-on-disc type apparatus was employed for determining the sliding wear rate in composite samples at different grain size (1 µm, 12µm, 50 nm) and different weight percentage (0.05-0.1-0.5-1) wt% of alumina respectively. Mechanical properties characterization which strongly depends on microstructure properties of reinforcement revealed that the presence of ( nano , micro) alumina particulates lead to simultaneous increase in hardness, ultimate tensile stress (UTS), wear resistances. The results revealed that UTS, Hardness, Wear resistances increases with the increase in the percentage of

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

Polymer additives binder system provides many properties useful in thermal energy storage (TES) then developed the efficient energy storage materials and green strength bodies system.

This paper studies the thermal energy storage property for polyvinyl alcohol (PVOH) / paraffin wax (WPw) blends. To enhance paraffin wax thermal conductivity, PVOH as a material which high conductivity was employed. A fixed weight of Paraffin wax was dispersed with PVOH heterogeneously at different additive weights ratios of PVOH/Pw (50/50, 67/33, 75/25, and 80/20) wt. ratio respectively. The composite material was prepared using wetted pressing method.

Both base materials (polyvinyl alcohol and paraffin wax) were scanned using differential

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu