Products’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers.       In this research, we pr

      Products’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers.

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The present study was conducted to investigate the antimicrobial activity of the hot water and the hot ethanolic extracts of Thuja orientalis against some pathogenic microorganisms (Staphylococcus aureus, Pseudomonas aeruginosa, Eschericha coli, Proteus mirrablis, Salmonilla typhi, Klebsiella pneumoniae, Bacillus cereus, Bacillus subtilus, Acinobacter, Staphylococcus epidermidis and Candida albicans). Results showed that both the water and alcoholic extracts of this plant exert marked inhibitory effect against all the bacterial isolates and yeast and at different ratio, and it was shown that ethanolic extract was more effective in microbial inhibition than the water extract. Maximum inhibition (16 mm) was recorded against Staphylococcus aur

The current study focuses on the bacterium Acinetobacter baumannii due to its importance as a nosocomial infections source in addition to its increased resistance against antibiotics. Different clinical and hospital environment samples were collected, and cultured on A. baumannii selective media: Leed Acinetobacter agar and Herellea agar. A. baumannii have been identified by traditional methods, followed by confirmation using molecular identification to detect bla_{oxa-51 like} gene which is considered a diagnostic gene since it is present in genome of all A. baumannii strains. The result was, nineteen bacterial isolates of A.baumannii were obtained, from twenty-seven suspected isolate

Chemical analysis for evaluation of Nigella sativa L. (black cumin) seeds showed a composition of Fat 39% ; Protein 28% ; Carbohydrate 21% ; Moisture 6% and Ash 4.5% . It was found that the black seed contains the following mineral element : Magnesium 0.26 gm /100gm seed ; Calcium 0.25 gm /100gm seed and Iron 25 ?g / gm /100gm seed ; zinc 4.51?g /gm /100gm seed and Copper 3.60 ?g /gm /100gm seed. The analysis also showed that mineral element I. e. ; lead ; Cobalt ; Nickel ; Chrom ; Cadmium and Aresenic are not present . It was found that the fat of the black seed contains the following fatty acids : Myristic 2.8%; Palmtic 16.6%; Stearic 0.8 % ; Oleic 13.79% ; Linoleic 64.2% and Arachidic 1.9% .

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

This work is aiming to study and compare the removal of lead (II) from simulated wastewater by activated carbon and bentonite as adsorbents with particle size of 0.32-0.5 mm. A mathematical model was applied to describe the mass transfer kinetic.

The batch experiments were carried out to determine the adsorption isotherm constants for each adsorbent, and five isotherm models were tested to choose the best fit model for the experimental data. The pore, surface diffusion coefficients and mass transfer coefficient were found by fitting the experimental data to a theoretical model. Partial differential equations were used to describe the adsorption in the bulk and solid phases. These equations were simplified and the

n this work, the adsorption of crystal violet dye from aqueous solution on charcoal and rice husk has been investigated, where the impact of variable factors (contact time; the dosage of adsorbent, pH, temperature, and ionic strength) have been studied. It has been found that charcoal and rice husk have an appropriate adsorption limit with regards to the expulsion of crystal violet dye from fluid arrangements. The harmony adsorption is for all intents and purposes accomplished in 45 min for charcoal and 60 min for rice husk. The amount of crystal violet dye adsorbed (0.4 g of charcoal and 0.5 g of rice husk) increased with an increasing pH and the value of 11 is the best. The effect of temperature on the adsorption process was studied