Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

Industrial and urban development has resulted in the spread of plastic waste and the increase in the emissions of carbon dioxide resulting from the cement manufacturing process. The current research aims to produce green (environmentally friendly) concrete by using plastic waste as coarse aggregates in different proportions (10% and 20%) and nano silica sand powder as an alternative to cement in different proportions (5% and 10% by weight). The results showed that compressive strength decreased by 12.10% and 19.23% for 10% and 20% plastic waste replacement and increased by 12.89% and 20.39% for 5% and 10% silica sand replacement respectively at 28 days. Flexural strength decreased by 12.95% and 19.64% for 10% and 20% plastic waste r

Abstract-Industrial and urban development has resulted in the spread of plastic waste and the increase in the emissions of carbon dioxide resulting from the cement manufacturing process. The current research aims to produce green (environmentally friendly) concrete by using plastic waste as coarse aggregates in different proportions (10% and 20%) and nano silica sand powder as an alternative to cement in different proportions (5% and 10% by weight). The results showed that compressive strength decreased by 12.10% and 19.23% for 10% and 20% plastic waste replacement and increased by 12.89% and 20.39% for 5% and 10% silica sand replacement respectively at 28 days. Flexural strength decreased by 12.95% and 19.64% for 10% and 20% plastic waste

This work examines numerically the effects of particle size, particle thermal conductivity and inlet velocity of forced convection heat transfer in uniformly heated packed duct. Four packing material (Aluminum, Alumina, Glass and Nylon) with range of thermal conductivity (from200 W/m.K for Aluminum to 0.23 W/m.K for Nylon), four particle diameters (1, 3, 5 and 7 cm), inlet velocity ( 0.07, 0.19 and 0.32 m/s) and constant heat flux ( 1000, 2000 and 3000 W/ m ²) were investigated. Results showed that heat transfer (average Nusselt number Nu_av) increased with increasing packing conductivity; inlet velocity and heat flux, but decreased with increasing particle size.Also, Aluminum average Nusselt number is about (0.85,2.

In this research, the performance of a two kind of membrane was examined to recovering the nutrients (protein and lactose) from the whey produced by the soft cheese industry in the General Company for Food Products inAbo-ghraab.Wheyare treated in two stages, the first including press whey into micron filter made of poly vinylidene difluoride (PVDF) standard plate type 800 kilo dalton, The membrane separates the whey to permeate which represent is the main nutrients and to remove the fat and microorganisms.The second stage is to isolate the protein by using ultra filter made of polyethylsulphone(PES)type plate with a measurement of 10,60 kilo dalton and the recovery of lactose in the form of permeate.
The results showed that the percen