The effluent quality improvement being discharged from wastewater treatment plants is essential to maintain an environment and healthy water resources. This study was carried out to evaluate the possibility of intermittent slow sand filtration as a promising tertiary treatment method for the sequencing batch reactor (SBR) effluent. Laboratory scale slow sand filter (SSF) of 1.5 UC and 0.1 m/h filtration rate, was used to study the process performance. It was found that SSF IS very efficient in oxidizing organic matter with COD removal efficiency up to 95%, also it is capable of removing considerable amounts of phosphate with 76% and turbidity with 87% removal efficiencies. Slow sand filter efficiently reduced the mass of suspended

Local communities are in need of self-resources so that they can perform their multiple functions which serve the objectives of the local development, and tax revenues are considered as important sources of their funding. However, despite the efforts of the state to reform the fiscal system and to improve the management in the local administration, tax collection can increase when the state adopts a more effective policy to combat tax evasion and tax fraud. Accordingly, this research aims to shed light on the role of local tax revenues in the local development. A set of conclusions are drawn; the most important one is that Algeria, in order to achieve local development, has taken a set of reforms, which are still valid until now. The mos

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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

In this work, results from an optical technique (laser speckle technique) for measuring surface roughness was done by using statistical properties of speckle pattern from the point of view of computer image texture analysis. Four calibration relationships were used to cover wide range of measurement with the same laser speckle technique. The first one is based on intensity contrast of the speckle, the second is based on analysis of speckle binary image,  the third is on size of speckle pattern spot, and the latest one is based on characterization of the energy feature of the gray level co-occurrence matrices for the speckle pattern. By these calibration relationships surface roughness of an object surface can be evaluated within the

This investigation aimed to explain the mechanism of MFCA by applying this method on air-cooled engine factory which was suffering from high production cost. The results of this study revealed that MFCA is a useful tool to identify losses and inefficiencies of the production process. It is found that the factory is suffering from high losses due to material energy and system losses. In conclusion, it is calculated that system losses are the highest among all the losses due to inefficient use of available production capacity.

The present study aimed to investigate the anatomy, histology, and immunohistochemistry of parathyroid gland in two Iraqi mammals (Weasel, Herpestes javanicus and Long-ear hedgehog, Hemiechinus auritus) as a comparative study. A total of (20) animal for each species were used in the present study. Animals collected were immediately anesthesia and dissected to get the parathyroid gland. Methods of Humason and Bancroft and Stevens were employed for histological techniques. Different stains were used (Hematoxylin- Eosin stain-(H & E), Periodic Acid Schiff stain-(PAS), Azan stain, and Methyl Blue stain-(MB)) for staining the histological sections. Anti-calcitonin, code140778 marker used for immune-histochemical study. Results of the present stu