Reinforced concrete barriers have been commonly used in protecting the important building because the response of R.C. barriers subjected to blast loading is practically more acceptable than other materials used to build the barriers. In this study, the response of R.C. barriers was detected due to the blast effects caused by two charge weights (50 kg and 400 kg); ANSYS 14 was used to simulate the problem. A horizontal distance of 2 m between the explosive TNT charge and the front face of wall was taken. The pressure on the front face of the concrete barriers was measured at three levels. The R.C. barrier was entirely damaged when subjected to the blast effects caused by 400 kg TNT explosion bomb. However, the 50 kg TNT charge had

KE Sharquie, HA Hassan, AA Noaimi, IRAQI JOURNAL OF COMMUNITY MEDICINE, 2010

Abstract. Froth flotation is a solid-liquid separation technique that uses hydrophobicity as a driving force. Bacteria and other drinking water microorganisms tend to be hydrophobic and can be removed from water using this application. The biggest limitation against using froth flotation in the drinking water industry is the difficulty of producing froth without chemical frothers and holding bacteria in this froth without chemical collectors which deteriorate water taste and odor. Recently, researchers at the University of Sheffield described a method for producing froth using only water and compressed air. This has enabled froth flotation to be studied as an alternative to biocides for the removal of bacteria from drinking water. T

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p