The performance of asphalt concrete pavement has affected by many factors, the temperature is the most important environmental one which has a large effect on the structural behavior of flexible pavement materials. The main cause of premature failure of pavement is the rutting, Due to the viscoelastic nature of the asphalt cement, rutting is more pronounced in hot climate areas because the viscosity of the asphalt binder which is
inversely related to rutting is significantly reduced with the increase in temperature resulting in a more rut susceptible paving mixtures. The objective of this study is to determine the effect of temperatures variations on the permanent deformation parameters (permanent strain (p), intercept (a), slope (b), Alpha and Mu) as well as resilient strain (r) and resilient modulus (Mr). To achieve this objective, one aggregate gradation with 12.5mm nominal maximum size, two grades of asphalt cements (40-50 and 60-70) brought form Al- Daurah refinery, limestone dust filler has been used to prepare the asphalt concrete mixtures. 30 Marshall specimens were prepared to determine the optimum asphalt cement content. Thereafter, 30 cylindrical asphalt concrete specimens (102mm in diameter and 203 mm in height) are prepared in optimum asphalt cement and optimum ±0.5 percent. The prepared specimens were used in uniaxial repeated load test to evaluate the permanent deformation parameters of asphalt concrete mixes under the following testing temperature (5, 15, 25, 40 and 60c). The test result analyses appeared that Mr is decrease 51 percent when temperature increased from 5 c to 25 c and then decrease 22 percent with further increase in temperature from 25 c to 60 c. Also, the Alpha value decreases by a factor of 1.25 and 1.13 when temperature increases from 5 c to 25 c and 25 c to 60 c, espectively.
Finally, statistical models were developed to predict the Alpha and Mu parameters of permanent deformation.
KE Sharquie, HA Hassan, AA Noaimi, IRAQI JOURNAL OF COMMUNITY MEDICINE, 2010
In this work 2-hydrazino pyrimidine (1) was prepared from 2-mercapto pyrimidine with hydrazine hydrate. Treatment of (1) with active methylene compounds gave 2-(3,5-dimethyl -1 H – Pyrazole-1-yl) pyrimidine , whereas the reaction of (1) with carboxylic anhydride namely maleic anhydride or 1,2,3,6-tetra hydro phthalic anhydride yielded 1-Pyrimidine-2-yl-1,2-dihydro pyridazine-3,6-dione (3) and 2 – Pyrimidin -2-yl -2,3,4 a ,5,8 a – hexahydro phthalazine 1,4 – dione (4) . Reaction of (1) with phenyl isothiocyanate and ethyl chloro acetate afforded 3-Phenyl-1,3-thiazolidine-2,4-dione-2( pyrimidine -2- yl hydrazone (6) Azomethine (7-10) were prepared through condensation of (1) with aromatic aldehydes or ketones, then comp
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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
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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
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