The distress of moisture induced damage in flexible pavement received tremendous attention over the past decades. The harmful effects of this distress expand the deterioration of other known distresses such as rutting and fatigue cracking. This paper focused on the efficiency of using the waste material of demolished concrete to prepare asphalt mixtures that can withstand the effect of moisture in the pavement. For this purpose, different percentages of waste demolished concrete (0, 10, 20, 30, 50, 70 and 100) were embedded as a replacement for coarse aggregate to construct the base course. The optimum asphalt contents were determined depending on the Marshall method. Then after, two parameters were founded to evaluate the moisture susceptibility, namely: the tensile strength ratio (TSR) and the index of retained strength (IRS). To achieve this, the indirect tensile strength test and the compressive test were performed on different fabricated specimens. The results show that mixtures with a higher percentage of demolished concrete possess higher optimum asphalt content as this parameter increased from 3.9 % for control mixture to 4.5 % for mixture with coarse aggregate that fully replaced by demolished concrete. This work indicated that optimum percent of waste demolished concrete that can be utilized in the asphalt mixtures is 30 %, whereas this percent recorded higher value of increased increments for TSR and IRS by 10.6 % and 7.9 % respectively.
Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.
A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.
A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.
Abstract: This research was performed to study the effect of some amino acids and vitamins on the growth of bacteria Staphylococcus aureas and its sensitivity against UV light. The results showed low inhibition in bacterial growth because amino acids repairs the damges caused by UV light. Besides the effect of two groups of antibiotics (β-lactame and tetracycline) on the growth of S. aureus and the possible interference of amino acids and vitamins in the activity of the antibiotics against this bacteria in the presence of UV light were studied. The result show increase in the sensitivity towards these antibiotics and provided protection against the antibiotics.
This study involves adding nano materials and interaction with cement mortar behavior for several mortar samples under variable curing time with constant water to cement ratio (W/C = 0.5). The effects of adding nano materials on the microstructure of cement mortar were studied by (Scanning Electronic Microscopy (SEM) and X-Ray (for samples at different curing time 28 and 91 days. Small ratio replacements of nano particles (SiO2 or Al2O3) were added to Ordinary Portland Cement (OPC) type (I). The percentage of nano materials additives replacement by weight of ordinary Portland cement includes (1, 2, 3, 4 and 5%) for both types of nano materials with constant (W/C) ratio, also the amount of the fin
... Show Moreسمير خلف فياض * و محسن طالب د.نوال عزت عبد اللطيف*, مجلة الهندسة والتكنولوجيا, 2010
Find goal to detect educational and technical staffing of style fiction in book reading school materials so determine the current research on the specific book reading in fourth grade of primary for the academic year 2014 issued by the Ministry of Education / Republic Iraq stories. Come in the importance of research to draw attention to the best of these stories and strengthened, and the negative deny it and claim his deportation in order to protect the thought of the learner, and the study may contribute to the development of some frameworks to write a story appropriate for elementary school. This McCann within the first chapter The second chapter included the theoretical framework has been contained on two main sections included the co
... Show MoreIn the present work a modification was made on three equations to represent the
experiment data which results for Iraqi petroleum and natural asphalt. The equations
have been developed for estimating the chemical composition and physical properties
of asphalt cement at different temperature and aging time. The standard deviations of
all equations were calculated.
The modified correlation related to the aging time and temperature with penetration
index and durability index of aged petroleum and natural asphalts were developed.
The first equation represents the relationship between the durability index with aging
time and temperature.
loge(DI)=a1+0.0123(2loge T
... Show More
