In present work examined the oxidation desulfurization in batch system for model fuels with 2250 ppm sulfur content using air as the oxidant and ZnO/AC composite prepared by thermal co-precipitation method. Different factors were studied such as composite loading 1, 1.5 and 2.5 g, temperature 25 ^oC, 30 ^oC and 40 ^oC and reaction time 30, 45 and 60 minutes. The optimum condition is obtained by using Tauguchi experiential design for oxidation desulfurization of model fuel. the highest percent sulfur removal is about 33 at optimum conditions. The kinetic and effect of internal mass transfer were studied for oxidation desulfurization of model fuel, also an empirical kinetic model was calculated for model fuels

This investigation was undertaken to evaluate the effectiveness of using Hydrated lime as a (partial substitute) by weight of filler (lime stone powder) with five consecutive percentage namely (1.0, 1.5, 2.0, 2.5, 3.0) % by means of aggregate treatment, by introducing dry lime on dry and 2–3% Saturated surface aggregate on both wearing and binder coarse. Marshall design method, indirect tensile test and permanent deformation under repeated loading of Pneumatic repeated load system at full range of temperature (20, 40, 60) C0 were examined The study revealed that the use of 2.0% and 1.5 % of dry and wet replacement extend the pavement characteristics by improving the Marshall properties and increasing the TSR%. Finally, increase permanent

With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

In the lifetime process in some systems, most data cannot belong to one single population. In fact, it can represent several subpopulations. In such a case, the known distribution cannot be used to model data. Instead, a mixture of distribution is used to modulate the data and classify them into several subgroups. The mixture of Rayleigh distribution is best to be used with the lifetime process. This paper aims to infer model parameters by the expectation-maximization (EM) algorithm through the maximum likelihood function. The technique is applied to simulated data by following several scenarios. The accuracy of estimation has been examined by the average mean square error (AMSE) and the average classification success rate (ACSR). T

This study discussed the effects of doping with silver (Ag) on the optical and structural properties of
CdO nanoparticles at different concentrations 0, 1, 2, 3, 4, 5 wt% prepared by the precipitation method. The
materials were annealed at 550˚C for 1 h. The structural, topographical, and optical properties were
diagnosed by X-ray diffraction analysis, atomic force instrument, and visible and ultraviolet spectrometers.
The results show that the average diameter of the grains depends on the percentage of added silver to the
material, as the diameter decreased from 88.8 to 59.7 nm, and it was found that the roughness increased from
5.56 to 26.5. When studying the optical properties, it was noted that th

The number of infections caused by microorganisms is increasing significantly over the last few years. A total of 140 patients admitted to the central teaching hospital of pediatrics from the 1st of Jun 2017 to 31 October 2017. The Clinical samples was processed from culture and sensitivity testing. Antibiotic discs used for gram negative isolates. The most prevalent gram negative isolates included Escherichia coli 63 (45.0 %), Pseudomonas spp. 21 (15.0 %), Klebsiella spp. 19 (13.6 %) predominantly. Escherichia coli were the most prevalent isolates from urine 45 (71.4 %), Klebsiella spp. 11 (57.9 %) and Enterobacter spp. 11 (68.8 %) followed by Escherichia coli 10 (15.9 %) predominant from blood. 68 (48.6 %) of specimens were urine, 47 (33.