The study was conducted to detect the effect of addition of 0.03, 0.06 and0.10% potassium sorbate and 0.10, 0.15, 0.20 and 0.30% sodium propionate upon the sensory properties of laboratory made biscuit. The statistical analysis of the results revealed no significant differences (P<0.05) between the propionate treatment (A,B,C,D) and between the sorbate treatment (E,F,G) and the control (H) in most sensory properties of biscuit prior to storage. Upon six month storage of biscuit made with addition of different levels of sodium propionate 20-40C (room temperature) no significant differences (P<0.05) were shown on softness, flakiness and color in comparison with the control . No significant differences (P<0.05) were also revealed among the different treatment in appearance, texture, flavor and overall acceptance while they different significantly than the control (H) with scores of 4.1, 3.7, 3.4 and 3.98 respectively. The potassium sorbate-added biscuit treatment (E,F,G) however, didn t show any significant differences as compared with the control (H) in appearance, softness, flakiness, color and overall acceptance. The sensory evalution results revealed the superiority of the treatment with o.15% sodium propionate and 0.03% potassium sorbate levels of addition as compared with the control so that scored 4.66 and 4.68 respectively, in the overall acceptance against 3.98 for the control.
As asphalt concrete wearing course (ACWC) is the top layer in the pavement structure, the material should be able to sustain stresses caused by direct traffic loading. The objective of this study is to evaluate the influence of aggregate gradation and mineral filler type on Marshall Properties. A detailed laboratory study is carried out by preparing asphalt mixtures specimens using locally available materials including asphalt binder (40-50) penetration grade, two types of aggregate gradation representing SCRB and ROAD NOTE 31 specifications and two types of mineral filler including limestone dust and coal fly ash. Four types of mixtures were prepared and tested. The first type included SCRB specification and
... Show MoreThis research is focusing on finding more effective polymers that leads to enhance the rheological properties of Water Base Muds. The experiments are done for different types of mud for all substances which are Polyacrylamide, Xanthan gum, CMC (Carboxyl Methyl Cellulose). This study shows the effect of add polymer to red bentonite mud, effect of add polymer to Iraqi bentonite mud, the effect of add bentonite to polymer mud. The mud properties of Iraqi bentonite blank are enhanced after adding the polymers to the blank mix, CMC gives the highest value of plastic viscosity and Gel strength than others; X-anthan gives the highest value of yield point and gel strength than others. For the red bentonite mud, Polyacrylamide ha
... Show MoreIn the current study, CuAl0.7In0.3Te2 thin films with 400 nm thickness were deposited on glass substrates using thermal evaporation technique. The films were annealed at various annealing temperatures of (473,573,673 and 773) K. Furthermore, the films were characterized by X-ray Diffraction spectroscopy (XRD), field emission scanning electron microscopy (FESEM), atomic force microscopy (AFM), and Ultra violet-visible (UV–vis). XRD patterns confirm that the films exhibit chalcopyrite structure and the predominant diffraction peak is oriented at (112). The grain size and surface roughness of the annealed films have been reported. Optical properties for the synthesized films including, absorbance, transmittance, dielectric constant, and refr
... Show MoreNanocomposite was prepared using unsaturated polyester (UP) resin as a matrix and graphene nanoparticles as a reinforcement material in six percentage weights (0, 0.1, 0.2, 0.3, 1 and 1.5%). Mechanical, calorimetric and thermal studies were performed on the (UP) resin/graphene nanocomposite. All tests showed a clear improvement of all mechanical properties examined (hardness, flexural strength (F.S), impact strength (I.S) and tensile strength (T.S)) with increasing graphene percentage. In addition, the temperature of glass transition and thermal conductivity of this composite increased with increasing graphene content.
Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati
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