Objective: To identify of the effect of the different concentrations of the special liquid (for mixing the investment, Gilvest)
and mixed with water/powder ratio on setting time of phosphate–bonded investment.
Method and materials: The present study is (60) specimens made from phosphate bonded investment divided into (4)
groups (control and experimental groups), (15) specimens for each group. The Gillmore needle device is used to setting
time of phosphate bonded investment mixed with different concentration of Gilvest and water.
Results: Showed that there is a high significant difference (P<0.01) between each groups in the ANOVA test and a
significant difference (P<0.05) between the group (A) and control group in the LSD test.
Recommendations: using other concentration of Gilvest (0.3ml) mixed with water (0.2ml) and powder (15g) on the setting
time of the phosphate–bonded investment. We can use another type of powder (gypsum–bonded investment) with the
Gilvest at different concentrations.
Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo
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