Thermodynamical Estimations on the Amount of Liquid Phase During Reaction Synthesis of Nickel-Aluminides

1994 ◽  
Vol 364 ◽  
Author(s):  
Seiji Miura ◽  
Yoshinao Mishima

AbstractThe amounts of intermetallic compounds and liquid phase are estimated as a function of temperature during reaction syntheses for Ni–Al binary alloys having arbitrary compositions under adiabatic conditions. It is assumed that the phases that would present during the process are only those appearing in the equilibrium binary phase diagram. Calculations are done by using available data on heat of formation of intermetallic compounds and those on enthalpy of liquid phase. The results obtained arc discussed in view of the estimation and control of the amount of liquid phase, which is significant for the densification and shape change of materials.

1997 ◽  
Vol 5 (1) ◽  
pp. 45-59 ◽  
Author(s):  
Seiji Miura ◽  
Tomonori Ohashi ◽  
Yoshinao Mishima

2021 ◽  
pp. 014662162110146
Author(s):  
Justin L. Kern ◽  
Edison Choe

This study investigates using response times (RTs) with item responses in a computerized adaptive test (CAT) setting to enhance item selection and ability estimation and control for differential speededness. Using van der Linden’s hierarchical framework, an extended procedure for joint estimation of ability and speed parameters for use in CAT is developed following van der Linden; this is called the joint expected a posteriori estimator (J-EAP). It is shown that the J-EAP estimate of ability and speededness outperforms the standard maximum likelihood estimator (MLE) of ability and speededness in terms of correlation, root mean square error, and bias. It is further shown that under the maximum information per time unit item selection method (MICT)—a method which uses estimates for ability and speededness directly—using the J-EAP further reduces average examinee time spent and variability in test times between examinees above the resulting gains of this selection algorithm with the MLE while maintaining estimation efficiency. Simulated test results are further corroborated with test parameters derived from a real data example.


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