In this regard, Shi et al. (2000) proposed a sensitivity- and statistical-based method to localize structural damage by directly using the incomplete mode shapes.
In that sense, this procedure is possible by using the series where the incomplete gamma function.
Capitalizing on (4), by using the well-known definition of incomplete Beta function, ([13], Eq. (A.7)), and by following same mathematical transformations as in ([13], Appendix), the previous integral can easily be solved.
We study the statistical testing of random effects in the balanced incomplete block design by using the locally most powerful (LMP) rank test and some assumptions.
Incomplete data was handled by using the maximum likelihood estimation approach, i.e. treatment of missing data assumption of multivariate normality, based on the direct maximation of the likelihood of the observed data.
By using this incomplete information and data from the entire T. thermophila genome it was possible to determine the whole sequence of the bifunctional DHFR-TS MAC gene in T. thermophila.
The hydrolysis stage was optimized by using an incomplete factorial design where the independent variables were the percentage of catalyzer, the reaction time and the temperature.
This arrangement was achieved by building a question matrix using the balanced incomplete block design technique described elsewhere [ 14, 24].
The focus of this work is on a majorization-MDS algorithm for the relative localization of underwater sensors, or nodes, by using only incomplete inter-node distance measurements, given no other a priori information.
By using an incomplete block design, this can be accomplished without increasing the number of samples required per lot, the pairs evaluated per panelist, or the time spent by each panelist in their evaluation.
