The problem of the optimal number of non-asymptotic coefficients in the double reduction method

1978 ◽  
Vol 18 (2) ◽  
pp. 165-169
Author(s):  
A.A. Andreev
Keyword(s):  
Reduction Method ◽  
Optimal Number ◽  
Double Reduction

Modified Double-Reduction Method considering Strain Softening and Equivalent Influence Angle

2020 ◽  
Vol 24 (11) ◽  
pp. 3257-3266 ◽  
Author(s):  
Yifan Chen ◽  
Hang Lin ◽  
Yixian Wang ◽  
Rihong Cao ◽  
Chunyang Zhang ◽  
...  
Keyword(s):  
Strain Softening ◽  
Reduction Method ◽  
Double Reduction

A strength reduction method based on double reduction parameters and its application

2013 ◽  
Vol 20 (9) ◽  
pp. 2555-2562 ◽  
Author(s):  
Wei Yuan ◽  
Bing Bai ◽  
Xiao-chun Li ◽  
Hai-bin Wang
Keyword(s):  
Reduction Method ◽  
Strength Reduction ◽  
Strength Reduction Method ◽  
Double Reduction

Dissipated Energy as the Evaluation Index for the Double Reduction Method

2021 ◽  
Vol 21 (6) ◽  
pp. 06021012
Author(s):  
Quan Chen ◽  
Bing Bai ◽  
Haibin Wang ◽  
Xiaochun Li
Keyword(s):  
Reduction Method ◽  
Evaluation Index ◽  
Dissipated Energy ◽  
Double Reduction

Optimal Experimental Design With Nesting of Persons in Organizations

2013 ◽  
Vol 221 (3) ◽  
pp. 145-159 ◽  
Author(s):  
Gerard J. P. van Breukelen
Keyword(s):  
Repeated Measures ◽  
Optimal Number ◽  
Health Centers ◽  
Randomized Experiments ◽  
Optimal Sample ◽  
Treatment Conditions ◽  
Sampling Cost ◽  
Nested Designs ◽  
Simple Equations ◽  
Number Of Individuals

This paper introduces optimal design of randomized experiments where individuals are nested within organizations, such as schools, health centers, or companies. The focus is on nested designs with two levels (organization, individual) and two treatment conditions (treated, control), with treatment assignment to organizations, or to individuals within organizations. For each type of assignment, a multilevel model is first presented for the analysis of a quantitative dependent variable or outcome. Simple equations are then given for the optimal sample size per level (number of organizations, number of individuals) as a function of the sampling cost and outcome variance at each level, with realistic examples. Next, it is explained how the equations can be applied if the dependent variable is dichotomous, or if there are covariates in the model, or if the effects of two treatment factors are studied in a factorial nested design, or if the dependent variable is repeatedly measured. Designs with three levels of nesting and the optimal number of repeated measures are briefly discussed, and the paper ends with a short discussion of robust design.

Type -Reduction Method on Fuzzy Critical Path

2019 ◽  
Vol 7 (1) ◽  
pp. 256-259
Author(s):  
P.Balasowandari ◽  
Dr. V.Anusuya
Keyword(s):  
Reduction Method ◽  
Critical Path ◽  
Type Reduction
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