In the linear fixed effects model Y = X.β, X (the design matrix) as well as β (the coefficients) are sometimes called fixed effects.
This report describes the mechanical, thermal and biological characterisation of a solid free form microfabricated carbon nanotube polycaprolactone composite, in which both the quantity of nanotubes in the matrix as well as the scaffold design were varied in order to tune the mechanical properties of the material.
Further attentions can be paid to the decrease of the distance between LSPs and excitons, which may be achieved via the optimization of the thickness of the luminescence matrix as well as the appropriate design of the luminescence structure.
Mattioli-Belmonte et al. [ 18] have recently reported the fabrication of MWCNTs-polycaprolactone composites, in which both the quantity of nanotubes in the matrix as well as the scaffold design were varied in order to tune the characteristics of the scaffolds.
An observation plan is proposed based on a scalar measure of the Fisher information matrix as the design criterion quantifying the accuracy of parameter estimators.
We use the log determinant of the inverse Fisher information matrix as the design criterion and run simulations to investigate the relationship between the inverse Fisher information matrix and the covariance matrix of the ML estimates.
Comparing with existing methods, new algorithms are more efficient, as demonstrated on known difficult examples in the literature, newly designed random matrices, as well as matrices from the University of Florida Sparse Matrix Collection.
The coefficients of the constraint matrix are taken as design variables and a set of equations defining the inverse structural modification problem is formulated.
All inference procedures assume all matrices being known, including the covariance matrix W. The matrices F t and G t operate as design matrices for the observation model and the state model and are commonly specified by the modeler for all t.
These functions serve for the initial sensitivity analysis, where we detect Poisson ratio of the rubber matrix as the crucial design parameter and its Young modulus as having secondary importance, while particle Young modulus and Poisson ratio are totally irrelevant.
