Objectives: (1) To understand what hardness is, and how it can be used to determine material properties. (2) To conduct typical engineering hardness tests and be able to recognize commonly used hardness scales and numbers. (3) To be able to understand the correlation between hardness numbers and the properties of materials (4) To learn the advantages and limitations of the common hardness test ...

—We address the problem of linear minimum meansquared error (LMMSE) estimation under constraints on the lter or the estimated signal. We develop a general formula that leads to closed form solutions for a wide class of constrained LMMSE problems. The results are applicable to both nite dimensional problems as well as to the Wiener ltering setup, in which in nitely-many measurements are availabl...

We study biased control variates (BCVs), whose purpose is to improve the efficiency of stochastic simulation experiments. BCVs replace the control-simulation mean with an approximation; the resulting control-variate estimator is biased. This bias may not be a significant issue for finite sample sizes, however, because our estimator minimizes the more general mean-squared-error (mse), i.e., the ...

Parameters of glucose dynamics recorded by the continuous glucose monitoring system (CGMS) could help in the control of glycemic fluctuations, which is important in diabetes management. Multiscale entropy (MSE) analysis has recently been developed to measure the complexity of physical and physiological time sequences. A reduced MSE complexity index indicates the increased repetition patterns of...

Memory Self-Efficacy (MSE) has been shown to be related to memory performance and social participation in a healthy elderly population. This relation is unclear in stroke. As about 30% of all stroke survivors report memory complaints, there is an urgent need for effective treatment strategies. Before implementing MSE as a potential target in memory training, it should be examined whether the as...

Neural network (NN) based modeling often requires trying multiple networks with different architectures and training parameters in order to achieve an acceptable model accuracy. Typically, only one of the trained networks is selected as "best" and the rest are discarded. The authors propose using optimal linear combinations (OLC's) of the corresponding outputs on a set of NN's as an alternative...