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Thus it appeared as if the orientation of cells re ected the biomechanical strain throughout the lattice The importance of strain was substantiated by experiments in which CMPMs were subjected to a 5-day cyclic stretch regimen imposed by a motorized device [Fink et al, 2000] This resulted in a three- to vefold increase in contractile force development, hypertrophy of cardiac myocytes in CMPMs, and improved tissue formation at the free edges A systematic analysis of the relation between the degree of stretch and force development showed that the strain had a threshold of approximately 5 7% that was necessary to induce CMPM formation with improved.

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It is widely known that texture is an important attribute of any meat product. This is the case with restructured steaks that can range in texture from almost the original muscle through what is almost a beef burger-type product. In all cases, there are many different systems that can produce a wide variety of textural properties; these may be centered on particle size and binding systems. An essential requirement is the development of a sensory analysis procedure that can provide the tools to evaluate the properties of restructured steaks. Berry and Civille (1986) produced a very detailed procedure to evaluate the texture of steaks that were made from

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with aji = 0 and aij = -ali, is skew-symmetric. In other words. an (m x m) matrix A is skew-symmetric if A' = -A. (See Section 9.1:3.) Skew-symmetric part of a matrix: The matrix ~(A - A') is the skewsymmetric part of an (171 X m) matrix A. Sparse matrix: A matrix is sparse if only a small fraction of its elements are nonzero. Spectral radius: p( A) == maxdAdabs is the spectral radius of an (m x 111) matrix Ii with eigenvalues AI,' ... Am. (See Section 5.4.) Spectrum of a matrix: The set of eigenvalues of a matrix A is called tlw spectrum of A. (See 5.) Square matrix: An (m x n) matrix with

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There are a lot of remote control units out there from which to choose, so pick the one that best suits your armchair-centric lifestyle. After that, there s relatively little work involved in making a remote work well with your system. Doing some simple Internet research to find out what experienced users say about the various remotes and checking your budget should make your decision fairly simple.

for all distributions on . Hence suf cient statistics preserve mutual information and conversely. Here are some examples of suf cient statistics: 1. Let X1 , X2 , . . . , Xn , Xi {0, 1}, be an independent and identically distributed (i.i.d.) sequence of coin tosses of a coin with unknown parameter = Pr(Xi = 1). Given n, the number of 1 s is a suf cient statistic for . Here T (X1 , X2 , . . . , Xn ) = n Xi . In fact, we can i=1 show that given T , all sequences having that many 1 s are equally likely and independent of the parameter . Speci cally,

models to one another statistically. Nested models are models that are subsets of one another. Each model might represent a different theory. These nested models are statistically compared, thus providing a strong test for competing theories (models). From the example in Figure 24.2, we could pose a nested model that could be compared to the fuller model that is illustrated. A possible nested model could remove the path from Weak Institutional Bonds to Acceptance of Risky Behavior. This nested model would hypothesize that gender is the only predictor of Acceptance of Risky Behavior. To test this hypothesis, the chi-square from the fuller model depicted in Figure 24.2 would be subtracted from the chi-square for the nested model that removed the path from Weak Institutional Bonds to Acceptance of Risky Behavior. The corresponding degrees of freedom for these two models would also be subtracted. The difference in chi-squares, based on the difference in degrees of freedom, would be evaluated for signi cance using a chi-square table of signi cance. If the difference is signi cant, the fuller model that includes the removed path is needed to explain the data. If the difference were not signi cant, the nested model, which is more parsimonious than the fuller model, would be accepted as the preferred model. This would imply that Weak Institutional Bonds is not needed when predicting Acceptance of Risky Behavior.

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