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    The variation in pressures exerted by commercially available compression garments

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    Commercially available compression garments (CGs) demonstrate the enhanced recovery from exercise in some, but not all studies. It is possible that in some cases the degree of compression pressure (ComP) exerted is not sufficient to produce any physiological benefit. The aim of this investigation was to identify the levels of ComP exerted by commercially available CGs. This study was composed of two parts. In part A 50 healthy, physically active individuals (n=26 male, n=24 female) were fitted with CGs according to manufacturer’s guidelines. ComP was measured in participants standing in the anatomical position with a pressure measurement device inserted between the skin and the garment. Data were compared to ‘ideal’ pressure values proposed in the literature. In part B ComP in three different brands of CG were compared in a population of 29 men who all wore a medium sized garment. A one way ANOVA indicated that there was a significant difference (P0.05) between observed and ideal pressures in the calf of the male population. No significant differences in pressure (P>0.05) were observed between CG brands at the quadriceps or calf. In conclusion a large number of individuals may not be experiencing an adequate ComP from CG, and this is true for all 3 of the major brands of CGs tested in this investigation

    On Modal {\mu}-Calculus over Finite Graphs with Bounded Strongly Connected Components

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    For every positive integer k we consider the class SCCk of all finite graphs whose strongly connected components have size at most k. We show that for every k, the Modal mu-Calculus fixpoint hierarchy on SCCk collapses to the level Delta2, but not to Comp(Sigma1,Pi1) (compositions of formulas of level Sigma1 and Pi1). This contrasts with the class of all graphs, where Delta2=Comp(Sigma1,Pi1)

    Don't bleach chaotic data

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    A common first step in time series signal analysis involves digitally filtering the data to remove linear correlations. The residual data is spectrally white (it is ``bleached''), but in principle retains the nonlinear structure of the original time series. It is well known that simple linear autocorrelation can give rise to spurious results in algorithms for estimating nonlinear invariants, such as fractal dimension and Lyapunov exponents. In theory, bleached data avoids these pitfalls. But in practice, bleaching obscures the underlying deterministic structure of a low-dimensional chaotic process. This appears to be a property of the chaos itself, since nonchaotic data are not similarly affected. The adverse effects of bleaching are demonstrated in a series of numerical experiments on known chaotic data. Some theoretical aspects are also discussed.Comment: 12 dense pages (82K) of ordinary LaTeX; uses macro psfig.tex for inclusion of figures in text; figures are uufile'd into a single file of size 306K; the final dvips'd postscript file is about 1.3mb Replaced 9/30/93 to incorporate final changes in the proofs and to make the LaTeX more portable; the paper will appear in CHAOS 4 (Dec, 1993
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