PubMed · 16596847
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Jerry Zion, Eric Anderson. 2006. Portable analysis tools help solve networking problems.. https://pubmed.ncbi.nlm.nih.gov/16596847/
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We present a mesoscopic model for simulating the dynamics of a non-volatile liquid on a solid substrate. The wetting properties of the solid can be tuned from complete wetting to total non-wetting. This model opens the way to study the dynamics of drops and liquid thin films at mesoscopic length scales of the order of the nanometer. As particular applications, we analyze the kinetics of spreading of a liquid drop wetting a solid substrate and the dewetting of a liquid film on a hydrophobic substrate. In all these cases, very good agreement is found between simulations and theoretical predictions.
Recently, we (P.A. O'Connell, G.B. McKenna, Science 307, 1760 (2005)) introduced a novel nano-bubble inflation method to measure the absolute creep compliance of nanometer thick polymer films. In that work it was shown that even at film thicknesses as small as 27.5nm the glass temperature was unchanged for poly(vinyl acetate) (PVAc). Perhaps more importantly, and the subject of the present work, was the observation that these ultrathin films show a dramatic stiffening in the rubbery plateau regime, i.e., the compliance was reduced by over two orders of magnitude compared to the bulk material. In the present work we substantiate the previous results in a study of the thickness dependence of the rubbery compliance of PVAc and polystyrene (PS) films for thicknesses from 13nm to 276nm. We show the substantial stiffening of the plateau region for both materials. Furthermore, the rubbery compliance (inverse of stiffness) scales with approximately the second power ( 1.8+/-0.2) in the film thickness for both materials.
In the present paper, the theoretical background of multivariate autoregressive modelling (MAR) is explained. The motivation for MAR modelling is the need to study the linear relationships between signals. In biomedical engineering, MAR modelling is used especially in the analysis of cardiovascular dynamics and electroencephalographic signals, because it allows determination of physiologically relevant connections between the measured signals. In a MAR model, the value of each variable at each time instance is predicted from the values of the same series and those of all other time series. The number of past values used is called the model order. Because of the inter-signal connections, a MAR model can describe causality, delays, closed-loop effects and simultaneous phenomena. To provide a better insight into the subject matter, MAR modelling is here illustrated with a model between systolic blood pressure, RR interval and instantaneous lung volume.