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Self-cleaning surface is potentially a very useful addition for many commercial products due to economic, aesthetic, and environmental reasons. Super-hydrophobic selfcleaning, also called Lotus effect, utilizes right combination of surface chemistry and roughness to force water droplets to form high contact angle on a surface, easily roll off a surface and pick up dirt particles on its way. Electrospinning is a promising technique for creation of superhydrophobic self-cleaning surfaces owing to a wide set of parameters that allow effectively controlling roughness of resulted webs. This article gives a brief introduction to the theory of super-hydrophobic selfcleaning and basic principles of the electrospinning process and reviews the scientific literature where electrospinning was used to create superhydrophobic surfaces. The article reviewed are categorized into several groups and their results are compared in terms of superhydrophobic properties. Several issues with current state of the art and highlights of important areas for future research are discussed in the conclusion. V C 2012 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 50: 2012

We introduce ASAP3, a refinement of the batch means algorithms ASAP and ASAP2, that delivers point and confidence-interval estimators for the expected response of a steady-state simulation. ASAP3 is a sequential procedure designed to produce a confidence-interval estimator that satisfies user-specified requirements on absolute or relative precision as well as coverage probability. ASAP3 operates as follows: the batch size is progressively increased until the batch means pass the Shapiro-Wilk test for multivariate normality; and then ASAP3 fits a first-order autoregressive (AR(1)) time series model to the batch means. If necessary, the batch size is further increased until the autoregressive parameter in the AR(1) model does not significantly exceed 0.8. Next, ASAP3 computes the terms of an inverse Cornish-Fisher expansion for the classical batch means t -ratio based on the AR(1) parameter estimates; and finally ASAP3 delivers a correlation-adjusted confidence interval based on this expansion. Regarding not only conformance to the precision and coverage-probability requirements but also the mean and variance of the half-length of the delivered confidence interval, ASAP3 compared favorably to other batch means procedures (namely, ABATCH, ASAP, ASAP2, and LBATCH) in an extensive experimental performance evaluation.