We investigate the influence of statistical measures of surface roughness on the turbulent drag reduction (DR) performance of four scalable, randomly rough superhydrophobic (SH) textures. Each surface was fabricated using readily scalable surface texturing processes to generate a random, self-affine height profile on the base substrate. The frictional drag on all four SH surfaces was measured when fully submerged in shear-driven turbulent flow inside a bespoke Taylor-Couette apparatus at Reynolds numbers in the range 1 × 104 ≲ Re ≲ 1 × 105. An “effective” slip length quantifying the overall drag-reducing ability for each surface was extracted from the resulting Prandtl-von Kármán friction plots. Reductions in the frictional drag of up to 26% were observed, with one of the hierarchically textured surfaces exceeding a wall shear stress of 26 Pa (corresponding to a Reynolds number Re ≈ 7 × 104) before the onset of flow-induced plastron collapse. The surface morphology of each texture was characterized using noncontact optical profilometry, and the influence of various statistical measures of roughness on the effective slip length was explored. The lateral autocorrelation length was identified as the key textural parameter determining the drag-reducing ability for randomly rough SH textures, playing the role analogous to the spatial periodicity of regularly patterned SH surfaces. A large autocorrelation length, a small surface roughness, and the presence of hierarchical roughness features were observed to be the three important design requirements for scalable SH textures for optimal DR in turbulent flows.
BackgroundTranslating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer.ResultsThe reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug.ConclusionsPredictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-017-0471-8) contains supplementary material, which is available to authorized users.
How do we model and find outliers in Twitter data? Given the number of retweets of each person on a social network, what is their expected number of comments? Real-life data are often very skewed, exhibiting power-law-like behavior. For such skewed multidimensional discrete data, the existing models are not general enough to capture various realistic scenarios, and need to be discretized as they often model continuous quantities. We propose FusionRP, short for Fusion Restaurant Process, a simple and intuitive model for skewed multi-dimensional discrete distributions, such as number of retweets vs. comments in Twitter-like data. Our model is discrete by design, has provably asymptotic log-logistic sum of marginals , is general enough to capture varied relationships, and most importantly, fits real data very well. We give an effective and scalable maximum-likelihood based fitting approach that is linear in the number of unique observed values and the input dimension. We test FusionRP on a twitter-like social network with 2.2M users, a phone call network with 1.9M call records, game data with 45M users and Facebook data with 2.5M posts. Our results show that FusionRP significantly outperforms several alternative methods and can detect outliers, such as bot-like behaviors in the Facebook data.
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