Collective dynamics of dense hairy surfaces in turbulent flow

Flexible filamentous beds interacting with a turbulent flow represent a fundamental setting for many environmental phenomena, e.g., aquatic canopies in marine current. Exploiting direct numerical simulations at high Reynolds number where the canopy stems are modelled individually, we provide evidence on the essential features of the honami/monami collective motion experienced by hairy surfaces over a range of different flexibilities, i.e., Cauchy number. Our findings clearly confirm that the collective motion is essentially driven by fluid flow turbulence, with the canopy having in this respect a fully-passive behavior. Instead, some features pertaining to the structural response turn out to manifest in the motion of the individual canopy elements when focusing, in particular, on the spanwise oscillation and/or on sufficiently small Cauchy numbers

Improved Adaptation and Survivability via Dynamic Service Composition of Ubiquitous Computing Middleware

These days, ubiquitous computing has radically changed the way users access and interact with services and content on the Internet: novel smart mobile devices and broadband wireless communication channels allow users to seamlessly access them anytime and anywhere. Middleware infrastructures to support ubiquitous computing need to support an extremely dynamic and ever-changing scenario, where novel contents/services, devices, formats, and media channels become available. Service-oriented architectures and service composition techniques have proven to be the key in designing flexible and extensible platforms that are able to reliably support ubiquitous computing. However, current trends in service composition for ubiquitous computing tend to be either too formal and, therefore, poorly used by average final users, or too vertical and poorly flexible and extensible. This paper proposes novel service composition middleware for ubiquitous computing that relies on a translucent composition model to achieve a flexible, extensible, highly-available, but also easily understandable and usable platform. The proposed system has been widely tested, benchmarked, and deployed on a number of different and heterogeneous ubiquitous scenarios

Estimation Accuracy and Computational Cost Analysis of Artificial Neural Networks for State of Charge Estimation in Lithium Batteries

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles

CaDrA: A Computational Framework for Performing Candidate Driver Analyses Using Genomic Features

The identification of genetic alteration combinations as drivers of a given phenotypic outcome, such as drug sensitivity, gene or protein expression, and pathway activity, is a challenging task that is essential to gaining new biological insights and to discovering therapeutic targets. Existing methods designed to predict complementary drivers of such outcomes lack analytical flexibility, including the support for joint analyses of multiple genomic alteration types, such as somatic mutations and copy number alterations, multiple scoring functions, and rigorous significance and reproducibility testing procedures. To address these limitations, we developed Candidate Driver Analysis or CaDrA, an integrative framework that implements a step-wise heuristic search approach to identify functionally relevant subsets of genomic features that, together, are maximally associated with a specific outcome of interest. We show CaDrA’s overall high sensitivity and specificity for typically sized multi-omic datasets using simulated data, and demonstrate CaDrA’s ability to identify known mutations linked with sensitivity of cancer cells to drug treatment using data from the Cancer Cell Line Encyclopedia (CCLE). We further apply CaDrA to identify novel regulators of oncogenic activity mediated by Hippo signaling pathway effectors YAP and TAZ in primary breast cancer tumors using data from The Cancer Genome Atlas (TCGA), which we functionally validate in vitro. Finally, we use pan-cancer TCGA protein expression data to show the high reproducibility of CaDrA’s search procedure. Collectively, this work demonstrates the utility of our framework for supporting the fast querying of large, publicly available multi-omics datasets, including but not limited to TCGA and CCLE, for potential drivers of a given target profile of interest