46 research outputs found

    How reproducible are methods to measure the dynamic viscoelastic properties of poroelastic media?

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    There is a considerable number of research publications on the acoustical properties of porous media with an elastic frame. A simple search through the Web of Science™ (last accessed 21 March 2018) suggests that there are at least 819 publications which deal with the acoustics of poroelastic media. A majority of these researches require accurate knowledge of the elastic properties over a broad frequency range. However, the accuracy of the measurement of the dynamic elastic properties of poroelastic media has been a contentious issue. The novelty of this paper is that it studies the reproducibility of some popular experimental methods which are used routinely to measure the key elastic properties such as the dynamic Young's modulus, loss factor and Poisson ratio of poroelastic media. In this paper, fourteen independent sets of laboratory measurements were performed on specimens of the same porous materials. The results from these measurements suggest that the reproducibility of this type of experimental method is poor. This work can be helpful to suggest improvements which can be developed to harmonize the way the elastic properties of poroelastic media are measured worldwide

    Adaptive Value of Phenological Traits in Stressful Environments: Predictions Based on Seed Production and Laboratory Natural Selection

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    Phenological traits often show variation within and among natural populations of annual plants. Nevertheless, the adaptive value of post-anthesis traits is seldom tested. In this study, we estimated the adaptive values of pre- and post-anthesis traits in two stressful environments (water stress and interspecific competition), using the selfing annual species Arabidopsis thaliana. By estimating seed production and by performing laboratory natural selection (LNS), we assessed the strength and nature (directional, disruptive and stabilizing) of selection acting on phenological traits in A. thaliana under the two tested stress conditions, each with four intensities. Both the type of stress and its intensity affected the strength and nature of selection, as did genetic constraints among phenological traits. Under water stress, both experimental approaches demonstrated directional selection for a shorter life cycle, although bolting time imposes a genetic constraint on the length of the interval between bolting and anthesis. Under interspecific competition, results from the two experimental approaches showed discrepancies. Estimation of seed production predicted directional selection toward early pre-anthesis traits and long post-anthesis periods. In contrast, the LNS approach suggested neutrality for all phenological traits. This study opens questions on adaptation in complex natural environment where many selective pressures act simultaneously

    Fluid challenges in intensive care: the FENICE study A global inception cohort study

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    Fluid challenges (FCs) are one of the most commonly used therapies in critically ill patients and represent the cornerstone of hemodynamic management in intensive care units. There are clear benefits and harms from fluid therapy. Limited data on the indication, type, amount and rate of an FC in critically ill patients exist in the literature. The primary aim was to evaluate how physicians conduct FCs in terms of type, volume, and rate of given fluid; the secondary aim was to evaluate variables used to trigger an FC and to compare the proportion of patients receiving further fluid administration based on the response to the FC.This was an observational study conducted in ICUs around the world. Each participating unit entered a maximum of 20 patients with one FC.2213 patients were enrolled and analyzed in the study. The median [interquartile range] amount of fluid given during an FC was 500 ml (500-1000). The median time was 24 min (40-60 min), and the median rate of FC was 1000 [500-1333] ml/h. The main indication for FC was hypotension in 1211 (59 %, CI 57-61 %). In 43 % (CI 41-45 %) of the cases no hemodynamic variable was used. Static markers of preload were used in 785 of 2213 cases (36 %, CI 34-37 %). Dynamic indices of preload responsiveness were used in 483 of 2213 cases (22 %, CI 20-24 %). No safety variable for the FC was used in 72 % (CI 70-74 %) of the cases. There was no statistically significant difference in the proportion of patients who received further fluids after the FC between those with a positive, with an uncertain or with a negatively judged response.The current practice and evaluation of FC in critically ill patients are highly variable. Prediction of fluid responsiveness is not used routinely, safety limits are rarely used, and information from previous failed FCs is not always taken into account

    Constructive cooperative coevolution for large-scale global optimisation

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    This paper presents the Constructive Cooperative Coevolutionary ( C3C3 ) algorithm, applied to continuous large-scale global optimisation problems. The novelty of C3C3 is that it utilises a multi-start architecture and incorporates the Cooperative Coevolutionary algorithm. The considered optimisation problem is decomposed into subproblems. An embedded optimisation algorithm optimises the subproblems separately while exchanging information to co-adapt the solutions for the subproblems. Further, C3C3 includes a novel constructive heuristic that generates different feasible solutions for the entire problem and thereby expedites the search. In this work, two different versions of C3C3 are evaluated on high-dimensional benchmark problems, including the CEC'2013 test suite for large-scale global optimisation. C3C3 is compared with several state-of-the-art algorithms, which shows that C3C3 is among the most competitive algorithms. C3C3 outperforms the other algorithms for most partially separable functions and overlapping functions. This shows that C3C3 is an effective algorithm for large-scale global optimisation. This paper demonstrates the enhanced performance by using constructive heuristics for generating initial feasible solutions for Cooperative Coevolutionary algorithms in a multi-start framework
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