Hierarchical robust design optimisation of shock control bump devices for airfoil drag reduction

The paper investigates a detailed Active Shock Control Bump Design Optimisation on a Natural Laminar Flow (NLF) aerofoil; RAE 5243 to reduce cruise drag at transonic flow conditions using Evolutionary Algorithms (EAs) coupled to a robust design approach. For the uncertainty design parameters, the positions of boundary layer transition (xtr) and the coefficient of lift (Cl) are considered (250 stochastic samples in total). In this paper, two robust design methods are considered; the first approach uses a standard robust design method, which evaluates one design model at 250 stochastic conditions for uncertainty. The second approach is the combination of a standard robust design method and the concept of hierarchical (multi-population) sampling (250, 50, 15) for uncertainty. Numerical results show that the evolutionary optimization method coupled to uncertainty design techniques produces useful and reliable Pareto optimal SCB shapes which have low sensitivity and high aerodynamic performance while having significant total drag reduction. In addition,it also shows the benefit of using hierarchical robust method for detailed uncertainty design optimization.

Jackknife for bias reduction in predictive regressions

One of the fundamental econometric models in finance is predictive regression. The standard least squares method produces biased coefficient estimates when the regressor is persistent and its innovations are correlated with those of the dependent variable. This article proposes a general and convenient method based on the jackknife technique to tackle the estimation problem. The proposed method reduces the bias for both single- and multiple-regressor models and for both short- and long-horizon regressions. The effectiveness of the proposed method is demonstrated by simulations. An empirical application to equity premium prediction using the dividend yield and the short rate highlights the differences between the results by the standard approach and those by the bias-reduced estimator. The significant predictive variables under the ordinary least squares become insignificant after adjusting for the finite-sample bias. These discrepancies suggest that bias reduction in predictive regressions is important in practical applications.

Available evidence does not support serosorting as an HIV risk reduction strategy

Recent data highlighted the association between penetration of antiretrovirals in the central nervous system (CNS) and neurocognitive impairment in HIVpositive patients. Existing antiretrovirals have been ranked according to a score of neuropenetration, which was shown to be a predictor of anti-HIVactivity in the CNS and improvement of neurocognitive disorders [1]. Main factors affecting drug penetration are known to be protein binding, lipophilicity and molecular weight [2]. Moreover, active translation by membrane transporters (such as p-glycoprotein) could be a key mechanism of passage [3]. The use of raltegravir (RGV), a novel antiretroviral drug targeted to inhibit the HIV preintegrase complex, is increasing worldwide due to its efficacy and tolerability. However, penetration of RGV in the CNS has not been yet elucidated. In fact, prediction of RGV neuropenetration according to molecular characteristics is controversial. Intermediate protein binding (83%) and large volume of distribution (273 l) could suggest a high distribution beyond extracellular spaces [4]. On the contrary, low lipophilicity (oil/water partition coefficient at pH 7.4 of 2.80) and intermediate molecular weight (482.51 Da) suggest a limited diffusion. Furthermore, in-vitro studies suggest that RGV is substrate of p-glycoprotein, although this efflux pump has not been identified to significantly affect plasma pharmacokinetics [5]. In any case, no data concerning RGV passage into cerebrospinal fluid of animals or humans have yet been published.