A Petrov-Galerkin method for a singularly perturbed ordinary differential equation with non-smooth data

In this paper, a singularly perturbed ordinary differential equation with non-smooth data is considered. The numerical method is generated by means of a Petrov-Galerkin finite element method with the piecewise-exponential test function and the piecewise-linear trial function. At the discontinuous point of the coefficient, a special technique is used. The method is shown to be first-order accurate and singular perturbation parameter uniform convergence. Finally, numerical results are presented, which are in agreement with theoretical results.

Modeling multivariate autoregressive conditional heteroskedasticity with the double smooth transition conditional correlation GARCH Model

In this paper, we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new double smooth transition conditional correlation (DSTCC) GARCH model extends the smooth transition conditional correlation (STCC) GARCH model of Silvennoinen and Teräsvirta (2005) by including another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. Applying the model to the stock and bond futures data, we discover that the correlation pattern between them has dramatically changed around the turn of the century. The model is also applied to a selection of world stock indices, and we find evidence for an increasing degree of integration in the capital markets.

Active flow control bump design using hybrid Nash-game coupled to evolutionary algorithms

In this paper, the optimal design of an active flow control device; Shock Control Bump (SCB) on suction and pressure sides of transonic aerofoil to reduce transonic total drag is investigated. Two optimisation test cases are conducted using different advanced Evolutionary Algorithms (EAs); the first optimiser is the Hierarchical Asynchronous Parallel Evolutionary Algorithm (HAPMOEA) based on canonical Evolutionary Strategies (ES). The second optimiser is the HAPMOEA is hybridised with one of well-known Game Strategies; Nash-Game. Numerical results show that SCB significantly reduces the drag by 30% when compared to the baseline design. In addition, the use of a Nash-Game strategy as a pre-conditioner of global control saves computational cost up to 90% when compared to the first optimiser HAPMOEA.

Shock control bump design optimization on Natural Laminar Aerofoil

The chapter investigates Shock Control Bumps (SCB) on a Natural Laminar Flow (NLF) aerofoil; RAE 5243 for Active Flow Control (AFC). A SCB approach is used to decelerate supersonic flow on the suction/pressure sides of transonic aerofoil that leads delaying shock occurrence or weakening of shock strength. Such an AFC technique reduces significantly the total drag at transonic speeds. This chapter considers the SCB shape design optimisation at two boundary layer transition positions (0 and 45%) using an Euler software coupled with viscous boundary layer effects and robust Evolutionary Algorithms (EAs). The optimisation method is based on a canonical Evolution Strategy (ES) algorithm and incorporates the concepts of hierarchical topology and parallel asynchronous evaluation of candidate solution. Two test cases are considered with numerical experiments; the first test deals with a transition point occurring at the leading edge and the transition point is fixed at 45% of wing chord in the second test. Numerical results are presented and it is demonstrated that an optimal SCB design can be found to significantly reduce transonic wave drag and improves lift on drag (L/D) value when compared to the baseline aerofoil design.

Double-shock control bump design optimization using hybridized evolutionary algorithms

This study investigates the application of two advanced optimization methods for solving active flow control (AFC) device shape design problem and compares their optimization efficiency in terms of computational cost and design quality. The first optimization method uses hierarchical asynchronous parallel multi-objective evolutionary algorithm and the second uses hybridized evolutionary algorithm with Nash-Game strategies (Hybrid-Game). Both optimization methods are based on a canonical evolution strategy and incorporate the concepts of parallel computing and asynchronous evaluation. One type of AFC device named shock control bump (SCB) is considered and applied to a natural laminar flow (NLF) aerofoil. The concept of SCB is used to decelerate supersonic flow on suction/pressure side of transonic aerofoil that leads to a delay of shock occurrence. Such active flow technique reduces total drag at transonic speeds which is of special interest to commercial aircraft. Numerical results show that the Hybrid-Game helps an EA to accelerate optimization process. From the practical point of view, applying a SCB on the suction and pressure sides significantly reduces transonic total drag and improves lift-to-drag (L/D) value when compared to the baseline design.

Learning object dynamics for smooth tracking of moving lip contours

A new technique is proposed for learning the dynamic characteristics of a deformable object, applied in particular to the problem of lip-tracking. Experimental results are given which demonstrate that the use of dynamic models allows the system to track more robustly under adverse conditions and to correct spurious, poorly tracked frames

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.

Interleukin-1beta induces human proximal tubule cell injury, alpha-smooth muscle actin expression and fibronectin production

BACKGROUND Tubulointerstitial lesions, characterized by tubular injury, interstitial fibrosis and the appearance of myofibroblasts, are the strongest predictors of the degree and progression of chronic renal failure. These lesions are typically preceded by macrophage infiltration of the tubulointerstitium, raising the possibility that these inflammatory cells promote progressive renal disease through fibrogenic actions on resident tubulointerstitial cells. The aim of the present study, therefore, was to investigate the potentially fibrogenic mechanisms of interleukin-1beta (IL-1beta), a macrophage-derived pro-inflammatory cytokine, on human proximal tubule cells (PTC). METHODS Confluent, quiescent, passage 2 PTC were established in primary culture from histologically normal segments of human renal cortex (N = 11) and then incubated in serum- and hormone-free media supplemented with either IL-1beta (0 to 4 ng/mL) or vehicle (control). RESULTS IL-1beta significantly enhanced fibronectin secretion by up to fourfold in a time- and concentration-dependent fashion. This was accompanied by significant (2.5- to 6-fold) increases in alpha-smooth muscle actin (alpha-SMA) expression, transforming growth factor beta (TGF-beta1) secretion, nitric oxide (NO) production, NO synthase 2 (NOS2) mRNA and lactate dehydrogenase (LDH) release. Cell proliferation was dose-dependently suppressed by IL-1beta. NG-methyl-l-arginine (L-NMMA; 1 mmol/L), a specific inhibitor of NOS, blocked NO production but did not alter basal or IL-1beta-stimulated fibronectin secretion. In contrast, a pan-specific TGF-beta neutralizing antibody significantly blocked the effects of IL-1beta on PTC fibronectin secretion (IL-1beta, 268.1 +/- 30.6 vs. IL-1beta+alphaTGF-beta 157.9 +/- 14.4%, of control values, P < 0.001) and DNA synthesis (IL-1beta 81.0 +/- 6.7% vs. IL-1beta+alphaTGF-beta 93.4 +/- 2.1%, of control values, P < 0.01). CONCLUSION IL-1beta acts on human PTC to suppress cell proliferation, enhance fibronectin production and promote alpha-smooth muscle actin expression. These actions appear to be mediated by a TGF-beta1 dependent mechanism and are independent of nitric oxide release.

A phase-locked-loop design for the smooth operation of a hybrid microgrid

A microgrid contains both distributed generators (DGs) and loads and can be viewed by a controllable load by utilities. The DGs can be either inertial synchronous generators or non-inertial converter interfaced. Moreover, some of them can come online or go offline in plug and play fashion. The combination of these various types of operation makes the microgrid control a challenging task, especially when the microgrid operates in an autonomous mode. In this paper, a new phase locked loop (PLL) algorithm is proposed for smooth synchronization of plug and play DGs. A frequency droop for power sharing is used and a pseudo inertia has been introduced to non-inertial DGs in order to match their response with inertial DGs. The proposed strategy is validated through PSCAD simulation studies.

Modeling conditional correlations of asset returns : a smooth transition approach

In this paper we propose a new multivariate GARCH model with time-varying conditional correlation structure. The time-varying conditional correlations change smoothly between two extreme states of constant correlations according to a predetermined or exogenous transition variable. An LM–test is derived to test the constancy of correlations and LM- and Wald tests to test the hypothesis of partially constant correlations. Analytical expressions for the test statistics and the required derivatives are provided to make computations feasible. An empirical example based on daily return series of five frequently traded stocks in the S&P 500 stock index completes the paper.

GABAB receptors are expressed in human aortic smooth muscle cells and regulate the intracellular Ca2+ concentration

The aim of this study was to investigate the expression of GABAB receptors, a subclass of receptors to the inhibitory neurotransmitter gamma-aminobutyric acid (GABAB), in human aortic smooth muscle cells (HASMCs), and to explore if altering receptor activation modified intracellular Ca(2+) concentration ([Ca(2+)]i) of HASMCs. Real-time PCR, western blots and immunofluorescence were used to determine the expression of GABABR1 and GABABR2 in cultured HASMCs. Immunohistochemistry was used to localize the two subunits in human left anterior descending artery (LAD). The effects of the GABAB receptor agonist baclofen on [Ca(2+)]i in cultured HASMCs were demonstrated using fluo-3. Both GABABR1 and GABABR2 mRNA and protein were identified in cultured HASMCs and antibody staining was also localized to smooth muscle cells of human LAD. 100 μM baclofen caused a transient increase of [Ca(2+)]i in cultured HASMCs regardless of whether Ca(2+) was added to the medium, and the effects were inhibited by pre-treatment with CGP46381 (selective GABAB receptor antagonist), pertussis toxin (a Gi/o protein inhibitor), and U73122 (a phospholipase C blocker). GABAB receptors are expressed in HASMCs and regulate the [Ca(2+)]i via a Gi/o-coupled receptor pathway and a phospholipase C activation pathway

A smooth transition logit model of the effects of deregulation in the electricity market

This paper introduces the smooth transition logit (STL) model that is designed to detect and model situations in which there is structural change in the behaviour underlying the latent index from which the binary dependent variable is constructed. The maximum likelihood estimators of the parameters of the model are derived along with their asymptotic properties, together with a Lagrange multiplier test of the null hypothesis of linearity in the underlying latent index. The development of the STL model is motivated by the desire to assess the impact of deregulation in the Queensland electricity market and ascertain whether increased competition has resulted in significant changes in the behaviour of the spot price of electricity, specifically with respect to the occurrence of periodic abnormally high prices. The model allows the timing of any change to be endogenously determined and also market participants' behaviour to change gradually over time. The main results provide clear evidence in support of a structural change in the nature of price events, and the endogenously determined timing of the change is consistent with the process of deregulation in Queensland.

Smooth bootstrap methods for analysis of longitudinal data

In analysis of longitudinal data, the variance matrix of the parameter estimates is usually estimated by the 'sandwich' method, in which the variance for each subject is estimated by its residual products. We propose smooth bootstrap methods by perturbing the estimating functions to obtain 'bootstrapped' realizations of the parameter estimates for statistical inference. Our extensive simulation studies indicate that the variance estimators by our proposed methods can not only correct the bias of the sandwich estimator but also improve the confidence interval coverage. We applied the proposed method to a data set from a clinical trial of antibiotics for leprosy.