Repressive adaptation in children with cancer.

The constructs of repressive adaptive style and avoidant coping (blunting) were assessed as possible explanatory factors for previously reported findings of lower self-reported depression in children with cancer. Pediatric oncology patients 7–16 years old (n?=?107) and healthy control participants (n?=?442) completed measures of depressive symptoms, trait anxiety, defensiveness, and approach and avoidant coping. Oncology patients scored significantly lower on measures of depression and trait anxiety, and higher on defensiveness. Applying the adaptive style paradigm, the oncology group showed a significant excess of repressors. Depressive symptoms differed as a function of adaptive style, with repressors demonstrating the lowest levels of self-reported depression. Children with cancer also reported greater use of blunting, but this difference was small and appeared unrelated to depression scores. Within the cancer group, repressive adaptation was unrelated to time elapsed since diagnosis. These findings are discussed with reference to the ongoing controversy regarding cancer–personality style associations. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

QoE Aware IoT Application Placement in Fog Computing Using Modified-TOPSIS

Mobile Networks and Applications - Over the years, fog computing has emerged as a paradigm to complement the cloud computing in handling the delay sensitive IoT applications in a better manner....     

Admission control and resource provisioning in fog-integrated cloud using modified fuzzy inference system

The Journal of Supercomputing - Fog-integrated cloud (FiC) contains a fair amount of heterogeneity, leading to uncertainty in the resource provisioning. An admission control manager (ACM) is...     

A novel hybrid PSO–GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems

This work presents a novel hybrid meta-heuristic that combines particle swarm optimization and genetic algorithm (PSO–GA) for the job/tasks in the form of directed acyclic graph (DAG) exhibiting inter-task communication. The proposed meta-heuristic starts with PSO and enters into GA when local best result from PSO is obtained. Thus, the proposed PSO–GA meta-heuristic is different than other such hybrid meta-heuristics as it aims at improving the solution obtained by PSO using GA. In the proposed meta-heuristic, PSO is used to provide diversification while GA is used to provide intensification. The PSO–GA is tested for task scheduling on two standard well-known linear algebra problems: LU decomposition and Gauss–Jordan elimination. It is also compared with other states-of-the-art heuristics for known solutions. Furthermore, its effectiveness is evaluated on few large sizes of random task graphs. Comparative study of the proposed PSO-GA with other heuristics depicts that the PSO–GA performs quite effectively for multiprocessor DAG scheduling problem.     

An energy-efficient reliable grid scheduling model using NSGA-II

Job scheduling in computational grid is a complex problem and various heuristics and meta-heuristics have been proposed for the same. These approaches usually optimize specific characteristic parameters while allocating the jobs on the grid resources. Many a times, it is desired to optimize multiple parameters during job scheduling. Non-dominated sorting genetic algorithm (NSGA-II) has been observed to be the best meta-heuristic to solve such multi-objective optimization problem. The proposed work applies NSGA-II for job scheduling in computational grid with three conflicting objectives: maximizing reliability of the system for job allocation, minimizing energy consumption and balancing the load on the system. Performance study of the proposed model is done by simulating it on some real data. The result indicates that the proposed model performs well with multiple objectives.     

The Slow Convergence of Ordinary Least Squares Estimators of α, β and Portfolio Weights under Long‐Memory Stochastic Volatility

We consider inference for the market model coefficients based on simple linear regression under a long memory stochastic volatility generating mechanism for the returns. We obtain limit theorems for the ordinary least squares (OLS) estimators of α and β in this framework. These theorems imply that the convergence rate of the OLS estimators is typically slower than if both the regressor and the predictor have long memory in volatility, where T is the sample size. The traditional standard errors of the OLS‐estimated intercept () and slope (), which disregard long memory in volatility, are typically too optimistic, and therefore the traditional t‐statistic for testing, say, α = 0 or β = 1, will diverge under the null hypothesis. We also obtain limit theorems (which imply slow convergence) for the estimated weights of the minimum variance portfolio and the optimal portfolio in the same framework. In addition, we propose and study the performance of a subsampling‐based approach to hypothesis testing for α and β. We conclude by noting that analogous results hold under more general conditions on long‐memory volatility models and state these general conditions which cover certain fractionally integrated exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models.     

Studies on ion—solvent interactions through viscosity measurements—potassium iodide in N,N-dimethylformamide—water mixtures

The viscosities of DMF—water mixtures and of KI solutions in these mixed solvents have been measured at 25°, 30°, 35° and 40° C, covering the whole range of solvent composition. The Jones—Dole B-Coefficient, after passing through a minimum in water-rich solvent composition region, increases with the increase in DMF content in the solvent mixture. The variation of viscosity of the solvent mixture and of B-coefficient with solvent composition and temperature have been discussed in terms of solvent—solvent and ion—solvent interactions.     

Supercritical fluid extraction of a crude oil, bitumen-derived liquid and bitumen by carbon dioxide and propane

Extractions of a crude oil, a bitumen-derived liquid and bitumen were conducted at several temperatures and pressures with carbon dioxide and propane in order to assess the effect of the size and type of compounds in the feedstocks on the extraction process. The pure-solvent density at the extraction conditions was not the only variable governing extraction, and the proximity of the extraction conditions to the pure-solvent critical temperature affected the extraction yields and the compositions of the extract phases. Higher oil yields were obtained at lower solvent reduced densities when the extraction temperatures were in the vicinity of the pure solvent critical temperature. In the crude oil and native bitumen extractions, as the extraction time and/or the extraction pressure increased, heavier compounds were found in the extract phases. This preferential extraction was not observed with the bitumen-derived liquid. The non-discriminatory extraction behaviour of the bitumen-derived liquid was attributed to its thermal history and to the presence of olefins and significant amounts of aromatics. Phase behaviour calculations using the Peng-Robinson equation of state and component lumping procedures provided reasonable agreement between calculated and experimental results for the crude oil and bitumen extractions, but failed to predict the bitumen-derived liquid extractions.     

HTPB-based polyurethanes for inhibition of composite propellants

Polyurethanes were synthesized by the reaction of hydroxyterminated polybutadiene (HTPB) and diisocyanates such as toluene diisocyanate (TDI), diphenylmethane diisocyanate (MDI), hexamethylene diisocyanate (HMDI), and isophoron diisocyanate (IPDI). A number of fillers such as carbon black, kaolin, and antimony trioxide (Sb₂O₃) were used in these formulations. Mechanical and thermal stability of these polyurethanes were studied. Based on its properties, HTPB-MDI derived filled polyurethane was selected and evaluated as an inhibitor for a composite propellant. © 1995 John Wiley & Sons, Inc.     

The restricted likelihood ratio test for autoregressive processes

The restricted likelihood is known to produce estimates with significantly less bias in AR(p) models with intercept and/or trend. In AR(1) models, the corresponding restricted likelihood ratio test (RLRT), unlike the t‐statistic or the usual LRT, has been recently shown to be well approximated by the chi‐square distribution even close to the unit root, thus yielding confidence intervals with good coverage properties. In this article, we extend this result to AR(p) processes of arbitrary order p by obtaining the expansion of the RLRT distribution around that of the limiting chi‐squared and showing that the error is bounded even as the unit root is approached. Next, we investigate the correspondence between the AR coefficients and the partial autocorrelations, which is well known in the stationary region, and extend to the more general situation of potentially multiple unit roots. In the case of one positive unit root, which is of most practical interest, the resulting parameter space is shown to be the bounded p‐dimensional hypercube (?1, 1] × (?1, 1)^p?1. This simple parameter space, in addition with a stable algorithm that we provide for computing the restricted likelihood, allows its easy computation and optimization as well as construction of confidence intervals for the sum of the AR coefficients. In simulations, the RLRT intervals are shown to have not only near exact coverage in keeping with our theoretical results, but also shorter lengths and significantly higher power against stationary alternatives than other competing interval procedures. An application to the well‐known Nelson–Plosser data yields RLRT based intervals that can be markedly different from those in the literature.