On monitoring the process mean of autocorrelated observations with measurement errors using the w-of-w runs-rules scheme

Runs-rules have been widely used since the 1950s in industrial and nonindustrial process monitoring applications to improve the performance of basic and other traditional monitoring schemes. However, none of the studies on runs-rules have accounted for a process with a combined effect of measurement errors and autocorrelation. Hence, in this paper, the use of the w-of-w runs-rules to improve the performance of the Shewhart $\overline{X}$ scheme using an additive model with a constant variance and a first-order autoregressive model is proposed. To reduce the combined negative effect of measurement errors and autocorrelation, we implement a sampling strategy based on rational subgroups in which (a) multiple measurements per item are taken (instead of a standard single measurement) and (b) non-neighboring observations are gathered. Moreover, the latter sampling strategy is incorporated into the values of probability elements of a Markov chain matrix which is used to derive some closed-form expressions for the zero- and steady-state run-length distribution. The main finding of this study is that, with respect to some overall performance measures, the proposed scheme outperforms the existing Shewhart $\overline{X}$ scheme by a significant margin. A real-life example is used to illustrate the practical implementation of the proposed scheme.     

Network and Service Management for Cloud Computing and Data Centers: A Report on CNSM 2012

This short paper reports on the CNSM’2012 conference (International Conference on Network and Service Management). It presents an overview on the technical sessions, keynotes, tutorials, and panel, especially focusing on the original and dominating topics that raised the most comments and discussion. It then aims at indicating what the dominant research directions were in the network and service management community.     

Individual and area socioeconomic inequalities in cause-specific unintentional injury mortality: 11-Year follow-up study of 2.7 million Canadians

This study investigated the association between individual and area socioeconomic status (SES) and leading causes of unintentional injury mortality in Canadian adults. Using the 1991–2001 Canadian Census Mortality Follow-up Study cohort (N = 2,735,152), Cox proportional hazard regression was used to calculate hazard ratios and 95% confidence intervals for all-cause unintentional injury, motor vehicle collision (MVC), fall, poisoning, suffocation, fire/burn, and drowning deaths. Results indicated that associations with SES differed by cause of injury, and were generally more pronounced for males. Low education was associated with an elevated risk of mortality from all-cause unintentional injury and MVC (males only) and poisoning and drowning (both sexes). Low income was strongly associated with most causes of injury mortality, particularly fire/burn and poisoning. Having no occupation or low occupational status was associated with higher risks of all-cause injury, fall, poisoning and suffocation (both sexes) and MVC deaths among men. Associations with area deprivation were weak, and only areas with high deprivation had elevated risk of all-cause injury, MVC (males only), poisoning and drowning (both sexes). This study reveals the importance of examining SES differentials by cause of death from a multilevel perspective. Future research is needed to clarify the mechanisms underlying these differences to implement equity-oriented approaches for reducing differential exposures, vulnerability or consequences of injury mortality.     

The adapted augmented Lagrangian method: a new method for the resolution of the mechanical frictional contact problem

The aim of this work is to propose a new numerical method for solving the mechanical frictional contact problem in the general case of multi-bodies in a three dimensional space. This method is called adapted augmented Lagrangian method (AALM) and can be used in a multi-physical context (like thermo-electro-mechanical fields problems). This paper presents this new method and its advantages over other classical methods such as penalty method (PM), adapted penalty method (APM) and, augmented Lagrangian method (ALM). In addition, the efficiency and the reliability of the AALM are proved with some academic problems and an industrial thermo-electromechanical problem.     

Bayesian density estimation from grouped continuous data

Grouped data occur frequently in practice, either because of limited resolution of instruments, or because data have been summarized in relatively wide bins. A combination of the composite link model with roughness penalties is proposed to estimate smooth densities from such data in a Bayesian framework. A simulation study is used to evaluate the performances of the strategy in the estimation of a density, of its quantiles and first moments. Two illustrations are presented: the first one involves grouped data of lead concentration in the blood and the second one the number of deaths due to tuberculosis in The Netherlands in wide age classes.