Subjective experiences of stress workload, and bodily discomfort as a function of age and type of computer work

An increasingly older workforce and a growing emphasis on jobs involving interaction with computers have resulted in a need to more carefully examine the relationship between ageing and computer-based work. In this study, a sample of 394 subjects ranging in age from 20-75 years performed a computer task across a 3-day period. Three different types of computer-based jobs (data entry, information retrieval, and accounts balancing) performed at three large US companies were simulated. Age differences in the subjective experience of stress, workload, and bodily discomfort were evaluated. The results indicated that age effects for these measures varied according to task. The older subjects perceived greater workload for the more mentally challenging problem-solving oriented accounts balancing task (Which involved a graphical user interface) than the younger participants, even with increased exposure to the task. However, the older subjects generally experienced less stress than the younger subjects on an information retrieval task that involved a more socially interactive telephone component. A positive relationship between the frustration component of workload and the measure of stress was also found, suggesting an important link between the constructs of stress and workload. Overall, the outcomes of this study provide important insights into design interventions intended to accommodate older as well as younger persons in the workforce.     

On determining the number of outliers in exponential and Pareto samples

In the present paper we suggest a procedure for the determination of the number of outliers in exponential and Pareto samples, using the predictive interval approach.     

Learning About the Market’s Periphery: IBM’s WebFountain

The web can provide valuable information to an organisation on upcoming trends, competitors’ movements and customers’ gripes. More important, the information is within reach of every organisation. The problem is that there is so much of it. This paper describes how IBM set up and utilised WebFountain—a tool to help it extract trends and detect patterns from the vast amounts of raw data available. Still, the authors argue, any organisation needs to do more to use to its full potential such a system for extracting analytic solutions from the web: the most important being to ask the right questions in the first place.     

Random field models in image analysis*

Image models are useful in quantitatively specifying natural constraints and general assumptions about the physical world and the imaging process. This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis. Random field models permit the introduction of spatial context into pixel labeling problems, such as segmentation and restoration. Random field models also describe textured images and lead to algorithms for generating textured images, classifying textures and segmenting textured images. In spite of some impressive model-based image restoration and texture segmentation results reported in the literature, a number of fundamental issues remain unexplored, such as the specification of MRF models, modeling noise processes, performance evaluation, parameter estimation, the phase transition phenomenon and the comparative analysis of alternative procedures. The literature of random field models is filled with great promise, but a better mathematical understanding of these issues is needed as well as efficient algorithms for applications. These issues need to be resolved before random field models will be widely accepted as general tools in the image-processing community.     

Random field models in image analysis

Image models are useful in quantitatively specifying natural constraints and general assumptions about the physical world and the imaging process. This review paper explains how Gibbs and Markov random field models provide a unifying theme for many contemporary problems in image analysis. Random field models permit the introduction of spatial context into pixel labeling problems, such as segmentation and restoration. Random field models also describe textured images and lead to algorithms for generating textured images, classifying textures, and segmenting textured images. In spite of some impressive model-based image restoration and texture segmentation results reported in the literature, a number of fundamental issues remain unexplored, such as the specification of MRF models, modeling noise processes, performance evaluation, parameter estimation, the phase transition phenomenon, and the comparative analysis of alternative procedures. The literature of random field models is filled with great promise, but a better mathematical understanding of these issues is needed as well as efficient algorithms for applications. These issues need to be resolved before random field models will be widely accepted as general tools in the image processing community.     

Does “fighting back” make a difference? The case of the Canadian auto workers union

Union revitalization requires mutually reinforcing efforts in organizing, political action, and collective bargaining. We examine the experience of the Canadian Auto Workers (CAW). In addition to membership growth, three bargaining outcomes are analyzed: (1) wage gains; (2) the ability to resist a trend towards longer duration collective agreements; and, (3) success in negotiating same-sex spousal recognition. In each of these areas the CAW has articulated a policy and then made gains in both absolute and relative terms. The CAW’s renewal strategy links these bargaining goals with social activism and membership growth through mergers and new organizing.     

Estimation of a linear transformation and an associated distributional problem

A multivariate “errors in variables” regression model is proposed which generalizes a model previously considered by Gleser and Watson (1973). Maximum likelihood estimators [MLE's] for the parameters of this model are obtained, and the consistency properties of these estimators are investigated. Distribution of the MLE of the “error” variance is obtained in a simple case while the mean and the variance of the estimator are obtained in this case without appealing to the exact distribution.     

Some properties of discrete bathtub-shaped distributions

The existence and the usefulness of discrete bathtub-shaped and upside down bathtub-shaped distributions have been demonstrated in some papers of recent origin. However, the general properties of these two classes of distributions do not seem to have been discussed. This article proposes to study some reliability properties of such distributions. We investigate the closure properties with reference to convolution, mixing, series and parallel systems, etc. and existence of bounds on reliability functions, moment properties, and convergence.     

Unionization and Income Growth of Racial Minority Immigrants in Canada: A Longitudinal Study

This study examines the effect of unionization on the labor market integration of newly arrived immigrants in Canada. We find that non‐white recent immigrants gain access to unionized jobs at a slower rate than do white recent immigrants. The effect of unionization on earnings is somewhat lower for non‐white recent immigrants than for white recent immigrants. These findings are based on growth curve modeling of longitudinal data from the Survey of Labour and Income Dynamics (SLID). Therefore, unionization does not contribute to reducing the earnings gap of non‐white recent immigrants relative to white immigrants and the native‐born.     

Quality Testing and Product Rationing by Input Suppliers

Quality testing by suppliers has significant ramifications for downstream supply chain participants and retail consumers. This article focuses on such implications accounting for the fact that suppliers often enjoy discretion in quality testing and reporting. Under a discretionary testing and reporting environment, we show that a supplier can improve the market's perception of product quality by engaging in self‐imposed production cuts. Production cuts dampen supplier incentives to engage in excessive quality testing, putting the supplier and the market on a more equal information footing. This reduces the market's need to skeptically discount product quality to protect itself. The improved market perception, then, reduces quality testing demand, introducing cost savings. The result that costly production cuts can improve quality perceptions indicates that the groundwork for influencing market perceptions may have to be laid upfront, even prior to acquiring private information, providing a contrast to routine signaling models.