On the evolutionary status of short-period cataclysmic variables

We present high-speed, three-colour photometry of seven short-period (Porb

A Longitudinal and Contextual Analysis of Media Representation of Business Ethics

Purpose – The purpose of this paper is to present an analysis of media representation of business ethics within 62 international newspapers to explore the longitudinal and contextual evolution of business ethics and associated terminology. Levels of coverage and contextual analysis of the content of the articles are used as surrogate measures of the penetration of business ethics concepts into society. Design/methodology/approach – This paper uses a text mining application based on two samples of data: analysis of 62 national newspapers in 21 countries from 1990 to 2008; analysis of the content of two samples of articles containing the term business ethics (comprised of 100 newspaper articles spread over an 18-year period from a sample of US and UK newspapers). Findings – The paper demonstrates increased coverage of sustainability topics within the media over the last 18 years associated with events such as the Rio Summit. Whilst some peaks are associated with business ethics scandals, the overall coverage remains steady. There is little apparent use in the media of concepts such as corporate citizenship. The academic community and company ethical codes appear to adopt a wider definition of business ethics more akin to that associated with sustainability, in comparison with the focus taken by the media, especially in the USA. Coverage demonstrates clear regional bias and contextual analysis of the articles in the UK and USA also shows interesting parallels and divergences in the media representation of business ethics. Originality/value – A promising avenue to explore how the evolution of sustainability issues including business ethics can be tracked within a societal context.

A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimisation benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better in finding feasible solutions but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridise it with a recently proposed simulated annealing hyper-heuristic within a local search and genetic algorithm framework. The hybrid algorithm shows significant improvement over both the genetic algorithm with stochastic ranking and the simulated annealing hyper-heuristic alone. The hybrid algorithm also considerably outperforms the methods in the literature which have the previously best known results.