Getting mad may not mean getting even: The influence of drivers' ethical ideologies on driving anger and related behaviour

The current study explored the influence of moral values (measured by ethical ideology) on self-reported driving anger and aggressive driving responses. A convenience sample of drivers aged 17-73 years (n = 280) in Queensland, Australia, completed a self-report survey. Measures included sensation seeking, trait aggression, driving anger, endorsement of aggressive driving responses and ethical ideology (Ethical Position Questionnaire, EPQ). Scores on the two underlying dimensions of the EPQ idealism (highI/lowI) and relativism (highR/lowR) were used to categorise drivers into four ideological groups: Situationists (highI/highR); Absolutists (highI/lowR); Subjectivists (lowI/highR); and Exceptionists (lowI/lowR). Mean aggressive driving scores suggested that exceptionists were significantly more likely to endorse aggressive responses. After accounting for demographic variables, sensation seeking and driving anger, ethical ideological category added significantly, though modestly to the prediction of aggressive driving responses. Patterns in results suggest that those drivers in ideological groups characterised by greater concern to avoid affecting others negatively (i.e. highI, Situationists, Absolutists) may be less likely to endorse aggressive driving responses, even when angry. In contrast, Subjectivists (lowI, HighR), reported the lowest levels of driving anger yet were significantly more likely to endorse aggressive responses. This provides further insight into why high levels of driving anger may not always translate into more aggressive driving.

Curious hybrids: Creating 'not-quite' beings to explore possible childhoods

Communications between adults and young children can expose different ideas and opinions. Adults and children have different capacities to speak, these discursive spaces can become filled with assumptions, stereotyping and conventional thinking about power and agency. If communication shifts away from the purely discursive, what might be exposed about the explorations, investigations and fantasies adults and children indulge in? Some time ago my young daughter obsessively drew hybrid beings. Created from mixtures of animal, object, human and creature forms, these beings, which are ‘not-quite’, are becoming, able to transform via myriad mutations. We agreed to collaborate and draw additional hybrid beings to experiment with becoming-other through complex entanglements of forms, to complicate, morph and (trans)form from our human selves to hybrid others. The ‘not- quite-ness’ of our monstrous hybrids subvert the conventions of ‘being’ and prompt contemplations about childhood subjectivities, identities, conventionalities and actively interrogate the assumptive knowledges and subjectifications that are held about young children in early childhood professional and academic systems.

Determination of preventive maintenance lead time using hybrid analysis

The time for conducting Preventive Maintenance (PM) on an asset is often determined using a predefined alarm limit based on trends of a hazard function. In this paper, the authors propose using both hazard and reliability functions to improve the accuracy of the prediction particularly when the failure characteristic of the asset whole life is modelled using different failure distributions for the different stages of the life of the asset. The proposed method is validated using simulations and case studies.

Texture for Script identification

The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed.