Shaping (reflexive) professional identities across an undergraduate degree programme: A longitudinal case study

In our complex and incongruous professional worlds, where there is no blueprint for dealing with unpredictable people and events, it is imperative that individuals develop reflexive approaches to professional identity building. Notwithstanding the importance of disciplinary knowledge and skills, higher education has a crucial role to play in guiding students to examine and mediate self in relation to context for effective decision-making and action. This paper reports on a small-scale longitudinal project that investigated the ways in which ten undergraduate students over the course of a three-year Radiation Therapy degree shaped their professional identities. Theories of reflexivity and methods of discourse analysis are utilised to understand the ways in which individuals accounted for their professional identity projects at university. The findings suggest that, across time, the participants negotiated professional ‘becoming’ through four distinct kinds of reflexive modalities. These findings have implications for teaching strategies and curriculum design in undergraduate programs.

Forces shaping the future of work in a changing regional economy

This paper presents research which examined perceptions on the future of work in Queensland. It highlights the major drivers of change including: changing technology, demographics, increasing globalisation and economic shifts. Focus groups were conducted and findings show that Queensland businesses are acutely aware of the coming changes, but are less certain about how to respond. Current good practices plus recommendations for the future - particularly the lead role government and industry bodies need to play - are discussed. These recommendations will support Queensland businesses to thrive and adapt to the forces shaping work in this changing regional economy.

Shaping urban consolidation debates: Social representations in Brisbane newspaper media

Recent international trends towards urban consolidation, intended to reduce outward urban sprawl by concentrating growth within existing neighbourhoods, can cause contention in cities. Understanding how the mass media represents urban consolidation can lead to more informed and democratic planning practices. This paper employs Social Representations Theory to identify and understand representations of urban consolidation in newspaper media. The theory recognises that the media is a key purveyor of public discourse and can reflect, shape or suppress ideas circulating in society. This novel approach has not previously been applied to understanding social representations of urban consolidation strategies in the mass media. The rapidly growing and changing city of Brisbane, Australia, is utilised as a case study. Brisbane is situated in South East Queensland, the fastest growing region in Australia, and is governed by regional and local planning policies that strongly support increased densities in existing urban areas. Findings from a quantitative textual analysis of 449 articles published in Brisbane newspapers between 2007 and 2014 reveal key clusters and classes of co-occurring words that represent dominant social representations apparent in the newspaper corpus. The paper provides two key conclusions. The first is that social representations occurring in mass media represent an important source of information about ‘common sense’ understandings and evaluations of urban consolidation debates. The second is that urban consolidation is represented as a ultifaceted issue, including interrelated themes of housing,sustainable population growth, investment strategies and the interplay between politics and planning

Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Spectral Values

This series of drawings takes a diagrammatically creative approach to understanding the economic theories and personalities at the centre of the Global Financial Crisis. Mimicking the form of US currency, the work removes labels from common economic diagrams and portrays financial titans in repose as a way to express a personal and ambivalent experience of contemporary capitalism.

Spectral unmixing with estimated adaptive endmember index using Extended Support Vector Machine

The most difficult operation in the flood inundation mapping using optical flood images is to separate fully inundated areas from the ‘wet’ areas where trees and houses are partly covered by water. This can be referred as a typical problem the presence of mixed pixels in the images. A number of automatic information extraction image classification algorithms have been developed over the years for flood mapping using optical remote sensing images. Most classification algorithms generally, help in selecting a pixel in a particular class label with the greatest likelihood. However, these hard classification methods often fail to generate a reliable flood inundation mapping because the presence of mixed pixels in the images. To solve the mixed pixel problem advanced image processing techniques are adopted and Linear Spectral unmixing method is one of the most popular soft classification technique used for mixed pixel analysis. The good performance of linear spectral unmixing depends on two important issues, those are, the method of selecting endmembers and the method to model the endmembers for unmixing. This paper presents an improvement in the adaptive selection of endmember subset for each pixel in spectral unmixing method for reliable flood mapping. Using a fixed set of endmembers for spectral unmixing all pixels in an entire image might cause over estimation of the endmember spectra residing in a mixed pixel and hence cause reducing the performance level of spectral unmixing. Compared to this, application of estimated adaptive subset of endmembers for each pixel can decrease the residual error in unmixing results and provide a reliable output. In this current paper, it has also been proved that this proposed method can improve the accuracy of conventional linear unmixing methods and also easy to apply. Three different linear spectral unmixing methods were applied to test the improvement in unmixing results. Experiments were conducted in three different sets of Landsat-5 TM images of three different flood events in Australia to examine the method on different flooding conditions and achieved satisfactory outcomes in flood mapping.