The role of the health surveillance program in the Queensland coal mining industry

The Queensland Coal Industry Employees Health Scheme was implemented in 1993 to provide health surveillance for all Queensland coal industry workers. Tt1e government, mining employers and mining unions agreed that the scheme should operate for seven years. At the expiry of the scheme, an assessment of the contribution of health surveillance to meet coal industry needs would be an essential part of determining a future health surveillance program. This research project has analysed the data made available between 1993 and 1998. All current coal industry employees have had at least one health assessment. The project examined how the centralised nature of the Health Scheme benefits industry by identi~)jng key health issues and exploring their dimensions on a scale not possible by corporate based health surveillance programs. There is a body of evidence that indicates that health awareness - on the scale of the individual, the work group and the industry is not a part of the mining industry culture. There is also growing evidence that there is a need for this culture to change and that some change is in progress. One element of this changing culture is a growth in the interest by the individual and the community in information on health status and benchmarks that are reasonably attainable. This interest opens the way for health education which contains personal, community and occupational elements. An important element of such education is the data on mine site health status. This project examined the role of health surveillance in the coal mining industry as a tool for generating the necessary information to promote an interest in health awareness. The Health Scheme Database provides the material for the bulk of the analysis of this project. After a preliminary scan of the data set, more detailed analysis was undertaken on key health and related safety issues that include respiratory disorders, hearing loss and high blood pressure. The data set facilitates control for confounding factors such as age and smoking status. Mines can be benchmarked to identify those mines with effective health management and those with particular challenges. While the study has confirmed the very low prevalence of restrictive airway disease such as pneu"moconiosis, it has demonstrated a need to examine in detail the emergence of obstructive airway disease such as bronchitis and emphysema which may be a consequence of the increasing use of high dust longwall technology. The power of the Health Database's electronic data management is demonstrated by linking the health data to other data sets such as injury data that is collected by the Department of l\1mes and Energy. The analysis examines serious strain -sprain injuries and has identified a marked difference between the underground and open cut sectors of the industry. The analysis also considers productivity and OHS data to examine the extent to which there is correlation between any pairs ofJpese and previously analysed health parameters. This project has demonstrated that the current structure of the Coal Industry Employees Health Scheme has largely delivered to mines and effective health screening process. At the same time, the centralised nature of data collection and analysis has provided to the mines, the unions and the government substantial statistical cross-sectional data upon which strategies to more effectively manage health and relates safety issues can be based.

Acoustic keyword spotting in speech with applications to data mining

Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spotting to data mining tasks. The thesis makes a number of major contributions to the ¯eld of keyword spotting. The ¯rst major contribution is the development of a novel keyword veri¯cation method named Cohort Word Veri¯cation. This method combines high level lin- guistic information with cohort-based veri¯cation techniques to obtain dramatic improvements in veri¯cation performance, in particular for the problematic short duration target word class. The second major contribution is the development of a novel audio document indexing technique named Dynamic Match Lattice Spotting. This technique aug- ments lattice-based audio indexing principles with dynamic sequence matching techniques to provide robustness to erroneous lattice realisations. The resulting algorithm obtains signi¯cant improvement in detection rate over lattice-based audio document indexing while still maintaining extremely fast search speeds. The third major contribution is the study of multiple veri¯er fusion for the task of keyword veri¯cation. The reported experiments demonstrate that substantial improvements in veri¯cation performance can be obtained through the fusion of multiple keyword veri¯ers. The research focuses on combinations of speech background model based veri¯ers and cohort word veri¯ers. The ¯nal major contribution is a comprehensive study of the e®ects of limited training data for keyword spotting. This study is performed with consideration as to how these e®ects impact the immediate development and deployment of speech technologies for non-English languages.

Beyond statistical procedures for predictive modelling: Data mining algorithms and support for university research at QUT

In a seminal data mining article, Leo Breiman [1] argued that to develop effective predictive classification and regression models, we need to move away from the sole dependency on statistical algorithms and embrace a wider toolkit of modeling algorithms that include data mining procedures. Nevertheless, many researchers still rely solely on statistical procedures when undertaking data modeling tasks; the sole reliance on these procedures has lead to the development of irrelevant theory and questionable research conclusions ([1], p.199). We will outline initiatives that the HPC & Research Support group is undertaking to engage researchers with data mining tools and techniques; including a new range of seminars, workshops, and one-on-one consultations covering data mining algorithms, the relationship between data mining and the research cycle, and limitations and problems with these new algorithms. Organisational limitations and restrictions to these initiatives are also discussed.

Transnational business masculinities : media representations of gender and management in global mining corporations

This paper examines Australian media representations of the male managers of two global mining corporations, Rio Tinto and BHP Billiton. These organizations are transnational (or multinational) corporations with assets and/or operations across national boundaries (Dunning and Lundan, 2008), and indeed their respective Chief Executive Officers, Tom Albanese and Marius Kloppers are two of the most economically (and arguably politically) powerful in the world overseeing 37 000 and 39 000 employees internationally. With a 2008 profit of US$15.962 billion and assets of US$ 75.889 Billion BHP Billiton is the world's largest mining company. In terms of its profits and assets Rio Tinto ranks fourth in the world, but with operations in six countries (mainly Canada and Australia) and a 2008 profit of US$10.3 billion it is also emblematic of the transnational in that its ‘budget is larger than that of all but a few nations’ (Giddens, 2003, p. 62).

12 fingers or one, it’s how you play? Genetic discrimination in the Australian workforce

For quite some time, debate has raged about what the human race can and should do with its knowledge of genetics. We are now nearly 60 years removed from the work of Watson and Crick who determined the structure of deoxyribonucleic acid (DNA), yet our opinions as how best to employ scientific knowledge of the human genome, remain as diverse and polarised as ever. Human judgment is often shaped and coloured by popular media and culture, so it should come as no surprise that box office movies such as Gattaca (1997) continue to play a role in informing public opinion on genetics. In order to perform well at the box office, movies such as Gattaca take great liberty in sensationalising (and even distorting) the implications that may result from genetic screening and testing. If the public’s opinion on human genetics is strongly derived from the box office and popular media, then it is no wonder that the discourse on human genetics is couched in the polar parlances of future utopias or future dystopias. When legislating in an area like genetic discrimination in the workforce, we must be mindful of not overplaying the causal link between genetic predisposition towards a disability and an employee’s ability to perform the inherent requirements of their job. Genetic information is ultimately about people, it is not about genes. Genetic discrimination is ultimately about actions, it is not about the intrinsic value of genetic information.

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

Globalization, suburbanization, and the creative workforce : Findings from Australian suburban communities

This paper considers the changing relationship between economic prosperity and Australian suburbs, noting that what has been termed “the first suburban nation” in experiencing an intensification of suburban growth in the 2000s, in the context of economic globalization. The paper reports on a three-year Australian Research Council funded project into “Creative Suburbia”, identifying the significant percentage of the creative industries workforce who live in suburban areas. Drawing on case studies from suburbs in the Australian cities of Brisbane and Melbourne, it notes the contrasts between the experience of these workers, who are generally positive towards suburban life, and the underlying assumptions of “creative cities” policy discourse that such workers prefer to be concentrated in high density inner urban creative clusters.

The social work & human services workforce : report from a national study of education, training and workforce needs

This report analyses the national curriculum and workforce needs of the social work and human services workforce. Australia’s community and health services are among the fastest growing sectors of employment in the nation but the sustainability of an appropriately qualified workforce is threatened. Yet there is little integration of education and workforce planning for the community services sector. This contrasts markedly with the health services sector, where key stakeholders are collaboratively addressing workforce challenges. Our research confirmed rapid growth in the social work and human services workforce and it also identified: • an undersupply of professionally qualified social work and human service practitioners to meet workforce demand; • the rapid ageing of the workforce with many workers approaching retirement; • limited career and salary structures creating disincentives to retention; • a highly diverse qualification base across the workforce. This diversity is inconsistent with the specialist knowledge and skills required of practitioners in many domains of community service provision. Our study revealed a lack of co-ordination across VET and higher education to meet the educational needs of the social work and human services workforce. Our analysis identified: • strong representation of equity groups in social work and related human service programs, although further participation of these groups is still needed; • the absence of clear articulation pathways between VET and higher education programs due the absence of co-ordination and planning between these sectors; • substantial variation in the content of the diverse range of social work and human service programs, with accredited programs conforming to national standards and some others in social and behavioural sciences lacking any external validation; • financial obstacles and disincentives to social work and human service practitioners in achieving postgraduate level qualifications. We recommend that: • DEEWR identify accredited social work and human services courses as a national education priority (similar to education and nursing). This will help ensure the supply of professional workers to this sector; • VET and higher education providers are encouraged to collaboratively develop clear and accessible educational pathways across the educational sectors; • DEEWR undertake a national workforce analysis and planning processes in collaboration with CSDMAC, and all social and community services stakeholders, to ensure workforce sustainability; and • COAG develop a national regulation framework for the social and community services workforce. This would provide sound accountability systems, and rigorous practice and educational standards necessary for quality service provision. It will also ensure much needed public confidence in this workforce.

An intelligent data mining technique for product quality improvement

Advances in data mining have provided techniques for automatically discovering underlying knowledge and extracting useful information from large volumes of data. Data mining offers tools for quick discovery of relationships, patterns and knowledge in large complex databases. Application of data mining to manufacturing is relatively limited mainly because of complexity of manufacturing data. Growing self organizing map (GSOM) algorithm has been proven to be an efficient algorithm to analyze unsupervised DNA data. However, it produced unsatisfactory clustering when used on some large manufacturing data. In this paper a data mining methodology has been proposed using a GSOM tool which was developed using a modified GSOM algorithm. The proposed method is used to generate clusters for good and faulty products from a manufacturing dataset. The clustering quality (CQ) measure proposed in the paper is used to evaluate the performance of the cluster maps. The paper also proposed an automatic identification of variables to find the most probable causative factor(s) that discriminate between good and faulty product by quickly examining the historical manufacturing data. The proposed method offers the manufacturers to smoothen the production flow and improve the quality of the products. Simulation results on small and large manufacturing data show the effectiveness of the proposed method.

Reconnecting the interrupted landscape : a cultural landscape approach to Australian open-cut coal mining landscapes

In the late 20th century, a value-shift began to influence political thinking, recognising the need for environmentally, socially and culturally sustainable resource development. This shift entailed moves away from thinking of nature and culture as separate entities - The former existing merely to serve the latter. Cultural landscape theory recognises 'nature' as at once both 'natural', and as a 'cultural' construct. As such it may offer a framework through which to progress in the quest for 'sustainable development'. This 2005 Masters thesis makes a contribution to that quest by asking whether contemporary developments in cultural landscape theory can contribute to rehabilitation strategies for Australian open-cut coal mining landscapes, an examplar resource development landscape. A thematic historial overview of landscape values and resource development in Australis post-1788, and a review of cultural landscape theory literature contribute to the formation of the theoretical framework: "reconnecting the interrupted landscape". The author then explores a possible application of this framework within the Australian open-cut coal mining landscape.