The Creative Workforce : How to Launch Young People into High-Flying Futures

Creativity has become the economic engine of the 21st century. No longer the preserve of creative industries, 'creative capital' – in the form of novel thinking, navigation, interactivity and border-crossing – has become crucial to success and productivity. But are young people being equipped for a work future in which creativity is the defining feature of economic life? In this important book, Erica McWilliam argues that young people’s creative capacities are not being properly developed and that education, particularly in Australia, demands a massive pedagogical shift. Using both Australian and overseas examples, McWilliam describes what creative capacities are, why they've become important to our work futures, and what can be done to optimise the creative capacities of young people.

Transaction mining for fraud detection in ERP Systems

Despite all attempts to prevent fraud, it continues to be a major threat to industry and government. Traditionally, organizations have focused on fraud prevention rather than detection, to combat fraud. In this paper we present a role mining inspired approach to represent user behaviour in Enterprise Resource Planning (ERP) systems, primarily aimed at detecting opportunities to commit fraud or potentially suspicious activities. We have adapted an approach which uses set theory to create transaction profiles based on analysis of user activity records. Based on these transaction profiles, we propose a set of (1) anomaly types to detect potentially suspicious user behaviour, and (2) scenarios to identify inadequate segregation of duties in an ERP environment. In addition, we present two algorithms to construct a directed acyclic graph to represent relationships between transaction profiles. Experiments were conducted using a real dataset obtained from a teaching environment and a demonstration dataset, both using SAP R/3, presently the predominant ERP system. The results of this empirical research demonstrate the effectiveness of the proposed approach.

Automation of an underground mining vehicle using reactive navigation and opportunistic localization

This paper describes the implementation of an autonomous navigation system onto a 30 tonne Load-Haul-Dump truck. The control architecture is based on a robust reactive wall-following behaviour. To make it purposeful we provide driving hints derived from an approximate nodal-map. For most of the time, the vehicle is driven with weak localization (odometry). This need only be improved at intersections where decisions must be made - a technique we refer to as opportunistic localization. The truck has achieved full-speed autonomous operation at an artificial test mine, and subsequently, at a operational underground mine.

Autonomous control of underground mining vehicles using reactive navigation

Describes how many of the navigation techniques developed by the robotics research community over the last decade may be applied to a class of underground mining vehicles (LHDs and haul trucks). We review the current state-of-the-art in this area and conclude that there are essentially two basic methods of navigation applicable. We describe an implementation of a reactive navigation system on a 30 tonne LHD which has achieved full-speed operation at a production mine.

Building research capacity in the nursing workforce : the design and evaluation of the nurse researcher role

Objectives: The Nurse Researcher Project (NRP) was initiated to support development of a nursing research and evidence based practice culture in Cancer Care Services (CCS) in a large tertiary hospital in Australia. The position was established and evaluated to inform future directions in the organisation.---------- Background: The demand for quality cancer care has been expanding over the past decades. Nurses are well placed to make an impact on improving health outcomes of people affected by cancer. At the same time, there is a robust body of literature documenting the barriers to undertaking and utilising research by and for nurses and nursing. A number of strategies have been implemented to address these barriers including a range of staff researcher positions but there is scant attention to evaluating the outcomes of these strategies. The role of nurse researcher has been documented in the literature with the aim to provide support to nurses in the clinical setting. There is, to date, little information in relation to the design, implementation and evaluation of this role.---------- Design: The Donabedian’s model of program evaluation was used to implement and evaluate this initiative.---------- Methods: The ‘NRP’ outlined the steps needed to implement the nurse researcher role in a clinical setting. The steps involved the design of the role, planning for the support system for the role, and evaluation of outcomes of the role over two years.---------- Discussion: This paper proposes an innovative and feasible model to support clinical nursing research which would be relevant to a range of service areas.---------- Conclusion: Nurse researchers are able to play a crucial role in advancing nursing knowledge and facilitating evidence based practice, especially when placed to support a specialised team of nurses at a service level. This role can be implemented through appropriate planning of the position, building a support system and incorporating an evaluation plan.

Where have all the workers gone? Exploring public sector workforce planning

Governments undertake extensive planning of many services and functions, but tend to neglect planning of public service workforces. Disruptions to public service delivery, such as shortages of nurses and doctors, have rejuvenated interest in workforce planning, but many organisations struggle to do it effectively. This historical study examines the capacity of central personnel agencies to predict workforce risks and support workforce planning, using a study of the Queensland public service. It identifies lack of workforce data as a barrier to effective workforce planning, as a result of factors such as changes in the direction of the central personnel agency, lack of appreciation for the value of comprehensive central workforce data, and limited agency human resource (HR) skills or capacity.

Developing the Primary Palliative Care Workforce in Australia

Current healthcare models promote the equitable provision of palliative care to oncology patients with advancing disease, in the setting of their usual care, often in conjunction with anti-cancer therapies. This has resulted in specialist cancer services, as well as primary care across metropolitan, rural and remote communities, being called upon to integrate palliative care principles into their practice. To meet this increased demand for skilled health care professionals several national strategies have been initiated over the last five years. In this paper two projects are discussed in detail: the Palliative Care Curriculum for Undergraduates and the Program of Experience in the Professional Approach.

Mining robotics

Mining is the process of extracting mineral resources from the Earth for commercial value. It is an ancient human activity which can be traced back to Palaeolithic times (43 000 years ago), where for example the mineral hematite was mined to produce the red pigment ochre. The importance of many mined minerals is reflected in the names of the major milestones in human civilizations: the stone, copper, bronze, and iron ages. Much later coal provided the energy that was critical to the industrial revolution and still underpins modern society, creating 38% of world energy generation today. Ancient mines used human and later animal labor and broke rock using stone tools, heat, and water, and later iron tools. Today’s mines are heavily mechanized with large diesel and electrically powered vehicles, and rock is broken with explosives or rock cutting machines.

Reactive navigation and opportunistic localization for autonomous underground mining vehicles

This paper describes an autonomous navigation system for a large underground mining vehicle. The control architecture is based on a robust reactive wall-following behaviour. To make it purposeful we provide driving hints derived from an approximate nodal-map. For most of the time, the vehicle is driven with weak localization (odometry). This need only be improved at intersections where decisions must be made – a technique we refer to as opportunistic localization. The paper briefly reviews absolute and relative navigation strategies, and describes an implementation of a reactive navigation system on a 30 tonne Load-Haul-Dump truck. This truck has achieved full-speed autonomous operation at an artificial test mine, and subsequently, at a operational underground mine.

Dragline automation - a decade of development : shared autonomy for improving mining equipment productivity

Draglines are massive machines commonly used in surface mining to strip overburden, revealing the targeted minerals for extraction. Automating some or all of the phases of operation of these machines offers the potential for significant productivity and maintenance benefits. The mining industry has a history of slow uptake of automation systems due to the challenges contained in the harsh, complex, three-dimensional (3D), dynamically changing mine operating environment. Robotics as a discipline is finally starting to gain acceptance as a technology with the potential to assist mining operations. This article examines the evolution of robotic technologies applied to draglines in the form of machine embedded intelligent systems. Results from this work include a production trial in which 250,000 tons of material was moved autonomously, experiments demonstrating steps towards full autonomy, and teleexcavation experiments in which a dragline in Australia was tasked by an operator in the United States.

Mining agreements : negotiated frameworks in the Australian minerals sector

Discusses the role of negotiated frameworks as a regulatory mechanism in the development of Australia's premier industry of the 20th century.

Overview of the INEX 2009 XML mining track : clustering and classification of XML documents

This report explains the objectives, datasets and evaluation criteria of both the clustering and classification tasks set in the INEX 2009 XML Mining track. The report also describes the approaches and results obtained by the different participants.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Safety impacts of alcohol and other drugs in the construction industry : a research methodology

Anecdotal evidence from the infrastructure and building sectors highlights issues of drugs and alcohol and its association with safety risk on construction sites. Operating machinery and mobile equipment, proximity to live traffic together with congested sites, electrical equipment and operating at heights conspire to accentuate the potential adverse impact of drugs and alcohol in the workplace. While most Australian jurisdictions have identified this as a critical safety issue, information is limited regarding the prevalence of alcohol and other drugs in the workplace and there is limited evidential guidance regarding how to effectively and efficiently address such an issue. No known study has scientifically evaluated the relationship between the use of drugs and alcohol and safety impacts in construction, and there has been only limited adoption of nationally coordinated strategies, supported by employers and employees to render it socially unacceptable to arrive at a construction workplace with impaired judgement from drugs and alcohol. A nationally consistent collaborative approach across the construction workforce - involving employers and employees; clients; unions; contractors and sub-contractors is required to engender a cultural change in the construction workforce – in a similar manner to the on-going initiative in securing a cultural change to drink-driving in our society where peer intervention and support is encouraged. This study has four key objectives. Firstly, using the standard World Health Organisation AUDIT, a national qualitative and quantitative assessment of the use of drugs and alcohol will be carried out. This will build upon similar studies carried out in the Australian energy and mining sectors. Secondly, the development of an appropriate industry policy will adopt a non-punitive and rehabilitative approach developed in consultation with employers and employees across the infrastructure and building sectors, with the aim it be adopted nationally for adoption at the construction workplace. Thirdly, an industry-specific cultural change management program will be developed through a nationally collaborative approach to reducing the risk of impaired performance on construction sites and increasing workers’ commitment to drugs and alcohol safety. Finally, an implementation plan will be developed from data gathered from both managers and construction employees. Such an approach stands to benefit not only occupational health and safety, through a greater understanding of the safety impacts of alcohol and other drugs at work, but also alcohol and drug use as a wider community health issue. This paper will provide an overview of the background and significance of the study as well as outlining the proposed methodology that will be used to evaluate the safety impacts of alcohol and other drugs in the construction industry.