Sustainable Road Management in Texas : network-level flexible pavement structural condition analysis using data mining techniques

The research team recognized the value of network-level Falling Weight Deflectometer (FWD) testing to evaluate the structural condition trends of flexible pavements. However, practical limitations due to the cost of testing, traffic control and safety concerns and the ability to test a large network may discourage some agencies from conducting the network-level FWD testing. For this reason, the surrogate measure of the Structural Condition Index (SCI) is suggested for use. The main purpose of the research presented in this paper is to investigate data mining strategies and to develop a prediction method of the structural condition trends for network-level applications which does not require FWD testing. The research team first evaluated the existing and historical pavement condition, distress, ride, traffic and other data attributes in the Texas Department of Transportation (TxDOT) Pavement Maintenance Information System (PMIS), applied data mining strategies to the data, discovered useful patterns and knowledge for SCI value prediction, and finally provided a reasonable measure of pavement structural condition which is correlated to the SCI. To evaluate the performance of the developed prediction approach, a case study was conducted using the SCI data calculated from the FWD data collected on flexible pavements over a 5-year period (2005 – 09) from 354 PMIS sections representing 37 pavement sections on the Texas highway system. The preliminary study results showed that the proposed approach can be used as a supportive pavement structural index in the event when FWD deflection data is not available and help pavement managers identify the timing and appropriate treatment level of preventive maintenance activities.

Justice for pollution-victims in China and Australia

The concept of environmental justice is well developed in North America, but is still at the evolutionary stage in most other jurisdictions around the globe. This paper seeks to explore two jurisdictions where incidents of environmental justice are likely to be seen in the future as a result of manufacturing and mining practices. The discussion will centre upon avenues to environmental justice for both private citizens and the public at large. The first jurisdiction considered is China, where environmental liability claims brought by Chinese citizens have increased at an annual average of 25% (Yang 2011). Manufacturing is at the core of the Chinese economy and is responsible for some of the unprecedented economic growth in the region. Less discussed are the industry impacts on water and air pollution levels and the associated implications of these pollutants on local communities. China introduced the Tort Liability Law (TLL) in 2010, which may provide avenues to justice for private citizens. The other jurisdiction considered by the paper is Australia, where the mining boom has buffered the Australian economy from the global financial crisis. There is some limited case law in Australia where private citizens have made a claim in toxic torts; however the framework is underdeveloped in terms of the significant risks facing indigenous and local communities in mining areas and also by comparison to the developments of the TLL framework in China. This paper traces the regulatory responses to the affects of major industries on communities in China and Australia. From this it examines the need for environmental justice avenues that align with rule of law principles.

Representing and discovering adversarial team behaviors using player roles

In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabe temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

An examination of the legislative framework governing commuter safety in the Queensland Mining Industry

Commuting in the mining industry -Background -The problem -Journey management -The structure of the legislative framework Legislation and Regulation -Workplace safety in Queensland mining -Risk management -Mining legislation and journey management -Commuting and employee responsibilities -Queensland Workers’ Compensation Scheme Industry standards -Industry standards and journey management Regulated and organisational policy documents -Policy documents and journey management Observations & Conclusions

Process mining for clinical processes: A comparative analysis of four Australian hospitals

Business process analysis and process mining, particularly within the health care domain, remain under-utilised. Applied research that employs such techniques to routinely collected, health care data enables stakeholders to empirically investigate care as it is delivered by different health providers. However, cross-organisational mining and the comparative analysis of processes present a set of unique challenges in terms of ensuring population and activity comparability, visualising the mined models and interpreting the results. Without addressing these issues, health providers will find it difficult to use process mining insights, and the potential benefits of evidence-based process improvement within health will remain unrealised. In this paper, we present a brief introduction on the nature of health care processes; a review of the process mining in health literature; and a case study conducted to explore and learn how health care data, and cross-organisational comparisons with process mining techniques may be approached. The case study applies process mining techniques to administrative and clinical data for patients who present with chest pain symptoms at one of four public hospitals in South Australia. We demonstrate an approach that provides detailed insights into clinical (quality of patient health) and fiscal (hospital budget) pressures in health care practice. We conclude by discussing the key lessons learned from our experience in conducting business process analysis and process mining based on the data from four different hospitals.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Perception Quality Evaluation with Visual and Infrared Cameras in Challenging Environmental Conditions

This work aims to contribute to the reliability and integrity of perceptual systems of unmanned ground vehicles (UGV). A method is proposed to evaluate the quality of sensor data prior to its use in a perception system by utilising a quality metric applied to heterogeneous sensor data such as visual and infrared camera images. The concept is illustrated specifically with sensor data that is evaluated prior to the use of the data in a standard SIFT feature extraction and matching technique. The method is then evaluated using various experimental data sets that were collected from a UGV in challenging environmental conditions, represented by the presence of airborne dust and smoke. In the first series of experiments, a motionless vehicle is observing a ’reference’ scene, then the method is extended to the case of a moving vehicle by compensating for its motion. This paper shows that it is possible to anticipate degradation of a perception algorithm by evaluating the input data prior to any actual execution of the algorithm.

The ‘feminine revolution in mining’ : a critique

This paper presents an analysis of media reports of Australian women in mine management. It argues that a dominant storyline in the texts is one of gender change; in fact, a ‘feminine revolution’ is said to have occurred in the mining industry and corporate Australia more generally. Despite this celebratory and transformative discourse the female mine managers interviewed in the media texts seek to distance themselves from women/female identity/femininity and take up a script of gender neutrality. It is demonstrated, however, that this script is saturated with the assumptions and definitions of managerial masculinity.

ACE research briefing paper 007 : Ken Talbot - an entrepreneur's contribution to the growth of the Australian Mining Industry

Ken Talbot was one of Australian mining’s most successful entrepreneurs and rose to the top of his industry to become one of Australia’s wealthiest men. Although the nation’s resources industry is synonymous with global names such as Xstrata, BHP Billiton and Rio Tinto, Ken was an individual who made a big impact on the development and growth of the sector. This case study examines Ken’s achievements, his transition from employee to entrepreneur, and the qualities that enabled him to succeed at such a high level. In particular, it focuses on his Jellinbah and Coppabella mining developments that directly led to the creation of Macarthur Coal and the Talbot Group. By the time of his premature death in an African plane crash in 2010, Ken had amassed a fortune estimated at almost $1 billion and was aged just 59. The last publically available Talbot Group annual report for calendar year 2009 showed that the investment portfolio of the group returned 113 per cent that year. Even throughout the global financial crisis the portfolio made a positive return on investment of no less than 10 per cent. Ken’s sense of mateship and his tremendous people skills were keys to his success in the mining industry and the wider community. In addition to excelling in business, he is also remembered for his philanthropy and leaving 30 per cent of his estate to charity through the Talbot Family Foundation.

Configurations of Gender, Class and Rurality in Resource Affected Rural Australia

Historically, class has been a key concern in studies of resource affected communities (e.g., Williamson 1982, Warwick and Littlejohn 1992). While work continues, particularly in Britain, today it reflects the rationalization of the British mining sector, and thus focuses largely on mining heritage (e.g., Strangleman et al. 1999, Dicks 2008). In contrast, this chapter examines class relations as manifest in a contemporary setting in rural Australia. This site, the Ravensthorpe Shire in the south west of Western Australia, relied largely on agriculture until 2004 when BHP Billiton commenced construction of a nickel mine in the area. This affected the entire Shire as well as the two rural communities of Ravensthorpe and Hopetoun. The mine, which was officially opened in June 2008, is one of a large number of new mineral and energy developments being established in non metropolitan areas of the country as high international demand for resources fuels significant growth in the sector. In a single six month period in 2009, for example, 15 major minerals and energy projects were completed across the nation and a further 74 projects were at advanced stages (Australian Bureau of Agricultural Economics 2009). A number of these were, as was the case in Ravensthorpe, in what had been traditionally agricultural communities.

Pro bono partnerships in environmental law : enhancing outcomes for universities and CLCs

CLE can be a life-changing event in a law student’s education. It can open their eyes to the day-to-day operation of justice and provide them with examples of possible career paths they may never have thought existed. Yet it can also provide long-term benefits for CLCs and academics. Recent CLE models have moved towards partnerships with external organisations and away from on-site legal clinics. Some examples have exhibited success with a multidisciplinary approach involving students from non-law disciplines to provide a holistic approach to a CLC’s needs. Such a multidisciplinary approach is of particular benefit in community lawyering clinics where students are engaged in social change lawyering. The QUT/EDO partnership presents a new model in the environmental clinic landscape in Australia. Initial feedback suggests that the clinic has assisted students in gaining insight into the access to justice issues arising from mining activities and to raise the level of understanding and awareness among community members of their legal rights to protect the environment. Looking at ways to increase partnerships between universities and CLCs is of vital importance in the future, given recent federal government CLC funding cuts. The legal clinic model has great potential to evolve and contribute in ensuring the continued operation of legal initiatives to protect the environment in the public interest.

Corporate sustainability reporting for water : water footprint, global reporting initiative and the water accounting framework

Three initiatives with respect to water reporting in the mining sector are compared in this paper to understand the quantities that are asked for by each initiative and the guidelines of those initiatives through means of a case study. The Global Reporting Initiative (GRI) was chosen because it has achieved widespread acceptance amongst mining companies and its water-related indicators are widely reported in corporate sustainability reporting. In contrast, the Water Footprint Network, which has been an important initiative in food and agricultural industries, has had low acceptance in the mining industry. The third initiative is the Water Accounting Framework, a collaboration between The Minerals Council of Australia and the Sustainable Minerals Institute of the University of Queensland. A water account had previously been created according to the Water Accounting Framework for the case study site, an open pit coal mine in the Bowen Basin. The resulting account provided consistent data for the Global Reporting Initiative (GRI) and the Water Footprint attributable to mining but in particular, a deficiency in the GRI indicator of EN10 reuse and recycling efficiency was illustrated quantitatively. This has far-reaching significance due to the widespread use of GRI indicators in mining corporate reports.

Measuring, modelling and understanding the mechanical behavior of bagasse

In the Australian sugar industry, sugar cane is smashed into a straw like material by hammers before being squeezed between large rollers to extract the sugar juice. The straw like material is initially called prepared cane and then bagasse as it passes through successive roller milling units. The sugar cane materials are highly compressible, have high moisture content, are fibrous, and they resemble some peat soils in both appearance and mechanical behaviour. A promising avenue to improve the performance of milling units for increased throughput and juice extraction, and to reduce costs is by modelling of the crushing process. To achieve this, it is believed necessary that milling models should be able to reproduce measured bagasse behaviour. This investigation sought to measure the mechanical (compression, shear, and volume) behaviour of prepared cane and bagasse, to identify limitations in currently used material models, and to progress towards a material model that can predict bagasse behaviour adequately. Tests were carried out using a modified direct shear test equipment and procedure at most of the large range of pressures occurring in the crushing process. The investigation included an assessment of the performance of the direct shear test for measuring bagasse behaviour. The assessment was carried out using finite element modelling. It was shown that prepared cane and bagasse exhibited critical state behavior similar to that of soils and the magnitudes of material parameters were determined. The measurements were used to identify desirable features for a bagasse material model. It was shown that currently used material models had major limitations for reproducing bagasse behaviour. A model from the soil mechanics literature was modified and shown to achieve improved reproduction while using magnitudes of material parameters that better reflected the measured values. Finally, a typical three roller mill pressure feeder configuration was modelled. The predictions and limitations were assessed by comparison to measured data from a sugar factory.

Regional trade-offs between mine water and energy use : a water treatment case study

The mining industry faces concurrent pressures of reducing water use, energy consumption and greenhouse gas (GHG) emissions in coming years. However, the interactions between water and energy use, as well as GHG e missions have largely been neglected in modelling studies to date. In addition, investigations tend to focus on the unit operation scale, with little consideration of whole-of-site or regional scale effects. This paper presents an application of a hierarchical systems model (HSM) developed to represent water, energy and GHG emissions fluxes at scales ranging from the unit operation, to the site level, to the regional level. The model allows for the linkages between water use, energy use and GHG emissions to be examined in a fl exible and intuitive way, so that mine sites can predict energy and emissions impacts of water use reduction schemes and vice versa. This paper examines whether this approach can also be applied to the regional scale with multiple mine sites. The model is used to conduct a case study of several coal mines in the Bowen Basin, Australia, to compare the utility of centralised and decentralised mine water treatment schemes. The case study takes into account geographical factors (such as water pumping distances and elevations), economic factors (such as capital and operating cost curves for desalination treatment plants) and regional factors (such as regionally varying climates and associated variance in mine water volumes and quality). The case study results indicate that treatment of saline mine water incurs a trade-off between water and energy use in all cases. However, significant cost differences between centralised and decentralised schemes can be observed in a simple economic analysis. Further research will examine the possibility for deriving model up-scaling algorithms to reduce computational requirements.