Perceptions of land-use uncertainty in Queensland's resource-based regions

Some of Queensland's regions are experiencing rapid changes related to the recent and growing capacity to more effectively exploit significant energy sources. These changes have triggered land-use conflicts between the mining sector and other economic sectors, mainly agriculture. These conflicts fuel existing uncertainty surrounding the current and future economic, social and environmental impacts of extractive industries. This paper explores the concept of uncertainty as it applies to planning for resource-based regions through a scoping analysis of regional stakeholders' perceptions of land-use uncertainty. It then investigates solutions to alleviate such an issue.

Transit passenger segmentation using travel regularity mined from Smart Card transactions data

Transit passenger market segmentation enables transit operators to target different classes of transit users to provide customized information and services. The Smart Card (SC) data, from Automated Fare Collection system, facilitates the understanding of multiday travel regularity of transit passengers, and can be used to segment them into identifiable classes of similar behaviors and needs. However, the use of SC data for market segmentation has attracted very limited attention in the literature. This paper proposes a novel methodology for mining spatial and temporal travel regularity from each individual passenger’s historical SC transactions and segments them into four segments of transit users. After reconstructing the travel itineraries from historical SC transactions, the paper adopts the Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm to mine travel regularity of each SC user. The travel regularity is then used to segment SC users by an a priori market segmentation approach. The methodology proposed in this paper assists transit operators to understand their passengers and provide them oriented information and services.

Lesson studies in Chinese kindergartens

Lesson studies are a powerful form of professional development (Doig and Groves, 2011). The processes of creating, enacting, analyzing, and refining lessons to improve teaching practices are key components of lesson studies. Lesson studies have been the primary form of professional development in Japanese classrooms for many years (Lewis, Perry and Hurd, 2009). This model is now used to improve instruction in many South-East Asian countries (White and Lim, 2008), as well as increasingly in North America (Lesson Study Research Group, 2004), and South Africa (Ono and Ferreira, 2010). In China, this form of professional development aimed at improving teaching, has also been adopted, originating from Soviet models of teacher professional development arising from influences post 1949 (China Education Yearbook, 1986). Thus, China too has a long history of improving teaching and learning through this form of school-based professional learning.

Towards a smart cancer registry

Aims Pathology notification for a Cancer Registry is regarded as the most valid information for the confirmation of a diagnosis of cancer. In view of the importance of pathology data, an automatic medical text analysis system (Medtex) is being developed to perform electronic Cancer Registry data extraction and coding of important clinical information embedded within pathology reports. Methods The system automatically scans HL7 messages received from a Queensland pathology information system and analyses the reports for terms and concepts relevant to a cancer notification. A multitude of data items for cancer notification such as primary site, histological type, stage, and other synoptic data are classified by the system. The underlying extraction and classification technology is based on SNOMED CT1 2. The Queensland Cancer Registry business rules3 and International Classification of Diseases – Oncology – Version 34 have been incorporated. Results The cancer notification services show that the classification of notifiable reports can be achieved with sensitivities of 98% and specificities of 96%5, while the coding of cancer notification items such as basis of diagnosis, histological type and grade, primary site and laterality can be extracted with an overall accuracy of 80%6. In the case of lung cancer staging, the automated stages produced were accurate enough for the purposes of population level research and indicative staging prior to multi-disciplinary team meetings2 7. Medtex also allows for detailed tumour stream synoptic reporting8. Conclusions Medtex demonstrates how medical free-text processing could enable the automation of some Cancer Registry processes. Over 70% of Cancer Registry coding resources are devoted to information acquisition. The development of a clinical decision support system to unlock information from medical free-text could significantly reduce costs arising from duplicated processes and enable improved decision support, enhancing efficiency and timeliness of cancer information for Cancer Registries.

Collaborative resource development for energy efficiency education

BACKGROUND There is a growing volume of open source ‘education material’ on energy efficiency now available however the Australian government has identified a need to increase the use of such materials in undergraduate engineering education. Furthermore, there is a reported need to rapidly equip engineering graduates with the capabilities in conducting energy efficiency assessments, to improve energy performance across major sectors of the economy. In January 2013, building on several years of preparatory action-research initiatives, the former Department of Industry, Innovation, Climate Change, Science, Research and Tertiary Education (DIICCSRTE) offered $600,000 to develop resources for energy efficiency related graduate attributes, targeting Engineers Australia college disciplines, accreditation requirements and opportunities to address such requirements. PURPOSE This paper discusses a $430,000 successful bid by a university consortium led by QUT and including RMIT, UA, UOW, and VU, to design and pilot several innovative, targeted open-source resources for curriculum renewal related to energy efficiency assessments, in Australian engineering programs (2013-2014), including ‘flat-pack’, ‘media-bites’, ‘virtual reality’ and ‘deep dive’ case study initiatives. DESIGN/ METHOD The paper draws on literature review and lessons learned by the consortium partners in resource development over the last several years to discuss methods for selecting key graduate attributes and providing targeted resources, supporting materials, and innovative delivery options to assist universities deliver knowledge and skills to develop such attributes. This includes strategic industry and key stakeholders engagement. The paper also discusses processes for piloting, validating, peer reviewing, and refining these resources using a rigorous and repeatable approach to engaging with academic and industry colleagues. RESULTS The paper provides an example of innovation in resource development through an engagement strategy that takes advantage of existing networks, initiatives, and funding arrangements, while informing program accreditation requirements, to produce a cost-effective plan for rapid integration of energy efficiency within education. By the conference, stakeholder workshops will be complete. Resources will be in the process of being drafted, building on findings from the stakeholder engagement workshops. Reporting on this project “in progress” provides a significant opportunity to share lessons learned and take on board feedback and input. CONCLUSIONS This paper provides a useful reference document for others considering significant resource development in a consortium approach, summarising benefits and challenges. The paper also provides a basis for documenting the second half of the project, which comprises piloting resources and producing a ‘good practice guide’ for energy efficiency related curriculum renewal.

Random Gabor based templates for facial expression recognition in images with facial occlusion

Robust facial expression recognition (FER) under occluded face conditions is challenging. It requires robust algorithms of feature extraction and investigations into the effects of different types of occlusion on the recognition performance to gain insight. Previous FER studies in this area have been limited. They have spanned recovery strategies for loss of local texture information and testing limited to only a few types of occlusion and predominantly a matched train-test strategy. This paper proposes a robust approach that employs a Monte Carlo algorithm to extract a set of Gabor based part-face templates from gallery images and converts these templates into template match distance features. The resulting feature vectors are robust to occlusion because occluded parts are covered by some but not all of the random templates. The method is evaluated using facial images with occluded regions around the eyes and the mouth, randomly placed occlusion patches of different sizes, and near-realistic occlusion of eyes with clear and solid glasses. Both matched and mis-matched train and test strategies are adopted to analyze the effects of such occlusion. Overall recognition performance and the performance for each facial expression are investigated. Experimental results on the Cohn-Kanade and JAFFE databases demonstrate the high robustness and fast processing speed of our approach, and provide useful insight into the effects of occlusion on FER. The results on the parameter sensitivity demonstrate a certain level of robustness of the approach to changes in the orientation and scale of Gabor filters, the size of templates, and occlusions ratios. Performance comparisons with previous approaches show that the proposed method is more robust to occlusion with lower reductions in accuracy from occlusion of eyes or mouth.

Plasma-produced phase-pure cuprous oxide nanowires for methane gas sensing

Phase-selective synthesis of copper oxide nanowires is warranted by several applications, yet it remains challenging because of the narrow windows of the suitable temperature and precursor gas composition in thermal processes. Here, we report on the room-temperature synthesis of small-diameter, large-area, uniform, and phase-pure Cu2O nanowires by exposing copper films to a custom-designed low-pressure, thermally non-equilibrium, high-density (typically, the electron number density is in the range of 10 11-1013cm-3) inductively coupled plasmas. The mechanism of the plasma-enabled phase selectivity is proposed. The gas sensors based on the synthesized Cu2O nanowires feature fast response and recovery for the low-temperature (∼140°C) detection of methane gas in comparison with polycrystalline Cu2O thin film-based gas sensors. Specifically, at a methane concentration of 4%, the response and the recovery times of the Cu2O nanowire-based gas sensors are 125 and 147s, respectively. The Cu2O nanowire-based gas sensors have a potential for applications in the environmental monitoring, chemical industry, mining industry, and several other emerging areas.

Adaptive finite element analysis of elliptic problems based on bubble-type local mesh generation

A new mesh adaptivity algorithm that combines a posteriori error estimation with bubble-type local mesh generation (BLMG) strategy for elliptic differential equations is proposed. The size function used in the BLMG is defined on each vertex during the adaptive process based on the obtained error estimator. In order to avoid the excessive coarsening and refining in each iterative step, two factor thresholds are introduced in the size function. The advantages of the BLMG-based adaptive finite element method, compared with other known methods, are given as follows: the refining and coarsening are obtained fluently in the same framework; the local a posteriori error estimation is easy to implement through the adjacency list of the BLMG method; at all levels of refinement, the updated triangles remain very well shaped, even if the mesh size at any particular refinement level varies by several orders of magnitude. Several numerical examples with singularities for the elliptic problems, where the explicit error estimators are used, verify the efficiency of the algorithm. The analysis for the parameters introduced in the size function shows that the algorithm has good flexibility.

Establishing statistical foundations early : data modeling with young learners

This paper addresses research from a three-year longitudinal study that engaged children in data modeling experiences from the beginning school year through to third year (6-8 years). A data modeling approach to statistical development differs in several ways from what is typically done in early classroom experiences with data. In particular, data modeling immerses children in problems that evolve from their own questions and reasoning, with core statistical foundations established early. These foundations include a focus on posing and refining statistical questions within and across contexts, structuring and representing data, making informal inferences, and developing conceptual, representational, and metarepresentational competence. Examples are presented of how young learners developed and sustained informal inferential reasoning and metarepresentational competence across the study to become “sophisticated statisticians”.

Energy Efficiency Graduate Attributes Project: Final Report

The Energy Efficiency (EE) Graduate Attributes Project focuses on engineering as a priority profession that has a significant role to play in addressing energy demand and supply issues in Australia. Specifically, this project aims to support embedding EE knowledge and skills throughout the engineering undergraduate curriculum, to help build capacity within the Australian workforce across major sectors of the economy, from mining, manufacturing and industrial applications to design, construction, maintenance and retrofitting built environments. The resultant report is intended to assist in future consultation with key groups such as Engineers Australia (EA), the Australian Council of Engineering Deans (ACED) and the eight EA colleges, to support systemic curriculum renewal and promote the design and development of high quality EE engineering education resources. The project is based on a whole-of-program outcomes-based approach to curriculum renewal, creating a transparent framework for integrating EE. This comprises collaborative consideration by academics and professional engineers who have experience in teaching and practising EE, to identify what students should learn to be equipped with relevant competencies by the time they graduate.

Coil enhancements for high efficiency wireless power transfer applications

Achieving high efficiency with improved power transfer range and misalignment tolerance is the major design challenge in realizing Wireless Power Transfer (WPT) systems for industrial applications. Resonant coils must be carefully designed to achieve highest possible system performance by fully utilizing the available space. High quality factor and enhanced electromagnetic coupling are key indices which determine the system performance. In this paper, design parameter extraction and quality factor optimization of multi layered helical coils are presented using finite element analysis (FEA) simulations. In addition, a novel Toroidal Shaped Spiral (TSS) coil is proposed to increase power transfer range and misalignment tolerance. The proposed shapes and recommendations can be used to design high efficiency WPT resonator in a limited space.

Issues in autonomous navigation of underground vehicles

This paper describes current research at the Australian Centre for Field Robotics (ACFR) in collaboration with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) within the Cooperative Research Centre (CRC) for Mining Technology and Equipment (CMTE) towards achieving autonomous navigation of underground vehicles, like a Load-Haul-Dump (LHD) truck. This work is being sponsored by the mining industry through the Australian Mineral Industries Research Association Limited (AMIRA). Robust and reliable autonomous navigation can only be realised by achieving high level tasks such as path-planning and obstacle avoidance. This requires determining the pose (position and orientation) of the vehicle at all times. A minimal infrastructure localisation algorithm that has been developed for this purpose is outlined and the corresponding results are presented. Further research issues that are under investigation are also outlined briefly.

Automation of underground truck haulage

This paper reviews the state-of-the art in the automation of underground truck haulage. Past attemps at automating LHDs and haul trucks are described and their particular strengths and weaknesses are listed. We argue that the simple auto-tram...

Case study: Dragline swing to dump automation

Dragline Swing to Dump Automation By Peter Corke, CSIRO Manufacturing Technology/CRC for Mining Technology and Equipment (CMTE) Peter Corke presented a case study of a project to automate the dragline swing to dump operation. The project is funded by ACARP, BHP Coal, Pacific Coal and the CMTE and is being carried out on a dragline at Pacific Coal's Meandu mine near Brisbane. Corke began by highlighting that the minerals industry makes extensive use of large, mechanised machines. However, unlike other industries, mining has not adopted automation and most machines are controlled by human operators on board the machine itself. Choosing an automation target The dragline automation was chosen because: ò draglines are one of the biggest capital assets in a mine; ò performance between operators vary significantly, so improved capital utilisation is possible; ò the dragline is often the bottleneck in production; ò a large part of the operation cycle is spent swinging from dig to dump; and ò it is technically feasible. There has been a history of drag line automation projects, none with great success.

Exploiting inference from semantic annotations for information retrieval

The increasing amount of information that is annotated against standardised semantic resources offers opportunities to incorporate sophisticated levels of reasoning, or inference, into the retrieval process. In this position paper, we reflect on the need to incorporate semantic inference into retrieval (in particular for medical information retrieval) as well as previous attempts that have been made so far with mixed success. Medical information retrieval is a fertile ground for testing inference mechanisms to augment retrieval. The medical domain offers a plethora of carefully curated, structured, semantic resources, along with well established entity extraction and linking tools, and search topics that intuitively require a number of different inferential processes (e.g., conceptual similarity, conceptual implication, etc.). We argue that integrating semantic inference in information retrieval has the potential to uncover a large amount of information that otherwise would be inaccessible; but inference is also risky and, if not used cautiously, can harm retrieval.