Queensland small towns Documentary project

This photographic documentary project explores diverse stories of Queensland mining towns. Images compare the interplay between communities, commerce and natural resources. The project is a collaboration between three Brisbane universities (Queensland University of Technology, Griffith University and University of Queensland) and is supported by the Brisbane Powerhouse and the State Library of Queensland. Funded by a $50,000 Arts Queensland grant.

Impacts of different wind power generators on power system small signal and transient stability

With the ever-increasing penetration level of wind power, the impacts of wind power on the power system are becoming more and more significant. Hence, it is necessary to systematically examine its impacts on the small signal stability and transient stability in order to find out countermeasures. As such, a comprehensive study is carried out to compare the dynamic performances of power system respectively with three widely-used power generators. First, the dynamic models are described for three types of wind power generators, i. e. the squirrel cage induction generator (SCIG), doubly fed induction generator (DFIG) and permanent magnet generator (PMG). Then, the impacts of these wind power generators on the small signal stability and transient stability are compared with that of a substituted synchronous generator (SG) in the WSCC three-machine nine-bus system by the eigenvalue analysis and dynamic time-domain simulations. Simulation results show that the impacts of different wind power generators are different under small and large disturbances.

The art of mooting : mooting and the cognitive domain

Mooting is modeled principally on appellate advocacy. However, the skill set developed by participating in a moot program – being that necessary to persuade someone to your preferred position – is indispensible to anyone practising law. Developing effective mooting skills in students necessitates the engagement of coaches with an appropriate understanding of the theories underlying mooting and advocacy practice and their interconnection with each other. This article explains the relevance of the cognitive domain to mooting performance and places it in context with the psychomotor and affective domains.

Yeondoo Jung

This article analyses Yeondoo Jung's exhibition at MAAP Space, Brisbane. Jung's video artworks works often reveal their mechanisms of pictorial illusion, and expose the apparatuses by which still and moving images are constructed more generally. This article examines the two specific works exhibited at MAAP Space, and situates them in the contexts of moving image theory and practice.

Abstraction : on ambiguity and semiosis in abstract painting

Drawing on the fields of philosophy, phenomenology, art history and theory as well as the candidate's own painting practice, this PhD explores the nature of ambiguity and semiosis in contemporary abstract painting. The thesis demonstrates how the aesthetic qualities of pause and rupture, transition and slippage work emergently to break established clichés, habits and intentions in the experiencing of abstract painting and artistic practice.

Performance-driven live migration of multiple virtual machines in datacenters

Live migration of multiple Virtual Machines (VMs) has become an indispensible management activity in datacenters for application performance, load balancing, server consolidation. While state-of-the-art live VM migration strategies focus on the improvement of the migration performance of a single VM, little attention has been given to the case of multiple VMs migration. Moreover, existing works on live VM migration ignore the inter-VM dependencies, and underlying network topology and its bandwidth. Different sequences of migration and different allocations of bandwidth result in different total migration times and total migration downtimes. This paper concentrates on developing a multiple VMs migration scheduling algorithm such that the performance of migration is maximized. We evaluate our proposed algorithm through simulation. The simulation results show that our proposed algorithm can migrate multiple VMs on any datacenter with minimum total migration time and total migration downtime.

A near-to-far non-parametric learning approach for estimating traversability in deformable terrain

It is well recognized that many scientifically interesting sites on Mars are located in rough terrains. Therefore, to enable safe autonomous operation of a planetary rover during exploration, the ability to accurately estimate terrain traversability is critical. In particular, this estimate needs to account for terrain deformation, which significantly affects the vehicle attitude and configuration. This paper presents an approach to estimate vehicle configuration, as a measure of traversability, in deformable terrain by learning the correlation between exteroceptive and proprioceptive information in experiments. We first perform traversability estimation with rigid terrain assumptions, then correlate the output with experienced vehicle configuration and terrain deformation using a multi-task Gaussian Process (GP) framework. Experimental validation of the proposed approach was performed on a prototype planetary rover and the vehicle attitude and configuration estimate was compared with state-of-the-art techniques. We demonstrate the ability of the approach to accurately estimate traversability with uncertainty in deformable terrain.

Traversability estimation for a planetary rover via experimental kernel learning in a Gaussian process framework

A critical requirement for safe autonomous navigation of a planetary rover is the ability to accurately estimate the traversability of the terrain. This work considers the problem of predicting the attitude and configuration angles of the platform from terrain representations that are often incomplete due to occlusions and sensor limitations. Using Gaussian Processes (GP) and exteroceptive data as training input, we can provide a continuous and complete representation of terrain traversability, with uncertainty in the output estimates. In this paper, we propose a novel method that focuses on exploiting the explicit correlation in vehicle attitude and configuration during operation by learning a kernel function from vehicle experience to perform GP regression. We provide an extensive experimental validation of the proposed method on a planetary rover. We show significant improvement in the accuracy of our estimation compared with results obtained using standard kernels (Squared Exponential and Neural Network), and compared to traversability estimation made over terrain models built using state-of-the-art GP techniques.

Augmenting traversability maps with ultra-wideband radar to enhance obstacle detection in vegetated environments

Operating in vegetated environments is a major challenge for autonomous robots. Obstacle detection based only on geometric features causes the robot to consider foliage, for example, small grass tussocks that could be easily driven through, as obstacles. Classifying vegetation does not solve this problem since there might be an obstacle hidden behind the vegetation. In addition, dense vegetation typically needs to be considered as an obstacle. This paper addresses this problem by augmenting probabilistic traversability map constructed from laser data with ultra-wideband radar measurements. An adaptive detection threshold and a probabilistic sensor model are developed to convert the radar data to occupancy probabilities. The resulting map captures the fine resolution of the laser map but clears areas from the traversability map that are induced by obstacle-free foliage. Experimental results validate that this method is able to improve the accuracy of traversability maps in vegetated environments.

Motion planning and stochastic control with experimental validation on a planetary rover

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This model is used to construct a control policy for navigation to a goal region in a terrain map built using an on-board RGB-D camera. The terrain includes flat ground, small rocks, and non-traversable rocks. We report the results of 200 simulated and 35 experimental trials that validate the approach and demonstrate the value of considering control uncertainty in maintaining platform safety.

Combining multiple sensor modalities for a localisation robust to smoke

This paper proposes an approach to obtain a localisation that is robust to smoke by exploiting multiple sensing modalities: visual and infrared (IR) cameras. This localisation is based on a state-of-the-art visual SLAM algorithm. First, we show that a reasonably accurate localisation can be obtained in the presence of smoke by using only an IR camera, a sensor that is hardly affected by smoke, contrary to a visual camera (operating in the visible spectrum). Second, we demonstrate that improved results can be obtained by combining the information from the two sensor modalities (visual and IR cameras). Third, we show that by detecting the impact of smoke on the visual images using a data quality metric, we can anticipate and mitigate the degradation in performance of the localisation by discarding the most affected data. The experimental validation presents multiple trajectories estimated by the various methods considered, all thoroughly compared to an accurate dGPS/INS reference.

How small-sided and conditioned games enhance acquisition of movement and decision-making skills

This article summarizes research from an ecological dynamics program of work on team sports exemplifying how small-sided and conditioned games (SSCG) can enhance skill acquisition and decision-making processes during training. The data highlighted show how constraints of different SSCG can facilitate emergence of continuous interpersonal coordination tendencies during practice to benefit team game players.

IL-23R is epigenetically regulated and modulated by chemotherapy in non-small cell lung cancer

The Interleukin-23 (IL-23)/IL-23R signaling axis is an important inflammatory pathway, involved in the stimulation and regulation of the T helper (Th) 17 lymphocytes, resulting in the production of IL-17. Aside from auto-immunity, this cytokine has also been linked to carcinogenesis and polymorphisms in the IL-23R gene are associated with an increased risk for the development of a number of different cancers. Activation of the IL-23 pathway results in the up-regulation of STAT3 and it is thought that the pathological consequences associated with this are in part due to the production of IL-17. We have previously identified IL-23A as pro-proliferative and epigenetically regulated in non-small cell lung cancer (NSCLC). The current study aims to evaluate IL-23R in greater detail in NSCLC. We demonstrate that IL-23R is expressed and epigenetically regulated in NSCLC through histone post-translation modifications and CpG island methylation. In addition, Gemcitabine treatment, a chemotherapy drug used in the treatment of NSCLC, resulted in the up-regulation of the IL-23R. Furthermore, Apilimod (STA 5326), a small molecule which blocks the expression of IL-23 and IL-12, reduced the proliferative capacity of NSCLC cells, particularly in the adenocarcinoma (A549) sub-type. Apilimod is currently undergoing investigation in a number of clinical trials for the treatment of auto-immune conditions such as Crohn's disease and Rheumatoid Arthritis. Our results may have implications for treating NSCLC patients with Gemcitabine or epigenetic targeted therapies. However, Apilimod may possibly provide a new treatment avenue for NSCLC patients. Work is currently ongoing to further delineate the IL-23/IL-23R axis in this disease.

Thromboxane synthase expression and correlation with VEGF and angiogenesis in non-small cell lung cancer

Background Thromboxane synthase (TXS) metabolizes prostaglandin H2 into thromboxanes, which are biologically active on cancer cells. TXS over-expression has been reported in a range of cancers, and associated with angiogenesis and poor outcome. TXS has been identified as a potential therapeutic target in NSCLC. This study examines a link between TXS expression, angiogenesis, and survival in NSCLC. Methods TXS and VEGF metabolite levels were measured in NSCLC serum samples (n=46) by EIA. TXB2 levels were correlated with VEGF. A 204-patient TMA was stained for TXS, VEGF, and CD-31 expression. Expression was correlated with a range of clinical parameters, including overall survival. TXS expression was correlated with VEGF and CD-31. Stable TXS clones were generated and the effect of overexpression on tumor growth and angiogenesis markers was examined in-vitro and in-vivo (xenograft mouse model). Results Serum TXB2 levels were correlated with VEGF (p<0.05). TXS and VEGF were expressed to a varying degree in NSCLC tissue. TXS was associated with VEGF (p<0.0001) and microvessel density (CD-31; p<0.05). TXS and VEGF expression levels were higher in adenocarcinoma (p<0.0001) and female patients (p<0.05). Stable overexpression of TXS increased VEGF secretion in-vitro. While no significant association with patient survival was observed for either TXS or VEGF in our patient cohort, TXS overexpression significantly (p<0.05) increased tumor growth in-vivo. TXS overexpression was also associated with higher levels of VEGF, microvessel density, and reduced apoptosis in xenograft tumors. Conclusion TXS promotes tumor growth in-vivo in NSCLC, an effect which is at least partly mediated through increased tumor angiogenesis.