Unravelling soil carbon response to warming in fire-affected ecosystems. This project aims to reveal the continental pattern of soil carbon (C) response to warming in fire-affected ecosystems across Australia and to unravel the biogeochemical mechanisms underlying fire’s role in shaping the temperature sensitivity of soil respiration. Fire has modified over 40% of the Earth’s land surface and wildfire frequency is predicted to increase under global warming. This project expects to generate new k ....Unravelling soil carbon response to warming in fire-affected ecosystems. This project aims to reveal the continental pattern of soil carbon (C) response to warming in fire-affected ecosystems across Australia and to unravel the biogeochemical mechanisms underlying fire’s role in shaping the temperature sensitivity of soil respiration. Fire has modified over 40% of the Earth’s land surface and wildfire frequency is predicted to increase under global warming. This project expects to generate new knowledge on how fire influences soil-to-atmosphere C fluxes in a warmer climate using a multi-disciplinary approach. Expected outcomes include an enhanced capacity to predict the terrestrial ecosystem-to-atmosphere C fluxes and their feedbacks to climate under increasing frequency of fire using Earth-system models. Read moreRead less
Rhizosphere mediation of soil greenhouse gas fluxes with climate change. Increasingly extreme heat waves, droughts and floods contribute major uncertainties in predicting natural land-based climate change mitigation. This project will quantify current and future greenhouse gas absorption in a managed grassland ecosystem, and the new knowledge will contribute to carbon emissions offsets in climate change accounting schemes. We will conduct this research using a manipulative field experiment, cont ....Rhizosphere mediation of soil greenhouse gas fluxes with climate change. Increasingly extreme heat waves, droughts and floods contribute major uncertainties in predicting natural land-based climate change mitigation. This project will quantify current and future greenhouse gas absorption in a managed grassland ecosystem, and the new knowledge will contribute to carbon emissions offsets in climate change accounting schemes. We will conduct this research using a manipulative field experiment, controlled laboratory incubations, microbial gene analysis and mechanistic modelling to provide new insights into future potential climate change mitigation by soils.Read moreRead less
Ecologically responsible mining to fuel a green energy transition. An energy transition is key to tackling climate change. However, renewable energy is mineral intensive and boosting its supply may create new mining threats to biodiversity. This project aims to facilitate strategic development of ecologically responsible mining. It expects to reveal where new mines will be needed to meet future energy demand, and create innovative tools to predict and mitigate threats to plants and animals. Expe ....Ecologically responsible mining to fuel a green energy transition. An energy transition is key to tackling climate change. However, renewable energy is mineral intensive and boosting its supply may create new mining threats to biodiversity. This project aims to facilitate strategic development of ecologically responsible mining. It expects to reveal where new mines will be needed to meet future energy demand, and create innovative tools to predict and mitigate threats to plants and animals. Expected outcomes include an improved ability to inform sustainable climate and energy policies, leading to strategic investment decisions, cleaner mineral supply chains and conservation outcomes that capture valuable environmental and social benefits and create a competitive advantage for Australia’s mining sector.Read moreRead less
Market segmentation methodology: attacking the 'Too Hard' basket. Businesses embrace market segmentation to identify and target clients. However, poor segmentation analysis leads to poor segment choice. This project will develop tools to improve segmentation analysis and will test the resulting tools in tourism, foster care and climate change mitigating behaviours, and produce usable, transferable recommendations.
Flotation in high salt concentration: resolving critical knowledge gaps relating the ion effect on bubble production and behavior. Flotation separation of coal and mineral particles by attaching to rising air bubbles is significantly affected in high salt concentration but its exact mechanism still remains unclear. This project employs state-of-the-art surface sensitive spectroscopy and modeling tools to investigate how salt ions influence drainage and rupture of liquid films between two bubbles ....Flotation in high salt concentration: resolving critical knowledge gaps relating the ion effect on bubble production and behavior. Flotation separation of coal and mineral particles by attaching to rising air bubbles is significantly affected in high salt concentration but its exact mechanism still remains unclear. This project employs state-of-the-art surface sensitive spectroscopy and modeling tools to investigate how salt ions influence drainage and rupture of liquid films between two bubbles, and bubble production and behaviour relevant to the flotation processes. The research will develop better water use for coal and mineral flotation to reduce reagent usage and environmental impacts of water pollution. The project will contribute significantly to knowledge advancement in the coal and mineral industry.Read moreRead less
Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predi ....Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.Read moreRead less
A new role for vibration analysis in gear wear modelling and prediction. This project aims to improve prediction of the remaining useful life of gears. Gears are widely used in industry and transport. This project aims to integrate the two main methods of gear condition monitoring, vibration and oil analysis, and perform model-based wear prediction with the tribology and dynamic models continually updated on the basis of measured wear debris and vibration. New signal processing tools should allo ....A new role for vibration analysis in gear wear modelling and prediction. This project aims to improve prediction of the remaining useful life of gears. Gears are widely used in industry and transport. This project aims to integrate the two main methods of gear condition monitoring, vibration and oil analysis, and perform model-based wear prediction with the tribology and dynamic models continually updated on the basis of measured wear debris and vibration. New signal processing tools should allow estimation of relatively weak friction forces, previously neglected, as an important prognostic tool. This would allow detailed root cause analysis and prediction of remaining useful life. Improvements in gear prognosis would have safety and economic benefits by eliminating unforeseen catastrophic failures and optimising maintenance schedules.Read moreRead less
Taming the uncertainty in trajectory data. This project aims to develop effective and efficient methods to manage large scale uncertain trajectory data. It provides individuals, business, government and social groups the ability to explore significant uncertain trajectories and their patterns, for important usages in location based services, logistic, transportation and tourism.
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less