ARC Research Network in Enterprise Information Infrastructure. EII targets consolidated research towards the comprehensive development & establishment of advanced information infrastructures. Its prime purpose is to provide a forum for intellectual exchange by diverse yet complementary research groups, to address the fundamental research problems faced by scientific & business communities when dealing with deployment of information technology to globally distributed, and data intensive environme ....ARC Research Network in Enterprise Information Infrastructure. EII targets consolidated research towards the comprehensive development & establishment of advanced information infrastructures. Its prime purpose is to provide a forum for intellectual exchange by diverse yet complementary research groups, to address the fundamental research problems faced by scientific & business communities when dealing with deployment of information technology to globally distributed, and data intensive environments. EII will address 3 tightly coupled research themes: Ability to interoperate across existing heterogenous platforms & applications; Efficient processing of very large data sets; Technology adoption & impact. Generic results will be applicable to e-science and large business information systems installations.Read moreRead less
Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provi ....Data-driven water quality treatment management decision support system. Data-driven water quality treatment management decision support system. This project aims to develop a robust decision support system to predict manganese and the character and concentration of dissolved organic matter in drinking water reservoirs, using intelligent algorithms and data collected through remote autonomous instrumentation. These predicted water quality parameters could be used as model input variables to provide real-time decisions for plant operators on the required treatment regime for incoming raw water, and advise them on the optimal reservoir offtake depth. This will potentially minimise treatment costs and health risks for consumers. The ultimate goal is to significantly enhance current water supply management practices.Read moreRead less
The Airport Metropolis: Managing the Interfaces. The project aims to develop coordinated and equitable decision-making to ensure that airport-urban development balances economic, social and environmental issues and produces a sustainable regional (and national) competitive advantage that is both secure and resilient. The project will develop modelling technologies for the innovative management of data to ensure efficient and resilient infrastructure coordination. The outputs will enable an open ....The Airport Metropolis: Managing the Interfaces. The project aims to develop coordinated and equitable decision-making to ensure that airport-urban development balances economic, social and environmental issues and produces a sustainable regional (and national) competitive advantage that is both secure and resilient. The project will develop modelling technologies for the innovative management of data to ensure efficient and resilient infrastructure coordination. The outputs will enable an open planning process whereby all stakeholders are able to provide informed input into decision-making. Strategic decision-making, based on increased certainty about future airport and regional planning and development will improve conditions for growth in a range of industries.Read moreRead less
Congestion recovery and optimisation of patient flows. Australian public hospitals often experience congestion due to growing demand and limited resources, resulting in disruptions in service delivery and risks in quality of care. This project will apply advanced techniques and methodologies from mathematical sciences and computer modelling to alleviate this important healthcare delivery problem.
Active Team – Examining An Online Social Networking Intervention To Increase Physical Activity In Controlled (RCT) And Ecological (ET) Settings
Funder
National Health and Medical Research Council
Funding Amount
$814,041.00
Summary
Lifestyle diseases, such as heart disease and diabetes, are key health problems facing Australia. Effective, low-cost, mass-reach physical activity interventions are urgently needed. This project uses online social networks to deliver an innovative physical activity intervention. This project will determine how effective the software is in changing people’s lifestyle over 12 months, and whether viral marketing techniques can be used to disseminate the program on a mass scale.
Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to ....Effective Recommendations based on Multi-Source Data. Large-scale data collected from multiple sources such as the Web, sensor networks, academic publications, and social networks provide a new opportunity to exploit useful information for effective and efficient recommendations and decision making. The project will propose a new framework of recommender systems that is based on analysing relationships between different types of objects from multiple data sources. A graph model will be built to represent the extracted semantic relationships and novel linkage-analysis based algorithms will be developed for ranking objects. The results from this project will underpin many critical applications such as healthcare.Read moreRead less
High shear fluid flow driving carbon foundry for advanced manufacturing. This project aims to develop versatile continuous flow thin film microfluidic device technology for harnessing contact electrification generated by sub-micron high shear flows in fabricating novel and high-performance nano-carbons for which current methods are ineffective or impossible. This project expects to generate new knowledge on complex vortex fluid fields, their intricate interactions with external electric and magn ....High shear fluid flow driving carbon foundry for advanced manufacturing. This project aims to develop versatile continuous flow thin film microfluidic device technology for harnessing contact electrification generated by sub-micron high shear flows in fabricating novel and high-performance nano-carbons for which current methods are ineffective or impossible. This project expects to generate new knowledge on complex vortex fluid fields, their intricate interactions with external electric and magnetic fields and carbon nanostructure formation. Expected outcomes for this project include exquisite control on reforming nanocarbon with tuneable properties and unprecedented hetero-structures. This should provide significant benefits, such as in generating new processes and products for advanced manufacturing. Read moreRead less
Patchy colloidosomes at interfaces: correlation of particle surface heterogeneity, wettability, and chemical activity at the nanoscale. The surfaces of natural mineral particles are made up of spots with such different chemical and physical properties. The complexity makes it hard to predict their behaviour. This project will provide insights into how the 'patchy' nature of particle surfaces affects their behaviour in processes such as flotation separation and bio-fuel production.
On-Chip Detection and Molecular Fingerprinting of Emerging Toxicants. The project aims to address key questions about the development and integration of advanced materials and functional molecules into cutting-edge analytical tools for screening emerging environmental pollutants. This is expected to generate fundamental and applied knowledge in analytical chemistry, using an interdisciplinary approach to engineer materials with precisely tailored properties for ultra-sensitive and selective dete ....On-Chip Detection and Molecular Fingerprinting of Emerging Toxicants. The project aims to address key questions about the development and integration of advanced materials and functional molecules into cutting-edge analytical tools for screening emerging environmental pollutants. This is expected to generate fundamental and applied knowledge in analytical chemistry, using an interdisciplinary approach to engineer materials with precisely tailored properties for ultra-sensitive and selective detection of extremely persistent toxicants in water. Anticipated outcomes are optical materials and functional molecules, integrated into lab-on-a-chip platforms with advanced features for real-life environmental applications – with significant benefits for addressing major environmental and health treats to our society.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC190100017
Funder
Australian Research Council
Funding Amount
$3,703,664.00
Summary
ARC Training Centre for Integrated Operations for Complex Resources. This Training Centre aims to increase value in mining through clever applications of ‘lean processing’ and train the next generation of scientists and engineers in advanced sensors and data analytics in complex resources; knowledge priorities for the mining industry. Sensor information will be linked to the resource’s in-place knowledge to enable data analytics of all embedded knowledge. Processing can then be tuned to resource ....ARC Training Centre for Integrated Operations for Complex Resources. This Training Centre aims to increase value in mining through clever applications of ‘lean processing’ and train the next generation of scientists and engineers in advanced sensors and data analytics in complex resources; knowledge priorities for the mining industry. Sensor information will be linked to the resource’s in-place knowledge to enable data analytics of all embedded knowledge. Processing can then be tuned to resource attributes, maximising value ‘on the fly’. Benefits will include increasing certainty on product quality and maximising throughput and recovery. Outcomes will include new tools to rapidly model geological and geometallurgical uncertainty with sensor inputs, to track the resource to product and enhance interpretation.Read moreRead less