SenShaMart: A Trusted Internet of Things Marketplace for Sensor Sharing. This project aims to devise a novel Internet of Things (IoT) sensor sharing marketplace that permits IoT applications to discover, integrate, and pay for any IoT sensor data that is made available by other parties. The project will devise highly-scalable sensor classification, query processing, and transactions solutions and incorporate them in a pair of novel blockchains that work in tandem to securely manage all the infor ....SenShaMart: A Trusted Internet of Things Marketplace for Sensor Sharing. This project aims to devise a novel Internet of Things (IoT) sensor sharing marketplace that permits IoT applications to discover, integrate, and pay for any IoT sensor data that is made available by other parties. The project will devise highly-scalable sensor classification, query processing, and transactions solutions and incorporate them in a pair of novel blockchains that work in tandem to securely manage all the information and contracts needed by IoT applications to discover, integrate, pay, and use sensors provided by another parties. These IoT advancements will provide significant economic, environmental, and social benefits via making low-cost and immediate sensing available across the world.Read moreRead less
Advanced Materials from Automated Synthesis of Sequence-Defined Polymers. The project aims to develop industrially scalable and environmentally friendly methods for synthesis of sequence-defined multiblock copolymers (polymer chains containing segments of different polymer types) using automated synthesis methods. The materials to be explored will be largely based on renewable biomass-derived monomeric building blocks. Such polymers are able to undergo microphase separation into spatially period ....Advanced Materials from Automated Synthesis of Sequence-Defined Polymers. The project aims to develop industrially scalable and environmentally friendly methods for synthesis of sequence-defined multiblock copolymers (polymer chains containing segments of different polymer types) using automated synthesis methods. The materials to be explored will be largely based on renewable biomass-derived monomeric building blocks. Such polymers are able to undergo microphase separation into spatially periodic compositional patterns, thereby providing access to a vast range of nano-engineered materials. This would enable design and synthesis of new advanced materials, making use of renewable resources and supporting the circular economy, with diverse potential applications ranging from nanomedicine to materials science.Read moreRead less
Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire ....Physics-aware machine learning for data-driven fire risk prediction. The 2019/20 Australian fire season was unprecedented in its extent, impact, and the response of fire agencies. In this project, we aim to answer the question: was the scale of these fires driven by known drivers of fire (drought, weather, fuels and ignitions), or were fundamentally new undescribed processes and phenomena involved? We will accomplish this by developing an innovative, physics-aware machine learning model of fire risk and spread, trained and validated on a two-decade satellite fire record. The predictive ability of the model will be tested on the 2019/20 fire season to determine if novel drivers of fire can be identified, and the model itself will be operationalised into a novel short-to-mid term fire risk prediction tool. Read moreRead less
The Great Barrier Reef in 2100. Our research aims to answer fundamental geomorphic questions about the future of coral reefs, focusing on the Great Barrier Reef (GBR). We will develop cutting-edge, fully open-source numerical models to quantify the eco-morphodynamic evolution of the GBR under IPCC climate-change scenarios. Our geomorphic numerical models will consider biotic/abiotic feedbacks including synergistic effects of multiple stressors such as waves, temperature, acidification and sedime ....The Great Barrier Reef in 2100. Our research aims to answer fundamental geomorphic questions about the future of coral reefs, focusing on the Great Barrier Reef (GBR). We will develop cutting-edge, fully open-source numerical models to quantify the eco-morphodynamic evolution of the GBR under IPCC climate-change scenarios. Our geomorphic numerical models will consider biotic/abiotic feedbacks including synergistic effects of multiple stressors such as waves, temperature, acidification and sediment transport, at individual reef scales. We will model the future of the GBR’s ecosystem-services, allowing for a quantum leap in the geomorphic knowledge and understanding of coral reef ecosystems. Expected outcomes include a gamechanger tool for future management of the GBR.Read moreRead less
DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting th ....DeepHoney: Automatic Honey Data Generation for Active Cyber Defence . This project aims to enhance the security of networks and information systems by empowering them with intelligent deception techniques to achieve proactive attack detection and defence. In recent times, the fictitious environment – honeypot designed by human experience becomes popular to attract attackers and capture their interactions. However, rules-based construction of honeypots fails in preserving the privacy, boosting the attractiveness and evolving the system. The project expects to advance deep learning and yield novel DeepHoney technologies with associated publications and open-source software. This should benefit science, society, and the economy by building the next generation of active cyber defence systems. Read moreRead less
Biofilm responses to cold atmospheric plasma . This project is focused on understanding the interaction of cold atmospheric plasmas with biofilms, with the aim of biofilm eradication and ultimately offering an environmentally friendly alternative to current detergents and antibiotics. The research expects to elucidate the fundamental mechanisms of action for breakthrough plasma intervention technologies, which are sufficiently active to cope with the resistant nature of biofilms, yet are of low ....Biofilm responses to cold atmospheric plasma . This project is focused on understanding the interaction of cold atmospheric plasmas with biofilms, with the aim of biofilm eradication and ultimately offering an environmentally friendly alternative to current detergents and antibiotics. The research expects to elucidate the fundamental mechanisms of action for breakthrough plasma intervention technologies, which are sufficiently active to cope with the resistant nature of biofilms, yet are of low energy, do not adversely affect surface properties and critically leave no residual chemistry. This should provide significant benefits by delivering a new method to tackle the ubiquitous problem of biofilm contamination in food, water and medical areas.Read moreRead less
Visualising molecular level detail in single cells and intact tissues. The goal of this project is to deliver a new toolkit for imaging cells at an unprecedented resolution and level of chemical detail. We will expand the capabilities of two existing, but complementary, methods: optical fluorescence microscopy with responsive probes and X-ray fluorescence imaging. Expected outcomes include improved techniques and benchmarks for visualising bacterial and mammalian cells; development of new molecu ....Visualising molecular level detail in single cells and intact tissues. The goal of this project is to deliver a new toolkit for imaging cells at an unprecedented resolution and level of chemical detail. We will expand the capabilities of two existing, but complementary, methods: optical fluorescence microscopy with responsive probes and X-ray fluorescence imaging. Expected outcomes include improved techniques and benchmarks for visualising bacterial and mammalian cells; development of new molecules for elucidating cellular chemistry; better utilisation of valuable synchrotron resources; and greater understanding of the strengths and limitations of current microscopy workflows. Results should benefit the biotechnology sector, and may lead to improved medical, diagnostic, and bioremediation capacity.Read moreRead less
Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while pres ....Privacy-preserving data processing on the cloud. This project aims to address the current lack of privacy of user data processed by common cloud computing web servers, including email, business data, and confidential files. This project aims to develop new techniques in cryptography. The anticipated outcome is a suite of practical tools enabling common cloud computing processing operations such as search, statistical analysis, and multi-user access control, to be performed efficiently while preserving the data privacy. These tools should provide significant benefits to the privacy of cloud users, as well as financial and reputation benefits to the IT industry, by significantly reducing the likelihood of massive user data privacy breaches in the event of a cyber-hacking attack on the cloud server.Read moreRead less
Assessing absolute sustainability of global cities. The project aims to create a quantitative modelling framework for assessing the absolute sustainability of cities by combining global multi-region input-output analysis with the 'safe and just space' concept for human development. The model will allow assessment of the full supply chain environmental and social impacts of urban economic activities against biophysical planetary limits as well as social foundation thresholds defined in the litera ....Assessing absolute sustainability of global cities. The project aims to create a quantitative modelling framework for assessing the absolute sustainability of cities by combining global multi-region input-output analysis with the 'safe and just space' concept for human development. The model will allow assessment of the full supply chain environmental and social impacts of urban economic activities against biophysical planetary limits as well as social foundation thresholds defined in the literature. The project will advance sustainability science methodology and will greatly benefit worldwide initiatives for urban sustainability. Case studies on Australian cities will assess where interventions can be most practically, realistically and effectively implemented.Read moreRead less
Machine Learning and Shape Optimisation of Fluid-Structure Interactions. This project aims to address vibrations of solid structures by utilising a combination of advanced experimental and computational methods. This project expects to generate new knowledge in the area of flow-induced vibrations utilising the new techniques of machine learning and evolutionary shape optimisation. Expected outcomes of this project include greatly accelerated discovery of mechanisms leading to structural vibratio ....Machine Learning and Shape Optimisation of Fluid-Structure Interactions. This project aims to address vibrations of solid structures by utilising a combination of advanced experimental and computational methods. This project expects to generate new knowledge in the area of flow-induced vibrations utilising the new techniques of machine learning and evolutionary shape optimisation. Expected outcomes of this project include greatly accelerated discovery of mechanisms leading to structural vibrations and optimising structure geometries to either enhance or suppress the vibrations. This should provide significant benefits, such as the design strategies for improved energy harvesters, such as current oscillators, or more stable structures, such as platforms for offshore wind turbines.
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