An experimentally-validated thermo-hydro-mechanical theory for waste containment lining systems. Geosynthetic clay liners are engineering systems that are widely used around the world to protect groundwater from municipal, industrial and mining contaminants. The project will conduct cutting-edge experimental, theoretical and computational research leading to a major improvement in their short-term and long-term performances.
Microplastics in Landfills and Surrounding Environments. This project aims to build a risk-based framework for managing micro- and nano-plastic particles in landfills and surrounding environments. It expects to develop a new experimentally validated theory of micro/nano-plastic transport in soils, focussing on lining systems used in landfills worldwide to protect aquifers from contamination. The project will use state-of-the-art experimental, theoretical and computational approaches to generate ....Microplastics in Landfills and Surrounding Environments. This project aims to build a risk-based framework for managing micro- and nano-plastic particles in landfills and surrounding environments. It expects to develop a new experimentally validated theory of micro/nano-plastic transport in soils, focussing on lining systems used in landfills worldwide to protect aquifers from contamination. The project will use state-of-the-art experimental, theoretical and computational approaches to generate new knowledge on micro/nano-plastic fate in lining systems and their effects on the mobility of heavy metals and organic pollutants. This should provide significant benefits including safe plastic containment and groundwater protection from landfill waste, a major reservoir of plastic in the environment.Read moreRead less
Geomechanics of multiple seam mining interactions. This project will address a highly significant and timely problem that has arisen in the coal mining industry. Through the application of scientific principles and advanced methods of engineering analysis, this research will develop practical guidelines that in turn will provide the means for rational planning of multi-seam mining operations.
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
Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowled ....Advanced Machine Learning with Bilevel Optimization. There is an urgent need to develop a new machine learning (ML) paradigm that can overcome data-privacy and model-size constraints in real-world applications. This project aims to develop an advanced paradigm of ML with bilevel optimisation, called bilevel ML. A theoretically-guaranteed fast approximate solver and a new fuzzy bilevel learning framework will be developed to achieve the aim in complex situations; a methodology to transfer knowledge and an approach to fast-adapt bilevel optimization solutions when required computing resources change. The anticipated outcomes should significantly improve the reliability of ML with benefits for safety learning and computing resource optimisation in ML-based data analytics.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
Building insights of our largest terrestrial carbon sink: rangelands soils. Rangelands soils represent Australia’s largest carbon sink. Yet, little is known about their potential for carbon sequestration or their vulnerability to climate and environmental change. This project leverages investments in national terrestrial observation platforms and integrates previous research outputs to develop new methods to measure and build understanding of soil carbon composition and dynamics in rangeland eco ....Building insights of our largest terrestrial carbon sink: rangelands soils. Rangelands soils represent Australia’s largest carbon sink. Yet, little is known about their potential for carbon sequestration or their vulnerability to climate and environmental change. This project leverages investments in national terrestrial observation platforms and integrates previous research outputs to develop new methods to measure and build understanding of soil carbon composition and dynamics in rangeland ecosystems. Under a framework that connects detailed measurements and small-scale processes, with machine-learning, data-model assimilation and large-scale next-generation biogeochemical modelling, it’ll allow more accurate predictions of soil carbon change and better decision-making to guide sustainable rangelands management.Read moreRead less
Combating Fake News on Social Media: From Early Detection to Intervention. The project aims to detect fake news early to minimise the negative impact of false information. This project expects to devise novel solutions to address technical challenges for detection of fake news with scarce signals. Expected outcomes of this project include a suite of data mining and machine learning models for identification of fake news from the social media stream, prediction of user propagation of false infor ....Combating Fake News on Social Media: From Early Detection to Intervention. The project aims to detect fake news early to minimise the negative impact of false information. This project expects to devise novel solutions to address technical challenges for detection of fake news with scarce signals. Expected outcomes of this project include a suite of data mining and machine learning models for identification of fake news from the social media stream, prediction of user propagation of false information as well as recommendation of truthful news to counteract adversarial fake news. This project should generate technologies that enhance the integrity of the online echo system and benefit media providers and online population within Australia and across the world. Read moreRead less
The sparrows in the mining towns: a century of adaptation to contamination. Our research will characterise how contamination from the extraction of precious metals can spread through the environment and how it effects a highly urbanised bird – the house sparrow. In many cases, populations of these birds have been intimately associated with mining operations for over a century, and our recent work has provided evidence of adaptation over time. House sparrows provide a great natural system to unde ....The sparrows in the mining towns: a century of adaptation to contamination. Our research will characterise how contamination from the extraction of precious metals can spread through the environment and how it effects a highly urbanised bird – the house sparrow. In many cases, populations of these birds have been intimately associated with mining operations for over a century, and our recent work has provided evidence of adaptation over time. House sparrows provide a great natural system to understand the genetic potential of organisms to adapt to anthropomorphic change in the environment connected with the resources industry. Our work, will bring new insight into the future management of environmental contamination, and the mitigation of adverse effects arising from resource extraction.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