Motor Functioning In Young People With Attention Deficit Hyperactivity Disorder – Combined Type: A Three-dimensional Motion Analysis Study.
Funder
National Health and Medical Research Council
Funding Amount
$477,065.00
Summary
Attention deficit hyperactivity disorder –combined type (ADHD-CT) is a complex neuropsychiatric disorder with a progressively devastating impact on psychosocial development. The first objective of this study is to use 3D-motion analysis to ‘probe’ the underlying brain dysfunction which characterises ADHD-CT. The second objective of this study is to improve our understanding of the link between movement problems, and (a) injury proneness, and (b) social-communicative problems, in children with AD ....Attention deficit hyperactivity disorder –combined type (ADHD-CT) is a complex neuropsychiatric disorder with a progressively devastating impact on psychosocial development. The first objective of this study is to use 3D-motion analysis to ‘probe’ the underlying brain dysfunction which characterises ADHD-CT. The second objective of this study is to improve our understanding of the link between movement problems, and (a) injury proneness, and (b) social-communicative problems, in children with ADHD-CT.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100326
Funder
Australian Research Council
Funding Amount
$425,231.00
Summary
Mass transfer enhancement for hydrate based carbon capture and cold storage. This project aims to generate the knowledge and techniques required to increase carbon dioxide (CO2) uptake in hydrate based carbon capture from current levels of 15.4% to up to 90% of its rated capacity. This marked improvement stems from identification of the mechanism of CO2-water mass transfer in CO2 hydrate formation and engineering of structurally modified porous hydrogels as the substrate of CO2 hydrates. Encapsu ....Mass transfer enhancement for hydrate based carbon capture and cold storage. This project aims to generate the knowledge and techniques required to increase carbon dioxide (CO2) uptake in hydrate based carbon capture from current levels of 15.4% to up to 90% of its rated capacity. This marked improvement stems from identification of the mechanism of CO2-water mass transfer in CO2 hydrate formation and engineering of structurally modified porous hydrogels as the substrate of CO2 hydrates. Encapsulation will be developed in a way that CO2 may be transported by CO2 hydrates in a concentrated form. Successful completion of the project will offer technical evaluation of a novel CO2 capture and transport solution with lower operational energy consumption and capital cost than incumbent carbon capture technologies.Read moreRead less
Ocular Motility In Autism And Asperger S Disorder: Dissociation Of Motor Deficits.
Funder
National Health and Medical Research Council
Funding Amount
$131,235.00
Summary
We will use ocular motor technology to investigate motor dysfunction in autism and Asperger's disorder, to advance our understanding of the neurobiological bases of these disorders. This will help clarify whether neural networks are differentially disrupted in these disorders, as our previous clinical research suggests. This dissociation and the subsequent development of an ocular motor clincal screen may improve diagnosis, and potentially treatment, of these devastating conditions.
Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a hig ....Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a high theoretical value, as the questions we shall address are of independent interest to pure mathematicians.Read moreRead less
Geological sequestration of carbon dioxide in deep saline aquifers: coupled flow-mechanical considerations. Deep saline aquifers have been routinely proposed as sites for long-term, large-scale storage of carbon dioxide (CO2) emissions, as an option to assist the abatement of global warming. This project investigates expected engineering behaviour of deep saline aquifer reservoirs and their stability following CO2 sequestration.
Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a hig ....Asymptotic Geometric Analysis and Learning Theory. Learning Theory is used in various real-world applications in diverse research areas, ranging from Biology (e.g. DNA sequencing) to Information Sciences. Therefore, having a deep understanding of fundamental questions in Learning Theory, and in particular, pin-pointing the parameters that make a learning problem hard would have a significant practical impact. This projects aims to achieve this goal, and in addition, we expect it would have a high theoretical value, as the questions we shall address are of independent interest to pure mathematicians.
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Industrial Transformation Research Hubs - Grant ID: IH230100005
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub in Zero-emission Power Generation for Carbon Neutrality. This Hub aims to develop sustainable zero-emission power generation technologies to transform gaseous waste (mainly CO2) from our energy and manufacturing sectors into valuable products and create scalable pathways to market for driving industry transformation. This Hub expects to harvest renewable energy from the environment by using zero-emission power generators and then store it in green and safer batteries for convert ....ARC Research Hub in Zero-emission Power Generation for Carbon Neutrality. This Hub aims to develop sustainable zero-emission power generation technologies to transform gaseous waste (mainly CO2) from our energy and manufacturing sectors into valuable products and create scalable pathways to market for driving industry transformation. This Hub expects to harvest renewable energy from the environment by using zero-emission power generators and then store it in green and safer batteries for converting gaseous waste from sectors that cannot easily avoid emission into useful chemicals, which in turn realize carbon neutrality and negativity. The outcomes of this Hub are likely to be transformative for industry, the economy, and society in new-type renewable energy resources through decreasing environmental pollutants. Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE120100098
Funder
Australian Research Council
Funding Amount
$230,000.00
Summary
A comprehensive gas/vapour sorption facility for the fast advancement of decarbonised energy technologies. Solutions to clean energy production, storage and use are critical to Australia’s prosperity, yet there is a significant lack of targeted research facilities for the development of the highly needed materials and technologies for powering a sustainable Australia. This facility will bring research efforts closer to practical solutions.
Cohort discovery and activity mining for policy impact prediction. Cohort discovery and activity mining for policy impact prediction. This project aims to develop an intelligent systematic framework to predict policy impacts on Australian patients, by discovering inherent patient cohorts and assessing the impact of the policies on these cohorts. The proposed methods lay the theoretical foundations for building intelligent automated tools for policy assessment. Expected outcomes are data-driven p ....Cohort discovery and activity mining for policy impact prediction. Cohort discovery and activity mining for policy impact prediction. This project aims to develop an intelligent systematic framework to predict policy impacts on Australian patients, by discovering inherent patient cohorts and assessing the impact of the policies on these cohorts. The proposed methods lay the theoretical foundations for building intelligent automated tools for policy assessment. Expected outcomes are data-driven patient group discovery, which could more precisely identify the patient cohorts most likely to benefit from a specific policy; and a model to predict the efficacy of policy options, which could increase the sustainability of the national health system by enabling smarter, more efficient policy decision-making.Read moreRead less
Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an ....Learning to Pinpoint Emerging Software Vulnerabilities. This project aims to develop learning-based software vulnerability detection techniques to improve the reliability and security of modern software systems. The existing techniques relying on conventional yet rigid software analysis and testing techniques are ineffective and/or inefficient when detecting a wide variety of emerging software vulnerabilities. The outcomes of this project will be a deep-learning-based detection approach and an open-source tool that can capture precision correlations between deep code features and diverse vulnerabilities to pinpoint emerging vulnerabilities without the need for bug specifications. Significant benefits include greatly improved quality, reliability and security for modern software systems.Read moreRead less