Built Environments And Child Health In WalEs And AuStralia (BEACHES)
Funder
National Health and Medical Research Council
Funding Amount
$797,256.00
Summary
A better understanding of how the built environment drives obesity in children will inform evidence-based planning policy and practice strategies to prevent the rise in non-communicable diseases (NCDs) in future generations. We will bring together five large UK and Australian cohort studies to understand how complex and interacting built environment factors influence modifiable risk factors (physical inactivity, sedentary time, unhealthy diet) for NCD’s across childhood.
Addressing Evidence Gaps And Developing A Novel Treatment To Reduce The Burden Of Post-traumatic Knee Osteoarthritis
Funder
National Health and Medical Research Council
Funding Amount
$645,205.00
Summary
Arthritis caused by knee injury has a devastating personal and economic impact. Research is needed to develop strategies to prevent arthritis and improve outcomes for people living with arthritis. This research will improve treatment of knee injury to reduce arthritis risk, understand why some people are more likely to get knee arthritis, and develop a new treatment to improve outcomes for people living with knee arthritis. A clinical trial will determine if this treatment achieves this aim.
Early Career Industry Fellowships - Grant ID: IE230100410
Funder
Australian Research Council
Funding Amount
$452,085.00
Summary
Bridging the gap between rockfall theory and engineering practice. Fragmentation is often observed post rockfall events and it is recognised as a critical aspect of adequate rockfall risk management. Yet, rockfall fragmentation is a complex phenomenon still poorly understood and not properly considered in engineering practice. This project aims at developing a theoretical and stochastic fragmentation framework, based on high-quality and comprehensive experimental data, in collaboration with lead ....Bridging the gap between rockfall theory and engineering practice. Fragmentation is often observed post rockfall events and it is recognised as a critical aspect of adequate rockfall risk management. Yet, rockfall fragmentation is a complex phenomenon still poorly understood and not properly considered in engineering practice. This project aims at developing a theoretical and stochastic fragmentation framework, based on high-quality and comprehensive experimental data, in collaboration with leading international industry partners that provide advanced geotechnical design tools to practitioners around the world. The outcomes of the project will bridge the gap between rockfall theory and engineering practice. It will allow for more cost-effective and safer design of rockfall protection structures.Read moreRead less
A novel granular stress sensor for soil exploration. The project aims to develop a novel way to measure the state of soils and improve the perception of soft ground robots by combining advances in sensor development with granular physics. The project expects to produce new insights in geotechnical engineering by utilising innovative sensors compliant with the surrounding medium, thus improving measurements across broader deformation conditions than existing technologies. Expected outcomes includ ....A novel granular stress sensor for soil exploration. The project aims to develop a novel way to measure the state of soils and improve the perception of soft ground robots by combining advances in sensor development with granular physics. The project expects to produce new insights in geotechnical engineering by utilising innovative sensors compliant with the surrounding medium, thus improving measurements across broader deformation conditions than existing technologies. Expected outcomes include an increased ability to prevent soil failures by utilising these sensors to monitor stress levels underground. This should provide significant benefits for saving critical infrastructure from environmental and geotechnical failures, including landslides, tunnel collapses, and tailings dam damages.Read moreRead less
Benchmarking the neurophysiology of human cortex models in vitro. This project aims to improve human brain models in vitro by developing an analytical tool benchmarking biophysical similarities to the adult human cortex. This project expects to generate new knowledge by testing for the first time the theory that integrating sensory-like inputs and awake/sleep-like cycles of electrical activity in vitro may complete the maturation of human brain organoid models. It will also generate new methods ....Benchmarking the neurophysiology of human cortex models in vitro. This project aims to improve human brain models in vitro by developing an analytical tool benchmarking biophysical similarities to the adult human cortex. This project expects to generate new knowledge by testing for the first time the theory that integrating sensory-like inputs and awake/sleep-like cycles of electrical activity in vitro may complete the maturation of human brain organoid models. It will also generate new methods to simplify the analysis of multimodal path-clamping data (Patch-seq). Expected outcomes will facilitate research collaboration and the reproducibility of accurate experimental replicates of the human brain. This will provide significant benefits in the global race to understand human brain computation mechanisms.Read moreRead less
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
Bio-inspired Computing for Problems with Chance Constraints. Bio-inspired algorithms have successfully been applied to a wide range of optimisation problems. Uncertainties in real-world applications can lead to critical failures of production schedules or safe critical systems. Chance constraints model such uncertainties and allow to limit the possibility of such failures. This future fellowship builds up the area of bio-inspired computing for problems with chance constraints. It develops high ....Bio-inspired Computing for Problems with Chance Constraints. Bio-inspired algorithms have successfully been applied to a wide range of optimisation problems. Uncertainties in real-world applications can lead to critical failures of production schedules or safe critical systems. Chance constraints model such uncertainties and allow to limit the possibility of such failures. This future fellowship builds up the area of bio-inspired computing for problems with chance constraints. It develops high performing bio-inspired algorithms for stochastic problems where the constraints can only be violated with a small probability. The outcomes will lead to more effective and reliable optimisation methods for complex planning processes in areas of national priority such as mining and manufacturing.Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less
Behaviour Bootstrapping for Ad Hoc, Heterogeneous Robot Swarms. This project aims to develop algorithms to permit groups of robots to evolve coordinated, collective, swarm behaviours. Groups of robots will be conceptualised as developmental swarm organisms with an initially limited set of behaviours, but equipped with structures and processes to permit them to evolve new behaviours. This project expects to deliver the next generation of computational intelligence technologies to enable humans to ....Behaviour Bootstrapping for Ad Hoc, Heterogeneous Robot Swarms. This project aims to develop algorithms to permit groups of robots to evolve coordinated, collective, swarm behaviours. Groups of robots will be conceptualised as developmental swarm organisms with an initially limited set of behaviours, but equipped with structures and processes to permit them to evolve new behaviours. This project expects to deliver the next generation of computational intelligence technologies to enable humans to harness large groups of robots for new kinds of transport and inspection tasks in smart cities, smart farming and defence. The expected outcomes of the project include new software frameworks for distributed developmental learning, extending developmental robotics to evolutionary robot swarms. Read moreRead less