A Biologically Responsive and Anatomically Authentic Human Nasal Model. As respiratory conditions caused by pollutants and viruses become more prevalent, human nasal models to study infection/protection mechanisms and nasal drug/vaccine delivery are increasingly important. This project aims to develop a world-first human nasal model to mimic both anatomical and biological aspects of the nasal cavity and predict the distribution and deposition of fine particles and the resultant biological respon ....A Biologically Responsive and Anatomically Authentic Human Nasal Model. As respiratory conditions caused by pollutants and viruses become more prevalent, human nasal models to study infection/protection mechanisms and nasal drug/vaccine delivery are increasingly important. This project aims to develop a world-first human nasal model to mimic both anatomical and biological aspects of the nasal cavity and predict the distribution and deposition of fine particles and the resultant biological response from the nasal mucosa. The aim is to overcome a key fabrication challenge - to 3D print an anatomically accurate nasal construct with a porous wall on which to grow and mature functional nasal tissue that lines a nasal cavity wall. The benefit would be enabling faster development of more targeted drugs and vaccines.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultu ....Remote presence for guidance on physical tasks. This project aims to transform remote collaboration on physical tasks. Current systems for remote collaboration on physical tasks are not as effective as working face-to-face. This could be overcome by sharing non-verbal cues, designing systems to account for cultural issues, and using a new model of communication. This project will develop theories and interaction methods for remote guidance based on natural non-verbal communication cues and cultural issues. This project is expected to benefit industries with widely distributed multi-cultural workforces such as mining, defence and medicine.Read moreRead less