Legal risk management of adverse health outcomes and injury in the fitness industry: developing evidence-informed regulation that improves safety. This project analyses Australian laws, policies and practices designed to manage legal risks and liabilities in the fitness industry, and assesses their effectiveness in preventing adverse health outcomes, injuries, and the legal liability associated with those risks.
Discovery Early Career Researcher Award - Grant ID: DE180100022
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
Characterising wind farm noise to reduce community disturbance. This project aims to address the issue of wind farm noise. The rapid global expansion of wind farm facilities has resulted in widespread community complaints regarding noise emission. This project aims to identify, quantify and characterise the signal components of wind farm noise that are responsible for annoyance and sleep disturbance. The anticipated outcome is establishment of dose-response relationships between wind farm noise ....Characterising wind farm noise to reduce community disturbance. This project aims to address the issue of wind farm noise. The rapid global expansion of wind farm facilities has resulted in widespread community complaints regarding noise emission. This project aims to identify, quantify and characterise the signal components of wind farm noise that are responsible for annoyance and sleep disturbance. The anticipated outcome is establishment of dose-response relationships between wind farm noise and community disturbance. Significant benefits include improved health and quality of life for people living near wind farms and greater public acceptance of wind farms in rural communities.Read moreRead less
Non-linear modelling for predicting patient presentation rates for mass-gatherings. Mass-gatherings are events where crowds gather. Access to health care at these events is critical, though difficult. Complex interrelationships exist between characteristics of events and presenting patient profiles. To prevent overwhelming local hospital and emergency services it is important to accurately predict patient volume. A predictive model constructed through linear modelling has been widely used. Key f ....Non-linear modelling for predicting patient presentation rates for mass-gatherings. Mass-gatherings are events where crowds gather. Access to health care at these events is critical, though difficult. Complex interrelationships exist between characteristics of events and presenting patient profiles. To prevent overwhelming local hospital and emergency services it is important to accurately predict patient volume. A predictive model constructed through linear modelling has been widely used. Key features affecting patient presentations are non-linear in character and non-linear modelling may provide more accurate patient predictive models. This project provides prospective analysis of data to develop a non-linear predictive model.Read moreRead less