ACEMS Research Briefs

These are short summary descriptions of our latest, just-published research.

ACEMS Research Briefs Projects

A population of bang-bang switches of defective interfering particles makes within-host dynamics of dengue virus controllable

Viral pathogens pose a continuous and shifting biological threat to military readiness and national security overall in the form of infectious disease with pandemic potential. Today’s limited vaccines and other antivirals are often circumvented by quickly mutating viruses that evolve to develop resistance to treatments that are carefully formulated to act only specific strains of a virus.

A Feature‐Based Procedure for Detecting Technical Outliers in Water‐Quality Data From In Situ Sensors

Water-quality sensors are exposed to changing environments and extreme weather conditions and thus are prone to errors, including failure. These technical errors make data unreliable and untrustworthy and affect performance of any subsequent data analysis. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a feature based procedure, named oddwater, for detecting technical outliers in water-quality data derived from in situ sensors.

Anomaly Detection in Streaming Nonstationary Temporal Data

Anomalies can be the main carriers of significant and often critical information, and the identification of anomalies is a key task in many fields such as cyber security, intrusion detection, water quality monitoring, system health monitoring, environmental monitoring. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a framework that provides early detection of anomalous series within a large collection of streaming time-series data.

Rapid spatial risk modelling for management of early weed invasions: balancing ecological complexity and operational needs

Invasive weeds can threaten native biodiversity and negatively impact on agriculture. A group of researchers at QUT and CSIRO, including an ACEMS member, designed and implemented a new framework for rapidly modelling the invasion risk of weeds using Bayesian belief networks.