ORCID Profile
0000-0002-0473-7611
Current Organisation
University of Adelaide
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Publisher: Cambridge University Press (CUP)
Date: 2016
DOI: 10.1017/PASA.2016.13
Abstract: We applied three statistical classification techniques—linear discriminant analysis (LDA), logistic regression, and random forests—to three astronomical datasets associated with searches for interstellar masers. We compared the performance of these methods in identifying whether specific mid-infrared or millimetre continuum sources are likely to have associated interstellar masers. We also discuss the interpretability of the results of each classification technique. Non-parametric methods have the potential to make accurate predictions when there are complex relationships between critical parameters. We found that for the small datasets the parametric methods logistic regression and LDA performed best, for the largest dataset the non-parametric method of random forests performed with comparable accuracy to parametric techniques, rather than any significant improvement. This suggests that at least for the specific ex les investigated here accuracy of the predictions obtained is not being limited by the use of parametric models. We also found that for LDA, transformation of the data to match a normal distribution led to a significant improvement in accuracy. The different classification techniques had significant overlap in their predictions further astronomical observations will enable the accuracy of these predictions to be tested.
Publisher: American Chemical Society (ACS)
Date: 10-08-2021
Publisher: Informa UK Limited
Date: 29-11-2021
Publisher: Springer Science and Business Media LLC
Date: 12-06-2023
Publisher: Springer Science and Business Media LLC
Date: 02-03-2021
Publisher: American Chemical Society (ACS)
Date: 13-04-2016
Abstract: Wastewater-based epidemiology is increasingly being used as a tool to monitor drug use trends. To minimize costs, studies have typically monitored a small number of days. However, cycles of drug use may display weekly and seasonal trends that affect the accuracy of monthly or annual drug use estimates based on a limited number of s les. This study aimed to rationalize s ling methods for minimizing the number of s les required while maximizing information about temporal trends. A range of s ling strategies were examined: (i) targeted days (e.g., weekends), (ii) completely random or stratified random s ling, and (iii) a number of s ling strategies informed by known weekly cycles in drug use data. Using a time-series approach, analysis was performed for four drugs (MDMA, meth hetamine, cocaine, methadone) collected through a continuous s ling program over 14 months. Results showed, for drugs with weekly cycles (MDMA, meth hetamine and cocaine in this s le), s ling strategies which made use of those weekly cycles required fewer s les to obtain similar information as s ling 5 days per week and had better accuracy than stratified random s ling techniques.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Informa UK Limited
Date: 09-09-2022
Publisher: Informa UK Limited
Date: 03-04-2023
Publisher: Elsevier BV
Date: 05-2020
Publisher: Elsevier BV
Date: 02-2015
Publisher: Elsevier BV
Date: 09-2022
DOI: 10.1016/J.FORSCIINT.2022.111386
Abstract: This manuscript presents a more accurate and easy to implement multivariate generalisation of the international standard 4σ forensic glass comparison technique. Many crimes result in glass breaking, and the broken glass found at a crime scene can be important forensic evidence. The chemical composition of this glass can be measured to establish whether it can be distinguished from glass fragments found on a suspect's clothing. The chemical composition can be measured using laser ablation-inductively coupled plasma mass spectrometry. A commonly used method to compare fragments of glass is the 4σ interval criterion. This method, however, compares each element in idually and does not take advantage of the multivariate nature of this data. We introduce a multivariate extension to this method, which makes use of the correlation structure between the elements. We demonstrate that this method results in an improvement in the false positive rate, with only a small compromise in the false negative rate. The improvement in false positive rate is desirable as false positives translate to misleading evidence against a potentially innocent defendant. The multivariate generalisation improves accuracy while retaining a similar interpretation, and so is suitable to present in court.
Publisher: Elsevier BV
Date: 10-2016
DOI: 10.1016/J.SCITOTENV.2016.05.181
Abstract: Wastewater analysis, or wastewater-based epidemiology, has become a common tool to monitor trends of illicit drug consumption around the world. In this study, we examined trends in cocaine, 3,4-methylenedioxymeth hetamine (MDMA) and meth hetamine consumption by measuring their residues in wastewater from two wastewater treatment plants in Australia (specifically, an urban and a rural catchment, both in South East Queensland) between 2009 and 2015. With direct injection of the s les, target analytes were identified and quantified using liquid chromatography-mass spectrometry. Cocaine and MDMA residues and metabolites were mainly quantifiable in the urban catchment while meth hetamine residues were consistently detected in both urban and rural catchments. There was no consistent trend in the population normalised mass loads observed for cocaine and MDMA at the urban site between 2009 and 2015. In contrast, there was a five-fold increase in meth hetamine consumption over this period in this catchment. For meth hetamine consumption, the rural area showed a very similar trend as the urban catchment starting at a lower baseline. The observed increase in per capita loads of meth hetamine via wastewater analysis over the past six years in South East Queensland provides objective evidence for increased meth hetamine consumption in the Australian population while the use of other illicit stimulants remained relatively stable.
No related grants have been discovered for Melissa Humphries.