https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 Geospatial Machine Learning Prediction of Arsenic Distribution in the Groundwater of Murshidabad District, West Bengal, India: Analyzing Spatiotemporal Patterns to Understand Human Health Risk https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54047 10 μg/L was much greater in the regions between two major rivers than in the regions close to the Ganges River on the eastern border of the study area, where higher proportions of As concentrations >10 μg/L had been observed prior to 2005. The greater likelihood that toxic concentrations of As are present away from the river channel and is found instead in the interfluvial regions could be attributed to the transport and flushing of aquifer As from intense irrigation pumping. We estimated that about 2.8 million people could be chronically exposed to As concentrations >10 μg/L. This high population-level exposure to elevated As concentrations could be reduced through targeted well-testing campaigns, promoting well-switching, provisions for safe water access, and developing plans for raising public awareness. Policymakers could use the ternary hazard map presented here to target high-risk localities for priority implementation of piped water supply strategies to help reduce human suffering.]]> Tue 30 Jan 2024 13:50:01 AEDT ]]> External validation of a predictive model of urethral strictures for prostate patients treated with HDR Brachytherapy boost https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:37248 50 = 116.7 Gy and m = 0.23; n was fixed to 0.3, based on numerical optimization of the likelihood. The calibration plot showed a good agreement between the observed toxicity and the probability predicted by the model, confirmed by bootstrapping. For the external validation, the calibration plot showed that the observed toxicity obtained with the RADAR patients was well-represented by the fitted LKB model parameters. When patients were stratified by the use of AD TD50 decreased when AD was not present. Conclusions: Lyman–Kutcher–Burman model parameters were fitted to the risk of urethral stricture and externally validated with an independent cohort, to provide guidance on urethral tolerance doses for patients treated with a HDRB boost. For patients that did not receive AD, model fitting provided a lower TD50 suggesting a protective effect on urethra toxicity.]]> Thu 09 Dec 2021 11:03:03 AEDT ]]> Predicting self-reported illness for professional team-sport athletes https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:29722 2282 AU, weekly-TL >2786 AU and monotony >0.78 AU) to best predict when athletes are at increased risk of self-reported illness. In addition, a reduction in overall wellbeing (<7.25 AU) in the presence of increased internal-TL as previously stated, was highlighted as a contributor to self-reported illness occurrence.These results indicate that self-report data can be successfully utilized to provide a novel understanding of the interactions between competition-associated stressors experienced by professional team-sport athletes and their susceptibility to illness. This may assist coaching staff to more effectively monitor players during the season and to potentially implement preventative measures to reduce the likelihood of illnesses occurring.]]> Sat 24 Mar 2018 07:33:25 AEDT ]]> The PLS agent: predictive modeling with PLS-SEM and agent-based simulation https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25007 Sat 24 Mar 2018 07:10:40 AEDT ]]>