Ground penetrating radar and electrical resistivity tomography surveys with a subsequent intrusive investigation in search for the missing Beaumont children in Adelaide, South Australia.

Jane, Arnna, and Grant Beaumont went missing from Glenelg Beach in Adelaide, South Australia on 26 January (Australia Day) 1966. Despite multiple land and sea searches over nearly 60 years, the children have not been found. New credible eyewitness testimony led to a site of interest at the now disused New Castalloy factory in North Plympton, Adelaide. This site has a complex stratigraphy of anthropogenic fill, which made ground penetrating radar (GPR) investigations unpromising. Electrical resistivity tomography (ERT), while not commonly used in a forensic capacity, provided an alternative approach that allowed suitable depth penetration to resolve a feature of interest, which was subsequently excavated by the South Australia Police. This feature did contain organic, and animal remains but, sadly, not the grave of Jane, Arnna, and Grant Beaumont. However, this investigation highlights the potential to use ERT in a forensic capacity, as well as the limitations of using geophysical techniques for covert burial detection.

Reconstructing archaeological palaeolandscapes using geophysical and geomatic survey techniques: An example from Red Lily Lagoon, Arnhem Land, Australia.

Arnhem Land is a key region for understanding the Pleistocene colonisation of Australia, due to the presence of the oldest sites in the continent. Despite this, conventional archaeological survey has not been effective at locating additional pre-Holocene sites in the region due to a complex distribution of geomorphic units caused by sea level rise and coastal aggradation. This research uses geophysical and geomatic techniques to map the subsurface distribution of the geomorphic units in the Red Lily Lagoon region in eastern Arnhem Land. This reveals a complex Pleistocene landscape, which offers the potential to locate additional archaeological sites and so reveal more about the lifeways of the earliest Australians.

A Deep Learning Approach to Classify Sitting and Sleep History from Raw Accelerometry Data during Simulated Driving.

Prolonged sitting and inadequate sleep can impact driving performance. Therefore, objective knowledge of a driver's recent sitting and sleep history could help reduce safety risks. This study aimed to apply deep learning to raw accelerometry data collected during a simulated driving task to classify recent sitting and sleep history. Participants (n = 84, Mean ± SD age = 23.5 ± 4.8, 49% Female) completed a seven-day laboratory study. Raw accelerometry data were collected from a thigh-worn accelerometer during a 20-min simulated drive (8:10 h and 17:30 h each day). Two convolutional neural networks (CNNs; ResNet-18 and DixonNet) were trained to classify accelerometry data into four classes (sitting or breaking up sitting and 9-h or 5-h sleep). Accuracy was determined using five-fold cross-validation. ResNet-18 produced higher accuracy scores: 88.6 ± 1.3% for activity (compared to 77.2 ± 2.6% from DixonNet) and 88.6 ± 1.1% for sleep history (compared to 75.2 ± 2.6% from DixonNet). Class activation mapping revealed distinct patterns of movement and postural changes between classes. Findings demonstrate the suitability of CNNs in classifying sitting and sleep history using thigh-worn accelerometer data collected during a simulated drive. This approach has implications for the identification of drivers at risk of fatigue-related impairment.