Australian threshold quantities for ‘drug trafficking’: are they placing drug users at risk of unjustified sanction?

This study uses data on patterns of drug user consumption and purchasing to evaluate Australian legal threshold quantities to see whether Australian drug users are at risk of exceeding the thresholds for personal use alone. Introduction Drug trafficking in Australia is deemed a very serious offence, one for which legislators and courts have ruled general deterrence is paramount and ‘little mercy’ should be shown. A principal challenge has been how to effectively differentiate and sanction participants in the drug trade—particularly how to differentiate ‘traffickers’ from those who consume or purchase illicit drugs for personal use alone. To assist in this endeavour, all Australian states and territories have adopted legal thresholds that specify quantities of drugs over which offenders are either presumed to have possessed the drugs ‘for the purposes of supply’ and liable to sanction as ‘drug traffickers’ (up to 15 years imprisonment in most states), or in the case of Queensland, liable to sanctions equivalent to drug traffickers (up to 25 years imprisonment). Yet, in spite of known risks from adopting such thresholds, particularly of an unjustified conviction of a user as a trafficker, the capacity of Australian legal thresholds to deliver proportional sanctioning has been subject to limited research to date. This paper summarises key findings from a Criminology Research Grant funded project. The broader project examined this issue in two different ways—whether the thresholds are designed to filter traffickers from users and whether they enable appropriate sanctioning of traffickers of different controlled drugs. Herein, the focus is on the former—to what extent Australian legal thresholds unwittingly place users at risk of unjustified and disproportionate charge or sanction as traffickers

In-situ Thermal and Deformation Characterization of Additive Manufacturing Processes

Additive manufacturing (AM) is a rapidly growing industry with numerous applications in the aerospace industry such as aircraft parts and emergency tools on the International Space Station. Defects in additively manufactured structures, however, can waste a lot of time and money. Being able to monitor the manufacturing process for defects is one of the first steps which can be taken to mitigate these losses. This study focuses on the use of thermography in conjunction with deep learning to identify flaws during 3D printing of composite structures made using Onyx, a mixture of chopped carbon fiber and nylon, composite prints. In addition, polymeric structures using polylactic acid (PLA) were analyzed using thermography and digital image correlation (DIC) to understand the interactions between the thermal variations and resulting deformation. The inclusion of a zero-bias deep neural network (ZBDNN) to classify given images can also show real-time monitoring of defects in composite prints as a realizable goal. The ZBDNN was trained to classify thermal images of undamaged prints based on which layer of the print they occurred on and to set aside any of these images containing defects. The addition of a non-bias layer in the deep neural network ensures the classifications of these images remain consistent and accurate, with a learning accuracy of over 90%. The algorithm was also used to analyze grayscale images from multiple angles of the prints and compared these images to thermal images as another means of detecting defects in each print. The use of these multiple data sources may be used as the basis for an early-warning detection system for real-time analysis

Sinusoidal Modeling Applied to Spatially Variant Tropospheric Ozone Air Pollution

This paper demonstrates how parsimonious models of sinusoidal functions can be used to fit spatially variant time series in which there is considerable variation of a periodic type. A typical shortcoming of such tools relates to the difficulty in capturing idiosyncratic variation in periodic models. The strategy developed here addresses this deficiency. While previous work has sought to overcome the shortcoming by augmenting sinusoids with other techniques, the present approach employs station-specific sinusoids to supplement a common regional component, which succeeds in capturing local idiosyncratic behavior in a parsimonious manner. The experiments conducted herein reveal that a semi-parametric approach enables such models to fit spatially varying time series with periodic behavior in a remarkably tight fashion. The methods are applied to a panel data set consisting of hourly air pollution measurements. The augmented sinusoidal models produce an excellent fit to these data at three different levels of spatial detail.Air Pollution, Idiosyncratic component, Regional variation, Semiparametric model, Sinusoidal function, Spatial-temporal data, Tropospheric Ozone

Diffusion Tensor Imaging in Pediatric Brain Tumor Patients

In this dissertation, we outline our efforts to introduce an advanced MRI imaging technique called Diffusion Tensor Imaging (DTI) to the pediatric brain tumor population. We discuss the theory and application of DTI as it was performed in a series of translational investigations at St. Jude Children’s Research Hospital. We present evidence of how the introduction of this technique impacted diagnosis, and treatment. And finally, we demonstrate how DTI was used to investigate cognitive morbidities associated with cancer treatment and how this research provided insight into the underlying pathophysiology involved in the development of these treatment sequela. This research has generated important insights into the fundamental causes of neuroanatomical and cognitive deficits associated with cancer and cancer therapy. The use of DTI has permitted us to identify potential targets for improved radiological and surgical techniques as well as targets for pharmacological and behavioral interventions that might improve cognitive function in cancer survivors. The discoveries here afford us an opportunity to reduce the negative effects of cancer therapy on patients treated in the future while maintaining successful survival rates