University of Adelaide

About: University of Adelaide is a education organization based out in Adelaide, South Australia, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 27251 authors who have published 79167 publications receiving 2671128 citations. The organization is also known as: The University of Adelaide & Adelaide University.

Sudden infant death syndrome and unclassified sudden infant deaths: a definitional and diagnostic approach.

Abstract: The definition of sudden infant death syndrome (SIDS) originally appeared in 1969 and was modified 2 decades later. During the following 15 years, an enormous amount of additional information has emerged, justifying additional refinement of the definition of SIDS to incorporate epidemiologic features, risk factors, pathologic features, and ancillary test findings. An expert panel of pediatric and forensic pathologists and pediatricians considered these issues and developed a new general definition of SIDS for administrative and vital statistics purposes. The new definition was then stratified to facilitate research into sudden infant death. Another category, defined as unclassified sudden infant deaths, was introduced for cases that do not meet the criteria for a diagnosis of SIDS and for which alternative diagnoses of natural or unnatural conditions were equivocal. It is anticipated that these new definitions will be modified in the future to accommodate new understanding of SIDS and sudden infant death.

Developing a multidimensional instrument to measure psychic distance stimuli

Abstract: This paper develops and tests a range of potential psychic distance stimuli including differences in culture, language, religion, education, and political systems. Using trade flows among a set of 38 nations as the dependent variable, it is shown that, whereas the majority of the proposed indicators prove to be statistically significant predictors of trade flows, the most common psychic distance surrogate – a composite measure of Hofstede's cultural dimensions – is not significant.

Efficient piecewise training of deep structured models for semantic segmentation

Abstract: Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information; specifically, we explore `patch-patch' context between image regions, and `patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-over-union score of 78.0 on the challenging PASCAL VOC 2012 dataset.

Persulfate Activation on Crystallographic Manganese Oxides: Mechanism of Singlet Oxygen Evolution for Nonradical Selective Degradation of Aqueous Contaminants.

Abstract: Minerals and transitional metal oxides of earth-abundant elements are desirable catalysts for in situ chemical oxidation in environmental remediation. However, catalytic activation of peroxydisulfate (PDS) by manganese oxides was barely investigated. In this study, one-dimension manganese dioxides (α- and β-MnO2) were discovered as effective PDS activators among the diverse manganese oxides for selective degradation of organic contaminants. Compared with other chemical states and crystallographic structures of manganese oxide, β-MnO2 nanorods exhibited the highest phenol degradation rate (0.044 min-1, 180 min) by activating PDS. A comprehensive study was conducted utilizing electron paramagnetic resonance, chemical probes, radical scavengers, and different solvents to identity the reactive oxygen species (ROS). Singlet oxygen (1O2) was unveiled to be the primary ROS, which was generated by direct oxidation or recombination of superoxide ions and radicals from a metastable manganese intermediate at neutral pH. The study dedicates to the first mechanistic study into PDS activation over manganese oxides and provides a novel catalytic system for selective removal of organic contaminants in wastewater.

A numerical comparison of 2D resistivity imaging with 10 electrode arrays

Abstract: Numerical simulations are used to compare the resolution and efficiency of 2D resistivity imaging surveys for 10 electrode arrays. The arrays analysed include polepole (PP), pole-dipole (PD), half-Wenner (HW), Wenner-α (WN), Schlumberger (SC), dipole-dipole (DD), Wenner-β (WB), γ -array (GM), multiple or moving gradient array (GD) and midpoint-potential-referred measurement (MPR) arrays. Five synthetic geological models, simulating a buried channel, a narrow conductive dike, a narrow resistive dike, dipping blocks and covered waste ponds, were used to examine the surveying efficiency (anomaly effects, signal-to-noise ratios) and the imaging capabilities of these arrays. The responses to variations in the data density and noise sensitivities of these electrode configurations were also investigated using robust (L1-norm) inversion and smoothness-constrained least-squares (L2-norm) inversion for the five synthetic models. The results show the following. (i) GM and WN are less contaminated by noise than the other electrode arrays. (ii) The relative anomaly effects for the different arrays vary with the geological models. However, the relatively high anomaly effects of PP, GM and WB surveys do not always give a high-resolution image. PD, DD and GD can yield better resolution images than GM, PP, WN and WB, although they are more susceptible to noise contamination. SC is also a strong candidate but is expected to give more edge effects. (iii) The imaging quality of these arrays is relatively robust with respect to reductions in the data density of a multi-electrode layout within the tested ranges. (iv) The robust inversion generally gives better imaging results than the L2-norm inversion, especially with noisy data, except for the dipping block structure presented here. (v) GD and MPR are well suited to multichannel surveying and GD may produce images that are comparable to those obtained with DD and PD. Accordingly, the GD, PD, DD and SC arrays are strongly recommended for 2D resistivity imaging, where the final choice will be determined by the expected geology, the purpose of the survey and logistical considerations.