Deakin University

About: Deakin University is a education organization based out in Burwood, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 12118 authors who have published 46470 publications receiving 1188841 citations. The organization is also known as: Deakin.

New insights into burnout and health care: Strategies for improving civility and alleviating burnout.

Abstract: What do we know about burnout, and what can we do about it? This article will provide an overview of what has been learned from current research on burnout, and what are the implications of the key themes that have emerged. One theme involves the critical significance of the social environment in health care settings. A second theme is the challenge of how to take what we know, and apply it to what we can do about burnout. What we need are new ideas about potential interventions, and clear evidence of their effectiveness. One example of this perspective addresses burnout by improving the balance of civil, respectful social encounters occurring during a workday. Research has demonstrated that not only can civility be increased at work but that doing so leads to an enduring reduction in burnout among health care providers. Lessons learned from this extensive research form the basis of recommendations for medical education. Specifically, the effectiveness of both the academic content and supervised p...

Photocatalytic reduction of carbon dioxide into gaseous hydrocarbon using TiO2 pellets

Abstract: It has been shown that CO 2 could be transformed into hydrocarbons when it is in contact with water vapour and catalysts under UV irradiation. This paper presents an experimental set-up to study the process employing a new approach of heterogeneous photocatalysis using pellet form of catalyst instead of immobilized catalysts on solid substrates. In the experiment, CO 2 mixed with water vapour in saturation state was discharged into a quartz reactor containing porous TiO 2 pellets and illuminated by various UV lamps of different wavelengths for 48 h continuously. The gaseous products extracted were identified using gas chromatography. The results confirmed that CO 2 could be reformed in the presence of water vapour and TiO 2 pellets into CH 4 under continuous UV irradiation at room conditions. It showed that when UVC (253.7 nm) light was used, total yield of methane was approximately 200 ppm which was a fairly good reduction yield as compared to those obtained from the processes using immobilized catalysts through thin-film technique and anchoring method. CO and H 2 were also detected. Switching from UVC to UVA (365 nm) resulted in significant decrease in the product yields. The pellet form of catalyst has been found to be attractive for use in further research on photocatalytic reduction of CO 2 .

MGAN: Training Generative Adversarial Nets with Multiple Generators

Abstract: We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem The main intuition is to employ multiple generators, instead of using a single one as in the original GAN The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapsing problem and delivering state-of-the-art results A minimax formulation was able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model We term our method Mixture Generative Adversarial Nets (MGAN) We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators’ distributions and the empirical data distribution is minimal, whilst the JSD among generators’ distributions is maximal, hence effectively avoiding the mode collapsing problem By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by the generators

Anode Improvement in Rechargeable Lithium-Sulfur Batteries.

Abstract: Owing to their theoretical energy density of 2600 Wh kg-1 , lithium-sulfur batteries represent a promising future energy storage device to power electric vehicles. However, the practical applications of lithium-sulfur batteries suffer from poor cycle life and low Coulombic efficiency, which is attributed, in part, to the polysulfide shuttle and Li dendrite formation. Suppressing Li dendrite growth, blocking the unfavorable reaction between soluble polysulfides and Li, and improving the safety of Li-S batteries have become very important for the development of high-performance lithium sulfur batteries. A comprehensive review of various strategies is presented for enhancing the stability of the anode of lithium sulfur batteries, including inserting an interlayer, modifying the separator and electrolytes, employing artificial protection layers, and alternative anodes to replace the Li metal anode.