La Trobe University

About: La Trobe University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Health care. The organization has 13370 authors who have published 41291 publications receiving 1138269 citations. The organization is also known as: LaTrobe University & LTU.

Ultrafast three-dimensional imaging of lattice dynamics in individual gold nanocrystals

Abstract: Key insights into the behavior of materials can be gained by observing their structure as they undergo lattice distortion. Laser pulses on the femtosecond time scale can be used to induce disorder in a "pump-probe" experiment with the ensuing transients being probed stroboscopically with femtosecond pulses of visible light, x-rays, or electrons. Here we report three-dimensional imaging of the generation and subsequent evolution of coherent acoustic phonons on the picosecond time scale within a single gold nanocrystal by means of an x-ray free-electron laser, providing insights into the physics of this phenomenon. Our results allow comparison and confirmation of predictive models based on continuum elasticity theory and molecular dynamics simulations.

Climate change and diseases of food crops

Abstract: Despite complex regional patterns of projected climate change, significant decreases in food crop yields have been predicted using the ‘worst case’ CO2 emission scenario (A1FI) of the Intergovernmental Panel on Climate Change. Overall, climate change is predicted to have a progressively negative effect on the yield of food crops, particularly in the absence of efforts to mitigate global CO2 emissions. As with all species, plant pathogens will have varying responses to climate change. Whilst the life cycle of some pathogens will be limited by increasing temperatures, e.g. Puccinia striiformis f.sp. tritici, other climatic factors such as increasing atmospheric CO2, may provide more favourable conditions for pathogens such as Fusarium pseudograminearum. Based on published literature and unpublished work in progress, we have reviewed the qualitative effects of climate change on pathogens that cause disease of four major food crops: wheat, rice, soybean and potato. The limited data show that the influence will be positive, negative or neutral, depending on the host–pathogen interaction. Quantitative analysis of climate change on pathogens of these crops is largely lacking, either from field or laboratory studies or from modelling-based assessments. Systematic quantitative analysis of these effects will be necessary in developing future disease management plans, such as plant breeding, altered planting schedules, chemical and biological control methods and increased monitoring for new disease threats.

Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing

Abstract: The increase in the number and diversity of smart objects has raised substantial cybersecurity challenges due to the recent exponential rise in the occurrence and sophistication of attacks Although cloud computing has transformed the world of business in a dramatic way, its centralization hammers the application of distributed services such as security mechanisms for IoT applications The new and emerging IoT applications require novel cybersecurity controls, models, and decisions distributed at the edge of the network Despite the success of the existing cryptographic solutions in the traditional Internet, factors such as system development flaws, increased attack surfaces, and hacking skills have proven the inevitability of detection mechanisms The traditional approaches such as classical machine-learning-based attack detection mechanisms have been successful in the last decades, but it has already been proven that they have low accuracy and less scalability for cyber-attack detection in massively distributed nodes such as IoT The proliferation of deep learning and hardware technology advancement could pave a way to detecting the current level of sophistication of cyber-attacks in edge networks The application of deep networks has already been successful in big data areas, and this indicates that fog-tothings computing can be the ultimate beneficiary of the approach for attack detection because a massive amount of data produced by IoT devices enable deep models to learn better than shallow algorithms In this article, we propose a novel distributed deep learning scheme of cyber-attack detection in fog-to-things computing Our experiments show that deep models are superior to shallow models in detection accuracy, false alarm rate, and scalability