The Effects Of Road Reclamation Method On Woodland Caribou And Other Boreal Species

This study will examine the relationship between reclamation methods and when deactivated roads become suitable for caribou, using developing UAV technology to monitor caribou while testing UAV effectiveness in the field. This will be done by establishing long-term cameras along reclaimed road sites, monitoring wildlife movement through the study areas and by completing aerial wildlife […]

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Integrated Far- and Near-Field Human Exposure Modelling for Organic Substances

While we enjoy the modern convenience brought by a multitude of man-made organic chemicals, such as surfactants and flame retardants, the exposure to these compounds, some of which are bio-accumulative, persistent and even toxic, may endanger our health. Humans are exposed to chemicals in consumer products during both product use in the indoor environment (near-field […]

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Integrated Far- and Near-Field Human Exposure Modelling for Organic Substances – Year two

Thousands of organic chemicals have been synthesized and commercialized for industrial and consumer uses. However, an increasing number of organic chemicals are revealed to be “hazardous” because of their potential adverse environmental and/or health effects. The project seeks to identify the primary route(s) that humans take up these organic substances, e.g., use of personal care […]

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Improving Powder Performance by Development and Optimization of Industrial Lubricants and Mixing Technology for Powder Metallurgy

Ideal flow, high-volume Powder Metallurgy (PM) manufacturing can achieve uniform, consistent filling of die cavities, leading to high productivity, low rejection rates, improved part integrity and consistent part dimensions. The type and amount of lubricant, size and shape of lubricant particles, mixing parameters and certain environmental conditions all significantly influence the flow characteristics and apparent […]

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Improving Powder Performance by Development and Optimization of Industrial Lubricants and Mixing Technology for Powder Metallurgy – Year two

Ideal flow, high-volume Powder Metallurgy (PM) manufacturing can achieve uniform, consistent filling of die cavities, leading to high productivity, low rejection rates, improved part integrity and consistent part dimensions. The type and amount of lubricant, size and shape of lubricant particles, mixing parameters and certain environmental conditions all significantly influence the flow characteristics and apparent […]

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Development of an information theory-based mutation detector for a commercial bioinformatics genome server

I have recently developed a piece of software that can be used to interpret the effects of DNA sequence differences in human genomes. The analysis produces results that predict disease mutations. Dr. Rogan’s laboratory has developed approaches of visualizing DNA sequence data, which I will incorporate into this software. I will modify the existing visualization […]

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Computational and experimental characterization of mechanical performance of cross laminated timber (CLT)

Cross-laminated timber (CLT) is an engineered wood panel typically consisting of multiple layers of glued timber stacked in a cross-ply layup. Timber shows a strong anisotropic mechanical behavior due to its microstructure. With a cross lamination, the CLT possesses superior dimensional stability, strength and rigidity, in comparison to traditional wood products. In Canada, CLT is […]

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Using Deep Learning for Auto-tuning of High Performance GPU Applications

Graphics Processing Units (GPUs) are increasingly used to accelerate applications and to reduce their energy use. GPUs are particularly attractive for mobile platforms, where battery life is important. However, GPUs are hard to use, requiring developers to apply optimizations to their code to realize the performance and energy benefits of GPUs–a tedious and error prone […]

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Using Deep Learning to Auto-tune GPU Application

The fellowship mainly investigates an analysis of the state-of-the-art approaches, design and implementation of cutting-edge deep neural network models to be used on a mobile platform. It explored ways to optimize the deployment of these machine-learning models for prediction tasks on the mobile devices which requires energy efficiency and accuracy.

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Applications of deep learning to large-scale data analysis in mass spectrometry-based proteomics

Rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, we aim to develop new applications of deep learning […]

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Applications of deep learning to large-scale data analysis in mass spectrometry-based proteomics – Year Two

As a result of recent advances in high-throughput technologies, rapidly increasing amounts of mass spectrometry (MS) data pose new opportunities as well as challenges to existing analysis methods. Novel computational approaches are needed to take advantage of latest breakthroughs in high-performance computing for the large-scale analysis of big data from MS-based proteomics. In this project, […]

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Toward an Understanding of Beautiful Feather Cover in Laying Hens

Feather pecking (FP) in egg-laying hens, where individuals peck repetitively and excessively at other birds to pull out and eat their feathers, is a challenge for the industry with large economic and welfare implications. High prevalence of FP is reported (60-80%) and this is associated with mortality rates of up to 20-40%, which translates to […]

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