The importance of nutrition for ice hockey players

One aspect of hockey science that remains elusive is the role of nutrition in performance. We propose to study the dietary intake of hockey players and also examine their nutritional knowledge with a questionnaire. We will also examine the nutritional knowledge of athletic trainers and examine the role that they play from a nutritional point […]

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Copper and nickel stable isotopes in overburden and transported cover as exploration tools for buried magmatic sulfide deposits

The rate of discovery of new, large mineral deposits has slowed, yet significant opportunity exists in many world-class belts where post-mineral cover obscures bedrock and can potentially hide world-class deposits beyond the reach of traditional geochemical tools. However, locating mineral deposits in areas of thick or transported overburden is challenging. The proposed research integrates trace-element […]

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High-Performance Sulfide-Electrolyte-Based All-Solid-State Lithium-Sulfur Batteries for Safe Electric Vehicle Applications

Lithium-sulfur (Li-S) batteries have been considered as one of the most promising candidates to meet the energy storage demand for electric vehicles due to their high theoretical energy density of 2600 Wh kg-1, low cost, natural abundance, environmental friendliness. State-of-the-art Li-S batteries, using liquid electrolytes, still have significant challenges in their safety and lifespan. Accordingly, […]

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Feasibility Study of Dual Input Clamp Undir DC-DC Converter for 737 SPU Replacement

The main objective of this project is to a) investigate feasibility and suitability of a new proposed DC-DC converter based on a novel “Dual Input Clamp Unidir DC-DC Converter”, and b). validate and quantify its projected benefits including reduced parts count, higher efficiency, lighter weight and more reliable as compared to existing commercial products. Cost-effective, […]

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Understanding Empirical Risk Minimization via Information Theory

Deep neural network (DNN) is a class of machine learning algorithms which is inspired by biological neural networks. Learning with deep neural networks has enjoyed huge empirical success in recent years across a wide variety of tasks. Lately many researchers in machine learning society have become interested in the generalization mystery: why do overparameterized DNN […]

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Next generation content development andmanagement

As a globally recognized credit service provider, DBRS plans to implement a new project called next generation content development and management. DBRS expects to expand its current online service offering to the users by launching a series of mobile apps with sophisticated analytical tools. The advent of these applications will fundamentally create an interactive environment […]

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Understanding the Role of Ex Situ Conservation to Enhance Public Understanding, Awareness, and Support for Biodiversity

The purpose of this research is to use an evidence-based approach to further the Toronto Zoo’s social science and education programs, in order to support in situ conservation and biodiversity in relation to Canadian goals and the Aichi Targets. Using a collaborative approach this research will result in the co-production of knowledge, where the researcher […]

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Understanding the Role of Ex Situ Conservation to Enhance Public Understanding, Awareness, and Support for Biodiversity – Year two

The purpose of this research is to use an evidence-based approach to further the Toronto Zoo’s social science and education programs, in order to support in situ conservation and biodiversity in relation to Canadian goals and the Aichi Targets. Using a collaborative approach this research will result in the co-production of knowledge, where the researcher […]

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Machine learning assisted quantum chemistry for Orquestra

Unsupervised machine learning has recently been introduced into the field of quantum many-body physics. A strategy based on generative models has been particularly successful in the data-driven learning of quantum states. In this proposal, we aim to adapt this technology to applications in quantum chemistry. The primary focus of this research will be on the […]

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Machine learning assisted quantum chemistry for Orquestra – Year two

Unsupervised machine learning has recently been introduced into the field of quantum many-body physics. A strategy based on generative models has been particularly successful in the data-driven learning of quantum states. In this proposal, we aim to adapt this technology to applications in quantum chemistry. The primary focus of this research will be on the […]

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