In this project we propose to evaluate the synergistic effects of fatigue and corrosion to further understand the degradation mechanism of the alloys used by our partner. This will help the partner defining their total cost of ownership & develop mitigating corrosion strategies to ensure long term sustainability of their infrastructure.
Energy storage systems (ESS) are vital components in the power grid to ensure the reliable performance of the power grid integrated a considerable amount of renewable energy resources (DER). ESSs are employed to compensate for the unexpected changes in DERs and shift load during peak hours. ESSs are complex structures consisting of several series and parallel connected battery cells. Battery cells may have different characteristics due to the ambient temperature and aging differences. As a result, the operation limits of each battery cell vary.
Beginner music composers often face difficulty in harmonizing a melodic idea. Particularly, if they lack music theoretical knowledge. It is well-understood that chord successions follow patterns. With some limitations, artificial intelligence algorithms can capture those patterns. A melody “harmonizer” model proposes harmonizations for the user’s melodies. The suggestions are often based on learned patterns from existing musical compositions, for example, the chorales by J. S. Bach. A model may learn patterns found in Bach’s compositions, applying them in new music.
Underground metro infrastructures are subjected to different types of dynamic loads that would hinder their ability to function properly. This proposed research aims to assess the risks of city-scale underground tunnel networks under both periodic human-induced vibrations (i.e., blasting and drilling vibrations) and short-term extreme earthquake hazards. We will develop a GIS model for the tunnel soil/rock profiles in Montreal by synthesizing a comprehensive dataset for tunnel designs, embedment depths, and soil/rock properties from geotechnical surveys.
Electric vehicle (EV) is the future of sustainable transportation to phase out the reliance on petroleum fuels. Despite the multibillion-dollar market potential, wide deployment of EV is challenging due to limited energy storage. Regenerative energy generation can be implemented to compensate for the energy consumption in EV to provide the much-needed extra mileage. Apart from regenerative braking, other energy harvesting options such as solar panels, wind turbines, and vibration/shock energy harvesting have yet to be implemented at larger
Adults exposed to extended periods of static seated computer work, especially females, have elevated risks of developping musculoskeletal symptoms. However, static standing work is also associated to issues with the musculoskeletal and vascular systems. To address this, alternating between seated and standing postures has been proposed in the form of sit-stand desks; however, their sex-specific impacts on biomechanical, performance and discomfort outcomes remain unclear. As a result, repeated measurements will be taken during a 90-minute computer task, involving both typing and mousing.
HyperMabs is a biopharmaceutical company that designs and develops innovative therapeutics targeting lung diseases. HyperMabs is currently evaluating a drug candidate developed using in-silico methods to target lung disease. For this project, we aim to study the biodistribution of HyperMabs therapeutic, currently in its pre-clinical stage designed to treat several lung diseases. To achieve this, we wish to label the HyperMabs therapeutic with a labeling molecule to facilitate visualization of distribution of the therapeutic in lungs.
As the heat fluxes produced by modern high-performance microprocessors continue to rise, so too must the effectiveness of the removal of these fluxes. Accordingly, a large amount of research has focused on developing techniques to enhance cooling in computer systems. A novel method of doing so involves replacing the single-phase liquid or two phase-liquid vapor coolants typically employed in such systems with binary fluid mixtures.
The goal of this project is to help build artificial intelligence algorithms for the diagnosis of disease using data derived from the human microbiome. This project will be focused on implementing new statistical methods to reduce “noise” found in data from different sources, allowing for us to improve the training of artificial intelligence algorithms. Another focus of this project will be to implement new types of models that are better suited for microbiome data, allowing for more accurate predictions.
Humic Land is a 100% organic fertilizer that was produced from black peat using innovative technology that protects live soil microorganisms. It contains complex organic compounds known as humic substances and a microbial consortia that can biologically promote the growth of field crops. Since corn plants that obtain a balanced nutrient supply from the soil microbiome are expected to grow larger and have faster phenological development, compared to their counterparts that have a suboptimal nutritional balance, we evaluated the agronomic performance of Humic Land on field-grown corn.