Solar energy offers a green energy future both for Canada and the world. To best collect this energy, solar farms, collections of solar panels, are often distributed to ensure efficient local collection. Weather is often a challenge for production; however, the failure of components can also adversely affect this as well. These failures can be difficult to track and predict; in this project, we propose to develop tools to help operators expect events that could lead to power losses and improve solar energy harvesting using both standard analytical tools and machine learning.
Quantum computing technology research has been a fast-growing academic field since the start of the century. In recent years, it has become apparent that in order to perform large, useful computations on a quantum computer, one must link multiple smaller quantum computers via a quantum communication channel, much in the same way that modern classical computers link many processor cores.
This project, a collaboration between the Arctic Youth Network (AYN) and Project CREATeS, is to create a virtual platform that supports suicide prevention in Arctic youth by building a virtual community, as well as to promote mental wellbeing through digital and creative media.
The proposed youth-led, community-based research agenda builds upon a successful grant completed in collaboration with the Arctic Council and the Inuit Circumpolar Council, from 2017-2019.
Heart failure (HF) is the third most common reason for hospitalization in Canada, where, the direct health care costs of HF are $2.8 billion. HF affects 600,000 Canadians with median survival of only 1.5 years after diagnosis. HF patients stay longer at hospital. Even after discharge, 1 out of 5 of patients returns to the hospital within a month. The major complaint of HF patients is shortness of breath due to extra water in their body and lungs. Thus, monitoring of body water is an important challenge in HF, especially at home settings.
Polytellurophenes are an emerging class of semiconducting polymers showing promises in organic electronics. However, their commercial potentials are still held back by the synthetic scale. In this collaborative efforts with 1-materials, we aim to develop a synthetic strategy to produce polytellurophenes from a laboratory scale to a pilot scale to meet the increasing market demands. The synthesis will be scale up to produce 500 mg and 2.5 g polytellurophenes in one single batch at Stage 1 and Stage 2, respectively.
pre-agreed price. The problem of what an option is worth is usually solved using costly numerical simulation methods.
We will apply Deep Learning methods to solve the Option Pricing Problem. This cutting edge technique has the advantage that, once trained, the model can simulate many scenarios at a low computational cost. Unfortunately, training is costly and can be unstable.
Our original contribution is to use Tensor Networks to improve this Deep Learning model.
This project will investigate how to develop a multifunctional surface coating which exhibits both dust and liquid repellencies for industries like wood mills where fire hazard is a major challenge caused due to the accumulation of liquid contaminants and dust particles. The U of T / Eclipson Technologies team will develop durable coatings possessing these properties along with high temperature resistance that could be readily applied on application of various substrates (metals, glass, ceramic). This involves developing surface coatings, characterizing them.
The box tree moth (BTM) (Cydalima perspectalis) is an invasive insect pest from Asia that was confirmed to be present in Etobicoke, Ontario by the CFIA in November, 2018. This is the first known introduction of BTM to North America. BTM attacks boxwood (Buxus spp.), a popular broadleaf plant used in residential and commercial gardens, hedges and topiary in Canada. For the nursery sector, boxwood represents a very high value, slow-growing crop in Ontario, Quebec and BC.
Deep learning is nowadays one of the most popular areas in computer science which has shown extraordinary performance and enjoyed wide use in a variety of fields. This research project is aiming at exploring the application of deep learning algorithms in two fields: autonomous driving with 5G networks, and facility maintenance with CT scanning. In the first field, a number of driving scenarios will be simulated and evaluated to understand the performance of deep learning-based algorithms, leveraging 5G to take advantage of quick data transfer and calculations.
The “Assessing forest harvesting impacts on furbearer habitat at the scale of Indigenous traplines - A case study in Indigenous consultation and forest conservation” project is planned to enhance the understanding of Indigenous rights holders concerning forest management activities that impact the distribution of forest types and ages upon which the forest dwelling wildlife that sustain traditional trapping and harvesting depend.