Innovative Projects Realized

Her Own Boss! Entrepreneurial Opportunities for Newcomer Visible Minority Women

This community-based action research project seeks to support visible minority newcomer women in establishing and succeeding in entrepreneurship activities, while identifying the challenges and opportunities they face while trying to start their own businesses. The benefits that this research is expected to bring to the Women’s Economic Council are to better understand visible minority newcomer women wishing to be self-employed; the local self-employment services, including any gaps and barriers for visible minority newcomer women in accessing these services as well as the challenges faced by service providers in reaching out to these women; and the impact of the training sessions on all participants in achieving their economic goals.

Safe and Effective Enclosure for Electric Baseboard Heaters

Vent & Cover wants to develop in conjunction with UBC and UWO, an enclosure that can slip over electric baseboard heaters and create a safe-to-touch exterior while still allowing the heater to heat the room safely and effectively. The project will have 2 major components, the first of which is to develop an efficient thermal design that redistributes the heat safely. The second component is to determine the optimal material to compliment the design and maintain the objective of a safe-to-touch exterior. At the conclusion of the project, Vent & Cover intends to patent the enclosure and develop new markets in both Canada and the US through direct and distributor sales. In addition, Vent & Cover wants to be the leader in reducing burns to vulnerable individuals as well as accidental fires through the development of this safety enclosure for electric baseboard heaters.

The function of estuarine habitat in the life history of juvenile ChinookSalmon: physical habitat, prey availability, and their relationship to fishbehaviour.

Estuaries are important to juvenile salmon as they transition to the ocean from their natal streams; however, a significant portion of estuary habitat has been lost in North America due to urban development. This loss of habitat has likely played a role in the decline of salmon populations throughout the Pacific Coast of North America. The Squamish River Watershed Society (SRWS) has funding to restore fish access to the Squamish River estuary and the study site presents a unique opportunity to study how restored habitat affects the survival of juvenile salmon. Using fish capture data, movement data from telemetry, and habitat characteristics, we will study how fish use the estuary as well as how the estuary differs from the river habitat in terms of food availability and water quality. Our research program will inform restoration priorities and objectives for the Squamish River Watershed Society.

Machine Learning-Assisted History Matching for Light and Tight Oil Reservoirs

The use of ionic liquids (ILs) in enhanced oil recovery is considered a new and promising technology as it has never been tested in any pilot plant or reservoir field. ILs are very similar to surfactants as they help reduce the interfacial tension, change the wettability of the reservoir, and some have strong viscous effect, all essential factors in recovering more heavy oil. The technology can also be used for medium and light oil recoveries with other kinds of ionic liquids. After an initial screening ILs will be selected taking into account stability in a basic environment, biodegradability and toxicity. The best ionic liquid will be used in a chemical enhanced oil recovery application where a weak alkali and a polymer are added to increase the recovery factor. ILs have the potential to be the most promising Chemical enhanced oil recovery (EOR) technology in the history of heavy oil production. The economic benefits to Saskatchewan and Canada could be extraordinary.

Separating Syntax and Semantics for Semantic Parsing

Study of language disorders, theoretical linguistics, and neuroscience suggests language competence involves two interacting systems, typically dubbed syntax and semantics. However few state-of-the-art deep-learning approaches for natural language processing explicitly model two different systems of representation. While achieving impressive performance on linguistic tasks, they commonly fail to generalize systematically. For example, unlike humans, learning a new verb like jump in isolation is insufficient for models to combine it with known words (like jump twice or jump and run). We continue the line of investigation recently started by Russin et al, whereby deep learning models are encouraged to learn separate representations for syntactic and semantic aspects of the input. Our goal is to achieve better systematic generalization on the task of semantic parsing, which requires transforming natural language utterances into executable programs. Building better models of semantic parsing is of high practical importance as they enable many important applications, such as e.g. natural language interfaces to databases.

Rapid scaling of viral spike protein production for SARS-CoV-2 testing using Phaeodactylum tricornutum

The SARS-CoV-2 virus is currently causing a pandemic that is overburdening the healthcare system and causing unprecedented economic disruptions on a global scale. A method called serological testing uses viral proteins to determine if the someone has been infected and has now become immune to the virus; however, it is currently very expensive to produce. If someone is known to be immune to SARS-CoV-2, then they can go back to work without spreading the virus.
The goal of the project is to facilitate a rapid test to identify people with immunity to SARS-CoV-2. This project will generate the proteins needed for this test using an alga called Phaeodactylum tricornutum. Most of the people affected do not display symptoms and can spread COVID-19 without even knowing it. Furthermore, identifying people who have been exposed would allow the healthcare system to identify people who can return to work without worry.

Social Innovation Language and Narratives in Edmonton, Alberta: An Intersectional Approach

This project seeks to build on existing research regarding the current challenges in social innovation work in Alberta, as articulated in Alberta Social Innovation (ABSI) Connect’s 2016 Report on the Future of Social Innovation.

To accomplish this, the intern will interview people in Edmonton who are from multiple marginalized identities (e.g. racialized, identifying as a gender or sexual minority, working class, disabled, etc.) who do work that’s considered as social innovation work through Frances Westley’s definition of social innovation, but may not have the same visibility or recognition as those who do social innovation work in mainstream networks. The intern will then gain a better understanding of the language and narratives that marginalized individuals and/or communities use when talking about social innovation work.

This project will employ an intersectional theoretical framework by seeking and centering individuals or communities doing social innovation work from multiple marginalized identities. An intersectional theoretical lens centers and analyzes the experiences of the most disadvantaged, while using this knowledge to design interventions that are rooted in social justice.

Carbon cycling, transport, and fate in Canada’s forests under a rapidly changing climate: Scale matters in systems with short biogeochemical attention spans

In order to better understand the role of Canada’s vast forested area in our country’s carbon budget, further work is needed to monitor ‘hot spots’ of carbon activity – the boundaries between land and lake, and how these landscape positions will react to a changing climate. Further, less is known about the timing around the activation of these hot spots outside of regularly spaced traditional monitoring practices. Working collaboratively with the International Institute for Sustainable Development-Experimental Lakes Area within their long-term mission to conduct watershed research at whole ecosystem level, this study will investigate carbon storage and movement in a representative forested landscape with novel measurements and new scales of space and time. The results of this work will have important implications for forested watersheds to be incorporated into a Canadian landscape-scale model of carbon storage and movement.

Synergistic Effects of Environmental Factors on Electrochemical Corrosion and Sulfide Stress Cracking (SSC) of a High?Strength Pipeline Steel

Sour corrosion originated from hydrogen sulfide in the pipeline has detrimental impact on the material’s surface of the pipeline, which ultimately causes cracks and pipeline failure. The mechanism behind this sour corrosion is complex because several environmental factors control the degree of sour corrosion, such as, temperature, acidity of the environment, hydrogen sulfide concentration, chloride ion concentration. This study will provide a clear understanding of how these all factors participate in the corrosion process. Therefore, the outcome of this project will offer better corrosion control management in pipeline sector and more accurate mitigation strategies.

Machine learning in the operating room: focus, performance, and the medical record

This proposed study will significantly enhance our current understanding of how specific intra-operative factors can impact patient outcomes. Our proposed work will provide a proof of concept that machine learning can objectively predict a specific, high-impact post-operative complication, allowing us to move forward with scaling this work to a wide variety of surgical settings. Moreover, the use of machine learning to automatically assess high-risk points of a surgery has many implications, including the ability to direct risk mitigation efforts, work towards real-time assessment of operations, as well as standard-setting and credentialing for surgical procedures. The ability to automate the evaluation of a high-risk step of an operation in real time, and potentially change a patient’s outcome, undoubtably has the potential to significantly improve patient safety on a large scale.

The real world of environmental pollutants: New approaches to identifying priority contaminants in the Endangered Southern Resident Killer Whale food web

The Southern Resident Killer Whales (SRKW, Orcinus orca) population is assessed at 73 individuals, and significant threats include high levels of endocrine disrupting contaminants, alongside other anthropogenic threats. Studies have painted a partial picture of the contaminants of concern in the SRKW food web. For example, preliminary studies of chinook salmon suggest that some resident populations are more contaminated than others which may be contributing to the high PCB burden in SRKW. This underscores the value of generating new information on contaminant concentrations and profiles in dominant prey species and / or stocks of this endangered whale population. This study presents a science-based evaluation of the contaminants found in their diet (food web) and habitat, the development, adaptation and/or application of new tools to prioritise (rank) the pollutants of concern, and the delivery of refined guidance to support of the wider conservation agenda for this (and other) at risk species

Employing Raman Spectroscopy for monitoring a vaccine manufacturing process

Vaccine antigen production is a process that entails numerous variables. In order to have a consistent and robust process, monitoring of process parameters and controlling output variables within a certain range is the best practice. To accomplish this objective, analytical tools are used, on-line, off-line, at line. Real time monitoring of the processes is advantageous as operating parameters can always be adjusted to keep the process in check. For the sake of efficiency, it would be reasonable to determine those parameters that are correlated to the productivity obtained at the end of the process. On-line measurements would facilitate calculating and defining these parameters. There are non-invasive tools that can collect continuous measurements without interfering with the integrity of the process. Raman probes and NIR probes are examples to this type of approach. In this study the intern will be tasked to use these tools to identify any patterns which can be linked to the final productivity. The instrument will be tested in all unit operation steps to map the evolution of measured metabolites/substrates upstream and downstream of the process. Mathematical modelling, such as multivariate analysis, Principal Component Analysis, Partial least Squares will be used in synthesizing and interpreting the data.