Innovative Projects Realized

Système de mise à jour de la cartographie routière basé sur les données géospatiales ouvertes

E-SMART conçoit un écosystème de produits qui offre aux flottes de camions une façon innovante d’assurer le respect des limites de vitesse par les conducteurs de camion. Dans le cadre de ce projet, la compagnie souhaite développer une technologie permettant d’utiliser la cartographie publique, par exemple OpenStreetMap (OSM) et de faire la mise en correspondance (« map-matching ») entre la trajectoire du véhicule et le segment de route correspondant, afin de savoir quelle limite de vitesse est rattachée. Ce développement technologique permettrait à E-SMART de mettre en œuvre des améliorations qui ne sont pas possibles en utilisant la solution commerciale utilisée par la compagnie.

Les buts à terme sont les suivants :
? Améliorer la précision des limites de vitesse retournées par le système, notamment en améliorant la mise en correspondance temps-réel ou map-matching.
? Diminuer la quantité de données transférée lors de la mise à jour des cartes.

Evaluation of Therapeutic Natural Killer cells

Cellular immunotherapy has yielded numerous clinical benefits in treating cancer. While many successes involve CAR T cells, unfortunately this potent anticancer cell therapy can cause serious and even fatal complications. Another immune cell, the natural killer cell (NK), may lead to effective treatment without unwanted side-effects. Furthermore, the use of stem cell-derived NKs provides additional advantages, including the ease of gene editing and ready availability of an off-the-shelf therapeutic. We have comprehensively characterized several potential NK products and demonstrated that they are highly cytotoxic against a variety of cancer cells. The intern will extend these studies to engineered NK cells that can specifically kill cancer cells expressing certain proteins.
Ankarys Therapeutics is a Canadian early stage biotech company that uses gene editing and stem cell technology along with scientific and clinical expertise to develop proprietary cell therapy products consisting of safe, powerful, engineered stem cell-derived NK cells.

Investigation and Development of an Automated Testing Platform for Digitally Implemented Clinical Guidelines in the Health Level (HL7) Fast Healthcare Interoperability Resources (FHIR) Standard

The project partner, JuniperCDS (Juniper), is a digital health company that is developing a care and treatment planning (software) application called JuniperCDS, which is a clinical decision support software solution that enables clinicians to create meaningful, person-centric care plans by delivering in-context recommendations based on the latest clinical guidelines at the point of care. A major challenge with clinical guideline digital implementation is testing the guideline to ensure it is complete and meets HealthLevel7 (HL7) Fast Healthcare Interoperability Resources (FHIR) standards, Currently, testing a guideline is a highly manual process with minimal existing tools that are difficult to use. With this project, Juniper would like to develop a digital testing tool that automates a major portion of the testing and provides significant time savings. The tool will evolve Juniper’s apps. Externally, the testing tool will be a global public good for testing and validating digitized guidelines.

Sales Micro-Patterns: ML-driven detection of deviant Store/SKU sales patterns

For almost a century, Canadian Tire has been offering products and services to help Canadians with the jobs and joys of life in Canada. Today, with a network of over 1,700 retail locations, Canadian Tire is dedicated to improving products and services offered to the community they serve. With this project, the company aims to examine the patterns of sales among its stores to identify anomalous product sales patterns. The objective is to design and develop a system capable of generating high-confidence automated notification to the stores. Sales patterns will be investigated to identify products deviating from normal patterns. The stores would be notified of the results and incentivized to investigate the flagged Stock Keeping Units (SKUs) for in-store issues, such as missing inventory or tags.

The impacts of biomass-burning aerosols on air quality, weather and renewable energy in British Columbia

In the last few years, wildfires’ have become more severe and frequent in British Columbia, Canada. In addition to the direct dangers of flames, threatening wildlife and human society, as well as vital infrastructures, wildfire plumes laden with Biomass-burning (BB) aerosols can adversely affect air quality, weather, and renewable energy generation on a large scale. BB aerosols can disrupt solar radiation, thus disturbing the surface energy budget, atmospheric stability, and cloud evolution.
The proposed international research will study the plume’s dynamics and its interplay with the atmosphere through an exhaustive computational campaign to shed more light on the mentioned impacts and enhance the Canadian’s analyzing and forecasting capability to address them.
For that purpose, an intern will be trained in ICON (ICOsahedral Nonhydrostatic), a high-resolution numerical weather forecasting model, at KIT, particularly its Aerosol and Reactive Trace gases(ART) module. The intern will then set up the model on SFU’s supercomputer upon his return and exercise the acquired knowledge on analyzing large-scale forest fires emissions.

Randomization in Byzantine Agreement

The topic of our research is to study how randomization is used to solve Byzantine Agreement problem in different computational models. Byzantine Agreement is a classic distributed computing problem, where processors try to agree on a value, but some of the processors try to disrupt the agreement or make sure that the algorithm never terminates. Byzantine Agreement problem can be solved in different computational models, e.g., partially asynchronous, asynchronous. Practical applications of Byzantine Agreement are clock synchronization problem (multiple machines trying to agree on what time it is right now) and blockchains (consensus algorithms for synchronizing the state of the blockchain database). We want to study how randomization is used to solve these problems and, as a final result, make a survey for these methods to further facilitate the research on consensus problem. To do so, we will study different methods for solving consensus, and possibly improve existing algorithms as a result.

Development and characterization of catalyst electrodes for electrochemical reduction of CO2

Electrochemical reduction of carbon dioxide (ERC) is a process by which carbon dioxide (CO2) is converted into valuable chemical products via chemical reactions driven by electricity. The goal of this project is to fabricate and test catalytic metal electrodes to increase the efficiency of ERC reactors converting carbon dioxide from industrial exhaust gas streams into formic acid. The catalytic metals/metal alloys will be electrochemically deposited onto porous, high surface area carbon foam electrodes and tested in the company’s ERC reactors to study their efficacy and also any catalyst degradation. These tests will help understand the catalyst degradation mechanism and could lead to development of reactivation techniques. The results from this project will thus allow Mantra Energy to develop a library of metals/ metal alloys to choose catalytic materials from and also contribute to prolonging the life of the ERC reactor electrodes, making the ERC process more profitable.

Generalization of SR.ai NLP Algorithms to New Data Sources and Stability Improvements

The field of responsible investing is rapidly expanding, with even greater attention on the importance of responsible investment with each passing year, as seen most recently in the aftermath of the impactful 2021 COP26 summit, where responsible investment was key focal point. Directing our financial resources in a sustainable direction has the potential to have a massive impact on helping us meet the Sustainable Development Goals set forth by the UN. Though the importance of better alignment between finance and sustainability is clear, a notion that now has very strong consensus built around it, investors still lack the right tools to support their research process in terms of sustainability. They rely on high level data that is largely known not to be reliable, which leads to massive misallocations of capital. The mission of SR.ai is to bring more rigour to the world of responsible investment, using our technology backed by peer-reviewed research conducted within the University of Toronto.

Optimal Transport in Quantum Dynamics and Chemistry

In the last 30 years, the theory of optimal transportation has emerged as a fertile field of inquiry, and a diverse tool for exploring applications within and beyond mathematics, in such diverse fields as economics, meteorology, geometry and engineering. More recently, it has empowers today’s machine learning research and become one of the most emerging topics to learn.

This project explores a novel avenue, by developing mathematical and machine learning methods based on optimal transport to overcome computational bottlenecks in chemical dynamics. Specifically, we aim to design new computational algorithms that can incorporate key quantum effects and accurately predict spectroscopic properties of molecules at close to classical computational costs.

Our research lies at the boundary between mathematics, physics, chemistry and machine-learning. It combines Augusto Gerolin’s expertise in Optimal Transport and Machine Learning, David Manolopoulos’s expertize in chemical dynamics, with Annina Lieberherr’s expertise in quasicentroid molecular dynamics.

Synthesizing Past, Present and Future Climate Variability in the Cambrien Lake, Nachicapau Lake and Fort McKenzie Area (Naskapi Territory)

The Cambrien Lake, Nachicapau Lake and Fort McKenzie area (the “CNFM”) has been attributed high conservation potential by Nunavik’s regional stakeholders due to its exceptional biophysical characteristics and its significance to the Naskapis, as it includes a major Naskapi gathering place. The CNFM’s important hydroelectric and mining potentials prevent strict protection status at this time, but it is the subject of an agreement setting out specific commitments regarding mining activities and hydroelectric development.
Little climate-related information is available for the CNFM. As a result, the capacity and vulnerability of geoecosystems remain difficult to quantify, preventing their proper consideration in decision-making. A synthesis of past, current and future climate variability is therefore required for the area. A state-of-the-art picture will act as a common basis to describe and quantify such variabilities, which will feed decision-making for future uses of the CNFM.

Development of chemical approaches and technology for the design and preparation of glycoconjugate vaccines.

Glycoconjugate vaccines have shown efficiency to control infectious diseases and hold great promises in cancer immunotherapy. Nonetheless, there is an important need to identify novel immunostimulating carriers for carbohydrate antigens as well as to optimize the currently used protein carriers. This Mitacs project aims at developing versatile biochemical approaches and technology for the design and preparation of glycoconjugate vaccines. The interns will design vaccines based on protein nanoparticles and will engineer a known immunogenic protein carrier to enhance its efficacy to deliver glycoantigens. This project will lead to key advancements in the methodology of glycopeptide synthesis, glycoconjugation, and in the engineering of protein carriers for glycoantigens. Accordingly, this proposal is entirely aligned with the mission of Glycovax Pharma Inc, an emerging, fast-growing, clinical-stage Canadian biopharmaceutical company that aims at implementing molecular architectures to develop glycovaccines able to prevent cancers and infectious diseases.

Développement d’un vaccin protéique contre la somatostatine porcine.

L’Organisation des Nations Unis pour l’alimentation et l’agriculture estime qu’il se consomme environ 112 millions de tonnes de porc annuellement. Chaque kilogramme de porc a son empreinte environnementale est de 2,92 kg GES équivalent CO2 et requiert entre 4 et 6 mètres carrés de surface cultivable. Afin de maintenir la production de viande porcine tout en réduisant son impact environnemental, nous devons travailler à améliorer l’efficacité d’utilisation des ressources alimentaires. L’hormone de croissance est connue pour son rôle sur la croissance. En fait, l’hormone de croissance favorise une utilisation plus efficace des nutriments par une meilleure déposition. Pour hausser la sécrétion de l’hormone de croissance, il est possible de réduire la sécrétion de l’inhibiteur de l’hormone de croissance, la somatostatine. Le projet présent de recherche cible donc spécifiquement l’hormone de croissance chez le porc et la somatostatine. Nous proposons donc de développer un vaccin qui réduira la somatostatine, provoquant ainsi un plus grand relâchement d’hormones de croissance.