Innovative Projects Realized

Development of an Optical Oxygen Sensor System for Real-Time Monitoring of Oxygen Release

Identification des profils de réussite chez les entrepreneurs agricoles de la Mauricie

Cette recherche permettra à la Fédération de l’Union des Producteurs Agricoles de la Mauricie (FUPAM) de connaître les différentes compétences et habiletés requises chez les entrepreneurs agricoles afin d’assurer leur succès en affaires. Pour y parvenir, nous identifierons des entreprises agricoles qui connaissent du succès ainsi que d’autres dont la performance est plus mitigée. Ces entreprises seront comparées au sein des trois (3) grands secteurs identifiés : production de masse, production de niche et production règlementée. Sur la base d’entrevues avec les producteurs agricoles et en regard de leur réalité ainsi qu’avec la perspective des conseillers en gestion de la FUPAM, des profils de réussite seront dressés. En fin de parcours, sur la base de ces nouvelles connaissances, un outil sera proposé afin d’aider les conseillers et les producteurs agricoles à accroître la performance de leur entreprise agricole.

Touchless sensor technology for people with chronic low back pain and fear avoidance

Community Mobile Media Lab

This application to MITACS supports the creation of an innovative mobile research lab. The Community Mobile Media Lab (CMML) is a purpose-designed mobile media production hub, which will house film production equipment that can be situated temporarily in communities we are collaborating with to explore innovative models of co-creation, training and mentorship. This infrastructure will allow us to engage with Nova Scotians to document the oral histories of community elders, and with whom we have initiated programs that empower youth by teaching them filmmaking skills. Through these unique education and production models, the CMML will facilitate acts of reconciliation by facilitating creative opportunities for communication among community members.

Technical intern(s) working within cross-functional teams to commercialize AI-powered solutions (1)

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Solid-state additive manufacturing of GRCop-42/84 copper alloys and their variants for combustion chamber liners

The partner organization, D.I. Self-Composite (DISC) Alloys Inc., specializes in large-format additive manufacturing for metals, with a particular focus on propulsion systems and aerospace applications. Leveraging advanced Cold Spray Additive Manufacturing (CSAM) technology, DISC Alloys has the capability to deposit multiple metals with precision and achieve high deposition rates (20 kg/h), enabling the production of complex, multi-material components.
Through this project, DISC Alloys aims to address a critical industry challenge: the high cost and limited availability of GR-42 and GR-84 copper alloys, which are essential for propulsion systems. These materials, while effective, present supply chain and economic constraints that hinder scalability and innovation in the aerospace sector.
By collaborating with academic expertise, DISC Alloys seeks to develop new materials with comparable or superior thermal and mechanical properties to GR-42 and GR-84. The anticipated outcomes include cost-effective solutions that enhance manufacturability while maintaining or exceeding performance standards.
The project’s success would yield significant social and economic benefits, including increased competitiveness for DISC Alloys in the aerospace market, reduced dependency on expensive and scarce materials, and fostering innovation that strengthens Canada’s position in advanced manufacturing and space technology sectors.

Development of Natural Language Queries Embedded Within a School Data Hub to Support Data-Informed Decision-Making by School-Based Practitioners

The University of Toronto Schools (UTS), through the Eureka Research Institute, is committed to advancing data-driven decision-making in K-12 education. This aligns with the growing recognition of the importance of data literacy in education, where students and teachers need to “read the world with data” and “write the world with data” (Louie, 2022). Recent research highlights that educators require user-friendly data tools that minimize the technical expertise needed to interact with educational data (National Academies of Sciences, Engineering, and Medicine, 2023). By integrating Natural Language Query (NLQ) capabilities into the Data Hub, this project enhances the ability of teachers and administrators to engage with school-wide data more effectively, supporting student performance analysis, curriculum planning, and intervention strategies.
This research will have a broader societal impact by making data-driven insights more accessible, ensuring equitable and effective educational strategies for student success.

Automated Threat Hunting for Early APT Identification

Advanced Persistent Threats (APTs) present a significant challenge to organizations due to their sophisticated, stealthy, and persistent nature. Traditional reactive security measures often do not detect these threats in real-time, leaving systems vulnerable to long-term exploitation. This research project will explore how to automate threat hunting to proactively identify APTs in real time, reducing manual intervention and enhancing early-stage detection. The partner organization, Ericsson, a leading multi-national networking and telecommunications company, focuses on delivering resilient 5G/6G solutions with threat detection, incident response, and security analytics to enterprise and Telco clients. Enhancing threat detection capabilities through automation, artificial intelligence (AI), and threat actor attribution-based analytics is extremely important to proactively identify APTs. This project will address the need to enhance threat-hunting capabilities, reduce the reliance on manual threat-hunting, and improve real-time detection and response. The development of an automated proactive threat-hunting solution is expected to provide significant economic benefits to the partner organization. By automating key aspects of threat hunting, the organization will reduce operational costs associated with manual threat hunting and improve the efficiency of its SOCs. Additionally, this solution will allow us to offer enhanced security services to verticals, customers, and clients potentially opening new revenue streams.

Development of a Physical Process for the Removal of Metallic Nanoparticles from Carbon Nanofiber

Given the global challenge of greenhouse gas emissions, capturing and converting these gases into valuable carbon-based products, such as carbon nanofiber (CNF), offers a sustainable solution. This is the primary goal of this project’s industry partner, Carbonova Corp, by utilizing greenhouse gas feedstocks and turning them into a valuable CNF. During the production of CNF, metal catalysts such as nickel (Ni) and iron (Fe) are employed to facilitate the synthesis process. However, such metal nanoparticles can become entangled with the CNF, leading to contamination. This impurity negatively impacts the physical properties of the CNF, degrading their performance in various applications. The extent to which metal impurities are present directly correlates with a decrease in the effectiveness of CNFs across different applications. This project aims at a novel physical purification process that integrates multiple steps, including ultrasonication, centrifugation, and magnetic separation, without relying on undesirable chemical processes that may negatively alter the properties of the produced CNF. One of the primary challenges in this research is ensuring that all process parameters work in harmony to achieve an optimal purification outcome. By achieving purified CNF from recycled carbon, the need for new raw materials is reduced for use in various applications.

Détection des rampes de puissance éolienne à l’aide de l’apprentissage machine

Le projet porte sur le développement et l’adaptation d’un modèle d’apprentissage profond existant afin de prévoir de façon automatique à partir de cartes de prévisions météorologiques à haute résolution la probabilité d’occurrence de rampes de puissance éolienne. Ces événements soudains de changement rapide de la production éolienne, difficiles à prévoir, peuvent avoir des impacts significatifs sur la stabilité du réseau électrique et poser des défis au niveau de sa gestion. Plusieurs études ont montré que l’occurrence de rampes est reliée à l’occurrence de phénomènes météorologiques précis. L’apprentissage profond sera ici utilisé pour analyser de façon automatique les patrons associés à ces phénomènes sur des cartes de prévisions et évaluer si les conditions sont propices ou non à l’occurrence de rampes, imitant ainsi le travail d’un météorologue expérimenté. Les avantages de ce projet pour l’organisation partenaire Hydro-Québec (HQ) seront d’augmenter la valeur économique de l’éolien et de faciliter son intégration dans le réseau, l’éolien étant appelé dans les années à venir à composer une part plus importante du mix énergétique au Québec avec des plans de développement ambitieux dont HQ sera le maître d’œuvre, et à remplacer une grande partie de l’électricité produite à partir de combustibles fossiles dans les réseaux

Assessment of Comprehensive Self-Care Programs in the Workplace: A Mixed Methods Study

The purpose of this 20-week study is two-fold. First, assess two comprehensive self-care programs on employee and organizational outcomes. Second, explore the self-care phenomenon. A single financial services organization will participate in the study. Financial consultants will be randomly assigned by office location into three groups. Group #1 will attend a comprehensive self-care program called the Corporate Athlete Training Program (CATP). The CATP provides skills instruction in movement, nutrition, mindfulness and recovery. Group #2 will receive the CATP followed up with ~60 minutes of co-active life coaching support delivered every 2 weeks for 3 months. Group 3, the control, will receive no treatment. Several quantitative measures will be taken before and after the self-care programs. Measures include weight, waist circumference, blood pressure, pulse, blood lipids, fasting blood sugar and employee productivity. Several self-rating survey instruments will be used. Surveys will measure work engagement, resilience and health. Qualitative data, used to explore the self-care journey, include observations, field notes, participant feedback, weekly e-journals, interviews, and a focus group.

Portrait stylization

This project explores how AI can create expressive, stylized portraits while keeping a person’s identity and emotions intact. By developing advanced AI models, it will help artists, enhance digital content creation, and support industries like entertainment, advertising, and cultural heritage. The technology can be used in museums for interactive exhibits, in film and gaming for virtual production, and in personalized digital experiences. Participating institutions will benefit from advancing AI-driven digital art, gaining research recognition, and fostering industry collaborations. A related conference or journal publication will showcase findings, increasing visibility and impact in both academia and industry.