Innovative Projects Realized

Constructing Global Competence: An Observational Study in Chinese K–12 Education and Implications for Ontario, Canada

This project will compare how Canadian and Chinese primary and secondary schools teach students aged 10-15 to develop global competence, understand and engage with the world. By observing classrooms in China and conducting a document analysis of the Ontario curriculum, the research aims to identify the most effective teaching methods from each context. The primary benefit for participating institutions is strengthened reciprocal insights that improve cultural sensitivity and global competence in teacher preparation programs and K-12 classroom practices in both Canada and China, thereby preparing students for an interconnected world with a shared future.

Design and development of a pillowcase for capturing EEG signals

CortexCare is a health-technology company focused on developing non-invasive and consumer-ready solutions for sleep improvement and neurological wellness. Their core activities include designing wearable and textile-integrated devices that combine sensors, stimulations, and analytics to support individuals with sleep disorders such as insomnia.
The company has previously developed a pillowcase capable of generating low-intensity electromagnetic fields that promote the onset of sleep. Its effectiveness, however, depends on accurately synchronizing stimulation within the user’s brain activity. CortexCare aims to address the lack of real-time EEG monitoring in their current system through the development and integration of a device into the pillowcase that is capable of capturing brain activity comfortably and reliably during sleep.
This project will provide CortexCare with a safe and commercially-scalable solution that enhances product efficacy, increases user safety, and strengthens the company’s competitive position in the growing sleep-technology market. The resulting innovation has a strong potential for economic return and broader social benefits through improved sleep health.

Listening to Music for Sleep Improvement: A Comprehensive Systematic Review

This research project aims to better understand whether listening to music can improve sleep and daytime functioning, such as mood, fatigue, and concentration, in the general population. The project will consist of a systematic review of scientific studies that have examined the effects of music listening on sleep across different age groups and health conditions. By bringing together results from a wide range of populations, this review will provide a clearer and more comprehensive picture of how music may support sleep health. The expected benefits of this research include improving scientific knowledge about a accessible, and low-cost approach to support sleep, identifying gaps in current research, and helping guide future studies and interventions aimed at promoting better sleep and overall well-being.

Investigate Synthetic Texture Generation Application for Integration into the Synthetic Data Pipeline

Applied Recognition Corp. (ARC), a Canadian software company based in Oakville, Ontario, develops facial recognition technology solutions designed to be both accurate and practical, enabling organizations to adopt biometric systems in a simple and cost-effective manner.ARC’s machine learning models depend on high-quality human facial image datasets to ensure reliable performance and accuracy in image detection and recognition. However, the limited availability of diverse, high-quality real-world facial data presents an ongoing challenge for training and testing purposes.ARC is collaborating with WIMTACH-CEDA to examine the technical limitations and operational challenges with generating synthetic face images using various artificial intelligence-based generation approaches.Building on previous research, this initiative focuses on identifying and documenting key system parameters and control features that influence consistency, scalability, and usability within the synthetic face image generation process.

This proposed project targets a critical operational challenge within ARC’s synthetic data pipeline: the need for scalable, consistent, and controllable texture generation methods. While the current pipeline supports variations in facial geometry and structure, limitations in texture consistency and bias control affect the reliability and repeatability of synthetic facial datasets at scale. Accordingly, the project aims to evaluate, optimize, and refine texture generation settings to enhance output consistency, reduce processing inefficiencies, and support large-scale data production aligned with ARC’s business and deployment requirements.The anticipated impact on ARC includes improved product scalability, accelerated model development, and enhanced revenue growth. By strengthening its synthetic data pipeline through improved texture control, ARC can efficiently expand training datasets and shorten development cycles, enabling faster deployment of facial recognition solutions. This approach reduces reliance on costly and sensitive real-world data, lowers compliance and acquisition costs, improves profit margins, and supports market expansion. The use of privacy-preserving synthetic data further strengthens customer confidence and reduces adoption barriers, contributing to sustainable long-term growth.

Catalytic Hydrodeoxygenation for Sustainable Fuel Synthesis: Characterizations (Canada-France 2)

This project brings together a multi-institutional collaboration among the University of Toronto (Canada), the Technical University of Munich (Germany), and the Université de Paris Cité (France) to investigate the role of reactive hydrogenation intermediates in the catalytic hydrodeoxygenation of lignin-derived phenolic compounds. Understanding these intermediates is critical for advancing sustainable catalytic pathways for biomass valorization.
The proposed Globalink Research Awards will support the mobility of PhD and undergraduate students across the three institutions, enabling hands-on training in sustainable catalysis research. Students will engage in complementary activities encompassing nanomaterial synthesis, advanced characterization, and kinetic investigations, fostering interdisciplinary skill development and international research exchange.
Within this tri-partite collaboration, the University of Toronto will contribute advanced kinetic and mechanistic analysis strategies; Université de Paris Cité will lead catalyst fabrication and materials characterization; and CRC-TUM will provide expertise in electrochemical techniques applied to catalytic systems. Together, these integrated efforts will accelerate fundamental understanding of catalytic hydrogenation processes while training the next generation of researchers in sustainable chemical technologies.

Personalized Virtual Patient Simulation for Decision Support in Intensive Care Units

Challenges in today’s Intensive Care Units (ICUs) involve the management of patients’ life critical physiological systems. ICUs must constantly make fast and personalized treatment decisions (e.g. ventilation settings, fluid management, and vasopressors) that require constant monitoring, patient specific response predictions, and real time decision support. This project explores the use of Cellular Discrete Event System Specification (Cell-DEVS) as a formal modeling framework to develop personalized virtual patient simulations for ICU decision support. The project will identify important physiological processes in ICU care and define discrete representations that capture their spatial and temporal dynamics. Strategies for mapping continuous ICU measurements into discrete Cell-DEVS state transitions will be investigated, alongside parameter personalization approaches and traceability. In order to validate the final framework’s functionality, it will be compared to existing physiological, machine-learning, and agent-based virtual patient models. Overall, this project aims to establish Cell-DEVS as a complementary modeling approach for personalized ICU risk scenario simulation and AI-assisted clinical decision support.

Boronate Ester-Based Strategies for Controlled Therapeutic Protein Release

The proposed project is focused on developing an improved therapeutic protein for the treatment of osteoarthritis (OA). More than 10% of the world population (primarily those over the age of 50) are impacted by OA, an inflammatory joint disease that affects all the joint tissues due to cartilage loss and to date lacks curative treatment.
We propose to develop a new strategy for the controlled intra-articular release of a protein that has demonstrated promising activity, and prolonged OA treatment. This protein works by stimulating chondrocytes, specialized cells that are responsible for forming, cartilage.
We hypothesize that engineered protein tethered to a hydrogel, a polymeric network in water, to enable sustained release of bioactive protein over 2 weeks. Our objectives to address this research question include:

1) Determine the yield and bioactivity of human natural and engineered protein.
2) Establish a potency for protein variants containing functional groups to immobilize and release it from hydrogels with reversible chemistry.

The Globalink Research Award will be used to facilitate collaborations between Dalhousie University and Nantes University with exchange of knowledge related to hydrogel formulation, protein expression and controlled release. Achieving these objectives will enable future research to focus on achieving potent protein-based therapeutics against OA.

Indigenous Faculty Members’ Experiences Pursing Tenure and Promotion in Canadian Postsecondary Institutions

This research project addresses documented systemic barriers Indigenous researchers face in pursuing tenure and promotion at Canadian postsecondary institutions, including the fundamental misalignment between institutional metrics and the relational, community-engaged work that often characterizes Indigenous research.

Frontiers in Quantum Sensing: Advanced Hybrid Platforms

Quantum sensing exploits quantum mechanical effects to achieve unparalleled precision in measuring magnetic and electric fields, temperature, and time, enabling transformative applications across healthcare, environmental monitoring, aerospace, secure communications, and next-generation navigation systems. As quantum computing and communication technologies accelerate globally, the development of robust, scalable, and highly sensitive quantum sensors has become a critical priority. A major barrier to progress is that sensing materials often behave inconsistently, driven by tiny atomic-scale imperfections, known as defects, that strongly influence performance. Through a Mitacs-supported internship, this project addresses that challenge by advancing a deeper, experimentally grounded understanding of defects in molybdenum disulfide (MoS2), a promising two-dimensional material with strong potential for scalable quantum sensing applications. By establishing a comprehensive picture of how real defect structures form and affect material behaviour in practical formats such as nanoparticles and coated substrates, the project strengthens the foundation for predictable, high-performance quantum sensing materials. These advances directly support 42MetaLink Inc. in accelerating technology development and contribute to Canada’s growing quantum and advanced materials innovation ecosystem.

Nouvelles approches GPU pour la visualisation interactive de la structure des protéines

Comprendre la structure des molécules est essentiel en biologie, notamment pour la recherche en santé et la conception de médicaments. Les chercheurs utilisent des outils de visualisation en trois dimensions pour analyser l’organisation des protéines, mais l’augmentation rapide des données rend ces outils de plus en plus difficiles à utiliser de manière interactive.
Ce projet vise à proposer une méthode open ource de visualisation moléculaire, performant et adapté aux grandes quantités de données actuelles. L’objectif est de développer des visualisations simplifiées de type Cartoon, très utilisées par les biologistes, en générant ces représentations directement sur les cartes graphiques (GPU) lors du rendu. Cette approche permettra d’accélérer l’affichage et de visualiser en temps réel des systèmes moléculaires plus complexes.
Le projet repose sur une collaboration entre l’Université de Limoges et l’Université de Sherbrooke, combinant des expertises en visualisation scientifique, en calcul haute performance et en sciences du vivant.

Comprehensive nutritional assessment of fat-soluble vitamins by liquid chromatography-atmospheric-pressure chemical ionization-mass spectrometry

Optimal nutrition is essential for normal growth and development throughout the lifespan. This includes fat-soluble vitamins (FSVs), A, D, E, and K given their key roles in promoting cardiometabolic and immune health. Chronic vitamin deficiencies are modifiable risk factors that contribute to chronic disease. Nutritional metabolomics is an emerging field of epidemiology involving the untargeted analysis of bioactive small molecules from the diet in complex biological samples. The aim of this metabolomic research proposal is to develop new methodologies to objectively measure a broad panel of FSV species and their metabolites. Currently, there exist few validated analytical methods that allow for reliable determination of the full spectrum of FSVs in small volumes of blood as required for new advances in personalized health. To achieve this, we propose a reversed-phase liquid chromatography (LC) assay as a high efficiency separation technique that can be used to resolve mixtures of FSVs and their bioactive metabolites. LC will be coupled to atmospheric pressure chemical ionization (APCI) with high resolution mass spectrometry, an ionization technique better suited for non-ionic and non-polar compounds, that offers a sensitive and selective approach for the quantification of FSVs with sub-nanomolar detection limits and minimal ion suppression effects.

The Integration of Machine Learning into Research Process: Methodological Reconfigurations in Social Science-Driven Disaster Research

This project explores how disaster researchers are using machine learning (ML) and what that actually changes in disaster research. It starts by collecting a broad set of published studies where ML is used to study disasters and climate risks such as floods, heatwaves, wildfires, and storms. For each study, the project records what kinds of data are used (for example, surveys, interviews, satellite images, sensor networks, administrative records, or social media), what types of ML models are applied, and what roles these models play in the research process, from data cleaning and feature extraction to prediction, classification, and scenario analysis. The project then looks closely at how authors talk about ML in their papers: the reasons they give for choosing ML, the benefits they highlight (such as speed, scale, flexibility, or better performance), the challenges and risks they note (such as bias, opacity, data gaps, or limited interpretability), and how they position ML in relation to more traditional qualitative and quantitative methods used in disaster social science. By combining these technical and narrative perspectives, the project builds a clear and accessible picture of how ML is reshaping disaster research, identifies areas where current practices may be improved.