Projets novateurs réalisés

Légitimité et acceptabilité des initiatives plurilatérales à l’Organisation mondiale du commerce

En raison de profondes divergences d’intérêts entre ses 166 membres et d’un système institutionnel peu flexible reposant sur le consensus, l’Organisation mondiale du commerce n’a pas su apporter de réponses concrètes aux nouveaux enjeux de la mondialisation économique. Face à cette impasse, de nombreux pays ont choisi de signer des accords plurilatéraux, conclus entre un groupe volontaire de membres de l’OMC, offrant une alternative plus pragmatique que les négociations multilatérales. Or, ces initiatives plurilatérales suscitent des contestations de la part de certains pays qui doutent de leur légalité ou qui remettent en cause leur légitimité.

Le projet de recherche vise précisément à éclairer ces controverses. Dans un premier temps, il s’agira d’analyser la légalité des initiatives plurilatérales et des accords qui en découlent en mobilisant les sources pertinentes du droit de l’OMC, le droit international coutumier et les principes généraux du droit international public. Dans un second temps, l’étude s’attachera à la question de la légitimité. Cette partie de l’analyse portera sur les déclarations officielles, les communications des États et des organisations, ainsi que sur des entretiens avec les négociateurs.

In-situ Carbon Dioxide Reduction Reaction in Bipolar Membrane Electrochemical Systems

Carbon dioxide (CO2) in air is commonly captured using alkaline electrolytes such as potassium hydroxide (KOH), which react with CO2 to form potassium bicarbonate (KHCO3). To convert the captured CO2 into useful chemical feedstocks or fuels, the absorbed solution is typically heated to regenerate pure CO2, which is then converted through chemical or electrochemical processes such as the CO2 reduction reaction (CO2RR). These thermal regeneration and separation steps are among the most energy-intensive and costly parts of the overall process. In this project, we aim to bypass these steps by directly converting KHCO3 into value-added chemicals, such as ethylene. This is achieved using a bipolar membrane (BPM), which supplies protons (H+) to locally acidify the electrolyte, thereby releasing CO2 in-situ from KHCO3. The generated CO2 is immediately converted to ethylene on the catalyst surface. The objective of this project is to enhance catalyst efficiency and optimize operating conditions to maximize ethylene production.

Experimental Validation: SINDy-Based Dynamic Identification of GFM/GFL Converters from PMU Data

This project develops a data-driven method to identify the internal dynamics of grid-forming (GFM) and grid-following (GFL) converters in renewable-rich power systems using only output-side PMU measurements. Small, continuous perturbations (e.g., 0.01 p.u. in active/reactive power setpoints) are injected during normal operation to safely excite the system, and SINDy is applied to the recorded voltage, current, and frequency data to obtain reduced-order models that capture the converters’ voltage–frequency behavior. From these models, we estimate key control parameters, such as PLL PI gains, P-f and Q-V droop coefficients, and virtual inertia and damping, and then design robust controllers that improve stability under high RES penetration. The full workflow has been implemented and validated in Simulink at McGill University, and the next phase will experimentally validate the approach using the double Power Hardware-in-the-Loop setup at Karlsruhe Institute of Technology (KIT). The project strengthens McGill’s capability in data-driven converter modeling and control and leverages KIT’s advanced laboratory infrastructure, fostering a long-term research collaboration between the two institutions.

An ensemble machine learning framework for streamflow data reconstruction

This project will develop a new framework to reconstruct missing streamflow data using advanced machine learning techniques. Reliable streamflow records are essential for flood forecasting, drought monitoring, and water resource planning, but many stations have missing or incomplete data. The proposed approach will combine traditional statistical methods with modern single-learner and ensemble machine learning models to estimate missing values more accurately across North American river basins. The collaboration between the University of Saskatchewan and UNAM will strengthen expertise in statistical hydrology and deliver practical tools that contribute to efforts toward improving water management and water security for both countries.

Understanding the effects of Early-Life Adversity on Microglia in the Developing Brain

Early-life adversity (ELA) is one of the most consistent and robust predictors of poor mental health outcomes across the lifespan. These are developmental periods characterized by high neural plasticity, in which the brain is shaped by experience. Thus, ELA can provoke detrimental, long-lasting changes in the brain and it is critical to understand the mechanisms of how ELA changes the developing brain that remain unclear. Thus, we propose to use the well-established Limited Bedding and Nesting (LBN) model, to perform bulk-RNA seq in the hypothalamus; a stress-sensitive brain region. Previous literature showed that ELA-caused developmental changes in the brain are mediated by microglia, the immune cells of the brain. Therefore, we will isolate microglia from the paraventricular nucleus of the hypothalamus of P8 mice from ELA or control conditions, to look for changes in gene expression that could provide potential therapeutic targets. This collaboration will capitalize in Dr. Ciernia’s expertise in RNA-seq and bioinformatics, as well as Dr. Bolton’s expertise in ELA and brain development to produce data that will be used for future grant writing to bring funding for future research.

System-of-Systems Solution with Quantum Simulation for Monitoring, Reporting, Validation, and Verification of GHGs/Carbon Cycle in Biodiversity Ecosystems for SEPLS to Strengthen Sustainable Frameworks Development

Title :System-of-Systems Solution with Quantum Simulation for Monitoring, Reporting, Validation, and Verification of GHGs/Carbon Cycle in Biodiversity Ecosystems for SEPLS to Strengthen Sustainable

Overview:
This project aims to develop a System-of-Systems solution using quantum simulation to monitor, report, validate, and verify greenhouse gases (CO2, CH4, N2O) and the carbon cycle in biodiversity ecosystems, particularly in SEPLS (Socio-Ecological Production Landscapes and Seascapes). By integrating satellite data, field sampling, AI, and quantum-based spectral analysis, the project will create accurate regional carbon-sequestration models, maps of soil and water carbon, and identify carbon-sink hotspots. Participating institutions will benefit by gaining access to cutting-edge tools and data for ecosystem carbon monitoring, enhancing research capacity, and strengthening collaborations for sustainable ecosystem management and climate action.

Academic supervisor: Cynthia Goh and Yen-Hsun Su
Intern:Ke-Hsin Chen

Mitigation des impacts opérationnels des erreurs de contextualisation dans une scène routière

E-Smart commercialise une solution innovante qui offre aux gestionnaires un moyen automatisé d’assurer la conformité aux limites de vitesse, sans qu’ils aient à surveiller les camionneurs. Une interface placée à la commande d’accélération contrôle activement la vitesse du camion en temps réel, en se basant sur la cartographie des limites de vitesse. Toutefois, lorsque la localisation GPS est imprécise ou que les vitesses sont absentes de la carte ou erronées, le système produit des erreurs. Ceci entraine un manque de confiance envers l’outil. En effet, les systèmes de vision artificielle basés sur la détection d’objets, la classification et la segmentation sémantique monoculaires, ne permettent pas de comprendre ce qui se passe dans une scène, comme les positions relatives d’un objet par rapport à l’autre ou leur orientation respective [1] [2]. Ces limitations découlent des défis inhérents à l’estimation de la profondeur et à la compréhension de la scène avec des images monoculaires [3]. Ces systèmes sont encore moins capables de comprendre la sémantique (signification des relations entre le sujet et les objets) d’une scène. Donc pour améliorer le produit et offrir du coup l’ensemble des fonctionnalités de vidéo-télématique classique aux gestionnaires de flotte, E-Smart souhaite développer une nouvelle génération de produit avec une caméra et des réseaux de neurones profonds. Ce système devra faire la détection d’objets et de conditions sur le réseau routier (ex. piétons et cyclistes, neige), afin de bonifier les informations disponibles, en plus d’accroître la précision globale de la solution.

Agrilo and Agrilo-VR: Field Validation and Real-World Deployment of AI-Driven Soil and Nutrient Sensing Systems in South Africa

Healthy soil is crucial for global food security, yet farmers face declining fertility, nutrient depletion, and rising fertilizer costs. These challenges are particularly pressing for smallholder farmers in South Africa, where timely soil information is limited. This project connects researchers from Canada and South Africa to advance Agrilo, a real-time soil nutrient sensing system, and Agrilo-VR, an immersive virtual reality platform for environmental education.
Agrilo’s portable, low-cost sensors measure essential soil indicators—including nitrate, phosphate, potassium, pH, boron, sulfur, CEC, NOM, magnesium, manganese, iron, and calcium—with near-laboratory accuracy. These data help farmers optimize fertilizer application, reduce nutrient runoff, and improve crop performance. Field validation will take place across South Africa’s diverse agricultural landscapes, allowing for assessment under real farming conditions.
A student intern will travel to South Africa to support field validation, collect soil datasets, and assist with the deployment and testing of Agrilo-VR in schools, training centres, and conservation programs. Agrilo-VR will transform real soil and ecological data into interactive learning environments that make environmental science accessible and engaging.
By integrating sensing technology, AI-driven analytics, and VR-based education, this project promotes climate-smart agriculture, strengthens international collaboration, and expands access to sustainable soil management tools across both partner regions.

Research and Development to Enhance Data Collection Functionality, Usability, and Outreach For An Educational Survey Platform

Xello is a leading college and career readiness platform designed to engage K-12 students in career exploration, academic planning, and skills development. It provides educators with data-driven insights to support student success, helping school districts make informed decisions.
Xello is looking to enhance its survey platform to improve their means of data collection, enhance the way the data can be visualized and interpreted by the educator, as well as supporting high concurrency access and the exporting of large datasets in a fast and reliable manner without performance issues. Xello believes that their data collection framework can be expanded as well as the efficiency and seamlessness of their exporting tool to further improve their educational platform. This is the case as efficient data collection is an important tool for helping educators better tailor their current and future curriculum to their students which would lead to an optimal experience for both the student and educator. Xello aims to collaborate with the WIMTACH team to develop these expanded survey tools such as student activity and data integration, survey reporting upgrades, and alumni surveys.
All in all, the development of the improved survey tools/data collection framework would benefit both the students and educators of their platform by allowing the educators to visualize and seamlessly export important insights provided by the students to better improve their curriculum, and it would allow the students to receive more optimal curriculum that is tailored to them based on the insights provided.

Quantum network sensor for speed tracking

The advance of sensors can have important impacts to our lives, ranging from developing safer self-driving cars to more precise medical imaging. Quantum properties like entanglement can dramatically improve the sensitivity of sensors. This project will study a new type of quantum sensing that aims to track speed. A new design will be considered that involves a spatially distributed quantum sensor network to detect the signal generated by a traveling object. The efficacy of this unexplored scheme will be verified through obtaining the optimal quantum control and understanding the role of entanglement. This Globalink internship will be a pilot project of this direction of research. A PhD student from Simon Fraser University will conduct 3-month internship at Chuo University to model the sensing scenario, and analyze the performance of many-body quantum sensors. The intern will learn analytical and numerical techniques that are valuable assets for quantum industries in Canada. The results will also lay the foundation of quantum speed sensing, facilitate collaboration and joint grant application between Profs. Lau and Matsuzaki.

G-BSIEM-PQC: Hybrid Consensus Architecture for Quantum-Resistant Security Information and Event Management (SIEM)

The G-BSIEM-PQC project is a pioneering cybersecurity initiative designed to fortify enterprise Security Information and Event Management (SIEM) systems against the existential threats posed by the post-quantum era. As quantum computing capabilities advance, the traditional cryptographic algorithms currently protecting critical infrastructure are becoming vulnerable to “harvest now, decrypt later” attacks. This research addresses this urgent vulnerability by developing a hybrid, quantum-resistant SIEM architecture that integrates the newly standardized NIST Post-Quantum Cryptography (PQC) algorithms – specifically ML-KEM-512 for secure key encapsulation and ML-DSA-44 for tamper-proof digital signatures. These advanced cryptographic primitives are synthesized with robust distributed consensus protocols, utilizing Practical Byzantine Fault Tolerance (PBFT) to ensure the integrity of critical event logs and RAFT for the high-availability replication of media streams. Developed using the memory-safe Rust programming language to minimize software vulnerabilities, the G-BSIEM-PQC system provides a scalable, standards-compliant blueprint for securing Canadian enterprise networks and data sovereignty in a quantum-enabled future.

Enhancing Sawmill Residue Utilization Through Mobile Biochar Production

This project will demonstrate on-site biochar production from sawmill residues at Patterson Sawmill (Hay River, NWT) using Saskatchewan Polytechnic’s mobile kiln. The mobile system avoids high transport and capital costs associated with stationary facilities and can process diverse residue types directly on-site, offering a practical solution for sustainable management of waste biomass at northern sawmills.
Patterson Sawmill in Hay River, Northwest Territories, produces substantial volumes of sawmill residues that have limited local disposal or value-added options. Transporting these materials is costly, and open-burning remains a common practice, resulting in lost revenue potential and avoidable emissions. Reclaimit Ltd., a northern reclamation and resource-management consulting firm, is working with the mill to identify practical ways to convert these residues into higher-value products while improving environmental outcomes.
This project will demonstrate that mobile biochar kilns are a cost-effective, flexible solution for increasing the sustainability of remote sawmill operations and diversifying their income. Unlike stationary biochar systems – which require high capital investment and consistent feedstock supply – mobile kilns can be deployed directly at the location of the material and can process diverse residue types. However, to support adoption of the technology, operational data, performance benchmarks, and hands-on experience are needed to ensure effective and efficient implementation for best results.