Projets novateurs réalisés

Adsorption of Greenhouse Gases from Mining Industries Using Zeolite Membranes Technologies

The mining sector plays a significant role in GHG emissions, contributing to climate change and environmental degradation. This proposal aims to investigate specific emissions associated with mining activities and evaluate the potential of zeolite membrane direct capture technologies as a viable solution for emission reduction. By addressing gaps in the current literature regarding the application of zeolite membranes in mining, this research will provide valuable insights into their effectiveness, economic feasibility, and contributions to sustainability goals. Overall, the findings could inform industry practices and policy decisions, promoting a more sustainable approach to resource extraction

Characterization of the stability of a novel antifungal peptide

Antimicrobial resistant infections are a burgeoning threat to humankind. Unfortunately, antimicrobial resistance (AMR) programs have traditionally focused on bacteria and excluded fungi, which are now considered critical priority pathogens. Candida albicans, a commensal in many sites in the human body, can become pathogenic in immunocompromised patients. It is resistant to almost all classes of clinically-available antifungals. We have recently engineered an antimicrobial peptide from a salivary host defence peptide and confirmed its ability to mitigate antifungal resistance. Although our peptide was degraded by some of the fungal proteases, it retained excellent antifungal activity and further in-depth investigation of this is necessary. To further develop this peptide, the intern will perform in-depth characterization of the effects of fungal proteolytic enzymes and salt conditions on the peptide. Investigations will include high performance liquid chromatography and mass spectrometry to characterize peptide degradation. Antimicrobial and antibiofilm assays will be performed against reference and multi-drug resistant clinical isolates of Candida albicans. The intern will also perform circular dichroism assays to characterize the secondary structure of the peptides. This MITACS Globalink Research Award funded project will take us a big step closer towards the development of a resistance-proof antifungal peptide.

Operational Optimization of PV-B (Photovoltaic-Battery) renewable energy communities with the objectives of minimizing OPEX and peak load

This project focuses on creating a smart, efficient energy management system for local communities powered by solar panels and batteries. By using advanced reinforcement learning (RL) techniques, the system will automatically control energy use, storing excess solar power in batteries to reduce costs and keep the grid stable. This approach helps communities rely more on renewable energy and less on traditional grid power, making them more independent and environmentally friendly. Ultimately, this project offers a flexible, future-ready solution to help communities lower energy costs, extend battery life, and actively contribute to a greener, more resilient energy system.

Optical Frequency Combs in Surface Nanoscale Axial Photonic Microresonators

This project aims to explore the generation of optical frequency combs (OFCs) in a surface nanoscale axial photonic microresonator (SNAPR). A SNAPR is an optical device created by modifying the radius of an optical fibre. This modification enables the trapping of light within the device, enhancing its intensity and leading to more pronounced optical effects.

One such effect is the generation of OFCs. An OFC is a pattern of light made up of multiple, evenly spaced lines across a spectrum. Under very specific conditions the incoming light does not exit the device with the same wavelength that was input. Instead, the device generates a series of evenly spaced spectral lines. These spectral lines can then be used as a hyper-precise ruler. This process of frequency comb generation is complex and not straightforward, and the goal of this project is to perform a detailed study of this phenomenon in SNAPRs.

The collaboration between Dr. Del’Haye’s and Prof. Bianucci’s research groups will strengthen their work in microphotonics and specifically nonlinear optics. This project will also provide valuable hands-on experience for the intern and foster future collaborations between the two research groups, advancing the field of optical technologies.

Do interpersonal relationships affect the impact of verbal encouragement on exercise performance?

Verbal encouragement aims to increase athletes’ performance by mobilizing resources, such as commitment and motivation, self-efficacy and/or the reduction of stress. A large body of literature supports its effectiveness in a variety of settings (e.g., sport, exercise). Yet, most studies suffer from important limitations, including the lack of external validity of the experimental stimulus. In particular, many verbal encouragement studies utilized standardized verbal stimuli, which were provided by research assistants who were strangers to the participants. The present study seeks to explore the interaction of characteristics of the sender and receiver of verbal encouragement, as well as their interaction during a task. The planned research will significantly advance our theoretical understanding of the process of verbal encouragement, which may in turn help to elevate athletic performance.
The present study is a collaborative effort between St. Francis Xavier University and the University of Münster. It is intended to be the first project in a line of studies, which will be conducted collaboratively in Germany and Canada, deepening the ties between both institutions, enriching the experiences of all stakeholders and producing cutting-edge research, which may be fundable by several agencies in both countries.

Arthroscopic Fixation vs. Allograft Replacement in Type Ia Glenoid Fractures: A Comparative Study of Outcomes

Glenoid fractures, which happen in the shoulder, are usually caused by either instability or severe injury. A specific type, called Ideberg-Goss Type Ia, involves a break in the front part of the glenoid (the socket of the shoulder joint) without affecting the neck of the bone or the rest of the shoulder blade. Treating these fractures without surgery doesn’t work well. Surgeons can use either open or minimally invasive (arthroscopic) methods, but there’s no clear agreement on the best option. Open surgery tends to have more complications. One minimally invasive method, called Arthroscopic Anatomic Glenoid Reconstruction (AAGR), uses a bone graft from a donor and is a safe and reliable option for some fractures. This study compares two approaches: fixing the bone arthroscopically or replacing the damaged part with a graft. This study’s results will give surgeons more data on clinical outcomes after different arthroscopic surgeries for Ia glenoid fractures.

“Playing with Nostalgia” Guide for Researchers of Game Studies, Nostalgia Studies, Sociology, and Psychology

This internship supports the creation of a comprehensive 80-100 page guide titled “Playing with Nostalgia” by a PhD student intern working in the field of game studies and sociology. The guide has one question: how can we use videogames to inspire people to feel nostalgic for, and thus work to preserve, the future? Research suggests that nostalgia is not regressive, but generative — it can give people a renewed appreciation of what they still have in the present, a critical attitude to history, and a speculation for what the future looks like. Videogames are apt tools since they attract over three generations of players and make serious topics accessible. In tandem with two leading game labs: the Technoculture, Art and Games (TAG) lab at Concordia University, Montreal, and the Centre of Excellence in Game Culture Studies (CoE) in Finland, “Playing with Nostalgia” is intended as a guide for researchers who are working on utilizing videogames to inspire nostalgic reflections about the past and future. The audience for this guide are psychologists, games scholars, nostalgia scholars, and sociologists. Its production is timely given that videogames are made to be nostalgic objects in popular media whilst the generative nostalgia literature is emerging.

Artificial Intelligence for Improved Dosimetric Calculation in Radiotherapy

Cancer treatment by radiotherapy (RT) is essential yet complex, requiring precise radiation doses to effectively target tumors while sparing healthy tissues. This project addresses the frequent challenge of dose discrepancies-differences between the planned and delivered doses-due to factors such as patient anatomy, organ movement, and equipment variability. These inconsistencies can lead to increased toxicity or reduced treatment efficacy if left uncorrected. To tackle this, the project combines Monte Carlo (MC) simulations, known for their high accuracy, with machine learning to develop an advanced, AI-driven quality assurance (QA) system for radiotherapy. While MC simulations are accurate, they are also computationally demanding, often limiting their clinical application. By integrating machine learning, this project aims to streamline dose calculations, making them faster and more adaptable for clinical use. A predictive model will analyze patterns in dose discrepancies and proactively adjust for potential variations, enhancing accuracy and reducing treatment errors. This approach not only improves patient safety and treatment outcomes but also advances a more adaptive, data-driven approach to cancer treatment. By establishing a robust AI-enhanced QA framework, the project aims to set a new standard for precision in radiotherapy, benefiting both individual patients and broader clinical practice.

Biochar: une solution durable pour la gestion de déchets au Nord-du-Québec

Ce projet se concentre sur la production et la caractérisation du biochar, un matériau carboné aux multiples applications prometteuses, obtenu par pyrolyse de la biomasse dans un environnement pauvre en oxygène. L’objectif principal est de soutenir le développement et l’installation de deux modèles de pyrolyseurs à petite échelle dans des communautés cries, contribuant ainsi à la gestion autonome des déchets organiques locaux, tels que des résidus de bois et des déchets organiques, et à analyser ses propriétés chimiques et physiques pour identifier le meilleur matériau à utiliser comme amendement du sol. Différentes techniques analytiques permettront de caractériser les biochars produits et d’ajuster les paramètres de production, tels que la température et le temps de séjour, afin d’optimiser leurs performances pour des applications agricoles et climatiques durables. Une analyse technico-économique sera également réalisée pour démontrer la faisabilité et la rentabilité du projet, assurant ainsi son potentiel de mise en œuvre à long terme. Un aspect essentiel de ce projet est son impact sur les communautés autochtones du Canada. Ces initiatives pourraient renforcer l’autonomie économique et sociale des communautés cries en leur offrant des outils pour le développement local alignés avec leurs traditions et objectifs environnementaux.

Integrating Shape Grammar and Transformer Architectures for Automated Text-to-BRep Conversion in Design Automation

This project aims to create a system that can turn simple text descriptions of designs into detailed 3D models automatically. By combining shape grammar rules (which act like guidelines for building shapes) with advanced language-processing AI models called transformers, the system will understand natural language inputs and generate precise 3D representations known as Boundary Representation (Brep) models. For example, if someone describes a “three-story building with large windows and a flat roof,” the system will produce an accurate 3D model of that building. This innovation will make it easier for designers and architects to bring their ideas to life quickly and accurately. The participating institutions will benefit by advancing research in artificial intelligence and design automation, fostering collaboration between experts in computational design and AI, and potentially developing new tools that can be used in industry and education to streamline the design process.

Doctoral student agency in career imagination: A qualitative case study in mainland China

This project aims to examine how doctoral students in mainland China navigate their agency in career imagination—how they think about and act on their career aspirations—during their studies. It investigates what personal, structural, and socio-cultural factors influence doctoral student agency, and how the interplay of these factors and agency shapes their career envisioning and action in the neoliberal employment landscape. As part of my doctoral thesis, which includes a qualitative case study in both Canada and China, this research will provide valuable insights for the University of Toronto and Xiamen University, the two participating institutions. Findings will help inform evident-based policies on doctoral education, career support, and training programs at both institutions.

Application of Machine Learning to optimize concrete properties, minimizing variability in concrete production

Self-consolidating concrete (SCC) is engineered to facilitate casting and accelerate the construction process while enhancing structural performance and durability. Its high deformability allows SCC to spread and fill formwork under its own weight, eliminating the need for external vibration. The mix design of SCC is critical for achieving an optimal balance between fluidity and stability, thus preventing the separation of its constituents. Traditional design methods can be extensive and time-consuming, requiring careful adjustments of mix parameters to meet specific performance targets. The integration of artificial intelligence to predict the properties of self-consolidating concrete represents a significant advancement, improving the accuracy and efficiency of mix design processes.