Innovative Projects Realized

A Scalable Nitrous Oxide (N2O) Emissions Predictor Utilizing Fuzzy Logic and Impulse Response Measurements

Nitrous oxide (N2O) is a powerful greenhouse gas with a global warming potential 298 times that of CO2, significantly contributing to climate change through agricultural activities, particularly the application of fertilizers. Accurate and scalable prediction of N2O emissions is essential for sustainable agriculture and climate mitigation. Current methods, such as LSTM-DLM neural networks proposed in previous MITACS projects, are computationally intensive, require extensive training data, and depend on costly equipment throughout the entire season (e.g., LICOR gas analyzers), which limits their practical application. This proposal outlines a scalable, cost-effective approach that combines fuzzy logic and impulse response measurements to rapidly and accurately predict N2O emissions across diverse agricultural landscapes.

Business Strategy Internship @ The forever Group

The Forever Group (TFG) is a 30-year-old Canadian owned and operated consumer-packaged-goods company that currently generates $2.5 million–$3 million in annual revenue across North America, the UK, Japan, and other export markets. To achieve its strategic goal of reaching $5 million–$10 million in the next three to five years, TFG must overcome the operational friction created by siloed legacy tools—AccountEdge for accounting, Shopify and Squarespace for e-commerce, Freightcom and ClickShip for logistics, Faire and Amazon FBA for wholesale and marketplace sales. These disconnected systems require heavy manual data entry, human errors, and inconsistent reporting, which in turn hinder timely, data-driven decisions. The business has loyal customer base, but it is unable to scale the business due to these hindrances which only increase cost of doing business with scale.

Toward a Volatile-Based Pest Management Strategy for the Lesser Clover Leaf Weevil

This project will support research on a pest insect called the lesser clover leaf weevil, which damages red clover seed crops and currently requires frequent insecticide use. The goal is to develop a safer and more targeted control method using natural scent cues that the insects rely on to find mates and food. To do this, I will spend two months at Murdoch University in Australia, working with experts to study the weevil’s sense of smell at the genetic level. By identifying the specific genes involved in detecting scent, we can better understand which natural compounds might be used to attract or confuse the pest. This will lay the foundation for designing traps that reduce the need for insecticides, protecting pollinators and other beneficial insects in the process. The research also supports international collaboration between the University of Saskatchewan and Murdoch University, while providing advanced training in molecular biology and bioinformatics. This work will contribute directly to my PhD, lead to a scientific publication, and help bring practical, environmentally friendly pest control solutions to Canadian agriculture.

Oxygen vs. Metabolites: investigating Mechanistic Drivers of Skeletal-Muscle Hypertrophy in Low-Load Hypoxic Resistance Training

Many athletes and patients use blood-flow-restriction (BFR) cuffs to make light-weight exercise feel like heavy training, but no one is sure why this type of exercise causes improvements similar to traditional exercise. Is it because the cuff blocks oxygen from entering the muscle, or because it traps byproducts (metabolites) that signal the muscle to grow? Our project will carefully tease apart these two ideas. We will have volunteers exercise in four conditions: (1) regular exercise with low weight, (2) low weight with inflated BFR cuffs (similar to a blood pressure cuff), (3) low weight while breathing low-oxygen air (like training at altitude), and (4) low weight with inflated BFR cuff and low-oxgen air together. We will match the amount of oxygen within the exercising muscle between the BFR and low-oxygen conditions, allowing us to see whether the lack of oxygen or metabolite build-up is the real driver of strength and size gains. The findings will help Delfi, a Canadian company that makes smart BFR devices, fine-tune their algorithms so users from rehab spaces to elite athletes get maximum benefit with minimal risk, strengthening Canada’s position in cutting-edge sports-medicine technology.

Exploration du rôle du complément dans la glomérulosclérose segmentaire et focale dans le syndrome néphrotique de l’enfant

Ce projet de recherche vise à mieux comprendre une maladie rénale fréquente chez les enfants, appelée syndrome néphrotique idiopathique (SNI). Cette maladie peut prendre deux formes principales, dont l’une (la HSF) est plus grave et répond mal aux traitements habituels. Aujourd’hui, il est très difficile de distinguer ces deux formes dès le début de la maladie, ce qui peut entraîner des traitements inutiles et des effets secondaires graves. Le stagiaire participera à une étude qui cherche à valider un marqueur présent dans les urines, appelé sC5b-9, qui pourrait permettre de différencier rapidement les deux formes de SNI. Ce biomarqueur pourrait éviter des biopsies douloureuses et aider les médecins à choisir le bon traitement plus tôt, ce qui améliorerait grandement la prise en charge des enfants atteints.

AI for Airplane Engine Inspection

This project aims to develop an AI-powered visual inspection system using image data from P&WC’s existing Computer Vision Inspection System (CVIS) to enhance aircraft engine assembly issue detection. By leveraging deep learning techniques, the proposed solution will address current limitations in accuracy and consistency, targeting a 100% visual detection rate for anomalies such as: part at wrong orientation, missing or extra component, and not-tospec installation. The model will be trained on historical CVIS images, validated against expert annotations, and integrated into a streamlined inspection workflow with human-in-the-loop capabilities for continuous improvement. This initiative will replace a less reliable visual inspection system currently used, reduce time and energy resources expended when out-of-spec parts are shipped by or require off-site repair, and establish a scalable foundation for future smart inspection technologies within aerospace manufacturing and maintenance environments.

Optimization and Expansion of Eventlinx

TicketWindow Inc. is a Canadian ticketing company providing digital event management solutions to promoters, venues, and organizations. Its new platform, EventLinx, is a comprehensive, cloud-based event management ecosystem that includes a promoter portal, patron website, mobile point-of-sale (POS) application, and administrative backend. The system is deployed via edge computing infrastructure using Cloudflare Workers and Pages, with real-time interactivity, Stripe payments, Supabase for authentication, and GitHub-based DevOps.

While the platform is functional and in active beta testing, TicketWindow faces challenges in elevating user interface (UI) consistency, responsiveness, and accessibility across multiple devices and user types (e.g., promoters, patrons, and mobile staff). Additionally, there is a pressing need to improve the platform’s user experience (UX) to support scale-up operations and increase adoption by larger venues and organizations. The current interfaces are based on rapid prototyping and beta-level UI decisions, and are not optimized for production-level user flow or visual coherence.

Stratégie climatique et réduction des émissions de GES en entreprise agroalimentaire

Le projet de recherche « Stratégie climatique et réduction des émissions de GES en entreprise agroalimentaire » vise à contribuer à la réduction de l’empreinte carbone des activités industrielles d’une grande entreprise de biofabrication alimentaire au Canada. Cette visée d’optimisation environnementale de processus de production industrielle vise à contribuer à la recherche de solutions à la lutte contre les changements climatiques sous l’angle d’un enjeu rencontré par plusieurs industries de tous les secteurs, quel que soit leur domaine d’activité.

Data Collection and Evaluation Support to Nova Scotia’s Collaborative Food Networks_Glace Bay Town House

Glace Bay Citizen’s Service League – Town House is addressing a critical issue that affects many communities: food insecurity. Although progress has been made through programs such as community meals, cooking classes, and a social supermarket, food insecurity is not only about access to food. It also relates to people’s dignity, health, and their sense of inclusion in the community. This evaluation project aims to better understand whether these programs are effectively addressing these broader needs. Are they reducing food insecurity in a meaningful way? Are they helping people feel more connected and respected?

The purpose of this evaluation is to assess the impact of Town House programs, identify areas for improvement, and provide recommendations for strengthening services over the long term. The project involves developing an evaluation plan, collecting and analyzing both qualitative and quantitative data, engaging with program participants, and producing insights that are grounded in evidence.

This initiative builds on a previous evaluation conducted for the period from April 1, 2024, to March 31, 2025. That evaluation, completed in collaboration with Town House, found that regular monitoring data was limited, making it difficult to assess program outcomes fully. Since the province—through its Collaborative Food Network funding—requires not-for-profit organizations to conduct annual evaluations, there is a need to strengthen data systems.

The current project is a continuation of this effort. Over the next six months, the intern will support Town House in organizing and documenting monitoring data, helping to ensure that the next evaluation is more evidence-based and useful for decision-making. This work will improve the quality of evaluation findings and support the organization in better meeting the needs of the community.

Data Collection and Evaluation Support to Eskasoni Community Foodbank

Eskasoni Community Foodbank is addressing a critical issue that affects many communities: food insecurity. Through programs such as community meals, cooking classes, and a social supermarket, the organization has made important progress in helping people access nutritious food. However, food insecurity is about more than food access alone—it also touches on human dignity, health, and a sense of connection within the community.

This evaluation project builds on a similar evaluation previously conducted with the Eskasoni Community Foodbank and aims to take that work further. While the earlier evaluation provided valuable insights into program reach and impact, it also revealed the need for more consistent data collection and deeper analysis of how programs contribute to social inclusion and community well-being.

The current project will assess how effectively these programs are addressing both the practical and emotional dimensions of food insecurity. The intern will work with the organization to develop an evaluation plan, collect and analyze quantitative and qualitative data, and engage with community members to capture their experiences. The goal is to generate evidence-based insights and actionable recommendations that will help the Foodbank enhance its programs and better serve the evolving needs of the community over the long term.

TRLUP – AI Tool for Structural Health Monitoring using Thermal Imaging

This project will create an AI-based tool that helps engineers detect hidden damage inside concrete structures—like bridges or buildings—using special thermal images. Right now, most damage is only found when it’s already serious, which can be costly and dangerous. This new tool uses heat patterns to find early signs of damage, making inspections faster, safer, and less expensive. The final product will be easy to use, even for people without technical backgrounds, and will help the partner organization prepare to bring this technology to the market.

Stochastic and Predictive Modeling to Optimize Stroke Care in Under-Resourced Health Systems

This project aims to develop computer models that can help improve how stroke care is delivered in hospitals with limited resources. By using data and predicting patient needs, we hope to design better ways to manage hospital staff, equipment, and patient care. The collaboration between Arizona State University and Dalhousie University will combine expertise in healthcare systems, data modeling, and real-world health challenges. This project will not only help improve stroke care in low-resource settings but also strengthen research ties and knowledge exchange between both institutions.