Projets novateurs réalisés

Market opportunity analysis for HER2 targeted cell therapy.

Precision medicine is showing great promise in the fight against cancer by selectively targeting hallmarks of cancer cells to spare normal ones. One such hallmark of cancer is the overexpression of the HER2 protein in many different types of cancer.
At Modulari-T we have developed a way to program the immune system to recognize and kill cancer cells which we call MARC cells by sensing the overexpression of HER2.
This project aims to find the optimal indication for a HER2-directed cell therapy. A thorough analysis of HER2 overexpressing cancer incidence, the current standard of care for secondary line treatment (refractory or relapsed cancer) and the patient population profile will be used to analyze the market opportunity for the different indications that the therapy could be applied to.

Secure Horizons – Building a Local Model for Cyber Resilience

This collaborative initiative between St. Clair College, Connecting Windsor-Essex (CWE), TELUS, Windsor Police Service, and Ontario Provincial Police aims to enhance digital literacy and cybersecurity awareness in the Windsor-Essex region. The project will focus on implementing the TELUS Wise program, which aims to educate various demographics—ranging from children to seniors—about online safety and responsible digital citizenship.

The project’s primary objective is to conduct a pilot study assessing the effectiveness of the TELUS Wise program at a local level. This involves delivering a tailored cybersecurity curriculum, executing a phased rollout, and conducting comprehensive research to evaluate the program’s impact on participants’ cyber hygiene and behaviour. Interns from St. Clair College will play a key role in this initiative, contributing to curriculum facilitation, data collection, and analysis. They will also gain valuable experience in cybersecurity, data analytics, and research methodologies, enhancing their professional skills and expanding their networks.

The project aims to create a replicable model for cybersecurity education that can be adapted to other regions, ultimately contributing to a more cyber-aware and secure community. The initiative also supports broader societal goals by promoting digital literacy, enhancing public safety, and fostering a culture of responsible online behaviour.

Optimisation du processus du maintien de la DEL (licence d’établissement) dans une industrie pharmaceutique générique

Ce projet de stage permettra d’optimiser le processus de maintien de la DEL (licence d’établissement) pour qu’elle reflète les sites réellement utilisés, en se concentrant uniquement sur les sites et les activités nécessaires et en évitant de payer pour des sites inutiles sur la DEL (licence d’établissement), tout en répondant aux exigences réglementaires. Selon la définition de Santé Canada, tous les établissements de produits pharmaceutiques au Canada doivent avoir une licence d’établissement de produits pharmaceutiques (LEPP) pour fabriquer, emballer-étiqueter, distribuer, importer, vendre en gros ou analyser un médicament. Il s’agit d’une exigence en vertu du titre 1A de la partie C du Règlement sur les aliments et drogues (RAD).

Real-time Garment Synthesis for Game Deployment

Cloth animation is essential in video games, but creating realistic cloth movements in real-time is challenging due to computing limitations. Traditional methods use simplified simulations combined with keyframing to manage performance and control, but these can be unstable and offer limited animator flexibility.

Our project proposes a solution using neural networks to animate garments. By leveraging the latest advancements in artificial intelligence, we model cloth dynamics as a complex spatiotemporal behaviour which cannot be modelled by traditional approaches. This involves considering the body’s shape and movement along with the garment’s properties to predict how the cloth should move.

By integrating AI-driven models, our approach promises a significant leap forward in cloth animation technology, offering a more realistic and responsive experience in video games. This innovation could transform the way cloth is animated, setting new standards in the gaming industry

Fast estimation of building stocks in the GTA for deconstruction assessment

There is a large stock of construction materials in existing built facilities, which when reaching their end of life, become a liability for their owners. The current practices of demolishing and disposing of these facilities when reaching their end-of-life generate considerable amounts of wastage and collectively can lead to landfill overuse; hence are not compliant with sustainable development goals. Retrieved materials can potentially be used in new construction (after refurbishment or recycling) or as inputs for other industries (after downcycling). This initially requires replacing building demolition practices with ‘deconstruction’ and ‘disassembly’. Given the higher costs and longer duration of deconstruction, feasibility studies and accurate planning are necessary when considering deconstruction as an end-of-life alternative. In a partnership between Adaptis Co. and Concordia University, this Accelerate project comprises four internship units and will develop fast (and approximate) methods to estimate the amount of valuable and recoverable materials in existing buildings without relying on detailed drawings and specifications. Since many older constructions lack such documents, the outcomes of this research project are expected to positively impact owners, builders, and demolition contractors. It will also offer a major contribution to the preservation and recovery of materials and resources in the building sector.

Advanced Irrigation Technologies for Sustainable Water Resource Management Strategies in Precision Cultivation of Cannabis

Efficiency of irrigation practices will become increasingly critical to both economic performance for agricultural businesses and environmental sustainability. Precision irrigation allows for reductions in resource consumption and waste products released to the environment as well as bolsters the ability for a crop-producing business to maintain consistent-market-quality production at the lowest possible cost. The efficacy or precision irrigation at producing biomass is best analyzed using the concept of water-use efficiency, which measures the amount of biomass produced per amount of water used. The use of sensor technology has the potential to create zero-runoff irrigation practices while simultaneously guiding the maintenance of optimal substrate water content for any crop. This collaboration between InnoKore Solutions and McGill University will comprise controlled studies on precision irrigation practices using tensiometers. These studies will explore the variability in rootzone water availability in globally-relevant commercial production substrates and use those data to optimize biomass production and quality of medical-grade cannabis, while simultaneously reducing water and fertilizer use by reducing wastewater production.

Automated fault detection in commercial refrigeration systems

A refrigerant leak is one of the major contributors of commercial refrigeration unexpected breakdowns. Conventional methods of leak detection using physical sensors are expensive, have limited capability of square footage coverage, and incapable of detecting slow and progressive refrigerant leaks. Accordingly, the development of a smart leak detection system without the need to include additional physical sensors using AI models based on actual operating conditions could significantly reduce the overhead costs associated with system shutdowns and refrigerant fill-ups in grocery stores. The goal for this project is to analyze data collected from various systems, driving meaningful insights, and developing AI models for detecting refrigerant leaks in the form of anomalies. The outcome of achieving the project objectives would have a significant environmental impact, substantial cost-saving, and most importantly, reduce human efforts and erroneous leak maintenance and monitoring processes.

Connector theory for entropy inequalities

Quantum entropy inequalities fundamentally limit how information can be distributed in a quantum system. However, it is an open problem to list and show all the entropy inequalities for the quantum setting beyond 3-party systems, which has proven extremely challenging both numerically and analytically. As the number of qubits we can experimentally initiate and control grows, it is becoming increasingly important to understand a quantum system’s ability to store information about correlations between subsystems. This project aims to combine methods from quantum information theory and tensor networks in order to study the entropy inequalities for system sizes well beyond the reach of current numerical tools. This approach is based on the recently introduced connector theory, which provides a novel tool to coarse-graining many-body quantum systems through convex optimization while preserving certain properties of interest.

Photosynthesis, stomatal conductance, and transpiration of strawberry plants.

The spectral composition of light-emitting diodes (LEDs) reportedly results in higher crop yield and reduced thermal damage to plants. Given that the stomata response represents a link between the plant and the outside environment, exploring the relationship between photosynthesis, stomatal conductance, and transpiration is paramount. This study will investigate the photosynthetic efficiency and stomatal conductance of strawberry plants to understand their plant development responses under specific environmental conditions. Using a combination of gas exchange measurements and leaf area analysis, photosynthesis rates (measured as net CO2 assimilation) and stomata conductance in strawberry plants exposed to established light intensities, humidity levels, and temperatures will be analysed. These findings will provide insights into the adaptability of plants to varying climates (strawberry, cannabis, etc) and can inform agricultural practices aimed at optimizing crop yield and quality.

Energy Disaggregation over Large-Scale Appliances

Energy Disaggregation is to find the energy consumption of individual appliances from only a single measure of household electricity consumption. Accurate energy disaggregation helps identify major energy guzzlers in the house and motivates users to take proper actions for energy saving. To pursue aneasy-to-use and scalable solution to energy disaggregation for contemporary large-scale appliances, we have proposed a solution of semi-intrusive appliance load monitoring (SfALM). Nevertheless, it is proved to be NP-hard to solve the optimization problem and achieve high-precision energy disaggregation in SIALM. Thus, it may be not feasible to find the optimal solution in our case where the appliance number is large. Therefore, we are motivated to design efficient algorithms and validate our solution via highperformance computers and servers. After this project, we are expected to provide efficient algorithms to solve the optimization problem in our case and achieve high-precision energy disaggregation over contemporary large-scale appliances.

Seed funding: Feasibility study of MacDon datasets for machine learning development

This project aims to determine if MacDon’s existing data can be used to develop machine learning models for image segmentation. Image segmentation means identifying different objects in an image by assigning each pixel to a specific category. MacDon has lots of unlabelled video and image data collected from their farming equipment. Traditionally, large amounts of labelled data are needed to create effective machine learning models. MacDon tried to create synthetic (artificial) data similar to their real field data, but the models trained with this synthetic data did not perform well on actual field data. In this project, we’ll analyze both MacDon’s real and synthetic data, as well as their current models, to find out what improvements are needed. We will look at the quality, relevance, size, variability, and noise in the datasets. We’ll also investigate the consistency of the labels in the data. For the models, we will examine why they did not meet performance expectations and suggest improvements. The goal is to provide a detailed report with recommendations on how MacDon can improve their data and models for better image segmentation, laying the groundwork for future collaboration and development.

Portrait de satisfaction, de motivation, de contraintes et de besoins en termes de loisir chez les Ahuntsicois et Ahuntsicoises.

L’arrondissement d’Ahuntsic-Cartierville et les organismes de loisir qui le compose aimerait produire un portrait des participants.es aux activités de loisirs offertes par le milieu associatif afin d’optimiser les services offerts par le milieu. Des questions relatives aux besoins, à la motivation, à la satisfaction, à la pratique et au processus d’inscription, pour ne nommer que celle-là, ont été identifiées par les acteurs locaux. Les objectifs de ce stage seraient de:
· Comprendre les croyances qui motivent les comportements de loisir, les contraintes qui encouragent ou dissuadent les comportements de loisir, et les comportements de loisir des habitants de l’Arrondissement Ahuntsic-Cartierville;
· Comprendre les significations du loisir et de loisirs variés dans diverses communautés culturelles présentes sur le territoire de l’arrondissement;
· Porter une attention particulière à certaines personnes appartenant à des groupes considérés comme minoritaires ou marginaux dans le contexte québécois, comme les personnes issues de l’immigration internationale, les personnes appartenant à des communautés culturelles minoritaires en territoire québécois, ou de la diversité de genre et sexuelle.