Projets novateurs réalisés

Application of Artificial Intelligence (AI) for improved and sustainable swine production

Artificial Intelligence (AI) is changing the way we do things, and pig farming is no exception. To improve pig production in Canada, researchers from the Canadian Centre for Swine Improvement, the Prairie Swine Centre, and the Centre de développement du porc du Québec are teaming up with tech companies. They’re testing new AI tools to help farmers make better decisions. These tools can help identify problems like early piglet death and health issues. The goal is to make pig farming more efficient, sustainable, and profitable. The project will focus on using real-time data to improve pig health and productivity. By using AI, farmers can make data-driven decisions that can lead to healthier pigs and more efficient farms.

The Feasibility, Fidelity and Acceptability of Implementing a High-Impact, Growth Mindset and Trauma-Informed Tutoring Model to Improve Educational and Well- Being Outcomes among Indigenous, Crown Ward and Child Welfare-Involved Youth

The proposed research project, aided by 2 interns (PhD students) from the University of Toronto and University of British Columbia, aims to evaluate the feasibility, fidelity and acceptability of a specialized tutoring program designed to support the social and emotional development of Indigenous and child welfare-involved youth. This program, offered by TutorBright, combines academic tutoring with growth mindset principles and trauma-informed practices to help students build essential skills, such as emotional regulation, resilience, and social awareness.
Through the study, the interns will gather data on the program’s impact on both academic performance and emotional well-being. For TutorBright, this research provides valuable insights into how to tailor tutoring services to meet the unique needs of vulnerable youth, enhancing the effectiveness and reach of their educational support.

3D Interactive Immersive Experience in Hospital and Long-Term Care

This study proposes to address these gaps using 3D Interactive Immersive Experience technology. By leveraging advanced 3D projection and sensor systems, the project will create accessible, engaging, and interactive virtual environments without reliance on headsets. Research will be conducted with older adults living with dementia in both a hospital unit and a Canadian LTC facility, employing Human-Centered Design to define user needs and system requirements. Implemented in three phases—planning, on-site intervention, and evaluation—this co-development strategy aims to enhance resident well-being, promote ethical data use, and explore large-scale feasibility. Findings will inform integration strategies, fostering the safe and effective use of 3D immersive technology in dementia care.

Conversational AI companion for older adults with memory loss

This study explores the use of an AI chatbot to support people with dementia in long-term care (LTC). Dementia affects memory and can cause anxiety, leading people to ask repetitive questions. The chatbot is designed to offer compassionate responses and emotional support. Using a collaborative approach, the study will involve residents, families, and staff in planning, testing, and evaluating the chatbot over a year. The goal is to create a helpful, user-friendly tool that meets the needs of LTC residents. The findings will guide future improvements, aiming to enhance care and address the emotional needs of those with dementia.

Cold-season subtropical air mass intrusions into the Saint-Lawrence River Valley: dynamic-thermodynamic impacts on extreme precipitation and regional climate change

The heavily populated region of Québec’s Saint Lawrence River Valley (SLRV) experiences
among the largest number of hours of freezing rain on the North American continent. Some of
the region’s most impactful freezing rain events, including the Great Ice Storm of 1998 and
the recent April 2023 ice storm, were characterized by very warm, moist subtropical air above
the valley’s entrenched cold air. The Intern’s research is designed to identify an associated
subtropical temperature- and moisture-based metric that signals an increased likelihood of
extreme winter precipitation, particularly freezing rain, in the SLRV region of Canada. This
research promises to provide improved guidance to the Québec stakeholders on the
likelihood of extreme winter precipitation in an environment of ever-increasing global
warming. The results of this research are expected to benefit the partner organization,
Ouranos, in providing regional climate-change projections to the citizens of Québec.

Outil d’aide à la décision pour réduire les émissions de gaz des installations de production d’œufs du Québec

Pour répondre aux nouvelles exigences des consommateurs, l’industrie ovocole vise à modifier la façon de produire en transitant des systèmes de cages traditionnelles vers des systèmes de logement alternatifs, tels que les cages enrichies et les volières. Ce changement, bien qu’avantageux pour le bien-être animal, complique la gestion de la qualité de l’air en raison des facteurs interdépendants influençant les émissions tels la ventilation, les variables environnementales, ainsi que la gestion de la litière et du fumier. Cette complexité nécessite des stratégies robustes pour atteindre la neutralité carbone tout en maintenant la santé animale et humaine.
Ce projet vise à développer un prototype d’outil d’aide à la décision pour les installations de production d’œufs du Québec afin de réduire les gaz à effet de serre (GES), l’ammoniac (NH3), les particules fines (PM) et les bioaérosols. Dans un premier temps, les outils existants tels que Comet-farm, ALFAM2 et DATAMAN seront étudiés tout en sachant bien que ces outils ne soient pas axés sur l’industrie des œufs. Les étudier aidera à comprendre comment ils estiment les GES et le NH3 en fonction des caractéristiques spécifiques du système de production.

Collaborer pour une gestion circulaire et durable des eaux usées

Divers types organisations doivent de plus en plus travailler ensemble pour résoudre des problèmes mondiaux et
locaux. Cependant, des divergences d’opinions et de secteur d’activité peuvent rendre cette collaboration difficile.
Ce projet de recherche, en partenariat avec La Serre+, a pour objectif de comprendre, par une analyse historique
de la démarche, comment différents groupes peuvent travailler efficacement ensemble pour gérer les eaux usées
en utilisant les principes de l’économie circulaire, c’est-à-dire réemployer les rejets de manière bénéfique.
Faire un bilan de l’initiative de La Serre+, en tirer des leçons et documenter son évolution permettra de développer
des cadres de travail flexible qui pourront être utilisés pour guider d’autres projets de collaboration, non seulement
au Québec, mais aussi ailleurs. En résumé, ce projet pourrait avancer des recommandations à partir des leçons
tirées, en proposant une feuille de route de projet pluripotent multidisciplinaire et porté par plus de 10 partenaires.

Light and Darkness in Counter-Reformation Painting of Repentance

In the late sixteenth century, a distinctive Italian painting style emerged, characterized by the contrast of light and darkness, known as chiaroscuro. While this artistic development is often seen as a stylistic or optical advancement, My PhD dissertation argues that the light-dark interplay in Italian Counter-Reformation paintings, especially those showing a repentant figure, has deep theological and intellectual roots. Grounded in the writings on repentance, these paintings were meant to engage viewers and encourage their own repentance. The study will explore how the spirituality that emerged during the Counter-Reformation influenced the use of light and darkness, focusing on painters who incorporated these ideas and examining evidence of audience responses. This thesis will argue that without this theological context, our understanding of the era’s painting style and light manipulation would be incomplete.

PCBA AI Testing

Price Electronics, a leader in the design and manufacturing of commercial-grade HVAC products, also operates as an Electronics Manufacturing Services (EMS) provider, producing electronic components for a variety of industries. A critical aspect of their production process is the testing and verification of Printed Circuit Board Assemblies (PCBAs), which currently relies heavily on manual inspection. This manual process is not only time-consuming but also prone to human error, which can compromise the quality of the final product.

To address these challenges, Price Electronics seeks to implement an AI-driven solution that automates the PCBA testing process. By leveraging machine learning and advanced imaging techniques, the company aims to enhance the accuracy and efficiency of its testing procedures. This project will introduce a cost-effective, automated system that uses cameras to capture images of PCBAs and verify their quality based on pre-defined criteria. The system will be trained to detect defects and ensure that all components, such as LEDs, function correctly.

This initiative is crucial for Price Electronics to maintain its high standards of quality while scaling its production capabilities. The automated system will not only reduce labor costs but also ensure that every product meets stringent quality requirements, thereby minimizing returns and enhancing customer satisfaction.

Development of Advanced Machine Learning and Artificial Intelligence Models in an Open Cloud-Based Platform to Accelerate Drug Discovery (AIRCHECK)

The Artificial Intelligence Ready CHEmiCal Knowledgebase (AIRCHECK) is an open platform developed to share and analyze large-scale, freely accessible chemical activity data. AIRCHECK is specifically designed to integrate high-throughput chemical screens data generated from DNA-encoded libraries (DEL) and Affinity Selection Mass-Spectrometry (ASMS). AIRCHECK aims to become a global platform that advances Machine Learning (ML) and artificial intelligence (AI) in drug discovery through open collaboration across academia and industry. The goal is to create a dynamic, inclusive, and collaborative community that accelerates innovation in chemical biology through the availability of analysis-ready datasets and open-source AI models that are transparent, reproducible, and reusable. To support researchers worldwide, AIRCHECK aims to offer resources (data and computation) and host workshops, webinars, and competitions to become a cornerstone for collaborative drug discovery and a way to open science in chemical biology.
The Structural Genomics Consortium (SGC) supports the project by contributing its expertise in data curation, standardization, and fostering open science collaborations between academia and industry. SGC plays a key role in ensuring the quality and accessibility of the datasets integrated into AIRCHECK.
This proposal seeks to enhance AIRCHECK by improving data management strategies, standardizing DEL and ASMS datasets, and developing machine learning models with benchmark datasets. We will focus on building novel algorithms for large-scale chemical screening and a Machine Learning Operations (MLOps) framework to streamline data ingestion, model development, testing, and deployment, enabling collaborators to focus on advancing ML/AI techniques without the complexities of managing the research lifecycle.

Novel next-generation large diameter fiber reinforced polymer piping

This project addresses the significant demand for composite piping capable of withstanding elevated temperatures and/or pressure in water service and oil and gas transportation. In collaboration with Flexpipe at Mattr Infrastructure Technologies, we aim to develop rapid testing methods for designing and manufacturing large-diameter spoolable fiber-reinforced composite piping. Our focus is on exploring alternative procedures that are safe, efficient, cost-effective, and ensure high quality. This initiative is vital for our industrial partner and for Canada because: (1) There is an increasing need to enhance the cost-effectiveness and efficiency of fabrication and testing methods in key sectors such as oil and gas. (2) There is currently a lack of comprehensive understanding regarding the long-term performance of commercially available multilayer composite pipe in specific applications, such as linepipe for oil, gas, and water. (3) There are opportunities to transfer knowledge to other Canadian organizations facing similar technological challenges, specifically the use of durable fiber-reinforced composite materials in harsh environments.

Implementing and Testing of Additively Manufactured High-Temperature Ceramics in Hydrogen Production Reactors

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