Projets novateurs réalisés

Sleep for gold: a comprehensive assessment and development of sleep health recommendations for Quebec’s elite athletes

The difficulty for elite athletes to have sufficient restorative sleep is confirmed for years. This study is here to help identify the issues triggering this lack of sleep thanks to the validation of the French version of a sleep questionnaire exclusively made for athletes and a global evaluation of their sleep and performance. This research project will provide great insights of what can be done in order to improve the athletes’ performance while adjusting their sleep health. Finally, it will help coaches of the INSQ to know how to rethink their training according to each sport so their athletes sleep enough for gold.

Development of Advanced Lithium Rechargeable Batteries and Scalable Manufacturing Processes

Demand for higher-capacity batteries has been rising exponentially from industries such as zero-emission vehicles, smart grids, unmanned aerial vehicles, and many others. However, current battery technology is limited by graphite anode materials, which have a low specific capacity. Furthermore, the manufacturing process of battery-grade graphites is criticized for the amount of GHG and environmental damage created.
Silicon, with eleven times the energy density than graphite, introduces a promising avenue for innovation to enhance battery performance. However, silicon’s significant volume expansion during lithiation cycles leads to rapid degradation. Current efforts to make silicon anodes commercially viable involve complex, energy-intensive, and costly manufacturing processes. These challenges require innovative materials engineering, advanced manufacturing, and sustainable practices.
We aim to develop a scalable, cost-efficient, and greenhouse gas emission-reducing manufacturing process for silicon-based anode materials. In this research, we will develop techniques applicable to different aspects of manufacturing processes, including utilization of industry byproduct materials, surface engineering of silicon particles, thin and conforming carbon coating, and energy-efficient heat application. The processes will be evaluated by battery electrochemical performance, energy consumption, greenhouse gas generation, and manufacturing costs.

Geoconservation and site monitoring of the geoheritage of Discovery UNESCO Global Geopark

UNESCO Global Geoparks are a comparatively new designation with the focus being clearly on protection of Geoheritage, but also its promotion in the form of Geotourism Geoeducation and sustainable development. Since 2015 some 213 geoparks have been designated including one in Newfoundland Discovery UNESCO Global Geopark, which is based around the fact that it is the home of the earliest animal fossils, and as such of Global significance. The fact that Discovery covers a wide area not within a national or provincial park creates a need to document and address geoconservational challenges that might compromise the designation. The potential threats to the fossil sites relate to climate and climate change as well as human activities. The project aims to survey the sites in Fall 2024 to provide a baseline with which change over the period of intense storm and freeze-thaw activity occurs (Fall through Winter). The methodology involves creating photogrammetric models of the fossil sites and surrounding soils and beaches, we will also install solar powered cameras that will take one photo a minute throughout the day every day to document the extent of wave action but also footfall from visitors to the fossil sites and any other activities (e.g.

Hybrid Quantum-Classical Image Processing by Merging Enhanced Flexible Qubit Representation for Quantum Images equipped with Probability Distribution (EFQRQI-PD) and Machine Learning for Enhanced Accuracy

Image processing principles and techniques represent a significant advancement in modern technology, offering invaluable contributions to numerous sectors. Despite their efficacy, conventional AI and ML-based image processing algorithms encounter inherent limitations that obstruct their scalability and performance. The main aim of the proposed research project is to develop image processing algorithm based on quantum computing and machine learning principles, utilizing amplitude encoding for transition between classical bits and qubits. This initiative seeks to meet the urgent requirement for developing effective image processing techniques by crafting a system capable of efficiently predicting unseen patterns. By achieving this goal, the project aims to significantly enhance the efficiency and accuracy of image processing tasks, thereby enabling more informed decision-making and promotion developments in various fields reliant on visual data analysis. The team involved in this project has an existing research partnership. This partnership is focused on advancing the field of quantum image processing and creating theoretical algorithms. This partnership involves joint research, sharing of knowledge, and co-authoring papers in respected academic journals and conferences.

Economic Assessment Of A Predictive Technology For The Early Detection Of Oral Cancer

Health economic evaluation provides information about the value for money of new healthcare technologies, and is increasingly used to guide the allocation of scarce resources based on maximizing health gain. The molecular diagnostics company, PDI, has developed a technology to improve the identification of patients at high risk for oral cancers for early intervention, and distinguish abnormal cell growth that will not become malignant, compared to the current gold standard alone. This more accurate diagnosis could save lives, reduce morbidity from traumatic surgeries, increase the duration of productive work lives, and save healthcare costs. The purpose of this internship is to develop a health economic model to evaluate the cost-effectiveness of the technology, as well as its social impact. The results of the model will help demonstrate the economic value of this novel technology.

DEM/CFD simulation to minimize dust and biomass debris on farm equipment systems

Dust and biomass debris have serious negative effects on the performance of material handling farm equipment, such as reduced efficiency, increased maintenance costs, and heightened fire hazards. However, there is a lack of understanding of dust and debris generation and dispersion within farm equipment. CNH Industrial is searching for innovative solution for dust and biomass debris control in farm equipment. On this basis, this study aims to develop an integrated DEM and CFD model for simulating the behavior of dust and biomass debris generated during harvest, and use the model to optimize the design of agricultural harvesters to improve the efficacy of in-harvester dust and biomass debris removal. The rheological and aerodynamic properties of dust and biomass debris will be measured at CNH facility, and the proposed model will be validated with small scale lab experiments as well as in-field experiments. CNH Industrial will benefit from training highly qualified personnel, technological advancements, and product optimization in terms of dust and biomass debris minimization.

Measurement of silicon quantum dots using RF-reflectometry

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Process design for interpreting deep learning model decisions using large language models

EBSCO and McMaster University’s Health Information Research Unit (HiRU) collaborate to enhance the delivery of high-quality, clinically relevant content to healthcare professionals. Through DynaMed, EBSCO’s evidence-based clinical resource, and HiRU’s literature surveillance program, McMaster PLUS, they provide a continuous stream of rigorously appraised articles from PubMed to clinicians globally and to DynaMed expert authors. Machine learning models have been developed by HiRU to expedite access to this quality evidence, but we do not understand how decisions are made by the models when they classify evidence. To adhere to transparency and responsible AI practices, this project will test whether large language models, like GPT-4o, can provide explanations for the machine learning model decisions. We will test GPT-4o with a series of prompts and approaches and evaluate how well it performs the task. The goal is to develop a process to be implemented within the support services for DynaMed authors to increase their trust in the model decisions.

CivicLabTO – AccessTO: Exploring the true cost of safety for undocumented people in Toronto

The City of Toronto, as a sanctuary city, is committed to ensuring that “residents have full rights to access City services without fear, regardless of their immigration status”. Consultation on the renewal of the Toronto Newcomer revealed that the City falls short of this commitment with regards to issues of safety that involve the Toronto Police Services. Through a jurisdictional scan, policy analysis, review of the literature, and interviews, this project will analyze the current policy frameworks at the City and Toronto Police Service, examine the policies and practices of other sanctuary cities with regards to policing and safety of undocumented members of society, and ultimately provide tailored recommendations to the City on how to address the raised concerns within the mandate of the City’s SafeTO commitments. In doing so, this project will review the efficacy of Toronto’s sanctuary city policy (first past in 2013) with regards to safety and policing while providing concrete recommendations for ways forward. As Canada’s largest, most diverse, and first sanctuary city, this study will shed light on the challenges of the implementation of this policy initiative while highlighting opportunities to improve its functionality.

Optimal design and operation of a sustainable building purposed as a professional training centre

This project will focus on the design and operation of a sustainable institutional building for vocational/professional training. This building, located at the CÉGEP de Victoriaville site, will apply sustainability principles and life cycle analysis as the cornerstone of its design. The intern will perform data analysis and carry out building performance simulations to provide additional information to the design team. Another important aspect to analyzed will be the operation of the building in the context of interaction with the electrical grid; from this point of view, load management strategies will be an essential design requirement. The final goal is to produce general guidelines that could be applied in similar buildings.

Démarche de recherche sur le portrait et la destination éducative et socioprofessionnelle des élèves inscrits aux Parcours de formation axée sur l’emploi

Pour les élèves handicapés ou en difficulté d’adaptation ou d’apprentissage (EHDAA), terminer le secondaire présente un défi considérable. Pour aider les élèves EHDAA présentant des retards scolaires importants à terminer leur cheminement au secondaire, le Ministère de l’Éducation du Québec a mis en place le Parcours de formation axée sur l’emploi (PFAE), qui offre aux élèves d’alterner entre les études et le travail. Très peu de recherches ont examiné ces programmes, et comment ils répondent aux besoins des jeunes y participant. Ce projet, réalisé avec Réseau Réussite Montréal (RRM), vise à comprendre les profils des élèves qui fréquentent ces programmes ainsi que leur cheminement scolaire et professionnel. D’une part, nous allons préparer la constitution d’un ensemble de données sur les élèves du PFAE à partir des données possédées par les Centre de services scolaires (CSS). D’autre part, nous allons analyser le parcours d’élèves du PFAE ayant pris part à l’étude Parcours pour obtenir des résultats préliminaires.

Persuasive Micro-Data Videos (MDVs) to support children’s outdoor play and learning in schools

Micro Data Videos (MDVs) are short videos, typically under one minute in length, that incorporate data visualizations to tell compelling, data-driven stories. In today’s fast-paced world and with increasingly shorter attention spans, MDVs have become a highly popular format for disseminating data-based information. Their strength lies in the ability to present factual data to the general public, regardless of their level of data literacy, in a timely, accessible, and engaging way. However, crafting an engaging and informative data-based story for such a brief timeframe is challenging, especially when the goal is to influence viewers’ perceptions and/or behaviors.

This research aims to advance the understanding of persuasive data storytelling and narrative visualization by exploring the persuasive potential of MDVs. We plan to develop a set of design guidelines for creating persuasive MDVs, which can be used to produce videos that swiftly raise viewers’ awareness and influence their attitudes/behaviours regarding health-related issues. Specifically, we intend to design, produce, and evaluate persuasive MDVs targeting educators, with the goal of advocating for the importance of outdoor play for children’s mental, emotional, and physical development.