Innovative Projects Realized

Parc de l’Exposition de Trois-Rivières. l’analyse de l’infrastructure et des caractéristiques architecturales de l’Exposition de 1938

Développer et perfectionner une méthode d’étude, d’analyse et d’interprétation du patrimoine architectural moderne. Il s’agit en premier lieu de documenter et de reconnaître les différentes approches conceptuelles, méthodologiques et techniques. Deuxièmement, la démarche vise à élaborer des prémisses, des méthodes et des stratégies opérationnelles pour un projet de conservation et de restauration contemporain. Enfin ces propositions sont conscientes des nouveaux défis économiques, sociaux et environnementaux pour explorer comment atteindre les objectifs de la politique nationale de l’aménagement du Québec énoncé en 2023.

L2M – A biosensor for the detection serum breast cancer markers

This Mitacs Business Strategy Internship (BSI) Grant Application is focused on developing an analytical assay for detecting several breast cancer (BC) markers in serum. As an upgrade to the existing methods for quantifying BC serum markers, we aim to integrate several electrodes on the same sensor platform that will be bio-engineered to target one type of BC marker specifically. These markers will be stage-dependent (HER2 biomarker), together with diagnostic (healthy vs. BC), prognostic (early vs. advanced), and predictive markers (providing information regarding the treatment). Therefore, with the confirmation of BC, identifying the above-listed subcategories of BC would also represent an upgrade in breast cancer diagnostics and, thus, subsequent care.

A Mitacs BSI Market Assessment for proposed technology will be conducted considering the patient population who are at the highest risk of breast cancer. The accuracy of mammography is highly dependent on the density of breast tissue. While conventional mammography can be as accurate as 98% in a fatty breast, sensitivity can drop to as low as 30% in women with extremely dense breasts.
This Mitacs BSI project will develop a novel diagnostic tool that will improve the accuracy of cancer detection in the patient population with dense breast tissue. Estimated market size for novel breast cancer screening tests considers women within the age category of 50-74 eligible for mammography screening in the United States as of the year 2022 (49,567,600), percentage of women screened (75.9%), and 1.6% of women who receive a biopsy (601,949).

Harnessing first row transition metal complexes for photocatalytic hydrogen evolution reactions

My Ph.D. research with Prof. Hanan focuses on developing first-row (3d) transition metal complexes as photosensitizers for photocatalytic hydrogen evolution reaction (PHER). This is crucial due to the depletion of fossil fuel reserves. PHER is crucial in global material, physical, and chemical science research. We are designing organic ligands to fine-tune the coordination sphere of 3d metal complexes, enhancing lifetime of their photo-excited states. While second and third-row transition metal photosensitizers, such as tris(2,2′-bipyridine)ruthenium(II) hexafluorophosphate, have been extensively studied over the past 50 years, interest in abundant 3d metal counterparts is recent as these are less toxic and economic. Understanding their luminescent properties is critical for optimizing PHER processes. This internship aims to explore the photophysical pathways of luminescent Cobalt(III) complexes synthesized in Prof. Hanan’s lab using transient absorption spectroscopy (TAS), luminescence studies, and lifetime measurements. TAS is an efficient and powerful technique not available in University of Montreal. It will help us understand the role of photo-excited states in PHER. Additionally, Prof. Campagna’s lab at the University of Messina, working on analogous iron(III) complexes, will benefit from our collaboration enhancing our understanding of fundamental photophysics of these novel complexes for advancements in PHER and related photoredox chemistry.

Governance of Smart Cities at the Nexus of Quantum and Intelligent Transportation Systems: A Comparative Analysis of Regulatory Frameworks among G7 States

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

AI-Driven Approach to Score ESG-Related Text Data from Company Reports

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW.

L2M QC 2024 – «Nettoyage et certification sanitaire des plants de cannabis pour les cultivateurs Québecois »

Le projet de recherche proposé vise à développer un service de diagnostic de l’état sanitaire, de nettoyage et de certification des plants-mères utilisés pour la multiplication par bouturage dans les cultures de cannabis thérapeutique et récréatif. Les méthodes de culture et de multiplication des plants de cannabis couramment utilisées entrainent une accumulation de microorganismes et de virus dans les plantes cultivées. Cette accumulation peut drastiquement dégrader les rendements et la qualité du produit fini jusqu’à le rendre impropre à la consommation dans certains cas. La totalité des cultivateurs font actuellement face à ces problématiques, parfois même sans en avoir connaissance. L’objectif de la participation au programme L2M est d’explorer l’écosystème des cultivateurs Québécois pour préciser leur besoin quant à l’état sanitaire de leurs plants. Le programme permettra également d’identifier les entreprises concurrentes et de déterminer leur aire d’influence en précisant si elle s’étend aux entreprises Québécoises. Il est attendu, à la suite de ce programme, d’adapter l’offre de l’entreprise aux besoins réels de l’écosystème et de tenir compte des avancées durant la phase de développement du service. Le développement pourra se faire en interaction avec des partenaires identifiés dans le cadre du programme dans le but de bâtir des relations pérennes à la base des innovations de demain dans la culture du cannabis.

L2M QC 2024 – «SysNergie – Fabrication de modules et de blocs batteries lithium-ion modulaires pour application stationnaire.»

Le projet de stage en stratégie d’entreprise du stagiaire se concentre sur la validation de marché pour les batteries innovantes développées par l’entreprise, spécifiquement destinées aux applications de stockage d’énergie stationnaire. L’objectif principal de ce projet est d’évaluer la demande et le potentiel commercial de ces batteries sur différents segments de marché. Cela implique une analyse approfondie des besoins des clients, des tendances du marché, ainsi que des opportunités et des défis spécifiques à ce secteur. En outre, le projet vise à identifier les partenaires potentiels, les canaux de distribution optimaux, et à élaborer une stratégie de mise en marché efficace pour positionner SysNergie comme un leader dans le domaine du stockage d’énergie stationnaire.

Soil remediation using tannin’s hydrogel: a potential solution to forestry and mining industries

Within the Canadian context, this project is expected to solve an environmental problem related to the contamination of soils by heavy metals from the mining industry and deal with a great amount of forest waste generated from the forest industry. This project aims to develop tannin hydrogel from wood residues from Canada, evaluate its performance to adsorb metal pollutants, and compare it with commercial hydrogel based on fossil fuels, sodium polyacrylate. For that, the tannin, extracted from the Canadian forestall waste, will be used to produce a hydrogel and applied to the soil to evaluate its potential adsorption. The latter will be characterized as pH, elemental composition, morphology, surface chemistry, specific surface area, and pore volume. Then, a laboratory dispositive in the form of a column will be developed with three layers, the hydrogel under the contaminated soil and above the standard soil. A controlled water flow will be added to the system, to leach the contaminants and allow the hydrogel to swallow and adsorb it. The soils will be characterized for metal content and these results will be compared to the sodium polyacrylate hydrogel to evaluate which polymer is the most efficient for metal adsorption.

Deep Learning for Acoustic-based Pipe Leak Detection

Current leak detection methods for water pipelines, such as fixed acoustic devices and Pipeline Inspection Gadgets (PIGs), require shutting off the water supply and are often impractical for field inspections. These methods demand extensive workforce and time and often fail to detect smaller leaks effectively, leading to significant water loss, increased operational costs, and potential infrastructure damage. XK Innovate Inc., a leader in water pipeline technology based in Toronto, Ontario, will collaborate with UBC Okanagan to develop a groundbreaking in-service water pipe leak detection system. This project introduces the HZ1 Free-Swimming Device, advanced deep learning models, and an intuitive Graphical User Interface (GUI) to detect leaks without disrupting the water supply. This innovative solution aims to reduce workforce requirements, operational costs, and water loss by enhancing accuracy and efficiency while fostering Canadian economic growth and public welfare. The project aligns with Canada’s innovation and workforce development policies and supports sustainable development by minimizing environmental impact and resource consumption. Additionally, it offers an invaluable opportunity for an intern to gain practical experience in data analytics, systems engineering, and environmental technology, equipping them with competitive skills for the job market.

Research and Development of an AI based Software to Recognize Realtime TV Commercials on Low-power Computing Equipment

This research will explore the possible use of video analysis, object detection, and machine learning models to identify TV commercials and advertisements in real-time on low-power computer systems. The partner organization focuses on software development and advertisement technology. The partner aims to solve the challenge of developing a lightweight power-efficient AI-based software that would be able to identify TV commercials or other advertisements in live video feeds in real time. This technology will help the company identify and quantify advertisements for tracking purposes. The interns would work on categorizing advertisement data, optimizing the system, and identifying key characteristics associated with TV commercials transitions.

An exploratory investigation of Geographic Information Systems (GIS) applications to support the Vietnam Cleaner Production Centre’s sustainable Pangasius bocourti (Basa Fish) industry project

This research project will explore how a Geographic Information System (GIS) analysis can aid in enhancing the sustainable Basa fish industry in Vietnam. Enhancement is achieved through increasing market value, reducing the impact on global seafood supply chains and reducing negative local environmental impacts. GIS is an application that allows for complex spatial analysis of large quantities of data, some of its frequent uses are to:
1. Determine optimal locations for: service centres, businesses based on demand, resource extraction industries, forestry or hydrology studies etc.
2. Create market segment analyses based upon locations, customer preferences or product preferences.
3. Create and implement models to predict future growth, demand, or environmental impacts.
The goal is to determine how a GIS application can help build the capacity of ongoing VNCPC and Hanoi University of Science and Technology work to improve the Basa fish industry in Vietnam. The expected outcomes of the research project are that GIS analysis will be used by VNCPC for network and point-based analyses to contribute to a sustainable Basa fish industry in Vietnam

Remote Video Surveillance using LEO Satellites Communications

Video surveillance systems have rapidly expanded, driven by their critical roles in security and traffic monitoring. This expansion has produced vast data volumes, causing bottlenecks in communication systems due to the time-sensitive and bandwidth-intensive nature of surveillance data. To address these challenges, research has increasingly focused on developing algorithms to compress redundant data effectively, evolving from traditional methods to advanced neural video compression techniques. These methods aim to transmit minimal data without sacrificing quality. Our approach utilizes the semantic communication paradigm, emphasizing the transmission of data’s meaning rather than the data itself, to enhance efficiency in low-bandwidth environments. We propose a semantic compression framework specifically for video surveillance that employs novel object detection models and varying levels of semantic abstraction. This framework is designed to convert surveillance footage into compact, meaningful representations, optimizing data transmission in scenarios like remote surveillance via satellite.