Innovative Projects Realized

Characterization of Sustainable Solid Composite Electrolyte for Lithium Ion Batteries

Lithium ion batteries are under constant attention of researchers all over the world due to their advantages such as high energy density, light weight and cycling performance. However they still struggling from some safety issues associated with potential leakage of electrolyte liquid components, their toxicity and flamability hazards. Lithium passivating layer formation and dendrite growth occuring even in fully solid polymer or ceramic based systems. Combination of a solid filler and a polymer matrix allows to improve solid electrolyte resistance to spherulite growth during battery utilization. However, specific requirements needed to be met to not compromise ionic conductivity while pursuing improvements in mechanical and thermal stability. The research activities include: (I) laboratory training, (II) utilization of electrochemical strain microscopy and laser scanning confocal microscopy for interpretation of ion pathways in a solid polymer composite electrolyte, (III) data collection and processing, and (IV) results analysis. The expected outcome will be a better understanding of the defined system in order to evaluate the potential results for its application for lithium ion batteries and understand the way of further improvements can be done in solid polymer electrolytes properties.

Automating sleep stage classification using Contactless BCG Sensor

It is estimated that 5.4 million Canadian adults have chronic sleep abnormalities. Symptoms are not visible to patients because they happen during the night. Hence, they remain undiagnosed. Besides, sleep abnormalities can cause different chronic health problems, that is sleep apnea, diabetes, stroke, brain injury, Parkinson’s disease, depression, and Alzheimer’s disease. Thus, measuring sleep behavior can diagnose sleep disorders and enable the early detection of other health conditions. Current sleep monitoring systems are expensive, labor-intensive, complex. Also, it is not possible to emulate the usual sleep environment in a sleep laboratory. Furthermore, manual scoring has considerable inter-scorer and intra-scorer variability, making its reliability and reproducibility questionable. That said, in this project, we propose a deep learning-based method for the automatic detection of sleep architecture using a contactless system that is based on the ballistocardiographic principle in an attempt to address one of today’s health care issues.

Travailler dans le mouvement communautaire autonome à l’ère de la COVID-19 : analyse des conditions de travail

Le mouvement communautaire autonome a été au cœur de la réponse gouvernementale durant la pandémie de la COVID-19. Que ce soit pour répondre aux besoins des personnes en situation d’itinérance, d’assurer la sécurité des femmes victimes de violences conjugales ou encore pour assurer la sécurité alimentaire des personnes affectées par des pertes d’emploi, les organismes communautaires sont intervenus pour répondre aux besoins émergeant de la population et des personnes les plus vulnérables.
Or, pour ce faire, les organismes communautaires ont dû transformer leurs pratiques et leurs manières de faire. Ils ont également dû faire face à l’augmentation des demandes d’aide tout cela dans un contexte où la santé financière des organismes s’est rapidement détériorée et où de nombreux organismes étaient déjà, avant la pandémie, en situation précaire.
Il ne fait donc aucun doute que la pandémie de la COVID-19 a eu des répercussions sur les conditions de travail dans ce secteur. Ce sont ces répercussions que cette recherche collaborative propose de documenter ainsi que les pratiques de gestion et les processus de prises de décisions autour de ces conditions de travail dans les organismes communautaires.

Transfer Learning for precise detection of individual cells in multimodal microscopic image data

Automatic segmentation and detection of cells is a fundamental task in relevant medical fields such as histopathology, hematology, and cytopathology. Deep Learning methods show promising results, but often require excessive amounts of data, which is a major barrier to entry, especially for experimental cellular imaging data.
This project aims to develop a state-of-the-art cell detection framework that is applicable or transferable to many different data modalities, i.e. imaging techniques or biological staining protocols, with minimal effort. To achieve this goal, a potent Deep Learning architecture is combined with a newly compiled dataset, consisting of several existing cell-datasets, as well as newly generated synthetic datasets. The latter may be created with algorithmic approaches and generative Deep Learning models. Moreover, the benefit of such a dataset and applicability of Transfer Learning to trained Deep Learning models will be studied. The created framework will be evaluated against state-of-the-art methods and other datasets.

Proteomic approach to understand the tripartite interaction of coniothyrium minitans against sclerotinia sclerotiorum that causes stem rot of canola

Characterization of mode of action of C.minitans against S.sclerotiorum and documentation of lysis, hyhal-parasitism via electron microscopy. Studying its metabolite production during tripartite interaction of plant, pathogen and biocontrol agent through FTIR and NMR. Analysis on proteomic approaches during tripartite interaction by Fluorescent Two-Dimensional Difference Gel Electrophoresis (2D-DIGE), identification of protein and data analysis through MS analysis or MALDI TOF

Effect of agro?climatic conditions on the cannabinoid quantities in hemp crops

We want to determine the relationship between weather such as rainfall and temperature on outdoor grown hemp. Specifically what these variables do in terms of changing the content of THC and CBD in certain varieties of hemp. Our goal is to give farmers the knowledge so that they can determine when is optimal time to harvest their crop based on that years weather if they want to be below a .3 thc content, and have a high CBD content. However, the research we do will lend itself to farmers seeking other CBD and THC outcomes in their crops.

The Community Learning Hub Knowledge Mobilization

While the use of specific drugs including cannabis cocaine, ecstasy, and heroin by youth 15-24 in Canada decreased in 2011 (Health Canada), the rate of drug use by youth 15-24 years of age remains much higher compared to that of adults 25 years and older. (Health Canada). Early intervention and education for youth has been suggested to provide protective effect (Hurry & Lloyd), and interactive approaches to that education and intervention have been found to be beneficial (Shiner & Newburn).

Sustainability analysis of biomass-based ethylene production

Ethylene (C2H4) is a critical chemical feedstock for polymer production (e.g., plastics); its global production is about 150 million ton/yr. Ethylene production accounts for 51.6% of the petrochemical production in Canada, and it is mainly produced in Alberta, Ontario, and Quebec, according to the Statistics Canada. Meanwhile, the industry is dealing with a variety of environmental issues such as emission of greenhouse gas (GHG), NOx and SOx. In order to achieve net-zero emission in 2050, significant efforts have to be done to improve the sustainability in the ethylene production.

In this project, we are interested in understanding the sustainability of two novel fossil fuel-based and biomass-based technologies. We will compare the sustainability metrics such as material acquisition, process efficiency, lifecycle GHG emission, recyclability, health and safety for the two technologies. The results from the project will allow us to evaluate the potential of implementing biomass-based ethylene production in Canada and India.

Etude la séparation de phase photo-induite d’un système hybride radicalaire/cationique

Le développement de revêtements de faible brillance polymérisés aux UV est une tendance dans le domaine des revêtements des couvre-planchers. La brillance d’un revêtement dépend de la diffraction de la lumière, et il est essentiel d’avoir un certain degré de rugosité de surface pour obtenir un aspect mat. La technologie étudiée ici pour réduire la brillance des vernis polymérisés aux UV est basée sur “l’auto-rugosité” induite par la polymérisation de systèmes hybrides radicalaire/cationique Le mélange de composés époxy et acrylate peut se séparer en phases/domaines lorsque la polymérisation est initiée par photo-induction. Cette séparation de phase des systèmes hybrides entraîne une hétérogénéité de phase microstructurale et une séparation microphasique des différents composants. La morphologie de surface formée par la séparation de phases résulte de la compétition entre la séparation de phases due à la thermodynamique et les changements physiques associés à la conversion des monomères. Ces changements physiques comprennent l’augmentation de la viscosité ainsi que la gélification et la vitrification, qui limitent toutes deux la diffusion des phases incompatibles.

Post-quantum cryptography for signing software on avionic systems

Avionics on modern aircraft are becoming complex computer systems supported by increasingly sophisticated software. These systems often require software upgrades that are performed during aircraft maintenance on the ground. In order to ensure the integrity of software upgrades and database updates, digital signature of the corresponding data files has been used as means to prevent cyber attacks that could replace the upgraded software with versions including malicious software or bad data. However, the potential advent of quantum computers present a threat to this code signing mechanism, as an attacker having access to a suitable quantum computer could fake these signatures.
Indeed, quantum computers introduce a new type of algorithm using the properties of quantum mechanics. It is believed that with enough quantum bits (qubit), a quantum computer using this algorithm would be able to crack most of the current public key signing and encryption algorithms used today.

Optimal recharging scheduling for urban electric buses

Implementation of alternative fuel technologies for public transportation modes stems from increasing environmental concerns, technological improvements, and increasing demand for autonomous transportation, which is well exhibited by recent trends to replace diesel buses with battery electric buses (e-bus). Yet, scheduling and operational planning of electric vehicle (EV)-based transit modes is challenging due to additional considerations for energy consumption models and driving modes. In this project, a holistic scheduling and optimization modelling tool is developed for battery and fuel-cell operated e-bus transit. For a given timetable and charging constraints at the depot or on-route location, this study identifies the best charging strategy that minimizes the infrastructure investment and operating cost.

Développement d’approches par apprentissage actif et en budget contraint à partir de données de cardiologie

La sténose de la valve aortique est la deuxième maladie cardio-vasculaire la plus fréquente et la maladie cardiaque valvulaire la plus fréquente. Sa prévalence au sein de la population ne cesse de croître. Non traitée, cette maladie est mortelle et seule une chirurgie permet d’empêcher sa progression. Il y a un besoin urgent de progrès majeurs dans le dépistage, le diagnostic, la stratification des risques et le traitement de cette maladie. Le principal défi dans ce contexte est l’absence de biomarqueurs et de stratégies pour identifier le moment optimal pour l’intervention dans le traitement. Dans le cadre de ce projet, des jeux de données comportant des résultats de cardiologie seront utilisés pour élaborer des modèles prédictifs. Le travail des étudiantes sera d’identifier les données les plus représentatives des sous-maladies de la sténose de la valve aortique afin de les caractériser par des cardiologues, de façon à créer des modèles robustes.