Projets novateurs réalisés

Drug Delivery Vehicle for Gene Silencing in the Posterior Segment of the Eye

Retinal diseases, such as age-related macular degeneration (AMD), cause vision blur and distortion, eventually leading to vision loss. AMD is the leading cause of blindness in patients over 60, affecting an estimated 200 million people worldwide. Current treatments require patients to receive monthly injections into the back of the eye, risking adverse effects and placing a tremendous burden on both the patient and healthcare system. RNA interference offers greater potency compared to traditional AMD treatments, with the potential to reduce injection frequency and volume. However, clinical implementation is limited by stability and long-term delivery, and methods for ensuring safe and effective delivery to the target cells are needed. The proposed project is focused on the development of a novel material for RNA encapsulation and delivery for the treatment of AMD. This project has the potential to seed a collaboration between the home and host institutions as we have overlapping research interests and differing areas of expertise.

Développement d’un système informationnel 360 pour l’optimisation des opérations (suite)

Videns Analytics est une compagnie qui approche cinq ans d’existence et qui regroupe une quarantaine de collaborateurs. Pour sa gestion interne, la compagnie utilise plusieurs outils qui touchent différents domaines comme par exemple:
– la gestion des heures des collaborateurs, utile notamment pour la facturation;
– la planification de l’affectation des ressources humaines sur les différents projets;
– les ressources humaines;
– la comptabilité;
– la gestion des clients et prospects (CRM).
L’objectif du projet est de continuer à développer un système d’informations analytique qui permettra de rassembler ces données et de les présenter sous un format qui facilitera la prise de décision et permettra d’optimiser la gestion de la compagnie. Le projet qui est la suite d’une demande Mitacs SSE précédente couvre:
– l’infrastructure cloud qui va héberger le système d’informations;
– le modèle de données;
– la base de données;
– la génération de code ETL en python qui va extraire et transformer les données des systèmes existants pour les intégrer au système informationnel;
– des tableaux de bord qui vont permettre de présenter les données de façon synthétique.

Par rapport à l’année dernière le projet se concentre sur les données de comptabilité et les tableaux de bord afférents

Big Data Analytics for GPS Fleet Management

The project will develop a data mining strategy on how to efficiently use Geotab’s data from multiple sources to isolate network connectivity logs in Geotab’s data, interpolate the GPS coordinates associated with network logs, acquire fuel consumption rates and related opportunities. It will involve a literature survey of the existing algorithms to gain valuable insights. It will also include an important research component to analyze, understand, and develop trends using the available data to compare and corelate the results. The interns shall work toward enriching the data files as required, compare the performance of big data source providers, upload the data to the big data source and use appropriate data visualization tools for the fleet productivity and safety.

Supporting Patients by Family Education in Psychotic Illness

Family caregivers are an essential component of care for young adults with severe mental illness including psychosis. While their involvement improves outcomes in multiple modalities, family caregivers are often overwhelmed with the care of their loved one. Currently, there are not enough resources for family caregivers of young people with severe mental illnesses. The study involves administering a nine-week course on caring for a person with mental illness to family caregivers. Family burden, as well as service user quality life, symptom severity and service used, will be assessed pre- and post-intervention, as well as at a 6 and 24 month follow up. The intern will also assist with program development, including creating a website, online resources for participants, and facilitator’s manual.

Reconciling Measurement-Based Methane Inventories in the Oil and Gas Sector

This research from Arolytics and StFX’s FluxLab, led by Nadia Tarakki, PhD, focuses on accurately calculating methane emissions from Canada’s oil and gas sector, a major contributor to global climate change. The project will develop innovative emissions frameworks for reconciling diverse methane measurement methods and create accurate methane inventories, overcoming current limitations. Emphasizing statistical properties and automation, these frameworks, which will become part of Arolytics’ services, will improve the reliability of emissions reporting. This research will give the oil and gas industry enhanced tools for precise methane measurement, aligning with international standards and regulatory frameworks for effective climate mitigation. The generated technical knowledge will inform regulatory decisions and accelerate progress toward environmental targets. In essence, this project will improve emissions management practices in the oil and gas sector.

McLeod Lake Indian Band Moose Enhancement Project

This project is designed to look at the effects, and potential impacts of various forest harvest treatments on wildlife use, with a focus on moose, within the McLeod Lake Indian Band Traditional Territory, located north of Prince George, BC. This project will examine wildlife species and their use of the various treatments, plant abundance, diversity and nutrient quality, local weather data, and snow depth interception of trees remaining in the treatments. This project will then track the changes of these attributes over time to determine long term use of the areas by wildlife, which can then provide information on other forest treatment practices to benefit similar local wildlife species.

Thermodynamic modelling of binary and ternary deep eutectic solvents

Green solvents are expected to present certain features: availability, non-toxicity, recyclability, biodegradability, non-flammability and low cost. Deep eutectic solvents (DESs) have been studied for the last two decades as a greener alternative to organic solvents since they have cheaper production, are less toxic and are commonly biodegradable. Although phase diagrams of several DESs have been measured, little attention has been paid to ternary systems. An extensive literature review revealed that only a few reports provide physicochemical properties and phase equilibrium data for these mixtures. This project is intended to measure reliable experimental data of ternary DESs that will permit to calibrate a thermodynamic model compatible with the FactSage thermochemical software package, both developed at Polytechnique Montréal. DESs are of interest to the research group in Portugal, a reference in the area. For the first time, our thermodynamic model will be applied to systems dominated by hydrogen bonds, and it is intended to be a powerful tool for modelling DESs.

Analysis of the Evolution of Early Streaming Services: Development, Successful Strategies, and Future Directions. Case Study of Netflix, Disney+, and Amazon Prime Video.

This project explores the evolution and strategic development of early streaming services, including Netflix, Disney +, and Amazon Prime Video. The aim is to study the technological, social, and economic catalysts that have defined the landscape of digital media consumption. An in-depth analysis of these largest platforms’ growth tactics and operational activities will reveal the factors contributing to their market dominance and predict future trends in the streaming sector. The study promises to provide a critical understanding of the academic industry and the streaming industry and provide stakeholders with strategies to skillfully navigate the complexity of the digital ecosystem. For participating institutions, the main advantage is the enrichment of the knowledge base and research capabilities and a deeper understanding of the dynamics of digital media development.

Enhancing behavioural and mental health services for youth impacted by developmental trauma by strengthening caregiver and provider networks

Developmental trauma is often defined as experiencing family violence and/or caregiver disruption early in life. People who experienced developmental trauma have worse health and mental health outcomes throughout their life. The focus of this research will be on developing and evaluating mental health supports for youth and families impacted by developmental trauma. There are two key projects within this proposal. One project will provide support for caregivers by using “peer support” where a caregiver of anchild who experienced developmental trauma supports another caregiver whose child faced a similar situation. The second project is evaluating the impact of providing professionals with training in a therapy for youth and families impacted by developmental trauma, known as the Neurosequential Model. Both projects are thematically linked in that they try to improve care for youth impacted by developmental trauma by increasing the capacity of the child’s support network (family and care providers).

Asset Management using machine learning techniques

Perform an exploratory research to investigate the potential to extract knowledge by aggregating historical data across the company’s clients and applying machine learning techniques on them. The goal is to device prediction models that can forecast the condition of certain events(such as life of pipelines) using statistical analysis. Riva Modeling( partner organization) will benefit from the results of the research by being able to draw more meaningful inferences from the large amount of historical data available from the clients. The greatest benefit of the research goes to the clients of Riva, which includes more than 15 Canadian municipalities and additional cities and utilities in the USA, Australia and New Zealand. The research would lead to a large savings in the expenditure towards the maintenance of their assets.

Development of normal physiological behaviour classification using multi-modal biomarker dataset towards machine learning-driven medical devices.

This project aims to develop a large, labelled dataset of normal canine behaviour for use in developing machine learning algorithms to detect abnormal animal physiological behaviour. These machine learning algorithms will enable veterinarians to collect high-quality data of their patients in their natural environments. This innovative solution has the potential to improve the quality of care for canines experiencing chronic conditions such as epilepsy and cardiac syncope and enhance veterinary medicine’s understanding of these conditions.

Large Scale MultiModal Question Answering

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