Projets novateurs réalisés

Personalization algorithms for behavioural inclusion in trading strategies

This project is to further Finliti’s proprietary behavioral science-backed trading algorithms by creating and analyzing data models. This will also provide the basis for quantifying the value proposition of specialized financial advice and education at scale for the wealth management landscape in Canada to direct Finliti’s growth and competitive advantage in the market place. Finliti intends to assess to what extent its behavioural science framework allows it to significantly improve on the quality of the recommended portfolios and trading strategy parameters by incorporating the desired parameters into a conditional portfolio optimization framework.

Développement de jumeaux numériques pour le développement et la mise en service virtuels

L’intérêt pour les jumeaux numériques dans les secteurs de la fabrication et de l’automatisation est en hausse, notamment pour répondre à des besoins spécifiques tels que la mise en service virtuelle, qui intervient avant la matérialisation effective de la machine. Cette étude propose d’effectuer la modélisation du mécanisme « ferme-sac » de la machine VP-415 de Premier Tech et comprend la connexion du modèle virtuel à la logique de contrôle programmée pour la machine physique. Ceci sera accompli en utilisant un PLC virtuel ainsi qu’un PLC réel. L’utilisation de ces deux méthodes permettra de les comparer. Nous cherchons plus précisément à évaluer le niveau de difficulté et comparer le temps nécessaire pour l’application de chaque méthode et de voir les avantages et les limitations de chaque approche. De plus, il reste primordial de considérer les besoins de l’entreprise. Ainsi, il est important de déterminer le niveau minimal de détails mécaniques nécessaires dans le modèle pour correctement effectuer la simulation du contrôle et de proposer une méthode ou procédure à l’intérieur du logiciel qui permet la réutilisation des modèles créés.

Demande SSE | Jihane Lazaar

Click & Mortar est une agence de marketing numérique intégrée basée à Montréal. Elle se spécialise dans le développement de stratégies basées sur les données pour des entreprises innovantes. Forte de ses trois pôles d’expertise – performance numérique, créativité, et science des données – l’agence accompagne ses clients dans la conception et la mise en œuvre de campagnes numériques à fort impact. L’agence se démarque par son approche pédagogique et sa quête de solutions éthiques, intégrant notamment des innovations en intelligence artificielle (IA) dans ses services.

SSE – QV Studio – Shirin Mahinnezhad

This project pioneers advanced sensor solutions by integrating scalable manufacturing technologies, such as printed electronics, with innovative energy-harvesting capabilities. These sensors are designed for autonomous operation in challenging environments and are cost-effective, energy-efficient, and highly customizable, catering to industries such as agriculture, forestry, environmental monitoring, and smart infrastructure.

Leveraging cutting-edge materials like Metal Organic Frameworks (MOFs) and quantum sensing materials, the sensors deliver exceptional precision, adaptability, and reliability. The integration of quantum materials for energy harvesting ensures sustainable and independent operation, making them ideal for deployment in remote or resource-constrained areas.

This innovation addresses critical market needs, including early wildfire detection, energy optimization, agricultural productivity, and tailored IoT solutions. By equipping businesses with precise monitoring tools and sustainable technologies, this project takes a transformative approach to environmental and resource management challenges, positioning itself as a key driver of clean and sustainable growth.

Access in Counterpoint 2.0: Wayfinding in Curatorial Situations.

This project will develop accessible wayfinding practices for an interdisciplinary art exhibition. The project combines research-creation and auto-ethnographic methodologies to explore and develop accessibility practices within the Canadian arts sector. The intern will work with partner organization Mass Culture to adapt and revise “Access in Counterpoint,” an accessibility framework intended for a broad interdisciplinary arts contexts.

Alteration of plant cell wall structure for improvement ofbiomass hydrolysis

Biobased products, mostly derived from plant biomass, have the potential to improve the sustainability of Canada’s natural resources and environmental quality while competing economically. Plant biomass, composed primarily of cell walls and modification of cell wall properties has the potential to improve biomass conversion to biobased products such as biofuels. Progress towards achieving this goal is currently impeded by a lack of knowledge of how cell walls are assembled and how their structure affects the processing of biomass. The scope of the proposed research is to identify new varieties of crops (rice, wheat, corn and switchgrass) having improved plant biomass properties. These will be identified using a multipronged approach that utilizes state of the art plant molecular biology techniques and a novel screening method.

A Machine Learning Method for Automated Intraoperative Assessments in Total Hip Surgery

This research project aims to create advanced computer tools to help surgeons during hip replacement
surgery by automatically identifying important parts of the hip anatomy and providing accurate
measurements in real-time. The intern(s) will work on developing a large, realistic set of simulated X-raylike
images to mimic what surgeons see during different stages of the procedure. They will also design and
train neural network computer models to analyze these images, helping surgeons determine critical surgical
details like implant placement and leg alignment. By improving surgical accuracy and efficiency, this
research will support the partner organization in creating innovative tools that enhance patient outcomes
and open new opportunities for the company in the joint replacement market.

Nanostructured fibers for the fabrication of hierarchical thermoplastic composites

Flexpipe, the industrial sponsor, is recognized as a global leader in the production of fully organic reinforced thermoplastic pipelines (RTPs). Flexpipe is currently offering non-metallic, non-bonded spoolable fiber-reinforced thermoplastic composite linepipes with a maximum service rating of up to 82 °C and up to 1500 psi, for high-temperature fluid transportation, CO2 injection and other corrosive, high-pressure applications. It takes advantage of a multi-layered single-polymer pipe design, comprising on an inner HDPE liner, a glass fiber reinforced HDPE composite as the reinforcement, and an outer HDPE jacket. In order to capitalize on an emerging competitive global market, Flexpipe is interested in the development of permeation resistant RTPs which have the ability to store and transport harmful chemicals at increased temperature and pressure ratings. One such example is for Carbon Capture, Utilization and Storage (CCUS) which involves the transportation of CO2 from point sources to useful applications. Safe transportation of hydrocarbons and H2S by drastically limiting their permeation through RTPs to the environment is also highly desirable to Flexpipe and their customers. This proposal aims to establish a technology to coat reinforcing fibers with nanoparticles and develop hierarchically reinforced thermoplastics with the goal of improving the mechanical strength, creep and fatigue resistance of composites used in the production of RTPs. This program is designed to integrate with and support concurrent research being conducted by Flexpipe and their technical partners.

Estimation of Weather and Energy Price Relationship and Impact on Load Following Cash Flows

Weather is a significant driver of daily natural gas and electricity price moves in various North American markets. Weather drives retail energy consumption, which in turn drives market prices through a supply and demand process. The combined volume and price uncertainty is one of the main financial risks for an energy retailer like Direct Energy – we call this “load following risk.” The project will seek to improve the estimation of this risk for a number of settings and also to provide a better estimation of the range and probability of possible outcomes at the level of a complex portfolio of retail load obligations. The relationship between weather and prices is continually evolving, and varios models underlying this relationship will be tested. Finally, a better understanding of the risk mitigation effects of hedges such as weather insurance or natural gas storage will be sought.

Déploiement de la maintenance prédictive basée sur l’intelligence artificielle

Ce projet introduit la maintenance prédictive chez Aliments Morehouse Canada, en exploitant des technologies avancées d’intelligence artificielle et de l’Internet des objets (IoT). En centralisant les données de production et de maintenance dans une base robuste pour résoudre le problème des données fragmentées et en développant des modèles prédictifs performants, il permettra de réduire les arrêts imprévus, d’optimiser les coûts de maintenance et de prolonger la durée de vie des équipements. Grâce à des tableaux de bord dynamiques, les performances des machines seront visualisées en temps réel, facilitant une prise de décision stratégique et alignant l’entreprise sur les standards de l’Industrie 4.0.

Investigation of thermal losses of various configurations of wet and Dry thermally insulated systems and with and without deployment of novel Mica Spacer system

In many industries, particularly those using steam to heat or move heavy oil, keeping pipes and equipment as hot as possible is very important. When heat escapes, it lowers the efficiency of the operation, raises costs, and increases the carbon footprint. Insulation is supposed to keep heat inside, but it does not always work well at high temperatures or when it gets wet.

There is currently no straightforward way to measure and compare how different types and thicknesses of insulation perform, especially when they get wet. Without a standard measure of heat loss per unit length of pipe, it’s difficult to decide which insulation is most effective in real-world conditions.

Peliplat Growth System for Creators

This project will be pivotal in transforming Peliplat into a more dynamic and interactive platform, where creators are supported and motivated to share their passion and knowledge, ultimately enriching the experience for all users. Through this initiative, Peliplat will solidify its position as a leading destination for film and television content and discussions, globally.