Projets novateurs réalisés

Multi-Fidelity Design Optimization of Hydro

From an energy production perspective, the mobile component of the turbine, the runner, plays a key role in the operation of a water turbine. In the present competitive situation of deregulated energy markets, there is a great demand for more efficient runners which can withstand severe operating conditions. Traditional trialand- error runner design methods largely depend on the designers’ experience, and always need long design cycles. In this project, a new design optimization methodology has been developed, which uses a combination of highly accurate evaluations and inexpensive moderately accurate evaluations in a proper design cycle, to integrate their advantages and alleviate their weaknesses, with respect to important industrial design limitations such as time and cost. This robust methodology is able to consider several different design objectives and constraints to obtain high-performance runners. Since the majority of Canada’s power stations work with water turbines, even by a few percentage points improvement of runner efficiency and durability, extra millions of dollars can be gained each year.

PandoPartner Implementation Automation & Asset Library Project

Jessie will help us manage a project we will be executing with MMAI students as part of their Capstone project. She will help us manage their efforts to develop this tool to ensure we’re meeting our deadlines, while also preparing data for the MMAI students to use in AI models.

She will also help us tinker with our process of managing and updating our sponsorship asset library and will help us set it up for our clients to use.

Strategic Arc Flash Risk Management

Strategic Arc Flash Risk Management project aimed at enhancing electrical safety in industrial settings. Arc flash incidents pose a significant threat, and our project seeks to develop a robust framework for preventing, mitigating, and managing such risks.

Novel product and prototype development for soft tissue regeneration

Laetech is developing novel technology for the treatment and reconstruction of soft tissue. Specifically, Laetech is harnessing the regenerative capacity of cells found in a patients own fat tissue. Due to the abundance of fat and relative confidence of harvesting it, fat derived cells have become a large area of interest for researchers and commercialization efforts. Laetech’s technology is enabling surgeons to add cells from a patient’s fat tissue to biomaterials to improve patient outcomes. We are currently exploring two main different products: 1. A functional biodegradable material that is to be used in conjunction with harvested and processed fat tissue (Adipograft). 2. The second product is a similar material formulation as the first product described but in the form of a thicker and more porous hydrogel material for the application as an alternative to fat grafting. The intern will be working towards the completion of three main objectives: Objective 1. To scale the material manufacturing to meet the requirements for a large animal model study. Objective 2. To develop a crosslinking strategy and methodology for the gingival tissue material product, while making regulatory considerations. Objective 3. To develop a functionalized version of Laetech’s material using antibody conjugation methodology or other methods that allow for a specific cell type (endothelial cells) to adhere to the material.

Upgraded Automation Internship

A leading platform faces challenges in producing such content quickly and affordably through traditional methods. To address this, it plans to develop an AI system for automating content generation, aiming to enhance personalization, reduce costs, and streamline production.

Objectives
Automate Content Creation: Implement AI to generate targeted sales and marketing content, reducing dependence on manual processes.
Increase Personalization: Use AI to craft content that resonates with individual preferences, boosting engagement and conversions.
Efficiency & Cost Reduction: Cut production time and costs, enabling faster and more economical content deployment.
Content Evaluation Innovation: Develop new methods for assessing content effectiveness and engagement, incorporating analytics and feedback.
Methodology
The project will use advanced neural language models trained on successful campaigns and customer data. The AI will adapt its output for different customer segments and marketing channels, informed by real-time behavior and trend analysis.

Evaluation
Evaluation will involve automated analytics to measure engagement and conversion, alongside customer feedback to ensure content meets expectations. An adaptive improvement process will refine the AI’s algorithms based on performance data, ensuring continuous optimization.

Impact
This initiative aims to revolutionize the platform’s content strategy, making it more agile and impactful. By leveraging AI for content creation, the platform can better serve its diverse customer base, setting a new industry standard for efficiency and personalization in sales and marketing efforts.

Skagit Headwaters Indigenous-led Conservation

UNDRIP and the Aichi Biodiversity Targets have increased consideration of Indigenous Rights and Title and the role of Indigenous Governance in land protection and conservation.

The Skagit Headwaters is an area of overlapping significance to many First Nations and Tribes in Canada and the United States. This region presents a unique opportunity to consider the creation of a culturally informed, cross-jurisdictional Indigenous Protected and Conserved Area (IPCA). Given the diversity of stakeholders and rights-holders with interests in the region, there is a need for a collaborative approach to respect the various beliefs systems, while understanding what jurisdictional options exist to potentially establish an international IPCA of this complex nature.

The objective of this research is to identify various governance mechanisms that can be used to create an IPCA in the Skagit Headwaters. The broader project is led by the Stó:lo Research and Resource Management Center (SRRMC) which is supporting operations of the S’ólh Téméxw Stewardship Alliance (STSA), and in conjunction with the Province of British Columbia. This research directly contributes to the broader project by providing the foundations to create a framework for the Skagit Headwaters IPCA, tribal park or other Indigenous-led conservation mechanism.

Applying methods established through the Collaborative Stewardship Forum’s S’ólh Téméxw Integrity Analysis ensures that the project is Indigenous-led and representative of Stó:lo principles. By doing so, this project works to take a holistic, interconnected, intergenerational and cumulative approach to relationships, that operates on a watershed-based scope, representative of the Stó:lo worldview.

Developing a Novel Reinforcement Learning Algorithm for Managing Mass- Casualty Trauma Scenarios

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Title of Project: Comparative analysis of three spider mite genomes

We are focusing on three genomes of spider mite, an agricultural pest. One mite feeds on 1200 plants, the other one only feeds on one plant while the third feeds on several plants. Most of the plants these mites feed on are important in agriculture and therefore spider mites cause huge economic damage for agriculture world-wide. Currently, we have three spider mite genomes sequenced and by comparing their gene variance, we are aiming to find some interesting genes which help the first spider mite eat so many different plants. Using genetic technology we are hoping to control/mutate these genes, to decrease the feeding ability of mites. By doing this project, millions of dollars will be saved every year, not only for the Canadian government but worldwide.

Zn-doped Coating Impact on the Titanium Salivary Pellicle

Teeth loss significantly impacts the quality of life of over 700 million individuals and is commonly addressed with implant-supported prostheses. Dental implants face biological and microbiological challenges, leading to potential infectious diseases and implant loss. Proteins from blood and saliva adhere to implant surfaces, influencing subsequent biological interactions crucial for implant success. These proteins quality and quantity (proteomic profile) are vital for osseointegration, while also serve as active binding sites for microbial adhesion. Zinc incorporation onto implant surfaces shows promise in enhancing biological response and reducing microbial colonization. However, the effect of zinc-doped coatings on protein adsorption remains unknown. Therefore, this research aims to evaluate the proteomic profile of salivary and blood plasma protein layers formed on both control and zinc-doped coatings, as well as the proteomic profile of biofilms formed on these surfaces. This research could provide innovative solutions in oral healthcare by reducing peri-implant diseases and improving implant success rates by offering implants with enhanced biocompatibility and antimicrobial properties. Moreover, it fosters knowledge exchange, expertise sharing, and multicultural contact among participating institutions, enriching interdisciplinary collaboration. In essence, this research holds significant promise for innovative solutions in oral healthcare, improving clinical outcomes, and developing highly trained human resources.

Harnessing Advanced Algorithms and Machine Learning Techniques for Precise Location Tracking and Efficient Route Planning

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A Fair and Efficient Transactive Energy Trading Framework for Secured Blockchain-based Smart Grid

Decentralized P2P energy trading using blockchain technology has been the top research and development subject around the globe during past years, especially in the presence of large-scale integrated RESs. The blockchain-based applications could facilitate the energy transition and paradigm shift in energy and pave the way for reaching future net-zero smart energy systems. The main objectives of this project are 1) to leverage the applications of scalable, secure, and interoperable blockchain-based P2P energy trading compatible with the future decarbonized and decentralized energy systems 2) to develop new effective fair, and energy-efficient consensus methods 3) to develop middleware for co-simulating with existing power grid simulation platforms at IREQ.

Telematics-Driven Risk Assessment and Insurance Modeling for Fleet Operations

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