Projets novateurs réalisés

Denture internship

Prime Denture Clinic operates in a modern facility in South Edmonton, staffed by a full-time Denturist and a full-time Dental Technologist. The denture industry is rapidly evolving with the integration of digital technology in fabricating dental prostheses. However, finding staff with adequate training in these emerging technologies has been challenging. Additionally, the demands of a busy clinic make it difficult to allocate the necessary time to fully explore and utilize new equipment. To address these challenges, the clinic aims to leverage research and testing through an intern with recent post-secondary experience in modern technologies. This work would facilitate the implementation of new processes and the evaluation of potential equipment investments.

Increasing dairy intake for the reduction of obesity and diabetes in adolescents

Dairy consumption has decreased in the last 30 years. Canada’s Food Guide no longer recommends three servings of dairy and emphasizes plant-based diets and dairy alternatives. Associated with this decrease is an increase in overweight and the risk of obesity and Type 2 Diabetes (T2D) in adolescents. Consistent with this advice, many adolescents have increased their consumption of highly processed plant-based alternatives. The primary objective of this project is to compare the effects of two servings of dairy with compared to plant-based substitutes on blood glucose control and latter food intake. We hypothesize that dairy will reduce blood glucose responses, appetite, and later food intake. The results will support greater emphasis on dairy consumption for this age group and the use of dairy premeal to control food intake. The intern will recruit the adolescents and conduct the studies in our Nutrition Intervention Center on the University campus. The data will be used in support of the requirements for her MSc degree.
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Developing a predictive tool for electrical grid stability

The proposed project focuses on developing a Grid Disruption Prediction Tool that helps grid operators, asset owners, and dispatchers predict and mitigate power outages, brownouts, and blackouts. By analyzing historical and real-time data from Independent System Operators (ISOs) and Regional Transmission Organizations (RTOs) such as AESO, ERCOT, UK National Grid, and the Australian System Operator, the tool will identify low-frequency events and measure grid volatility through frequency variance. The model will use regression analysis and statistical techniques to detect patterns leading to supply-side constraints and grid failures. The expected benefit to the partner organization is enhanced decision-making capabilities, improved grid stability and reliability, and actionable insights to prevent disruptions, ultimately leading to technical and market design solutions that enhance system resilience.

Intergenerational Transmission of Child Abuse

This project focuses on understanding how child abuse is passed down through multiple generations by analyzing interviews with mothers about their experiences (collected previously) and then assessing maltreatment in subsequent generations. Using a unique and specialized coding system, the research will provide new insights into how abuse is perpetrated and perpetuated. The findings will help improve strategies to prevent child maltreatment and support at-risk families. The participating home institution will have access to a seminal and comprehensive data repository as provided by the host institution. For both the participating institutions, this project helps to advance the established knowledge on the transmission of abuse, a research topic both home and host supervisors are interested in.

Long-Duration Testing of Concrete-Based Molten Salt Thermal Energy Storage Tanks Under Forecasted Electricity Supply Conditions

Molten salt-based thermal energy storage (TES) systems offer a promising way to store and manage heat efficiently, especially for industries that rely on stable high-temperature processes. However, these systems are still in development, and key challenges—such as heat loss, material durability, and long-term performance—must be addressed to ensure their reliability and cost-effectiveness.

While some studies have explored concrete-based tanks for molten salt storage, most research has focused on short-term performance. There is little understanding of how these systems behave over weeks or months, especially under fluctuating electricity inputs from renewable sources.

This project aims to fill that gap by testing TES tanks over long periods under real-world conditions. By examining their thermal stability, efficiency, and durability, this research will help develop optimized storage solutions, making molten salt TES more practical for industries like food processing and mining while supporting the transition to renewable energy.

Effects of flooding and rising temperature on C storage and release dynamics in alluvial forests

Advancing Land Classification with AI: Exploring Kolmogorov-Arnold Networks

Land classification is crucial for environmental monitoring, resource management, and urban planning. This research explores using Kolmogorov-Arnold Networks (KAN), a novel machine learning model, for multispectral land classification. Unlike traditional neural networks, KAN utilizes univariate functions as activation mechanisms, enhancing its ability to capture complex spatial and spectral patterns in satellite imagery. The study focuses on classifying land in the Edmonton-Calgary corridor using hyperspectral data, which contains rich spectral information but presents computational challenges. The Finnish Centre for Artificial Intelligence (FCAI) will provide expertise in optimizing KAN for high-dimensional datasets, reducing computational demands, and improving model interpretability. A key goal is to compare KAN’s performance with conventional models regarding accuracy, robustness, and efficiency. The study will also assess whether KAN’s high accuracy results from superior learning abilities or potential overfitting. This project contributes to Canada’s leadership in AI and environmental monitoring, offering a more efficient, data-driven approach to land classification. The findings will aid policymakers in making informed urban development, agriculture, and conservation decisions. Additionally, the collaboration between the University of Alberta and Aalto University strengthens global research ties in AI-driven remote sensing.

Set-up of a custom light-sheet microscope for electro-mechanical investigation of neuronal organoids

Calcium alginate microbeads loaded with lipid nanoparticles as precursors for lipid-loaded pellets

Adversarial Attacks on 3D Object Detectors

A Risk-Based Continuous Authentication Engine Using a Probabilistic Model around Behavioral Biometrics

Traditional static authentication systems have a fundamental deficiency; it assumes the presence of the validated user through the length of the session. Continuous authentication algorithms periodically validate the identity of a user during the entire session. It relies on information that can be automatically extracted from the user such as biometrics and behavior patterns. A probabilistic approach can naturally model the noise and latent variables present in the data. The probabilistic output of such models is a confidence value. Risk-based authentication makes use of this value to define task-specific requirements. The proposed research project has two components: an anomaly-based Intrusion Detection System (IDS), and a risk-based authentication system that uses biometrics and behavior patterns. If our research in this area proves to be successful, we can hopefully replace the use of password challenges as a primary authentication factor, in exchange for continuous algorithms that are more reliable and less of hindrance to the end-consumer

Development of high-efficiency semi-transparent organic solar cells with enhanced infrared absorption and tunable transparency

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