The goal of this project is to develop and pilot a sustainability materiality assessment framework for the Calgary Stampede. It is essential to understand stakeholder group’s values and motivations to make the best strategic decisions possible for organizations and the Calgary Stampede is no exception. By piloting the framework, it will be possible to test […]

This project explores the application of the Koopman Operator to system dynamics for the application within model predictive control of hydrogen-enhanced diesel engines. Investigating different implementation options and leveraging data-driven machine learning approaches, the aim is to reduce computational effort while enabling system-theoretic analysis of the dynamic system representation. The topic aligns closely with the […]

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 […]

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 […]

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 […]

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, […]