Building Trust in AI-Generated Content: Innovative Strategies for Quality and Integrity Verification

In an era where AI can write articles, create reports, and even craft stories, ensuring this content is accurate, free from errors, and trustworthy is crucial. Our project, “Building Trust in AI-Generated Content: Innovative Strategies for Quality and Integrity Verification,” aims to tackle this important challenge and to measure and increase the reliability and trustworthiness […]

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2D material band-gap prediction by machine learning

Two-dimensional (2D) semiconductor materials are materials with thickness on the atomic scale that provide unique properties compared to their 3D counterparts. One important property of semiconductors is their band gap, which dictates how the semiconductor material will behave. However, manufacturing and testing 2D semiconductors can be costly and difficult, so the ability to predict the […]

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Integrating graph-based data management into materials acceleration platforms

This research project aims to significantly improve the way data are managed in a specific self-driving laboratory in the AUTODIAL group of Prof. Hattrick-Simpers at the University of Toronto, focusing on discovering new materials that are resistant to corrosion. This class of labs, known as Self-driving labs (SDL) or Materials Acceleration Platforms (MAPs), use advanced […]

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Promoting an integrative landscape approach in vineyards for greater resilience in the face of climatic and environmental changes

Grape growers are increasingly interested in finding strategies to enhance vineyard’s long-term productivity while improving the overall ecosystem health. The market for organic wine is also an incentive that encourages some growers to adopt these practices. Using a landscape approach, we will examine how increased native biodiversity in the vineyards and perimeters can contribute to […]

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Sustainable Hydrogel Filters for Efficient Microplastic and Nanoplastic Removal in Wastewater

Micro- and nanoplastic (MP/NP) pollution is a pressing global issue resulting from the widespread use of plastics. These minute particles, present in everyday products, accumulate in the environment, even being detected in infant feces and blood. Addressing this concern is paramount, with wastewater treatment processes (WWTPs) playing a pivotal role. However, conventional WWTPs encounter challenges […]

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Cross-Domain Recommender Systems with Limited Human Annotated Data

Recommender systems are artificial intelligences that, as their name would suggest, make recommendations based on provided inputs. For example, recommending jobs a person can apply to based on their resumes. Existing research on recommender systems in the Job and Education domains have focused on a single domain. Our research focuses on bridging the gap between […]

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Physics-informed Neural Networks for Time-Dependent Transport Equations

Companies can realize the cost-saving benefits of having access to large amounts of data provided they have digital tools that allow efficient and accurate extraction of information from the dataset. The goal of this project is to conduct the research required for machine learning algorithms to provide reliable engineering predictions for industrial applications. To achieve […]

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Development of advanced absorber/emitter for wireless power transfer thermophotovoltaic systems

The demand for wireless power transmission is growing due to the increasing costs of transporting conventional fuels for satellites, drones, and aerospace systems. A promising solution lies in Thermophotovoltaic (TPV) technology, with an impressive system efficiency of around 50%, converting heat from any high-temperature sources into electric power. TPV systems, which are lightweight and have […]

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Population Survey and Mitigation of Amphibian Roadkill along Heart Lake Road

Heart Lake Road (Brampton, Ontario) bisects a Provincially Significant Wetland. Increases in traffic volumes on the road have led to large numbers of road kill during the spring migration. The TRCA has been monitoring the road kill for two years and has found that thousands of frogs and toads as well as hundreds of turtles, […]

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Enhancing EEG Data Analysis and Brain Health monitoring through Self-Supervised Learning and Corticothalamic Modeling

Sleep is a central function of many species, and is studied widely in neurophysiology and computational neuroscience, yet its underlying mechanisms remain elusive. The common method for recording brain activity during sleep is polysomnography (PSG) which is expensive to operate and can prove to be heavy and bothersome for the recording subjects. In recent years, […]

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