Complete destruction of perfluoroalkyl and polyfluoroalkyl substances (PFAS) in water and by-products minimization via a novel Boron-Doped-Diamond anodes electrochemical oxidation

Perfluoroalkyl and Polyfluoroalkyl substances (PFAS) are anthropogenic compounds with unique properties and wide applications. The consequence of using such persistent chemicals is widespread contamination reported for groundwater, soil, sediment, and wastewater, especially in industrialized countries such as Canada. The endocrine-disrupting and likely carcinogenic nature of PFAS have resulted in strict regulations on PFAS in drinking water.

Network-wide bicycle monitoring

Bicycle and pedestrian counts are important data for the planning and design of safe roads. However, these data need to be inspected for quality, a time-consuming task. Part of this project is to make this project simpler, quicker and more accurate. Installing pedestrian and bicycle counters across an entire city road network is not financially viable. Therefore, a good option is to estimate counts at the network scale, using knowledge from a handful of pedestrian and bicycle counters (strategically placed) and trip data from users who willingly share their position from their smartphones.

UV Mapping Assistance through Deep Learning

The goal is to create a conversation loop between 3D designers and artificial intelligence programs. This will help the AI provide suggestions to the designer, while the designer provides the AI with feedback. This can help make it easier for designing complicated objects as well as complicated textures that belong to the surface of 3D objects. Through this interaction, the hope that AI can extend the utility of design software.

Multi-source data fusion to assess the impact of roadworks on secondary travel demand

Since recent years, many roadwork projects and nearby events will affect the flow of visitors at Casino de Montreal, a model is needed to analyze the impact of these roadwork events and provide solutions to mitigate the effect. In this project, multisource data will be provided and applied into traffic flow prediction. The main objective of this project is to build a predictive model and then using the results of this model to perform analysis.

Integration of hippocampal subfield measurements into a streamlined MRI volumetry pipeline using deep learning

The hippocampus is a small brain region that has a key role in brain diseases including Alzheimer’s and epilepsy. The measurement of hippocampal volume on MRI is important for the diagnosis of these diseases, but this is a complex task because it is a small structure. Further, the hippocampus is divided in sub-regions that are impacted differently by the diseases. This project aims to use artificial intelligence technology to rapidly and reliably measure all sub-regions of the hippocampus and develop this as a clinical tool that could be used in hospitals and clinics.

Mesurer l’impact socio-économique d’initiatives en éducation financière dans un pays en voie de développement : Méthodologies proposées

De nombreuses ONGs implantent des plans éducatifs dans des pays en voie de développement. Habituellement, des indicateurs de l’atteinte des objectifs sont proposés afin d’évaluer les retombées directes du projet. Mais qu’en est-il des retombées indirectes auprès des populations impliquées dans le projet? Comment utilisent-elles les nouveaux apprentissages dans les autres domaines de leur vie? Comment évaluer ces retombées indirectes lorsque des participants adultes ne savent pas lire ni écrire?

Covalent and non-covalent interactions self-supervised representation of molecules for chemotherapeutic drug design

Most of the drugs used to treat cancer have been originally identified from natural sources. While Nature did a great job selecting those compounds, some of them have shown limitations in the treatment of cancer and others have shown to be insufficient on some cancer types. Furthermore, it exists a gigantic number (nearly infinite) of small molecules human can synthetize.

Milk spectral signature of transition success or failure

A successful transition to lactation is of high importance in dairy production as this period affects the entire productive life of a dairy cow. Poor transition are often associated with clinical or subclinical diseases. The objective is to assess whether milk components are associated with a poor or good cow transition. Emphasis is put on milk components other than the ones routinely measured in monthly milk samples. The spectral signatures of milk from cows analyzed through infrared spectroscopy are therefore assembled.

Multi-scale direct measurements and source determination of methane emissions in Montréal

Methane is a potent greenhouse gas and reducing methane emissions is a key strategy in many climate change action plans. Because cities and their methane emissions are growing, it is essential to characterize and mitigate methane emissions from urban sources. Because there are no published studies of methane emission measurements in Montréal, we propose to conduct direct measurements to quantify methane emission rates and attribute the emissions to sources in Montréal.

Separating Syntax and Semantics for Semantic Parsing

Study of language disorders, theoretical linguistics, and neuroscience suggests language competence involves two interacting systems, typically dubbed syntax and semantics. However few state-of-the-art deep-learning approaches for natural language processing explicitly model two different systems of representation. While achieving impressive performance on linguistic tasks, they commonly fail to generalize systematically. For example, unlike humans, learning a new verb like jump in isolation is insufficient for models to combine it with known words (like jump twice or jump and run).

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