Projets novateurs réalisés

Litho-minéralogie automatisée de carottes de forage et de poudres de minerais pour des applications géométallurgique et géoenvironnementale

Le projet présenté ici vise le développement d’une technique de caractérisation lithologique automatisée adaptée pour les carottes de forage en exploration minière. La technique sera basée sur divers capteurs (optiques, chimiques et autres) ainsi que la microscopie optique. Les données obtenues (big-data) serviront à l’optimisation des procédés de traitement des minerais ainsi que la bonne gestion environnementale des rejets issus des exploitations minières.

Safe Magnetic Resonance Imaging of Interventional Devices

Magnetic resonance imaging (MRI) is a non-ionizing modality; therefore, there is no

known damage on human tissue as opposed to ionizing imaging modalities, such as

X-ray. Interventional procedures take long time and in order to prevent ionization,

interventional MRI is very promising. On the other hand, there are safety concerns

because of the possible temperature rise on current carrying conductive wires, such

as guidewires and biopsy needles, during MR scans when it is inserted inside the

human body. In this project, we are aiming to eliminate the dangerous temperature

rise by using different methods and improve safety of the devices used in

interventional MRI.

Habitat security mapping for improving human-wildlife coexistence in the Bow Valley

Tourism, nature-based recreation and residential development within the Bow Valley (BV) of Alberta’s Rocky Mountains are all valued and continually increasing. The BV is also important for large mammals as it provides habitat for iconic species like grizzly bears to live in and move through. The Yellowstone to Yukon Conservation Initiative (Y2Y) is dedicated to balancing the needs of people and wildlife, and collaborated with stakeholders to recommend how to improve human-wildlife coexistence in the BV. Targeting one of those recommendations, this research project will help wildlife managers and stakeholders by developing a method for identifying areas of high-quality, undisturbed grizzly habitat. Land managers can use this information to manage human use in these areas accordingly. By establishing methods that help balance the needs of people with wildlife, Y2Y and the people of the BV continue to exemplify efforts to maintain wide-ranging populations both inside and outside of protected areas.

Violence and Smuggling Markets: State-smuggler interactions on the Brazilian border

Different states’ laws and law enforcement capacities meet in border areas. The legal and regulatory differences, the distinct police and administrative capabilities involved, as well as the degree of commitment of state authorities, create rent potentials that are central to border dynamics and the frequent instability that prevails in border regions. This has been traced to the smuggling of legal and illegal goods and to the peculiarities of military and police enforcement of the movement of people and goods. On average, border violence in Brazil from 2000 to the present has been higher than in the rest of the country and concentrated in municipalities with smuggling markets. However, in these markets there have been large changes in the levels of violence over time and across municipalities without obvious reasons for such variation. Those levels of violence, however, have been highly volatile over time. The proposed research thus seeks to understand why, and under what conditions smuggling markets become violent, focusing on the interaction of state authorities with non-state actors.

Développement d’un système d’aide à la détection et à la localisation des défauts présents sur les photographies aériennes par intelligence artificielle

Le projet proposé vise à développer un système d’aide à la détection et à la localisation des défauts présents sur les photographiques aériennes par intelligence artificielle, et plus particulièrement par l’utilisation des réseaux de neurones convolutifs. Ces derniers sont reconnus pour leur performance en vision par ordinateur, notamment dans la détection automatisée d’éléments fournis à travers des exemples d’entrainement. Le système d’aide à la détection proposé sera composé de trois architectures d’apprentissage profond complémentaires pour répondre aux problématiques rencontrées par la compagnie dans le processus d’analyse des photographies aériennes : (1) la détection des nuages et/ou des ombres ; (2) la détection du flou ; et (3) la détection de huit défauts majeurs connus lors de la construction des orthophotos et des orthomosaïques. Les algorithmes développés seront intégrés dans un système opérationnel qui permettra d’automatiser une bonne partie de la phase d’analyse et ainsi de réduire le temps et les coûts de cette opération pour la compagnie.

Assessing Equipment and Technologies for use in the Development of Dykelands and Upland in Atlantic Canada for Sustainable Agricultural Production

The research in this Master’s project is focused on documenting and analyzing the contemporary costs associated with maintaining and/or developing agricultural dykeland and the costs associated with the development of upland. It will also recognize the evolution of land development equipment and identify modern equipment and technologies appropriate to maximize agricultural potential on both dykeland and upland. Primary outcomes from this research will involve determining the financial cost of improving under-utilized dykeland and a return on investment using contemporary technology and innovative techniques versus traditional methods. This will involve researching, and potentially testing, cutting-edge technologies used in agriculture and dyke maintenance including, unmanned aerial vehicles (UAVs), structure from motion (SfM) software, remote sensing, topographical surveying, land development machinery and precision agriculture. These technologies will be subjected to a cost-benefit analysis to determine their efficacy in modern dykeland management.

Evaluation and development of a waste heat utilization strategy for the Hamilton Bayfront Industrial Area

The project deal with identifying the sources of waste heat recovery in the industries located in Hamilton Bayfront area and utilizing the available waste heat in either within the process of waste heat generator or utilizing the energy in any of the partner industry. The project also deal with harnessing the waste heat and utilizing it for district energy needs. The research of this project will have two benefits

1. Reducing GHG emission in Hamilton by reducing the emissions of industries operating in Hamilton Bayfront Area
2. Exploring potential for district heating in Hamilton City by utilizing low grade industrial heat for domestic heating water, Space heating and cooling. This will also result in GHG reduction for residential and commercial building operating within city of Hamilton

Chlorine dioxide as antibiofilm of surfaces in the extraction process of maple sap

This project proposes to expand the investigation of the use of chlorine dioxide (ClO2) as an antimicrobial agent in the sap extraction of maple syrup industry. It is well documented that chlorine dioxide exhibits antibacterial, antifungal, disinfectant, air sterilizer, antiviral, etc., properties. However, its mechanisms of action in specific applications, tolerable concentrations, toxicity etc., must be studied to understand its properties as antibiofilm, its impact on surfaces, materials time of life, and the consequences in the relation of surface damages vs bacterial growth.
This proposal describes our protocol of study and characterization of efficacy and safety of chlorine dioxide against biofilms, and the levels of surface degradation and traces of chlorine chemical residues in polyethylene (PE) and polyvinyl chloride (PVC) (pipes commonly used in this industry), in contrast with isopropyl alcohol as the antibacterial agent commonly used in this industry. Isopropyl alcohol has been shown not to be very effective against biofilms, which suggests an urgent call for the investigation of alternative methods since the demand for maple syrup increases year by year, and the quality is seen to be threatened and at risk by bacteria that affect the quality standards of this product.

SN-38 (or 5-FU) drug encapsulation in Iiposomes transported by magnetotactic bacteria for localizedcolorectal cancer treatment

Cancers are treated today with the appropriate combination of chemotherapy drugs, surgery

and radiation. Chemotherapy is almost invariably dosed intravenously, and enters the

systemic blood flow where it circulates around the whole body, coming into contact with

healthy cells as well as cancerous ones. This systemic dosing has two big problems: too little

drug gets to cancer cells, and too much drug comes into contact with healthy cells, causing

side effects. Frequently, these dose-limiting side-effects prevent us from delivering the

maximally effective anti-cancer drug dose.

Professor Sylvain Martel of Ecole Polytechnique de Montreal’s Nanorobotics laboratory has

pioneered an approach using special bacteria that can be directed by a magnE)tic field to

deliver chemotherapy directly to the site of tumors, reducing side-effects. With the support of

a team of co-investigators from McGill University (Drs. Te Vuong, Gerald Balisl, Maryam

Tabrizian, Nicole Beauchemin, Danuta Radzioch) and the Universite de Montreal (Dr. Louis

Gaboury),

Photo-curable Polysiloxane Resins for Engineered Ferroelectret Films

Polymer ferroelectrets are light weight and flexible materials that display piezoelectric behaviour. These properties make them ideal for use as transducers in the aerospace industry. However, the traditional methods used to prepare high performance polymer ferroelectrets cannot be used for large scale manufacturing due to poor reproducibility or high cost. Furthermore, these manufacturing techniques rely on commercially available materials that are not intended for use as ferroelectrets. Due to these manufacturing challenges, polymer ferroelectrets have not been adopted for widespread use in industry. To address these barriers, we propose a novel two part approach that consists of: (1) developing polysiloxanes tailored to enhance the piezoelectric behaviour of the films and that are compatible with vat photopolymerization and (2) using vat photopolymerization to fabricate polysiloxane ferroelectret films with a highly ordered internal structure. Combining purpose made materials with an advanced additive manufacturing technique will allow for the fabrication of high performance ferroelectret films at an industrial scale. This will enable the industry partner to manufacture electronic devices incorporating polymer ferroelectrets.

The impact of ore recovery uncertainty in geometallurgy

Mining systems are complex to model since they involve (1) creating adequate representations on how sub-models (mining processes) are interconnected (the ore/waste flowcharts) and (2) there is a lack of understanding of the impact that small variations have over the entirely system. Moreover, developing adequate models requires the use of modern predictive tools that account for high dimensional and big data information, for instance remote sensed geochemical ore composition and laboratory test responses. Although such digital twins intuitively seem the best option, is uncommon to find them used in real mine projects. Even more, it is still unusual to find mine projects developed under a unified mining system concept. The aims of this project are (1) to present and prove a suitable representation of mining processes, centered in the mine planning and design stage, and (2) to quantify the impact of small disturbances in the ore recovery prediction into the mine plan. By doing so, we deliver proof that uncertainty quantification in mining systems is feasible and profitable. Results will be presented in internal reports and conference articles.

Recurrent neural network modeling of working memory

The temporary maintenance of information is known as working memory, and it enables behaviour that we take for granted, such as remembering a phone number or avoiding an obstacle after turning off the lights. Despite its importance for everyday behaviour, the neural mechanisms underlying working memory are/remain unclear. While some single-neuron recordings in monkeys correlate with working memory performance in humans, it has proven difficult to connect these sets of data with existing network models for the human brain that/as they include thousands of neurons. Here, we propose leveraging recent advances in machine learning to re-analyze existing data and bridge this gap. We will develop a flexible cortical network model by training an artificial recurrent neural network (RNN) to perform a memory task originally completed by monkeys. Individual units of the RNN will be validated with single-neuron data, and the population-level dynamics of the RNN will be compared to mechanisms proposed by existing network models.