Projets novateurs réalisés

Development of Advanced Process Control for the Production of Specialty Nickel Powder Products

With the global movement towards electric vehicles (EVs) to combat pollution and climate change, one of the potential limiting factors for public embracement of the technology is the limited vehicle range and high cost largely due to the current state of battery technology. Battery makers require high-purity nickel in an appropriate physical form (e.g. powder), but the availability of such nickel is limited to a small number of producers in the world. This project investigates increasing the yield of nickel powder from a production process through the use of in-plant experimentation, modelling, and advanced process control and optimization techniques.

Feasibility analysis of printing a drug-eluting flexible antibacterial mesh for treatment of post-surgical infections

Surgical site infections (SSIs) are caused by germs after surgery. Germs such as Staphylococcus, Streptococcus, and Pseudomonas can infect a surgical wound through various forms of contact, such as from the touch of a contaminated caregiver or surgical instrument, through germs in the air, or through germs that are already on or in your body and then spread into the wound. The development of an SSI leads to a substantial increase in the clinical and economic burden of surgery due to the direct costs incurred by prolonged hospitalization of the patient, diagnostic tests, and treatment. Eupraxia Pharmaceuticals Inc. has developed a proprietary controlled?release system that reduces the side effects of intravenous injection of antibiotics. In this work, will perform a feasibility study on the use of 3D printing to manufacture patient-specific meshes using Eupraxia’s formulation. To achieve this goal, we will perform a series of studies on the characterization of different formulations of Eupraxia’s polymer to determine the best formulation that is printable using extrusion-based printers. We will also fabricate a costume-made 3D printer with components that are compatible with the used materials.

Rethinking Byzantine Fault Tolerant (BFT) Protocols in the Age of Blockchains

Blockchain technology is gaining an increasing amount of attentions in the past few years, partially due to the popularity of Bitcoin and other cryptocurrencies. It has the potential to profoundly disrupt a wide range of industries. Despite its great potential, today’s blockchain has several major hurdles. First, it can be slow. Second, it can be costly. Third, it has scalability issues. In this proposed research, we aim to create a blockchain testnet for evaluating different blockchain algorithms, such as Tendermint, Casper, Thunderella, Honey Badger, and Algorand. The testnet will not only serve as a fair means of comparison but also provide us with an in-depth understanding of design space and tradeoffs among different blockchain algorithms. The research outcomes are expected to provide the partner company with substantial insights into the security and performance of various blockchain algorithms.

Robust Inventory Routing Problem Under Uncertainty

We dedicate to inventory routing problems with uncertain demand, arising from supply chain systems. We determine replenishment times, quantities, and vehicle routes to serve customers. To predict customer demand, we use machine learning techniques to extract information from historical data, which is normally available in big data era. We use distributionally robust optimization method to build mathematical models and develop software packages that can directly be used by suppliers to plan their supply chain activities. The outcomes of our project can help suppliers effectively control inventories and allocate transportation vehicles, thereby optimizing resource allocations, reducing operation costs, and better serving customers. Furthermore, optimizing vehicle schedules makes a good environmental sense, because transportation produces most of the CO2 in supply chain activities.

Quantum Optical Experiment to Understand Black Holes

Black holes are very intriguing astronomical objects. They do not emit light, so we can only get indirect evidence about their existence. When it comes down to understanding how they change in time, things get more complicated. Stephen Hawking argued that black holes have a finite lifetime. The question that arises then is, what happens to the objects swallowed by a black hole and, in particular, the information carried by them when the black hole evaporates away? Does the information disappear? If so, this would be in contradiction with quantum mechanics, which preserves information. This is known as the information paradox and it is an unresolved problem in physics.

Our project focuses on an quantum optical process, parametric amplification, that is equivalent to the evaporation of a black hole. By using powerful lasers and special crystals we push parametric amplification to a new high-gain regime that might teach us where the information goes in black hole evaporation.

The aim of this overseas collaboration is to exploit the expertise of the German group in high gain parametric amplifcation and our theoretical knowledge on the black hole dynamics. TBC

BIM-based Component-level Construction Sequence Planning for Precast Concrete Building

Precast concrete building is one type of prefabricated construction, in which some of the building components are produced in factories and transported to construction site for assembly. Because it focuses more on individual building components rather than the whole floor or construction area, traditional construction scheduling based on the floor or area level is not appropriate for precast concrete building, considering the duration, cost and resource of the project. A component-level construction scheduling is then needed. This research aims to develop a Building Information Modeling (BIM)-based component-level construction sequence planning method. The scheduling required information of building components is extracted from BIM model and being used for generating a component-level schedule network, which describes the technical precedence relationships of components. This research lays the foundation for subsequent resource-constraint project scheduling for precast concrete building and will improve the construction management level of precast concrete building.

Investigating Gangliosides in Breast Cancer Subtypes by Imaging Mass Spectrometry

Breast cancer is the most common cancer amongst women. Early detection through mammography and new breakthroughs in therapy have significantly improved the survival rate. Nonetheless, in Canada, approximately one in eight women diagnosed will not live past five years. The study of gangliosides is one avenue to improve prognosis. Found on the cellular membrane, gangliosides help communication between cells and have shown to be dysregulated in many diseases. Previous studies have found links between certain gangliosides and breast cancers. More recent studies have even shown important nuances in the expression of gangliosides depending on the breast cancer subtype. This project will employ imaging mass spectrometry to corroborate these findings and provide information on the distribution and quantity of these gangliosides in breast cancer by subtype.

Relationship between Elastomeric Material and 3D Printing

Additive manufacturing (AM) offers designers more Degree-Of-Freedom (DOF) in design with fewer limitations and they can focus more on the intended functionality of a product. For example, functionality can be referred to assigned mechanical deformation by printing rigid and elastomeric materials. Soft robot, artificial heart, and customized shoe mid-sole are some functional products. One important foundation of the technique is that spatially distributing the build material differently can generate various mechanical properties of a part. Although the tensile strength and tensile modulus of an elastomeric material are generally provided in the material specification sheet, the 3D printing process will also affect the mechanical properties significantly. This is the PSPP relationship in material science, and it is crucial to understand this relationship at the very beginning. TO BE CONT’D

Encounters and tensions in the French Pyrenees: New herding practices and predatory behavior of the returned brown bear

This research analyzes the interaction between herding practices and bear predatory behavior in the Ariège Pyrenees (France). The LIFE program was launched in 1996 by the European Union (EU) to establish a permanent bear population in the Pyrenees through the introduction of Slovenian sub-species. As a direct response to this policy, shepherds have introduced new techniques of ‘regrouping’ sheep herds for safety, new shepherds have been hired, livestock-guarding dogs were reintroduced and fenced sheepfolds were installed with EU funds to prevent livestock loss. This research aims at bringing together environmental anthropology and behavioral ecology to conduct the first study in the Pyrenees regarding what environmental and social factors are conditioning predation and whether existent means of protection are effective to prevent bear attacks. It will contribute towards improving livestock protection policy outcomes, as well as informing regional policy debates in the EU regarding how land and livestock managerial institutions are emerging as tools to reshape the politics of the commons.

Using Machine Learning to Decipher Controls Water Quality

Water in the United States is a valuable resource. It is used from everything from drinking water to tourism. However, by the 1970s, many rivers and coastal areas were severely degraded from decades of using water to assimilate pollution. After billions of dollars spent and decades of effort on all levels of government, the increasing intensification of the agricultural system and the growing population is still putting U.S. water quality at risk.

Specifically, in many areas, nitrogen concentration trends are not responding to mitigation attempts and are remaining flat or continuing to increase. The overall objective of this work is to quantify water quality trends in rivers across the U.S. and determine how the landscape, climate, or land management may be controlling river nitrogen concentrations. We will be using novel methods in machine learning to find the patterns in river concentrations across the U.S. and determine what is controlling the concentration trends.

This information is crucial for those trying to improve water quality in the U.S. at a national or local level. The first step to addressing water quality issues is to understand how and why water quality has changed over time. TBC

Resolving membrane protein dynamics and self-association characteristics by fluorescence fluctuation analysis

This project aims to exploit an analysis of cellular and molecular dynamics which focuses on understanding protein-protein interactions in living cells. It will focus on the development and application of a suite of novel single molecule super-resolution imaging strategies that will address fundamental questions surrounding how the association of specific membrane proteins drives localization and function. The project contains a robust suite of fluorescence fluctuation analysis tools, including Numbers and Brightness, Spatial Intensity Distribution Analysis (SPIDA). These tools will be complemented by imaging total internal reflection FCS (ITIR-FCS), an approach well-suited for examining surface-specific interactions and spatial variations in molecular diffusion and dynamics on millisecond time scales ). As for expected outcomes, it is supposed to detect actin and ezrin rate on membranes as well as the distribution in different intensity areas. Furthermore, the project would be the basis for mapping the spatially heterogeneous diffusion characteristics in live cells.

A Novel Gan-Based Solar DC-Optimizer

Le marché de l’énergie solaire a récemment connu une croissance exponentielle en raison de l’augmentation de la demande énergétique et des enjeux environnementaux. Dans ce programme de recherche, un système DC-Optimizer sera conçu et développé pour exploiter la puissance maximale des tuiles solaires installées sur les toits des bâtiments résidentiels de manière efficace et rentable. Ce programme de recherche a une grande valeur pour la fourniture de panneaux LED afin d’acquérir une part de marché significative et d’obtenir plus de revenus. Ce programme de recherche peut faire de DEL Sign la meilleure entreprise dans les futurs systèmes solaires avancés.