Projets novateurs réalisés

Mechanical characterization of phage-coated implants for the prevention and treatment of periprosthetic joint infections in high risk patients

Caused by planktonic and biofilm drug-resistant bacteria on implants, periprosthetic joint infections (PJI) is one of the most devastating complication in orthopedics and is in line with forecasted rise in joint replacement. From the perspectives of patients, surgeons, hospitals, and health care system, PJI thus present a great unmet medical need, resulting in high morbidity, and even mortality, among affected patients. Therefore, clinicians would find invaluable a technology with a potential to manage PJI on implants. With the rise of antimicrobial resistance (AMR), a new technology to prevent or treat PJI would be invaluable.

Modélisation d’un pont à thyristors dédié à l’excitation des machines synchrones dans le référentiel qd0

Les systèmes de production d’énergie basés sur l’utilisation de la force hydraulique utilisent une machine synchrone afin de convertir l’énergie mécanique en énergie électrique. Pour que ces machines fonctionnent, il faut fournir une puissance d’excitation sur la partie tournante de la machine (que l’on nomme système d’excitation.) Ce système d’excitation peut produire des courants électriques de grande intensité variant de 500A à plus de 3000A. Afin de contrôler ces courants, un convertisseur de puissance est requis. Il est proposé dans ce projet de recherche d’effectuer une modélisation de ce convertisseur afin de bien connaître ses réactions à diverses perturbations.

Development of a numerical model for the simulation of fluidized bed reactors

This project concerns the development of a 3D virtual model of a fluidized bed gasifier reactor. Relying on the so-called CFD-DEM approach and supercomputer technology, the virtual model will allow visualization of the fluid dynamic inside the reactor, highly valuable insight otherwise unreachable by physical mean. This project specifically focus on model calibration and validation as well as coupling heat and mass transfer to the model. A series of lab benches will be operated and modeled in order to quickly identify various model parameters before application to actual reactor. This project will help Enerkem engineers and scientists to better understand reactor performance sensitivity to design parameters and subsequently optimize the technology.

Multi-morbidity Characterization and Polypharmacy Side Effect Detection for designing Optimal Personalized Healthcare with Machine Learning

Despite a significant improvement in healthcare systems over the past decades, the rapid growth in the number of patients with multiple chronic diseases – called multimorbidity – stands as a complex challenge to healthcare services that are primarily designed to treat individuals with single conditions. Advances in machine learning as well as in computing power now enable us to exploit a vast amount of healthcare data. The main goal of this project is to propose a data-driven approach to characterize patients with multimorbidity in such a way that an optimal care can be given to each of them, using machine learning techniques. The project will use the ICES (Institute for Clinical Evaluative Sciences) dataset, Ontario public health data that is completely anonymized and collected from 1992, containing information on around 15 million Ontario residents. TO BE CONT’D

Testing and applying machine learning techniques in monitoring and detecting anomalies in membrane cell electrolyzers at R2.

In this project we aim to develop a computer software system that is capable of predicting anomalies in membrane cell electrolyzers before they arise. It will closely monitor the electrolyzers’ operating condition and identify the hints that suggest that a failure is coming down the line. We will then notify the plant operators, so they can plan the preventive replacement of the soon-to-break equipment without causing damages. This system will be part of the services offered by the partner organization and will help them maintain their leadership as market experts and innovators.

Applying Machine Learning to Predict Demand Transference

The project will help us design a machine learning model that can determine the demand transference of our customers. The key objective of this project is to design, research, build, and experiment with machine learning models to ensure low product waste and high customer satisfaction. The model will have several impactful applications across the organization.

Brown Builds: Optimizing Build Performance and Comprehension

Modern software organizations use continuous integration (CI) practices to build and test their products after each code change in order to detect quality issues as soon as possible. Unfortunately, the number of builds scales super-linearly with the number of hardware and feature configurations that should be supported. In order to avoid running out of build resources, organizations are no longer able to build individual code changes, but instead need to build groups of successive code changes. Worse, certain “flaky” tests executed during a build lead to inconsistent results, i.e., not every failure is a real failure. This project aims to prototype and evaluate approaches for (1) early detection of flaky build results and (2) reduction of build volume by grouping related code changes.

Productivity and Risk Analysis for CIPP Projects

The Cured-In-Place Pipe (CIPP) method is a trenchless technology for curing and rehabilitating the

existing pipelines. In CIPP method, the damaged pipe is lined inside, using a resin-impregnated

polyester tube. However, CIPP projects can be complicated due to the inherent uncertainties associated

with the underground projects. The lack of knowledge about the production rates and unanticipated

issues (risks) associated with CIPP projects can affect the project’s costs and time schedule in different

stages of planning, design, and construction. To avoid the unnecessary costs and delays, a

comprehensive risk and productivity studies are required. The present study aims at improving the

CIPP administration and planning system by focusing on risk identification and productivity analysis

for CIPP projects. Field observations and analysis of the historical data are planned to obtain the

needed information. The result of the study would be a classified check list of risk factors that

associated with CIPP and general work process rate (production rate) guide that is applicable………….TBC

Micro-fluidized bed reactor: gas phase hydrodynamics simulation

Fluidized bed reactors are related to different industrial applications such as petroleum process, synthesis of chemicals, waste treatment and coating of solid particles. Engineers select fluidized beds for catalytic reaction that are highly exothermic, endothermic or explosive. Scale up and back mixing of the gas are the main challenge of this type of reactor. Residence time distribution (RTD) analysis is a technique that allows to understand the behavior of the gas phase inside the catalytic bed and thus making the kinetic modeling as well as the scale up possible. This project focus on RTD analysis in an 8 mm internal diameter micro reactor with two different catalysts (sand and FCC). The first part of this studied consisted to analyze the behavior of the gas phase with tests in the laboratory. Then, two different computational simulations will be performed. Experimental data will be inserted in the simulation to confirm the veracity of the analysis.

Statistical Learning for Financial Time Series

Given a time series of returns for a portfolio of financial instruments, develop a model that accurately predicts returns which maximize profits. The objective function will take an input of financial indicators from the previous time interval and the returns from the current time interval. These indicators can explain relationships between financial instruments in the portfolio of interest, thus are important for explaining their returns and associated risk. A common challenge with these types of problems is how easy it can be to over-fit your model. In this project, we seek to explore state-of-the-art machine learning models to determine a model with high prediction accuracy that generalized well to unseen data.

Improving Coded-wire tag sampling and submission adequacy through better understanding of barriers.

Salmon stock in BC have declined substantially since the 1990’s. A US/Canada Coded Wire Tag (CWT) fishery monitoring program was implemented in the 1980s with the intention of helping managers understand salmon stocks in order to make decisions about harvesting. The program relies on the fishing community return salmon heads so managers can retrieve CWT information. Currently, the return rate is not meeting requirements to provide managers with enough data to base management decisions. Participation from the non-commercial fishing community in monitoring programs like the CWT program are crucial for sustainable fisheries management. While educating the community of why participation is important is important, it is also important to understand their values because values are often the source of what makes people act in environmentally responsible ways. In order to enhance participation in the CWT program, it is necessary to understand the community in terms of motivation, values and potential barriers to participation

Evaluation of an integrated heat exchanger in a sewage pipe

Increased usage of renewable energy is required to reduce climate change. Wastewater from housings release a vast amount of energy which is lost to the soil. The @Source-Energy pipe system is a new type of a sewage water pipe which has a heat exchanger pipe in its concrete. In this proposed research, the main objective is to create methods to evaluate the efficiency of the @Source-Energy pipe system under changing weather conditions. A numerical model will be used to simulate the heat flow during heating phases of the @Source-Energy pipe system for a whole city quarter. The results will determine the feasibility of installing the @Source-Energy pipe system in sewage pipelines in the Canadian Prairies which could reduce greenhouse gas emissions and better protect the environment.