Mapping the surface flow velocity of Minas Passage using RADAR data

This project will investigate the use of RADAR data to estimate the ocean surface velocity in regions of interest, specifically where tidal turbines will be deployed in Minas Passage, Bay of Fundy. The Fundy Ocean Research Center for Energy (FORCE) currently owns a single RADAR on the North side of the Minas Passage. Initial investigations have been done with this single RADAR; however, more intensive analysis must be done to reach the long-term goal of having a network of RADARs in the area.

Prediction of Insurance Coverage and Wait Times

The purpose of this project will be to develop a flexible and statistically sound methodology for leveraging BPI’s database of current and historical publicly available coverage information to model coverage trends in the U.S. health insurance industry. The project will also include the development of an R Markdown template for future predictive analytics reports. The templates and methodologies developed for this project will be integrated into BPI, Inc.'s custom consulting division.

Optimization tools for short-term hydropower generation management

Short-term hydropower optimization models are used on a daily basis to dispatch the available water for production between the turbines of the power plants that compose an hydropower system. Rio Tinto owns and operates power plants in the Saguenay Lac-St-Jean region of the province of Quebec and is currently lacking efficient tools to help the engineers in the daily decision making for the management of their hydropower system. The objective of this project is to develop tools to solve the short-term optimization model and therefore improve the water productivity of the hydropower system.

Evaluation of Measures to Control and Prevent Clostridium difficile Infection

Clostridium difficile infection (CDI) has become the leading cause of hospital acquired nosocomial diarrhea worldwide. The prolonged hospital stays associated with CDI has enormous impact on the healthcare systems in terms of costs and patient outcomes. While treatment of CDI is an important area for ongoing research, prevention efforts will need to be enhanced to interrupt CDI transmission.

Development and validation of a machine learning predictor for the early detection of prostate cancer

Prostate cancer is the third leading cause of death from cancer in men in Canada. However, prostate cancer is highly treatable if diagnosed early. Unfortunately, due to lack of cost-effective and meaning test detecting the early presence of the cancer. Most prostate cancer (92%) are found when the career is spreading to nearby organs.

Development of a hybrid seismic data inversion method for determining well-drilling location at complex geophysical area - Year two

Due to the current economic downturn, especially the lower crude oil price, the drilling success rate become the most important goal for any oil/gas company. For a start-up company, any failure in drilling will be a disaster. To this end, the Deep Treasure Corp wishes that through the combination of mature hydrocarbon prediction techniques and new research results in seismic inversion, the success rate of hydrocarbon prediction, the theoretical basis for well placement can be provided in Roncott field, which will improve the success rate in drilling.

Data Analytics for Social Network Marketing

Influencer marketing is a new and innovative way for brands to target their customers on social media in a highly accurate and trusted way. Brand partners work with hundreds of influencers over a period of time, which is called a campaign, to create marketing material. This marketing material is shared by both the brand and influencer to the audience of the influencers, who are followers on social networking platforms such blogs, YouTube, Instagram, and Facebook. A key challenge in influencer marketing is to identify influencers with the greatest social networking reach.

Development of a model for computational sea ice monitoring

The proposed research project focuses on the development of a novel model for the computation of sea ice parameters in near real- time relying on satellite data. The interdisciplinary team will investigate solutions for high performance computing to monitor sea ice and calculate ice parameters with the high spatial resolution. This project includes R&D activities in sea ice modeling, calculating parameters of ocean interaction with sea ice and designing algorithms for satellite data processing and analysis.

Testing and applying machine learning techniques in monitoring and detecting operating modes and faults of a membrane cell electrolyzer online and in real time at R2

The production of Chlor-Alkli by using electrolysis of aqueous solutions of sodium chloride (or brine) is one of the largest industrial scale electro-synthesis worldwide. Plants with more than 1000 individual reactors, in which 0.2 mm thin membranes separate chlorine and hydrogen, are common. This process is quite sensitive and any wrong operating conditions can cause irreversible damages. The most common accident associated with this industry are fire, explosion and toxic gas releases that can cause fatalities and long term health impact on the exposed population.

Uplift models extension for smart marketing

Insurance companies heavily fund marketing campaigns such as, for instance, customer retention or cross-sell initiatives. Uplift modeling aims at predicting the causal effect of an action such as medical treatment or a marketing campaign on a particular individual by taking into consideration the response to an action. Typically, the result of an uplift model is used to call customers for marketing some products based on important attributes of a customer.