Innovative Projects Realized

Acylketenes in Catalytic Cycloaddition Reactions

The reactivity of acylketenes in carbon-carbon bond forming reaction has been scarcely explored, despite their ease of access and stability. The student funded through this program will investigate the reactivity of these unexplored species in intramolecular transition metal-catalyzed cycloaddition reactions.

These studies will establish the reactivity of acylketenes in cycloadditions, and reveal novel reactivity pathways, leading to tunable catalytic carbon-carbon bond forming reactions, and allowing to access a diversity of structures by simply modifying the catalysts/ketenophiles. The resulting synthetic strategies will provide expedient entries into an array of complex carbocyclic frameworks found in biologically active natural products and pharmaceuticals. During the course of this project on the exploration of the synthetic potential of acylketenes, the student will receive an excellent theoretical and practical training in synthesis and catalysis.

Feasibility Study of Wave Energy Conversion with Grid Connectivity

A renewable energy source that has not received much attention is tidal and wave energy, although numerous studies have concluded that wave power, and to a lesser extent tidal power, could contribute massive amounts to the overall energy picture. This project aims to explore the feasibility of tidal/wave energy absorption and its storage in the form of on-shore energy bank and using that to power on shore devices/generators. The primary activities in the proposed R&D activity include developing a proof-of-concept system in the lab as a pilot system for performing certain experiments using a hardware-in-the-loop platform. The lab-scale system can help to identify feasibility and performance of a scaled-up system under more practical conditions and the possibility of commercialization. The concept to be explored is an offshore surface floating foil that acts as a buoy, which acts to raise and lower a mechanical arm located at a nearby onshore pivot point.
This study will produce proof-of-concept results and help the team and the partner organization, Greenergy, in making commercialization decisions on the developed technology that has desirable features such as high-efficiency, economic viability, and environmental friendliness.

Market making for digital assets

Market makers facilitate trading in electronic financial markets by simultaneously offering to buy and sell the same asset at any given time. Their role is to provide price stability and increase market liquidity to improve its overall efficiency. Digital assets markets are extremely fragmented and present both challenges and opportunities for market makers. These latter must offer participants accurate prices while balancing their asset inventory on many venues at the same time, what represents a difficult synchronization task. At the same time, this complex environment leaves room to arbitrage opportunities, which market makers can use to offset inventory risk. The project consists of developing dynamic programming and reinforcement algorithms that take advantage of this environment to improve the profitability of market makers while reducing their risk. To achieve this goal, a large volume of historical data is used in conjunction with a machine learning-based market simulator.

Leveraging Deep Learning in Asset Pricing in a Multi-Factor Modelling Framework

The purpose of this project is leverage Machine Learning technology to develop and test effective trading strategies in order to properly hedge an investment strategy. Hedging is an integral part of the investment process and allows portfolio managers to protect their positions against any adverse change in asset prices. As it is a task that requires solving a range of highly complex problems, using Artificial intelligence is proving to outperform traditional techniques currently used, especially when it comes to decreasing costs and improving returns.
By optimizing the effectiveness of the trading models, this project will allow Quantolio to integrate a superior hedging technique in its final product (platform). As a result, the organization will be able to offer solutions that help portfolio managers enhance their investment strategies.

AI decision support tools for optimizing alfalfa yield and nutritive value

Alfalfa forage is the queen of forages in Canada, both for its nutritional value and for its global distribution across Canada. However, alfalfa yield and nutritive value are affected by multiple environmental and agronomic factors. The interaction among all of those factors makes it difficult for producers and field advisors to determine which of these leads to poor yields and how better management practices can improve yield. Producers and field advisors do not have adequate tools to take all of those factors into consideration. During this project we will develop a decision support tool targeting specific actions for improving yield and forage nutritive value from the Alfalfa field by integrating agrarian factors with artificial intelligence. This project will develop a Diagnostic and decision support tool using machine learning integrating proximal and remote data collected in the field to 1) Identify potential environmental and soil-related factors affecting alfalfa yield and nutritive value derived from the nutritional value of forage samples; 2) Predict potential yield and nutritive value loss derived from soil nutrient analysis related to actual conditions in the field.

AI-driven decision support tools for alfalfa’s winter survival and persistency

Alfalfa forage is the queen of forage in Canada, both for its nutritional value and for its global distribution across Canada; however, its weakest feature is poor winter survival and persistence. This parameter is affected by a multitude of environmental and management factors making it difficult to understand the reasons for this low persistence. This project will develop a Diagnostics and decision support tool using machine learning integrating proximal and remote data collected in the field to allow producers and field advisors to scout using drones, to understand the reason for the problem and to identify the best management practice to adopt for improving winter survival and persistency. The tool developed during the project will be accessible under cloud services to all producers and field advisors in Canada and will result in increased productivity, profitability and a reduction in the environmental footprint.

Développement des capacités d’assemblage des robots collaboratifs

Ce projet vise à mettre au point des techniques inédites pour résoudre le problème de faire de l’assemblage complexe avec un robot collaboratif. Dans le domaine industriel, les lignes d’assemblage sont composées de robots industriels. Les trajectoires que doivent faire ces robots sont calculées et codées minutieusement. Des senseurs particuliers sont installés, sans compter l’infrastructure les entourant. Cette intégration est très lourde, ainsi ces lignes ne sont ensuite pas modifiables aisément. Pour de plus petites séries et pour ajouter de la flexibilité, il serait utile d’utiliser des robots collaboratifs. Ils nécessitent une infrastructure plus légère, ont des programmes facilement modifiables et peuvent cohabités avec des humains. Il est donc nécessaire de développer les capacités d’assemblage des robots collaboratifs se trouvant dans l’industrie pour permettre leur utilisation dans les opérations d’assemblage complexe. Il peut s’agir par exemple de prendre des pièces, de les joindre ensembles puis de les visser.

Portable Device for COVID-19 Nucleic Acid Tests

We have seen the recent surge of COVID-19 because we cannot afford another massive shut-down. A portable and timely detection device for the COVID-19 nucleic test is more desirable. In this proposal, we propose to develop an impedance-based nucleic acid testing device. Our LOC devices are low cost, user friendly alternatives to centralized lab tests. By partnering with Hidaca Ltd., our ultimate goal is to have this made-in-Canada technology available on the market as soon as possible to benefit Canadians and the Canadian economy.

Evaluation, Analysis and Design of Flood-related Climate Change Adaptation Policies for Coastal B.C.

Under the 2010, Preparing for Climate Change: British Columbia’s Adaptation Strategy (Ministry of Environment, 2010) the Government of British Columbia has been tasked with integrating aspects of adaptation planning into their policies, legislation and regulations. By evaluating the policies and programs that the Government of B.C. has legislated and implemented to date, this internship will assess the preparedness and resiliency of the coastal communities of the province. In collaboration with the Arlington Group, the intern will focus on a qualitative analysis of current flood management policies and programs within the Government of B.C., designing flood management policy alternatives when existing policies hinder adaptive measures, and then exploring the effects of flood-related climate change adaptation policies on spatial planning. Through these three broad steps, this internship will help to evaluate, and then support, flood-related policies relevant to coastal communities in B.C.

Modelling and Assessment of Cloud Based Smart Dual Fuel Switching System (SDFSS) of Residential Hybrid HVAC System for Simultaneous Reduction of Energy Cost and Greenhouse Gas Emission Under Smart Grid

The objective of this research is to develop models to assess potential benefits of cloud-based Smart Dual Fuel Switching System (SDFSS) of the residential hybrid HVAC system of electric air source heat pump (ASHP) and natural gas furnace/boiler (NGFB) for simultaneous reduction of energy cost and greenhouse gas (GHG) emission. It will entail detailed modelling, simulation, and optimization of three different well studied residential houses in four regions of Ontario to assess the potentials of such smart cloud-based supervisory control under different Time-of-Use (TOU) electricity prices and federal carbon tax schemes in terms of maximizing energy cost saving and GHG emission reduction while providing a flexible and ubiquitous mechanism for utilities (both electric and natural gas) to better manage their infrastructure for distributed renewable energy generation and load management under the smart grid framework. The aim of this project is to answer the following research question: “How effective are the SDFSSs in different cold climate cities, and how does it perform in future high-carbon pricing scheme from a GHG emission and economic perspective?”

Moisture accumulation in a cavity-insulated thick-wall assembly with an exterior air barrier system as a result of natural convection in cold climates

The intent of the proposed research project is to measure the performance of a highly-insulated wall assembly system when an exterior air barrier system is used and air movement within and across the wall occurs. In cold climates, the movement of warm, moist air within and across an exterior wall may result in moisture accumulating on some of the surfaces within the wall if the conditions allow, causing long-term damage if it does not dry out. The proposed research will study the amount of moisture that accumulates within a high-performance wall under specific parameters to provide valuable information to the building industry and improve the long-term performance of energy efficient buildings. RDH Building Science is an important partner in this project and will help in the dissemination of the results of this study, contributing to the success of the success and continued growth of the provincial and national building industry.

Development of Low-footprint Brine Remediation Technology at Melville Compressor Station

Brine water is often produced in mineral extracting processes. It can cause damage to soil and biota when being spilled during storage and transportation. The damage can last for decades. Even after a mineral extracting process is closed, the damage of soil structure and composition would still not be recovered. The long-lasting devastation may cause irreversible environmental impacts and huge economic losses. Therefore, it is necessary to develop low-footprint on-site remediation technologies.