Innovative Projects Realized

Towards the Development of a Prognostic tool for Harmful Algal Blooms – Year two

The Laurentian Great Lakes and many Canadian inland waters have experienced a resurgence of cyanobacteria-dominated harmful algal blooms (cHABs), which negatively impact recreational uses, aesthetics, taste and odor in drinking water. The presence of toxins can also have dire repercussions on aquatic wildlife and human health. The factors that influence the occurrence and magnitude of algal blooms and toxin production (e.g., nutrient enrichment, climate change) vary in space and time and are poorly understood. Thus, our ability to predict cHABs is currently limited and represents a major challenge for the management of our water resources. Founded upon cutting-edge machine-learning and Bayesian inference techniques, this research project aims (i) to identify the factors that regulate the occurrence of cHABs; (ii) to provide predictions of cHABs under different land-use and climate change scenarios; and (iii) to obtain a probabilistic mapping of areas around the Great Lakes that are characterized by an excessively high risk of cHAB formation. To showcase this modelling framework, the intern will use data collected from the Bay of Quinte (Lake Ontario, Ontario, Canada), a system that has been experiencing water quality issues, and where the elimination of cHABs represents one of the major challenges of eutrophication management.

Towards the Development of a Prognostic tool for Harmful Algal Blooms

The Laurentian Great Lakes and many Canadian inland waters have experienced a resurgence of cyanobacteria-dominated harmful algal blooms (cHABs), which negatively impact recreational uses, aesthetics, taste and odor in drinking water. The presence of toxins can also have dire repercussions on aquatic wildlife and human health. The factors that influence the occurrence and magnitude of algal blooms and toxin production (e.g., nutrient enrichment, climate change) vary in space and time and are poorly understood. Thus, our ability to predict cHABs is currently limited and represents a major challenge for the management of our water resources. Founded upon cutting-edge machine-learning and Bayesian inference techniques, this research project aims (i) to identify the factors that regulate the occurrence of cHABs; (ii) to provide predictions of cHABs under different land-use and climate change scenarios; and (iii) to obtain a probabilistic mapping of areas around the Great Lakes that are characterized by an excessively high risk of cHAB formation. To showcase this modelling framework, the intern will use data collected from the Bay of Quinte (Lake Ontario, Ontario, Canada), a system that has been experiencing water quality issues, and where the elimination of cHABs represents one of the major challenges of eutrophication management.

Politique linguistique & Valadas Occitanas : l’analyse d’un occitan périphérique

Ma recherche de maîtrise effectuée dans le Massif Central (France) avec le dialecte nord-occitan auvergnat, s’est appuyée sur un terrain en terres marginales de l’Occitanie (extrémité nord) et sous l’influence d’une politique française unilinguiste. J’aimerais ici réaliser une analyse comparative avec d’autres régions marginales d’Occitanie. En cela, les vallées occitanophones situées en Piémont (Italie) forment un terrain d’étude fascinant : elles disposent d’une politique linguistique représentée par le gouvernement italien, avec un statut semi-officiel pour l’occitan. La diglossie est de plus un phénomène sociolinguistique connu de longue date en Italie, qui s’oppose à la position française centralisatrice.
Je m’intéresse donc aux concepts de statuts en politique linguistique, à la standardisation, à la dialectologie et aux zones dites « marginales » d’un espace régional et linguistique occitaniste dont les frontières, structures et ramifications, apparaissent toujours discutables et modifiables. L’objectif sera donc de réaliser des observations dans les communautés qui pratiquent la langue (lieux publics, évènements socioculturels, pratiques éducatives, etc.) et d’animer quelques entretiens avec des “représentants” de la culture ou de la vie linguistique locale. Ces données permettront de confronter les données déjà obtenues en Massif Central, TBC

The influence of cloud-technologies and other technological advance on the print industry

Cloud-based technologies are being used more and more in industry to benefit from the large data-sets that are being created so these data-sets can be analyzed to get a better overview on the effectiveness of processes and how to optimize current processes. The print industry is starting to see the benefits of cloud-based technologies. Since the print industry is just starting to use cloud-based technologies this project will analyze what is currently offered and assist with developing further cloud-based solutions that will enable the print industry to become more efficient in the utilization of their equipment, analysis their workflow systems, and leveraging automation through the computing power of cloud-based technologies.

A low complexity face recognition for consumer devices

Due to the rapid growth of consumer grade devices and corresponding application market, the incorporation of

vision capabilities into embedded systems has gained significant attention from researchers lately. Similarly to

the human visual system, embedded computer vision systems analyze and extract information from visual

content in a wide variety of products. Face recognition has been one of the most successful applications in this

field. A cost effective implementation of reliable face recognition (FR) solutions can be useful for a wide range

of applications, such as identity authentication, entertainment, and content based retrieval system. However,

embedded system based face recognition solutions often suffer from not only common problems such as

variation in illumination (pose variation does not appear to be a problem for face recognition at a short distance),

but also relatively low quality input face images, and limited computational resources. Although many

researchers have attempted to develop robust FR algorithms, relatively few initiatives have been undertaken to

adapt FR solutions………………………………………

Fly-By-Wire INDI-Based Generic Control Laws for Flexible Civil Transport Aircraft

On the one hand, new generation of civil transport aircraft can present aeroelastic coupling between flight mechanics and structural dynamics. The lower-frequency flexible dynamics can be perceptible by a fly-by-wire (FBW) controller. This requires control law design that takes into account the flexible dynamics.
On the other hand, recent developments on feedback linearization by means of Incremental Nonlinear Dynamics Inversion (INDI) allow considering the design of generic control laws that can be applied to several aircraft with a minimum effort of adaptation and tuning.
Bombardier developed linear and nonlinear civil transport aircraft dynamics models that take into account the flexible dynamics. Bombardier also developed a prototype INDI-based CLaws with promising results demonstrated on a rigid aircraft model. This project aims to improve this prototype CLaws so that it can also manage the aircraft flexible dynamics.
Based on literature review and previous Bombardier researches, the student will conduct a conceptual design of these enhanced INDI-based CLaws functions and will validate the proposed solution by simulation using Bombardier high fidelity simulator and performance assessment tools.

Machine learning-based data analysis for cancer targeted gene panels

Cancer develops from the accumulation of mutations in key genes, which drive disease progression in individual patients. Currently, cancer genomic analysis results in the reporting of dozens to hundreds of mutations and other genomic alterations, without providing any indication of which genes are the most functional, and relevant for the survival of the cancer cell.
In this project, we aim at performing a comprehensive targeted gene sequencing of DNA and RNA extracted from over 60 cancer patient samples. Our main goal is to develop a bioinformatics pipeline to perform variant calls on the sequencing data, which will be used to generate reports on functional oncogenes and effective targeted drugs in individual cancer patient samples.
Our main methodology will utilize Artificial Intelligence techniques for Deep Machine Learning to understand the contributions, interactions, and relationships among the genomic variants. We will validate our findings by using existing large-scale cancer specific genomic databases.

An Integrated Multi-Case Study Approach to Advancing Business-to-Business (B2B) Online Marketing Strategies

With the growth of the internet, online and social media marketing have replaced tv and print advertising. Many companies have become extremely savvy in the use of online marketing tools to reach consumers. But how should online marketing be used when a company is working within a business-to-business context? Online marketing techniques often do not apply in this context. Developing appropriate strategies for business-to-business companies are important as they will drive brand recognition, acquire new customers and create significant financial returns for the company.
Through a multiple sector approach, this study will create a new series of best practices for how business-to-business companies can use online and social media marketing tools for their context

Modular quantum efficiency devices

Quantum efficiency (QE) test equipment becomes abundantly important in numerous applications such as solar cells devices, charge-coupled devices (CCD), photodetectors, both in visible range and in infra-red (IR) spectral range. Although QE tests are becoming prevalent, every application has specific requirements to the excitation source, photovoltaic devices or instrument geometries. Sciencetech Inc. is a global designer and manufacturer of optical spectroscopic instrumentation, for industry and academia. This project will focus on developing modular QE test system, with possibility to exchange different components and extended detection capability in short-wave (SWIR) range. A postdoctoral researcher, working in collaboration between Western and Sciencetech, will use her expertise in growth of the multilayer thin film structures at Western to test new developed structures and to incorporate them into SWIR detector technologies at Sciencetech. She will work on developing a scalable visible-to SWIR device directly integrated on silicon platform. This technology provides a potential for lower cost and larger size QE systems operating in a broader wavelength range.

High Throughput Vacuum Chamber

Building a high throughput vacuum chamber for our group’s transient grading spectroscopy (TGS) project, which uses picosecond, ultrasonic laster acoustics to probe the sub-surface, thermo-elastic properties of materials. We are particularly interested to study the thermal diffusivity of material under irradiation, which requires probe depths of single microns conducted in vacuum. Our current chamber is small, fragile, and restricted in sample size & throughput. We often need to analyze many similarly irradiated materials in vacuum, and having a larger, optically transparent vacuum chamber to do so will greatly increase the throughput of out experiments. Once completed, it will be used to study the performance of alumina coatings on Zircaloys as potential accident tolerant fuel cladding for light water reactors, in addition to comparing the changes in thermal transport in irradiated SiC and FeCrSi alloys under ion and neutron irradiation.

Magnetic field measurement with Thomson Scattering

Counter-streaming plasma has been featured in many experiments due to its prevalence in astronomy around supernova shocks, as well as in other lab plasma applications. These plasmas develop magnetic fields which are interesting due to their ability to accelerate particles as well as their roll in the formation of collisionless shocks.
These fields are normally measured by proton radiography, where the deflection of protons projected through the plasma is measured to infer the field strength of the whole plasma. Thomson scattering, however, gives information about the plasma at a small location, depicting the characteristics of an individual filament. We show measurements of magnetic field strength from the Thomson scattering spectrum, and asses the capabilities and limitations of the Thomson scattering spectrum.

Development of nanoscale and microscale delivery vehicles for plant immune aids using protein coacervation and spray drying – Year two

Modern pesticides have revolutionized agriculture. However, growing concerns with pest resistance, environmental run-off, and a lack of capacity to deal with abiotic stresses associated with drought or extreme heat (critical issues with regards to climate change) demand new solutions. Suncor has developed new plant immune aids that address these challenges by fortifying the immunity of plants, with demonstrated efficacy in crop models. However, current formulations suffer from low bioavailability and low leaf retention, resulting in the need for frequent and costly re-applications. In response, in this proposed Elevate PDF, food proteins and carbohydrates will be used to encapsulate plant immune aids on both the microscale and the nanoscale using scalable and inexpensive complexation techniques. The encapsulation, degradation profile, stability, and cellular uptake of the delivery systems will be assessed. Furthermore, the transport of the developed particles through the waxy leaf barrier will be optimized by modifying the surface hydrophobicity and charge of the complexes. TO BE CONT’D