Innovative Projects Realized

Understanding the Mechanism of Probiotics for Human Health

DoseBiome has developed a new probiotic that targets several aspects of oral health: dental caries, periodontal diseases, and halitosis. This project aims to understand the exact mechanism of our probiotic’s inhibitory properties against the pathogens associated with the above diseases. DoseBiome is a new start-up in the probiotic/microbiome health sector, and this project will allow for improved technology and manufacturing processes for our oral probiotic line.

Identification of blood-based biomarkers predictive of pulmonary exacerbations in cystic fibrosis

Individuals with cystic fibrosis experience recurrent episodes of worsen in respiratory symptoms, termed pulmonary exacerbations (PEx). Early identification of individuals who are at elevated risk of PEx can improve their clinical outcomes and rescue their lung function. In this project, we will collaborate with the Prevention of Organ Failure Centre of Excellence (PROOF) to develop a simple blood test to predict the PEx in CF individuals. We will also evaluate the genetic influence on blood biomarker candidates during the project and refine the blood test based on these genetic variants. Our interactions with PROOF Centre will directly benefit their organization by providing proof-of-concept of their goal, to develop blood-based biomarkers and move towards personalized medicine.

Prediction and Mitigation of Drillstring Vibration

An oil well drillstring is a slender slru(.;ture whidl vibrales in 3 major coupleu moues. Unwnlrolkd

vibration of the drillstring interferes with the drilling procedure. Tool and bit failure, improper

borehole quality and lower penetration rates are examples of the side effects of unwanted vibration of

the drillstring. Prior research by others has attempted to model the drillstring under different effects

and interactions. However, due to the nonlinear characteristics of the combined vibration modes, and

interacting loads such as bit-formation interaction and contact with the wellbore, it is not feasible to

analytically solve the system equations and investigate the vibration trends for models with a large number of realistic downhole conditions. Numerical modeling is an effective way to overcome the

complex problem of coupled equations of motion of the drillstring in the presence of contact with the

wellbore. Several factors such as drillcollar and drillpipe length, material and cross sections affect the

vibration behavior….TOBECONTINUED

Stable isotope measurements of vanadium and molybdenum as tracers for treated oil-sands process affected water

Petroleum coke (PC) is a by-product of the extraction of crude oil from the Oil Sands in northern Alberta and has been shown to effectively remove total acid-extractable organics from oil sands process-affected water. This treatment may also lead to an increase in some heavy metals in the treated water and it is important to distinguish between coke-derived elements and those found naturally. The objective of the project is to develop an understanding of the sources and sinks of vanadium and molybdenum in the petroleum coke treated water in the Athabasca Oil Sands Region. This knowledge can be used to identify and potential environmental concerns.

Development of an NLP Sales Assistant using Machine Learning Techniques

The main goal of this project is to develop machine learning and natural language processing approaches to help customers to communicate their preferred brands and/or retailers via Heyday solutions. These approaches will automate answers and help to humanely engage with customers. In order to reach these objectives, some challenges will be tackled such as automatically recognizing the users intent and replying to frequently asked questions. Recognizing ambiguous words is another challenging task to provide accurate answers. In order to reduce the risk related to sensitive subjects and questions (e.g. health related questions), a machine learning approach will be developed and integrated, also.

Cyclic Freeze-Thaw Behavior of Geocell-Reinforced Bases

Geocells are a type of three-dimensional honeycomb geosynthetics that are widely used to improve the performance of paved and unpaved roads by reinforcing the base/subbase courses. It is approved to benefit the long-term performance of roadways and reduction of construction cost. Although geocell has been widely used in roadway construction in cold regions, showing measurable improvements, research regarding the cyclic freeze-thaw behavior of geocell-reinforced roadways is rather scarce. A good understanding of factors influencing the cyclic freeze-thaw performance of geocell-reinforced bases is vital to the design and construction of roadways. This project aims to perform field monitoring and a series of experimental investigations of the behavior of geocell-reinforced bases subjected to the freeze-thaw cycles. The final goal is to improve the design practices for construction of the durable roadways in Canada.

Exploring race representation and the racial wage gap in Southern Ontario’s Tech Industry

There is a lack of “diversity” in Canada’s tech industry and this is what the research is based on. The lack of research considering racial diversity in the tech industry in Canada leaves a significant gap in understanding issues that would be critical in addressing such a lack of diversity. This research will explore how, to what extent, race is represented in Southwestern Ontario’s tech industry. In so doing, it will determine if, and if so, to what extent, there is there is a race equity pay gap. It will provide a baseline assessment of to what extent race is represented in SWO’s tech industry. Important to this research is understanding how intersection race, gender, age and other such factors intersect. The research will take place in three phases over a one-year period using both qualitative and quantitative measures. The research will have three potential outputs with relevance to both the tech industry and academia.

Sodium Manganese Oxide Coated with Polymers as Zinc and Sodium Dual Ions Battery Cathode

Rechargeable aqueous zinc sodium dual-ions batteries are considered as alternatives of lithium ion batteries because of their safety and low-cost. As an available cathode for the zinc sodium dual-ions batteries, sodium manganese oxide (NMO) shows relatively high specific discharge capacity. Polyaniline (PANI) is promising for coating NMO to stabilize the NMO system because it can supress the crystalline structure collapse. Metanilic acid is promising for doping PANI to increase the conductivity of PANI. In this research, we will modify different parameters such as the molar ratio of Na and Mn, the weight percentage of PANI coated on NMO to obtain better cycling performance than bare N

Steering the Innovation Process: Accelerating “Ideas to Impact” in Water Treatment

Demand for water and wastewater treatment equipment has grown substantially over the last several

years, leading to a significant increase in the market size for technology providers such as Trojan. To

maintain the cutting-edge nature and quality of Trojan products, it is essential that the research and

testing performed at Trojan match this expansion in demand. Historically, one of the distinguishing

features of Trojan’s products has been the rigorous analysis and testing that the products undergo

during the design and development phase. Trojan uses cutting-edge tools for modeling fluid flow, UV

radiation distribution, and biological and chemical kinetics to predict reactor performance. Computerbased

experiments are accomplished by using Computer Aided Engineering tools such as

Computational Fluid Dynamics (CFD) models. Trojan also performs extensive physical testing to

validate integrated CFD-optical-kinetic models used during product development to optimize design

and to validate completed designs and control algorithms. The strategic objective of this research

program is to determine a holistic algorithm able to guide….TOBECONTINUED

Modèle d’occupation individualisé pour le secteur résidentiel – Partie 2

Le marché énergétique est en transition. La demande et la consommation d’énergie dans le secteur résidentiel seront impactées par l’adoption de nouvelles technologies et l’évolution des comportements. Le projet de recherche vise le développement d’un modèle statistique générant des profils temporels individualisés de présence des individus à leur résidence en fonction des attributs des individus. Le modèle retenu sera construit à partir des données de l’Enquête sociale générale de Statistique Canada de 2010 et de 2015. À terme, les résultats du modèle d’occupation développé pourront alimenter des modèles de consommation énergétiques de résidences pour quantifier l’impact de scénarios d’évolution technologiques et comportementales sur la demande et la consommation d’énergie.

Evaluation of a radiomic approach based on hyperspectral retinal imaging to predict the cerebral amyloid status for the diagnosis of Alzheimer’s disease

The project will help Optina validate and further develope a novel technology to predict the presence of significant amyloid (A?) deposition in the brain from a simple, non-invasive hyperspectral retina scan in combination with an artificial intelligence algorithm. Accumulation of A? plaques in the brain is a key hallmark of Alzheimer’s disease (AD), but current methods to evaluate its presence in vivo (A? positron emission tomography imaging and quantification of A? proteins in the cerebrospinal fluid obtained) are not practically implementable as screening methods due to cost, availability and/or invasiveness nature. The continued development of the device and the design of a clinical study is an important milestone towards raising the required capital (possibly 15-20M$) to reach the AD diagnostic market. The project will train individuals who are specialized in medical device development, clinical trial design and coordination, project management and regulatory affairs.

Machine Learning and Data Mining Approaches for Smart Buildings

The goal of this project is to develop machine learning and data mining algorithms relying on non-intrusive common sensor data to estimate and predict smart buildings’ occupancy and activities. Efficient feedbacks are automatically supplied to the end user to involve occupants and increase their awareness about energy systems. This consists of generating reports helping the occupant to understand his/her energy management system and thus to be involved in the decision-making process. The project directly aligns with Ericsson’s IoT mission and expands its operator potential opportunities by exploring a dimensionality in real-time automation, monitoring and tracking, and smart surveillance. It also further strengthens Ericsson’s position in the IoT market which has a projected additional revenue potential of up to 36% (USD 619 billion) by 2026. Moreover, the machine learning techniques to be developed can be easily adapted to other problems of interest to Ericsson.