Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Potential of pyrolysis oil from biomass as a source for phenolic resins

Phenolic resin, also known as phenolic formaldehyde resin (PF), is a synthetic resin produced from the polymerization of phenol (an aromatic alcohol derived from benzene) and formaldehyde (a reactive gas derived from methane). Resins have applications from use as laminates to in construction materials such as wood and composite materials. The use of petroleum-based compounds as sources of phenols is limited by the climate change and other environmental impacts associated with petroleum derived products. As such there are increased efforts to use renewable resources for the production of useful polymers or composites. As the only renewable source of fixed carbon biomass is the primary candidate for the production of composites. Converting solid biomass (saw dust, corn stover, wheat straw etc.) to a liquid makes extraction of phenols less intensive from a process perspective. The overall objective of this work is to identify and quantify (where feasible) key compounds in py-oil (derived from wheat) and lignin (extracted using an organosolv process), that could serve as a feedstock for phenolic resins to replace petroleum sources.

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Faculty Supervisor:

Kelly Hawboldt

Student:

Partner:

Karlsruher Institut für Technologie

Discipline:

Engineering

Sector:

Education

University:

Memorial University of Newfoundland

Program:

Globalink Research Award

Utilization of the exhaust gas of the smelting process for cooling and heating applications

Aluminum smelting is a highly energy consuming industrial process. The process generates a large amount of the heat that leaves through the exhaust gas. The exhaust gas must be scrubbed of its contaminants before release to the atmosphere at the gas treatment unit exit. The scrubbing process is more effective if this gas is cooled before entering the gas treatment unit. The main objective of this project is to find a technical and economical method to cool the smelting process exhaust gas upstream of the gas treatment unit. An ejector heat driven system is proposed for cooling the exhaust gas by heat recovery, and for improving the overall plant efficiency.

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Faculty Supervisor:

Mikhail Sorin

Student:

Partner:

Rio Tinto Alcan (Jonquière, QC)

Discipline:

Engineering

Sector:

Manufacturing; Mining; Professional, scientific and technical services

University:

Université de Sherbrooke

Program:

Accelerate

Determining Properties of Agricultural Straw forEquipment Design and as Feedstock for theBiofuel Industry

Information on the physical and mechanical properties of the wheat stem could be greatly helpful in
effective harvesting of the crop biomass as well as feedstock preparation (post-harvest processing)
for the biofuel industry. Appropriate design of the harvesting systems or post-harvest processing
equipment which is energy efficient depends on the access to physical properties and strength as well
as lignocelluosic material characteristics. In the proposed project, experiments will be conducted
based upon independent variables such as crop genotype, moisture content, type of cutting knife, and
loading rate and the bending and shearing properties and other mechanical and physical properties of
straw will be measured. Finally, dependent variables will be analyzed using regression modeling to
find out and express the importance of abovementioned factors on energy requirement of the
equipment.

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Faculty Supervisor:

Lope Tabil

Student:

Partner:

CNH Industrial;University of Saskatchewan

Discipline:

Engineering

Sector:

Other

University:

University of Saskatchewan

Program:

Accelerate

Machine-Learning for design and discovery of next generation CO2 electrocatalysts

Mitigation of CO2 emissions in conjunction with the implementation of renewable energy generation and storage are widely recognized among the most pressing technological challenges of the twenty-first century that aim to address runaway climate scenarios. The UBC team in collaboration with its industry partner (AGORA Energy Inc) has introduced the concept of CO2-to-energy via its unparalleled and proprietary CO2 Redox Flow Battery (CRB) technology. There is an ongoing collaboration among UBC and IEK-13 (Julich) to accelerate the commercialization and large-scale deployment of the CRB. The visiting student will participate to ongoing data-driven tasks related to materials discovery for new electrocatalysts based on Metal Organic Framework (MOF) where Artificial Intelligence (AI) models based on data analytics and machine learning are utilized. The collected data and ML-based modelings will be complemented by high-throughput electrochemical characterization methods for rapid screening of advanced catalysts, enhancing system design and optimizing operating conditions at UBC and AGORA.

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Faculty Supervisor:

Elod Lajos Gyenge

Student:

Partner:

Forschungszentrum Jülich

Discipline:

Engineering

Sector:

Green/Alternative Energy; Artificial Intelligence; Information and Communications Technology

University:

The University of British Columbia

Program:

Globalink Research Award

High-Precision Imitation Learning for Real-Time Robotic Control

In recent years, an increase in industrial robots in manufacturing has emerged. However, there are still possible safety issues and difficulty in specifying tasks for the robots to perform. The objective of this research project is to make a path planning system that uses demonstrations of how to perform a task to learn how to perform the task using techniques from the field of machine learning. These demonstrations will also show the robot how to move in the workspace safely and without entering collision with items in its surroundings. This system aims to be integrated into Mecademic’s Meca500, which will make the robot more user-friendly, safer and more accessible to people unfamiliar with industrial robotics.

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Faculty Supervisor:

Hsiu-Chin Lin

Student:

Partner:

Mecademic

Discipline:

Engineering

Sector:

Manufacturing

University:

McGill University

Program:

Accelerate

Identification of Polymorphic Malware using Fractal Complexity Analysis

In the proposed collaborative research, the focus will be on the application of fractal complexity analysis in anomaly detection. Many features are hidden deep within time series information such as network traffic, and complexity analysis will facilitate the extraction of such features. Complexity analysis takes advantage of the self-similar structure which is found widely in nature and which has been shown to be evident in network traffic. These features will be used for the presence of Malware which has compromised a host system. The features will also be used to detect incoming denial of service attacks or other resource crippling behavior which would indicate intrusion or the attempted disruption of normal operations. This research will improve the ongoing research aims of the intern by including complexity analysis as a tool towards the detection of obfuscated forms of Malware. This work will also produce a deliverable in the form of a manuscript which will summarize the work carried out and the core findings.

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Faculty Supervisor:

Ken Ferens

Student:

Partner:

National Institute of Informatics

Discipline:

Engineering

Sector:

Education

University:

University of Manitoba

Program:

Globalink Research Award

Characterization of Sustainable Solid Composite Electrolyte for Lithium Ion Batteries

Lithium ion batteries are under constant attention of researchers all over the world due to their advantages such as high energy density, light weight and cycling performance. However they still struggling from some safety issues associated with potential leakage of electrolyte liquid components, their toxicity and flamability hazards. Lithium passivating layer formation and dendrite growth occuring even in fully solid polymer or ceramic based systems. Combination of a solid filler and a polymer matrix allows to improve solid electrolyte resistance to spherulite growth during battery utilization. However, specific requirements needed to be met to not compromise ionic conductivity while pursuing improvements in mechanical and thermal stability. The research activities include: (I) laboratory training, (II) utilization of electrochemical strain microscopy and laser scanning confocal microscopy for interpretation of ion pathways in a solid polymer composite electrolyte, (III) data collection and processing, and (IV) results analysis. The expected outcome will be a better understanding of the defined system in order to evaluate the potential results for its application for lithium ion batteries and understand the way of further improvements can be done in solid polymer electrolytes properties.

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Faculty Supervisor:

Milana Trifkovic

Student:

Partner:

Forschungszentrum Jülich

Discipline:

Physics

Sector:

Green/Alternative Energy; Clean Technology; Sustainability & the Environment

University:

University of Calgary

Program:

Globalink Research Award

Automating sleep stage classification using Contactless BCG Sensor

It is estimated that 5.4 million Canadian adults have chronic sleep abnormalities. Symptoms are not visible to patients because they happen during the night. Hence, they remain undiagnosed. Besides, sleep abnormalities can cause different chronic health problems, that is sleep apnea, diabetes, stroke, brain injury, Parkinson’s disease, depression, and Alzheimer’s disease. Thus, measuring sleep behavior can diagnose sleep disorders and enable the early detection of other health conditions. Current sleep monitoring systems are expensive, labor-intensive, complex. Also, it is not possible to emulate the usual sleep environment in a sleep laboratory. Furthermore, manual scoring has considerable inter-scorer and intra-scorer variability, making its reliability and reproducibility questionable. That said, in this project, we propose a deep learning-based method for the automatic detection of sleep architecture using a contactless system that is based on the ballistocardiographic principle in an attempt to address one of today’s health care issues.

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Faculty Supervisor:

Bessam Abdulrazak

Student:

Partner:

Mediterranean Institute of Technology

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Artificial Intelligence; Information and Communications Technology

University:

Université de Sherbrooke

Program:

Globalink Research Award

Travailler dans le mouvement communautaire autonome à l’ère de la COVID-19 : analyse des conditions de travail

Le mouvement communautaire autonome a été au cœur de la réponse gouvernementale durant la pandémie de la COVID-19. Que ce soit pour répondre aux besoins des personnes en situation d’itinérance, d’assurer la sécurité des femmes victimes de violences conjugales ou encore pour assurer la sécurité alimentaire des personnes affectées par des pertes d’emploi, les organismes communautaires sont intervenus pour répondre aux besoins émergeant de la population et des personnes les plus vulnérables.
Or, pour ce faire, les organismes communautaires ont dû transformer leurs pratiques et leurs manières de faire. Ils ont également dû faire face à l’augmentation des demandes d’aide tout cela dans un contexte où la santé financière des organismes s’est rapidement détériorée et où de nombreux organismes étaient déjà, avant la pandémie, en situation précaire.
Il ne fait donc aucun doute que la pandémie de la COVID-19 a eu des répercussions sur les conditions de travail dans ce secteur. Ce sont ces répercussions que cette recherche collaborative propose de documenter ainsi que les pratiques de gestion et les processus de prises de décisions autour de ces conditions de travail dans les organismes communautaires.

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Faculty Supervisor:

Yanick Noiseux

Student:

Partner:

Table nationale des corporations de développement communautaire

Discipline:

Sociology

Sector:

Other services (except public administration)

University:

Université de Montréal

Program:

Accelerate

Transfer Learning for precise detection of individual cells in multimodal microscopic image data

Automatic segmentation and detection of cells is a fundamental task in relevant medical fields such as histopathology, hematology, and cytopathology. Deep Learning methods show promising results, but often require excessive amounts of data, which is a major barrier to entry, especially for experimental cellular imaging data.
This project aims to develop a state-of-the-art cell detection framework that is applicable or transferable to many different data modalities, i.e. imaging techniques or biological staining protocols, with minimal effort. To achieve this goal, a potent Deep Learning architecture is combined with a newly compiled dataset, consisting of several existing cell-datasets, as well as newly generated synthetic datasets. The latter may be created with algorithmic approaches and generative Deep Learning models. Moreover, the benefit of such a dataset and applicability of Transfer Learning to trained Deep Learning models will be studied. The created framework will be evaluated against state-of-the-art methods and other datasets.

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Faculty Supervisor:

Alan Evans

Student:

Partner:

Forschungszentrum Jülich

Discipline:

Computer science

Sector:

Artificial Intelligence; Health and Related Sciences & Technology

University:

McGill University

Program:

Globalink Research Award

Proteomic approach to understand the tripartite interaction of coniothyrium minitans against sclerotinia sclerotiorum that causes stem rot of canola

Characterization of mode of action of C.minitans against S.sclerotiorum and documentation of lysis, hyhal-parasitism via electron microscopy. Studying its metabolite production during tripartite interaction of plant, pathogen and biocontrol agent through FTIR and NMR. Analysis on proteomic approaches during tripartite interaction by Fluorescent Two-Dimensional Difference Gel Electrophoresis (2D-DIGE), identification of protein and data analysis through MS analysis or MALDI TOF

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Faculty Supervisor:

Balakrishnan Prithiviraj

Student:

Partner:

Tamil Nadu Agricultural University

Discipline:

Life Sciences

Sector:

Agriculture and Food; Clean Technology; Sustainability & the Environment

University:

Dalhousie University

Program:

Globalink Research Award

Effect of agro?climatic conditions on the cannabinoid quantities in hemp crops

We want to determine the relationship between weather such as rainfall and temperature on outdoor grown hemp. Specifically what these variables do in terms of changing the content of THC and CBD in certain varieties of hemp. Our goal is to give farmers the knowledge so that they can determine when is optimal time to harvest their crop based on that years weather if they want to be below a .3 thc content, and have a high CBD content. However, the research we do will lend itself to farmers seeking other CBD and THC outcomes in their crops.

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Faculty Supervisor:

Donald Smith

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Life Sciences

Sector:

Agriculture and Food; Environmental Science and Technology

University:

McGill University

Program:

Accelerate

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