Projets novateurs réalisés

Explorez des milliers de projets réussis issus de la collaboration entre organisations et talents postsecondaires.

29 670 projets achevés

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Projets par catégorie

DEVELOPMENT OF CONTEXT-SPECIFIC INTEGRATED BIOLOGICAL NETWORK MODELS WITH APPLICATIONS TO STRAIN ENGINEERING

Biological-system-based production of chemicals and fuels (Eg: ethanol, bio-diesel, bio-nylon, etc.) has lesser or no impact on the environment than their conventional, chemical processes But, these biofactories are not evolved to produce industrially important chemicals yet. Hence, the productivity of the desired chemical is low in an isolated biological system. Metabolic engineering modifies the metabolic network (network of biochemical reactions) by blocking or appending a few reactions in a static or dynamic manner to increase the chemical production. We have to determine which reactions to target? Or what pathways to modify? before performing metabolic engineering. Mathematical models of the biosystems enable us to analyze these biosystems in different conditions, like what happens if a reaction is removed or added in an in-silico manner. These models are mathematical equations that capture bio-entities as symbolic variables and their interactions as mathematical operations among the symbolic variables. In this work, we propose to model the biosystem, Kluyveromyces lactis, as an integrated model to design new strains for chemical production using our group’s advanced algorithms like MoVE, mcPecaso, cRegMCS.

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Superviseur du corps professoral :

Radhakrishnan Mahadevan

Étudiant :

Partenaire :

Indian Institute of Technology Madras

Discipline :

Engineering

Secteur :

Biotechnology; Green/Alternative Energy; Life Sciences (not health)

Université :

University of Toronto

Programme :

Globalink Research Award

Evaluation of the effects of peripheral CB1 receptor antagonism on obesity and metabolic dysfunction

Obesity is a growing public health concern that is characterized by increased fat mass. It is also an important risk factor for various diseases, such as type 2 diabetes. Most treatments for obesity act on the brain, and interestingly, none of the available treatments target fat cells, the key cell type involved in the development of obesity. While it is known that blocking the actions of the CB1 receptor can reduce food intake and treat obesity, the effect of CB1 receptor blockade in peripheral fat tissue has not been investigated in great detail. This project is aimed at evaluating the effectiveness of new generation CB1-receptor inverse agonists, that preferentially act in the periphery, in treating obesity. The benefit of this project to Inversago, the partner organization, is to advance preclinical candidate compounds towards clinical use in treating obesity and metabolic disorders.

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Superviseur du corps professoral :

Gareth Lim

Étudiant :

Partenaire :

Inversago Pharma

Discipline :

Life Sciences

Secteur :

Manufacturing

Université :

Université de Montréal

Programme :

Accelerate

5G-Enabled Smart Infrastructure Applications

Canada’s critical infrastructure (e.g., bridges, pipelines, and nuclear power plants) is deteriorating at a rate that far outpaces the ability to replace it. A recent analysis of Ontario’s transportation infrastructure alone indicates over $5.1B is needed to replace assets that have reached the end of their lifecycle. Justifying the finances required for replacement/repair is challenging given the limitations of current inspection practices. This necessitates a quantitative inspection practice that can be deployed at scale for objective measure of existing infrastructure life. This project aims to harness the power of 5G to address unique challenges related to the assessment of state of infrastructure objectively so that maintenance prioritization and replacements can be planned for based on quantitative data. This project will spur an entirely new innovation ecosystems consisting of network providers such as Rogers and vendors and contributes a significant cost reduction and increases reliability in assest inspection and maintenance.

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Superviseur du corps professoral :

Chul Min Yeum

Étudiant :

Partenaire :

Rogers Communications Inc.;University of Waterloo

Discipline :

Engineering

Secteur :

Information and Communications Technology; Artificial Intelligence

Université :

University of Waterloo

Programme :

Accelerate

AI-enabled food waste differentiation for at-home compost nutrients estimation

The main goal of this project is to digitalize food waste at home for a sustainable future using at-home composters. We will develop dedicated machine learning algorithms to detect, segment, and classify various food waste generated in the kitchen, making it possible for everyone to immediately estimate the quality and nutrients of the generated compost using a simple mobile App. The project will collaborate with VCycene Inc., a cleantech company dedicated to providing sustainable solutions to the food-waste problem. The intern will work with the domain experts at VCycene, especially the team for data collection and the team for model deployment and mobile App development, to conduct experiments and evaluate the performance of the proposed machine learning algorithms.

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Superviseur du corps professoral :

Guanghui Wang

Étudiant :

Partenaire :

VCycene Inc.

Discipline :

Computer science

Secteur :

Manufacturing

Université :

Toronto Metropolitan University

Programme :

Accelerate

Temporal Preferences versus Song Learning Across Populations of Ormia ochracea

This project will measure the behavioural responses in a certain fly species, ‘Ormia ochracea’, to a variety of cricket mating songs, and from geographically separated North American populations of the fly. This type of fly attacks crickets so that their larvae may feed and mature within the cricket’s body. They locate the cricket’s position by following the cricket’s unique mating song. Each song is very different, especially in its time-pattern (number of chirps etcetera). Experiments in the wild have shown that the fly populations will show distinct preferences even when all the cricket songs are equally available. How it is the flies are preferentially choosing dissimilar songs is not yet known. I will address this research gap by measuring fly preferences in the laboratory. It is expected this project will help us find out whether the fly populations are tuned differently to certain song patterns.

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Superviseur du corps professoral :

Andrew Mason

Étudiant :

Partenaire :

University of Strathclyde

Discipline :

Life Sciences

Secteur :

Life Sciences (not health); Biotechnology; Environmental Science and Technology

Université :

University of Toronto

Programme :

Globalink Research Award

La mise en oeuvre en pratique d’un plan stratégique dedéveloppement durable

Le projet de recherche vise à étudier le processus de réalisation d’une stratégie en développement durable au sein d’une entreprise minière. Pour se faire, une étude longitudinale de quatre à huit mois sera réalisée dans deux ou trois secteurs de l’entreprise Rio Tinto Fer et Titane (RTFT) afin d’obtenir une idée globale de la perception du développement durable (DD) au sein de différents paliers hiérarchiques de la compagnie. Des séances d’observation ainsi que la réalisation d’entrevues individuelles constitueront la grande partie du projet sur le terrain. L’étude comportera deux étapes : 1) compréhension des processus de travail dans certains départements (à déterminer en collaboration avec la direction de développement durable à RTFT) et 2) l’identification, à travers des discussions et entrevues avec les employés concernés, des façons d’intégrer dans ces processus des nouvelles pratiques de travail qui sont cohérentes avec la stratégie DD de l’entreprise .

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Superviseur du corps professoral :

Charlotte Cloutier

Étudiant :

Partenaire :

Rio Tinto Fer et Titane inc.

Discipline :

Business

Secteur :

Mining

Université :

HEC Montréal

Programme :

Accelerate

Le pont Kintai et l’architecture des ponts au Japon à l’angle des crises environnementales (16 – 18e siècle)

La période s’étalant du 16e au 18e siècle au Japon fût marquée par des épisodes successifs de guerre et de paix, de crises environnementales et de vagues de construction pour marquer la fin des conflits. De nouveaux bâtiments, routes et infrastructures marquent en ce sens l’époque Edo, qualifiée largement pour sa prospérité relative. Or, ce boom de construction a mis en péril l’écosystème japonais, entraînant notamment des innondations et la mise en péril de nombreuses espèces d’arbres. En prenant pour exemple le pont Kintai, ce projet cherche à mettre en lien le développement d’une identité architecturale japonaise unique et l’émergence de nombreux problèmes écologiques, ainsi que les solutions environnementales mises en place.

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Superviseur du corps professoral :

Bernard Bernier

Étudiant :

Partenaire :

The Graduate University for Advanced Studies

Discipline :

Sociology

Secteur :

Construction; Forestry; Other

Université :

Université de Montréal

Programme :

Globalink Research Award

How the Ronald Lake Bison Herd (RLBH) interact with wetland meadows

The purpose of this research is to further the understanding of how wood bison (Bison bison athabascae) interact with wetland meadows. This includes examining the environmental factors that contribute to the structure of these wetlands, determining what environmental factors influence bison selection of foraging sites within individual wetlands, and showing to what extent individual bison display site fidelity to wetland foraging locations within their range. This research conducted under the support of this grant will contribute to a body of knowledge on the Ronald Lake Wood Bison Herd located in northeastern Alberta. The funding partner (ACFN-DLRM) is committed to continuing research with a goal of protecting the RLBH and their habitats, this research will enable strengthened protection and understanding.

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Superviseur du corps professoral :

Scott Nielsen;Mark Edwards

Étudiant :

Partenaire :

Athabasca Chipewyan First Nation

Discipline :

Life Sciences

Secteur :

Agriculture; Professional, scientific and technical services

Université :

University of Alberta

Programme :

Accelerate

A computer-vision assisted system for on-site construction to monitor safety and track productivity

Construction sites are accident-prone sites and despite numerous efforts to reduce incidents, they continue to occur. Among others, fall accidents, in particular, account for the majority of these incidents, necessitating the implementation of proper management strategies. Traditional techniques for providing safety and preventing accidents are adequate and appropriate for improving safety, however, they are not able to adapt to the changing environment. In this proposal, a computer vision based system is proposed that is able to detect workers and take appropriate actions depending on the situation. The system will be able to prevent accidents by alarming the workers about the hazardous situation.

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Superviseur du corps professoral :

Rafiq Ahmad

Étudiant :

Partenaire :

North Forge

Discipline :

Engineering

Secteur :

Education; Management of companies and enterprises; Professional, scientific and technical services

Université :

University of Alberta

Programme :

Accelerate

The potential to produce lower carbon intensive (CI) transportation fuels via co-processing at existing oil refineries

Co-processing has been commercialised by Parkland Burnaby refinery which allows the refiner to produce lower carbon intensity fuels. Significant amount of process data has also been generated. The intern made use of the commercial data and evaluated the impact of these alternative feedstocks to existing refining operation, like yield, operating conditions. More importantly, the intern is exploring the possibilities to develop new method (beyond simple correlation) that can quantify the renewable content in different streams (tracking the green molecules). The expected results hope to incentivise refineries in Canada and the rest of the world to co-process and lower the overall carbon intensity of petroleum products while helping policy makers to credit these producers.

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Superviseur du corps professoral :

Jack Saddler

Étudiant :

Partenaire :

Parkland Burnaby Refinery

Discipline :

Engineering

Secteur :

Manufacturing

Université :

The University of British Columbia

Programme :

Accelerate

Lab2Market West: Advancing echocardiography scanning through multiview fusion using robotics and machine learning

Echocardiography is widely used for scanning cardiac patients to assess the health of their hearts. Despite its wide use, echocardiography suffers from a few limitations, including the limited field-of-view and longer scanning times. We propose a product using a collaborative robot arm to overcome these limitations. Our project allows scanning the heart from different locations and fusing them to get a combined single scan. It also makes the overall scanning process efficient by moving the ultrasound probe to the desired locations quickly. In this project, an internship student will perform the market analysis by consulting numerous stakeholders to prepare a business plan for product development.

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Superviseur du corps professoral :

Kumaradevan Punithakumar

Étudiant :

Partenaire :

North Forge

Discipline :

Computer science

Secteur :

Education; Management of companies and enterprises; Professional, scientific and technical services

Université :

University of Alberta

Programme :

Accelerate

Domain incremental learning in forgery detection

Digital image forgery has become a worldwide pandemic, with many forms of forgery (e.g., insurance fraud, fake news, identity theft) negatively affecting our life. This effect could be attributed to the accessible costs of mobile phones and digital cameras, which has led to an exponential proliferation of digital images, and the availability of many image editing tools that allow easy manipulation of images, resulting in high-quality forgeries. In this project, we intend to leverage the powerful representation capabilities of deep models to address the problem of forgery detection in realistic and challenging scenarios, such as incrementally learning across continuously evolving domains.

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Superviseur du corps professoral :

Jose Dolz

Étudiant :

Partenaire :

Thales Recherche et Technologie

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

École de technologie supérieure

Programme :

Accelerate

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