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

Optimization of plant sources grinding process and their food applications

In recent years, plant-based food products, such as plant-based beverages, ice-cream, and yogurt, have been much admired by health and environment conscious consumers due to their sustainability and health benefits. This industrial collaborative project aims to optimize grinding processing to treat plant proteins of different sources to achieve sufficient particle size reduction, while avoiding the adverse impact on the product physicochemical and functional properties. The knowledge generated from this study will allow industry to precisely control the grinding parameters for new food development for different plant sources.

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Superviseur du corps professoral :

Lingyun Chen

Étudiant :

Partenaire :

Earth's Own Foods

Discipline :

Life Sciences

Secteur :

Life Sciences (not health); Clean Technology; Agriculture and Food

Université :

University of Alberta

Programme :

Accelerate

La bistabilité optique pour de l’intelligence artificielle moins énergivore

L’apprentissage profond, un type d’intelligence artificielle, consomme une quantité phénoménale d’électricité. Les réseaux neuronaux, à la base de cette technologie, sont présentement implémentés sur ordinateurs. Or, il est possible de créer des réseaux de neurones fonctionnant à l’aide de la lumière, des réseaux de neurones photoniques. Cette technologie pourrait permettre de réduire la consommation d’électricité des algorithmes par un facteur 1000. Pour l’instant, celle-ci se heurte à la difficulté de concevoir des dispositifs 100 % optiques avec une réponse non linéaire suffisamment forte pour permettre un fonctionnement à basse puissance. En combinant un phénomène s’appelant la bistabilité optique, les récents progrès dans le domaine de l’ingénierie des résonateurs et des matériaux fortement non linéaires caractérisés par notre groupe, nous souhaitons concevoir des dispositifs avec une forte réponse non linéaire à basse puissance qui pourront être utilisés dans les futurs réseaux neuronaux photoniques.

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Superviseur du corps professoral :

Stéphane Kena-Cohen

Étudiant :

Partenaire :

FOM Institute for Atomic and Molecular Physics

Discipline :

Engineering

Secteur :

Information and Communications Technology; Artificial Intelligence

Université :

Polytechnique Montréal

Programme :

Globalink Research Award

Investigation of sulphur compound behaviour on surface operations on offshore platforms

The process of an oil and gas reservoir being “soured” by hydrogen sulphide is complex due to the many operations (e.g. changing production methods and waterflooding). The phenomena of souring occurs in most reservoirs were secondary or enhanced oil/gas recovery takes place. Hydrogen sulphide casues a number of operational and safety problems such as scaling, air quality issues, and corrosion. Due to the reactivity of sulphur, the process that produces the hydrogen sulphide and related sulphur compunds is a complex chemical and biological process. In this project we will study the behaviour of sulphur compounds as they move through the surface handling facilities in an effort to better understand the chemical flux of the sulphur between different species. This will aid in the overall understanding of how to better handle soured gases and wastewaters on offshore plaforms where reservoir souring is occurring.

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Superviseur du corps professoral :

Kelly Hawboldt

Étudiant :

Partenaire :

Suncor Energy Inc (St. John's, NL);Petroleum Research Newfoundland & Labrador

Discipline :

Engineering

Secteur :

Mining; Professional, scientific and technical services

Université :

Memorial University of Newfoundland

Programme :

Accelerate

The role of neural oscillations in sensorimotor integration of pleasure and performance in music

Our experience of music is multifaceted, including aspects of movement, emotion, timing, and expectation. Accordingly, music cognition recruits a wide range of brain areas during both passive listening, and musical performance. However, the extent to which these brain regions interact with each other is not fully understood. Recent research has indicated that synchronization of neural oscillations – the rate at which groups of neurons fire – may be an indicator of how distant brain areas interact with each other during music processing. Using electroencephalography (EEG), the project investigates the relationship between how much we like a piece of music and the amount of synchronization between brain areas during music listening. We will also examine the role of oscillations in music learning during a piano task. We expect to observe increased synchronization as liking for a piece of music increases and as piano performance improves, particularly between brain regions associated with pleasure and motor activity.

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Superviseur du corps professoral :

Virginia Penhune

Étudiant :

Partenaire :

University of Barcelona

Discipline :

Sociology

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

Novel approaches to fault detection

The objective of this internship is to develop a multidisciplinary collaboration to better understand the complex factors that influence industry adoption of fault detection and diagnostic (FDD) tools for commercial buildings. More specifically I will focus on how interface design influences usability and perceived usefulness, and how information from FDD tools ultimately lead to fault correction. With the supervision and support of Dr. Marco Pritoni at the Lawrence Berkeley National Laboratory, I will carry out two research projects in parallel . These projects will include (1) applying an iterative and user focused design methodology to FDD interface design and (2) investigating socio-technical barriers to the implementation of FDD measures. The outputs from these two projects will help both researchers and industry professionals better design FDD tools for successful and wide scale adoption.

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Superviseur du corps professoral :

Mohamed Ouf

Étudiant :

Partenaire :

Lawrence Berkeley National Laboratory (LBNL)

Discipline :

Engineering

Secteur :

Sustainability & the Environment; Energy and Utilities; Other

Université :

Concordia University

Programme :

Globalink Research Award

Deformation Behavior and Fatigue Failure Analysis of Functionally Graded B4C Particle Reinforced Aluminum Alloy-Based Metal Matrix Composite

The proposed work under this award program involves fabrication of functionally graded B4C particle reinforced AA6061 metal matrix composite through stir-centrifugal casting technique. Subsequently, the deformation behavior and fatigue failure analysis of the fabricated composite will be carried out. The 2D microstructure, constituent phases, particle distribution, and gradient will be revealed by light and electron microscopy, XRD, and EDS analysis. Further, flexural strength tests, quasi-in-situ compressive deformation tests, and bending-fatigue tests will be run on specimens extracted from different regions of interest of the bulk composite. The deformation and failure mechanisms will be studied afterwards. The planned work will provide a thorough understanding of these lightweight materials and their functioning, which will allow aerospace, automotive, and other transportation sectors use these materials in potential areas, improving fuel efficiency and lowering carbon emissions. This suggested study will result in the publication of high-quality research articles in reputable international journals, attracting a large readership.

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Superviseur du corps professoral :

Daolun Chen

Étudiant :

Partenaire :

Indian Institute of Technology Kharagpur

Discipline :

Engineering

Secteur :

Education

Université :

Toronto Metropolitan University

Programme :

Globalink Research Award

Development and numerical reactor modeling of a novel conversion system for methanol production

It is imperative to have sustainable infrastructure that emits as little greenhouse gases as possible by 2050 in order to reach net-zero emissions. The key component of this strategy is development of CO2 capture units, as well as the use of renewable energy sources. By incorporating renewable electricity to meet the energy demand of high-temperature reactions, chemical processes can become much more efficient and compact, and CO2 emissions can also be drastically reduced. A novel electrified tri-reforming system has been developed for the efficient use of CO2 and conversion of this to methanol. The detailed design of the electrified reactor is critical to ensuring the feasibility of this process. Therefore, we will numerically model the reactor using a variety of heating approaches, including microwave and plasma. COMSOL modeling software will be used to develop the computational fluid dynamics (CFD) model for electrified reactors by incorporating standard equations such as energy, mass transfer, and electrical fields.

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Superviseur du corps professoral :

Yaser Khojasteh-Salkuyeh

Étudiant :

Partenaire :

Eindhoven University of Technology

Discipline :

Engineering

Secteur :

Education

Université :

Concordia University

Programme :

Globalink Research Award

Optimization of the expoCube Prototype for Cannabis Vapor Exposure to ALI culture

In 2018, Canada legalized the use of recreational cannabis. Despite legalization, there are still many things we do not know about how cannabis can impact our health. It is important that scientists study what cannabis does to our bodies so that Canadians can be informed consumers. A common way to consume cannabis is to use a dry herb vaporizer, which heats the plant to release compounds called cannabinoids. THC and CBD are important cannabinoids and affect the way our bodies function. At the same time, vaping involves inhaling other chemicals into the lungs, so it is important to understand how this vapor impacts lung health. We study lung cells by exposing them to cannabis vapor in a new device called the expoCube. This device is unlike any other because it allows us to study cells that are exposed to the air and the whole vapor product, unlike traditional cells which are kept under liquid making exposing them to a vapor difficult.

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Superviseur du corps professoral :

Carolyn Baglole

Étudiant :

Partenaire :

SCIREQ Scientific Respiratory Equipment

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

Research Institute of the McGill University Health Centre

Programme :

Accelerate

Tiresias: Client Private Malware Protection

Tiresias is a client private solution to malware protection and threat intelligence. Tiresias allows a user to put all their incoming files in a cryptographically secure Data Chest locally. After sending the Data Chest to our cloud environment, our AI scans and infers if it is malicious without seeing the actual file content from the Data Chest. This method protects the client data privacy and confidentiality. The Data Chest is a novel research outcome at the Queen’s School of Computing. They are cryptographically secure, and it is mathematically impossible with current technology to view the plaintext within a Data Chest without end users active consent. Tiresias conducts client private malware analysis, stays compliant with data privacy laws, and utilizes state-of-the-art AI for accurate malware detection. We are open source, which keeps us accountable for what we install on our users computers.

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Superviseur du corps professoral :

Steven Ding

Étudiant :

Partenaire :

Springboard Atlantic Inc.

Discipline :

Computer science

Secteur :

Technology; Public Service, Policy, and Governance; Cyber Security

Université :

Queen's University

Programme :

Accelerate

High-performance Machine Learning Models for Financial Forecasting

Reliable long-term and short-term financial performance predictions are crucial for financial corporations to make major financial decisions and assess their financial health. Our work will develop high-performance machine learning models to detect the underlying drivers of various financial affairs such as balance, revenue, expense and cash flow, etc. And effectively using these key features to forecast future financial results which will help to improve decision making accuracy of the partner organization. Thus, improves various financial performances of our partner corporation. The well trained Artificial Intelligent Machine Learning models help understand complex financial time series data that affect the financial performance, and continuously updated according to the real time series data to achieve a sustainable financial growth both in the short-term and long-term.

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Superviseur du corps professoral :

Linglong Kong

Étudiant :

Partenaire :

ATB Financial

Discipline :

Mathematics

Secteur :

Finance and Insurance

Université :

University of Alberta

Programme :

Accelerate

Development of an online and customizable corporate challenge and integrated workplace wellness tracking and self-monitoring platform

The main objective of the project is to develop a customizable corporate challenge and integrated workplace wellness tracking and self-monitoring platform. Physical inactivity has a direct impact on employees’ medical condition, workplace stress, health metrics, self-reported work impairment, and absenteeism. Thus there is a crucial need for wellness programs that maximize employee participation to physical activity programs. Several initiatives have been developed to manage workplace wellness programs, but most of them are limited to walking and/or running, and no current system automatically monitors the participant’s response to the exercise program and provides timely and individual counseling, advices, reminders and personalized alerts. FITSTATS enterprise proposed a comprehensive approach by combining four methods that have demonstrated their efficiency in increasing physical activity and long-term adherence to an active lifestyle: (i) Administration of health-risk assessment questionnaires and clinical/biometric screenings. (ii) Team-based corporate wellness challenges (within the same company or between companies. (iii) Self-monitoring of physical activity (pedometer and self-reporting via mobile phone), automated graphing of progress, goal achievement. (iv) Automatic reminders, alerts and personal coaching advices (SMS or email) to participants via smartphone.

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Superviseur du corps professoral :

Eric Hervet

Étudiant :

Partenaire :

FITSTATS Technologies Inc

Discipline :

Computer science

Secteur :

Information and cultural industries

Université :

Université de Moncton

Programme :

Accelerate

Effets du réchauffement climatique sur les communautés de macroinvertébrés benthiques du Québec

Les larves d’insecte, les mollusques et les crustacés qui habitent le fond des rivières servent de bioindicateurs de la qualité de l’eau pour plusieurs programmes de suivi au Québec et ailleurs. Ces espèces varient énormément quant à leur tolérance à la pollution, ce qui permet de distinguer les milieux pollués des milieux naturels selon la composition des espèces qui habitent une rivière. Cependant, avec le climat qui se réchauffe, certaines espèces sensibles aux températures élevées pourraient être amenées à disparaître même en milieu naturel. En travaillant avec le Groupe d’éducation et d’écosurveillance de l’eau (G3E) et en combinant diverses approches de pointe en écoinformatique et en biostatistiques, nous proposons de déterminer la vulnérabilité des invertébrés des rivières du Québec au réchauffement climatique. Ce projet nous permettra de mieux comprendre la réponse des bioindicateurs de la qualité de l’eau au réchauffement anticipé dans les années à venir.

Voir la description complète du projet
Superviseur du corps professoral :

Vincent Fugère

Étudiant :

Partenaire :

Groupe d’éducation et d’écosurveillance de l’eau

Discipline :

Life Sciences

Secteur :

Other services (except public administration)

Université :

Université du Québec à Trois-Rivières

Programme :

Accelerate

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