Completed Projects

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

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13268 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Medical cannabis and cancer: A qualitative exploration.

In recent years Medical Cannabis (MC) has become a much discussed and ever-evolving topic in Canada. A growing number of studies have highlighted the medical and multidimensional benefits of cannabis. The question of the spiritual benefits of MC forms part of this holistic approach. Qualitative research on medical cannabis is limited, and most of it has explored the experiences of people who took cannabis illegally. it must be determined prescribed MC is effective in relieving the spiritual pain and counteracting the “fear factor” associated with a terminal diagnosis with regards to patients taking cannabis legally, under medical supervision, for the treatment of chronic pain. The research question of this study will be “What are the effects of cannabis on quality of life and suffering in cancer patients”, and it will be examined through descriptive semi-structured interviews (SSI) will be used to obtain qualitative data.

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Faculty Supervisor:

Antonio Vigano

Student:

Konstantinos Mastorakis

Partner:

Santé Cannabis

Discipline:

Medicine

Sector:

Life sciences

University:

McGill University

Program:

Accelerate

Dimensionnement mécaniste empirique de chaussées

Le dimensionnement des chaussées au Canada est actuellement effectué avec des méthodes empiriques. Ces méthodes limitent la possibilité d’utiliser de nouveaux matériaux et de nouvelles technologies qui permettraient de prolonger la durée de vie des chaussées tout en limitant leur impact négatif sur l’environnement. Il existe des méthodes de dimensionnement mécaniste-empirique, comme PavementME et des méthodes rationnelles comme la méthode française qui utilisent le comportement thermomécanique et les performances des matériaux testés en laboratoire afin de faire un dimensionnement optimal. Ces deux méthodes utilisent par contre différents intrants et différents modèles de calcul qui complexifie la comparaison entre les deux. Ce projet de recherche porte sur l’étude et l’optimisation du dimensionnement mécaniste-empirique des chaussées bitumineuses pour le Canada. Le projet est séparé en trois phases. Une première phase théorique dans laquelle des corrélations entre la procédure française et la procédure utilisée dans PavementME seront effectuées. La deuxième phase consiste en des essais de laboratoire pour avoir les données nécessaires aux différentes corrélations, mais également des essais de caractérisations de matériaux de chaussées à faible empreinte environnementale non usuels au Canada. La troisième phase porte sur la calibration des modèles de calculs à partir de résultats sur chantier.

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Faculty Supervisor:

Alan Carter

Student:

Saeed Badeli

Partner:

Colas Canada

Discipline:

Engineering - other

Sector:

Construction and infrastructure

University:

École de technologie supérieure

Program:

Elevate

Development of a neural network algorithm to quantify chronic osteoarthritic pain in rats

The progression of pain research has been limited because of the overreliance on nociceptive assessment tools. These nociceptive assessment tools only assess the sensory component of pain and neglect its emotional component. It has been suggested that behavioural tools, such as the grimace scales, can assess the emotional component of pain. The use of various molecular markers are also promising new avenues to assess pain in various experimental models. The concurrent use of all three types of assessment methods will build a more accurate and complete picture of pain. Different types of pain assessment tools will be used to assess for the presence or absence of pain in an osteoarthritis pain model in rats. Osteoarthritic pain is the greatest cause of morbidity, and chronic pain in Western countries results in enormous financial losses from reduced productivity. It is also common in companion animals. The data gathered will be used to establish a neural network algorithm to build a sensitive and specific pain quantification profile. This will be compared to previously established (invasive) standards that have been validated by our laboratory. Once the algorithm has been established, it will also be tested in dogs.

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Faculty Supervisor:

Éric Troncy

Student:

VIVIAN SZE-YUEN LEUNG

Partner:

ArthroLab Inc

Discipline:

Animal science

Sector:

Life sciences

University:

Université de Montréal

Program:

Elevate

Development of machine learning and artificial intelligence based tools to improve efficiency in financial services

Our interactions with actors in the financial services industry, including our partner company, uncovered that they possess large amounts of data pertaining to investors and markets, but have yet to extract/learn information of significant value from that data such as expected actions by clients. The industry is conscious of this, but while they are making the needful investments in IT, they report lack of academic expertise in machine learning (ML) / artificial intelligence (AI) to unlock full potentials of such investments. This project will combine academic and industrial expertise to resolve this bottleneck. We will develop ML / AI based tool to allow predictions of actions by client, specifically client churn and to help identify optimal fee structures as well as targeted populations, which Purefacts views as necessary to improve productivity and earning potential. We will also develop descriptors of accuracy of such predictions.The feasibility of the project is assured by deep expertise of each party in respective domains: this applicant’s in applied math, coding and ML, academic supervisor’s in ML methodologies, and Purefacts’ expertise in financial services to individuals and major financial institutions. Methods and tools developed in the project will be applicable to other industries.

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Faculty Supervisor:

Sergei Manzhos

Student:

Owen Ren

Partner:

PureFacts

Discipline:

Journalism / Media studies and communication

Sector:

University:

Université du Québec : Institut national de la recherche scientifique

Program:

Elevate

Développement d’un modèle mathématique thermo-hydrodynamique transitoire de la trempe thermique pour la production d’aciers de haute dureté

La fabrication de pièces en acier de haute dureté et de hautes propriétés mécaniques pour différentes applications industrielles (pétrochimiques, moule d’injection de plastique, etc.) se fait par différents traitements thermiques. Ces pièces sont fabriquées par différents procédés et subissent une trempe. Les pièces peuvent être de différentes tailles et de formes diverses. Plus la taille est importante, plus le trempage devient complexe. La trempe des grandes pièces est réalisée dans des bassins d’eau ou de composés polymériques dans lesquels les pièces sont plongées pour différentes périodes. La vitesse de refroidissement de chaque point de la pièce détermine le degré de dureté et les propriétés mécaniques de l’acier. Cependant, la taille importante de la pièce rend le contrôle du taux de refroidissement très difficile et peut résulter en la production de pièces déformées et non conformes aux propriétés exigées. La recherche proposée dans ce projet vise à modéliser mathématiquement les phénomènes thermomécanique et hydrodynamique de ce processus pour proposer des améliorations sur le plan industriel et proposer une approche du point de vue scientifique et d’ingénierie pour l’analyse des paramètres de ce procédé.

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Faculty Supervisor:

Mohammad Jahazi

Student:

Mounir Baiteche

Partner:

Finkl Steel - Sorel

Discipline:

Engineering - mechanical

Sector:

Manufacturing

University:

École de technologie supérieure

Program:

Elevate

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.

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Faculty Supervisor:

Frederic Lesage;Sylvia Villeneuve;Marie Beauséjour

Student:

Xuecong Lu

Partner:

Optina Diagnostics

Discipline:

Engineering - biomedical

Sector:

Life sciences

University:

Program:

Accelerate

Designing Machine Learning algorithms for clinical level high-precision predictions of off-target mutations in CRISPR-Cas9

This project will provide software tools to predict where a gene modifying system will make changes in the DNA of a living cell or animal. This is particularly important to investigate since this new system called CRISPR-Cas may one day be use for human gene therapy. We will test our system in animal models and develop a predictive platform to determine if the therapy will be safe for human by predicting with CRISPR-cas system should be use for specific diseases. Our work will hopefully pave the way for gene therapy applied in an ethical fashion in human.

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Faculty Supervisor:

Jacques Corbeil

Student:

Elsa Rousseau;Elina Francovic-Fontaine;Émanuel Paré

Partner:

AI-Genetika inc

Discipline:

Biochemistry / Molecular biology

Sector:

Life sciences

University:

Université Laval

Program:

Accelerate

Development of rheological methods to investigate the viscoelastic behavior and stability of paints

Paints are complex colloidal suspensions that usually contain binders (resins), solvents, pigments and rheological modifiers. From production to applications, these raw materials should remain uniformly mixed and stable during pumping processes and storage, as well as after application with brushes, rollers, spray guns, etc. It is very important to know how the paint behaves under different deformation. Rheology is the study of the deformation and flow of matter. Thus, it can provide some quantitative information, such as yield stress, viscoelastic properties, shear thickening and shear-thinning behavior, which could be useful for the production plant and for establishing correlations between final properties and formulation parameters. Hence, rheological methods will help better control the paint quality. Therefore, the aim of this work is to develop rheological methods that will generate data that could be correlated with empirical test data used in the paint industry and used to guide the production and control the quality of paints. Different from previous work, solvent evaporation will be taken into consideration in order to better simulate the actual condition in this work. The performance of paint on surfaces with different texture and roughness will also be evaluated to better predict the paint behavior after application.

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Faculty Supervisor:

Marie-Claude Heuzey

Student:

Changsheng Wang

Partner:

Industries Pépin Ltée

Discipline:

Engineering - chemical / biological

Sector:

Manufacturing

University:

École Polytechnique de Montréal

Program:

Elevate

Social impacts on users and community : methodological approach and a sustainable building case study

The buildings have an important environmental and social footprint, and to respond to that, the sustainable buildings are the new tendency of the building sector. To reduce the negative impacts associated to it, accreditations like LEED were developed, but it is mostly focused on the environmental aspects, and the social criteria are sadly neglected. This project research aims to develop a methodological approach to measure the social impacts of sustainable building projects, on the users (residents, employees) and the local community. The methodology includes a literature review on various field of research (architecture, urbanism, environmental psychology, social life cycle assessment), a series of semi-directed interviews with experts in these fields, and finally a case study of a sustainable building. The internship at Gestion Immobilière Quo Vadis will enable to develop the appropriate tool to evaluate the social performance of a sustainable building.

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Faculty Supervisor:

Cécile Bulle

Student:

Marie-Hélène Vaillancourt

Partner:

Gestion immobilière Quo Vadis Inc

Discipline:

Environmental sciences

Sector:

Construction and infrastructure

University:

Université du Québec à Montréal

Program:

Accelerate

Procedural Character Animation for Heavy Equipment Training Simulation

The proposed research project will improve CM Labs’ operator training simulators with animated human characters that interact and respond to the underlying physical simulation. This will relay important dynamical cues to the trainee about the safety and correctness of the procedures. Furthermore, the developed animation controllers will allow objects to be manipulated in a realistic and plausible manner, thus improving the overall quality of the training simulation. This will expand the animation capabilities of their current software toolkit and reduce the dependence on third party software used for the animation of 3D characters.

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Faculty Supervisor:

Sheldon Andrews

Student:

Abdesselam Guerroudj

Partner:

CMLabs

Discipline:

Engineering - computer / electrical

Sector:

Information and communications technologies

University:

École de technologie supérieure

Program:

Accelerate

Design and Prototyping of a Power Logger for Power Quality Monitoring

Power characteristics of CAE equipment are regularly requested by the customers (some customers are penalized by their service providers for poor power factor, etc.), but there are presently no quick and safe means of logging power at the main power distribution and motion control cabinets. The goal of the power logger, to be designed and prototyped, is to capture hours/days of data for quick upload to laptop for analysis.

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Faculty Supervisor:

Kamal Al-Haddad

Student:

Mohammad Sleiman

Partner:

CAE Inc.

Discipline:

Engineering - computer / electrical

Sector:

Energy

University:

École de technologie supérieure

Program:

Accelerate

Development of New Chromophores for Soil Detection on Surgical Instruments (part 2)

Small surgical instruments can be used multiple times if cleaned properly after each surgery. At the end of the cleaning process, a visual inspection of each instrument is performed in order to detect any traces of soil such as blood, tissue or any other biological materials. However, the human eye is not a flawless sensor and soils that are not visible to the naked can be present. The objective of this project is to develop new chromophores that could bind selectively to biological materials and emit light in a specific region of the electromagnetic spectrum, allowing quantitative analysis of the soil in an ambient environment without the need for complex setups that are difficult to operate. This technology will give STERIS a significant edge over the competition as no other reliable method for quantitative analysis has been commercialized yet. The results obtained in the first phase of this project are highly encouranging, but improvement still need to be done to make these molecules even more water-sluble to ensure an organic solvent-free process.

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Faculty Supervisor:

Jean-François Morin

Student:

Karimulla Mulla

Partner:

STERIS Canada Corporation

Discipline:

Chemistry

Sector:

Life sciences

University:

Université Laval

Program:

Accelerate

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