Projets novateurs réalisés

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

31 620 projets complétés

2978
AB
5221
C.-B.
856
MB
696
NL
899
SK
9419
ON
9858
QC
98
PE
619
NB
1192
NS

Projets par catégorie

Increasing Innovation and Collaboration Opportunities in the Oceans Sector

Ocean industries in Canada contributes about $31.7 billion a year to the economy and make up 1.6 percent of the country’s gross domestic product. Canada has tremendous opportunity to build on the outcomes achieved to date, harness our collective national strengths and resources. Canada’s ocean industries sector can help drive our economic recovery. It will take a diverse and inclusive talent base to help us achieve this objective. Today, there are significant talent hurdles that Canada must address to increase its competitiveness, innovation and market leadership in this global sector. Today, women comprise only 2% of the global ocean industry. According to research from McKinsey, by advancing women’s economic empowerment, Canada can add $150 billion in incremental GDP by 2026. The proposed Canadian Women in Ocean Industries Leadership initiative – CWOIL will directly address this goal, increasing Canada’s leadership, competitiveness and contribution to ocean industries. It will foster an ocean talent pipeline by attracting women and under-represented people including equity seeking groups: 2SLGBTQ+ community; persons with disabilities; and racial groups, increasing the competitiveness of this industry.

Voir la description complète du projet
Superviseur du corps professoral :

Shawn Pendergast

Étudiant :

Partenaire :

Oceans Advance

Discipline :

Physics

Secteur :

Other services (except public administration); Professional, scientific and technical services

Université :

Memorial University of Newfoundland

Programme :

Business Strategy Internship

Developing a therapeutic defined microbial community for the treatment of Depression

Depression is an illness that impacts 1 in 6 people during their lifetime. While there exist a number of medications to treat this illness, half of those whom need them either do not respond or can not tolerate current medications because of side effects. Therefore there is an urgent need to develop new ways to treat depression. One of the areas being explored as a potential option is based on what is now known as a strong link between the bacteria that live in our gut and the brain. We are now looking at what specific bacteria are responsible for this effect so that we can design a bacterial based medication for the treatment of depression. We will identify bacteria which are different between people with and without depression and then use machine learning to predict which specific strain will have the potential to improve depressive symptoms, treat depression.

Voir la description complète du projet
Superviseur du corps professoral :

Prameet Sheth

Étudiant :

Partenaire :

Taylored Biotherapeutics

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

Queen's University

Programme :

Accelerate

An accelerated COVID-19 diagnosis tool using interpretable deep learning

This research aims to develop an efficient machine learning model to detect COVID-19 patients by using their cough signals. The model is trained using thousands of audio recordings of cough signals from different subjects. The audio signals can be converted into spectrogram images that can be visually inspected to determine relevant regions of interest. There are two challenges in building the prediction models, including 1) complexity of finding the best architecture of the machine learning model and 2) understanding the reasons behind specific predictions. Since we model the problem as a set of images, we can exploit previously published state-of-the-art deep learning models under certain modifications. Further, we address interpretable approaches that can identify different conditions and highlight areas of interest while predicting positive or negative COVID-19 cases. Domain experts can then look at the outcome and confirm if the right parts of the cough spectrum are considered.

Voir la description complète du projet
Superviseur du corps professoral :

Othman Soufan

Étudiant :

Partenaire :

Zensark, Inc.

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

St. Francis Xavier University

Programme :

Accelerate

Building the Evaluation Capacity of the Alberta Mentoring Partnership – Part 2

The goal of the proposed research project is to assist the Alberta Mentoring Partnership (AMP) is building the capacity of Alberta mentoring organizations to evaluate their mentoring programs using a community-driven approach. In particular, this year, we hope to develop and implement more accessible supports that will build the capacity of those organizations who have lower pre-existing knowledge, skills, and motivation to evaluate their programs. We hope that by helping these organizations build their capacity to gather evidence on their programs, they will be able to use that evidence to inform the best possible programs, practices, and policies for supporting children, youth, and families.

Voir la description complète du projet
Superviseur du corps professoral :

Rebecca Gokiert

Étudiant :

Partenaire :

Boys and Girls Clubs Big Brothers Big Sisters of Edmonton and Area

Discipline :

Sociology

Secteur :

Education; Health and Related Sciences & Technology; Other services (except public administration)

Université :

University of Alberta

Programme :

Accelerate

Efficacy of Self Myofascial Release Techniques

Roller-massager is a convenient tool that uses upper body strength (rather than body weight) to move the roller across a muscle to imitate massage effects. Massage has been used to remove the knots and tensions sometimes found in muscle that can lead to pain and stiffness. It is not known if a roller massager can on its own or when used with massage can help alleviate the pain and stiffness associated with these muscle restrictions (knots or cramps). To date there are no published studies to validate the effectiveness of the roller massager. Therefore, the aims of the present study are to investigate the effectiveness of using Roller-massager as a technique similar to massage (i.e. removal of trigger points) to improve muscle pain, range of motion (ROM), strength, endurance and muscle activation. Secondly we will compare the results of the roller-massager as a follow up protocol after trigger point release by a massage therapist (combination of two techniques). Hopefully these results will provide insights into how the roller massager works and the best ways to use it in order to alleviate pain and stiffness

Voir la description complète du projet
Superviseur du corps professoral :

David Behm

Étudiant :

Partenaire :

ProActive Physiotherapy Inc

Discipline :

Life Sciences

Secteur :

Health and Related Sciences & Technology

Université :

Memorial University of Newfoundland

Programme :

Accelerate

Modeling disease networks using graph machine learning

Using Simmunome simulations, researchers and companies can predict the likelihood of success or failure before embarking on, or continuing with, costly clinical development programs. We focus on understanding the biological system and applying this towards higher accuracy in disease simulations. We achieve this by using different types of data from various public and proprietary sources to better characterize the biological system. This process requires increasing complexity in our AI algorithms which therefore significantly increases the complexity of the computational models. The aim of this partnership is to research and apply cutting edge machine learning approaches from the fast-moving AI domain graph machine learning by working with experts in this area.

Voir la description complète du projet
Superviseur du corps professoral :

Gilles Caporossi

Étudiant :

Partenaire :

Simmunome Inc.

Discipline :

Life Sciences

Secteur :

Professional, scientific and technical services

Université :

HEC Montréal

Programme :

Accelerate

Enhancing ecosystem service provisioning by natural enemies of arthropod herbivores in the agroecosystems of BC’s northern central interior.

Arthropods – particularly spiders and certain insects – provide pest management services to agricultural ecosystems in BC and elsewhere. However, very little work has been done to understand either the pest insects or the insect and spider species that might control them in BC’s central interior agroecosystems. For instance, this is no current catalog of the species that are present in the region, and any prior assessments may be outdated due to climate change-induced shifts in geographic range of species. We plan to catalog the pest and predator biodiversity and to assess how landscape features (e.g. fields with surrounding forested land, nearby woodlots, hedgerows, etc.) encourage the growth and health of beneficial spiders and insects to help to increase agricultural productivity in this area while reducing pesticide use.

Voir la description complète du projet
Superviseur du corps professoral :

Dezene Huber;Jasmine Janes

Étudiant :

Partenaire :

BC Forage Council (BCFC)

Discipline :

Life Sciences

Secteur :

Agriculture

Université :

University of Northern British Columbia

Programme :

Accelerate

Mechatronics Co-op Position

Improving and optimizing a full scale prototype which is being used to develop electric vehicle control software

Voir la description complète du projet
Superviseur du corps professoral :

Norah McRae

Étudiant :

Partenaire :

Potential Motors

Discipline :

Engineering

Secteur :

Information and cultural industries; Professional, scientific and technical services; Transportation and warehousing

Université :

University of Waterloo

Programme :

Business Strategy Internship

La gestion des revenus en restauration; la pratique en période de reprise après Covid-19 pour les établissements de restauration à service complet

Ce projet de recherche porte sur les pratiques de gestion des revenus en restauration en période de reprise à la suite de la pandémie Covid-19, plus particulièrement pour les restaurants qui offrent un service complet.
Les restaurateurs ont l’une des marges bénéficiaires les plus faibles de l’industrie hôtelière et en cette période de reprise, ils voient leur marge se réduire encore en raison de l’augmentation prévue des coûts de main-d’oeuvre et de nourriture. Ils sont encore soumis à des restrictions sanitaires qui font qu’ils ne peuvent pas opérer à pleine capacité. Les restaurateurs vont donc chercher des moyens de renouer avec les profits afin de rester en activité. Une façon d’augmenter les revenus est d’utiliser des stratégies dîtes de gestion des revenus. La question qui se pose est : comment et jusqu’à quel point ces stratégies sont-elles mises en oeuvre pendant et après la pandémie de la Covid-19?
Des entrevues avec des gestionnaires de restaurants indépendants et des gestionnaires des revenus d’hôtels permettront d’identifier les bonnes pratiques ainsi que de mieux comprendre la nature des difficultés rencontrées.

Voir la description complète du projet
Superviseur du corps professoral :

Riadh Ladhari;Marie-Claire Louillet

Étudiant :

Partenaire :

Société des Casinos du Québec

Discipline :

Business

Secteur :

Arts, entertainment and recreation

Université :

Université Laval

Programme :

Accelerate

Modeling and optimization for designing smart freight platform

This research is motivated by the need to coordinate matching and pricing decisions in resource sharing platforms. In general, matching decisions can be seen from different standpoints of shippers, carriers, and the platform. We are particularly interested in investigating the matching problem considering all standpoints simultaneously, which has not received much attention in the literature review. Furthermore, trending toward new freight procurement mechanisms managed by a third party increases the importance of applying dynamic pricing policies to respond to market fluctuations and satisfy all trading parties. This mechanism needs to valorize real-time data to provide the best price recommendation according to the market circumstances. The recommended price can facilitate the trading mechanisms and provide a balanced tradeoff between different sides of the market. This trade-off can, in turn, result in less need for negotiation in the market and more facilitated transportation service procurement mechanisms.

Voir la description complète du projet
Superviseur du corps professoral :

Samira Keivanpour;Maha Ben Ali

Étudiant :

Partenaire :

ShipHaul Logistics Inc.

Discipline :

Engineering

Secteur :

Professional, scientific and technical services; Transportation and warehousing

Université :

Polytechnique Montréal

Programme :

Accelerate

Anomaly Detection in Highly Noisy Signals from Electrical Rotating Machines

Equipment failure is the primary source of unplanned downtime in industries working with rotating electrical machines. Fault detection at the early stages is an essential solution for reducing this downtime. Condition monitoring of machinery is the process of capturing and monitoring parameters such as vibrations to identify a developing fault. This project uses the data resulting from condition monitoring to develop anomaly detection algorithms for improving early-stage fault detection and diagnosis processes.

Voir la description complète du projet
Superviseur du corps professoral :

Faramarz Samavati

Étudiant :

Partenaire :

AB Cognitive System Inc.

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

University of Calgary

Programme :

Accelerate

Développement de nouveaux algorithms pour accroître la precision, la robustesse et la reproductibilité d’instruments intelligents en réponse aux exigence de l’industrie

Le monitoring en temps réel des bioprocédés permet d’augmenter les performances et de réduire les pertes. La principale
difficulté provient de la quantité limitée d’information accessible en temps réel et des phénomènes complexes et fortement
non-linéaires en présence. Ainsi, les algorithmes statistiques conventionnels ne permettent pas d’interpréter adéquatement et
avec la précision requise en pharmaceutique les signaux mesurés par des sondes spectrales. L’objectif du projet est de
développer de nouveaux algorithmes non-linéaires complexes qui permettront de réaliser la transformation de signaux
spectraux bruts complexes en indicateurs de performances précis, robustes et reproductibles. Ce projet permettra de
développer un algorithme qui améliore les performances d’estimation en temps réel des concentrations de produits dans des
cultures cellulaires pharmaceutiques. Ces algorithmes utiliseront l’apprentissage profond pour modéliser les phénomènes nonlinéaires
et s’appuieront sur le transfert d’apprentissage pour permettre l’entraînement avec peu de données.

Voir la description complète du projet
Superviseur du corps professoral :

François Grondin

Étudiant :

Partenaire :

BioIntelligence Technologies inc

Discipline :

Computer science

Secteur :

Manufacturing; Professional, scientific and technical services

Université :

Université de Sherbrooke

Programme :

Accelerate

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Mitacs — Retour sur innovation

Mitacs est financé par le gouvernement du Canada, le gouvernement de l’Alberta, le gouvernement de la Colombie-Britannique, Research Manitoba, le gouvernement du Nouveau-Brunswick, le gouvernement de la Nouvelle-Écosse, le gouvernement de l’Ontario, Innovation PEI, le gouvernement du Québec, le gouvernement de la Saskatchewan, le gouvernement de Terre-Neuve-et-Labrador et le gouvernement du Yukon.