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
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856
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696
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899
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9419
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9858
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98
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619
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1192
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Projets par catégorie

Efficacité énergétique en serres agricoles

Une composante importante de l’alimentation durable, dans le contexte du développement durable, est de mettre le consommateur beaucoup plus près, psychologiquement et physiquement, de la production de sa nourriture quotidienne. En particulier, la culture en serre en climat nordique (ex. Québec, Canada) diminue la distance entre le consommateur et le producteur d’aliments à haute teneur nutritive. Cependant, la consommation d’énergie par ce moyen reste élevée. L’organisme partenaire souhaite bénéficier des développements les plus récentes dans les concepts d’efficacité énergétique et de les incorporer dans la conception de serres pour la culture locale en serre. L’objectif principal est d’évaluer le potentiel pour la minimisation de l’utilisation de l’énergie de haute qualité (électricité et carburants fossiles) dans une étape de préconception d’une serre innovatrice modulaire d’environ 3 000 pieds carrés pour la culture de tomates, sur un terrain se situant à Bishopton, Québec.

Voir la description complète du projet
Superviseur du corps professoral :

Mikhail Sorin

Étudiant :

Partenaire :

Les Écuries RD S.E.N.C.

Discipline :

Engineering

Secteur :

Agriculture

Université :

Université de Sherbrooke

Programme :

Accelerate

Diversification of atmospheric perils: top-down flood and tropical cyclone models with applications to the (re)insurance industry

Just like the idiom says, to not put all your eggs in one basket, insurance and reinsurance are founded on diversification. Pooling among policyholders, lines of businesses, perils, etc., are typical ways to achieve an adequate level of diversification to lower required capital. However, there are several factors that can act to hamper diversification, even on an international basis: namely the concentration of wealth and population in a few large countries (USA, China), and natural climate phenomena such as El Niño and climate change that both affect many countries. The research project aims to analyze how these factors may affect the reinsurance industry’s capability to diversify risks and thus, its ability to insure losses from natural hazards that are too significant for local insurance companies or governments to assume. As a result of this research, the partner will improve its risk management and long-term solvency, in addition to reduce costs to its clients and enhance their protection.

Voir la description complète du projet
Superviseur du corps professoral :

Mathieu Boudreault

Étudiant :

Partenaire :

AXA XL;AXA XL (UK)

Discipline :

Mathematics

Secteur :

Finance and Insurance; Professional, scientific and technical services

Université :

Université du Québec à Montréal

Programme :

Accelerate

Perioperative Opioid Usage Quality Improvement [CDTS-PDF2] – Year two

Our aim is to use machine-learning to improve treatment of post-surgical pain in children and adults. Most people addicted to opioids were initially exposed through the treatment of pain from trauma and/or surgery. The opioid crisis is reaching the pediatric population, in whom effective post-surgical pain management, with less reliance on prescription of opioids, is more important than ever. Recent advances in machine-learning, combined with approaches to patient-oriented research, provide significant prospects for a learning health system. Such a system could risk-stratify children and adults before surgery, so that pre-habilitation and optimized analgesic combinations can be employed to reduce persistent post-procedural pain. Artificial intelligence-augmented systems will also give clinicians actionable feedback on their practice, so they can learn how to improve their care, reduce their patients’ risk further, and
help them to recover more quickly from their procedure.
Postdoctoral fellows will lead day-to-day project activities and spend significant time working with both our clinical sites (St. Paul’s Hospital and BC Children’s Hospital) as well as industry partners (Careteam and Xerus) who will benefit from their methods expertise, ability to collaborate with clinicians and academic researchers, design, implementation, and evaluation skills.

Voir la description complète du projet
Superviseur du corps professoral :

Matthias Görges

Étudiant :

Partenaire :

Careteam Technologies Inc

Discipline :

Engineering

Secteur :

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

Université :

The University of British Columbia

Programme :

Elevate

Perioperative Opioid Usage Quality Improvement [CDTS-PDF2]

Our aim is to use machine-learning to improve treatment of post-surgical pain in children and adults. Most people addicted to opioids were initially exposed through the treatment of pain from trauma and/or surgery. The opioid crisis is reaching the pediatric population, in whom effective post-surgical pain management, with less reliance on prescription of opioids, is more important than ever. Recent advances in machine-learning, combined with approaches to patient-oriented research, provide significant prospects for a learning health system. Such a system could risk-stratify children and adults before surgery, so that pre-habilitation and optimized analgesic combinations can be employed to reduce persistent post-procedural pain. Artificial intelligence-augmented systems will also give clinicians actionable feedback on their practice, so they can learn how to improve their care, reduce their patients’ risk further, and
help them to recover more quickly from their procedure.
Postdoctoral fellows will lead day-to-day project activities and spend significant time working with both our clinical sites (St. Paul’s Hospital and BC Children’s Hospital) as well as industry partners (Careteam and Xerus) who will benefit from their methods expertise, ability to collaborate with clinicians and academic researchers, design, implementation, and evaluation skills.

Voir la description complète du projet
Superviseur du corps professoral :

Matthias Görges

Étudiant :

Partenaire :

Careteam Technologies Inc

Discipline :

Engineering

Secteur :

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

Université :

The University of British Columbia

Programme :

Elevate

Using Social Media to Address Misinformation about HPV and Influenza Vaccines: An Intervention on Facebook

Vaccination is a widely accepted public health intervention, yet public confidence in vaccines is affected by misinformation about vaccine safety shared mainly on Facebook. This project aims to increase availability of credible information about HPV vaccines and influenza vaccines on Facebook. It includessix phases: 1) identify the most relevant approaches for marketing campaigns; 2) conduct pre-campaign surveys with parents and health professionals; 3) prepare advertisements to address misinformation; 4) publish advertisements on Facebook; 5) conduct post-campaign surveys to assess the success of the campaigns; 6) assess the relevance of the campaign strategies for long-term outcomes. This study focuses on the phase 2. We will conduct two pre-campaign surveys: one with parents to gather their knowledge and attitudes regarding HPV vaccines and vaccination, and another one with health professionals to gather their knowledge and attitudes regarding influenza vaccines and vaccination. This study will help prepare advertisements addressing misinformation on Facebook.

Voir la description complète du projet
Superviseur du corps professoral :

Kieran O’Doherty

Étudiant :

Partenaire :

Canadian Public Health Association

Discipline :

Sociology

Secteur :

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

Université :

University of Guelph

Programme :

Accelerate

Perioperative Opioid Usage Quality Improvement [CDTS-PDF1] – Year two

Our aim is to use machine-learning to improve treatment of post-surgical pain in children and adults. Most people addicted to opioids were initially exposed through the treatment of pain from trauma and/or surgery. The opioid crisis is reaching the pediatric population, in whom effective post-surgical pain management, with less reliance on prescription of opioids, is more important than ever. Recent advances in machine-learning, combined with approaches to patient-oriented research, provide significant prospects for a learning health system. Such a system could risk-stratify children and adults before surgery, so that pre-habilitation and optimized analgesic combinations can be employed to reduce persistent post-procedural pain. Artificial intelligence-augmented systems will also give clinicians actionable feedback on their practice, so they can learn how to improve their care, reduce their patients’ risk further, and
help them to recover more quickly from their procedure.
Postdoctoral fellows will lead day-to-day project activities and spend significant time working with both our clinical sites (St. Paul’s Hospital and BC Children’s Hospital) as well as industry partners (Careteam and Xerus) who will benefit from their methods expertise, ability to collaborate with clinicians and academic researchers, design, implementation, and evaluation skills.

Voir la description complète du projet
Superviseur du corps professoral :

Matthias Görges

Étudiant :

Partenaire :

Careteam Technologies Inc

Discipline :

Engineering

Secteur :

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

Université :

The University of British Columbia

Programme :

Elevate

Developing rAAV-mediated retinal gene therapy to improve vision for ZellwegerSpectrum Disorder – Year Two

Peroxisome Biogenesis Disorders of the Zellweger Spectrum (PBD-ZSD) are a group of inherited genetic disorders caused by mutations in any one of 13 PEX genes. Individuals with the common PEX1-G843D mutation consistently develop a retinopathy that progresses to blindness. To test whether we could slow visual loss in these patients, we performed a proof-of-concept trial for PEX1 retinal gene augmentation therapy using our mouse model homozygous for the equivalent PEX1-G844D mutation. We found that expression of functional PEX1 in the retina of this mouse model resulted in twofold improvement of retinal function over 6-7 months, and was effective at both earlier and later disease stages. In partnership with AmorChem Therapeutics, we will advance the preclinical, and eventually clinical, development of this therapy. Dr. Argyriou will lead the maturation plan over the 2-year Postdoctoral fellowship. The project includes experiments to establish the optimal design of the clinical gene delivery vector, dose, and efficacy, as well as establishing a business plan towards commercialization. These experiments will lead to safety and toxicology studies in nonhuman primates and an ultimate transition to the clinic.

Voir la description complète du projet
Superviseur du corps professoral :

Nancy Elise Braverman

Étudiant :

Partenaire :

Amorchem Therapeutics Inc

Discipline :

Life Sciences

Secteur :

Finance and Insurance; Professional, scientific and technical services

Université :

Research Institute of the McGill University Health Centre

Programme :

Elevate

Developing rAAV-mediated retinal gene therapy to improve vision for ZellwegerSpectrum Disorder

Peroxisome Biogenesis Disorders of the Zellweger Spectrum (PBD-ZSD) are a group of inherited genetic disorders caused by mutations in any one of 13 PEX genes. Individuals with the common PEX1-G843D mutation consistently develop a retinopathy that progresses to blindness. To test whether we could slow visual loss in these patients, we performed a proof-of-concept trial for PEX1 retinal gene augmentation therapy using our mouse model homozygous for the equivalent PEX1-G844D mutation. We found that expression of functional PEX1 in the retina of this mouse model resulted in twofold improvement of retinal function over 6-7 months, and was effective at both earlier and later disease stages. In partnership with AmorChem Therapeutics, we will advance the preclinical, and eventually clinical, development of this therapy. Dr. Argyriou will lead the maturation plan over the 2-year Postdoctoral fellowship. The project includes experiments to establish the optimal design of the clinical gene delivery vector, dose, and efficacy, as well as establishing a business plan towards commercialization. These experiments will lead to safety and toxicology studies in nonhuman primates and an ultimate transition to the clinic.

Voir la description complète du projet
Superviseur du corps professoral :

Nancy Elise Braverman

Étudiant :

Partenaire :

Amorchem Therapeutics Inc

Discipline :

Life Sciences

Secteur :

Finance and Insurance; Professional, scientific and technical services

Université :

Research Institute of the McGill University Health Centre

Programme :

Elevate

Perioperative Opioid Usage Quality Improvement [CDTS-PDF1]

Our aim is to use machine-learning to improve treatment of post-surgical pain in children and adults. Most people addicted to opioids were initially exposed through the treatment of pain from trauma and/or surgery. The opioid crisis is reaching the pediatric population, in whom effective post-surgical pain management, with less reliance on prescription of opioids, is more important than ever. Recent advances in machine-learning, combined with approaches to patient-oriented research, provide significant prospects for a learning health system. Such a system could risk-stratify children and adults before surgery, so that pre-habilitation and optimized analgesic combinations can be employed to reduce persistent post-procedural pain. Artificial intelligence-augmented systems will also give clinicians actionable feedback on their practice, so they can learn how to improve their care, reduce their patients’ risk further, and
help them to recover more quickly from their procedure.
Postdoctoral fellows will lead day-to-day project activities and spend significant time working with both our clinical sites (St. Paul’s Hospital and BC Children’s Hospital) as well as industry partners (Careteam and Xerus) who will benefit from their methods expertise, ability to collaborate with clinicians and academic researchers, design, implementation, and evaluation skills.

Voir la description complète du projet
Superviseur du corps professoral :

Matthias Görges

Étudiant :

Partenaire :

Careteam Technologies Inc

Discipline :

Engineering

Secteur :

Health and Related Sciences & Technology; Information and cultural industries; Professional, scientific and technical services

Université :

The University of British Columbia

Programme :

Elevate

Étude préliminaire de la situation d’envahissement du nerprun: Revue de littérature et mise en place d’un dispositif expérimental pour une gestion efficace du nerprun dans les bois de Laval

Depuis des milliers d’années, les déplacements des humains ont introduit des espèces exotiques dans des milieux où elles ne se retrouvaient pas naturellement. Une de ces espèces, le nerprun, a été introduite en Amérique du nord vers la fin des années 1800. Elle prend aujourd’hui de plus en plus de place dans nos environnements forestiers, menaçant les l’équilibre des écosystèmes forestiers. Le présent projet vise à faire l’inventaire de la dispersion des colonies de nerprun dans 3 bois de Laval afin de proposer des méthodes pour leur éradication, en prenant en compte les risques pour la faune, la flore indigène et les milieux avoisinants. Il s’agit donc de répertorier l’information pour élaborer un dispositif expérimental dans le but de créer un outil d’aide à la décision. Cet outil permettra de comprendre les différentes méthodes d’éradication du nerprun, facilitant ainsi les choix des gestionnaires des bois urbains à Laval.

Voir la description complète du projet
Superviseur du corps professoral :

Daniel Kneeshaw;Ira Tanya Handa

Étudiant :

Partenaire :

CANOPÉE

Discipline :

Earth science

Secteur :

Professional, scientific and technical services

Université :

Université du Québec à Montréal

Programme :

Accelerate

Modelling glacial overdeepenings and future lake locations for the western Canadian Cordillera

The Canadian Western Cordillera is a glaciated region within Canada which has seen significant glacial retreat due to recent climatic warming – estimated glacial volume reduction by 70% by 2100. As a result of this glacier retreat, depressions beneath are exposed (known as glacial overdeepenings) and lakes begin to form, dammed by the bedrock or deposited moraines. These can have important consequences both in terms of hazards and the possibility of economic benefits, including hydroelectric dams and a water resource. This research aims to predict where these glacial overdeepenings may occur under the present-day glacial ice in the British Columbia and Alberta glaciers. We will estimate the depth and volume of the potential lakes and understand how they will develop by using modern-day analogues, currently transitioning from a glaciated to deglaciated environment. This will aid future decision making of the local governments into water availability in an unpredictable climate.

Voir la description complète du projet
Superviseur du corps professoral :

Dan Shugar

Étudiant :

Partenaire :

Newcastle University

Discipline :

Earth science

Secteur :

Sustainability & the Environment; Water; Environmental Science and Technology

Université :

University of Calgary

Programme :

Globalink Research Award

Few-shot Generative Adversarial Networks

The most successful computer vision approaches are based on deep learning architectures, which typically require a large amount of labeled data. This can be impractical or expensive to acquire. Therefore, few-shot learning techniques were proposed to learn new concepts with just one or few annotated examples. However, unsupervised methods such as generative adversarial networks (GANs) still require a huge amount of data to be trained. As such, this project will focus on few-shot learning for GANs. This means that at inference time, the user can input a few images of a class never seen before by the model and the model can generate new images from that class. The proposed project will use a standard

Voir la description complète du projet
Superviseur du corps professoral :

Christopher Pal;Derek Nowrouzezahrai

Étudiant :

Partenaire :

ServiceNow Canada

Discipline :

Computer science

Secteur :

Professional, scientific and technical services

Université :

McGill University; Polytechnique Montréal

Programme :

Accelerate

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