Innovative Projects Realized

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

29670 Completed Projects

2811
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4990
BC
801
MB
663
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825
SK
8841
ON
9197
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95
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568
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1088
NS

Projects by Category

Advanced nanoformulation approaches for anti-biofilm innate defense regulator peptides

In the framework of this project, it is aimed to develop a novel therapeutic approach for the treatment of chronic rhinosinusitis. Since its pathology is complex, application of the conventional antibiotics is not much efficient therefore it is defined as a difficult-to-treat disease. To overcome those challenges encountered in the usage of conventional therapeutic approaches, we are aiming to develop a novel approach that bases on novel technologies. For this purpose, Host Defense Peptides will be used as therapeutic agents which are more promising than conventional antibiotics. To deliver those peptides to the site of the diseases, naturally derived polymer(chitosan)-based nano-sized delivery systems are going to be developed. These nano-sized delivery systems will protect peptides and deliver them to the site of disease safely and enable us to use the full potential of the host defense peptides to treat chronic rhinosinusitis.

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Faculty Supervisor:

Ellen Wasan

Student:

Partner:

ABT Innovations Inc.

Discipline:

Life Sciences

Sector:

Professional, scientific and technical services

University:

University of Saskatchewan

Program:

Accelerate

Effects of enriching IgG concentration in low-quality colostrum with colostrum replacer on IgG absorption in newborn Holstein calves

Newborn calves need to ingest maternal colostrum, which is rich in immunoglobulin G (IgG) content, to help them develop their naïve immune system. This requires calves to ingest high masses of IgG, generally above 100 g in the first hours of life. In addition to feeding high IgG masses to newborn calves, research suggests that low-quality colostrum (<50 g/L IgG) can be enriched with colostrum replacer in order to increase its IgG concentration. Recently, it was reported that enriching maternal colostrum with 30 g/L IgG concentration with colostrum replacer resulted in increased IgG absorption, with none of the calves experiencing failure of passive immune (FPI) transfer. As a result, enriching low-quality colostrum could lead to desirable passive immune transfer rates providing adequate total IgG mass to the calf without compromising its absorption. The enrichment of colostrum with colostrum replacer represents an alternative for dairy farms that produce low-quality colostrum. Thus, enriching low-quality colostrum with colostrum replacer will enable farms to achieve adequate passive immune transfer rates, which will benefit the short-term health of calves, and more importantly, improve long-term health implications by preventing illness early in life.

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Faculty Supervisor:

Michael Steele

Student:

Partner:

Saskatoon Colostrum Company Ltd.

Discipline:

Life Sciences

Sector:

Manufacturing

University:

University of Guelph

Program:

Accelerate

Can Knowledge Transfer in a Genetic Algorithm Accelerate Hyper-Parameter Tuning of a Deep Neural Network?

Solid State of Mind uses automatic methods to adjust the details of the architectures and learning methods (i.e., hyperparameters) for some of its algorithms. However, this process is often slow and costly in terms of resources and energy. Through this internship proposal, Solid State of Mind wishes to study the possibility of accelerating the automatic adjustment of these hyperparameters through a knowledge transfer process. The proposed project consists in developing a genetic algorithm allowing not only the evolutionary transfer of the hyperparameters of an artificial neural network from one generation to the next, but also of the knowledge acquired by the preceding generation to the next. In other words, not only transferring innate structure through reproduction, but also having the parents teach their kids what they know before the kids leave the nest to learn on their own and try to best their parents.

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Solid State of Mind Inc

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Developing a novel pharmacoviral approach combining immunemodulatory antibody-drug conjugates with oncolytic viruses for enhanced immunotherapeutic efficacy

Vector potentiator (VEPOs) are compounds that can enhance the ability of cancer-killing viruses to replicate and kill cancer cells. However, administering these compounds can be problematic as they are inherently toxic. To remedy this problem, we will link these compounds to a highly specific antibody, Trastuzumab, forming a new antibody-drug conjugate compound, T-VEPO. By means of the specific tumour-targeting afforded by the antibody, the VEPOs will now be able to home in on tumours to improve safety, reduce toxicity, and improve effectiveness. These T-VEPOs can also be combined with gene therapy strategies to enhance efficiency of gene delivery, and overall efficacy. Our project focuses on optimizing these combinations for the best approach, and to gather evidence supporting taking these compounds to the clinical stage.

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Faculty Supervisor:

Jean-Simon Diallo

Student:

Partner:

Virano

Discipline:

Life Sciences

Sector:

Biotechnology; Health and Related Sciences & Technology; Pharmaceuticals

University:

University of Ottawa

Program:

Accelerate

A Classification and Comparative Study of Existing Fast Reinforcement Learning Techniques

Deep reinforcement learning has achieved great successes in recent years. One of the primary challenges faced by such methods is the high cost involved in training a system that demonstrates the desired competency and performance. This project aims to study and compare the available techniques for improving the training efficiency and effectiveness of reinforcement learning and establish a method of integrating such techniques to the existing models. The proposed project consists of conducting a review on fast reinforcement learning techniques, categorizing the key recent innovations to be explored, run an empirical comparative study between the corresponding techniques and create an internal library allowing to integrate these different techniques for future investigation and implementation.

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Faculty Supervisor:

Ioannis Mitliagkas

Student:

Partner:

Solid State of Mind Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program:

Accelerate

Caractérisation minéralogique pour une application de jumeau numérique

Des technologies numériques présentent un potentiel considérable pour traiter le risque géométallurgique en amont et de manière continue pendant la production. Au nombre de celles-ci, la simulation phénoménologique, la commande prédictive, l’optimisation en temps réel et les capteurs virtuels forment le noyau autour duquel pourra s’articuler une proposition pour concrétiser le volet minéralurgique de la Mine 4.0. La distribution de libération du minerai constitue la pierre angulaire pour réussir la mise en œuvre intégrée de ces différentes technologies numériques. C’est elle qui détermine les limites ultimes de rendement et de teneur au concentré pour tout procédé de séparation minérale. Si elle peut désormais être déterminée par des analyses en laboratoire pour un échantillon donné par microscopie électronique automatisée, la modélisation de cette caractéristique fondamentale n’a pas encore atteint la pleine maturité. Les travaux récents ont permis d’établir une première paramétrisation, ils n’ont cependant pas considéré d’autres caractéristiques (p. ex. la densité ou la composition minérale). Le projet de stage vise à fournir les mesures expérimentales pour combler ces lacunes.

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Faculty Supervisor:

Jocelyn Bouchard

Student:

Partner:

Université de Liège

Discipline:

Engineering

Sector:

Education

University:

Université Laval

Program:

Globalink Research Award

INTERACTIVE DISCOVERY WALKS: A MOBILE APPLICATION FOR EXPLORING TORONTO PARKS AND NATURAL HERITAGE FEATURES

City parks and outdoor recreational opportunities are among Toronto’s most valued resources. The Discovery Walks Program has been one of Toronto Parks, Forestry and Recreation’s flagship outreach efforts to engage residents and tourists alike in learning about culturally and environmentally significant areas across the city. The purpose of this project is to develop and implement a mobile interactive Discovery Walks application that delivers an in-depth and customizable experience to users. Information describing the local environment, recreational opportunities, as well as cultural and historical features will be combined with mobile geolocation technology to allow users the opportunity to participate in self-guided and interactive tours. Development of the mobile mapping tool will use HTML5 with several Application Programming Interfaces (APIs) and libraries such as jQuery and Google Maps/Fusion Tables. The project’s organizational partner is the Toronto Parks and Trees Foundation, which believes that engaging a new generation of visitors to parks and natural areas (a mobile generation) provides a contemporary approach to the promotion of many of Toronto’s most valued characteristics and may also serve to enhance stewardship of the city’s prominent natural heritage features.

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Faculty Supervisor:

Andrew Millward

Student:

Partner:

Toronto Parks and Trees Foundation

Discipline:

Sociology

Sector:

Environmental Science and Technology

University:

Toronto Metropolitan University

Program:

Accelerate

Measuring the Impact of Public Art for Main Street Communities

Public art brings numerous economic and socio-cultural benefits to main street communities, however, it can be challenging for communities to effectively measure these impacts. Therefore, this research project involves the development of a toolkit of instruments to be used within main street communities to aid in measuring the impact of public art. This will involve review of academic and industry publications and consultation and interviews held with related stakeholders to inform and guide development of the toolkit. Once complete, the toolkit will be piloted on selected main street communities and art initiatives. This project is of benefit to STEPS Public Art, as the outcome should contribute to their ability to concretely validate the benefits of investing in public art to related stakeholders, and to more effectively communicate and demonstrate these benefits for advocacy purposes.

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Faculty Supervisor:

Tom Griffin;Walter Jamieson

Student:

Partner:

STEPS Public Art

Discipline:

Sociology

Sector:

Arts, entertainment and recreation

University:

Toronto Metropolitan University

Program:

Accelerate

DMDC Open-source Asset Manager (DOsAM)

Downsview Open-source Asset Manager (DOsAM) is a research collaboration between the Carleton Immersive Media Studio (CIMS) and Northcrest Developments. The objective of the project is to develop an online, opensource asset information modelling and public engagement tool to facilitate democratized urban development.

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Faculty Supervisor:

Stephen Fai

Student:

Partner:

Downsview Metro Devco Inc

Discipline:

Sociology

Sector:

Construction and infrastructure

University:

Carleton University

Program:

Accelerate

Creative Approaches to Access: Implementing sustainable accessibility at Centre CLARK

This purpose of this research project is it implement creative access affordances at Centre CLARK, an artist-run gallery in Montreal’s Mile End Neighbourhood, to increase the participation of visitors with various disabilities and access needs. Divided into four sub-projects, this research will use interviews, access audits and participatory workshops to find creative solutions to access issues in the categories of curatorial design, structural design, digital interventions, and artist representation at Centre CLARK. This will result in a valuable and sustainable programme of access at Centre CLARK that will increase the number of visitors who will meaningfully engage with exhibits. The broader aim of this research is to increase the participation of disabled individuals in Montreal’s art and culture scenes

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Faculty Supervisor:

Arseli Dokumaci

Student:

Partner:

Centre Clark

Discipline:

Sociology

Sector:

Arts, entertainment and recreation

University:

Concordia University

Program:

Accelerate

Limits to Cryptocurrency Arbitrage

Rapid growth of cryptocurrencies has drawn the attention of practitioners, regulators, and scholars. The trading venues for these digital assets display a number of features that make them a sui generis laboratory for studying price formation and market efficiency. One notable feature of cryptocurrency is high and strongly time-varying yields, whether measured by APRs offered on staking sites, secured borrowing costs, or apparent contango of the futures curve. This study will help understand the nature of these yields-–what drives their levels and variation, and how they can persist despite arbitrage pressures. Drawing on the limits to arbitrage literature, we will explore cross exchange, cross product, and cross time variation in the implied yields of cryptocurrencies. Our preliminary results of a series of event studies suggest that cryptocurrency yields are highly sensitive to changes in market conditions such as shifts in regulatory frameworks and issuance of close substitutes.

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Faculty Supervisor:

Will Gornall

Student:

Partner:

AquaNow

Discipline:

Business

Sector:

Professional, scientific and technical services

University:

The University of British Columbia

Program:

Accelerate

Natural Language Processing for Medical Billing Code Prediction

It is extremely challenging for clinicians and health researchers to extract insights from unstructured text
data at scale. Recently, there has been a significant advancement in Natural Language Processing. Large
deep-learning models, most famous of which are the transformers, generate pre-trained embeddings and
are developed to extract insights from massive amounts of text.
This project develops AI models to assist the billing code assignment tasks. We focus on the ability of the
pre-trained embeddings to assist the identification and appropriate assignment of billing codes for the
insurable services defined by the Ontario Health Insurance Plan (OHIP). Training a model to generate rich
enough embeddings for the automatic coding task has the potential to improve predictive models for other
clinical NLP tasks, such as symptom extraction, cohort analysis, disease tracking, adverse effect
identification.

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Faculty Supervisor:

Helen Chen

Student:

Partner:

IntelAGENT Billing

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

Accelerate

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