Innovative Projects Realized

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

30156 Completed Projects

2861
AB
5059
BC
812
MB
673
NL
842
SK
8957
ON
9368
QC
96
PE
579
NB
1120
NS

Projects by Category

Investigating the knowledge, attitudes, practices, and perceptions of the eco-bio-social drivers of dengue virus transmission among residents living in urban São Paulo city, Brazil

Dengue virus (DENV) is a virus that is spread by some mosquitoes in Latin America and the Caribbean. In recent years, DENV has spread rapidly, particularly in Brazil. The climate greatly influences the biology of the mosquito and the virus. On top of that, there are social factors that allow DENV to spread including living in big cities where people are close together with mosquitoes living among them. Research has shown that misconceptions about DENV still exist. An individual’s knowledge, attitudes, practices, and perceptions (KAPP) about transmission pathways, treatment, and prevention can influence dengue infection. This is why it is important to understand the KAPP regarding the ecological, biological, and social (eco-bio-social) drivers of DENV outcomes among members of urban Brazilian communities. It is also critical to understand the relevant socioeconomic and demographic factors associated with dengue KAPP. We are proposing a parallel mixed-methods research project in collaboration between McMaster University in Canada and the University of Sao Paulo in Brazil. The implementation of this project will promote knowledge exchange, capacity building, cross-cultural understanding, and policy/public health impact. Findings from this study may inform dengue health education campaigns and vector control awareness in the study region and across the globe.

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Faculty Supervisor:

Lawrence Mbuagbaw

Student:

Partner:

Universidade de São Paulo

Discipline:

Sociology

Sector:

Education

University:

McMaster University

Program:

Globalink Research Award

The Atlantic Canada Retrofit Accelerator

This project aims to develop a systematic approach to deep energy efficiency retrofits for prevalent building archetypes in Atlantic Canada, resulting in a comprehensive roadmap for regional and national deep retrofit acceleration. As part of a larger federally-funded endeavor, it contributes to the establishment of regional hubs, known as “Retrofit Accelerators,” aimed at guiding building owners in implementing deep retrofit projects across Canada and aiding in meeting emission reduction targets outlined in the 2030 Emissions Reduction Plan. Collaborating with over 20 partner organizations, including NSCC, the project encompasses diverse research goals: understanding building quantities and types, establishing baseline data for current building conditions and energy performance, developing a comprehensive database, tracking retrofit progress, and assessing housing affordability and demographics. NSCC’s pivotal role involves academic support to identify building archetype data and articulate a retrofit roadmap for Atlantic Canada. The project aims to create tools, share findings, and foster collaboration for widespread progress.

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Faculty Supervisor:

Wayne Groszko

Student:

Partner:

ReCover

Discipline:

Engineering

Sector:

Construction and infrastructure; Professional, scientific and technical services

University:

Nova Scotia Community College

Program:

Accelerate

Single protein isolation in Free flow counter-gradient focusing

The free-flow counter gradient focusing technology provides a solution to tackling the problem of separating and collecting large amounts of protein sample. This technology is to be high performance and scalable, and is meant to compete against current batch mode protein separation techniques. The intern will assist in developing the experimental set-up necessary to validate this protein separation system. They will also be responsible for attaining the data necessary to continue towards the goal or rendering this technology scalable. The partner organization AMS is expected to benefit from the developed technology by gaining stake in a technology that they can leverage for their soft robotic and microfluidic applications.

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Faculty Supervisor:

Carolyn Ren

Student:

Partner:

Air Microfluidic Systems Inc

Discipline:

Engineering

Sector:

Manufacturing

University:

University of Waterloo

Program:

Accelerate

Applying Artificial Intelligence for Sustainable Agricultural Management of Soil Water and Nutrients in Saskatchewan

To feed the globally increasing population and increasing costs of producing food, this project seeks to use artificial intelligence to develop an app to assist in sustainable agriculture management. By monitoring soil moisture, nutrient levels, climate and crop type, a machine learning algorithm will link the farming action (application of fertilizer, flood irrigation and other) and the crop output to capture current farming practises. Also, the participating farmers will gain access to the real-time, collected soil moisture and nutrient data to aid in their farming decision making. This improved understanding of soil moisture, nutrient levels, climate, crop and related data will lead to developing and adapting future sustainable farming strategies as climate change continues.

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Faculty Supervisor:

Denise Stilling

Student:

Partner:

Custom Agricultural Intelligence Inc.

Discipline:

Earth science

Sector:

Manufacturing

University:

University of Regina

Program:

Accelerate

Outils soutenus par une IA pour accompagner les propriétaires de puits privés

Une eau potable de qualité est essentielle à la santé de tous. Plus d’un dixième des ménages québécois consomment l’eau
de puits privés. Il incombe aux propriétaires de ces puits de s’assurer de la potabilité de leur eau. Ceux qui suivent la
recommandation consistant à faire régulièrement analyser leur eau éprouvent souvent des difficultés à comprendre leurs
résultats d’analyse et, en cas de contamination, à identifier les mesures correctrices appropriées. Le présent projet vise à
déterminer si des outils soutenus par une intelligence artificielle (IA) pourrait permettre d’accompagner les propriétaires de
puits privés à travers ce processus et de soutenir leur prise de décision. Ce faisant, ce projet souhaite ultimement contribuer
à prévenir les maladies d’origine hydrique chez les ménages ruraux. Les apprentissages effectués dans le cadre de ce projet
alimenteront également le développement d’une offre de services en IA pour le Centre intégré de santé et de services sociaux
de Chaudière-Appalaches.

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Faculty Supervisor:

Mehdi Adda;Patrick Archambault

Student:

Partner:

Centre intégré de santé et de services sociaux de Chaudière-Appalaches

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology

University:

Université du Québec à Rimouski; Université Laval

Program:

Accelerate

New Analytical Approaches to Social Media Data

In general, market research agencies use surveys and social media analytics as different ways to explain customer behaviour. Vision Critical, a Vancouver software and market research firm, is currently testing how the two can be combined to generate new findings. While the company intends to offer this approach as a service, various important questions to be answered beforehand: for example, how can panelists’ activities on social media correlate with their responses to survey questions? Which factors or benefits motivate respondents to take part in social media integration studies? Analyzing results of past and upcoming pilot projects in SPSS, this project will address these points so that Vision Critical can extend its range of services with this new offering and effectively compete with other large agencies that offer similar social media features. The findings will be published in white papers and also documented in a project report for the Master of Publishing program at SFU.

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Faculty Supervisor:

John Maxwell

Student:

Partner:

Vision Critical

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

Simon Fraser University

Program:

Accelerate

Lead Scoring in using Artificial Intelligence and Network Ranking Methods

This research project works to improve the industry partner’s customer engagement prediction by using advanced analytics. The project focuses on understanding which contacts are most likely to engage with a customer’s marketing campaign, such as opening emails, clicking links, or making transactions. By analyzing a large amount of data, the research team will develop models and techniques to accurately predict customer behavior. This will help the industry partner provide targeted and effective communication strategies for their customers, enhancing their overall experience. The project will also contribute to the academic discourse on data analysis and predictive modeling, potentially offering new insights and strategies for improving customer engagement in the digital marketing domain.

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Faculty Supervisor:

Stanko Dimitrov

Student:

Partner:

Constant Contact

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program:

Accelerate

Alisa McNaughton – 51 Food and AgTech Fund

Food & agtech innovation are important contributors to Canada’s innovation ecosystem and the productivity of Canada’s agriculture and food sectors. This project will support the development of novel business processes at an emerging Canadian venture capital firm, as well as research into early stage science and technology companies within the sector through market analysis and due diligence on investment opportunities.

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Faculty Supervisor:

Ari Pandes

Student:

Partner:

The51 Food and AgTech Fund

Discipline:

Business

Sector:

Agriculture and Food; Biotechnology; Finance and Insurance

University:

University of Calgary

Program:

Business Strategy Internship

Time-Series Based Machine Learning

The proposed project aims to enhance the interpretability of machine learning models, particularly in the context of time series data analysis. By extending the capabilities of the tsfresh library, a widely used tool for time series feature extraction, the project seeks to make complex models more transparent and understandable. This effort will improve confidence in the predictions and decisions made by these models, benefiting both research institutions (University of Toronto and University of Auckland) and industry partners. Additionally, the project addresses specific challenges in benchmark datasets like the UCR Time Series Classification Archive, aiming to enhance model robustness and reliability in real-world applications. Ultimately, the project aims to foster trust and usability in various domains by providing clearer insights into the workings of machine learning models.

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Faculty Supervisor:

Arthur Chan

Student:

Partner:

University of Auckland

Discipline:

Engineering

Sector:

Artificial Intelligence; Technology; Other

University:

University of Toronto

Program:

Globalink Research Award

Electrospun composite solid-state electrolyte membranes based on lithium conductive metal-organic framework for high-energy-density lithium metal battery

As Canada advances to a sustainable and prosperous net-zero future by 2050, investment and research into the battery industry can help accelerate progress towards net-zero emissions targets and are a vital resource to support Canadian industry and workers in seizing economic opportunities for next-generation Li-ion batteries. Li metal batteries, as one of the most promising next-generation high-energy-density storage devices, can meet the rigid demands of new industries. However, the direct utilization of metallic Li as anode can induce harsh safety issues caused by the dendrite formation and electrolyte decomposition. Replacing organic liquid electrolytes with solid-state electrolytes is considered to be the most promising method to solve the safety problem of Li metal batteries. Herein, we propose a series of novel metal-organic framework-based composite solid-state electrolytes based on electrospinning engineering as an alternative route to conventional electrolyte manufacturing, which can enhance the interfacial contact and increase the electrochemical stability.

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Faculty Supervisor:

Michael Fowler

Student:

Partner:

Solid UltraBattery Inc.

Discipline:

Engineering

Sector:

Mining

University:

University of Waterloo

Program:

Accelerate

Le transport informel comme composante de l’intermodalité, le cas de Hanoi

Hanoi, la capitale vietnamienne, connaît une forte croissance démographique. Les déplacements, autrefois effectués à bicyclette, sont désormais réalisés principalement à moto et, de plus en plus, en voiture, générant des problèmes de congestions routières et de pollution. Le réseau de transport collectif, en forte expansion, peut difficilement rivaliser avec le transport individuel, compte tenu de la complexification des déplacements et de la morphologie particulière de la ville (rues étroites dans lesquels les autobus ne peuvent circuler, plusieurs pôles de développement, etc.). Le transport informel, très présent à Hanoi sous la forme de motos-taxis (xe ôm en vietnamien) peut jouer un rôle de premier plan dans la mobilité de la population, en complémentarité avec le réseau de transport collectif. Cette étude vise donc à identifier les conditions nécessaires pour que le xe ôm contribue à l’intermodalité et, conséquemment, au développement d’une mobilité plus durable dans la région de Hanoi

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Faculty Supervisor:

Danielle Labbé

Student:

Partner:

National University of Civil Engineering

Discipline:

Sociology

Sector:

Education

University:

Université de Montréal

Program:

Globalink Research Award

Relativistic localisation of Unruh-DeWitt detectors

In this project, we study the Unruh-DeWitt (UDW) model, which features a two-level system (an “atom”) coupled to a quantum field. In a previous publication, the UDW model was extended to include a relativistic centre of mass, which was introduced via a first- and second-quantised treatment of the atom’s dynamics. However, an issue with this model is that the wave function describing the atom spreads out over time. In the UDW model, one conventionally requires the atom to remain bound within a finite region of spacetime. In previous works, Tales Rick Perche—a student of Prof. Eduardo Martin-Martinez—has investigated a separate UDW model, where the atom remains localised to finite regions of spacetime. In joint discussion, Tales has proposed a means by which these two models can be combined. For this project, we will synthesise the two models and investigate this extended UDW model in the context of relativistic quantum information. This collaboration will be beneficial to both the University of Waterloo and the University of Queensland, as it will further the study of the atom-light interaction in relativistic quantum mechanics and its applications to relativistic quantum information.

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Faculty Supervisor:

Eduardo Martin-Martinez

Student:

Partner:

The University of Queensland (Australia)

Discipline:

Physics

Sector:

Education

University:

University of Waterloo

Program:

Globalink Research Award

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