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

Cannabis and Death-Anxiety

In partnership with True North Clinical Research, two studies are proposed to examine the moderating effect of using cannabis on anxiety in response to death-thoughts. In Study 1, participants will be randomly assigned to consume cannabis either before or after thinking about death. Self-reported affect will be assessed to determine if cannabis helps reduce death-anxiety. Study 2 will examine several potential moderators (e.g., personality factors associated with sensitivity to rewards and punishments; investment in pro-cannabis beliefs and values) and mediators (e.g., reactive approach motivation; reduced threat sensitivity) of the relationship between cannabis and anxiety following death-contemplation. This research will help True North understand the potential benefit of prescribing cannabis for the clinical treatment of anxiety. More broadly, it may also contribute to a growing literature on the medicinal effects of cannabis, as well as the literature on how people cope with the awareness of death.

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Faculty Supervisor:

Joseph Hayes

Student:

Partner:

True North Clinical Research

Discipline:

Sociology

Sector:

Health and Related Sciences & Technology

University:

Acadia University

Program:

Accelerate

Data-driven building simulation for thermal and electrical energy demand prediction by using deep machine learning algorithms, RNN and LSTM for energy management

Building energy consumption prediction is becoming increasingly vital for energy management, equipment efficiency improvement, cooperation between building energy and power grid, and so on. However, it is still hard work to obtain accurate prediction results because of the complexity of the building energy behavior and the frequent undulations in the energy demand. In the building energy consumption prediction, the existing historical data are usually used to construct the traditional machine learning models and the deep learning models. This project, will mainly focus on developing a deep learning algorithm using recurrent neural networks and LSTM to have a more efficient and accurate prediction of electrical and thermal demand based on the energy consumption data from buildings. I have been selected to do my thesis in Canada for developing my project by starting a collaboration between DataOptima.it and CIISE in Concordia University.

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Faculty Supervisor:

Jia Yuan Yu

Student:

Partner:

Politecnico di Milano

Discipline:

Engineering

Sector:

Education

University:

Concordia University

Program:

Globalink Research Award

The Leisure, Sport, And Recreation Labor Market In Alberta: History And Current Trends

The project looks to explore the trends and directions of the labor market in the recreation, sport, and leisure industry for the Province of Alberta. Within this 6-month project, a review of the current literature in the human resource area of the field will be undertaken along with look at the current and past trends of the labor market within the province. These activities will lead to two chapters written for the partner organization which will help guide their future policies and directions as it relates to both awareness and advocacy of the industry to the general public and the various key stakeholder groups throughout the province.

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Faculty Supervisor:

Brian Soebbing

Student:

Partner:

Alberta Recreation & Parks Association

Discipline:

Sociology

Sector:

Arts, entertainment and recreation

University:

University of Alberta

Program:

Accelerate

LGBTQI2S Seniors’ Safety in Public Services

This project will result in a national environmental scan on LGBTQI2S seniors’ safety in health care, social care and municipal public services. It aims to identify promising policies and practices as well as systemic and structural barriers. It will also include the experiences of LGBTQI2S workers who serve seniors, a largely unexplored area. Egale Canada Human Rights Trust (Egale) and the Canadian Union of Public Employees (CUPE) are national organizations with common interests and distinct positions from which to influence change. They have identified this environmental scan as an important step in mapping out challenges and opportunities for collaboration and member engagement in education and advocacy. The results will be shared in a discussion paper, a fact sheet and lists of non/government service providers and champions, offering resources to improve safety for LGBTQI2S seniors and workers across the country.

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Faculty Supervisor:

Susan Braedley

Student:

Partner:

Egale Canada Human Rights Trust

Discipline:

Sociology

Sector:

Other services (except public administration)

University:

Carleton University

Program:

Accelerate

Assessing the impact of STEM programming on children and youth.

In the twenty-first century, coding literacy provides individuals with a host of useful skills and competencies as well as accompanying psychological benefits. Yet, at present, very few pedagogical models or curricula include coding literacy as a central focus. STEM interventions, often found in after-school care programs and summer camps, help address this gap by offering targeted programming aimed at developing these competencies in children and youth. The present project will help assess the benefits and effectiveness of one of these programs by investigating how an exposure to STEM programming impacts the grit levels of the children in grades 1 through 8. This study will also assess the program’s structure, and include interviews with program participants assessing their experience.

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Faculty Supervisor:

Carolyn FitzGerald;Julie Mueller

Student:

Partner:

Canada Learning Code

Discipline:

Sociology

Sector:

Education

University:

Wilfrid Laurier University

Program:

Accelerate

Experimental testing and characterization of a new wireless heart pump

Heart failure is a common cardiovascular disease that is becoming even more prevalent worldwide. The current gold standard treatment for end-stage heart failure is heart transplantation. However, heart transplant therapy is a limited option due to the available number of donors, thus heart pumps emerged to offer circulatory support to patients in the heart transplant waiting list. Although heart pumps’ therapy improves the quality of life and survival rate, a significant number of patients still suffer from blood trauma adverse events such as infections and blood flow-channel obstructions, leading to further surgical interventions.
Therefore, blood trauma remains an unmet challenge in the field of mechanical circulatory suport. The purpose of this study is to experimentally test and characterize through in-vitro test rig different heart pump configurations of a new design. The primary outcome of the project is to select an optimal design that could eventually provide a long-lasting and minimally invasive wireless solution for patients with heart failure. It is believed that in the future, heart failure treatment will rely heavily on improved cardiovascular technologies that provide the reuired physiological function with the least invaiveness while remaining cost-effective. TO BE CON’T

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Faculty Supervisor:

Renzo Cecere;Rosaire Mongrain

Student:

Partner:

Yale University

Discipline:

Life Sciences

Sector:

Education

University:

Research Institute of the McGill University Health Centre

Program:

Globalink Research Award

Mechanical characterization of phage-coated implants for the prevention and treatment of periprosthetic joint infections in high risk patients

Caused by planktonic and biofilm drug-resistant bacteria on implants, periprosthetic joint infections (PJI) is one of the most devastating complication in orthopedics and is in line with forecasted rise in joint replacement. From the perspectives of patients, surgeons, hospitals, and health care system, PJI thus present a great unmet medical need, resulting in high morbidity, and even mortality, among affected patients. Therefore, clinicians would find invaluable a technology with a potential to manage PJI on implants. With the rise of antimicrobial resistance (AMR), a new technology to prevent or treat PJI would be invaluable.

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Faculty Supervisor:

L'Hocine Yahia

Student:

Partner:

Phagelux (Canada) Inc

Discipline:

Engineering

Sector:

Professional, scientific and technical services

University:

Polytechnique Montréal

Program:

Accelerate

Modélisation d’un pont à thyristors dédié à l’excitation des machines synchrones dans le référentiel qd0

Les systèmes de production d’énergie basés sur l’utilisation de la force hydraulique utilisent une machine synchrone afin de convertir l’énergie mécanique en énergie électrique. Pour que ces machines fonctionnent, il faut fournir une puissance d’excitation sur la partie tournante de la machine (que l’on nomme système d’excitation.) Ce système d’excitation peut produire des courants électriques de grande intensité variant de 500A à plus de 3000A. Afin de contrôler ces courants, un convertisseur de puissance est requis. Il est proposé dans ce projet de recherche d’effectuer une modélisation de ce convertisseur afin de bien connaître ses réactions à diverses perturbations.

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Faculty Supervisor:

Handy Fortin Blanchette

Student:

Partner:

ANDRITZ Canada Inc.

Discipline:

Engineering

Sector:

Technology; Energy and Utilities; Advanced Manufacturing

University:

École de technologie supérieure

Program:

Accelerate

Development of a numerical model for the simulation of fluidized bed reactors

This project concerns the development of a 3D virtual model of a fluidized bed gasifier reactor. Relying on the so-called CFD-DEM approach and supercomputer technology, the virtual model will allow visualization of the fluid dynamic inside the reactor, highly valuable insight otherwise unreachable by physical mean. This project specifically focus on model calibration and validation as well as coupling heat and mass transfer to the model. A series of lab benches will be operated and modeled in order to quickly identify various model parameters before application to actual reactor. This project will help Enerkem engineers and scientists to better understand reactor performance sensitivity to design parameters and subsequently optimize the technology.

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Faculty Supervisor:

Stéphane Moreau

Student:

Partner:

Enerkem Inc (Sherbrooke, QC)

Discipline:

Engineering

Sector:

Manufacturing; Professional, scientific and technical services

University:

Université de Sherbrooke

Program:

Accelerate

Multi-morbidity Characterization and Polypharmacy Side Effect Detection for designing Optimal Personalized Healthcare with Machine Learning

Despite a significant improvement in healthcare systems over the past decades, the rapid growth in the number of patients with multiple chronic diseases – called multimorbidity – stands as a complex challenge to healthcare services that are primarily designed to treat individuals with single conditions. Advances in machine learning as well as in computing power now enable us to exploit a vast amount of healthcare data. The main goal of this project is to propose a data-driven approach to characterize patients with multimorbidity in such a way that an optimal care can be given to each of them, using machine learning techniques. The project will use the ICES (Institute for Clinical Evaluative Sciences) dataset, Ontario public health data that is completely anonymized and collected from 1992, containing information on around 15 million Ontario residents. TO BE CONT’D

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Faculty Supervisor:

Marzyeh Ghassemi

Student:

Partner:

Layer 6 AI

Discipline:

Computer science

Sector:

Finance and Insurance; Professional, scientific and technical services

University:

University of Toronto

Program:

Accelerate

Testing and applying machine learning techniques in monitoring and detecting anomalies in membrane cell electrolyzers at R2.

In this project we aim to develop a computer software system that is capable of predicting anomalies in membrane cell electrolyzers before they arise. It will closely monitor the electrolyzers’ operating condition and identify the hints that suggest that a failure is coming down the line. We will then notify the plant operators, so they can plan the preventive replacement of the soon-to-break equipment without causing damages. This system will be part of the services offered by the partner organization and will help them maintain their leadership as market experts and innovators.

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Faculty Supervisor:

Soumaya Yacout

Student:

Partner:

R2

Discipline:

Engineering

Sector:

Manufacturing

University:

Polytechnique Montréal

Program:

Accelerate

Applying Machine Learning to Predict Demand Transference

The project will help us design a machine learning model that can determine the demand transference of our customers. The key objective of this project is to design, research, build, and experiment with machine learning models to ensure low product waste and high customer satisfaction. The model will have several impactful applications across the organization.

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Faculty Supervisor:

Qiang Sun

Student:

Partner:

Loblaws Inc

Discipline:

Computer science

Sector:

Technology; Agriculture and Food; Other

University:

University of Toronto

Program:

Accelerate

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