Innovative Projects Realized

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

30156 Completed Projects

2861
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5059
BC
812
MB
673
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842
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8957
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9368
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96
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579
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1120
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Projects by Category

Altitude & Heat – environmental synergies to optimize human performance – Year two

The environmental stress of altitude and heat have both been shown to elicit divergent adaptive responses and are used by elite athletes to augment training adaptation and subsequent performance. Indeed, 3-4 weeks at moderate altitudes can increase the body’s natural erythropoietin (EPO) responses, raising hemoglobin by 4-6% and enhancing endurance performance. Conversely, as little as 5-7 days of exercise induced heat acclimation can increase blood volume by 5-10%, resulting in increased tolerance to heat, increased VO2max /cardiac output and improve endurance performance as well. However, the concept of “cross-tolerance” has recently emerged, which is the use of heat and altitude synergistically to augment adaptation and performance; however studies in humans are sparse. Furthermore, there is an opportunity to implement non-invasive / wearable near-infrared spectroscopy (NIRS) technology to further our understanding of peripheral mechanisms of muscle oxygenation/utilization in elite athletes in various environmental conditions, and to better elucidate performance determinants in endurance sport. TO BE CONT’D

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Faculty Supervisor:

Michael Koehle

Student:

Partner:

Canadian Sport Institute Pacific

Discipline:

Physics

Sector:

Arts, entertainment and recreation; Health and Related Sciences & Technology; Professional, scientific and technical services

University:

The University of British Columbia

Program:

Elevate

Altitude & Heat – environmental synergies to optimize human performance

The environmental stress of altitude and heat have both been shown to elicit divergent adaptive responses and are used by elite athletes to augment training adaptation and subsequent performance. Indeed, 3-4 weeks at moderate altitudes can increase the body’s natural erythropoietin (EPO) responses, raising hemoglobin by 4-6% and enhancing endurance performance. Conversely, as little as 5-7 days of exercise induced heat acclimation can increase blood volume by 5-10%, resulting in increased tolerance to heat, increased VO2max /cardiac output and improve endurance performance as well. However, the concept of “cross-tolerance” has recently emerged, which is the use of heat and altitude synergistically to augment adaptation and performance; however studies in humans are sparse. Furthermore, there is an opportunity to implement non-invasive / wearable near-infrared spectroscopy (NIRS) technology to further our understanding of peripheral mechanisms of muscle oxygenation/utilization in elite athletes in various environmental conditions, and to better elucidate performance determinants in endurance sport. TO BE CONT’D

View Full Project Description
Faculty Supervisor:

Michael Koehle

Student:

Partner:

Canadian Sport Institute Pacific

Discipline:

Physics

Sector:

Life Sciences (not health); Environmental Science and Technology; Other

University:

The University of British Columbia

Program:

Elevate

Using wearable sensor-based technologies to detect changes in health status for prevention of adverse health events and to improve overall quality of life – Year two

The project goal is to determine the clinical utility of Orpyx LogR technology to detect gait changes and their efficacy to predict and monitor fall risk. Project I will use existing data to determine sensitivity and specificity for prospective classification of fallers and non-fallers for a composite measure drawn from an extensive battery including single and/or dual-task IMU-derived gait metrics as well as from force plate gait initiation data. Respectively, Project II and III will concurrently provide validation of Orpyx LogR technology measurements and then determine sensitivity and specificity for retrospective and prospective classification of fallers and non-fallers for a composite measure drawn from a battery including clinical tests of dynamic balance and Orpyx LogR derived measures including postural sway during quiet stance, as well as gait measures during gait initiation and single and/or dual task walking. Project IV will use will use custom algorithms to develop client-specific models of fall prediction incorporating relevant measures identified in Project II. These measurements of gait and balance can act as biomarkers to provide early detection of changes in health status. Providing timely information to caregivers about changes in health status will allow for appropriate interventions with potential to mitigate adverse health events.

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Faculty Supervisor:

Marc Klimstra

Student:

Partner:

Orpyx Medical Technologies

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Victoria

Program:

Elevate

Using wearable sensor-based technologies to detect changes in health status for prevention of adverse health events and to improve overall quality of life

The project goal is to determine the clinical utility of Orpyx LogR technology to detect gait changes and their efficacy to predict and monitor fall risk. Project I will use existing data to determine sensitivity and specificity for prospective classification of fallers and non-fallers for a composite measure drawn from an extensive battery including single and/or dual-task IMU-derived gait metrics as well as from force plate gait initiation data. Respectively, Project II and III will concurrently provide validation of Orpyx LogR technology measurements and then determine sensitivity and specificity for retrospective and prospective classification of fallers and non-fallers for a composite measure drawn from a battery including clinical tests of dynamic balance and Orpyx LogR derived measures including postural sway during quiet stance, as well as gait measures during gait initiation and single and/or dual task walking. Project IV will use will use custom algorithms to develop client-specific models of fall prediction incorporating relevant measures identified in Project II. These measurements of gait and balance can act as biomarkers to provide early detection of changes in health status. Providing timely information to caregivers about changes in health status will allow for appropriate interventions with potential to mitigate adverse health events.

View Full Project Description
Faculty Supervisor:

Marc Klimstra

Student:

Partner:

Orpyx Medical Technologies

Discipline:

Life Sciences

Sector:

Manufacturing; Professional, scientific and technical services

University:

University of Victoria

Program:

Elevate

The Essence of Improvisation: Promoting Emotional Fitness in Employees, Products, and Customer Experiences

My proposal directly builds on my Ph.D. work, which has concerned how musical and theatre improvisation can be applied in business and public health settings to promote cognitive and social-cognitive goals. In particular, Lululemon is interested in harnessing the cognitive principles underlying improvisation in order to promote emotional fitness in two complimentary ways––at a human resource level through targeted, research-informed training sessions with Lululemon employees, and at a customer level by the creation of new products and/or in-store experiences that embody the concept of ‘holistic fitness’. The goal of my proposal is to facilitate these aims for Lululemon by developing a line of basic cognitive neuroscience research at UBC that seeks to distill down the cause-and-effect impacts of improvisation training so that they can be more directly applied to meet customer and human resource needs.

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Faculty Supervisor:

Rebecca Todd;Todd Handy

Student:

Partner:

Lululemon

Discipline:

Business

Sector:

Other

University:

The University of British Columbia

Program:

Elevate

Simulation and Analysis of Aerators at Fish Farms

Fish farms are a way to satisfy the increasing demand for fish without directly impacting wild stocks. In fish farms water quality significantly impacts fish health and development. Industrial fish farms rely on aerators for multiple purposes, including the oxygenation of water and the isolation of fish from plankton blooms. However, at this time, several critical aspects of aerators are not completely understood.
This study will be conducted in several stages, with a combination of experimental and simulation techniques. The first stage will involve field measurements in an operating fish farm. Next, computational techniques will be implemented in order to predict the flow from one aerator, and subsequently, from an array of aerators. The final stage will use the experimental and simulated results to determine the optimal design for individual aerators as well as their preferred placement within a fish pen.
Poseidon Ocean Systems Ltd. will use the results of this research to assess the current behavior of aerators and then improve upon their novel conceptual design. There is also potential for significant environmental benefits as fish farm aerators are powered by diesel generators, and optimization of the aerators will result in reduced consumption of diesel fuel.

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Faculty Supervisor:

Sheldon Green

Student:

Partner:

Poseidon Ocean Systems

Discipline:

Engineering

Sector:

Agriculture

University:

The University of British Columbia

Program:

Elevate

Analysis and Optimization for Industrial Safety and Efficiency: Novel Methods for scheduling Resources under Learning-Forgetting Effects

Irwin’s Industrial Safety, as a leading provider of safety and project management services, has collected safety and efficiency data over a list of projects since 2013. As a part of Mitacs Accelerate project, those data have been analyzed and the results have been visualized to enlighten opportunities for future optimization. This research proposes a framework to integrate the safety and efficiency data of projects in the consulting services that Irwin’s provide for its clients. As a result of the proposed framework, stakeholders (Irwin’s, its clients, contractors) will reduce incidents and accidents in the projects as well as reduce inefficiencies in terms of time and cost. In addition, an optimal resource allocation model that incorporates the unique attributes of this project will be developed. Studies show that learning (the ability to do a job faster due to repeating) and forgetting (increasing the time to finish a job due to interruptions and breaks) significantly affect the duration of activities and the utilization of resources. This project will incorporate learning and forgetting in the scheduling of resources benefits Irwin’s to reduce the delays, financial penalties, and incidents due to congested activities.

View Full Project Description
Faculty Supervisor:

Warren Hare

Student:

Partner:

Irwin's Safety and Industrial Labour Services Ltd

Discipline:

Engineering

Sector:

Construction and infrastructure; Other services (except public administration)

University:

The University of British Columbia - Okanagan

Program:

Elevate

Foamy Oil Direct Visualization during Solvent Injection Processes

This project is aimed for an accurate and highly convenient methodology to visually investigate the multiphase flow behavior, foamy oil stability and solvent mass transfer in solvent injection processes. Therefore, a novel real-time direct visualization methodology, focusing on the study of foamy oil equilibrium and non-equilibrium PVT phase behavior and stability in bulk and porous media, solvent mass transfer efficacy etc, by utilizing the newly designed Hele-Shaw-like 2D high pressure visual cell, has been developed to significantly overcome the inevitable shortcomings of the invisibility of traditional apparatuses such as a hardly-visual 3D PVT cylindrical cell or a non-visual transfer cylinders. With the aid of professional image processing, the experimental results could be vividly seen and quantified. By incorporating micromodel technique, specified pore patterns could be built and a pore-scale characterization of solvent-heavy oil system under multiple operation schemes could be easily fulfilled. Real-time measurement of mixture gaseous solvent fractions in the gas-heavy oil system in every test would be achieved.

View Full Project Description
Faculty Supervisor:

Farshid Torabi

Student:

Partner:

Petroleum Technology Research Centre

Discipline:

Engineering

Sector:

Mining; Professional, scientific and technical services

University:

University of Regina

Program:

Accelerate

Development of climate sensitive growth functions for western North America’s boreal tree species – Year two

The Mixedwood Growth Model (MGM) is used by forest managers in estimating growth and yield outcomes for common boreal tree species in North America. MGM has been shown to effectively model both managed and unmanaged stands in Alberta and surrounding regions. Currently, climate effects are not accounted for in growth functions used in MGM. Recent work for black spruce has shown that there is need to understand and model the effect of climate for other boreal tree species including white spruce, aspen, balsam poplar, lodgepole pine and jack pine. This study is designed to examine the effect of climate, competition, site quality, and their interactions with climate on the growth of the aforementioned tree species. Long term measurement data with at least 11,673 Permanent Sample Plots (PSP) established and measured between 1931 and 2015 across western Canada and Alaska will be analysed for this project. This will include evaluating a wide variety of climate variables, competition indices and tree and site variables as potential predictors of growth . The addition of climate to growth functions in MGM would improve its ability to represent effects of climatic variation in the western boreal and support modeling of climate change impacts.

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Faculty Supervisor:

Phil Comeau

Student:

Partner:

Canadian Forest Products Ltd;Weyerhaeuser;West Fraser Mills Ltd (Alberta Plywood)

Discipline:

Life Sciences

Sector:

Agriculture

University:

University of Alberta

Program:

Elevate

Development of climate sensitive growth functions for western North America’s boreal tree species

The Mixedwood Growth Model (MGM) is used by forest managers in estimating growth and yield outcomes for common boreal tree species in North America. MGM has been shown to effectively model both managed and unmanaged stands in Alberta and surrounding regions. Currently, climate effects are not accounted for in growth functions used in MGM. Recent work for black spruce has shown that there is need to understand and model the effect of climate for other boreal tree species including white spruce, aspen, balsam poplar, lodgepole pine and jack pine. This study is designed to examine the effect of climate, competition, site quality, and their interactions with climate on the growth of the aforementioned tree species. Long term measurement data with at least 11,673 Permanent Sample Plots (PSP) established and measured between 1931 and 2015 across western Canada and Alaska will be analysed for this project. This will include evaluating a wide variety of climate variables, competition indices and tree and site variables as potential predictors of growth . The addition of climate to growth functions in MGM would improve its ability to represent effects of climatic variation in the western boreal and support modeling of climate change impacts.

View Full Project Description
Faculty Supervisor:

Phil Comeau

Student:

Partner:

Canadian Forest Products Ltd;University of Alberta;Weyerhaeuser;West Fraser Mills Ltd (Alberta Plywood)

Discipline:

Life Sciences

Sector:

Agriculture

University:

University of Alberta

Program:

Elevate

Improving Resource Estimation and Reconciliation with Machine Learning

Models quantifying the grade and tonnage of mineral deposits form the basis of important and costly decisions for planning, optimization and extraction of a natural resource. Models are initially generated from sparse exploration sampling; however, information is continuously collected until resource extraction. Predicted values that reconcile well with true values following extraction instill confidence in the production forecasts. Failure to meet production forecasts can have crippling effects on cash flow and ultimately result in failure of the project.
In this research a neural-network-based prediction framework is proposed that incorporates production information to the predictive algorithm to improve forecasts of future production, thereby improving reconciliation at a mining project. The proposed method could be used to continually update resource models to improve decisions being made at all scales. This research will benefit the partner company since the incorporation of a wide array of data in manual reconciliation is complex. The proposed research will simultaneously simplify the workflow for the practitioner and improve reconciliation by improving predicted values in unmined areas. This will generate value through increased operational efficiency.

View Full Project Description
Faculty Supervisor:

Jeff Boisvert

Student:

Partner:

Teck Resources Ltd (Calgary, AB)

Discipline:

Engineering

Sector:

Mining

University:

University of Alberta

Program:

Elevate

Developing Optimally Discriminative Subnetwork Markers for Predicting Response to Chemotherapy

Molecular profiles of tumour samples have been widely and successfully used for classification problems. Many algorithms have been proposed to predict classes of tumor samples based on expression profiles. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein–protein interaction data with gene expression profiles for the development of subnetwork markers in classification problems. We hope to design a novel network-based classification algorithm using color coding technique to identify optimally discriminative subnetwork markers. We hope to provide better and more stable performance compared with other subnetwork and single gene methods. Another issue of designing our subnetwork method is to make it being capable of producing predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy.

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Faculty Supervisor:

Cenk Sahinalp

Student:

Partner:

University of British Columbia

Discipline:

Computer science

Sector:

Manufacturing

University:

Simon Fraser University

Program:

Accelerate

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