Innovative Projects Realized

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

13270 Completed Projects

1072
AB
2795
BC
430
MB
106
NF
348
SK
4184
ON
2671
QC
43
PE
209
NB
474
NS

Projects by Category

10%
Computer science
9%
Engineering
1%
Engineering - biomedical
4%
Engineering - chemical / biological

Mathematical Modeling of Porous Structure and Operation of Cathode Catalyst Layers in PEM Fuel Cells

Highly efficient and environmentally clean energy conversion in Polymer Electrolyte Membrane (PEM) Fuel Cells is driven by electrochemical reactions that convert hydrogen and oxygen molecules into water. Water, the product of the overall reaction, is involved in all essential processes in the cell. Water management is, thus, a critical issue for fuel cell operation. It entails controlling water fluxes and maintaining appropriate levels of liquid water saturation in the different cell components. There are strong indications in experiment and modeling that the cathode catalyst layer (CCL) plays a major role in this context. Good operation of the CCL is closely linked to its composition (platinum/support phase, ionomer phase and pore space), porous structure, and wetting properties. The research team, in partnership with the NRC Institute for Fuel Cell Innovation, further analyzed a basic mathematical model of CCL function using the structural details of the layer, water transport from the membrane, liquid water formation by electrochemical reaction in the CCL, water transformation via evaporation and condensation and two-phase flow in liquid and vapor phases. Effects of composition of the CCL, porous structure, wetting properties of pores, operating conditions and boundary conditions at interfaces with membrane and gas diffusion layer were systematically studied. Suggestions for the optimization of water handling capabilities of CCLs and fuel cell power densities emerged, which are currently being tested in experiment.

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Faculty Supervisor:

Dr. Michael Eikerling

Student:

Jianfeng Liu

Partner:

NRC - Institute for Fuel Cell Innovation

Discipline:

Chemistry

Sector:

Fuel cells

University:

Simon Fraser University

Program:

Accelerate

Mathematical Modeling in Pharmaceutical Development

Microtubules are a key constituent of the cell’s structural framework and are responsible for a diverse range of functions within the cell. They are cylindrical polymers, 25 nm in diameter and can grow to be several hundred micrometers in length. Tubulin, the protein which is the main component of microtubules, self-assembles to form the walls of the cylinder in a highly-ordered, helical lattice arrangement. Functionally, microtubules fill a wide variety of roles within the cell. The function often considered most important is the role played in cell division. This requires repeated assembly and disassembly phases in microtubule dynamics. Not only is this important in healthy cells but is required for the proliferation of cancer and tumor growth. By interfering with this process one can prevent the cell from dividing, thereby halting the growth of a tumor. This makes tubulin and microtubules one of the most important chemotherapeutic targets. In collaboration with Dr. Andriy Kovalenko of the National Research Council’s National Institute for Nanotechnology, the intern research team applied the integral equation theory of molecular liquids to the self-assembly and stability of microtubles to gain insights to the role of tubulin in the human body. These insights will help create and test the next generation of anti-cancer drugs, those that target cancer cells specifically.

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Faculty Supervisor:

Dr. Jack Tuszynski

Student:

Tyler Luchko

Partner:

NRC - Institute for Nanotechnology

Discipline:

Physics / Astronomy

Sector:

Pharmaceuticals

University:

University of Alberta

Program:

Accelerate

Dispersion and Deposition of Particulate Matter from Stacks

Teck Cominco Ltd. is a diversified mining, smelting and refining group, a world leader in the production of metallurgical coal and zinc and a major producer of copper and gold. The company was interested in investigating the dispersion of particulate matter from its Trail facility into the surrounding area under the influence of diffusion, advection by the wind and settling effects. In particular, the company was interested in estimates of yearly release rates from particular release points which are very difficult and costly to measure directly. The intern research team studied the modelling of the problem including the equations governing the flow from these points, the effects of the gravity and drag forces on the particles, the outflow and the deposition boundary conditions under varying atmospheric states. The release rates for different chemicals were then estimated using constrained numerical optimization techniques resulting from the modelling of the inverse problem and work is currently being done on improving the accuracy, stability and convergence of the algorithm.

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Faculty Supervisor:

Drs. Mary Catherine Kropinski & John Stockie

Student:

Enkeleida Lushi

Partner:

Teck Cominco Metals Ltd.

Discipline:

Mathematics

Sector:

Mining and quarrying

University:

Simon Fraser University

Program:

Accelerate

Algebraic Foundations of Structure Prediction

Axonwave Software Inc. of Burnaby, BC, is a leader in the field of unstructured data analysis and processing. The company provides solutions that enhance business intelligence and improve the efficiency of business processes through the ability to analyze and extract information from text-based information sources. In collaboration with Axonwave, the project involves unifying a number of algorithms unified for structure prediction into a single formal framework that will be the basis of a software toolkit. Axonwave provides businesses with software that uses Natural Language Processing (NLP) techniques to analyze freeform textual content. The software toolkit will be employed to process the natural language data that is currently being used by Axonwave. The Intern will test the toolkit on natural language datasets in order to provide effective analytical tools.

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Faculty Supervisor:

Dr. Anoop Sarkar

Student:

Yudong Li

Partner:

Axonwave Software

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Simon Fraser University

Program:

Accelerate

Statistical Learning for Technical Portfolio Management

ApSTAT Technologies Inc. provides insurance companies with analytical systems based on exclusive data mining technologies, enabling insurers to maximize the profitability of their operations. The company is working with a large financial institution to develop new financial products based on statistical learning techniques. The research team based at l’Université de Montréal had two objectives. The first was to develop a modular framework which would combine a large class of time-series processing modules in combinatorial fashions in a way that would make them accessible to an econometrician with little knowledge of computer science principles. Secondarily, the team was to improve the performance of several risk management models using state-of-the-art machine learning techniques. In the end, the team managed to improve the readability and usability of the company’s time-series processing tools as well as augment the robustness of the financial models used to build trading portfolios.

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Faculty Supervisor:

Dr. Yoshua Bengio

Student:

Christian Dorion

Partner:

ApSTAT Technologies Inc.

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program:

Accelerate

RuleML FOAF: A Web Rule Language for Social Networking

NRC-IIT creates and commercializes software and systems technology to help Canada prosper in the knowledge economy. The institute in Fredericton has been working with rule systems, semantic web standards and social networking since its inception. Web-based Social Networking is emerging as a major application area for semantic web metadata. Most social networking sites are based on a centralized architecture: all user descriptions are stored in one database. There is, however, growing user and business interest in portability between such sites, and for ‘single sign-on’ mechanisms that reduce the need for data re-entry, while allowing users to publish different aspects of themselves in different contexts. The Friend-Of-A-Friend (FOAF) vocabulary allows such sites to address the user demand for control of ‘their’ data. A Web Rule Language is now seen as the next research target for the semantic web. While metadata were traditionally handcrafted and stored statically, rules can be employed for dynamically deriving required metadata on demand. The Rule Markup Language (RuleML) enables the XML-based elicitation, interchange and execution of rules. The FOAF vocabulary does not currently capture rule knowledge, and the RuleML specification has not before been applied to social networking. The RuleML FOAF research combines both strands by studying the mathematical properties and use of RuleML rules for FOAF homepages.

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Faculty Supervisor:

Dr. Virendra Bhavsar

Student:

Jie Li

Partner:

NRC - Institute for Information Technology

Discipline:

Computer science

Sector:

Information and communications technologies

University:

University of New Brunswick

Program:

Accelerate

Pattern Recognition and Its Application in Customers’ Family Composition Detection

Rogers Communications, a diversified communications and media company, wanted to investigate the kinds of strategies that it should employ to attract new customers and meet customer demands thus maintaining a profitable market share. To develop these strategies, the client needed new efficient tools that could extract useful patterns, which was representative of actual customer behaviour, from the company's data warehouses. The team explored the structure of the company's data sets, identifying available data mining tools as well as designing new, efficient algorithms and validating the proposed approaches.

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Faculty Supervisor:

Dr. Jiming Peng

Student:

Huarong Chen

Partner:

Rogers Communication

Discipline:

Computer science

Sector:

Information and communications technologies

University:

McMaster University

Program:

Accelerate

Metric Development to Interpret Measures of Medication Compliance

Medication non-compliance is associated with poor outcomes in patients. Although different methods are currently used to measure compliance, the ability to translate this measure into a meaningful metric, which is interpretable for both methodologists and clinicians, has not yet been realized. Medication compliance has become an increasingly important area of research as policy makers perceive the extent and effect of the problem that non-compliance poses. The intern, in partnership with the London Health Sciences Centre, Division of Critical Care, proposed to develop a new metric to interpret measures of medication compliance which would provide better insight into disease patterns, which would involve clearly defining the statistical properties of the new measure. In addition, the intern estimated the distributions of both the current and new compliance measures to quantify the uncertainty of compliance measures.

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Faculty Supervisor:

Drs. Ying Zhang & Paul Cabilio

Student:

Maja Grubisic

Partner:

Centre for Critical Illness Research at the Lawson Research Institute

Discipline:

Mathematics

Sector:

Pharmaceuticals

University:

Acadia University

Program:

Accelerate

Grown-in Defects Modeling of InSb Crystals and Computation

Semiconductor materials are essential for today’s fast growing electro-optic and computer industries and semiconductor crystal growth is a key stage in the manufacturing process. There is constant market pressure to increase the size and quality of crystals so that more and better devices can be put on a single wafer. The most widely used crystal growth technique is the Czochralski (Cz) method, in which a semiconductor crystal is grown at the tip of a seed-crystal while the seed is slowly extracted from a pool of molten material. Crystals grown in this way begin to develop thermal stress defects if the thermal gradients are too large. A trial-and-error approach to process improvement either becomes too expensive or is ineffective. The client, Firebird Technologies, uses the Cz technique to manufacture crystals but an incomplete understanding of the thermal environment during processing was hampering progress. The research team developed a model for the distribution of point defects inside crystals grown by Cz-techniques and developed a series of parameters for optimal crystal growth. Modelling of the processes via mathematical, computational and small-scale experimental methods proved to be an inexpensive and efficient alternative for understanding the fundamental physical processes and improving the manufacturing processes. The team has written a paper on the research results which will be published in Communications on Computational Physics next year in 2006.

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Faculty Supervisor:

Dr. Huaxiong Huang

Student:

Naveen Vaidya

Partner:

Firebird Semiconductor

Discipline:

Mathematics

Sector:

Energy

University:

York University

Program:

Accelerate

Estimation of Joint Distribution from Marginal Totals with Applications to Database Marketing

In many customer surveys and database marketing applications such as segmentation, profiling and predicting consumers’ choices, the joint distributions of covariates of interests are required. However, for variety of reasons, such as protection of clients’ confidentiality, the data are often available only in marginal frequency distribution format. This makes it difficult to use the covariates in a meaningful way where joint distributions are required for analysis decision making. Availability of such joint distributions would enable the client, Bell Canada, to define customer segments, improve classification and predictive models and enhance the Company’s understanding of customers’ behaviour. This research will enhance Bell’s flexibility to define customer segments according to marketing needs and generate marketing profiles for segments of interest thereby improving their understanding of customers’ behaviour and allowing for development of customer-focused strategies.

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Faculty Supervisor:

Dr. Fassil Nebebe

Student:

Debaraj Sen & Jordie Croteau

Partner:

Bell University Laboratories

Discipline:

Business

Sector:

Information and communications technologies

University:

Concordia University

Program:

Accelerate

Constraint Programming based Column Generation for Employee Timetabling

Employee timetabling problems form a large class of widely-encountered issues in service organizations. Often, the issue lies in the design of work shifts, which often have to satisfy many complex regulatory constraints coming from legislation and contractual agreements. The client, Omega Optimisation, was looking for a unified and flexible solution for these shift scheduling problems which address legal issues, activity requirements and labour costs. The approach developed by the research team at École Polytechnique de Montréal involved an algorithm consisting of a hybridization of constraint programming techniques and linear programming, which added flexibility to Omega’s current scheduling modules. The research team had the opportunity to evaluate their approach on a scheduling problem submitted by a client of the partner company and found that their approach was able to be quickly adapted to the regulation constraints of the problem.

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Faculty Supervisor:

Dr. Gilles Pesant

Student:

Sophie Demassey

Partner:

Omega Optimisation

Discipline:

Engineering

Sector:

University:

Polytechnique Montréal

Program:

Accelerate

Characterization and Prediction of Cancer Drug Resistance Markers Based on Data Mining of Microarray Profiles

The internship will concern studying the role and impact of specific proteins (in particular tubulin) in cancer, and the identification of prognostic markers of cancer progression and predictors of cancer response to existing and new compounds (mainly based on microarray data analysis and protein structure prediction). This project will involve working on preparation of relevant datasets, characterization of cancer resistance markers based on computational analysis of microarray profiles of patients during various stages of neoadjuvant chemotherapy and computational analysis and prediction of tubulin structure. We will perform advanced computational analysis of biomedical data and will work in collaboration with the Cross Cancer Institute in Edmonton. These activities are associated with the larger project undertaken at the Cross Cancer Institute, which aims at design, synthesis and test of a group of novel drugs for epithelial ovarian cancer.

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Faculty Supervisor:

Dr. Lukasz Kurgan

Student:

Ke Chen

Partner:

Cross Cancer Institute

Discipline:

Engineering

Sector:

Life sciences

University:

University of Alberta

Program:

Accelerate

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