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Abstraction

Project Leader(s): 

Postdoctoral fellow: Dr. Natalie Nakhla, Electronics, Carleton University

Lead faculty member: Dr. Q. J. Zhang, Electronics, Carleton University

With today’s rapidly increasing energy demands and the emergence of smart grids and renewable energy resources, the current energy and power technologies need to be advanced to keep up with these changes. Simulation and modeling plays a vital role in understanding, designing and planning of electrical power systems. The proposed research aims at developing a new generation of advanced mathematical models and simulation tools for electrical power systems and smart grids.

Project Leader(s): 

Dr. Kim McAuley, Queen's University

Project team: 
Dr. Thomas Harris, Queen’s University
Dr. James McLellan, Queen’s University
Dr. James Ramsay, McGill University
Dr. David Campbell, Simon Fraser University
Dr. Amos Ben-Zvi, University of Alberta
Dr. Carl Duchesne, Université Laval
Funding period: 
April 1, 2021 - March 31, 2021

Engineers use mathematical models to describe the production of plastics and other chemicals. The models contain unknown parameters that are estimated from plant data. In the past year, the research team analyzed several criteria that modelers use to decide how complex or how simplified their models should be. They showed that one popular model-selection criterion, the corrected Akaike Information Criterion, tends to select very simple models, and that another, the adjusted coefficient of determination, tends to select models with many parameters.

Project Leader(s): 

Dr. Mads Kaern, University of Ottawa

Project team: 
Dr. Theodore Perkins, McGill University
Dr. Matthew Scott, University of Waterloo
Dr. Brian Ingalls, University of Waterloo
Non-academic participants: 
Funding period: 
April 1, 2021 - March 31, 2021

The goal of the MITACS-funded research program on reverse-engineering cellular complexity is to develop new mathematical tools and algorithms for analyzing genetic switching networks. Many genes operate as switches and are turned on and off, like light bulbs, when needed. Understanding the regulatory circuits that control this switching behaviour would improve our ability to modulate gene activity, provide clues to fundamental biological design principles, and lead to better synthetic circuits for biotechnological applications.

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