Grants and Contributions

The objective of this project is to reduce greenhouse gas emissions through the deployment of renewable energy and grid modernization technologies.

This project is to improve energy efficiency in Canada's residential, commercial, and institutional building sector.

The purpose of this program is to increase Canadians' access to flood hazard information and maps in order to make informed decision-making related to flood mitigation, land use planning and prevent costly impacts of flood.

The purpose of this Interdepartmental Letter of Agreement (ILA) is to record the common understanding of the Parties regarding the administration of the usage of NRCan's CERRC-Capacity and Deployment funding to ISC's SPI to be utilized as top-up funding i

The purpose of this Interdepartmental Letter of Agreement (ILA) is to record the common understanding of the Parties regarding the administration of the usage of NRCan's CERRC-Capacity and Deployment funding to ISC's SPI to be utilized as top-up funding i

The purpose of this Interdepartmental Letter of Agreement (ILA) is to record the common understanding of the Parties regarding the administration of the usage of NRCan's CERRC-Capacity and Deployment funding to ISC's SPI to be utilized as top-up funding i

The objective of this project is to reduce greenhouse gas emissions through the deployment of renewable energy and grid modernization technologies.

The objective of this project is to reduce greenhouse gas emissions through the deployment of renewable energy and grid modernization technologies.

Digital twin technology is used to model, explain, understand and predict behavior of complex systems in order to make their performance more reliable or to provide an in silico platform for high throughput testing of the effect of different interventions. Models combined under a digital twin umbrella can also be used to study mechanistic phenomena of a disease. While there are extensive data using genetically and environmentally manipulated laboratory mice to model human disease, these data are study- dependent and have yet to interrogate how metabolism dictates outcome and modulates genetic susceptibility. Moreover, there are no in silico models that combine the multiple behavioral outcomes of these in vivo modelling data with the metabolome. The lack of a murine digital twin is a major missing link in intelligent design of therapeutic strategies for complex diseases. The driving hypothesis of this project is that a digital mouse developed through optimized AI approaches and extensive, available biological data can provide a framework to support the design of evidence- based therapies for neurodegenerative diseases. The main outcome of this project will be an in silico reconstruction and simulation of the dependence of behavior, as a measure of neurodegeneration, on the lipidome and its application in corrective intervention development. The proposed digital mouse will be built using novel AI algorithms and unique, extensive, and longitudinal murine datasets (from birth to death) made available through collaboration.

Monitoring dam structures under climate change conditions is essential in terms of assessing their safety, adapting their operations, and ensuring their long-term viability in the face of changing environmental factors. Such monitoring helps mitigate risks, protect downstream communities and maintain critical water storage and management infrastructure. Canada, with its numerous dams, faces unique challenges like harsh climates and rugged terrain, making innovative monitoring techniques essential. Traditional monitoring methods of dams and their infrastructures have limitations, but Interferometric Synthetic Aperture Radar (InSAR) offers a cost-effective and non-invasive solution. This project aims to make InSAR technology more accessible and cost-effective for dam managers and operators in Canada. It plans to establish a near-real-time monitoring system for dams using automated InSAR processing and analysis. The system will use SAR data from Sentinel-1 and/or RCM satellites, potentially the future SAR data in L- and S- bands from NISAR mission, and L-band SAOCOM. The chosen InSAR technique, Small Baseline Subset (SBAS), is effective for monitoring and focus earth-fill dams in Ontario, Canada in vegetated areas. Open-source software packages like ISCE and MintPy will be used for data processing. The projects primary objectives include developing a user-friendly monitoring tool and advancing knowledge in the field of remote sensing-based dam safety monitoring. The anticipated outcomes include cost-efficient monitoring, near-real-time monitoring capabilities, and valuable scientific contributions to the field of infrastructure monitoring.