Grants and Contributions

March 2021 – Following the outbreak of violence in Northern Rakhine State in August 2017, more than 700,000 Rohingya people crossed from Myanmar into Bangladesh over a period of several months. Currently, an estimated 855,000 Rohingya refugees live in 34 highly congested settlements in Cox’s Bazar, Bangladesh. The speed and scale of the influx resulted in significant humanitarian needs.

With GAC’s support, Development and Peace and its local partner, Caritas Bangladesh, provide safe shelters and secure settlements to Rohingya refugees in Cox’s Bazar, using a community-based approach. Project activities include: (1) providing training on shelter construction, maintenance and upgrade to refugee households; (2) distributing construction materials and providing technical assistance; (3) facilitating community groups to develop site improvement action plans; and (4) undertaking site improvement works.

This Project seeks to develop an Artificial Intelligence (AI) enabled new multimodal microscope, based on coherent nonlinear optics and having the ability to learn from previous measurements to selectively choose subsequent imaging techniques. This will increase measurement capacity and allow for new microscopy techniques which can be used to advance the design of materials such as those found in solar cells and soft materials associated with biological applications. The initial focus will be on the challenging but important field of soft matter systems which have applications ranging from bio-implants to flexible batteries.

The development of sustainability assessment based on techno-economic analysis (TEA) and life cycle analysis (LCA) of pulse-based protein processing techniques has not been well established in Canada. Most of the available data for LCA/TEA
practitioners currently use US and European data inventories, when available, in proxy for Canadian data to complete sustainability assessments. Given the sensitivity of assessment results to data inputs, it is essential to capture local/regional data input and make them available in an organized database to support rigorous and meaningful LCA/TEA output. While Canada spearheads the plant-based diets, it is essential to establish environmental and economic implications and trade-offs of current routes to their production. This Project will provide a detailed technical, economic, and environmental assessment of current processes for both protein and starch production. By combining experimental data and process simulations, the Project will provide optimization pathways that will enhance sustainability assessments using plant protein based on technical, economics and environmental criteria.

This Project will develop new machine learning algorithms for automating aspects of physical chemistry by discovering new pathways for creating materials with desired properties. The goal is to use the framework of reinforcement learning as a means of discovering, controlling, and optimizing the production of new materials. This Project will explore a novel new direction to perform decision making associated with established but highly complex processes used to transform one chemical compound into another. From an algorithmic point of view, this research will tackle the challenge of working with highly nonlinear relationships and generating simulation data that is of high quality.

Current technology relies on radio-frequency communication to and from satellites to ground stations. Optical satellite communications can support much larger bandwidths but require adaptive optical correction systems to correct for atmospheric turbulence and maintain high throughput, but conventional bulk-optics systems can be prohibitively expensive. To mitigate cost and space issues, the Project will develop an on-chip adaptive optics (AO) phase shifting system using the integrated silicon photonic analogue of a deformable mirror. The on-chip “deformable mirror” operates through an array of grating couplers, phase sensors, and phase shifters to correct the wavefront of a free-space optical beam directly in the plane of an integrated photonic chip, leading to numerous advantages, including reduced bulk, reduced cost, and increase volume production.

This Project is a collaboration between NRC and the University of Victoria (UVic) to develop Transfer Learning methods that utilize a large dataset for healthy patients with the aim of developing a model for the analysis of smaller patient populations. In this way, it will provide a base model for early personalized-diagnosis that is accurate across different patient subpopulations. The Project will focus on two neurodegenerative diseases - Alzheimer's and Parkinson's disease. The transfer Learning model will be used for the development of sex and race optimized early diagnostic methods for Alzehimer's and Parkinson's disease.

The Project is to optimize unique latex agglutination tests (LATs) for the detection of SARS-CoV-2 virus in saliva in an inexpensive "chewable diagnostic" format. The LATs for SARS-CoV-2 are to be performed in a single step, with the aim that the results will be readable by visual inspection, with the potential for automated readout via a smart-phone. By the end of this Project, LATs implemented in the chewable cartridge format will have been developed, optimized, and evaluated in a pilot scale clinical trial.

The Earth Data Store (EDS) is a cloud repository for earth observation data that is supported by Canada's Digital Technology Supercluster. The use of aerial data is transitioning from manual processes to approaches based on machine learning. As
this automated approach to information extraction progresses, advances are needed to provide automated ways of aligning aerial imagery with existing models and other data sources. This is a highly domain-specific task, and every set of new data
sources requires at least some manual work to identify the best alignment methodologies. In this Project, the focus will be on exploiting EDS satellite data in a form that can be used with Sensair software. This involves the creation of interfaces
between the systems to allow for real-time access, and the derivation of 3D model data derived from lidar point clouds, satellite data, and other sensor data.

The Habitat StewardshipProgram (HSP) contributes to the recovery of endangered, threatened, and other species of concern, by engaging Canadians in conservation actions to benefit wildlife to protect aquaticspecies at risk.

To support the participation of Indigenous peoples in activities relating to theconservation and protection of fish and fish habitat, including the development of theDepartment of Fisheries and Oceans' (DFO) policies, program elements and regulationsand the development of their technical capacity to support greater involvement in the conservation and protection of fish and fish habitat.