Grants and Contributions

Advances exploratory research under the New Beginnings Initiative

Thermal pre-treatment, specifically Radio Frequency (RF) treatment of pulses prior to protein extraction has been proven to improve fractionation efficiency and functionality of protein concentrates. Towards fully adopting these methods at industrial scale, it is necessary to establish the optimum process configuration and conditions that maximizes the overall techno?eco-environmental performance. While the experimental optimization approach provides a reflective model for optimization, just a few scenarios can be carried out. Therefore, this project seeks to develop a digital twin model to reflect pilot-scale pea and faba bean flour and protein fractionation, thus providing a means for control, optimization, and product?process performance prediction. This project will support the CSTIP goals in terms of enabling the innovation of new technologies. The dataset generated will support the design and improvement of plant protein fractionation technologies. Moreover, this will strengthen collaborations among different stakeholders in pulse protein production towards enhancing the overall Canadian bioeconomy.

Advances exploratory research under the New Beginnings Initiative

Advances exploratory research under the New Beginnings Initiative

Advances exploratory research under the New Beginnings Initiative

Scientific discovery, by nature, is a lengthy and arduous journey largely due to the vastness of its exploratory scope. To illustrate, the design of a 100 amino-acid protein sequence entails the exploration of mind-boggling 20^100 possibilities. Historical approaches to scientific discovery in realms such as protein, molecular, and material design have relied heavily on the artful blend of intuition, experience, and a dash of serendipity. These empirical-driven methods, while valuable, are markedly time-intensive and laden with numerous unsuccessful attempts. The advent of artificial intelligence (AI) promised a new velocity in the discovery process. Yet, prior efforts have generally centered on small-scale models with constrained generalizability, resulting in suboptimal outcomes. The emergence of large language models (LLMs), with their remarkable reasoning and generalization prowess, holds the potential to catalyze a seismic shift in this field, thereby dramatically streamlining the scientific discovery journey. The project aims to accelerate scientific discovery with big data. By utilizing the currently popular large foundation models, it will be possible to unleash their generalization capabilities to help researchers significantly shrink the search space. By leveraging AI techniques in the process, it can make the search process more explainable and systematic.

Decarbonizing the built environment through extensive green renovation requires a carbon footprint of deconstruction/renovation activities, which includes the carbon footprint of materials and components and a holistic approach. High-precision live digital twin models (DTMs) could integrate data repositories and provide a platform for scenario analysis, but standards and maturity are lacking. This project defines a model-building and application strategy, analyzes the requirements for standardizing renovation DTs by creating a digital building product passport and urban-scale DTs through open MIBSIG integration, automating the processes for generating renovation alternatives and their evaluation, combined with life-cycle cost and life-cycle analyses, integrated into DTs, with methods for calculating deconstruction and recoverability scores for renovation deployed by DT standards.

In the realm of Autonomous Driving (AD), the application of Deep Learning (DL) and Deep Reinforcement Learning (DRL) has been pivotal. However, ensuring the robustness of these technologies against cyber threats remains a critical challenge. This project proposes the development of a high-fidelity virtual platform to assess the certified robustness of DL and DRL algorithms in AD, focusing on cybersecurity vulnerabilities. The proposed framework leverages the capabilities of the Carla open-source simulator to create a realistic virtual environment that mimics real-world cyberattack scenarios on AD systems. This project will have three phases: the first phase will involve identifying and training baseline DL/DRL algorithms for AD and selecting the state-of-the-art methods suitable for various AD scenarios. In the second phase, the focus will be on developing a virtual platform prototype capable of accurately simulating diverse cyberattack scenarios, thereby providing a testbed for evaluating the resilience of DL/DRL-based AVs. The final phase involves training certified defense algorithms designed explicitly for DL/DRLbased AV systems, emphasizing their optimization for AD requirements and testing against a spectrum of cyber threats.

This contribution will support the Little Red River Cree Nation to participate in the implementation of the Action Plan for Wood Buffalo National Park World Heritage Site.

This grant supports Avalanche Canada to participate in a regional meeting to foster vital conversations related to coastal waterfront safety.