Innovative Projects Realized

Assessing the viability of potential monetization strategies for the NS Health Innovation Hub’s test and try program.

This project is aimed at assisting the NS Health Innovation Hub (hereinafter referred to as the Hub) in the delivery of its mandate to “solve critical health challenges for the benefit of all Nova Scotians. One way the Hub delivers on this mandate is through what it calls the ‘test & try portfolio’ – an organizational function through which external partners/technologies are trialed in a controlled setting within NS Health operations. The activity of the test & try portfolio is a critical step in both de-risking external health-tech innovation and in triaging suitable partners to the most pressing clinical challenges within NS Health. While the Hub produces (and/or sources) the majority of funding to run projects through the test & try portfolio, partner organizations also receive material benefit with very little cost of participation. Despite the clear value created for its partner organizations, the Hub does not currently have a mechanism for recovering the portfolio’s cost to operate. The aim of this project is to better understand the commercial value of the Hub’s test & try portfolio and to vet potential pathways to monetize its activities. If a viable new revenue stream can be identified, this would allow the Hub to (1) recoup a greater return of dollars invested into the program, and (2) re-invest earned dollars back into the speed and scale of the test & try portfolio.

Estimating in-situ consolidation properties of tailings deposits using CPT

The successful integration of tailings deposits into existing landforms requires knowledge on how much they will compress and release water under load over time. Typically this is characterized using laboratory based methods, but these tests on small samples cannot capture the behaviour of a full scale deposit. This project will develops a method to use conventional field measurements in a novel way to estimate this rate of compression.

Nonlinear adaptive neural controllers – Year two

Contemporary machine learning has been very successfully applied to processing static images and words in consumer applications, resulting in billions of dollars in recent acquisitions of machine learning companies by Microsoft, Amazon, Facebook, and Google. However, applications to dynamic information (e.g. movies, controlling robotics) has been less well-developed. In this project, will develop and apply a novel machine learning method to neural control system for a sophisticated robotic arm. We will use hierarchical optimal neural control, dynamic trajectory generation, and non-linear adaptive methods. These same algorithms lay the foundations for processing dynamic perceptual information as well. The methods allow for the generation of neural network controllers that require limited or no knowledge of the system, allow for one-shot learning, provide generalizable trajectory generation, exhibit online error correction, and provide a natural implementation in neuromorphic hardware. This will provide the company with a clear lead in state-of-the-art controllers for robotics applications.

Characterizing Next-Generation Aerogels: From Plant-Based Cellulose interfacial assembly to Enhanced Energy Storage and electromagnetic interference (EMI) Shielding

The proposed project aims to develop sustainable cellulose-based aerogels containing conductive materials like MXenes and graphene for electromagnetic interference shielding and energy storage applications. By exploring interfacial assembly mechanisms through advanced characterization techniques available at CEMES-CNRS, such as in-situ electron microscopy and Raman spectroscopy, the research will clarify how these materials form structured aerogels. Understanding these assembly processes will directly enable the optimization of aerogel performance. This international collaboration will significantly enhance UBC’s capacity to create tailored materials with improved functional behaviors, benefiting research capabilities at both participating institutions.

Comparative Life Cycle Assessment and Uncertainty Evaluation of Seaweed-Based Biofertilizer Pathways for Climate-Smart Agriculture

The proposed project will compare different ways of turning seaweed into eco-friendly fertilizers by using a life cycle assessment (LCA) approach that includes uncertainty and regional differences. This research will help identify the most sustainable and scalable production methods, contributing to climate-smart agriculture and reducing reliance on synthetic fertilizers. The collaboration between the University of British Columbia and Cornell University combines strengths in environmental modeling and systems engineering, providing advanced training for the intern. Both institutions will benefit through shared expertise, new research data, and future opportunities for joint projects and innovation in sustainable agriculture.

COR Certification and HRM System/Online Onboarding Process

S3 Technical Inc., a growing manufacturer of aluminum and stainless-steel cladding products for the oil and gas industry, is undertaking a project to improve its internal processes and support continued growth. The company plans to update its standard operating procedures, work toward COR certification, and develop a Human Resources Management System (HRMS) to streamline onboarding and employee management. These improvements will help S3 Technical increase efficiency, meet industry safety standards, and access new business opportunities that require COR certification. With limited internal capacity, the company is working to bring on a qualified NAIT intern to support this work, gaining valuable hands-on experience while helping S3 Technical build a safer, more resilient, and scalable operation. The project will also contribute to Alberta’s workforce development and economic diversification by preparing future professionals and strengthening a small business’s ability to compete in evolving markets.

Prédiction de la rémission de l’asthme sévére sous biothérapie chez les enfants.

L’asthme est une des maladies chronique courante chez les enfants. Elle peut parfois devenir sévère, affectant gravement leur qualité de vie. Dans ces cas, des traitements appelés biothérapies peuvent aider à mieux contrôler les symptômes. Cependant, ces traitements sont très coûteux et ne sont prescrits qu’à un petit nombre d’enfants, souvent parce qu’il est difficile de savoir à l’avance quels patients en bénéficieront réellement.
Mon projet vise à combler cette lacune en développant un outil qui oriente la décision médicale. Cet outil utilisera des données cliniques issues de l’hôpital Sainte-Justine pour aider les médecins à identifier plus précisément quels enfants sont les meilleurs candidats pour les biothérapies.

Objectifs du projet:
Comparerl’efficacité de 2 types de biothérapie chez les enfants atteints d’asthme sévère afin de mieux comprendre leurs différences.

Définir des critéres capables de prédire la réponse aux biothérapies sur une période particuliére.

Tester ce modèle sur une nouvelle cohorte d’enfants pour s’assurer qu’il fonctionne dans différents contextes.

Ce projet pourrait permettre à plus d’enfants d’avoir accès à des traitements efficaces, tout en évitant les prescriptions inutiles. Il s’agit d’un pas important vers une médecine personnalisée et plus équitable pour les jeunes souffrant d’asthme.

Centre for Local Innovation & Collaboration Cohort 5 2025

The Centre for Local Innovation & Collaboration (CLIC) project will team up with small businesses from Richmond Hill’s Small Business Enterprise Centre. Interns from OCAD University’s Strategic Foresight and Innovation (SFI) Master of Design program will (through research) help these businesses with their innovation challenges using various methods like human-centered design, futures thinking, and strategic planning. This collaboration aims to boost growth and innovation for the businesses, producing action plans and strategies for success.

L2M -Vibrational Energy Harvester With Self-Resonant Frequency Adjusting For Wireless Sensing Network

This project will support the development and validation of a novel vibration energy harvester (VEH) with resonant frequency tuning. VEHs convert mechanical vibrations into electrical energy and can be used to power wireless sensor network (WSN) nodes for machine condition monitoring in manufacturing and processing. Conventional VEHs are resonant devices with inherently narrow bandwidths, making them effective only at a specific frequency, presenting a practical challenge as vibration conditions can vary. The proposed design uses a mechanism that can passively tune its resonant frequency, enabling energy harvesting over a wider range of operating conditions. By providing a more versatile power supply for WSN nodes, this technology can reduce battery dependence, lower operating costs, and enable wider adoption of machine condition monitoring. The project will engage stakeholders to ensure market fit and will test the system under representative conditions to advance commercialization for the partner organization.

Community Benefits Network in Prince Edward County

This project, in partnership with The County Foundation (TFC), supports the work of the Community Benefits Network (CBN), a coalition of 23 local organizations working to ensure that new development in Prince Edward County delivers lasting, positive outcomes for the community. With major public and private development projects on the horizon—including housing developments, a new hospital, long-term care facility, school, and water infrastructure—the County is at a turning point. Community Benefit Agreements (CBAs) offer a tool to align these developments with community-defined priorities such as affordability, workforce development, and social infrastructure.

Through this four-month research initiative, a graduate intern will lead a jurisdictional scan of rural CBA case studies, identify practical tools and governance approaches, and explore both legal and goodwill-based frameworks that can work in rural settings like PEC. Key deliverables will include a rural CBA case study report, an annotated resource guide, a draft CBA toolkit tailored to PEC, a plain-language brief for community and developer partners, and a final presentation of findings. The goal is to build local capacity, deepen community-developer collaboration, and support more inclusive, equitable development outcomes in the County.

Nonlinear adaptive neural controllers

Contemporary machine learning has been very successfully applied to processing static images and words in consumer applications, resulting in billions of dollars in recent acquisitions of machine learning companies by Microsoft, Amazon, Facebook, and Google. However, applications to dynamic information (e.g. movies, controlling robotics) has been less well-developed. In this project, will develop and apply a novel machine learning method to neural control system for a sophisticated robotic arm. We will use hierarchical optimal neural control, dynamic trajectory generation, and non-linear adaptive methods. These same algorithms lay the foundations for processing dynamic perceptual information as well. The methods allow for the generation of neural network controllers that require limited or no knowledge of the system, allow for one-shot learning, provide generalizable trajectory generation, exhibit online error correction, and provide a natural implementation in neuromorphic hardware. This will provide the company with a clear lead in state-of-the-art controllers for robotics applications.

L2M Validation / QC Automne 2025 / Apertum – Pierre-Briac METAYER / Validation du potentiel commercial de la plateforme Apertum : Analyse des applications des biomarqueurs multimodaux pour le dépistage en santé

Ce projet vise à mieux comprendre les marchés et les partenaires auxquels s’adresse Apertum, une entreprise québécoise en santé numérique qui cherche à combler le fossé entre les innovations développées en laboratoire et leur application concrète dans la société. Trop souvent, des outils prometteurs comme les biomarqueurs numériques pouvant détecter précocement des troubles comme la maladie d’Alzheimer ou la dépression restent confinés aux laboratoires et n’atteignent jamais les citoyens.

Apertum souhaite faire tomber ces barrières en développant une plateforme technologique qui standardise la manière dont on collecte, structure et analyse les données en santé (voix, vidéo, questionnaires, capteurs, etc.), tout en assurant l’interopérabilité des protocoles et la robustesse scientifique des résultats. L’objectif est d’accélérer le transfert de ces innovations vers la clinique et la vie quotidienne, dans un cadre éthique/transparent.

Dans ce projet, le stagiaire mènera une étude de terrain pour aller à la rencontre des professionnels de santé, des institutions publiques, des entreprises du secteur, des laboratoires, mais aussi des experts en propriété intellectuelle et en valorisation de l’innovation. L’objectif est de mieux comprendre les besoins réels, les obstacles actuels et les opportunités de collaboration pour orienter le développement d’Apertum en cohérence avec les attentes du marché.