Projets novateurs réalisés

Research on Marine Fuel Usage Prediction Systems

Research will be conducted to determine the best way to visualize and operationalize engine performance data collected in real-time for vessel owners and operators. Performance data will be correlated to weather and sea conditions to understand their impacts on fuel usage and emissions. The methods of displaying data will be explored to determine the best way to visualize the interaction of the various data points in real time. The goal is to research the characteristics of a “fit-bit” for a vessel, tracking vessel performance and supplying feedback in real-time. Prior research has shown that feedback on the fuel use of a vessel in real time can cause a reduction in fuel use of 15%.

Developing an automated tool for SAR parameter optimization

Synthetic aperture radar (SAR) satellites provide wide area global perspectives through images of the Earth’s surfaces and its governing processes. Capable of detecting small
variations in surface roughness, and unconstrained by lighting and weather conditions, SAR is a robust remote sensing instrument. Designing and operating a SAR is a complex process, as there are numerous design and operational parameters. The challenge is to optimize a few key parameters that are the primary determinants of image quality and coverage. Traditionally, this task has been handled manually in a spreadsheet, and can be extremely time consuming. In the instance when a SAR platform undergoes frequent changes in altitude and attitude, continual manual updates of optimal parameter calculations is infeasible. For this project, we propose developing an automated tool that can be used to solve for SAR operating parameters. For Urthecast, the rapid response of such an automated tool is invaluable to providing near-live SAR imagery of the earth.

Retour social sur l’investissement des interventions en santé primaire et communautaire

La continue augmentation des investissements en innovation en santé vise à trouver des solutions efficaces et pérennes pour contrôler ou réduire le coût des systèmes de santé. La télémédecine représente une des solutions mises de l’avant pour répondre à ces défis économiques. Toutefois, peu de projets accompagnent leur processus de développement et validation technologique par une évaluation économique, tel que le retour social sur l’investissement (RSSI). Dans le domaine de la télémédecine et les interventions en réalité virtuelle, bien que le RSSI ait prouvé son utilité, il est encore peu utilisé. Le but du présent projet est de conduire un RSSI pour évaluer les effets d’un modèle de soins primaires virtuels (TH@CLINIC) destiné aux camionneurs du Nouveau-Brunswick et d’un programme de prévention des chutes auprès des personnes aînées vivant en communauté (BalantBoost). Deux stagiaires au master en économie, à l’Université de Poitiers, seront impliqués dans le projet. Ils participeront, sous la supervision d’une équipe de recherche interdisciplinaire (médecine et économie de la santé) à toutes les étapes de la méthode RSSI. Ils acquerront des compétences en recherche (analyse de données et de coûts (RSSI), revue de littérature, rédaction d’articles et présentations orales) et élargiront leur réseau de contacts.

Identifying agronomy and potential of Intermediate wheatgrass in the Canadian Maritime climate to build climate change adaptation capacity in agriculture

Modern agriculture has focused on cultivating annual crops for their uniformity and quick production timeline. However, a sustainable ecosystem includes the combination of annual and perennial plants, with perennials providing soil health services and resilience under dynamic conditions. Newly developed lineages of intermediate wheatgrass, a perennial crop that uniquely produces grain and forage, were introduced to the Canadian Maritimes in 2022. These research plots established well, but the agronomy and associated yield potential has not yet been identified. This project will expand on the cultivation methods for incorporating intermediate wheatgrass into annual crop rotations, as well as in biculture with annual commodity crops, to develop an environmentally and economically sustainable agriculture system that has the potential to aid an industry impacted by climate change.

Machine learning for satellite-based monitoring and condition assessment of critical infrastructure

This project aims to improve the monitoring and assessment of critical infrastructure using machine learning and satellite technology. By integrating machine learning techniques, we aim to improve the accuracy of detecting structural and geotechnical deterioration in public infrastructure, such as marine port wharves and airport runways. Led by experts from the NRC’s Construction Research Centre (NRC-CRC) and UBC Applied Science, the project will develop AI-trained probabilistic models to process satellite data and provide clearer insights into structural health. With the potential to monitor infrastructure across Canada in all weather conditions, satellite-based health monitoring has the potential to improve the quality and reduce service disruptions of critical infrastructure. This project will contribute to the NRC-CRC’s work towards a more data-driven and condition-based maintenance strategy for infrastructure assets by providing a new perspective on satellite-based deformation monitoring. This project will also contribute to UBC’s work on data-driven predictive analytics for infrastructure monitoring. Ultimately, the approach explored in this project promises more efficient and cost-effective asset management, ensuring safer transportation networks for communities nationwide.

How to decode cellular signals: Investigating the biochemical mechanism-of-action of calcium-dependent protein kinases

Microbial diseases pose significant threats to agriculture, jeopardizing food security by reducing yields and damaging crops. Understanding how plants sense and respond to pathogens while maintaining growth is crucial. Signal transduction is governed by protein kinases that phosphorylate targets to regulate their activity, localization, and stability. Our research aims to unravel plant immune signaling, to eventually enhance disease resistance in crops. We investigate signal perception, transduction, attenuation, pathway organization, and how information is integrated for optimal plant health. This research proposal focuses on a family of kinases that sense and respond to changes in cellular calcium levels – a hallmark of stress. These ‘calcium-dependent protein kinases’ (CPKs) are important signal transducers in plant immune pathways and are considered ‘signaling hubs’ because they integrate multiple stress. This proposal fosters collaboration between the Monaghan Lab at Queen’s and the Romeis Lab at the Leibniz Institute of Plant Biochemistry in Germany, ensuring a lasting partnership to advance plant health research.

Responses of marine organisms to multiple stressors

Fiber-optic based point of care genetic testing device

Point-of-Care DNA tests have grown in importance throughout recent years. Incorporation of devices in patient beside treatment requires fast turn-around times for results as well as economic feasibility. Spartan Bioscience, a local biotechnology company, has developed a technology that can detect abnormalities in a person’s DNA. In an effort to develop next generation technology, Spartan Bioscience has collaborated with several faculties at Carleton to develop a platform that will have improved performance. The device is making use of Spartan’s core technology and optical fiber techniques pioneered at Carleton University. The joint teams will be making a proof-of-concept device that will hopefully, lead to a full-on product development

The reforestation of damaged spruce stands in a changing climate

Testing collective cognition in response to size-selection in zebrafish (Danio rerio)

Tracking and Navigation of Medical Instruments for Image Guided Therapy

Machine Learning Internship: Scene Graph Generation Model Development