Projets novateurs réalisés

Community Engaged Architecture

The research project aims to investigate how racialized communities relationship with public space can be
improved with community-engaged architecture. The intern will use a post-occupancy evaluation to understand
the impacts of the community-engaged designed public space, 1700SPOT, for the residents of Russell Heights.
Impact is evaluated with consideration to racial and gender equity.

Propagation and Conservation of Heritage Grapevines – Vitis vinifera Pinot Gris ‘Gray Monk’

Cold temperatures in January 2024 devastated vineyards in the Okanagan and commercial vineyards responded by pulling out damaged vines. Surprisingly, in mid-June latent buds were observed on some of the cold-damaged grapevines. In collaboration with the vineyard team at Gray Monk Winery and Andrew Peller Estates, a MSc student in Sustainability at UBC Okanagan proposes to work to understand the impacts of the cold damage and climate resilience in Vitis vinifera L. Pinot Gris ‘Gray Monk’. The subvarietal ‘Gray Monk’ was planted in 1977 and the objective of the vineyard is a replanting program for expansion and conservation of their brand type. We propose to use plant tissue culture protocols to produce sufficient vines for replanting. Mass propagation of the vines requires that we select the appropriate explant tissue with the potential to regenerate, optimize of the culture media to establish a small number of clean maternal stock plants, induce of new shoots and roots, graft scions onto rootstocks and then acclimatization of the young vines for outdoor planting. Our preliminary data indicate that melatonin and serotonin are indoleamine plant growth regulators (IPGRs) that increase the rates of survival, regeneration and shoot proliferation in grapevines. We hypothesize that IPGRs enhance plant cell survival through detoxification of reactive oxygen species (ROS). This research program will determine the role(s) of IPGR in grapevine resilience and regeneration and will provide maternal stock plants for commercial propagation and replanting.

L2M – KelpAI: Leveraging AI and satellite data to monitor and protect kelp forests in the ocean for effective climate change mitigation

Kelp forests in the oceans provide considerable ecological benefits but are threatened by global warming, pollution, and human activities. This project aims to monitor kelp forests based on satellite data and artificial intelligence methods, beneficial for marine environmental conservation and climate change mitigation.

L2M – AquaFishSense

AquaFishSense addresses the pressing issue of outdated and insufficient information available to fisheries, which impedes their ability to operate sustainably and efficiently. Fisheries face significant challenges due to the lack of real-time data on fish populations and aquatic habitats. This gap in information leads to overexploitation of marine resources, with approximately 34% of global fish stocks being overexploited or depleted. The absence of accurate and up-to-date mapping of fish locations results in operational inefficiencies, causing fisheries to waste time and resources on less productive areas. The root cause of these challenges is the reliance on traditional, imprecise data collection methods that fail to provide integrated, near-real-time insights. Fisheries often lack the tools to adapt to changing environmental conditions or to identify optimal fishing zones, which exacerbates overfishing and habitat destruction. This inefficiency not only threatens marine biodiversity but also impacts the economic sustainability of fishing operations. The inability to pinpoint productive fishing areas results in reduced catch efficiency and increased operational costs. Addressing these issues with advanced satellite and AI-driven solutions is crucial for enhancing fishing practices, improving sustainability, and supporting better management of marine resources.

L2M – An Adaptive Instructional System for Simulator-Based Ice Management Training

Our project addresses a critical gap in maritime training, specifically the challenge of developing effective ice management skills in extreme environments. Traditional training methods rely heavily on human instructors, whose expertise is both scarce and costly, limiting the scalability and consistency of training programs. This issue is especially significant in industries like oil and gas, where inadequate training can lead to severe safety risks and operational inefficiencies. The core problem lies in the reliance on human instructors, which introduces inconsistencies in training outcomes and limits the ability to deliver comprehensive, standardized training. If unresolved, this inconsistency could lead to catastrophic failures in real-world operations.
Our solution introduces an AI-driven Adaptive Instructional System (AIS) that automates training in a simulator- based environment, providing personalized feedback to trainees and ensuring consistently high competency levels. By addressing the root cause of reliance on human instructors, the AIS has the potential to improve safety, reduce training costs, and provide more uniform skill set among trainees.
One of the primary challenges we face is convincing the traditionally cautious maritime industry to adopt AI-based training systems, especially for safety-critical tasks like ice management. There may also be skepticism about replacing human instructors with an automated system. In addition, obtaining regulatory approval and proving the system’s effectiveness through pilot programs are key hurdles.
This project aims to overcome these challenges by conducting pilot programs that demonstrate the AIS’s effectiveness in improving training outcomes. Independent validation and regulatory approval will also be pursued in the long-term to build trust with potential customers. By clearly communicating the cost savings, safety improvements, and consistency of our system, we will address industry concerns and pave the way for wider adoption.

L2M – Steel Catenary Riser Tracking

This project aims to develop an AI-enhanced autonomous underwater vehicle (AUV) system designed to track Steel Catenary Risers (SCRs) in real time. Accurate tracking of SCRs is critical for maintaining the integrity of subsea infrastructure in the offshore oil and gas industry. The AUV will leverage advanced computer vision and machine learning techniques to continuously track the riser’s position and adjust its own movement to follow the structure precisely.

L2M – Integrated 3D Reconstruction and SLAM for precise 3D visualization and localization of underwater assets

Our project integrates AI-driven Simultaneous Localization and Mapping (SLAM) with cutting-edge 3D reconstruction techniques to provide precise localization and high-fidelity 3D visualizations of underwater features. This fusion enables accurate measurement of size, depth, and spatial orientation of anomalies, enhancing inspection precision. By adopting this technology, industries can improve inspection accuracy, streamline maintenance, and reduce operational costs. Additionally, it boosts safety and efficiency, leading to better management and extended operational life of subsea assets. Through the Lab2Market Validate program and the Mitacs Business Strategy internship, we aim to develop commercially viable solutions tailored to the unique conditions of the Atlantic Ocean. This collaboration will help bridge the gap between innovative academic research and practical applications, enhancing the safety and efficiency of underwater operations and creating valuable commercialization opportunities for advanced technological research.

L2M – Sustainable photobioreactor

Overview and Main Activities of the Partner Organization
The partner organization is dedicated to enhancing urban environments through innovative solutions. One of their primary activities is developing decorative photobioreactors to improve air quality and provide aesthetic appeal in urban areas.

Innovation Challenge or Improvement Priority
The partner organization faces several challenges:
• Urban Air Pollution: There is a need to reduce CO2 levels and improve air quality in cities.

• Lack of Green Spaces: Enhancing urban environments with functional green infrastructure is a priority.

• Aesthetic Appeal: Providing visually appealing solutions for urban areas is essential.

Project Contribution
This project aims to address these challenges by developing an autonomous photobioreactor that:
• Reduces CO2 and improves air quality.

• Enhances urban environments with green infrastructure.

• Provides visually appealing solutions for urban areas.

Project Background, Business Ideas, and Industry Need
The project involves creating a decorative photobioreactor that can be used in urban areas to attract tourism and improve air quality. The need for such a device is driven by increasing urbanization and the associated environmental challenges.

Project Impact
This project aims to analyze the components/ characteristics of liquids resulting from pyrolysis/ hydrothermal liquefaction (HTL), / anaerobic digestion to determine their suitability as nutrients and mitigate biomass waste and eliminate minor risks associated with excess microalgae disposal.

L2M – PoseidonPulse

The proposed project focuses on developing a business and go-to-market strategy for PoseidonPulse, an advanced AI-powered water quality monitoring system designed to improve sustainability and operational efficiency in Canada’s aquaculture industry. By continuously tracking essential water parameters, this system enables aquaculture farms to proactively manage water quality, supporting healthier fish populations and optimized farm performance. Additionally, this project aims to adapt PoseidonPulse for Indigenous communities, where it could provide real-time monitoring solutions to address ongoing water supply challenges.
The L2M Validate program complements the proposed BSI project by offering crucial entrepreneurial skills development for researchers, enabling us to assess the market potential of research-driven products. Resources from L2M, including industry expert sessions and financial strategy training, will equip me with the skills needed to implement a successful business strategy throughout the BSI internship.

L2M – An adaptive communication module for real-time network switching between satellite and cellular or radio to optimize connectivity for autonomous maritime vessels (MASS).

The project aims to create a mountable communication system, for Maritime Autonomous Surface Ships(MASS) that enables them to switch between satellite and cellular networks like LTE and 5G depending on signal strength automatically. This innovation ensures communication for the smooth and secure operation of unmanned ships. By investigating market requirements and strategic plans this initiative will provide information to the collaborating company regarding uses and customer engagement.This will lay the groundwork for product enhancements. Unveil fresh prospects, in the maritime technology industry.

Determination of postharvest practices that impact the physicochemical properties cannabis biomass ash

This academic-industry collaboration aims to determine postharvest storage methods, combustion
parameters and consumer behaviour for dried cannabis products that influence the physicochemical properties of
cannabis ash, of which appearance (colour and integrity) has become an inadvertent dried cannabis product attribute
that may drive consumer decisions towards legal distribution in a highly competitive market. Data generated from this
work will provide valuable information for postharvest practices, product maintenance/storage and shelf life that
ensure quality and safety, as well as offering a baseline of biomass ash properties for cannabis product intended for
inhalation, or for future work on industrial cannabis or hemp waste valorization. Together, findings will provide
scientific evidence and highlight Canada’s expertise for consumers while improving legal product trust, license
producers, and regulatory bodies invested in evolving cannabis and hemp industries on a global stage.

ESG-washing Detection using NLP

“THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW”