Innovative Projects Realized

Biodegradation of microplastics in Environmental media

La gestion des déchets plastiques est difficile à gérer par leur quantité mais aussi leur recyclage coûteux et sélectif. Les plastiques non recyclés sont incinérés ou libérés dans l’environnement, ce qui entraine une accumulation dans le milieu. Ces dernières années, des microorganismes, (bactéries, champignon…) ont été identifiés et isolés pour leur capacité à dégrader différents types de plastique. Cependant, il existe un manque de moyen analytique afin de mieux comprendre les processus de dégradation. L’objectif de ce stage est de mettre en place des méthodes analytiques à partir d’outils existants afin de détecter, identifier et quantifier les molécules libérées lors de la biodégradation du plastique. Pour ce faire, des milieux de culture, où ce seront développées des bactéries (déjà connues pour dégrader le plastique) en présence de plastique, seront analysés de différentes façons afin d’optimiser la méthode. Ensuite des échantillons environnementaux seront analysés. Une meilleure connaissance des produits de dégradation formés permettra le développement du “biorecyclage” c’est à dire le recyclage du plastique à partir des bactéries. D’un point de vue environnemental, il sera également plus facile de comprendre le cycle de vie du plastique dans l’environnement et les éventuels impacts écologiques qui y sont lié.

Evaluation of tools for assessing organic chemicals for human health and ecological exposure and risk assessment

Society uses thousands of chemicals and the potential risks to humans and the environment for the vast majority of these chemicals are largely unknown. It is not feasible to measure all of the chemicals and there are substantial data gaps; therefore, models are required to screen and evaluate chemicals for potential exposures and risks to humans and the environment and to address data gaps. ARC Arnot Research & Consulting develops models for screening-level exposure and risk assessment. There is a need to test these models. This project includes the development of monitoring databases and subsequent testing of the models.

Interpolation and optimization in the graph space using the Pasqal architecture of quantum processors

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Développement d’une plateforme B2Bpour favoriser le circuits court

Amélioration d’une plateforme B2B d’approvisionnement en agroalimentaire afin de favoriser le circuit-court. En particulier, l’ajout de fonctionnalités permettant de facilement créer et gérer des offres collectives à partir de la plateforme Arrivage. Ces nouvelles fonctionnalités permettront aux régions, pôles alimentaires et autres organismes collectif d’utiliser la plateforme pour créer et gérer une offre combinant les offres de leurs membres.

Développement stratégique de Mégaphone, pour un développement communautaire durable

MÉGAPHONE a pour mission de développer les compétences socioprofessionnelles des jeunes par la culture scientifique. Par le présent projet, Projet Mégaphone fait appel à une étudiante de maîtrise en gestion de l’innovation sociale pour lui confier un mandat de développement stratégique. Il comporte plusieurs éléments, notamment, l’élaboration d’un schéma de transformation sociale et une stratégie de financement, mettant en valeur l’originalité de l’initiative. Une stratégie associant subventions gouvernementales, autofinancement des activités et philanthropie.

Stability Modelling of a Five Axis CNC Machine

The Canadian tool, die and mold making industry had sales of $1.2 billion in 2002 that dropped by 40+% in the ensuing decade as manufacturing moved offshore in search for lower labor cost. Trade tensions, conflicts (Ukraine/Russia; Taiwan/China etc.), COVID pandemic and accompanying supply chain disruptions, increase in labour cost overseas, improved automation technology and rising transportation cost have eroded the advantage, and the Canadian market is once again on a growth trend predicted to reach $128 billion. The growth is fuelled by novel ways of machining to reduce production cost and increase productivity, greater employment, and higher wages (Cook, 2022) . Furthermore, transportation and labour accounts for 45% and 32% of the logistics cost and its reduction with reshoring is advantageous. A Canadian study, conducted by Deloitte Economic Advisory, identified Machinery manufacturing as a hotspot for reshoring. An estimated five to seven jobs are created in related industries for every job reshored in the manufacturing sector (Cook, 2022); an additional boost for Canadian employment. However, lack of skilled labor, manufacturing knowledge and lack of interest among youth in this domain is a challenge.

The focus of this, university/industry, partnership is on developing automation technology that assists operators during machine programming and by detecting and avoiding anomalies during machine setup and machine operation.

Big Data Research for Open Source Applications

Big data is a collection of data sets so large and complex that it becomes difficult to process using onhand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization. In this internship, we analyze a real-world big data set(s) to make sensible inferences by taking into account a selected range of criteria. A number of methods and algorithms are investigated, evaluated and evolved to advance the development of specialized tools and processes.

Evaluation and enhancement of the Craven SPORT services osteoarthritis program provision

For the past 5 years, Craven SPORT Services has been providing an osteoarthritis (OA) exercise intervention programs within their Saskatoon clinic. Demand for the OA program has grown considerably over the last two years with waitlists for each cycle of the program and increased client retention for consecutive program cycles (often several months and years). During this time there have been few avenues for a detailed assessment and evaluation of the OA program. The current metrics used to assess client engagement and objective progressions are time consuming. With the increase in program demand efforts need to be made to enhance the existing program for further expansion. A review of the existing practices and current time consuming metrics being used is needed. The current project is designed to analyze the current industry and research standards for objectively measuring the client outcomes when participating in an OA exercise intervention. Alongside a review of the existing measures being used by Craven SPORT services the implementation of new and efficient practices will be an objective of this project. The second half of the project will be to assess the impact and effectiveness of the implemented practices.

Monitoring Marsh Extent and Vegetation Composition in British Columbia’s South Coast using Remote Sensing and Machine/Deep Learning Models

Coastal salt marshes, such as those in southern British Columbia (BC), offer valuable ecosystem services, including providing habitats for various plant, fish, and bird species, protecting shorelines from storm surges and
erosion, and sequestering and storing carbon to mitigate climate change. However, this low-lying system is under threat due to rising sea levels and human disturbances, leading to significant marsh die-off and ecosystem
degradation in the past few decades. Therefore, it is critical to take action to restore disturbed marshes and conserve local ecosystems. This project was proposed to monitor marsh die-off and restoration using remote sensing (e.g., satellite imaging), which is effective for large-scale and repeated monitoring. Research results are expected to provide more insights into marsh die-off processes, reveal associated ecological and geomorphological mechanisms, and support local restoration efforts by partner organizations.

Data visualization and evidence synthesis to inform the Sensitive Measurement Project

To ensure that PPPH priority projects are developed using the most up-to-date evidence and to increase the likelihood of project success, evidence synthesis will be crucial in facilitating a deeper understanding of community health issues, the contributing factors, associated health inequities, and appropriate interventions to improve health outcomes. The objectives of this project are: 1) To develop effective evidence-based data visualization strategies for conveying sensitive measurement results for cervical and colorectal cancer screening participation; 2) To conduct evidence synthesis on country-wise incidence and prevalence of hysterectomy. Key findings from the evidence synthesis will be summarized into a comprehensive final report and a presentation. The evidence synthesis will contribute to a better understanding of important issues affecting the health of Albertans, as well as the contributing factors and interventions to improve health outcomes in the province.

Market Research

This project aims to thoroughly understand the diagnostic testing market in Canada, the US, and Globally. The project will task the intern to elucidate and report on the current global, North American, and Canadian landscape for Lateral Flow Assays (LFA’s), including identification of the current manufacturers, distributors and sellers, and test manufacturer and retail price points. The intern will discover and report on the current technologies in use and any new trends or technologies in colorimetric testing. This includes providing a comprehensive list of tests currently available and their pricing. Finally, the intern will be expected to produce a detailed report of a synthesis of their work with custom analysis, including charts and graphs, where helpful. There will also be a reference section capturing all the work referenced in their report.

The work performed by the intern will be invaluable to the Partner organization as a means of developing a strong understanding of the testing landscape that will include a go-to-market strategy.

Machine learning approach to monitoring and decarbonizing mineral processes

Within Canada’s commitment to the 2030 Agenda for Sustainable Development, a pivotal goal involves taking immediate action to combat climate change and its far-reaching effects. The specified target is a 40% to 45% reduction in the country’s total greenhouse gas (GHG) emissions below 2005 levels by 2030. To transition away from fossil fuels and embrace renewable energy sources, a strategic combination of solar and wind power,
coupled with energy storage solutions like lithium-ion batteries, emerges as a transformative approach.
While this renewable energy transition holds immense promise for long-term carbon emissions reduction, it is imperative to address and mitigate the environmental impacts associated with the production of these batteries. The proposed research seeks to refine mineral processing activities by leveraging machine learning, aiming to decrease carbon emissions significantly. The focus is on developing a versatile machine learning model designed to optimize energy consumption in processing plants, thereby bolstering the production of essential minerals for lithium-ion batteries in Canada. This innovative strategy not only aligns with the nation’s commitment to GHG reduction targets but also underscores a commitment to sustainable practices in resource extraction.