Innovations Realized

Evaluation and development of a waste heat utilization strategy for the Hamilton Bayfront Industrial Area

The project deal with identifying the sources of waste heat recovery in the industries located in Hamilton Bayfront area and utilizing the available waste heat in either within the process of waste heat generator or utilizing the energy in any of the partner industry. The project also deal with harnessing the waste heat and utilizing it for district energy needs. The research of this project will have two benefits

1. Reducing GHG emission in Hamilton by reducing the emissions of industries operating in Hamilton Bayfront Area
2. Exploring potential for district heating in Hamilton City by utilizing low grade industrial heat for domestic heating water, Space heating and cooling. This will also result in GHG reduction for residential and commercial building operating within city of Hamilton

Habitat security mapping for improving human-wildlife coexistence in the Bow Valley

Tourism, nature-based recreation and residential development within the Bow Valley (BV) of Alberta’s Rocky Mountains are all valued and continually increasing. The BV is also important for large mammals as it provides habitat for iconic species like grizzly bears to live in and move through. The Yellowstone to Yukon Conservation Initiative (Y2Y) is dedicated to balancing the needs of people and wildlife, and collaborated with stakeholders to recommend how to improve human-wildlife coexistence in the BV. Targeting one of those recommendations, this research project will help wildlife managers and stakeholders by developing a method for identifying areas of high-quality, undisturbed grizzly habitat. Land managers can use this information to manage human use in these areas accordingly. By establishing methods that help balance the needs of people with wildlife, Y2Y and the people of the BV continue to exemplify efforts to maintain wide-ranging populations both inside and outside of protected areas.

Insecurity of tenure as a lesser known health and wellbeing crisis: Health and wellbeing perspectives of young families and children living without security of tenure

Major cities in Canada and across the world are undergoing housing crises that are pushing its vulnerable residents into housing insecurity. Security of tenure is a lesser-known and researched element that contributes to housing insecurity and its negative health and wellbeing impacts. To ensure the positive health and wellbeing outcomes of cities’ families and their children, developers and housing policy-makers should understand how insecurity of tenure, a critical challenge in housing insecurity, can affect the health and wellbeing perspectives of families and their children. This research will provide unique insight into the experiences of young families with children as they navigate housing insecurity, and the lesser known housing barrier of insecurity of tenure.

Covalent and non-covalent interactions self-supervised representation of molecules for chemotherapeutic drug design

Most of the drugs used to treat cancer have been originally identified from natural sources. While Nature did a great job selecting those compounds, some of them have shown limitations in the treatment of cancer and others have shown to be insufficient on some cancer types. Furthermore, it exists a gigantic number (nearly infinite) of small molecules human can synthetize. The goal of this project is to use existing machine learning technics and novel input representation approaches in order to efficiently search in this gigantic chemical space, newly potential molecules that could be used as new chemotherapeutic drugs.

Research into Convolutional Neural Network (CNN) Explainability

Machine Learning is advancing at an astounding rate. It is powered by complex models such as deep neural networks (DNNs). These models have a wide range of real-world applications, in fields like Computer Vision, Natural Language Processing, Information Retrieval and others. But Machine Learning is not without some serious limitations and drawbacks. The most serious one is the lack of transparency in their inferences, which works against relying completely in these models and leaves users with little understanding of how particular decisions are made. In this research we will explore new ways to represent the evolution of a CNN during training, and how the artifacts generated by these representations can be traced back to the inputs they’ve used during training. We will also explore the issue of imbalance in datasets, from the perspective of Parallel Coordinates and how to visualize dataset imbalance using this technique. The preliminary prototypes are going to be measured through usability tests with experts within the partner organization.

Development of Inexpensive Bio-based Insulation Foams from Crop Residues and Agricultural Wastes

Spray foam plays an important role in building insulation. By far, the only commercially available spray foam is petroleum-based polyurethane foam produced by reacting isocyanate and polyol. This project aims to investigate bio-oil acrylate-amine (BOAA) foam insulation materials with the following characteristics: 1) Bio content >60%; 2) R value 3.4-6.7 (the same R range for polyurethane foams); 3) Limiting oxygen index (LOI) 30 vol%; 4) Compressive strength 10-25 psi; 5) A cost lower than polyurethane foam; 6) Foam preparation by onsite spray.

Storytelling for Justice: Documentary, Semi-Documentary, and Fictional Media

We explore the creation of accurate representations of social issues in factual and fictional media; and the relative effectiveness of different media representation modes and types, to support and improve Partner Eagle Vision’s effectiveness as a media producer. Our objective in collaborating is to link the researcher and intern’s expertise in qualitative research with Eagle Vision’s media production knowledge and experience to inform our shared concerns for different forms of narratives in culture and their relation to the justice. This research contributes to Eagle Vision’s production’s value through association with academic experts, and contributes to their mandate to “create content that creates change” (Eagle Vision n.d.).

Enhancement of Canine Adipose Tissue-Derived Mesenchymal Stromal Cells Immune-Modulatory Properties and In Vivo Feasibility Testing in Elbow Dysplasia Patients

Osteoarthritis is a major cause of disability in dogs. Today, the therapeutic role of mesenchymal stromal cells (MSCs) as a potential treatment for immune and inflammatory disorders is well-defined. We are interested in exploring the immune-modulatory property of canine MSCs with the aim of exploiting it as a potential therapy in dogs with Elbow Dysplasia. This foundational work will support future funding applications to conduct a randomized clinical trial (RCT) to evaluate the effectiveness of the treatment. This project will provide a baseline for a long-term research program to discover new drugs and/or cell-based products that can develop novel treatment strategies to help osteoarthritis patients.

Mesurer l’impact socio-économique d’initiatives en éducation financière dans un pays en voie de développement : Méthodologies proposées

De nombreuses ONGs implantent des plans éducatifs dans des pays en voie de développement. Habituellement, des indicateurs de l’atteinte des objectifs sont proposés afin d’évaluer les retombées directes du projet. Mais qu’en est-il des retombées indirectes auprès des populations impliquées dans le projet? Comment utilisent-elles les nouveaux apprentissages dans les autres domaines de leur vie? Comment évaluer ces retombées indirectes lorsque des participants adultes ne savent pas lire ni écrire? L’objectif général du projet est de fournir un répertoire de méthodologies qui peuvent être appliquées dans des populations émergentes afin d’évaluer des retombées indirectes d’une formation. Nous effectuerons une recension des écrits afin de sélectionner les textes qui peuvent nous être utiles. À ce répertoire, nous ajouterons les méthodologies que la chercheure et les doctorants ont utilisées dans le cadre de divers projets internationaux.

Classification and Anomaly Detection of Network Traffic at the edge using Transfer Learning

Anomaly network traffic threat detection has been used in many companies to detect threats. Usually, it is deployed on-premises for efficiency and privacy reasons. Anomaly detection system usually consists of a machine learning algorithm that needs tons of time to train and test. Because each company may have different network setup, it is hard to build one system that suits for everyone. So, we need to customize the system for each client. Nevertheless, it is very inefficient to retrain the algorithm from zero for each company. Therefore, we hope to prove that by using transfer learning, we can move the knowledge from an existing detection system to a new network environment without retraining the whole algorithm.

Effects of Cattle Grazing Intensity on Soil Nematode Communities in Wet Meadow Rangelands

Rangelands store carbon, regulate water, and conserve biodiversity. This research will study the effect of cattle grazing on wet meadow rangelands and their soils. Specifically, this project will measure the effect of changes in plants on soil nematodes. Soil nematodes are important in the cycling of nutrients and carbon and help sustain soil health. We will use surveys of soil nematodes to measure the changing function of soil food webs following grazing. Using a long-term grazing experiment in a wet meadow rangeland in Manitoba, we will test how communities of nematodes change following grazing and what these changes mean for the function of rangeland soils. Findings from this research will help with the sustainable management and conservation of wet meadow rangelands in western Canada.

Integration of hippocampal subfield measurements into a streamlined MRI volumetry pipeline using deep learning

The hippocampus is a small brain region that has a key role in brain diseases including Alzheimer’s and epilepsy. The measurement of hippocampal volume on MRI is important for the diagnosis of these diseases, but this is a complex task because it is a small structure. Further, the hippocampus is divided in sub-regions that are impacted differently by the diseases. This project aims to use artificial intelligence technology to rapidly and reliably measure all sub-regions of the hippocampus and develop this as a clinical tool that could be used in hospitals and clinics.