Innovative Projects Realized

R&D of Cannabis extraction and purification technologies

The intern will work with the company PBE Expert on the field; she will gain technical experience on the cannabis industry. Specifically, she will research and work on cannabis extraction and purification techniques. She will research problems and issues faced by cannabis producers in regards to the extraction and purification techniques. In the lab, she will focus on developing new or improved technologies.
The partner organisation will supervise the intern. The intern will work on projects with the partner organisation and be in charge of several aspects of these projects, like arranging meetings with clients, creating layouts on AutoCAD, providing cGMP compliance services…

Effets biologiques de synergies d’extraits naturels sur l’anxiété et certaines conditions inflammatoires en relation avec l’axe microbiome-endocannabinoïdome

Mon projet de maîtrise vise à développer des produits de santé naturels à base d’extraits naturels, non issus du cannabis, qui ont des effets bénéfiques sur la santé, plus précisément de prévenir l’inflammation et l’anxiété. Plus spécifiquement, il a pour but de démontrer que la modulation du système endocannabinoïdome par certains extraits naturels entraîne des effets positifs sur la santé. L’objectif final est de concevoir des suppléments alimentaires vendus sous forme de capsules.

Un des composés étudiés est riche en ADP, un omega-3 qui est rare. En effet, la teneur en ADP est faible dans les huiles de poissons classiques retrouvées sur le marché. L’étude de l’ADP est donc novatrice et le projet permettra de mieux comprendre l’impact de cet oméga-3 sur la santé humaine et plus spécifiquement sur le système endocannabinoïde.

Robust Magnetic Resonance Imaging of Short T2 Tissues – Part 2

Magnetic resonance imaging (MRI) is an imagig modality which can visualize the internal structure of the human body and is free of radiation. In this project, we would like to write a multi-functional and robust MRI software package using ultrashort echo time (UTE) technique to realize the imaging of the tissues, such as tendons, ligaments and cotrical bone, which are difficult to view through conventional MRI techniques. This project will contribute a reliable imaging tool to our partner, Siemens Healthcare Canada, to further transform this tool into a medical solution which is able to aid the diagnosis of the associated diseases such as osteoarthritis (OA).

Validation of a Computer Vision-Based Basketball Training Application

The proposed research project aims to advance the development of a novel basketball training application catered to youth basketball players. The prototype application designed by Pipeline Studios Ltd. uses machine learning to record and track youth basketball performance in a fun and entertaining atmosphere. The research project is designed to pair experts in human movement and mobile technologies with experts in computer science and youth content (Pipeline Studios Ltd.) to expand the development of this innovative system. Specifically, this project will examine the current validity of the prototype application and support the development of new performance metrics through the collection of gold-standard high-speed camera and wearable sensor data during shooting drills for youth and young adult basketball players. In doing so, this research collaboration will be foundational in both evaluating and improving the performance of the prototype application which is poised to support the development of youth basketball in Canada.

Innovating Technologies for Reducing Falls Risk in COVID-19 Healthcare Settings

One of the side effects of the COVI D-19 pandemic is that older people in institutional care tend to be more socially isolated and get less physical exercise. This is likely to increase falls risk both directly through reduced strength and balance because of insufficient exercise, and indirectly due to effects of depression leading to reduced awareness of obstacles and trip hazards in the environment. In this project we will test an innovative exercise technology (2RaceWithMe, developed by AGEWELL NCE researchers) to see if it can reduce falls risk in institutional care environments. 2RaceWithMe uses hand and foot exercise peddlers with onboard computers, engaging video courses and a large curved screen with videoconferencing (where people can cycle with each other either co-presently or remotely). Using standard measures of falls risk that are collected before, during and after the three month 2RaceWithMe intervention, we will measure the impact of 2RaceWithMe on falls risk.

Développer une nouvelle génération d’agent cognitif avec raisonnement intégré : un pas vers l’identification des facteurs causaux de la COVID-19.

Bien que des systèmes de monitoring des patients existent déjà, leur accessibilité est souvent limitée en milieu hospitalier, réservé aux soins aigus. De plus, ils sont de nature particulièrement encombrante de par le nombre d’électrodes, capteurs et fils qui les compose. Un système de monitoring (‘wearables’) plus léger, plus accessible, et pas cher intégré aux algorithmes d’intelligence artificielle permettrait la surveillance d’un plus grand nombre de patients dans des environnements plus variés (c.-à-d. salle d’attente, à domicile et CHSLD) et permettrait d’avoir un avis plus précoce sur quelconque détérioration clinique sur laquelle il nous serait possible d’intervenir. Notre but est d’utiliser les résultats des recherches fondamentales et appliquées effectuées dans le domaine de l’intelligence artificielle par les chercheurs à l’UQTR et JACOBB, au centre de recherche de l’hôpital Sacré-Cœur, et les compagnies spécialisés de wearables, afin de créer un modèle causal capable de faire dépistage de COVID-19 et les maladies similaires.

Privacy-preserving Federated Learning Algorithms and Dashboarding to enable COVID-19 health analysis

This research project will enable Krate Distributed Information Systems Inc. (Krate) to provide two software as a service (SaaS) cloud products that will integrate with the distributed computer platform of fellow startup Distributed Compute Labs (DCL) to aid COVID-19 researchers. DCL’s distributed computer is in use by scientists and researchers across Canada (this company’s product was approved by the Government’s “Help Canada Combat Coronavirus Disease” fast-track program). The two services that are being researched as part of this project are (1) Privacy-preserving artificial intelligence (A.I.) and (2) Privacy-preserving dashboard. Research outcomes of these studies provides services to COVID-19 health researchers that enables them to collaboratively focus on their research without worrying about protecting the participant’s data privacy.

Flexural behaviour of strengthened prestressed hollow-core slabFlexural behaviour of strengthened prestressed hollow-core slabs with bonded and unbonded prestressed carbon fiber reinforced polymer (CFRP) plates under various prestress levels.s with bonded and unbonded prestressed carbon fiber reinforced polymer (CFRP) plates under various prestress levels

The overall objective of this project is to enhance the strengthening efficiency of prestressed hollow-core slabs (PHCS) by using prestressed CFRP plates. More specifically, this project aims to investigate the behavior of the PHCS elements strengthened with prestressed CFRP plates. The application of the innovative patented anchor system developed at the University of Waterloo offers a good platform to facilitate the strengthening process in an efficient and cost effect way. The proposed research is expected to tackle problems of wide implication yet rarely addressed in the PHCS strengthening domain by introducing a site applicable strengthening technique for existing and in-service concrete structures.

LimoiJoule : Énergie urbaine rationnelle et résiliente – Part 1

Canaliser la valeur des matières abondantes gaspillées et les recycler afin d’obtenir une meilleure qualité de l’air et un chauffage plus abordable pour tous les citoyens.

Improving protein-ligand binding models using multidimensional, mixed-confidence training data – Part 2

There currently are no approved treatments for COVID-19, as global case counts increase daily. High infectivity and long time-to-recovery for COVID-19 cases is straining health-care systems globally. There is an urgent need to discover therapeutics that treat patients and improve clinical outcomes. Research and Development timelines associated with vaccines and new chemical entities will not reach clinics in time to mitigate the current patient surge, leaving drug repurposing as the most practical short-term solution. Most repurposing efforts to date focus on drugs that have previously shown antagonistic activity against other viruses. A broad patient population with diverse pre-existing conditions calls for as many diverse treatment strategies as possible. This proposed project will apply AI technologies to mine the biological networks formed between virus and human proteins and predict existing FDA-approved drugs that can interrupt these interactions.

Polyamide-Based Triboelectric Nanogenerator for High Performance Energy Harvesting & Self Powered Sensing Applications

Fast development of more powerful and smaller mobile electronics suggested the need for development of sustainable energy resources as well as innovative sensor systems with improved accuracy and reduced power consumption. Moreover, utilizing appropriate sustainable energy resources would be beneficial for environmental protection. Such demands resulted in a great interest toward development of the triboelectric nanogenerators (TENG) to be used in future electronics. Triboelectric Nanogenerators are known as a robust system for mechanical sensing and mechanical energy harvesting by converting the mechanical energy into an electrical output. Given this background, the objectives of the proposed project would be design and development of polyamide TENG material with improved performance. This can be achieved by producing the TENG materials with enhanced effective surface morphologies, as well as improved dielectric and capacitive properties. Furthermore, with respect to the BASF goals, this project would be beneficial both scientifically and economically to the partner organization.

Estimating Apple Crop Yield using Images

As a high-value crop, apples are intensively managed with much of this management being associated with yield estimates. Currently, estimates are done manually by experience farmers and by Scotian Gold experts. Unfortunately, this results in costly estimates that vary in their quality from farm to farm and between apple varieties. A system that can provide consistent and accurate estimates using low cost digital photography and cloud-based artificial intelligence can provide an opportunity to improve production and marketing for the industry. The objectives of this project are: (1) to develop a method of capturing images of an apple variety on the tree in the mid-August time frame; (2) to develop a computational vision and machine learning prototype for estimating the size and number of apples on a tree using the images captured; and (3) to use these estimates to predict the harvest yield (bushels per acres) adjusting for forecasted weather.