Innovative Projects Realized

Exploratory pharmacokinetic and preliminary efficacy modelling of select orally administered antiviral compounds following DehydraTECH formulation enhancement

Researchers around the world are racing to find treatment solutions to combat COVID-19, the disease cause by infection of the novel coronavirus (SARS-CoV-2). The use of antiretroviral therapy has recently shown preliminary promise. However, a barrier relates to bioavailability challenges, i.e., poor uptake, of these drugs. Poor bioavailability limits drug utility which could be paramount in combating rapid health declines in COVID-19. DehydraTECH is a patented formulation processing technology developed by Lexaria Bioscience Corp that has been shown to enhance the body??s uptake of these drugs. In turn, the purpose of this study is to determine the plasma uptake of the lipophilic antiviral compounds darunavir and efavirenz with and without the DehydraTECH formulation. Through collaboration of Lexaria Bioscience Corp with the University of Windsor, two randomized placebo-controlled studies will be performed. Given positive results from this research, the Company will make its technology available to researchers throughout the world looking to maximize the effectiveness of their own drug investigations.

Personnaliser l’accompagnement de consommateurs dans une plateforme numérique de coaching virtuel

Les Éditions Protégez-Vous souhaitent innover dans la diffusion de leur contenu afin d’accompagner les consommateurs dans leurs choix, en particulier avec une plateforme numérique de coaching virtuel. Une première phase consiste à proposer une plateforme avec le contenu du guide pratique « 100 gestes pour la planète » pour accompagner les consommateurs à faire des choix sensés et écoresponsables. Cependant, ce contenu doit être présenté de manière précise afin que le processus d’accompagnement se révèle efficace. Pour cela, des techniques d’intelligence artificielle seront explorées pour analyser les traces laissées par les utilisateurs dans la plateforme, et les transformer en actions en vue de sensibiliser ces utilisateurs à leur propre cheminement. Le stage a pour but de proposer un premier modèle d’analyse, qui pourra être testé plus tard avec un échantillon de consommateurs.

Mobile Data Usage & Signal Strength – Manage, Analyze and predict estimated data usage and signal strength to conduct automatic cause analysis using deep neural network and unsupervised learning techniques

Enterprise mobility management enables to collect various metrics from million of devices. This industrial research project focuses on identifying the key performance indicators and formulas to identify and predict coverage issues and identify data usage problems within a device. Using the key performance indicators, the intern will explore all feasible machine learning approaches. Final goal is to design an AI smart diagnostic system to detect the issue related to data usage and signal strength and conduct automatic cause analysis. This would allow the clients to act proactively by getting deeper insights in their mobile applications. The solutions aim to provide a data usage and signal strength diagnostic solution to help client avoid their business disruption due to issues in mobile devices. This will help the Canadian organization to minimize the maintenance cost while reducing downtime and minimal business disruption.

Transfert de données anonymes en mobilité à travers la « preuve à divulgation nulle de connaissance »

A-Malgam Technologies Inc. est une entreprise québécoise spécialisée dans le développement Blockchain et IoT pour la mobilité des données massives. Dans un souci d’offrir des solutions plus conviviales et sécurisées à ses membres-clients, A-Malgam souhaite explorer les capacités des technologies émergentes et ce qu’elles peuvent apporter lorsqu’il est sujet de transfert de données anonymes en mobilité. Le secteur des transports, et plus particulièrement celui de la mobilité des personnes, est un domaine où la diffusion d’équipements connec-tés, collectant constamment des données, est probablement la plus évidente. Avec cette démocratisation des systèmes connectés en mobilité, il devient de plus en plus important pour les intervenants de protéger la vie privée des citoyens. C’est afin de remédier à cette problématique que l’anonymisation des transferts de don-nées prend tout son sens. Malheureusement, les processus actuels de transfert de données n’offrent présen-tement aucun moyen viable pour lutter contre la problématique de traçabilité de transactions et sont le plus souvent vulnérables à plusieurs failles notamment la possibilité d’ajout d’émetteurs fautifs. Ce projet vise à créer un tout nouvel algorithme qui assure et certifie l’anonymat des données collectées.

Methods for the Estimation of Traffic Matrices

The accurate knowledge of origin/destination traffic matrices allows network operators to efficiently
perform network management operations to maximize network performance (minimize network
congestion, average delay, jitter, energy consumption, etc.) and increase network reliability in case
of device failures and other exceptional events. However, traffic matrices cannot be directly
measured, and network operators, such Videotron, can only rely on estimations based on the
elaboration of a limited amount of information, such as partial peak flow, link utilization, SNMP link
counts, multicast group composition and multicast trees. The aim of this project consists in
developing an efficient method for the optimal estimation of traffic matrices, when both multicast
and unicast traffic is considered. In this way Videotron will improve the performance of network
management operations that are crucial for the efficient functioning of the network.

Development of a solution to assess the quality and to optimize AI-based video codecs

Current video codecs consider algorithms to analyze video imagery in order to find out which bits can be removed for file size reduction without subjective video frame degradation. Integrating AI with encoding process improves the quality of encoding and decoding. AI permits the software to proactively assess the quality of the encoded video before transmission. This allows the compressing system to detect and remedy any possible artifacts in the video frames. The main objectives of the company regarding this project can be summarized as 1) Quality assessment regarding the AI-based video codecs from the industry perspective. 2) Determination of codec capability of running on off-the-shelf hardware. 3) Evaluation of AI-based codecs optimization techniques such as vectorization, SIMD, GPU, etc. The enhancement in such technologies could improve the entertainment industry in the country and also potentially assist smart city projects in Canada.

Automated Part Numbering for 3D Printed Medical Devices for Covid-19 Response using Generative Design Methodologies

Thousands of 3D printers across Canada are producing PPE for frontline workers. Different printers and materials affect the quality and safe re-usability of parts. Trying to track down this information for every single one of millions of parts would be impossible. The purpose of this project is to improve the quality and safe re-use of 3D printed PPE by developing a system for printing a unique identification code onto every single part that will let end users know what type of material was used and how and when the part was printed.

Dimethyl Sulfoxide (DMSO); A novel technique for gold recovery from mercury-contaminated tailings from artisanal and small-scale gold mining

The proposed research aims to optimize the use of DMSO as a lixiviant in gold recovery. To date, mercury and cyanide are the popular lixiviants used in the gold processing industry. These chemicals are harmful to both the environment and humans, and have been the major concern in gold mining. The proposed lixiviant, DMSO, is an FDA approved chemical that is cheap and environment friendly. The successful use of DMSO in gold mining is beneficial to the gold mining industry.

Upon the successful use of DMSO for gold leaching, Newlox Gold Ventures Corp, being the partner organization for the study, will have the opportunity to pilot the use of DMSO in gold leaching. The company also has the advantage of promoting the technique to other miners in the industry, as well as establish collaborations with local and national governments.

Enhanced Content-Based Similarity Detection for Book Recommendation

Recommendations is one of the main ways Kobo users discover content on the platform. By using purchase history, Kobo can suggest other books similar to a certain item. However, this does not provide meaningfulrecommendations in some cases, especially for bestsellers and fiction books. Currently, only for books that have no purchase history does Kobo supply recommendations based on text. The purpose of this project is to improve its text-based similarity analysis to provide better recommendations for all titles regardless of popularity and genre, as well as for all users who want recommendations that better reflect their purchases. Through this project, Kobo will benefit from an enhanced recommender system, and in effect, an improved customer experience and an increase in purchase conversion.

Characterization of antiviral coatings: Wetting studies and efficacy

There is an urgent need to develop antiviral coatings that would be applied to functional surfaces such as personal protection equipment (PPE) and others to arrest the spreading of COVID-19. In this project, the University of Waterloo researchers will work with industry partner, SiO2 Innovation Labs, to develop an optimal composition of such antiviral coating by characterizing the wetting and associated properties along with the efficacy of the coating against different viral loads. Once successfully achieved, the desired coating material will propel the industry partner in marketing their coating to large number of end-users and would eventually save millions of lives worldwide.

Developpement d’un outil numerique amallorant la versatilite des impulseurs en vue dereduire Ie nombre de pompes intermildiaires, et conception d’un plus gros impulseur pour les pompescentrifuges mult

Pour des constructeurs des pompes hydrauliques, la conception, la fabrication et la caracterisation des pompes
centrifuges presentent toujours un grand defi. Une meilleure fabrication de ce type des pompes exige une
determination avec precision de tous les parametres cies des composantes de la pompeo II s’agira, dans Ie cadre
de celie proposition de recherche, de developper des modeles numeriques fiables et precis d’impulseur de
pompe centrifuge permellant d’etudier de maniere approfondie les ecoulements complexes de liquide dans la
pompe, les contraintes, Ies vibrations, les poussees axiales et radiales induites, ainsi que la cavitation. Tout ceci
dans Ie but d’ameliorer les performances des pompes centrifuges etla versatilite des impulseurs disponibles chez
Technosub.

Development of paper-based Rapid Diagnostic Kit for COVID-19

Until an effective vaccine becomes widely available for COVID-19, a frequent home test of the coronavirus on a weekly basis might become a new norm. Therefore, developing rapid, accurate, stable, and cost-effective tests is critical for combatting the spread of COVID-19 over the long term. Aptamers are single-stranded DNA oligonucleotides that can be selected from a large DNA library to bind to many target molecules. They can overcome the problems associated with antibodies since DNA is highly stable, cost-effective with a little batch-to-batch variation. However, aptamer sequences for COVID-19 viral proteins are not yet available. Therefore, we aim to isolate such aptamers to fill in this gap. The industry partner, ChitoLytic will provide chitosan and other functionalized paper surfaces for paper-based biosensor development. They already have acquired a patented technology that demonstrated the detection of lipids on a paper-based microfluidic device. Chitolytic platform will be tested for our proposed aptamer-based detection of COVID. The expected outcome is a paper-based colorimetric assay using aptamers for specific binding of the COVID-19 virus in the saliva, thus aiming to develop the first paper-based biosensor for the detection of COVID-19 virus in the saliva.