Innovative Projects Realized

Étude de la résistance au broutement de plants forestiers dans un contexte de migration assistée en forêt mixte tempérée

Les changements climatiques pourraient modifier la composition des forêts d’Amérique du Nord, notamment parce que les espèces méridionales migreraient vers le nord. Les gestionnaires de nos forêts peuvent accompagner la forêt dans ces changements en plantant des espèces méridionales dans la forêt mixte. Cette migration assistée pourrait toutefois échouer si le broutement par les grands herbivores nuit à la survie et à la croissance des jeunes plants. Ce projet vise à déterminer la susceptibilité au broutement des espèces déplacées en quantifiant leur qualité nutritive. Comme les plantes peuvent contenir des composés de défenses variés, nous identifierons premièrement les méthodes à utiliser. Nous comparerons ensuite la susceptibilité de huit espèces d’arbres utilisées dans une étude de migration assistée, ce qui contribuera à bonifier cette étude. À long terme, les connaissances acquises pourront être utilisées dans la planification forestière.

Magnetic gradiometry using a rotary-wing unmanned aircraft system

Balch Exploration Consulting Inc. (BECI) develops low cost geophysical survey solutions to the natural resources sector. BECI recently built an unmanned helicopter for magnetic surveying for mineral exploration. The objectives of this internship is to equip the BECI unmanned helicopter with a custom-designed frame carrying two magnetometers recording simultaneously and to perform a demonstration survey over a gold deposit. The main project deliverable is a series of maps highlighting the variations of the magnetic field in different directions. These maps will help geoscientists to identifying subtle geological trends associated with ore bodies in the subsurface. The intern will gain hands-on experience with UAS development, sensor integration, survey execution, and data processing. The internship will help BECI’s expansion into the aeromagnetic UAS surveying industry.

Feasibility Study of Biometric Technologies for Calgary Drop-In & Rehab Centre Society

This project focuses on solving problems related to safety and security of clients and personnel of the Calgary Drop-in & Rehab Centre Society. Their IT department tasked the Biometric Technologies Laboratory Lab, University of Calgary with determining what biometric technologies can be used for identification of clients for access to services, to monitor the safety of both clients and the personnel. The Lab will investigate the feasibility of usage of infrared and video recordings of face biometrics for these purposes. The outcome will include software for data processing, analysis, risk assessment, as well as recommendations regarding the suitable type of biometrics.

Analyse et évaluation de la résilience d’une usine de production d’eau potable

Ce projet vise à doter les gestionnaires des usines d’eau potable d’outils concret et opérationnelle pour améliorer leur résilience, c’est-à-dire, assurer un fonctionnement acceptable et ce, malgré des perturbations. L’eau potable et sa disponibilité sont des éléments essentiels pour les citoyens d’une municipalité. À terme, les outils identifieront les mesures nécessaires pour assurer ce fonctionnement et améliorer la réponse lors de perturbations.

Efficacy of the plant extract CELEXT07 and the botanically derived Thymox in suppression of Cannabis fungal diseases under greenhouse production systems

The legalization of Cannabis (marijuana) is now supported by 66% of Canadian voters. Aphria Inc., was the first approved and licensed producer under the MMPR to begin growing in a greenhouse and is currently the second-largest marijuana producer in Canada. Unfortunately, Cannabis is attacked by a plethora of phytopathogens leading to a number of diseases with the most commonly reported are grey mold and powdery mildews. Pesticide use is regulated by the Health Canada Pest Management Regulatory Agency (PMRA) ensuring compliance and appropriate guidelines for use of the pesticides and maximum residue limit (MRL) so as to not pose any health concerns in humans. The project will focus on the use of biodegradable botanically-derived compounds CELEXT07 (pending registration) and Thymox® with strong antimicrobial properties to suppress disease incidence and severity of Cannabis fungal diseases.

Application of Deep Learning Artificial Intelligence Tools in Pharmaceutical Processes

The last 5 years have seen remarkable advances in speech, image and language recognition tools that have been made available to the public through computer and mobile devices’ applications. Some of these significant improvements were achieved by an artificial intelligence tool known as deep learning (Hinton et al., 2006) that generally refers to a set of novel neural networks algorithms.
It is recognized that training of deep neural nets require a vast amount of data. In this sense manufacturing processes are ideal candidates for deep learning applications since they utilize computers and information systems for monitoring and control thus generating massive amount of data. This is especially true in pharmaceutical industries such as Sanofi where large data sets are routinely stored for monitoring and regulatory purposes. TO BE CONT’D

Fate of Toxigenic and non-Toxigenic Escherichia coli during storage and brewing of tea

Tea is often considered as a healthy drink that is rich in antioxidants and other health constituents. However, tea can become contaminated during production and pathogens such as Shiga toxin producing Escherichia coli (STEC) that could potentially can persist over extended storage periods. The true risk of STEC linked to tea is unclear as on one side, the beverage is brewed in hot water and although contains natural antimicrobials. However, on the other hand, STEC can become heat resistant in the dry state that could enable survival during brewing. The proposed study will look at survival of STEC on different tea blends and the ultimate fate of the pathogen during brewing. The research will benefit the collaborating partner through identification of the risk and possible solutions. The Intern will benefit by working on a commercially relevant project given how much tea is drank every day in Canada.

Predicting and Detecting Data Exfiltration using Machine Learning

Bell’s Cyber Threat Intelligence (CTI) team is collaborating with academic institutions in order to further research and develop cyber security analytics for the protection of critical infrastructure and data. The focus of this research is to create and leverage Machine learning for Data Exfiltration on Big Data specifically for network security purposes. This research to design distributed algorithms fast enough for analyzing massive high-dimensional data generated by on web application and firewall logs to detect cyber threats/ attacks and anomaly in the network.

Maximizing solar energy penetration in the smart grid using innovativepower system architecture.

Electrical Energy represents one of the fastest growing types of energy; however this growth

is accompanied by diminishing fossil fuel resources. Renewable sources of energyespecially

solar- represent one of the possible ways to meet this increased demand. For that

reason , many countries have ambitious plans for increasing their electricity production from

solar energy. Utilities believed that integrating solar electricity into their systems is not a

difficult task, however when the penetration level of solar electricity started to increase, they

began to face new untraditional problems. Most of these problems are due to the intermittent

nature of solar energy. The high variability in production-due to temperature variations,

moving clouds, etc. – greatly affects the reliability, stability and security of the electric power

system. In this project, we are trying to develop a new innovative architecture for the power

system that helps utilities integrate solar electricity into their grids while maintaining the

performance of the power system.

UBCO-FortisBC collaborative research partnership: Rehabilitation options analysis for dewatering systems in power generation facilities

The dewatering systems in the FortisBC hydropower generation facilities need long-term rehabilitation solutions. The purpose of this project is to develop a risk assessment model for the dewatering systems of hydropower generation facilities, and to identify the best long-term rehabilitation solutions for such facilities. Recommendation on long-term maintenance and rehabilitation will be provided on the basis of failure risk, safety risk, maintainability, costs, and environmental impacts. Component level failure events and probabilities will be considered in establishing the overall system risk for all possible rehabilitation options. The life cycle system costs and environmental impacts will be considered together with the risks to identify the most suitable rehabilitation option through a generalizable risk assessment model.

L’adoption de l’Automatisation Robotisée de Processus : Une approche par l’analyse de processus d’affaires

L’Automatisation Robotisée de Processus (ARP) est une approche efficace pour améliorer la productivité des organisations par l’automatisation des tâches répétitives. La mise en oeuvre de l’approche ARP est peu coûteuse et facile à réaliser (Willcocks & Lacity, 2016). Cependant, aucune étude n’a porté spécifiquement sur l’élaboration d’une approche permettant aux organisations d’adopter efficacement l’ARP pour automatiser leur processus d’affaires. Plus précisément, il n’existe aucune approche pour guider les organisations à identifier les processus d’affaires qui conviennent le mieux à l’ARP. Ce projet de recherche vise à élaborer une nouvelle méthode permettant d’analyser des processus d’affaires et d’identifier les processus qui conviennent à l’ARP. La méthode développée dans le cadre de ce projet de recherche permettra aux experts-conseils de Cofomo d’aider leurs clients à bénéficier efficacement de l’adoption de l’approche ARP afin optimiser leurs processus et ainsi diminuer leurs coûts d’opération et améliorer leur productivité et la qualité des services fournis.

Connecting Healthcare Data with the Blockchain

This project focuses on using blockchain technology and smart contracts to manage the health care data access. Blockchain is the distributed ledger based on verified transactions, and the smart contract is the programmable part of the blockchain which can automate more complex transactions. Blockchain provides a secure and durable distributed database to store data accesses and we can automatically grant or revoke access to the users by using smart contracts. Diffusing health care data over different organizations and managing these data is still a challenge which jeopardizes patients’ privacy. The main goal of this project is investigating the potential and performance of the new health data access management system with the blockchain as an infrastructure.