Innovative Projects Realized

Étude comparative de points d’opérations sans charge sur une turbine hydraulique de moyenne chute à échelle prototype

Le projet réalisé dans le cadre de stages Mitacs vise à renforcer et augmenter les connaissances sur la dynamique de l’écoulement traversant les turbines hydrauliques de type Francis de moyenne chute lorsqu’elles sont opérées en régimes hors production (sans charge). Ces conditions d’opération de plus en plus fréquentes et faisant partie intégrante des régimes transitoires, tels que les arrêts ou démarrages de turbine, sont parmi les plus dommageables pour les turbines. Donc, en améliorer la compréhension permettra, à long terme, d’allonger la durée de vie des machines, augmenter leur fiabilité et réduire les coûts d’opération. L’association des connaissances et compétences de l’Université Laval et Andritz Hydro permettra d’approfondir les connaissances sur la mécanique des fluides grandement turbulents lors d’opérations sans charge de turbines hydrauliques ainsi qu’une amélioration des méthodes de simulation visant reproduire la dynamique de l’écoulement.

Researching Subsea Equipment for Lab Testing and Plume Migration

The project will research software and models of plume migration for subsea oil and

gas productions. Plume migration is important because when a leak takes place

subsea it needs to set off a sensor, and to do that for point sensors, the product must

come near the sensor. Knowing how oil will move under water will help the placement

of sensors and improve the overall leak detection of the system for any given

environment. Another aspect of the project is to develop a lab model for testing leak

detection technologies. Instead of testing products in the ocean, or as a software

simulation, a lab could be setup to model the near conditions of the ocean. This will

allow the best testing of subsea leak technologies without deploying them in the field.

Exploring action plans for enhancing community sustainability in Baie Verte

The mining industry forms an integral part to the economy of Newfoundland and Labrador. The island of Newfoundland is particularly important to the NL mining industry, as it houses several active mining operations, as well as minerals exploration and developments in several areas including the Baie Verte peninsula. As mining operations are noted as impacting and contributing to the sustainability of communities, the proposed research seeks to understand and give an overview of the sustainability vision of Baie Verte, assess the state of sustainability in Baie Verte, and recommend community action plans that can help enhance sustainability in Baie Verte. In doing so, the project will rely on review of community documents, review of literature, a three-capital based sustainability assessment framework, public surveys, and consultations with community stakeholders.

Parenting an adult child with autism spectrum disorder: A qualitative examination of the lived experience

Parenting an adult child with autism spectrum disorder and an intellectual disability (ASD-ID) presents unique challenges, which often leads to parental distress, anxiety, depression, and uncertainty of their child’s future. To date however, there has not been an intimate, authentic account of this parental experience. Moreover, this strength-based approach will provide an important addition to what has traditionally been a deficit-based narrative. Therefore, the purpose of this project is to explore the lived experience of parenting an adult child diagnosed with ASD-ID, with the aim of creating a rich, authentic narrative that outlines positive outcomes, navigation of challenges, and overcoming parental concerns. Through a two-step interview approach, intimate details of this unique parental role will be explored. In doing so, Community Living Essex County will gain knowledge related to the experience of parenting an adult child with ASD-ID, which will inform future supports and services provided by this partner agency.

Photonic Quantum Computing for Machine Learning

In the recent years, the advances of machine learning, and more precisely deep learning, have lead to incredible progress in areas such as computer vision, speech recognition and natural language processing. Training algorithms based on deep neural networks can take a huge amount of time and computing resources. The first generation of quantum computers currently being built, although imperfect, is excepted to provide a speed-up over classical computers in several areas including machine learning. One particular paradigm of quantum computing where machine learning algorithms could be accelerated is called Continuous-Variable (CV) ?? or photonic ?? quantum computing. The proposed research project is to identify and implement suitable machine learning algorithms on a photonic quantum computer.

Additive Manufacturing Exploration

The project will involve making and designing reference parts, and then producing 3D scans of those parts and providing some measurement analysis. Documentation on collection and safety must be followed throughout. 3D printing is not only a very unique way of creating something, but it also gives its user a very wide range of design options as there are few restrictions. This research project is of great interest to me because the accuracy of these machines is crucial to their progression. If there are other machines that can fabricate something to a greater degree of accuracy, they may be favoured over 3D print manufacturing. Researching this issue will help narrow down the variables that cause these uncertainties in the dimensioning of the parts and will help increase the accuracy that these machines can print to. TO BE CONT’D

Mechanism of the Spinal Oxygen Sensors

Hypoxia (deprivation of adequate oxygen supply to the body) associated with cardiorespiratory crisis initiates vigorous respiratory and sympathetic “fight/flight” responses. The carotid bodies (CB) are the main respiratory oxygen sensors but without their influence sympathetic responses persist, suggesting a cardiovascular-specific oxygen chemoreceptor. Recently we discovered that neurons within the thoracic region of the spinal cord (the preganglionic sympathetic neurons – intermediolateral nucleus – IML) are highly responsive to oxygen. Therefore we named those neurons: Spinal Oxygen Sensors (SOS)
However, the cellular mechanisms of oxygen sensing used by the SOS are unknown. Therefore, the purpose of this project is to investigate the cellular signaling cascade that gives rise to their oxygen sensitivity. TO BE CONT’D

Novel Immunization Strategies for the Treatment of Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common form of dementia afflicting over 37 million

people worldwide, and half a million people in Canada. This disorder manifests as memory

deterioration and cognitive impairment. There is currently no efficient treatment available for

AD. Even though vaccination strategies were promising in treating AD animal models, this

approach showed limitation in clinical trials as a subgroup of patients presented severe side

effects following its administration. GSK Biologicals is a leading company in the field of

immunotherapy and vaccine. The objective of the company is to provide vaccines to the

population for a wide array of disease and pathologies, including neurodegenerative disease

such as AD. The proposed project will impact on the development of novel immunization

approaches and investigate the physiologic mode of action of promising candidates.

Proposed studies will facilitate the discovery of biomarkers that will improve the prediction of

unwanted side effects and may lead to the development of a novel and effective vaccine

targeting….

Biomechanical modelling of tissue stiffness following exposure to sub-concussive head trauma in collegiate athletes

In this project, we propose integrating well-known neuroimaging algorithms to model axonal integrity of the white matter over the course of a full season, in collegiate football players. Using these findings as a basis for further investigation, we intend to develop a novel imaging processing tool that will integrate information from different neuroimaging methods to model tissue strain, and highlight region-specific sensitivity of brain tissues to repetitive head trauma. This information will then be compared longitudinally over the course of a season, and contrasted across players based on their pattern of exposure to head trauma, characterized using helmet accelerometers and biomechanical analysis of impact properties. This novel multi-modal approach will allow our team to examine whether certain players are at higher risk for changes in tissue integrity, based on their exposure, in an effort to inform better coaching practices aimed at protecting athletes against head trauma, and promote safer playing fields.

Automatic Approach to Design Efficient Deep Neural Networks

Deep neural networks have demonstrated state-of-the-art modeling accuracy on a wide range of real-life problems, with some cases surpassing human performance. Despite the promise of deep neural networks as an enabling technology for a large number of industries and fields, there are two particular key challenges in the design of deep neural networks in real-world, operational scenarios. First, the design of deep neural networks is a very time consuming process for a machine learning expert, and often results in complex, non-optimal deep neural networks. Secondly, many of the deep neural networks designed in recent years, while achieving high accuracy, are often very complex with high millions to billions of parameters, making such networks intractable for real-time scenarios on mobile device, IoT devices, and other embedded devices. TO BE CONT’D

Identification and characterization of new antimicrobial chemical series

Antibiotic resistance represents a major health problem for society. With the expanded use of antibiotics, microorganisms have developed various mechanisms of resistance to overcome the effects of once highly effective agents. There is therefore an urgent need to identify new therapies to counteract resistant strains. The intern will design and identify new drugs that are capable of treating patients infected with bacterial strains showing resistance to current drugs used in the clinic. The new drugs’ activity will be tested using microbiology assays on bacterial strains that have shown resistance to current drugs. The project will allow the partner organization to better understand ways to overcome microbial resistance and to develop new antimicrobial drugs.

Investigating the Paradoxical Adoption of Blockchain in Healthcare Data Sharing: The Patterns, Consequences and Mediating Mechanisms of Challenging yet Succumbing to Incumbents

In this Mitacs project, we examine how nascent technology advocators can successfully implement the technology in highly institutionalized settings. Using the implementation of Blockchain in healthcare data sharing as an example, we compare and contrast the implementation strategies of 6-8 start-ups or divisions of established companies, and examine how the different types of implementation strategies lead to different implementation outcomes. Based on this research, BI will learn about the key advocators of Blockchain for healthcare data sharing, their strategies and performance. Also, BI can reflect on its own experience and learn from others’ success or failure. In addition, BI will know how to position itself amongst other Blockchain advocators in healthcare data sharing.