Innovative Projects Realized

Implication of the nucleotide pool sanitizing enzyme NUDT1 in vascular remodeling in pulmonary arterial hypertension (PAH)

Pulmonary arterial hypertension (PAH) is a progressive condition characterized by the chronic elevation of pulmonary artery (PA) pressure leading to failure of the heart. In spite of recent advances in approved therapies, PAH remains a serious disease with significant mortality. In contrast to the earlier belief that contraction of pulmonary vessels played a key role in PAH, it is now established that excessive proliferation of the cells within the pulmonary artery wall is a pivotal component of pulmonary vascular obliteration, a currently irreversible feature of PAH for which available pharmacotherapies have no/limited efficacy. In recent years, it has become increasingly appreciated that damage to cells’ DNA (where our genes are encrypted) largely contributes to this phenomenon in PAH by influencing numerous functions of the cells.
This proposal addresses a very novel and promising therapeutic avenue to treat PAH by studying the role of NUDT1 in the progressive vascular remodeling seen in PAH.

AI based acoustic signal processing and analysis and its applications

Artificial intelligence (AI) provides powerful tools to many acoustic related applications. This proposal aims at acoustic signal processing and analysis and its applications using AI technologies. In order for AI to make progress in understanding the acoustic world around us, it needs to be interpreted along with multimodal messages. Three subprojects, i.e., multi-modal acoustic scene recognition, audio-visual speech recognition and audio signal processing for healthcare purpose using deep learning techniques are included in this proposal. The project is expected to enhance the AI capabilities of the partner organization for its 5G applications in acoustic related application scenarios.

Effect of Shape Parameterization and Optimization Algorithms on Adjoint Based Aerodynamic Shape Optimization

A very important phase in aircraft design is the aerodynamic design of the wing and

various other components: wing-body fairing, tailplane, etc. With today’s computer

power and progress in algorithm development, the design is made through complex

mathematical models which are amenable to automatic optimization. An industrial

adjoint-based three-dimensional viscous optimization framework, developed at

Bombardier Aerospace, will be made more efficient by performing code-to-code

validation with research codes at UTIAS. Improvements in all area of the framework

will be examined, ie. aerodynamic parameterization, mesh movement and optimization

algorithms. The importance of overall robustness of the framework will be

demonstrated on test cases. The benefits will be to increase the t

Therapeutic Strategies for Restoring Palmitoylation and Autophagy in Neurodegeneration

The cellular clearance pathway autophagy is required for degrading toxic proteins and damaged organelles, but is disrupted in many neurodegenerative diseases. During induction many autophagy regulators quickly localise to membranes from the cytosol. The Martin lab has identified palmitoylation, which involves the addition of the fatty acid palmitate to cysteine residues, as a potential regulator to direct autophagy proteins to membranes. The Martin lab has confirmed that several regulators of autophagy are palmitoylated, including the key autophagy receptor p62 that tags and delivers toxic substrates to lysosomes for degradation. p62 palmitoylation was significantly decreased in brains of Huntington disease (HD) patients and an HD mouse model suggesting that decreased p62 palmitoylation may lead to the impaired cargo loading seen in HD cells. Therefore, rescuing p62 palmitoylation may represent a therapeutic target for HD.

Mechanical design of prototype down hole products, test rigs and manufacturing equipment for oil and gas sector – part 2

Down hole tools in the oil and gas (O&G) industry has long been used to increase the oil recovery and limit unwanted products like water, sand and steam. RGL is a world leader on both Sand control and Flow control products, with a focus on technology driven solutions. Various sand screens, slotted liner designs, flow control devices and shifting tools are in development in the engineering and research group. A critical understanding of the science behind the measured phenomena is pursued with fundamental research in partnership with the UofA. Applied research is conducted in house through the proLAB team, and once the research is taking shape in the form of a product, the Engineering services team engage to design and develop the product, the manufacturing methods and equipment and the process documentation.
The aim of this project is to engage students, skilled with drafting and design capabilities, to grab hold of a specific product or research area and develop the opportunity into a commercialized product, or improve the equipment and process through design changes, under the supervision of the lead Mechanical Engineer.

Protection Coordination Planning with Distributed Generation (DG) and the Impactof DG on Safety, Equipment and Distribution System Operation

Driven by economic, technical and environmental reasons, the energy sector is moving into an era where

Distributed Generation (DG) will meet a large segment of increasing electrical energy demand. Local

distribution companies (LDCs) are required to accept a given percentage of customer-owned renewable DG

units in their system. Generally, DG introduces new possibilities such as shaving of peak loads and

reliability enhancement. On the other hand, DG causes a number of protection problems in the distribution

networks, such as loss of coordination, de-sensitization, nuisance fuse blowing, relay bidirectional

operation, and overvoltages. In this work, all protection problems caused by DGs will be studied. The DG

penetration level that ignites each problem will be investigated. Solutions will be suggested to avoid the

adverse effects of the DGs based on simulation results and a prototype will be built to realize the suggested

solutions.

Tensor based machine learning with human computer interaction applications

In this research, first we will using tensor network techniques speedup the processing in neural network since computational cost is a major bottleneck in neural network based deep learning. Note that the weight matrix in each layer of neural network is with huge size, because of millions of parameters. This may cause large time complexity to calculate all the parameters. To overcome the large time complexity, we could use tensor decomposition to calculate the low-rank weight matrix, to reduce a large amount of parameters, therefore to reduce the time complexity. The tensor networks include Hierarchical Tucker (HT), Tensor Train (TT), CP, and Tucker decomposition etc. Second, we propose to develop tensor based algorithms to solve spatiotemporal corrupted problem, complex background problem in human action recognition (HAR) and denoising problems in medical images. The human action videos are represented as a high-dimensional tensor, and we can solve the spatiotemporal corrupted problem by tensor completion, and recurrent neural network to deal with the missing frames problem; also we could denoise the high-dimensional medical images by tensor completion.

Assessment of indoor environmental quality in the school building

Indoor environmental quality in schools is one of the major issues that should be considered. Indoor air quality (freshness and cleanness of air inside the building) and thermal comfort (the comfortable condition when no one is feeling either too hot or too cold) in schools can have significant impact on children’s health, learning and productivity. The purpose of this research is to evaluate indoor environmental quality (indoor air quality, thermal comfort, lighting, and acoustics) in First Nation School. To achieve this goal we conduct long-term measurements of indoor air quality and thermal comfort parameters (CO2, temperature, and relative humidity) and ask the teachers to participate in voluntary and anonymous questionnaire survey about their perception of parameters of indoor environmental quality. The data will be shared with the partner organization to develop different retrofit packages to improve indoor environment quality and energy efficiency of the school building depending on potential to achieve goals such as energy savings, CO2 reduction, cost savings, improvement in thermal comfort by using Building Information Modeling optimization tool.

Predicting 30-Day Risk of Hospitalization, Emergency Department Visit and Death Among Albertans Receiving Opioid Prescriptions Using Machine Learning Algorithms: A Prognostic Study

Prescribers rely on guidelines for appropriate use and prescribing of opioids. Currently, there are no risk prediction calculators available to quantify the risk of prescribing an opioid that uses a patient’s history and drug utilization pattern. Machine learning algorithms offer an opportunity to perhaps develop such a risk calculator as a result of the large amount of heath data being collected in Alberta. OKAKI, a clinical analytics company, may benefit from this project as it may be able to offer a risk calculator to prescribers as a clinical decision aid tool. This may ultimately enhance patient safety.

Ultra-thin graphene oxide membranes for efficient humidity harvesting (Part II)

The main goal of the proposed work is to develop an ultra-thin and selective GO membrane capable of separating water vapor or steam from air. For this purpose, a suitable membrane supports required to hold the GO sheets. Therefore, the GO sheets will be deposited on various membrane supports and their performance in terms of selectivity, permeability, and mechanical strength will be evaluated. Then, the effect of GO layer number on the selectivity and permeation rate will be investigated. Once the best performing membranes have been determined, the impact of feed humidity and temperature on the permeation rate and selectivity of water vapor transport through the membranes will be evaluated. Evercloak will supervise the intern and support out of lab research activities on their premise. Evercloak will benefit as the results of this project will inform key decisions within their technology development roadmap in addition to talent acquisition/training.

Developing and evaluating multi-functional rain gardens

Rain gardens are shallow planted depressions in the ground that infiltrate, and evaporate urban rainwater. They are a kind of green infrastructure that provide an alternative to putting polluted urban runoff into the storm sewer system. The researchers will build and test new designs and installation methods for rain gardens. The new designs will incorporate habitat for bees and other pollinators and will use a mix of planted and seeded plants. The public will be shown pictures of the research rain gardens in an urban setting and asked to give their opinions as to which ones they prefer. The costs of installation and maintenance of each of the rain gardens will be recorded and compared.

The Hidden Subgroup Problem in Post-Quantum Cryptography

Quantum computers are new technologies that harness the power of quantum mechanics to store and manipulate information. Recent years have seen accelerated progress on their development, and while their potential benefits are significant, we must now prepare the cryptographic infrastructure underlying cybersecurity to ensure the continued safety of our data in the face on an adversary with access to a quantum computer.
Sectigo, a world leader in cybersecurity, together with researchers at the University of Ottawa, are teaming up to ensure that the latest academic research is integrated into the cutting-edge cryptographic protocols they deploy. The key focus of their work is ability of a core mathematical problem – called the Hidden Subgroup Problem – to withstand attack in the face of a quantum adversary, and what this means to our post-quantum cryptographic contenders.