Innovative Projects Realized

Predictive modeling in multiple sclerosis: using real-world data to inform practice, policy, and research

Multiple sclerosis (MS), a progressive disease of the brain and spinal cord, affects millions globally, including 93,500 Canadians. With one of the highest existing global MS rates, the number of Canadians living with MS is expected to increase to 133,600 by 2031 and the economic burden to reach $2 billion by the same date.

Treatments for MS are available to help manage and reduce the number episodes of increased disability, and control symptoms. But, while there have been many studies on independent risk factors for patient health outcomes, no study to date has attempted to combine patient, disease, and treatment-related factors to better predict treatment outcomes.

This study will link provincial health care records data with clinical data, to develop predictive models on MS-related outcomes, and make it available on a website to guide better treatment decisions. Better patient care will ultimately facilitate the cost-effective use of health care resources.

Mapping Building Conditions – perhaps a bit more – for what purpose

Despite advancement in asbestos education and knowledge, there remains a shortcoming in area of management of asbestos exposure, which results in human health impact. This shortcoming was documented in a study carried out in Minneapolis, Minnesota (Alexander et al 2012). Alexander and colleagues examined the population exposed to asbestos and looked into evidence of lung changes consistent with asbestos exposure. Their investigation demonstrated a strong relation between asbestos exposure due to poor detection and management practices, and measurable human lung modifications. Unfortunately, despite the invested efforts to reduce asbestos exposure, Ontario still allows the import and utilization of asbestos products (2013, April). The goal of this research project is to target issues concerning accurate classification of ACM conditions, thereby improving the well-being of workers health, and hence preventing future harm from asbestos. This project is in partnership with ECOH Management Inc.

Development of High-Performance Additively Manufactured Wicks for Two-Phase Heat Transfer Applications

The objective of this project is to leverage the design freedom offered by additive manufacturing (AM) to create and high-performance capillary structures optimized for two-phase heat transfer applications (e.g., heat pipes). Moreover, compared with conventional heat pipe systems, these wick structures can be directly fabricated as part of one-piece AM metal parts and housings, allowing a two-phase thermal management system to be directly integrated into virtually any component. This will serve to lower the weight and improve the thermal performance of heat sinks in applications such as electronics cooling, lighting, and thermal comfort.
As the auto industry pivots to EVs and autonomy, Ontario industries need to develop transformative automotive technologies to ensure their relevance in this quickly changing industry. These project outcomes will allow Magna to design and develop lightweight, high-performance, two-phase AM thermal management solutions for a myriad of applications and enable Magna to offer unique solutions to a wide and diverse range of potential customers, resulting in new product lines and revenue streams. By developing this technology, Magna will be positioned to pioneer next-generation electronics cooling and electric vehicle (EV) thermal management technology.

Techno-economic assessment of operating grid-scale super-capacitor energy storage for a wind power producer

Battery and energy storage units are one of the main sources of backup power in modern power systems to support the renewable power generation deficiencies in meeting the load demand. In recent years, the advancements in the charge/discharge efficiency, low cost, and long life of super-capacitor energy storage (SCES) technology have challenged conventional battery technologies. Besides, due to their high-power delivery capability, the SCES units are a strong candidate for supporting uncertain wind power production in a wind power generation complex. Atlas power generation Inc. has developed a SCES technology from inexpensive and widely available minerals which have the above-mentioned advantages. This research project is intended to execute a techno-economic assessment of investing in SCES units in a wind power production complex from the prospect of the wind power producer.

Deep learning-based application for construction and mining industries exploiting drone data

Drone technologies become more robust, and easier to use across important civil sectors, such as construction and mining, with the assurance to deliver unparalleled performance and consistency in every operation. However, one of the significant challenges in integrating drones into civil applications is related to data; i.e. lack of standards for high-quality data collection, complexity of data analysis for converting the overwhelming volume of raw data to concise information, as well as long delays before the data-driven information can be integrated to the process of decision making. Tackling these challenges is the main objective of this project. Hence, Centre de Géomatique du Québec (CGQ) active in developing an automated platform for collecting, processing, and analyzing field data using drone technology elevated by the power of visual and geospatial artificial intelligence (AI). The drone will be provided by the partner of CGQ, ARA Robotique Inc. Another partner of CGQ, Chaac Technologies Inc, will help from data collection to data management. The platform mainly targets the construction and mining sectors for earthwork progress monitoring, predictive inventory analysis, and site safety assessment. It helps managers extract site data to build information dashboards and automated reports in a timely, cost-effective, and safe manner.

Transport Properties of Layered 2D and TMD Materials Based Devices

My doctoral research work is based on the optoelectronic, and transport properties of layered two-dimensional (2D) and transition metal dichalcogenide (TMD) materials-based devices. Currently I am fabricating devices and studying their electron transport properties. The next target, to characterize the heat transportation in these devices, can’t be completed at my home institution because it requires additional sophisticated instruments (e.g. for frequency-domain thermoreflectance, magneto-optic kerr effect and for sheet resistivity) and simulation facilities. This Programme gives me opportunity to carry out this work by using the facilities of our collaborators in Canada.

Expected outcomes:
1. Less-explored thermal transport properties can be reported for these important and much-in-demand materials
2. The analysis can be implemented in several fields such as optoelectronics, sensors, and spintronics to improve the device performance related to heat dissipation in the devices.
3. The hands-on training on new experimental facilities would strengthen my research career and promote collaborations.

Analysis of Salmonid Spawning and Rearing Habitat in the Seymour River in North Vancouver, British Columbia

Salmonid populations are increasingly challenged by interruptions in watershed connectivity, and limited access to critical habitat. For spawning and rearing, salmonids require diverse habitat features, including gravel beds and large streamwood. However, many watersheds are deficient in large streamwood and gravel, in part due to dam structures limiting downstream transport. Therefore, there is a need to evaluate whether gravel and large streamwood deficiencies are occurring in the lower Seymour River (River) downstream the dam. Furthermore, there is a need to address whether these deficiencies can be mitigated to improve salmonid spawning and rearing habitat. This project will provide the Seymour Salmonid Society with an assessment of spawning and rearing habitat availability in the River. Based on these findings, habitat restoration recommendations to restore limiting factors will be provided. The Seymour Salmonid Society can use the results of this study to recover wild salmonid populations, including the River’s summer-run steelhead population.

Fathers’ use of parental leave and flexible work arrangement policies: Towards a relational understanding of work-family well-being

The benefits of parental leave and the necessity of flexible work policies such as flexplace and flextime are well known. To date, parental leave and flexplace/time –– both critical levers for remediating gender inequality in parenting and child rearing –– are examined separately by scholars of work, family, and social policies, and gaps remain in our understanding of how these levers fit together. Such policies are not experienced in siloes, and how they fit together may have a tremendous impact on work and family well-being. This project examines fathers’ use and perceptions of both parental leave and flexplace/time to understand how they affect quality of life for fathers and their families. In exploring this topic, this project equips the Vanier Institute of the Family with a more relational understanding of how such policies are interwoven into the lives of families in Canada.

Artificial Intelligence based pavement distress detection and monitoring system

The Road Pavements are subjected to distress due to heavy traffic load and environmental factor and is a common cause of accidents resulting in loss of valuable life and economic losses. Regular monitoring and timely maintenance is the key to ensuring a healthy roadway infrastructure. Traditional methods of manual monitoring are time consuming, expensive and limited to human biases. The aim of my research is to provide an automated, cost effective and easy to install pavement health assessment system to detect potholes in pavements at an early stage by application of Artificial Intelligence. This will provide the authorities with an automated system to detect pavement distress effortlessly, preventing further deterioration of roads and take timely actions. Road infrastructure system is an area which demands huge investments,thus, a low-cost automated pavement health assessment system using commercially available off-the-shelf equipment will not only deliver a cost-effective solution but will also accelerate corrective measures to ensure timely maintenance

Improving user experience with a social gaming platform: Identifying and adapting to significant user traits and behaviors

This project will involve using statistical modeling and machine learning techniques in order to identify significant factors that exist in user interaction logs collected from a social gaming system. Next, these factors will be used to inform, implement, and test an adaptive platform for managing and improving behaviors that relate to user experience and/or user retention.

Adapting Automatic Speech Recognition to In-Ear Microphone Speech

Traditionally, speech is captured from in front of the mouth. Existing an Automatic Speech Recognition (ASR) are all made to work with this type of speech. However, noise and other factors can be detrimental to the performance of ASR systems. When using an in-ear device that blocks the earcanal, a microphone can be placed inside the ear to capture speech that is relatively not affected by noise. Speech captured from inside the ear sounds different from speech captured in front of the mouth. It has a lot more low frequency content and not enough high frequency content. The aim of this project is to develop an ASR that is adapted to in-ear microphone speech. For this work, it will be implemented on an advanced earpiece developed by EERS to work with an e-bike developed by Nerra.

Dynamic Network Re-dimensioning via Accurate Prediction of 5G Throughput and Reinforcement Learning

The emerging 5G technology has tremendous potential in empowering a wide range of demanding applications. However, the 5G throughput can fluctuate wildly based on several factors like handoffs, user moving speed, user moving direction with respect to the radio base station, the terrain (e.g., obstacles between the user and the radio base station), the density of users and the workloads covered by the radio base station, the temporal patterns of usage (e.g., time of the day, days of the week, etc.), and more. These fluctuations in the throughput can sometimes drop below the throughput of 4G or sometimes to nearly zero (5G dead zones). It is of great importance to a 5G provider to better understand how any of these factors affect its end-users to provide better service. This project focuses on how to accurately model and predict the throughput of 5G network, and how to better manage the network’s resources to adapt to the changes in the patterns of usage.