Projets novateurs réalisés

Dynamic Model for Lead Cable of Inconel Self-Powered NeutronDetectors

CANDU reactors use a large number of self-powered, in-core flux detectors (ICFDs) for their

reactor regulating system (RRS) and their two shutdown systems (SDS-1 and SDS-2).

Dynamic characteristics of ICFDs and lead cables are very important to reactor control and

safety. Although, to date, several studies have modeled dynamic characteristics of ICFDs

and lead cables, additional effort is deemed necessary to model the dynamic response of

lead cables, both at their beginning of life and as they age. The objective of the project proposal is to develop a dynamic model for the lead cables based on the physics of the neutron and gamma interactions contributing to the signal and to use the developed model to simulate previous experiments in Chalk River and observations at OPG.The ability to predict the characteristics of these lead cables at their beginning of life and as they age will benefit OPG’s ability to ensure safe operation of its reactors.

Measuring the effectiveness of a novel treatment of Chronic Lateral Epicondylitis: the ArmLock sleeve.

Lateral epicondylitis is a common source of lateral elbow pain and causes restrictions in performance during daily activities as the pain increases with wrist and hand movements. It is necessary to explore new treatments that decrease the symptoms of lateral epicondylitis. We aim to investigate the effects of a new non-surgical treatment (the ArmLock Sleeve) on pain, movement, and performance in daily activities in adults diagnosed with lateral epicondylitis. We also want to investigate the acceptance of the ArmLock Sleeve by the study participants. The partner organization will benefit by having its product (the ArmLock Sleeve) validated for use by its clients. The feedback will also help the partner organization to scale up its product by marketing it to a wider range of users and/or industries.

Translating behavioural science into effective hiring practices

While companies are looking to hire the most qualified candidate to fill their positions, they often have a difficult time identifying the right candidate for the job using traditional hiring practices. However, one major barrier is that companies may over-rely on traditional hiring methods such as interviews that may not be the best way to select the most qualified candidates. This is because employers may overly-rely on their experience and intuition, even though they are often led astray. The current project seeks to provide a systematic literature review to examine whether work done in cognitive and behavioural science can address these issues and improve the hiring process.

Diversity and Abundance of Beneficial and Pest Insects in Canadian Prairie Agroecosystems – Year two

The proposed research project will assess the insect fauna present associated with prairie wetlands, as well as those found in adjacent fields of crop plants (canola, barley, wheat) and restored grasslands. Insects will be collected using various trapping methods to sample taxa exhibiting different lifestyles. Collected specimens will be identified as specifically as possible to determine taxa found in sampled habitats. This will provide information regarding species diversity and richness of insects in prairie wetlands, which act as nutrition for the waterfowl that Ducks Unlimited are focused on protecting. This information can further be used to assess the ecological health of habitats where Canadian waterfowl exist, as well as determine if beneficial or pest insects may be present in the vegetation surrounding those wetlands which may be affecting local waterfowl or other types of animals, or which may exert an effect on the croplands that prairie potholes exists in.

Diversity and Abundance of Beneficial and Pest Insects in Canadian Prairie Agroecosystems

The proposed research project will assess the insect fauna present associated with prairie wetlands, as well as those found in adjacent fields of crop plants (canola, barley, wheat) and restored grasslands. Insects will be collected using various trapping methods to sample taxa exhibiting different lifestyles. Collected specimens will be identified as specifically as possible to determine taxa found in sampled habitats. This will provide information regarding species diversity and richness of insects in prairie wetlands, which act as nutrition for the waterfowl that Ducks Unlimited are focused on protecting. This information can further be used to assess the ecological health of habitats where Canadian waterfowl exist, as well as determine if beneficial or pest insects may be present in the vegetation surrounding those wetlands which may be affecting local waterfowl or other types of animals, or which may exert an effect on the croplands that prairie potholes exists in.

Sustainable Purchasing and Official Community Plan Sustainability Indicators

This internship at the City of Surrey incorporates two parts: one on sustainable purchasing, and one on integrating sustainability objectives into neighborhood concept planning. In part one, the intern contributes to creating an internal inventory of existing sustainable purchasing practices and initiatives at the City. This inventory informs the draft of a sustainable purchasing policy, aligning with leading practice in municipal sustainable purchasing. Sustainable purchasing refers to the integration of social, environmental and ethical considerations in purchasing decisions. In part two, the intern contributes to the Official Community Plan update process currently underway in the City, helping to incorporate sustainability indicators that are measurable, appropriate, and effective at monitoring progress. Both parts of the internship benefit the City by building off of existing projects, working towards its Sustainability Charter commitments, and integrating departmental objectives.

Développement d’un modèle mathématique thermo-hydrodynamique transitoire de la trempe thermique pour la production d’aciers de haute dureté – Year two

La fabrication de pièces en acier de haute dureté et de hautes propriétés mécaniques pour différentes applications industrielles (pétrochimiques, moule d’injection de plastique, etc.) se fait par différents traitements thermiques. Ces pièces sont fabriquées par différents procédés et subissent une trempe. Les pièces peuvent être de différentes tailles et de formes diverses. Plus la taille est importante, plus le trempage devient complexe. La trempe des grandes pièces est réalisée dans des bassins d’eau ou de composés polymériques dans lesquels les pièces sont plongées pour différentes périodes. La vitesse de refroidissement de chaque point de la pièce détermine le degré de dureté et les propriétés mécaniques de l’acier. Cependant, la taille importante de la pièce rend le contrôle du taux de refroidissement très difficile et peut résulter en la production de pièces déformées et non conformes aux propriétés exigées. La recherche proposée dans ce projet vise à modéliser mathématiquement les phénomènes thermomécanique et hydrodynamique de ce processus pour proposer des améliorations sur le plan industriel et proposer une approche du point de vue scientifique et d’ingénierie pour l’analyse des paramètres de ce procédé.

Développement d’un modèle mathématique thermo-hydrodynamique transitoire de la trempe thermique pour la production d’aciers de haute dureté

La fabrication de pièces en acier de haute dureté et de hautes propriétés mécaniques pour différentes applications industrielles (pétrochimiques, moule d’injection de plastique, etc.) se fait par différents traitements thermiques. Ces pièces sont fabriquées par différents procédés et subissent une trempe. Les pièces peuvent être de différentes tailles et de formes diverses. Plus la taille est importante, plus le trempage devient complexe. La trempe des grandes pièces est réalisée dans des bassins d’eau ou de composés polymériques dans lesquels les pièces sont plongées pour différentes périodes. La vitesse de refroidissement de chaque point de la pièce détermine le degré de dureté et les propriétés mécaniques de l’acier. Cependant, la taille importante de la pièce rend le contrôle du taux de refroidissement très difficile et peut résulter en la production de pièces déformées et non conformes aux propriétés exigées. La recherche proposée dans ce projet vise à modéliser mathématiquement les phénomènes thermomécanique et hydrodynamique de ce processus pour proposer des améliorations sur le plan industriel et proposer une approche du point de vue scientifique et d’ingénierie pour l’analyse des paramètres de ce procédé.

Competency-Based Education for Airside Professionals

This project will analyze competencies (knowledge, skill, and attitude) of airside professionals conducting the taxi-ground run of an aircraft in an operational airport environment. Both cognitive task analysis and consensus modeling methodologies will be used to identify competencies and draft a competency framework of the task. Based on the competency framework, training implementations (including those using Virtual and/or Augmented Reality, Gamification and other immersive technologies) will be developed and evaluated to assess the effectiveness of these approaches.
This work is significant because airside worker human error can cause damage to aircraft, injury to persons, time loss causing costly flight delays and other significant operational costs (such as loss of luggage). Competency-based education is increasingly used within aviation to align training with the actual professional competence required to complete a task safely and efficiently. This project will evaluate several methodologies in the identification of competencies, the results of which can translate to other professional positions.

Internet-based mental state monitoring using patient’s textual data – Year two

Among all chronic diseases, mental health issues have the highest burden on health care systems. However, unlike other chronic diseases, like Diabetes or hypertension, no monitoring procedures exist to monitor patients’ mental health status to prevent relapse and crisis situations. It is therefore necessary to develop cheap, convenient and accessible monitoring systems that could be used outside clinical setting. Most mental health diseases demonstrate a range of physical and behavioral symptoms (e.g. change in tone, posture and use of words, aka psychomotor symptoms) that could be measured using smart devices prevalently used by patients. Recent Internet-based methods of care delivery (eg online psychotherapy) provide the opportunity to utilize such digital evaluations of behavior (behavioral phenotyping) for long-term and remote monitoring of mental health status. Our proposal is to process digital behavioral data generated by the patients in an online platform (i.e. text, voice and video feedback) using machine learning approaches to develop an algorithm to predict their mental status. Furthermore, using recent advancements of deep learning in natural language processing, we are going to generate more personalized therapy content for patient interactions to improve the quality of the care.

Internet-based mental state monitoring using patient’s textual data

Among all chronic diseases, mental health issues have the highest burden on health care systems. However, unlike other chronic diseases, like Diabetes or hypertension, no monitoring procedures exist to monitor patients’ mental health status to prevent relapse and crisis situations. It is therefore necessary to develop cheap, convenient and accessible monitoring systems that could be used outside clinical setting. Most mental health diseases demonstrate a range of physical and behavioral symptoms (e.g. change in tone, posture and use of words, aka psychomotor symptoms) that could be measured using smart devices prevalently used by patients. Recent Internet-based methods of care delivery (eg online psychotherapy) provide the opportunity to utilize such digital evaluations of behavior (behavioral phenotyping) for long-term and remote monitoring of mental health status. Our proposal is to process digital behavioral data generated by the patients in an online platform (i.e. text, voice and video feedback) using machine learning approaches to develop an algorithm to predict their mental status. Furthermore, using recent advancements of deep learning in natural language processing, we are going to generate more personalized therapy content for patient interactions to improve the quality of the care.

A Reliable Lora based Tracking and Monitoring System for Underground Mines

The mining industry directly employs more than 426,000 workers across the Canada and contributed $97 billion to Canada’s GDP in 2017. However, mining workers are exposed to five-fold higher occupational hazards than the industrial average. Reliable underground communication is essential to alleviate incidents and escalate rescue operations. However, wireless communications in mines is a big challenge. Electromagnetic wave propagation is very poor in mines due to irregular confined shapes and rough walls. A recent wireless standard LoRa (Long Range) is promising in mine environments, due it’s, ultra-low power consumption, long range and deep penetration capabilities. This project aims to develop a unique and comprehensive monitoring and control system for underground mines using LoRa. It intends to develop a LoRa based tracking system that will use different range based techniques to estimate distance and apply advanced Machine Learning (ML) algorithms such as particle filtering, recursive neural networks or Kalman filtering on the estimated fingerprinting result to improve the accuracy. In addition, it will develop a medium access control (MAC) protocol to collect different mine data timely and ensure the Quality of Service (QoS) requirements. The outcome of this work will lead to significant improvements in miner safety and productivity.