Innovative Projects Realized

Oncology Nursing Training: Innovating for Quality of Care in Mumbai, India

Given the complex issues related to the Indian health care systems, the need for excellent nursing training and quality of care, the objectives of the proposed internship are to explore the contribution of simulation training to provide optimal oncology nursing care at the Prince Aly Khan Hospital (PAKH), Mumbai. The research activities will include : 1. A critical review of the literature in order to identify best practices guidelines of simulation nursing oncology training; 2. Individual and focus group interviews with health care (medical and nursing) experts in simulation training, in nursing training and in oncology care. Nursing Competencies Framework developed by the School of Nursing at the Université de Montréal will provide the backdrop for the integration of simulation and care competencies in oncology nursing. PAKH has been committed to carry out general simulation training for quality of nursing care on the one hand and oncology specific training on the other over the past few year. The proposed objectives will be directly anchored within these mandates for optimal oncology and general care.

Assessment of Climate Change-induced Geohazards for Ice-clad Canadian Volcanoes and Mountains

In Canada’s mountains, climate change is leading to the retreat of glaciers, permafrost thawing and accelerated snowmelt. These factors contribute to a significant increase in slope stability hazards and the risk of landslides, placing numerous communities and critical infrastructure at risk. Volcanoes are particularly vulnerable as they are commonly hydrothermally altered and weakened, thereby compounding the effects of climate change and further increasing the associated risk of collapse. Municipal, regional, provincial and federal agencies recognize the critical importance of effectively monitoring unstable slopes on ice-clad volcanoes and mountains in order to more effectively mitigate and respond to these potentially catastrophic hazards. By coupling satellite-based Earth Observation data with ground-based geological and geotechnical information, this project will help TRE Altamira develop a framework for a semi-automatic satellite monitoring service to monitor ice-clad volcanoes and mountains within the Garibaldi Volcanic Belt and ore broadly throughout the Pacific Northwest.

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Glocal Linearization for Feedback Control for Significantly Nonlinear Systems

Closed-loop control of nonlinear systems is typically performed by combining a number of linear controllers, each designed to perform well around an operating point. Although this solution generally works in many cases, there are some important practical applications, e.g. in diesel engine control or in robotics in which it fails and leads to serious performance degradation or outright instability during rapid transition between two operating points. This project aims at developing a systematic way to design a readily and easily applicable solution to this important practical problem.

An information-theoretic framework for understanding generalization in neural networks

Deep neural network (DNN) is a class of machine learning algorithms which is inspired by biological neural networks. DNNs are themselves general function approximations, which is the reason they can be applied to almost any machine learning problem. Their applications can be found in visual object recognition in computer vision, translating texts in unsupervised learning, etc. DNNs are prone to overfitting because DNNs usually have many more parameters than the available training data. However, they usually have a low error on the test data. This surprising fact has motivated the scientific community to study the generalization performance of DNNs. Nevertheless, the previous attempts do not lead to a satisfying answer to the aforementioned question. In this project, we aim to introduce an information-theoretic framework which let us find a promising answer to the question of why DNNs generalize well in practice.

The Contribution of Invertebrates to the Seasonal Diets of Walleye in Lake St. Joseph – year 2

The aim of this project is to better understand the onshore and offshore feeding habits and movement of walleye on Lake St. Joseph. There is special emphasis on answering the question: if, when and how much do Walleye rely on invertebrates in general, and Mayflies in particular. Walleye are an economically and ecologically significant sport fish and Mayflies are an important bioindicator of ecosystem health and potentially have an intricate predator prey relationship. To better understand these interactions, we will reconstruct the seasonal diet of walleye, across many age classes and determine their foraging habits both onshore and offshore. Biological information will be collected from the walleye to determine any specific foodweb interactions and transient movements that may exist. To conserve and protect ecological processes in an everchanging world understanding natural interactions has never been more important.

Technology innovations in Wheelchair Sport measurement and analysis

This project will assess the game demands of wheelchair court sports. While this has been attempted in the past, new methods using inertial measurement units (IMU) allow each push to be identified and offer new ways to analyze these game demands and connect them to key performance metrics. With the help of Own the Podium, and Canadian Sport Institute Pacific, these key performance indicators will become an important for developing and developing elite athletes in wheelchair court sports. Further, the development of IMU algorithms will speed up the integration of these devices in other sport applications being developed by Proskida. Overall, this project will further help the high performance sport industry in Canada.

The use of machine learning tools to identify organisms and contaminants in lake ecosystems

The ultimate objective of this research project is to use a form of artificial intelligence to be able to classify and identify images of microscopic particles. Machine Learning is the term applied to this type of process, in which an algorithm is created by the computer software itself (i.e. mostly hidden from human intervention) to complete the task. The intern will complete a Masters of Science degree at the University of Toronto, and work with EcoVision Consulting Group, to develop a framework for testing machine learning packages and to parameterize some machine learning tools to identify microscopic organisms called zooplankton and classify inorganic contaminants (example, plastic fibres) in lake water samples. This work will benefit academic and government environmental monitors by providing an automated process for identifying microscopic species within lakes, and benefits EcoVision (and private industry in general) in automating contaminant monitoring in environmental effects monitoring projects.

Self-Adaptive Pattern Recognition with Deep Neural Networks

The purpose of this project is to investigate self-adaptive forecasting and anomaly prediction algorithms based on deep neural networks (DNNs). DNNs present a compelling technology due to their wide-spread availability through open-source projects (e.g. TensorFlow, MXNet). However, usability of DNNs in scenarios outside of image, speech or text pattern recognition is mostly unproven. This project aims to reduce the knowledge gap that exists in the usage of DNNs in the context of pattern recognition with DNNs in network management and network equipment manufacturing. The output of the project will be a set of hyper-parameter optimization and concept drift adaptation algorithms, which can be used to optimize DNNs for pattern recognition in network management data and network equipment manufacturing data.

Création de stratégies démonstratrices hybrides de conception et fabrication d’outillages aérospatiaux

La fabrication de pièces aérospatiales présente plusieurs défis importants, dont ceux du délai et du coût de réalisation. Dans le contexte économique d’aujourd’hui, `bien faire du premier coup` est impératif pour maintenir la compétitivité des entreprises. La recherche de l’efficience est alimentée par diverses opportunités provenant de technologies émergentes telles que l’assemblage par métrologie 3D, la fabrication additive (FA) et la fabrication hybride (FH) lesquelles proposent une multitude de possibilités ayant un potentiel significatif. Ben que ces nouvelles technologies permettent d’envisager une évolution du processus de fabrication des pièces en aérospatiale, elles ne sont cependant pas applicables dans toutes les circonstances. En effet, les critères de rentabilité et de robustesse demeurent essentiels à l’implantation de toute nouvelle technologie. Plus spécifiquement, le présent projet s’attardera à la conception et fabrication des outils employés pour la fabrication des composants aéronautiques. Deux aspects seront examinés de près, l’utilisation de la métrologie 3D sans contact pour positionner les composants sur les gabarits et la révision du processus du tolérancement des composants.

Characterization and Pelletization of Western Red Cedar Residue

The key objective of this research is to test the Refuse-Derived Fuel supplied by ICC and investigate parameters involved in making durable pellets from these residues. This will include conducting a series of pelletization tests with different mixture recipe, pre-conditioning of material as well as adding binders. The produced pellets will then be tested for their calorific value, chemical composition, chlorine content and ash content. ICC plans to convert RDF to heat, and electricity through gasification. To develop the technology, this research focuses on providing the optimum operating conditions to produce durable pellets for use in gasification systems. A successful project would allow ICC Group to provide such a system for conversion of waste to energy.

Reconstitution paléolimnologique des effets des activités anthropiques de la péninsule de Fildes, Îles Shetland du Sud, Antarctique

Les sédiments de sept lacs de la péninsule de Fildes, située dans les Îles Shetland du Sud en Antarctique seront analysés afin de savoir si les activités humaines sur la péninsule ont eu un effet sur le milieu aquatique. Les diatomées, des microalgues unicellulaires, serviront d’indicateur pour déceler les changements dans le milieu puisque celles-ci réagissent fortement aux modifications de leur milieu. En effet, dès que leurs conditions favorables ne sont plus respectées, les espèces tendent à disparaître pour être remplacées par d’autres auxquelles les nouvelles conditions correspondent. Une analyse de la composition des communautés de diatomées à travers le temps sera effectuée et permettra donc de reconstituer les conditions des lacs pour la période à laquelle correspondent les sédiments récoltés pour chaque lac. Il est possible de dater les sédiments ou son contenu à l’aide des méthodes de datation au carbone 14 ou au plomb 210. L’évolution naturelle des conditions des lacs seront observées afin de déterminer l’effet des activités humaines sur les lacs. Il s’agit entre autres des conditions climatologiques et géomorphologiques liées aux processus naturels.