Projets novateurs réalisés

Une meilleure littératie financière pour les jeunes du Nouveau-Brunswick

Ce projet de recherche porte sur la littératie financière (LF) des jeunes Néobrunswickois, concept que l’Agence de la consommation en matière financière du Canada définit comme: «[…] le fait de disposer des connaissances, des compétences et de la confiance en soi nécessaires pour prendre des décisions financières responsables.» Or plusieurs études nous indiquent que bon nombre de Canadiens adoptent des comportements financiers inadéquats, ce qui les empêche de jouir d’une sécurité financière, leur permettant de vivre en toute quiétude. Cette constatation nous amène à nous poser la question de savoir comment motiver les jeunes du Nouveau-Brunswick, à acquérir et à disposer des connaissances, des compétences et de la confiance en soi nécessaires en vue de prendre des décisions financières responsables pour en arriver à un revirement de la situation. CPA NB souhaite utiliser les résultats de cette étude pour élaborer des stratégies permettant d’améliore niveau de LF au sein de la population Néobrunswickoise.

The Future of Learning

Today’s technological advancements are changing job requirements and skills expectations at a rapid rate. Businesses are consistently striving to investigate these trends and prepare for upcoming “disruptions.” In terms of job automation, there are many barriers that people experience when trying to access learning platforms such as online learning tools and micro learning sessions, as the majority of these platforms are aimed at individuals who already have a post-secondary education, and other credentials. This project will develop a white paper outlining strategies and recommendations to help businesses and individuals structure collaborative learning platforms aimed at helping people consistently refresh their skill sets and stay relevant in their chosen industry, or give them the ability to move into new industries.

Better predictions of employee events II

Machine learning can be used to predict employee events around retention, promotion or movement. This project explores how to generate better predictions by exploring correlations and exploiting them through features that increase predictive strength. Furthermore, the project explores how to reliably fine-tune the predictive model to a particular data set in the presence of interdependence of data points. The results will enable improved Machine learning predictions related to employee events.

Graphics Processing Unit Solutions for Power Systems Computer Aided Design: Collaborative Exploration with the University of Winnipeg

Graphics Processing Units (GPUs) are usually employed to quickly render images on everyday computer screens, and do so quickly and efficiently for relatively little cost. Modern GPUs are able to do hundreds or thousands of simultaneous calculations; rewriting conventional computer problems in the language of GPUs offers the potential to dramatically decrease the computing time for complex problems such as Electromagnetic Transmission (EMT) simulations. EMTs are used in the modelling of electrical systems with multiple generators and transmission lines, and are at the heart of power engineering software. This project will focus on how to integrate the power of GPUs into established software for EMTs, with the aim of providing power engineers around the globe with a cutting-edge tool to help them design ever greater electrical systems.

Pilot-scale preparation of phospholipid-free small unilamellar vesicle formulations with potential in treatment of hepatic diseases

Lipid-based nanoparticular drug formulations are a successful technique to enable targeted treatment. The Canadian company Precision Nanosystems Inc. (PNI) develops the innovative instrument family NanoAssemlr for lipid nanoparticle preparation based on microfluidics. These instruments are fast, easy-to-use and provide a high batch-to-batch reproducibility and quality. Recently, we developed a novel lipid nanoparticle formulation with the unique feature of selective liver targeting, which could only be prepared with NanoAssemlr Benchtop at relatively small scale. In order to further investigate this technology in vivo, the formulation needs to be produced in larger scale. PNI can support this project by helping to scale-up their production on the NanoAssemblr Blaze, by assisting in optimization of purification methods and by providing guidance on analytical method characterization. We will then utilize this innovative formulation to deliver a rescuing agent (glutathione) to treat acetaminophen-induced acute liver injury in a mouse model to demonstrate a potential utility.

Hybrid Testing of an Steel Frame Structure under Earthquakes

Steel structures are commonly considered highly ductile and less sensitive to seismic effects provided that adequate seismic detailing for ensuring sufficient energy dissipating mechanism are guaranteed. However, many existing steel residential framed buildings have been designed before the implementation of modern seismic structural design codes. These buildings show low energy absorption and inadequate dissipation capacity under the seismic effect which can cause severe deformation and damage. Therefore, it is deemed necessary to assess the seismic vulnerability of the existing frame structures as well as to develop adequate methods for an accurate evaluation of the behavior of the structures.
The objective of this project is 1) Conducting the hybrid simulation test using steel frame structures and 2) Development of analytical method for evaluating the behavior of the steel frame structures based on the test results. TO BE CONT’D

Intégration et impact des outils web 2.0 pour une PME dans un contexted’un projet de mise en marché d’un développement immobilier écologique innovant

Notre projet vise à intégrer l’utilisation des outils web 2.0 en tant que nouveaux médiums de

communication interactifs, dans les pratiques d’une firme d’ingénieurs conseil. Le contexte de

notre intervention sera de soutenir le processus de mise en marché d’un projet de

développement immobilier innovant. Notre objectif sera de démontrer la pertinence de

l’exploitation des plateformes web 2.0 pour la dynamisation des échanges et de la

collaboration avec la clientele et les partenaires ainsi que I’utilite des outils Internet de 2″ et

de 3″ generation (blogues, Calendriers interactifs, formulaires en ligne, outils collaboratifs en

design, … ) dans Ie cadre d’une approche d’innovation « ouverte ».

Use of temperature and activity monitoring system as predictor for parturition and estrus

Monitoring dairy cows individually around the time of calving and during lactation has the potential to identify calving difficulties or cows at risk of developing disease, as well as cows in estrus to create alerts for dairy farmers. Therefore, there has been an increase in research investigating methods to accurately predict timing of calving, disease diagnosis, and estrus detection via activity and temperature monitoring. Recently, a novel cattle activity and core temperature monitoring system that uses new sensor technology has been developed. The purpose of this proposal is to create opportunities to improve transition cow health by more accurately predict calving time and cows at risk for developing diseases.
Herdstrong is relatively small start-up, but with a very active R&D department and products in several operations and Universities particularly across North America. TO BE CONT’D

Automating Configuration Management and Deployment in Large-scale Data Centers Augmented with Edge Data Centers – Year two

Data centers are now growing and expanding massively. They are large scale and heterogeneous. In addition, they rely more and more on emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV) with “network softwarization” as their key feature. Moreover, they are now being augmented with edge data centers rooted in concepts such as cloudlets, ETSI Mobile Edge Computing (MEC), and fog computing. Such data centers bring a host of new challenges when it comes to the automation of configuration management and deployment. For instance, edge data centers can be mobile. This mobility may cause unpredictable network topology changes and migration of the hosted applications to hardware with different configuration requirements. Accordingly, operators must implement increasingly sophisticated network policies that have to be translated into low-level configuration commands and adjusted to the changes in the network condition. In addition, some applications may have location constraints on some of their components for legislative reasons. Due to this mobility, the mapping of application components to the infrastructure might dissatisfy such constraints. Traditionally, these tasks are done manually. However, this process is tedious, costly, and does not scale. TO BE CONT’D

Automating Configuration Management and Deployment in Large-scale Data Centers Augmented with Edge Data Centers

Data centers are now growing and expanding massively. They are large scale and heterogeneous. In addition, they rely more and more on emerging technologies such as Software Defined Networking (SDN) and Network Functions Virtualization (NFV) with “network softwarization” as their key feature. Moreover, they are now being augmented with edge data centers rooted in concepts such as cloudlets, ETSI Mobile Edge Computing (MEC), and fog computing. Such data centers bring a host of new challenges when it comes to the automation of configuration management and deployment. For instance, edge data centers can be mobile. This mobility may cause unpredictable network topology changes and migration of the hosted applications to hardware with different configuration requirements. Accordingly, operators must implement increasingly sophisticated network policies that have to be translated into low-level configuration commands and adjusted to the changes in the network condition. In addition, some applications may have location constraints on some of their components for legislative reasons. Due to this mobility, the mapping of application components to the infrastructure might dissatisfy such constraints. Traditionally, these tasks are done manually. However, this process is tedious, costly, and does not scale. TO BE CONT’D

Jordan Shapes for Deep Learning

The proposed project aims to develop a systematic approach for improving deep-learning-based computer vision systems by augmenting the local pixel data with the global shape data (more specifically, Jordan curves) and by adjusting system architectures to accommodate the augmented input. Three canonical computer vision problems will be investigated in this project. They are respectively image dehazing, alpha-matting, and face detection. The potential roles of Jordan curves in these applications will be examined. The research results will provide an in-depth analysis on how shape data may impact deep learning training, inference and transparency and suggest a general guideline on how to effectively utilize shape data in deep learning systems.

Orebody Heterogeneity Assessment for Sensor Based Sorting

Teck Resources Limited is searching for a method to characterize and quantify the heterogeneity of ore based on numerous parameters. Naturally, when characterizing an ore body’s heterogeneity, the variability in the deposit can contribute towards the sortability of the deposit.
The main objective of this research is to investigate a method to quantify the sortability and ore heterogeneity in a systematic manner with clear ranking criteria. Throughout the research, 5 – 6 operations’ resource models will be reviewed and the ore heterogeneity will be assessed for the purposes of ranking them for ore sorting