As a result of climate change and other pressures that result in extreme events like wildfire and flooding, many
drinking water utilities are at risk of potentially catastrophic failure and need treatment adaptation strategies to
prepare for increasingly variable and potentially rapid deterioration in source water quality. Currently there are no
recognized tools for evaluating the anticipated impacts of such events on water treatment plant operations. This
research will enable Stantec Consulting Ltd.
As the Canadian population is aging, more and more Canadians will show cognitive decline. Aging and certain types of
neurological disorders is often associated with deficits in executive functions: goal maintenance, planning, task
switching and attention. These functions are critical for the maintenance of functional independence. Few validated
rehabilitation approaches for these types of deficits exist. One rehabilitation approach, Goal Management Training
(GMT), has shown promise. In its standard implementation, GMT is led in small groups.
Envenio is a Canadian company that specializes in advanced analysis of fluid dynamics and fluid structure interaction in the technological and environment settings. Envenio is also a software development company that owns an advanced Computational Fluid Dynamics solver, EXN/Aero, that uses many core technology in the parallel computation of unsteady and fully turbulent flows. Envenio seeks experimental data to validate their unsteady separation codes. To this end, an intern in Dr.
Nature of the work: This project will assess a novel lululemon clothing ensembles during transitions between differing environments.
Anticipated Outcomes: An optimization of the design of clothing ensembles for transitions between different environments is the anticipated outcome.
Relevance: The project will provide garments for Canadians to improve their health, safety and comfort during outdoor activities.
Ethernet networks are typically best effort networks where traffic flows may contribute on creating network congestion and lead the switches to start dropping packets randomly. This results in unstable network latency that some applications cannot tolerate, especially in the context of 5G networks where delay constraints are very tight.
The Salish Sea is a highly productive, dynamic coastal ocean with substantial temporal and spatial variability at lower trophic levels (e.g. phytoplankton and zooplankton). This variability, in turn, may directly impact resident and migratory fish populations that are of major economic importance in the region. The main goal of this research is to investigate the level of synchronicity between phytoplankton and zooplankton phenology in the Salish Sea.
With worldwide efforts to increase the utilization of renewable energy, traditional power distribution networks are being transformed into active distribution networks with the interconnection of distributed generation. The status of DGs connected to an active distribution network can change frequently, and this creates many challenges to network protection. The aim of this project is to implement a new protection solution for active distribution systems and microgrids in hardware and validate its performance.
In a multi-tenant cloud environment, several tenants share the same physical resources. To ensure security of tenants data and process, appropriate security measures should be implemented by the cloud provider at multiple layers. Particularly, appropriate controls for end-to-end network isolation must be put in place. The proposed research project aims at elaborating innovative and efficient approaches and methods to audit end-to-end network isolation in the cloud.
The proposed research aims to target large-scale consumer-generated data to analyze, visualize, and make predictions out of. The data will be collected from the consumers to make assessments on their lifestyles, and will come in forms such as heart-rate variance, that is, being temporal data. Researchers with visual analytics background will apply new visualization techniques on the data in order to grasp the insights and improve the model to interpret the data. The research problem is to relate measures of stress, recovery and mindful activities to the data obtained.
During the internship in collaboration with RootCellar Technologies, research will be conducted towards the design of an adaptive machine-learning solution and its integration with the existing RootCellar framework for automated evaluation and management of information security risk in small and medium size enterprise networks. The existing framework is very advanced in terms of end-point risk monitoring as well as its compliance with the NIST CVSS System.