Long before hosting the second largest port in the Pacific, the territory around Prince Rupert, British Columbia, was home to the Metlakatla First Nation. Rich in resources and with a history of productive economic opportunities, this region has gone through many changes as a result of development projects and human activities, impacting not only the environment, but the wellbeing of the community.
The VEC, under its Economic Transformation Lab and in partnership with SFU and MITACS, seeks to research, design, and publish an affordability dashboard that consolidates all important metrics/statistics on affordability, relevant to Vancouver businesses and talent. The Dashboard will not only focus on affordability of office, retail, and industrial space, as well as other pertinent business operation affordability metrics, but will also contain affordability metrics that inform businesses of the cost of living for their talent.
The proposed project will develop a system that combines ambient environmental sensors with sleep detection methods to measure sleep quality and allow the user to improve their focus during activities of their daily lives. Observing sleep patterns through all the stages of sleep to model the users body clock and quality of sleep. In addition, using environmental sensors to help users identify optimal sleeping conditions. This will be done by developing an algorithm that will estimate sleeping patterns with environmental sensors and produce a report to optimize their sleep.
The objective of this project is to research new technologies and methods to help in the assessment and functional imaging of neuroplasticity related to music therapy. The approach utilizes key novel technologies being researched in partnership with Dr. Ryan D’Arcy.
The overall objective for this project is to support the research of one master’s student who will help advance the research related to the social discourse of sustainable transportation and climate policy in Canada. In this field, START, in the School of Resource and Environmental Management at SFU, is one of the leading research teams in the country and Navius Research Incorporated is the leading Canadian consulting firm, providing support to governments and other stakeholders in the development and assessment of climate policy.
D-Wave’s quantum computer is good at solving a specific type of problems known as Ising spin problems. However, in order to solve one of these spin problems, you must first solve another hard problem—embedding the spin problem on D-Wave’s quantum processor.
From the land of discrete mathematics, this embedding problem falls into a well studied branch of graph theory known as graph minors. Being that this problem is difficult in and of itself, D-Wave has developed a heuristic solution. This project’s main aim is to help improve this embedding process.
The project will evaluate the Community Scholars Program (CSP) to promote program sustainability, growth and further development. The CSP provides free research database access for 500 community organizations in British Columbia and is a partnership between United Way of the Lower Mainland, Mindset Social Innovation Foundation, and the SFU Library. Now in its second phase, the program was created in 2016 to address the problem of access to research outside of the academic community.
This project will explore the ways that businesses communicate internally, with their employees and other stakeholders. In order to determine what the current best practices are in terms of how to communication to employees, through which platforms or media, or using specific strategies, the intern will conduct a thorough review of academic and ‘grey’ literature (not quite academic and not quite popular, for example, business magazines). The intern will compile a report for the partner organization in order to help with their own internal communication best practices and product development.
Computational thinking is a recent and very popular addition to elementary school curricula. Computational thinking projects students undertake include five basic parts: identifying key features of a problem (decomposing), creating a model of relationships among factors (modeling) in a causal system or data, designing steps (algorithm) to solve the problem or analyze data, trying out and repairing missteps (debugging), and generalizing findings.