As the COVID-19 pandemic complete eradication will at least take 2-3 years, putting the country under complete lockdown is an impractical solution. Hence, Computing and Data Analytics research group at Saint Mary’s University (SMU-CDA) proposes a collaboration with Agyle Intelligence to develop a complete framework using Data Analytics and Machine Learning that can help citizen, civic authorities and local business to make a decision regarding its community movement.
This research has two main foci: (1) resilience and resilience intervention and (2) workplace dignity. The first main focus of this project is to evaluate Air Institute’s current evidence-informed Pathway to Resilience program, specifically their effectiveness in impacting the key resilience factors targeted in each workshop. This research will also examine data in order to identify certain resiliency factor correlations and/or profiles in order to provide more value to current organizational reports and key insights.
Internet of things (IoT) includes of multitude of sensors from a wide variety of applications. These sensors produce high volume and high velocity data. Recently there has been much interest in application of such technologies to improve agricultural practices. The sensors that are installed in the field transmit real time data regarding numerous environmental variables of interest. This data is then used to forecast a future state and to make a well informed business/operation decision according to an expected future state.
Internet of things (IoT) includes of multitude of sensors from a wide variety of applications. These sensors produce high volume and high velocity data. Recently there has been much interest in application of such technologies to improve energy management and agricultural practices. The sensors that are installed in the field transmit real time data regarding numerous environmental variables of interest. This data is then used to forecast a future state and to make a well informed business/operation decision according to an expected future state.
The purpose of the proposed research is to identify the factors and indicators that enable, or block community actors from taking ownership of a solution previously led by a social leader or government actor. The project will allow our partner and the communities they work with to gain a better understanding of factors that enable the transition of projects to ownership within the community, as well as further the available research and knowledge for other government or social led business ventures transitioning ownership to the community.
The proposed research will examine potential alternatives for respooling buoy line from trap fishing applications into caged underwater rope enclosures. Previous research conducted for Ashored Innovations found that cage rope enclosures demonstrated the most favorable results for ropeless fishing given the environmental conditions in Atlantic Canadian trap fisheries. However, one major concern highlighted from fishers is the time required to reload the system with buoy line in order to redeploy.
As part of Envirosoil’s re-usable energy division in Nova Scotia and Newfoundland, drilling fluid waste product will be re-purposed as a potentially viable fuel supplement and additive in other industrial processes. The proposed research would look into viable options for reusing this material in a way that is commercially beneficial to the current operations, while also diverting the waste from landfills. The general objective of the proposed research is to better characterize the nature of the waste and how it can be safely pushed into the commercial market.
Citco provides financial products and services to hedge funds, private equity and real estate firms, investors, institutional banks, Global 1000 companies, and high net worth individuals in the Netherlands and internationally. The proposed research is focused on optimizing operations by automating trade resolution and reducing risk using machine learning. Outlier detection algorithms will be proposed to improve the accuracy of existing mismatch detection procedures. Unsupervised and supervised machine learning will be used to further improve this capability.
In Canada, approximately 40,000 out of hospital cardiac arrests (OHCAs) occur annually. Survival rates are under 15%, and the only treatment is immediate use of an Automated External Defibrillator (AED), coupled with CPR. This project will focus on finding solutions to identified problems associated with locating and using an AED. Some of these solutions will focus on in-emergency technology that can increase the accessibility of publicly available AEDs, along with the ability for bystanders to locate and use these life-saving devices.