The vision of Own The Podium is for Canada to be a world leader in high performance sport. With this vision comes the required investment into our understanding of how athletes adapt to training and how this understanding can be leveraged to provide our national level athletes a competitive edge on the world stage. This project aims to understand the adaptations that lead to the incredible improvements in aerobic fitness in high-level endurance athletes observed following blood-flow restriction training.

Coastal habitats are critical for the health, livelihoods and social well-being of Canadian communities. In British Columbia, log handling and storage areas create wood waste that falls to the seafloor and does not decompose, degrading important habitats and substantially reducing the number of species can live there. To manage the impacts of habitat degradation, it is important to have accurate methods for measuring species diversity, but marine animals are difficult and time-consuming to identify and many remain unknown to science.

Industry and environmental consultants are increasingly using environmental DNA (eDNA) metabarcoding approaches to complement traditional environmental risk assessments. However, the application of these technologies to field locations where sampling is limited due to access to secure sites, or remote locations where the environment is very heterogenous, poses challenges to acquiring representative eDNA samples. Furthermore, the interpretation of amplicon sequencing data from environmental samples presents extreme analytical challenges.

Insurance companies collect huge volumes of text on a daily basis and through multiple channels, which can be used for lots of different analyses, including identifying “cause of death”. It is difficult to overestimate the importance of an insurance company’s need to understand the facts and circumstances surrounding an insured individual’s death. These facts, including the manner and cause of death, along with other data about the decedent, are critical to an insurance company’s ability to measure mortality rates.

For organizations that use the Internet, their employees will visit thousands of websites every day. However, there is a chance that the destination website is not safe to visit. Such websites may be fraudulent, phishing, or even data-stealing related. On the other hand, determining if the target website link is suspicious or not could help to prevent potential harm. Using a filtered list is the most straightforward way. The problem is, as the database for malicious websites is growing, hackers’ minds are also developing, which requiring a more profound way to deal with such a problem.

Imagine a refrigerator automatically orders the eggs from the online store to deliver at its location and make the payment on behalf of the owner. In today’s world, the network infrastructures are not limited to traditional datacenters or company’s premises. IoT (Internet of Things) and BYOD (Bring Your Own Device) are growing rapidly in our life. Network and Data Security Access Control plays a vital role to protect our data from being stolen intentionally or unintentionally.

Speed is incredibly important when addressing issues with computer security. The longer the time between the attack’s start and resolution, the more assets that attackers can steal from a company. There are various security platforms that can alert a company to a cyber-attack. This research project aims to combine knowledge from all these platforms together at faster speeds than a human would be able to do. The cooperation between security platforms will allow ISA Cybersecurity Inc. to detect and respond to cyber-attacks faster than previously possible.

Businesses are collecting more and more data, but they do not have the manpower to properly analyse it. This project will implement a proof of concept for a system that uses machine learning to improve the detection of cyber threats. The machine learning algorithm will receive information from many different data sources, detect where there is suspicious activity, and alert a cyber analyst. By adding a machine learning algorithm to the arsenal of cyber analysts, the analysts will be able to cut down on the time it takes to react to the threats.

Most collaborations and government departments share their threat data feed in Data Exchange. Inescapably, nowadays with increasing threat data, it is a challenge to extract a large amount of threat data and unify the format more quickly. And as more and more companies join in sharing, the redundancy of this duplicate data will increase dramatically. This project proposes machine learning algorithms for automatic format conversion to extract threat information from the traffic data, and convert them into STIX format and detect whether these structured feeds already exist in CCTX.

In today’s world, organizations protect themselves and their customer’s data through the implementation of complex cybersecurity solutions composed of many different nodes, each generating constant streams of data. Building reports from this data through the calculation of various metrics can provide much needed visibility into the state of the environment. However, building such reports can be a tedious and time-consuming process.