The ever-growing demand for energy storage, especially with high density and low-cost, has both academia and industry research communities working hard to develop and optimize energy storage technologies. Among the top energy storage technologies are Lithium metal batteries (LMBs) which have an exceptionally high specific capacity (3860 mA h g?1) in comparison to that of the conventional graphite-based LiC6 batteries (372 mA h g?1).
The multi-billion dollar fashion industry has a problem: many of the high-performance fabrics used in clothing are neither sustainably produced or environmentally friendly. However, there is a considerable market gap in sustainable textiles. Canadian corporations like Proteins Easy Corp are tackling the sustainability problem head on and actively seeking environmentally friendly solutions. This proactive approach has led them to the discovery of Textile Replacement iMolecules (TRiMs), a new class of computationally designed proteins that could rejuvenate the Canadian textile market.
The proposed research will develop novel distributed machine learning techniques for stable resource allocation and improving traffic estimation in networks. It is a well-known fact that networks are becoming complex and user demand is growing in many directions including the traditional demand for capacity and less delay, as well as improvements in Quality of Experience (QoE). Backhauling the multiplexed demand over the core networks calls for accurate traffic estimation. On the other hand, control of the resource allocation, based on such predictions, needs stable and robust solutions.
Coffee is one of the world’s most popular beverages due to the delivery of caffeine and diverse flavours. The genetics of how caffeine is processed in the human body is fairly well understood. With the decrease in cost and increase in popularity of consumer genetics, we are interested in learning how modern genetic analysis techniques can benefit the choice of caffeine and flavour for coffee consumers.
Recreational river waves are gaining more and more popularity, but there is not enough academic research to support them and a few companies around the globe can artificially create them by adjustable structures in rivers. Surf Anywhere, the Calgary-based partner organization in this research, is one of those few companies which has completed and is working on many wave projects in Canada, USA and Europe.
PolyCSAM is a new industrial-scale cold spray additive manufacturing (CSAM) facility created by Polycontrols to address production and industrialization challenges. It serves as a demonstrator and an innovation/development platform. A recent strengths, weaknesses, opportunities, and threats (SWOT) analysis revealed that the supply chain is solid and diversified for all key components, with the exception of the cold spray (CS) gun itself.
As the COVID-19 pandemic has made painfully clear, it is both important and difficult to analyze the large volumes of patient data collected by hospitals and other healthcare providers. Ideally, data would be widely-shared between institutions, and experts and teams with diverse backgrounds would be able to contribute to the analysis. Unfortunately, this is not possible: sharing of healthcare data would severely compromise patient privacy, with many negative consequences.
It is essential to develop a vaccine against SARS-CoV-2, the virus causing the global COVID-19 pandemic. The most efficient vaccines are built on attenuated live viruses, which can be engineered to display specific antigens and, once administered in humans, can safely induce an immune response and immunity to the disease of interest. Fast, reliable, and safe platforms are needed to develop a COVID-19 vaccine and move promising candidates to clinical trials. To support global vaccination campaigns, the vaccine should be easily produced, stored, and administered.
The proposed project aims to apply artificial intelligence methods to augment in-place non-destructive testing technologies in order to reduce or eliminate the need for intrusive methods (i.e. concrete core extraction) for concrete strength estimation. The proposed approach is based on the SonReb method, which combines two non-destructive testing technologies, namely ultrasonic pulse velocity and rebound hammer, for assessing subsurface and near-surface concrete properties.
Emerging public health risks are typically characterized by uncertainty in the evidence-base (i.e., a mosaic of clinical, laboratory, and/or epidemiological evidence, that is often unclear and not uncommonly conflicting). Given this uncertainty, it is often difficult for decision-makers to discern if action is warranted. The question of how compelling the evidence base ought to be in order to warrant action points to our topic, namely the question of a standard of proof (i.e., related to policy creation/ governance and the assessment of evidence).