Biological manufacturers are now starting to make chemicals and biological products (like proteins) by growing large amounts of microorganisms like bacteria or algae. One major quality control step in the manufacturing process is to check the genetic sequences of these microorganisms because they often not correctly made. This is due to the biological method for manipulating the sequences is not perfect. To check that the genetic sequences are correct, manufacturers typically send samples for DNA sequencing at international service providers.
Due to the length and intrinsic flexibility of cable-supported bridges, wind causes serious challenges to designers of such structures. To ensure the safety of these bridges, it is common practice to test scale models of bridges in the wind tunnel. As bridges are getting longer, simplifications used for typical wind tunnel test become questionable. Therefore, this project aims at developing a new type of wind tunnel tests for bridges in order to check whether these simplifications are safe for very long bridges.
Persistent Non Player Characters (NPCs) in many modern video games follow schedules guiding their routines and behaviors over time as the player engages in play inside the game’s virtual world. In a game like Ubisoft’s recently released Watchdogs: Legion, where schedules are a player-facing game mechanic, a robust scheduling system is highly important and critical to the game’s financial and critical success.
Making good schedules for NPCs in this context, however, is surprisingly complex.
The bank swallow is a species of insectivorous songbird considered to be threatened in Canada and has been prioritized for conservation action. All swallow species in Canada are migratory, and in their annual cycle, the post-breeding to first migration period is considered to be a difficult and dangerous time for juvenile birds. We propose to conduct an ecological study of bank swallow post-breeding movements and survival in southern Ontario.
Securing autonomous vehicle environments has recently become a hot topic for both industry and academia due to the significant safety and monetary costs associated with security breaches of such environments. This requires different approaches to address the challenges and propose potential solutions at multiple levels of these environments. To that end, machine learning (ML) and blockchain (BC) techniques can play a vital role in ensuring that the safety and security standards are satisfied to protect vehicles from failures that may cause an accident and/or possible attacks.
The risks of natural hazards in Canada are increasing, and studies have shown many disaster risk reduction projects have benefits greater than their costs. However, municipal climate adaptation projects face limited resources that support implementation. This Project will research and write two books of case studies about successful municipal climate adaptation projects in Canada, extending ICLR’s “Cities Adapt” series.
The problem is that currently food waste and municipal wastewater biosolids are produced in abundance, which necessitates a proper treatment rather than disposal to landfills, and increasing CO2 emissions. In this research proposal, the main goal is to enhance both biomethane production and biodegradability of organic solids by applying subsequent Lystek thermo-alkaline hydrolysis technology followed by anaerobic digestion/co-digestion (mixing different organic wastes at different mass ratios) of organic solids such as food waste and municipal wastewater biosolids.
BOLD-100 is a promising new drug that initial studies have shown has potent activity against the SARS-CoV-2 (the cause of COVID-19) in cell culture experiments. Before being able to start clinical studies with BOLD-100, additional research into the mechanism of action is required, plus testing the safety and efficacy of BOLD-100 in animal models of COVID-19. The purpose of this project is to utilize a range of cell culture and animal models to test BOLD-100 against COVID-19 to better understand the drug.
XLScout is a startup engaged in democratizing innovation and connecting research and development with intellectual property (IP) departments across the world. The company is developing proprietary algorithms, using Artificial Intelligence and Machine learning, to mimic the behaviour of an expert searcher.
XLScout hosts a data vault of about 130+ million patent documents which occupies approximately 8TB of storage. Searching such documents is a cumbersome process requiring extensive effort, time and strategies that a novice searcher might not be aware of.
The objective of this research is to create a data architecture and a state-of-the-art machine learning algorithms to build a robust user-profile system that (i) extracts, stores, builds, and analyses synchronously up to 1 million user profiles generating at least 50 behavioral data (alpha-numeric value of 64 bytes) per second, (ii) provides over 5 millions user-profile recognitions per day through predictive modeling and REST API call, (iii) authenticates continuously to detect suspicious activities and anomalies without using cookies, location, and hardware information, and (iv) tolerates ef