Medication used everyday to help cure various diseases, or even alleviate pain, ends up in the sewage system due to the less than perfect consumption of these medicines in the body and excretion of the unused portion. These compounds eventually find their way to the environment since the current treatment methods in wastewater treatment plants are not capable of complete elimination of pharmaceutical compounds. In the environment, pharmaceutical contaminants can harm aquatic species and potentially have negative effects on humans if they end up in drinking water sources.

Wedge Networks is a leading cybersecurity solution provider in Canada. In this project, we aim to investigate the application of statistical machine learning and deep learning to cyber threat detection, aiming to detect both network intrusions and malware binaries transmitted in the network.

Harmful bacteria in drinking water can be a great threat to humans, causing diseases and possibly death. This project is aimed at determining the safety of drinking water for consumers, especially in communities where access to sophisticated laboratory facilities is limited. This research project will help to further develop a portable bacteria sensor for water, capable of determining the presence of harmful bacteria in water. The technology will offer faster analysis than the typical 1-2 day water analysis for bacteria.

The iodination of water has been identified as a means to improve animal performance, particularly in the poultry industry. Iodine has been used as an antimicrobial agent under several applications, however, it is unclear how water iodination results in improved animal performance. We hypothesize that iodinated water can improve performance either by reducing pathogen load, or by altering the intestinal microbial community. BioLargo Water, Inc., specializes in leveraging iodine chemistry for applications in water treatment.

Representations are fundamental to Artificial Intelligence. Typically, the performance of a learning system depends on its data representation. These data representations are usually hand-engineered based on some prior domain knowledge regarding the task. More recently, the trend is to learn these representations through deep neural networks as these can produce significant performance improvements over hand-engineered data representations. Learning representations reduces the human labour involved in any system design, and this allows in scaling of a learning system for difficult problems.

Schizophrenia is a chronic mental disorder associated with a significant health, social and financial burden, not only for patients but also for their families, and society. However, the current treatment methods have been only partially successful, mainly due to the inter-individual differences between patients, which means that a treatment that is successful for one patient, might not work for another.

The University of Alberta proposes to hire an industrial postdoctoral fellow funded through the Mitacs Accelerate program to develop enhanced constraint equation solution methods and 3D graphical authoring tools in partnership with a local company in Edmonton, Alberta. The field of application is educational web software for creating randomized scaled mathematical drawings, delivered in an interactive browser environment.

The project examines the use of recycled plastics sourced from waste landfills for construction and infrastructure
applications such as fence panels, an idea developed by EcoFence. Through the course of this project a
comprehensive study is conducted to evaluate the mechanical and physical characteristics, such as strength
and sound absorption. The measured data is compared to those of conventional fencing materials such as vinyl,
composite wood and concrete.

International Financial Reporting Standards (IFRS) for loss allowances are changing, and financial institutions are proactively adapting existing methodologies and developing new ones to remain compliant. The main ingredient in the myriad of evaluations that banks are required to perform for compliance is risk assessment. The first goal of this research project is to review best practice risk models, with a special focus on modeling the evolution of default probabilities and potential losses given a default.

Data-Over-Cable Service Interface Specifications (DOCSIS) 3.1 is a technology that utilizes orthogonal frequency-division multiplexing (OFDM) technology to provide downstream (DS) transmission capacities of up to 10 gigabits per seconds (Gbps), as well as orthogonal frequency-division multiple access (OFDMA) technology to provide upstream (US) transmission capacities of up to 1 Gbps per channel. Full duplex (FDX) communication, via the concurrent transmission of the US and DS on the same frequencies, has been proposed for boosting the US capacity.