Numerical Modelling of Laser Cladding Technology using Pastes and Tapes

Laser cladding is an additive manufacturing technology for applying high-quality metal coatings to parts in order to improve their mechanical wear properties, and thus increase their lifespan. Currently, these metal coatings are created by depositing metal powders on the work piece and welding them together with the laser. A significant amount of powder is lost in this process, which is a large factor in the cost of this type of cladding.

Laboratory and Field Assessment of Performance of Treated Wildland Vegetative Fuels

The proposed project will assess and quantify the energy transfer from wildland fires as it relates to coverage of vegetative fuel with wildland fire chemicals for protection of wildland/urban interfaces. The project will extend on preliminary work on the relative performance of wildfire chemicals (e.g., water, gel, foam, and long-term retardants) on forest vegetation. The results of this proposed project will further develop proactive fire control measures, a priori to the occurrence of a fire, for community protection.

The Challenges of Curating Latin American Cinema in the 21st Century

Curatorial studies is a well-established field of research in the visual arts. However, curating and programming are probably among the most understudied areas in film and media scholarship. While I participate in defining the official selection of Latin American movies for the 2017 edition of the Toronto International Film Festival, I will gather tools to attempt answering the following questions: Which films are prioritized for exhibition and why? How can a film festival contribute to the development of ethnic inclusions?

Elimination of Emerging Contaminants in Wastewater by Electrical Plasma-Membrane Technologies Hybridization Method

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.

Applied Machine Learning for Malware and Network Intrusion Detection

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.

Portable Sensor for rapid, onsite detection of bacteria in water

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.

Microbial modifying properties of iodinated water in animal production

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.

Learning representations through stochastic gradient descent by minimizing the cross-validation error

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.

Learning tools to predict treatment responses for schizophrenia from neuroimaging data

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.

Algorithms for Nonlinear Geometric Constraints in Vector Graphics

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.