Innovative Projects Realized

Numerical Simulations on exotic magnetic materials

Data storage devices as hard drives or solid state disks rely on the magnetic properties of special materials. As the amount of data produced is increasing every day; new, more efficient, with more storage capacity, and faster devices are needed. One plausible option is to investigate exotic magnetic materials. We will run numerical simulations to study these exotic magnetic materials considering specific parameters as size, temperature, and magnetic properties. We expect by the end of the stay to be able to determine the most favorable experimental conditions to utilize such materials.

Biomimetic functional coating for flux control

We would like to develop slippery liquid infused surfaces for molding applications. Molding technology is of great importance in industry and understanding the wetting behavior of molding liquids (polymers/metals) is of fundamental interest and non-trivial as well because of the involved phase transition from liquid to gel/solid upon cooling. Thus, both wetting and post solidification adhesion needs to be accounted for while designing a surface. Often a de-wetting mold-liquid combination is desirable as it facilitates separation of the final product from mold cavity and prevents formation of unwanted defects due to sticking of molten liquids to the surface. We intend to address this by designing slippery liquid infused coatings. This novel surface manipulation technique was conceptualized independently by the groups of Aizenberg and Quéré in 2011. They proposed infusing a textured porous substrate with a wetting liquid to create pressure-stable slippery outer layer, which exhibits excellent liquid repellency and self-cleaning properties by making undesirable liquids slide off and wipe away surface contaminants in the process. In this collaborative project, we intend to utilize our knowledge on wetting behavior in conjunction with the advanced material processing and characterization facilities at NIMS to tackle this fundamentally interesting problem of significant practical relevance.

Investigating the efficacy of GelDerm* in detection of wound infection in a rat model

Burn injuries and wounds caused by burns are big health problems and in Canada alone cost nearly $290 million. Additionally, these wounds usually persist and become infected and subsequently drastically compromise patients’ health, result in significantly longer hospitalization, delayed wound healing, higher costs and higher risk of death. Therefore, prevention and management of wound infections have priority in treatment of burn patients. In order to early diagnose microbial infections in wounds and accelerate wound healing to such injuries, 4M Biotech under leadership of Dr. Akbari has developed a smart dressing in a form of a gel patch referred to as GelDerm* and confirmed it efficacy using in vitro and ex vivo models. The objective of the proposed study is to test the efficacy of GelDerm* in an animal model to evaluate the efficacy of GelDerm* to detect wound infection by sensing the variations in wound pH. We anticipate that the application of GelDerm would enhance the early detection of wound infection and thus provide the opportunity for timely interventions to treat wound infection.

Identifying probiotics that modulate mitophagy in models of mitochondrial dysfunction

Mitochondria are critical producers of energy and are the platform for various metabolic reactions that support cellular health. Mitochondria suffer from a variety of damage as a consequence of housing these reactive pathways. In order for cells and organisms to survive this damage, dysfunctional mitochondria are removed from the cell in a process termed mitophagy. The goal of this proposal is to identify probiotics that enhance mitophagy, thereby serving as ideal promoters of health by preserving mitochondrial, cellular and organismal function.

Évaluation du logiciel de dimensionnement de Cogeco pour la simulation destendances vidéo futures du réseau Internet

Le trafic vidéo occupe une proportion croissante de la capacité du réseau Internet. Dans

cette optique d’évolution, le stage proposé effectue une étude des tendances et technologie

de vidéo sur Internet. Le partenaire, Cogeco est un câblodistributeur et fournisseur d’accès

Internet. Le coeur du projet est l’utilisation d’un logiciel de dimensionnement de Cogeco qui

permet d’effectuer des simulations de capacité et de performance de réseau. Durant le stage,

plusieurs scénarios de demande future, en particulier reliés aux services Vidéo seront ainsi

évalués. Pour cela, l’étudiant devra se familiariser avec le logiciel, l’architecture et les

services de COGECO, faire une revue approfondie des nouvelles tendances en Services

Vidéo et proposer et effectuer des scénarios d’études de simulation. L’idée du stage étant de

fournir au partenaire des données qui montreraient comment maintenir l’efficacité de son

réseau en suivant les tendances d’évolution rapide de la demande par des « what if »

réalisés à l’aide du logiciel de dimensionnement.

Developing octenyl succinic maltodextrins as replacements for per- and polyfluoroalkyl substances in molded fiber bowls

In this Mitacs proposal, the Carbohydrate Chemistry and Utilization Program at the University of Saskatchewan (Usask) aims to collaborate with a leading producer of compostable molded fiber bowls ? SustainaPulp Canada Inc. ? to develop replacements for per- and polyfluoroalkyl substances (PFASs) in their products. The alternatives will be prepared from maltodextrins, a common food ingredient that is generated from starch. The maltodextrin-based replacements will be produced at the Usask, and their performance in molded fiber bowls will be assessed by the partner organization. It is the goal of the research team to develop safe, bio-degradable and sustainable materials that can be used in molded fiber bowls for serving hot, wet, greasy and healthy foods to consumers.

Cinema VR: between borders

The aim of this research is to understand the recent encounter of cinema with a specific type of technology called Virtual Reality (VR) and which gives rise to new arrangements: cinema remakes itself or becomes another and the virtual environment lends to cinema its orthogonal ability to elevate the senses. Therefore, if this recent mode of storytelling is being called cinema, does it interest us to know if the works produced for VR use the classic language of cinema in favor of the virtual environment or work in the formation of a new language? Evidently in Cinema VR the picture decompresses and presents itself in full 360-degree; the editing takes place in layers that overlap and not between one frame and another; and the purpose of the narrative is to be shared in a three-dimensional space where the viewer is co-creator of a story that now requires to be experienced from the inside. From these interactions, new instances arise, altering the processes of work, creation and autonomy. The creator needs so many other areas, from engineering to computer graphics, to compose their work.

Meta-information Extraction from Large-scale Streaming News for Entity-level Media Intelligence and Reporting – Year two

Gnowit is an Ottawa-based information services company that employs artificial intelligence and machine learning to automate the process of monitoring web sources at scale to provide real-time briefings and notifications for the purposes of competitive intelligence, evidence-based policy research and media monitoring. The company currently monitors more than 40 thousand web sources and generates atleast 1.2 million fully analysed documents daily. Gnowit’s customers currently only employ traditional Boolean full-text queries and simple meta-information-based filters to extract documents that are of interest to them. The current technology allows an undesirable quantity of noise and requires substantial improvement. Our main goal is to create (i) new set of customer-facing filters based on the geographic location of news publications, genres, central topics and themes (ii) extract meta-information that can be applied to web-sources, individual articles and segments of documents and (iii) develop entity-level analytics pipelines. Applying these tags to individual sources and documents is beyond the capacity of human effort, and so could benefit from techniques from the field of natural language processing and deep learning. Additionally, we will contribute to the field of machine learning research by developing innovative methods for tackling interpretability challenges associated with deep learning models.

Meta-information Extraction from Large-scale Streaming News for Entity-level Media Intelligence and Reporting

Gnowit is an Ottawa-based information services company that employs artificial intelligence and machine learning to automate the process of monitoring web sources at scale to provide real-time briefings and notifications for the purposes of competitive intelligence, evidence-based policy research and media monitoring. The company currently monitors more than 40 thousand web sources and generates atleast 1.2 million fully analysed documents daily. Gnowit’s customers currently only employ traditional Boolean full-text queries and simple meta-information-based filters to extract documents that are of interest to them. The current technology allows an undesirable quantity of noise and requires substantial improvement. Our main goal is to create (i) new set of customer-facing filters based on the geographic location of news publications, genres, central topics and themes (ii) extract meta-information that can be applied to web-sources, individual articles and segments of documents and (iii) develop entity-level analytics pipelines. Applying these tags to individual sources and documents is beyond the capacity of human effort, and so could benefit from techniques from the field of natural language processing and deep learning. Additionally, we will contribute to the field of machine learning research by developing innovative methods for tackling interpretability challenges associated with deep learning models.

A New Way Forward: Using Biocultural Approaches to Conservation in Key Biodiversity

Canada is a signatory to global conservation agreements to increase the number and coverage of protected areas in the country. Key Biodiversity Areas (KBAs) are a science-based planning tool that can help governments, industry, environmental groups and Indigenous Nations target the right places to protect in terms of habitat for wildlife and to ensure these areas are connected on the landscape. The proposed research will expand on the concept of KBA’s by integrating Indigenous bio-cultural information, such as traditional knowledge on cultural keystones or culturally-significant species like caribou, salmon and wild berries, to help identify and prioritize candidate protected areas with local Indigenous Nations. The intern will work with WCS Canada and Indigenous partner communities to develop a framework for incorporating biocultural information into KBA planning to identify potential areas that should be protected for their ecological and cultural significance.

Optimized Design of Building-Integrated Photovoltaic Sunshades

The proposed project involves the creation of a computational framework to optimize the design of building integrated photovoltaic sunshades. Such shades would allow improved lighting conditions within the indoor environment, while generating power through the photovoltaic panels. The design framework takes in design constraints such as the allowable size of the shade, the location and orientation of the building, and properties of the photovoltaics of interest, and provides the user with a design maximizing power generation, while providing improved daylighting conditions within the building, both spatially and temporally, and reducing cooling loads. Following the model development, a case study on a specific implementation of such a solar shade will be prototyped. The research will be a collaboration between an academic group, a PV supplier, and an architectural firm, including structural and electrical engineers, to implement the design soluti

Summer-season streamflow prediction model for the Oldman River Basin

Reliable monthly and seasonal streamflow predictions are essential for optimal planning of water resources, particularly for reservoir operation and planning applications. Streamflow predictions can also improve water use efficiency and provide early drought and flood warning. The importance of streamflow forecasting is rising with climate change, causing more frequent and hazardous flood and drought events. Current streamflow forecasts in the Oldman River Basin are uncertain, which poses a risk to irrigators, who rely on them to plan for the next irrigation season.
Our project aims to develop machine learning models for reliable summer-season and monthly streamflow predictions in the Oldman River Basin of Alberta. We will also study the risks associated with issuing predictions earlier in the year, up to three months ahead of the irrigation season. Reliable summer-season streamflow predictions in Alberta can help water managers and stakeholders make better-informed decisions on seasonal water allocation, flood and drought mitigation strategies, and environmental flow management.