Innovative Projects Realized

Improving Construction Permitting Process using Predictive Analytics

In public sector, the decision making of construction permitting can have direct impacts on the ongoing urban development. The efficiency and predictability of the review process is critical for municipalities to provide timely and accurate results to the public. As the review process is managed digitally with process data available, using data analytics to develop predictive models can result in improvement of efficiency and predictability. Therefore, the City of Edmonton collaborates with the University of New Brunswick on a research project to utilize simulation techniques to provide predictive analytics to assist construction permit decision making. A Discrete Event Simulation (DES) will be used to represent the construction permit procedures virtually and incorporate historical and real-time data to predict the permit processing time. The developed simulation engine will be able to run what-if analysis to improve resource assignment in the permit review process, and eventually reduce the inefficiencies and bottlenecks. The develop simulation model will be integrated with the City of Edmonton’s existing operating system to provide timely decision support information for its construction permit review process.

Financial Portfolio Reconciliation using Deconstructed Deep Learning

Citco provides financial products and services to hedge funds, private equity and real estate firms, investors, institutional banks, Global 1000 companies, and high net worth individuals in the Netherlands and internationally. The previous research was focused on optimizing operations by automating trade resolution and reducing risk using machine learning. Given its success in matching individual transactions, the proposed project plans to extend the research to overall portfolio management for identifying discrepancies in the portfolio at the end of the trading day. Outlier detection algorithms will be proposed to improve the accuracy of existing mismatch detection procedures. Unsupervised and supervised machine learning will be used to further improve this capability. The research will also look at improving data visualization and user interface for efficient monitoring of trade mismatch. Further, we will explore use of text mining for automated resolution of trades based on e-mails exchanged between those involved in a trade. The endeavor will significantly improve the data analytics capabilities of Citco and will potentially result in an increased operational footprint in Atlantic Canada.

The Impacts of Roads and Farming on Raised Bog Hydrology

This project will investigate the impacts of historical land use on Langley Bog to better understand how these unique ecosystems are affected by cranberry farming and fill roads. Bogs require a high water table and high acidity to maintain conditions suitable for bog-specific vegetation to grow. To determine whether the site is moving away from bog habitat, water levels, water quality, and vegetation will be monitored for four months across twelve different locations using a series of ground wells. Given the rarity of this ecosystem, this research will ultimately provide recommendations to the partner organization on how to restore Langley Bog.

Waterloo Affordable Housing Living Lab (WAHLL)

A Novel Cognitive Remediation Intervention for Public Safety Personnel with Post-Traumatic Stress Disorder (PTSD) – Year two

Urgent calls exist to address the mounting crisis of mental illness, and in particular posttraumatic stress disorder (PTSD), among public safety personnel (PSP; e.g., firefighters, police, paramedics) in Canada. Critically, PTSD is associated with alterations in cognitive functioning (e.g., poor memory and attention) that disrupt the ability of PSP to engage in activities such as work and family life. To date, however, no evidence-based treatment intervention exists to address cognitive dysfunction among PSP with PTSD. The objective of the current proposal is to examine potential durable improvements in cognitive and functional outcomes (e.g., return to work, family interactions) in PSP via Goal Management Training (GMT). GMT is a remediation strategy with demonstrated efficacy in improving cognitive function across a host of neurological conditions and healthy aging. This includes medium to large effect sizes (r = 0.45-0.69) on memory, attention, and complex reasoning observed in a small pilot study conducted by the proposed project supervisor’s laboratory (McKinnon) among severely ill inpatients (including PSP) with PTSD. A larger scale randomized control trial will inform the development of an evidence-based treatment that can assist in reducing the substantial disability and societal loss associated with PTSD among PSP.

Promoting economic development and vitality of rural communities in Ontario – Year two

Many rural regions do not have a sufficient labour force providing the skills that rural businesses need. Many job vacancies go unfilled or are filled by less than ideal candidates. This research looks at strategies to attract and retain the workers that Ontario rural communities need to generate economic development and vitality. By engaging local actors, the researcher will analyze the various dimensions influencing attraction and retention of an appropriate labour force such as affordable and attainable housing, transportation, access to health services, education/training services. The role of migration in the context of key local industry sectors will be included in the analysis. Best practices and stories of success will be identified, collected and shared. This research contributes to achieve two of the objectives of the Rural Ontario Institute which are 1) Facilitating dialogue and collaboration on rural issues; and 2) Collecting and sharing ‘stats and stories’ for impact and effective outreach.

Stability Testing for Cannabis Products

Understanding the stability profile of a product, whether a consumer packaged good or a pharmaceutical drug product, is key to ensuring the consumer has a safe and effective product. In the pharmaceutical realm, the internationally accepted approach to stability testing is codified within the International Conference on Harmonisation (ICH) Guidance: Q1A(R2) (Stability Testing of New Drug Substances and Products) and Q1B (Stability Testing: Photostability Testing of New Drug Substances and Products). Even within the Canadian Cannabis Regulations with regards to Expiry Dating, there is the requirement to have data to demonstrate that the potency is maintained within the proscribed range of label claim and microbial and chemical contaminant limits remain with release specification requirements.
The results of this research project will enhance future stability protocols and design better formulations and/or container closures, a deeper understanding of degradation pathways and appropriate stability indicating assays would be beneficial. The body of scientific literature in this area is limited due to the regulatory restrictions that have existed with regards to cannabis and its derivatives, and this study will contribute to building that body of literature.

Bacterial proteolytic function in inflammatory bowel disease

Differences in the composition of bacteria within the gut have been found in patients with inflammatory bowel disease (IBD) and healthy people. In addition to this, bacterial functional differences may also contribute to inflammation in IBD, but it is difficult to determine whether these changes are cause or the consequence of disease. Proteases, which are enzymes that break down proteins, are produced by bacteria in the gut. Proteases can be inflammatory, and it has been shown that patients with IBD have higher activity of proteases in stool, but whether this activity comes from bacteria is not known. We will investigate whether proteases are increased before the onset of IBD, whether this bacteria contribute to this, and whether they cause inflammation in animal models of colitis. Finally, we will determine whether we can inhibit bacterial proteases using specific probiotics to reduce inflammation. To address these objectives, we will use fecal samples collected from a unique cohort of patients: 1) subjects who are at healthy, but at risk for IBD, and who go on to develop IBD later; 2) these same patients at IBD diagnosis; 3) subjects who are at risk for IBD, but have remained healthy (controls).

Developing secure platforms for sharing First Nations owned information

The development and advancement of web mapping technologies is opening the doors to new mapping platforms that are accessible, interactive and engaging. In a conservation and resource co-management setting, there is a large potential for these web mapping platforms to be used to empower local communities by supporting local monitoring, planning and management decisions. However, there remains a disconnect between these technological advances and their capacity to address community needs and promote meaningful co-management. One emerging method to develop community-driven web mapping platforms is Participatory GIS (PGIS), which focuses on context and users, rather than technology. This proposed study will investigate PGIS methods in the context of First Nations fisheries management in British Columbia. Viewing local ecological knowledge as a valuable community resource, this project will focus on the development of secure and empowering web mapping platforms in partnership with the First Nations Fisheries Legacy Fund and the First Nations Fisheries Council to address a need for improved sharing and coordination of fisheries data between communities.

Cities Adapt to Climate Change: Celebrating Local Leadership

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. Suitable municipal disaster risk reduction projects will be identified by examining the list of projects supported by the Federation of Canadian Municipalities (FCM) Municipalities for Climate Innovation Program (MCIP) grants and through ICLR’s network of contacts. Interviews with project leaders will be conducted to develop these examples into case studies. Further information will be gained through a SSHRC Knowledge Synthesis Grant workshop on knowledge sharing across municipalities. Lessons learned and best practices common across these projects will be identified from these case studies to support future municipal projects.

Development of Durable Ultra-low-Pt-loading Catalyst Layers for Polymer Electrolyte Membrane Fuel Cells

Catalyst layers (CLs) determine the performance, durability, and cost of PEM fuel cells. The proposed research will focus on developing durable ultra-low-Pt-loading CLs with high performance and durability. The novel CLs will be fabricated by sputtering Pt onto a thin layer of carbon particles, pre-deposited on the membrane. The CL structure will be investigated by SEM, TEM, and the method of standard porosimetry. Electrochemical and mass transport properties will be characterized by cyclic voltammetry, electrochemical impedance spectroscopy, and Loschmidt Cell. The CL performance and durability will be investigated both experimentally and numerically. The intended impact of the proposed research will be on the improvement of cell performance and durability with reduced cost. The benefit to StarPower is in the generation of new knowledge and technology, as well as in the improvement of their products’ competitiveness.

Enhanced Modelling of Exfiltration Events in Sun Life Cybersecurity Data

Theft or loss of sensitive data is a growing concern for companies who may suffer losses of consumer confidence, market valuation and intellectual property when large amounts of data are stolen. In this research project we will use an enhanced “screen and review” approach to combating exfiltration in a large data set of activity logs within a large corporate network. We will create realistic simulations of data theft events that can be used as a basis for machine learning, and for the design and prototyping of a system where human experts work with AI algorithms to detect and prevent data theft. We will also build visualization and browsing tools that make it easier for people to judge whether or not observed data access activity indicates malicious intent. Our goal will continue to be the development of a state-of-the-art system for monitoring and reporting possible exfiltration events as they happen.