Investigating the Effect of Cognitive Training Simultaneously with Application of either Active or Sham Transcranial Alternating Current Stimulation on the Executive Brain Functions in Dementia Population

Built upon our successful pilot projects, the goal of this project is to investigate the effect of transcranial alternative current stimulation (tACS) paired with cognitive exercises in a placebo-controlled study on individuals with dementia, and develop novel technologies to monitor its effects and also predict a patient’s response to a treatment at baseline. This project can lead to an efficient optimized personalized treatment strategy for dementia.

Understanding the fate of tin in cyanide-free bronze electrodeposition baths

The Loony is a symbol, recognized by all Canadians. While it looks golden, the Royal Canadian Mint is producing its yellow color with an alloy coating on the coin surface. Yellow bronze is a hard and esthetically pleasing material that can be used as a coating for such coins. Internationally, yellow bronze coatings are predominantly produced from cyanide-based electroplating baths. This comes with significant risks to workers and the environment, regulatory restrictions and costs for waste treatment.

Nothing about us, without us: Indigenous data sovereignty

Dr. Moneca Sinclaire is a member of the Opaskwayak Cree Nation bordering the Saskatchewan River in Northern Manitoba. Having recently completed a postdoctorate under Professor Stephane McLachlan in the department of Environment and Geography at the University of Manitoba in Winnipeg, Dr. Sinclaire has been an integral member of the team responsible for Our Data Indigenous, a one-of-a-kind mobile app that collects important survey data that Indigenous communities can use to address health and wellness concerns. 

Community-based planning for a Just Inner-City Winnipeg COVID Recovery

Winnipeg’s inner city is home to low-income Indigenous, Black and people of colour communities that have long struggled with homelessness, poverty and the ongoing impacts of colonialism (CCPA-MB and CCEDNet, 2015; Silver, 2016). These challenges are now compounded by high COVID-19 rates and COVID-19-related barriers to accessing basic needs (CCPA-MB, 2020). Community-based organizations in the inner city have identified the inclusion of the needs and priorities of inner-city residents and communities as central to the social and economic recovery from the pandemic.

A Predictive Cluster-based Machine Learning Pricing Model

Dynamic pricing models create price by assessing total cost, demand, and timing to customize the price to the moment. The models enable both buyers and sellers to settle a price that is very custom to their specific needs. Bison Transport Inc. has a network model that monitors profit and a pricing engine that monitors margin. The network model needs to evolve in critical ways to facilitate dynamic pricing. The current model allows viewing of the network from a variety of vantage points- region, customer, driver, asset, service type and time (day of week, time of day, season of year).

Designing a Great Slave Lake Fishery by Northerners for Resilient Futures in the NWT

The Great Slave Lake Fishery can enhance food security and food sovereignty for northern residents of the Northwest Territories as indicated in many Government of Northwest Territories strategic plans. The Arctic Research Foundation will work with a Post Doctoral Fellow based at the Natural Resources Institute, University of Manitoba, to engage northern residents and governments of Northwest Territories in the project.

Vison-based frameworks for automated robotic machining of aerospace composite panels

Canada is a global leader in the aerospace manufacturing industry. Canadian companies produce complex assemblies and system solutions including carbon fiber composite panels used in the body of airplanes. Manufacturing of such panels requires a significant number of operations such as trimming, drilling, and abrasion. Currently, some of these operations are performed manually, which is labor-intensive and time-consuming. The overarching goal of this project is to develop automated and accurate robotic systems for aerospace composite manufacturing applications.

Ensuring Stability and Accuracy of Multi-rate Electromagnetic Transient Simulation

Real-time digital power system simulators are used for testing and debugging control equipment intended for field installation. They simulate the power network in ‘real-time’, i.e., the simulation computations are rapidly completed so as to retain synchronism with a real-world clock. This requires the level of complexity in different components of the network to be judiciously selected so that the computation speed-up does not significantly compromise accuracy. Multi-rate simulation is a widely used approach to achieve this.

Developing an unbiased robust algorithm for objective diagnostic classification of most common types of dementia

Alzheimer’s disease (AD) and Alzheimer’s with cerebrovascular disease (AD-CVD) are the two most common types of dementia in elderly population. Differential diagnosis of dementia type in early stage is challenging due to overlapping symptoms and mixed etiologies. Current diagnostic techniques are invasive, expensive, or lack independent validation. An early detection of dementia type helps enabling better personalized treatments. Electrovestibulography (EVestG) showed promising preliminary results in early detection of dementia types.

Deep learning models for compound design

Traditional drug development strategy is costly, tiresome, and labor-intensive. In the last decade, artificial intelligence (AI) technologies have shown promising results to overcome some of these limitations. However, these computational technologies still cannot efficiently generate novel drugs with expected properties for treating specific diseases. Here we will apply new generation of AI frameworks to design novel compounds with predefined properties. We will collect and analyze the publicly available data from peer-reviewed publications and in-house data to build the AI models.

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