Development of a High Efficiency LC-FAIMS-MS/MS Solution for Enhanced Lipidomic Analysis

Accurate and sensitive analysis of lipids is a challenge for the medical and nutritional fields. In this proposal, we describe a new method for their analysis using a technique seldom applied to these problems. We propose to make a number of modifications to existing instrumentation to facilitate this; in partnership with Dalhousie and the Atlantic Cancer Research Institute, an MASc student will lead the evaluation of several prototypes to optimize the analysis of complex lipid mixtures.

Real-Time VLT Player Data Personae Classification

The goal of this project is to train a machine learning model that can identify player’s personae using VLT (Video Lottery Terminal) data within a transaction\time limit. The personae are results of the previews MITACS project. Using unsupervised learning each playing session was associated with a playstyle. Identifying the playstyle as soon as possible is of great importance since it can be used later for problem gambling detection in early stages of playing. Another challenge this project tries to solve is to estimate an optimal limit for transaction\time.

Employing Data Mining and Visualization Strategies for the Analysis of Well-being Indicators

In this project, an online, interactive map visualization tool will be built to illustrate trends of community well-being indicators across Nova Scotia using the 2019 Quality of Life survey as the primary source of information. The goal is to empower residents and decision-makers to understand unique well-being patterns and socio-economic trends in communities, providing an invaluable resource for planners, researchers, and policymakers, and even allow Nova Scotians to make informed decisions on where to live.

Natural Health Products to Manage Cancers of Dogs

More than half of Canadian households have companion animals such as dog or cat. However, cancer has become the leading cause of death in dogs. Currently, available treatments have limitations and compromise the quality of life of dogs. Adored Beast Apothecary wishes to develop unique natural health products to prevent and treat cancers of dogs. The objective of the proposed research project is to develop optimized processes to generate safe and efficacious anti-cancer natural products using a sustainably grown phytoplankton strain.

Estimation of Disease Severity in Rice with Deep Learning Neural Networks

NIRS is a popular secondary analytical method that is being used for non-destructive quantification of compounds and mixtures in the agriculture and agri-food sector. The study aims to estimate the starch content (amylose and amylopectin) in rice samples with NIRS. A dataset is being established by obtaining NIRS spectra (400 to 2500 nm, 0.5 nm resolution) on over 400 milled and ground rice samples. Iodine-binding and spectrophotometric techniques will be used for acquiring the ground-truth.

A Sustainability Evaluation of Post-harvest Fisheries Opportunities for First Nations in Nova Scotia

Many First Nation communities are now exploring and developing post-harvest livelihood activities related to the purchase, transformation, and sale of catch from band harvesters. This research will assess post-harvest businesses currently operating within NS Mi’kmaq communities, and new livelihood opportunities currently being considered by Mi’kmaq Band Councils and entrepreneurs. This assessment will focus on the overall sustainability of the fishery operations, including social, economic, environmental, and cultural factors, among others.

Optimization and validation of collagen fiber manufacturing

Natural biomaterials such as cotton, wool and bamboo are common biofibres due to their accessibility and high performance characteristics in textiles. However, their strictly defined composition is severely limiting, as the materials cannot be adapted for alternative uses. Thus, producing biomaterials at an industrial scale has been an active area of research, where the goal is to create materials found in nature at a high throughput.

Using/An application (with) Artificial Intelligence in Telemedicine and Telehealth

There is a fact that over the last decades life expectation is increased, and human nation is experiencing a longer life span. The idea is to utilize technology in a way that people (senior or patients) can stay at their own home with less care facilities and less life quality degradation with less third-party assistance. In this research, I will be conducting a series of researches in which I can model seniors'/patients' behavior while they are home so we can have a better insight from their behavior, their patterns/trends of their habits.

Integrating multiple deep learning models to track and classify at-risk fish species near commercial infrastructure

Companies must not harm species at risk around their fixed infrastructure and need a way to detect and monitor at risk fish. However, a species at risk cannot be tagged and studied using conventional surgically implanted fish tracking technology. Innovasea is therefore developing a platform to monitor fish using a combination of sensors such as acoustic devices, visual and active sonar and optical cameras. This effort requires a robust accurate method to detect fish and classify them by species.

Aerial drone to perform adaptive in-water sampling in marine environments

In-water measurement and sample collection solutions for environmental marine monitoring will be studied. Fine-scale responsive measurements cannot be achieved cost-effectively with satellites or aircraft. For near-surface monitoring, an unmanned aerial system (UAS) could achieve the necessary spatial-temporal sampling.
The proposed solutions deploy a payload sensor and/or sample grabber from an UAS with a winchable tether. However, the winch and tether can impact the UAS dynamics.