Innovative Projects Realized

Optimizing the delivery of ministring DNA (msDNA) to the central nervous system for use in gene therapy applications

Gene therapy has risen as one of the more promising treatment options for disorders of the central nervous system (CNS). Using modified viruses to deliver genetic material has shown success however has several disadvantages including increased safety risks. Using a non-viral vector system has the potential to overcome many of the challenges that arise with using viral vectors. Mediphage Bioceuticals proprietary DNA construct, ministring DNA (msDNA) is a non-viral system that has the potential to be a safer and effective method of DNA delivery. This project will focus on optimizing the delivery of msDNA vectors to the brains of mouse models and will provide the necessary foundation to move forward with developing msDNA vectors for targeted therapies to treat CNS disorders in pre-clinical and clinical settings.

Quantum Deep Neural Nets for Spatial-Temporal Problems

Atmospheric behaviour will change substantially with Climate Change and is one of our generation’s most pressing challenges. The world requires accurate estimates of future the impacts from droughts, floods, heat waves, increase hazards, from freezing rain to hurricane to plan, prepare and mitigate. Combatting climate change requires reducing our emissions in a strategic way. Current computers can only approximate these coming changes. Employing quantum computing and machine learning algorithms, we’re developing open-sourced spatiotemporal models and tools that will improve forecasting accuracy of coming events from days to centuries. The developed models are expected to benefit Lakes Environmental in decreasing computational cost and expanding its weather forecasting market share in Canada and internationally.

Designing MVP for Atare Foods

This summer student position will help define Atare Food’s technology needs for future mobile application. Intern will work on several tasks that would ultimately define minimum viable product for the organization.

Béton bitumineux

Le projet de recherche a pour but de valoriser les sous-produits et résidus miniers de l’industrie de l’aluminium. Pour rendre possible cette valorisation, le projet consiste à évaluer des options d’utilisation des résidus de bauxite et sous-produits, comme ingrédients dans les recettes d’enrobés bitumineux (enrobés de surface, enrobés de liaison et grave-bitume). L’objectif principal est le développement de produits manufacturés et/ou matières premières alternatives qui implique le développement de procédés pour le traitement des résidus. Afin de pouvoir commercialiser ces produits dans la vie de tous les jours, les résidus de bauxite et sous-produits seront utiliser comme granulats dans les enrobés bitumineux ; cela permettra de réduire l’empreinte carbone ainsi que les coûts de production des infrastructures routières.

Standardized Ai-Based Corporate Sustainability Reporting

Corporate sustainability reporting is becoming mainstream. An increasing number of investors considers and integrates environmental, social and governance (ESG) criteria in their investment analyses. However, corporate sustainability data is still costly, not standardized, and sometimes even missing, Hence, the project will analyze how AI can be used for standardized corporate sustainability accounting and reporting. Based on Natural Language Processing (NLP) and machine learning tools will be created that help companies to report their sustainability performance in standardized forms compatible to standards, such as TCFD and SASB. The analyses of the results of these tools will help ESGai Technologies Inc. to validate the reports created by the AI-based tools.

Enhanced recommendation systems using machine learning

Artificial Intelligence (AI) and Machine Learning (ML) will play key roles in the RWA platform. A significant area to which these technologies will be applied is the recommendation algorithm connecting users to resources that are most relevant to their 3 personal wellness journey. The app features a collection of both community resources and in-app resources that have been developed by Refresh. Each of them meets different mental health needs, and not everyone will respond the same way to each resource. Therefore, Refresh is seeking a ML-based solution that will take in various user inputs–such as demographics, journal entry sentiments, and usage patterns–and output recommendations of resources that are highly suited to the user’s needs at that particular time. In this project, four interns will work together to develop and implement this model, beginning with just two features: journal entries and usage patterns. As this will represent the first ML component of the RWA platform, the implementation must be done in such a way so as to support additional ML applications in the future.

Designing and Implementing Accessible Software for the Canadian Media and Entertainment Space with the Cansbridge Fellowship

This project encompasses the research, design, and implementation of innovative improvements in a 3D animation software for a Canadian-based media and entertainment company. The main objective is to attract and better serve the business’ underrepresented customer segment of artists in the Asian market. This aligns with the Cansbridge Fellowship’s primary mission to support Fellows in creating disruptive impact and advocating for the causes they pursue. Through recommending product solutions for long-term business strategy, this project bridges the gap between the company and the needs of its Asian user base, contributing to Cansbridge’s ongoing Canada-Asia engagement initiatives. Lastly, the project will bring insights and data on Asian markets to the Cansbridge database, which will be made available to the Canadian business community and other stakeholders in the 3D industry who can leverage this information to deliver value to a more diverse range of users.

Artificial intelligence for automated identification of cannabaceae family plant diseases and gender on low-resource devices

Automated and early identification of plant diseases from their leaves is an important task in agriculture and can have positive impacts on crop yield and quality. Crop pathogens and pests reduce the yield and quality of agricultural production. They cause substantial economic losses and have a direct impact on food security and nutrition worldwide. Due to the wide variety of crops and diseases, even a farmer or pathologist can often fail to identify plant diseases by visualizing the affected leaves. However, visual observation remains the primary approach to disease identification. With the advances in artificial intelligence (AI) and mobile technologies in recent years, it is becoming possible to develop an embedded solution using machine learning that is accessible to farmers on their cell phones. In this project, we propose to develop and optimize deep learning models to detect diseases and gender for plants
in the cannabaceae family.

Improvements to Robotic Stairclimbing Assistant (ROSA)

Tasks involving lifting, pushing, and carrying (e.g., lifting laundry, cleaning the floor) are often challenging, especially for seniors and people with limited mobility. On stairs, performing these tasks becomes dangerous, potentially leading to serious injury (falls, back strain, etc.).

Quantum Robotic Systems Inc. (QRS) addresses this problem by making unique mobile service robots. In particular, QRS has developed and patented a novel stair-climbing technology that the company has incorporated into its Robotic Stairclimbing Assistant (“ROSA”).

Prior to this project, QRS developed a series of ROSA prototypes, together with a preliminary version of a Mobile Control App, allowing a user to specify rudimentary paths for the robot. However, ROSA’s capabilities have not yet reached the point of market viability.

The primary goal for this innovation project is to improve ROSA’s commercial readiness by addressing technological shortcomings These readiness gaps fall into the following categories, each with its own have specific objectives:

1. Electro-Mechanical: Improve climbing reliability, payload capacity, climbing speed, and robustness for outdoor use;
2. Robot Control: Improve control during climbing, introduce new capabilities, and make the robot compliant with the Robot Operating System (ROS) standard;

Profiling and Predictive Analytics with Smart Meters

With the aim of a cleaner, more reliable and affordable energy future for Nova Scotia, NS Power is modernizing the electricity grid. The system that has powered the needs of 500,000 households and businesses in Nova Scotia for a century is evolving, by adding more wind and solar generation, battery storage and electric vehicles to the grid. The first step that NS Power has taken towards a clean and sustainable energy future is the upgradation of smart meters – work that began in late 2019 and which will be completed later this year (2021). Smart meters are the standard in electrical meters. They are sophisticated, digital devices that record electricity use and send it to the company through a safe, secure wireless connection. Once the meter upgrades are complete, consumers in Nova Scotia benefit from increased convenience, reliability and control when it comes to managing their electricity consumption. These smart meters have now started providing a large amount of data streaming from every household in the province. While managing such a large amount of data is a challenge, the company is excited about the wealth of information that can be extracted from the dataset.

Workforce Analytics in Nova Scotia Hospitals

NS Health oversees the operations of a large number of hospitals and clinics distributed across the province. Given the data volumes that the organization processes on a regular basis, this project proposes workforce analytics for individual hospitals as well the entire collection of hospitals within the NS Health network. Payroll makes up a significant portion of the several billion-dollar budgets of NS Health hospitals. The primary objective of this project is to study the relationship between payroll and the resulting activities within a hospital as well as across all the hospitals in the province to optimize productivity. The project will use data summarization, visualization, profiling using unsupervised machine learning, and predictive analytics using supervised machine learning and statistics for anticipating demands for different personnel.

Culturally Inclusive Menu Planning in Long-term Care

Unmet food preferences result in meal dissatisfaction and is a significant risk factor for decreased intake of food and fluids in culturally diverse older adults residing in long-term care (LTC) homes. Food choices are based on traditional, religious, and personal taste that is part of an individual’s culture. Menu planning practices need to be culturally inclusive for the growing diverse older adult population within LTC homes in Canada. The intern will determine gaps in knowledge and training needs
of menu planners, determine resident and family perspectives on the challenges of attaining culturally inclusive foods, and develop and evaluate an on-line educational module for foodservice managers and dietitians on creating culturally inclusive menus. The partner organization will gain experience in developing educational material directed to cultural inclusivity in LTC and have access to a quality LTC education module that promotes meeting resident cultural food preferences and improving their quality of life.