Innovative Projects Realized

EPCOR / NAIT for a Summer COOP Student

This proposal is for the development of an exercise for catastrophic event that could significantly impact EPCOR’s operations and its ability to provide electricity, water or wastewater treatment services to over three million customers in Alberta. The findings from this project will significantly enhance EPCOR’s ability to plan for, respond to, continue operations and recover from such a large-scale event.

Prédiction de la demande chez les commerces de détail

Lila Solution offre une application de planification des achats (prévisions des ventes et gestion d’inventaire) afin de répondre aux défis majeurs auxquels font face les détaillants au quotidien : le manque d’outils efficaces pour la planification des achats et la gestion d’inventaire.

C’est dans ce contexte que notre solution a vu le jour. Notre plateforme révolutionnaire transforme la manière dont les détaillants abordent ces défis, remplaçant l’incertitude par la clarté. Grâce à nos outils, les détaillants prennent des décisions éclairées et maximisent leurs profits.

Lila Solution désire aller plus loin en développant un modèle de prédiction de la demande pour aider les commerçants à prédire ce qu’ils seront en mesure de vendre dans le futur en fonction de différents paramètres : nombre de produits, type de produits, inventaire en main au moment de la vente, tendance du marché, etc.

Development of Strategic Market Assessment Tools for Materials R&D

Chemia Discovery is a company that specializes in the research and development of new materials that can enhance the performance and sustainability of various technologies and enable emergent technologies. Chief among the materials explored at Chemia are those with applications for waste heat recovery and carbon capture. Chemia evaluates potential material R&D projects based on a comprehensive assessment of market potential, scientific viability, and technical considerations. Given the vast amount of information required for accurate decision-making, the R&D process for even a single material can be both time-intensive and resource-heavy. This project aims to enable Chemia to make assessments of the market potential for materials R&D, guiding its material R&D roadmap by mapping market data to technological and scientific data on specific classes of materials in target sectors. The main outcome of the project is the development of an easy-to-use and versatile tool designed to streamline the analysis of diverse market data, thereby facilitating strategic materials R&D planning.

Design a post-quantum cryptographic algorithm based on lattice for mitigating quantum computing threats.

Cryptographic algorithms are fundamental tools for securing digital information, with symmetric and asymmetric algorithms serving as the cornerstone of modern encryption techniques. Symmetric algorithms utilize a single key for both encryption and decryption, while asymmetric algorithms employ a pair of keys for these operations. Classical cryptographic algorithms, including RSA and AES, have been extensively utilized to ensure data security across various digital platforms.

However, the emergence of quantum computing poses a significant threat to the security provided by classical cryptographic algorithms. Quantum computers have the potential to exploit vulnerabilities in these algorithms using algorithms like Shor’s algorithm, which can efficiently factorize large prime number and solve discrete logarithmic problems. The advancement of quantum will imposes a threat to traditional cryptographic methods, prompting the need for exploring new solutions to maintain rigid security against evolving threats.

To address this vulnerability, we propose lattice-based cryptography as an advanced solution for post-quantum cryptography (PQC). Lattice-based cryptography offers resistance against quantum attacks due to its complex mathematical properties and hardness assumptions. The moto of our research is to successfully design and implement lattice-based cryptographic algorithm, providing a robust defense mechanism against potential quantum computing threats and contribute to the ongoing evolution of cybersecurity measures.

Exploring LLMs as a Foundation for Next-Generation Clinical Decision Support Systems  

The proposed project aims to develop an AI-powered Patient Assessment and Diagnostic (PAD) tool aimed at aiding in the diagnosis and management of chronic women’s health conditions. Leveraging Large Multi-Modal Models (LMMs), the project seeks to streamline the diagnostic process for hormonal health conditions, addressing the significant gap in timely healthcare access faced by millions globally. There are three main objectives including the design and development of an AI model with and without fine-tuning on a developed and curated women’s health dataset, validation using newly collected anonymized patient data, and optimization of output reports for clinicians, adhering to compliance standards.

Robust Portfolio Optimization

Financial investment is a crucial activity for both institutions and individuals. In particular, the construction of a portfolio of financial assets is at the core of financial investment decision making. Effective portfolio management enables many benefits such as better pensions and funds for education. The essence of portfolio construction is to balance expected returns (i.e. financial benefit) and risk (i.e. the possibility of financial loss). Quantitative methods play a crucial role in the generation of portfolios that optimizes the trade-off between risk and reward. However, most methods need estimates of important parameters such as future expected returns and other statistical quantities that go into the calculation of risk. A major limitation is that most models are not able to effectively incorporate that fact that these estimates change over time (e.g. due to unexpected turbulence in the markets) thus making previous estimates obsolete or misleading resulting in overly risky or underperforming portfolios. The research in this proposal aims to effectively mitigate parameter uncertainty by considering robust and dynamic models for portfolio optimization. As our partner organization Manulife Financial routinely constructs financial portfolios the research from this internship would be greatly beneficial for Manulife Financial.

Santé mentale et qualité des services prodigués par la brigade des feux : mieux comprendre les leviers potentiels d’intervention au travail

Ce projet de recherche en collaboration avec le service d’incendie de Moncton vise à mieux comprendre les difficultés vécues par les pompier(ière)s et les leviers potentiels d’intervention au travail face à ces problématiques. Ainsi, le présent projet vise à examiner le lien entre des exigences du travail (charge de travail et demandes émotionnelles), les ressources organisationnelles, la santé mentale et la qualité des services prodigués dans le cadre du travail de ces travailleur(euse)s. Environ 120 pompiers (ières) participent à cette étude où des entretiens et l’utilisation de questionnaires validés permettront de recueillir des informations sur les facteurs organisationnels (p.ex., charge de travail, perception de soutien, demandes émotionnelles, etc.) et individuels (p.ex., stress, symptômes d’épuisement professionnel, dépression, etc.). L’analyse de ces données devrait contribuer à mieux comprendre l’état de la situation, identifier les besoins des pompiers et discerner des pistes de solutions pour les soutenir. À la lumière de ces informations, une intervention ciblée pour le milieu partenaire sera développée afin de favoriser une meilleure santé mentale et des services de qualité offerts à la population.

Cloud Orchestration for Machine Learning

THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW

Développement d’hybrides PMMA-silice aux propriétés anticorrosives, bioactives et bactéricides pour les implants orthopédiques en alliages de titane.

Le titane et ses alliages métalliques sont largement employés dans les dispositifs médicaux pour la réparation ou le remplacement des os. Une intégration osseuse rapide est essentielle pour garantir la durabilité et l’efficacité des implants et éviter les infections bactériennes. Cependant, la formation de biofilms bactériens à la surface des implants en titane constitue un défi important et peut entraîner une inflammation et une défaillance prématurée du dispositif. Au Canada, un implant sur dix est infecté lors de l’implantation, ce qui oblige un traitement antibiotique post-opératoire.
Pour résoudre ce problème, nous étudions la modification des surfaces des implants au moyen de revêtements polymères antibactériens. Ces revêtements sont formulés avec des additifs conçus pour réduire l’infection, améliorer la compatibilité avec l’os et augmenter la résistance à la corrosion des implants. Parmi les additifs envisagés dans ce projet figurent les phosphates de calcium et d’argent, qui ont fait l’objet d’études scientifiques en raison de leurs propriétés bioactives et bactéricides, respectivement.
Outre les propriétés structurelles, la barrière contre la corrosion dans les fluides corporels simulés, la biocompatibilité et la bioactivité des revêtements précédemment développés, la candidate au doctorat impliquée dans cette recherche compte étudier et évaluer les propriétés antibactériennes des nouveaux recouvrements proposés.

Analysis of Data from Polymeric Pressure Sensor for Cardiovascular Risk Assessment

Cardiovascular disease (CVD) is a leading cause of death and disability, with 1 in 3 of us at risk of the disease.
Screening for relevant biomarkers regularly can help prevent and manage CVD. This project focuses on the
acquisition and analysis of CVD biomarkers obtained using a device consisting of polymeric pressure sensors and
advanced machine learning algorithms. The biomarkers will be obtained by extracting features from dynamic
pressure changes captured by the polymeric pressure sensor. Deep learning and neuromorphic models will be
developed to automatically classify pressure changes data based on their Signal Quality Index (SQI) scores and
to determine relevant CVD parameters in a computationally efficient manner on edge devices.

Exploring the decoration of nanostructured nickel sub-oxides with single atoms for energy conversion electrocatalysis

The proposed project aims to advance energy conversion processes by developing an innovative method to create single atoms (SAs) on a nanostructured nickel oxide support layer, facilitating electrocatalytic oxygen generation. With a focus on enhancing hydrogen and oxygen evolution reactions, crucial for renewable energy generation, fuel production, and energy storage, the project addresses pressing global energy demands. By devising efficient and cost-effective electrocatalysts, it holds promise to significantly improve performance and scalability, thereby facilitating the transition to cleaner and more sustainable energy systems. Through data sharing and publication of research findings stemming from this endeavor, the University of Alberta and the University of Siegen are poised to solidify their positions as prominent contributors to the renewable energy sector, actively shaping its trajectory. This collaborative effort not only bolsters their academic prestige but also establishes them as crucial entities in driving innovation within the industry. Additionally, by mitigating challenges associated with traditional catalyst materials, such as limited availability and high cost, the project aims to overcome barriers to the widespread adoption of clean energy technologies, ultimately contributing to a more sustainable future.

Data Analysis and Development of Cost Prediction Method for Renal Failure Care Delivery

Care of patients with renal disease is important and resource intense. Since we have an aging population with an increase in diabetes prevalence; the number of patients with renal disease is increasing and there is a need to develop strategies for more cost efficient care. The solution to this issue is home dialysis which is an alternative to the conventional hospital-treatment. Home dialysis is able to reduce the cost of the care delivery and also enhance the quality of health care for the patient. This process is monitored and facilitated by eQOL using a novel mobile technology solution. The goal of this project is to collect the data, gathered from home dialysis patients either electronically or by Health Care Team, and analyze it to quantify and predict the cost care delivery across different patient groups and time periods. This prediction will be able to forecast the total cost of care in a time period for a given patient with a set of input data. This information is valuable to health care administrators to accurately quantify the cost of care delivery and plan the budget accordingly.