Innovative Projects Realized

Internship Application for Natik Projects – May to Dec 2025

To assist in the day-to-day accounting tasks and contribute to our company’s growth.

Evaluating the impact of workplace indoor environmental quality on employee comfort and wellness

The design and quality of the indoor environment is understood to play an important role in the well-being and productivity of building occupants. While many building and design strategies have been employed to enhance the indoor environmental quality of workplace environments there is a surprising a lack of empirical evidence drawn from buildings in-use that shows how such strategies actually affect occupants. This research project involves a comprehensive post-occupancy evaluation, one that includes occupant surveys, observation, and physical measurements of environmental conditions, of four workplace environments. The purpose of the study will be to assess and compare the quality of these environments and to determine what features have the largest impact on employee satisfaction, wellness and self-reported productivity with a goal to provide feedback and improve existing and future workplace design and building practices. The results of this “pre-move” study will be used in a future “post-move” study that compares the IEQ performance of these “conventional” workplaces to the employee’s new “green” workplace.

Enrobé avec granulats bitumineux récupérés (GBR) : étude des différences entre la formulation en laboratoire et la production en usine

Ce projet de recherche vise à mieux comprendre pourquoi les enrobés bitumineux (asphalte) contenant des granulats bitumineux récupérés (GBR) ne se comportent pas toujours de la même façon lorsqu’ils sont fabriqués en laboratoire et en usine. Le but est d’identifier les différences entre ces deux méthodes de production et de proposer des solutions pour rendre les performances de ces matériaux plus prévisibles sur la chaussée à partir d’une formulation en laboratoire. En améliorant la fiabilité de ces enrobés contenant des GBR, Groupe ABS pourra aider l’industrie routière à réduire les coûts et l’impact environnemental des infrastructures routières. Cette recherche permettra aussi d’encourager une utilisation plus large des GBR dans la construction des routes, ce qui est bénéfique pour l’environnement et pour la gestion des ressources.

Modèles prédictifs pour la transmission d’information à énergie minimale

Les codes de correction d’erreurs (ECC) sont utilisés dans la grande majorité des systèmes de transmission et de stockage de données puisqu’ils permettent une importante réduction de la puissance du signal émis (ou stocké), et permettent donc à l’émetteur de consommer moins d’énergie. Malheureusement, les bons codes de correction d’erreurs nécessitent une quantité importante d’énergie pour être décodés à la réception, et il existe donc un compromis entre l’énergie économisée à l’émission et l’énergie additionnelle requise à la réception. Notre projet de recherche vise spécifiquement les applications où il est crucial de minimiser conjointement la consommation de l’émetteur et du récepteur, par exemple dans le cas de deux objets alimentés par batterie qui doivent communiquer entre eux. Pour ce faire, nous développons un outil d’exploration efficace pour la conception de systèmes de transmission de données à énergie minimale basé sur des modèles énergétiques des circuits émetteurs et récepteurs.

Impact of technostress on well-being and job satisfaction in Global Virtual teams

This research project aims to examine the impact of stress related to technology use on the well-being and job satisfaction of employees working in Global Virtual Teams. With the increasing use of digital tools like Microsoft Teams and Zoom, these teams must constantly adapt to technological changes, which can cause stress and affect their well-being. Using a quantitative approach, this study will analyze how these technologies influence employees’ mental health and motivation by surveying 1,500 professionals from various industries. The findings will help identify strategies to better manage technostress, promote a healthy work environment, and assist organizations in optimizing the use of digital tools while safeguarding employee well-being.

Research and Data Entry Intern

We are currently facing challenges in managing and organizing a growing amount of data. Our existing database structure is becoming increasingly inefficient, leading to difficulties in accessing, analyzing, and utilizing critical information. Additionally, we have identified a need for more thorough research and data analysis to support strategic decision-making.
Due to the amount of research that needs to be conducted, our current resources are insufficient.
Our innovation challenge lies in enhancing our data infrastructure and research capabilities. Specifically, the goal is to streamline the database management process, improve data accessibility, and provide more actionable insights through advanced data analysis. This project will address these challenges by focusing on improving data organization and conducting in-depth research using a variety of AI tools.
This project will go beyond day-to-day operations by introducing a structured, systematic approach to data management that we currently lack. It will help optimize our processes and better directly contribute to improved outcomes in our core activities for our clients.

Separation of sub-micron particles in flowing air streams: CFD modelling and design optimization

Classification and separation of solid particles immersed within an air stream plays a crucial role in aerosol separation, air filtration, fine particle recovery, and many other applications. Particle separators divide a mixture of different-sized solid particles in a flowing gas stream into fine and coarse streams. IMASCO Minerals produces ultrafine dry ground calcium carbonate (CaCO3) power for use in the agri-tech and industrial minerals sectors. IMASCO wishes to offer a new nanopowder product with a mean particle size of <1µm, which its current technology cannot achieve. Therefore, this project aims to develop an ultrafine industrial particle separator for nanometer-scale powders. The research will use computational fluid dynamics to investigate the turbulent multiphase flow of ultrafine particles to guide the design optimization of the prototype separator device.

Adaptive ML-Driven Detection of Scheduled Task Anomalies and Automated Threat Attribution

As cyber threats grow more sophisticated, attackers increasingly exploit scheduled tasks to maintain persistence and evade detection. Traditional security measures struggle to distinguish between legitimate and malicious task executions, especially when attackers modify execution parameters. Additionally, identifying and attributing threats to known adversaries remains a complex and resource-intensive process, relying heavily on human analysts and labeled data. This project, in collaboration with eSentire, aims to bridge these gaps by developing:
– An ML-driven anomaly detection system to identify suspicious scheduled task executions based on execution flow anomalies, registry modifications, and stealthy command-line manipulations.
– An automated threat actor attribution pipeline leveraging semi-supervised learning and the Diamond Model of Intrusion Analysis to enhance the accuracy, speed, and scalability of adversary identification.
By advancing cybersecurity analytics, this research will strengthen eSentire’s proactive threat detection capabilities, improve SOC efficiency, and contribute to industry-wide efforts in tackling automation-based persistence techniques and adversary attribution.

Dissecting the role of PCSK9 in sepsis

Severe sepsis strikes young and old alike with an increasing incidence of >75,000 per year in Canada at a cost of $40,000 per patient. In 40% sepsis is complicated by low blood pressure and organ failure with a mortality rate of 30-60%. The number of deaths due to severe sepsis and septic shock is greater than the number of deaths due to acute myocardial infarction. The process that links infection to organ failure and death is triggered by bacterial toxins, including lipopolysaccharide, lipoteichoic acid, and phospholipomannan. Pathogen toxins are cleared from the blood by the liver. We have recently discovered that inhibition of Proprotein Convertase Subtilisin/Kexin type-9 (PCSK9) improves survival in septic shock, possibly by altering clearance of pathogen toxins. This research will discover how best to neutralize PCSK9.

Sex differences and risk factors

It has been demonstrated that hypertension, type II diabetes, obesity, high cholesterol, and smoking are vascular risk factors that increase the chances of experiencing accelerated cognitive decline or developing dementia. Some studies have found that these vascular risk factors affect women’s cognition more severely than men’s. However, the reasons behind these observations remain to be elucidated. Some researchers have hypothesized that the decrease in estrogen levels during menopause increases women’s risk of developing vascular risk factors and experiencing accelerated cognitive decline. Hormonotherapy might compensate for this estrogen reduction. However, the complex interaction between sex, vascular risk factors, and cognitive decline, as well as the role of menopause and hormonotherapy in this interaction, still needs to be understood.

Our study aims to identify sex-related differences in the impact of vascular risk factors on cognitive decline and to better understand the role of menopause and hormonotherapy in this interaction by using data from the Canadian Longitudinal Study on Aging. This study will provide more knowledge about the impact of vascular risk factors in cognitive decline and the risk of developing dementia, but also contribute to a better understanding older women’s health, who face a double stigma in research, ageism and sexism.

Ultrafast Probing of Defect-Engineered 2D Quantum Materials

Two-dimensional materials (2DMs), such as graphene, have revolutionized the field of nanotechnology due to their exceptional electronic, optical, and mechanical properties. However, the introduction of controlled defects into these materials offers an exciting avenue for tailoring their functionalities for next-generation technologies. This project focuses on studying the effects of defects, such as grain boundaries and vacancies, on the structural and electronic behaviour of 2DMs.

The research involves synthesizing defect-engineered 2DMs using advanced epitaxial growth and chemical vapor deposition (CVD) techniques. State-of-the-art characterization methods, including high-resolution transmission electron microscopy (HRTEM), femtosecond electron diffraction (FED), and ultrafast transient absorption (TA) spectroscopy, will be used to investigate how these defects influence carrier dynamics, lattice vibrations, and optoelectronic performance.

By combining experimental techniques, this project aims to establish clear correlations between defect structures and material behavior, providing valuable insights into how defects can be harnessed for designing improved materials. The results will have broad applications in areas such as photodetectors, flexible electronics, and quantum devices. This research contributes to advancing 2D material science, paving the way for innovative technologies that rely on defect engineering.

Energy Audit Reporting Automation with Artificial Intelligence (AI) and Machine Learning (ML)

Currently, energy audits at Dillon Consulting Limited take 2 to 3 days to generate comprehensive reports, consuming significant auditor time due to the manual process. This inefficiency diverts auditors from critical tasks and introduces inconsistencies, especially in buildings with multiple facilities. It limits Dillon Consulting’s ability to meet increasing demand for energy management practices. There is a critical need for advanced AI techniques capable of processing inputted data, generating human-quality reports, and adapting to evolving needs. This project addresses this gap by developing an automated system using AI to transform the manual audit report process. The proposed system automates data transfer from site visits into pre-formatted Word templates using AI to generate unique descriptions. By leveraging past reports for references, the software will retrieve relevant data for similar building types and systems, automating 75–80% of the report. Auditors will only need minimal adjustments, reducing workload and improving consistency. Over time, the AI tool will adapt and improve with new data, creating a robust, efficient, and scalable solution for energy auditing.