Innovative Projects Realized

Automated Software Vulnerability Patching using Dynamic Symbolic Traces

Deep learning (DL) has emerged as a viable means for identifying software bugs and vulnerabilities. The success of DL relies on having a suitable representation of the problem domain. However, existing DL-based solutions for learning program representations have limitations – they either cannot capture the deep, precise program semantics or suffer from poor scalability. We plan to provide a DL system to learn program presentations by combining static source code information and dynamic program execution traces. By collaborating in two diverse multicultural research groups, participating institutions in Canada and United Kingdom will benefit from sharing knowledge and expertise in this cutting-edge field of cybersecurity and software engineering. This collaboration will enhance the reputation of these institutions in the security and software engineering communities, attracting top talent and opportunities. Moreover, participants will have the chance to develop new technical skills and social skills, and learn about the state-of-the-art technological innovations in vulnerability detection.

Development of a Comprehensive Neighborhood-based Measure of Cardiovascular Health in Children

Promoting healthy living and reducing the burden of cardiovascular diseases are public health priorities in Canada and the United States. In recent years, the importance of measuring neighborhood contexts to address inequities in cardiovascular health has been highlighted. Many neighborhood indices have been utilized by academic researchers to explore the complex relationship between neighborhood and health. However, within the context of cardiovascular disease, there remains a gap in knowledge regarding questions such as (a) which indices perform better or worse as predictors of cardiovascular disease in children, (b) how performance may vary based on individual or household characteristics within the neighborhood of interest, and (c) if we can improve on these indices for describing area-level patterns for cardiovascular health in children.

Thus, the proposed research project aims to fill this knowledge gap by directly comparing the empirical performance of established neighborhood disadvantage indices in a nationally representative dataset of adolescents in the United States. Furthermore, we will determine which index or individual index components perform better or worse as predictors of cardiovascular health in this population—thus, allowing clinicians and other stakeholders to gain a deep understanding of cumulative risk of cardiovascular disease over the life course.

Bimetallic Cooperation: Design of an Fe–Al Complex for Carbon Dioxide Activation

The proposed project aims to address the pressing challenge of reducing greenhouse gas emissions by leveraging synthetic chemistry. By designing a novel iron-aluminum complex for carbon dioxide activation, the project explores bimetallic systems to introduce innovative pathways for reducing carbon dioxide’s harmful environmental impact. The participating institutions, Imperial College London and the researcher’s home institution in Canada, stand to benefit in several ways. For Imperial College London, the project enhances its global research reputation, fosters international collaboration, and provides access to a highly motivated research intern. For the researcher’s Canadian home institution, it offers unique skills and knowledge acquisition, positioning the researcher as a catalyst for advancing expertise in sustainable chemistry and addressing climate change challenges. This project promises mutual enrichment, scientific advancement, and strengthened global research partnerships between the institutions and countries.

Multi-fidelity approach for the probabilistic assessment of dams

The proposed project seeks to evaluate the stability of concrete hydraulic structures using a progressive method that balances precision and computational expenses. By examining various simplification assumptions and analyzing different loading conditions, while incorporating machine learning techniques to merge data with different levels of accuracy, the project aims to enhance the evaluation of risks associated with hydraulic structures, facilitating better decision-making. Partnering with Hydro-Québec, a leading provider of renewable energy, the project will benefit the organization by enhancing safety assessments, optimizing resource management, and enabling proactive maintenance. Furthermore, by ensuring the stability of hydropower generation, the project contributes to Canada’s sustainability targets, reducing dependence on fossil fuels and minimizing CO2 emissions, which supports the nation’s commitment to fortify and sustain critical infrastructure in the long run.

Advancing Systems Architecture Development Methods for Aircraft with Hydrogen-based Propulsion

Ensuring the sustainability of air transportation is a priority for the global aerospace community. Disruptive technologies, such as hydrogen-based propulsion, are promising but present significant challenges for the design and operation. One challenge for designing these future aircraft is the large number of potential architectures and the need to consider safety already in the early design stages. The Institute of Aircraft Systems Engineering at the Hamburg University of Technology and the Aircraft Systems Lab at Concordia University are leading institutions in the field of aircraft systems design. In this project, a collaboration between the researchers aims to improve design tools in both institutions, particularly for safe and efficient system architectures for these future aircraft. As researchers from Canada and Germany are exposed to different industry applications (larger commercial aircraft vs. smaller business aircraft), a collaboration will significantly benefit the development of effective tools for different aircraft types, contributing to the development of future, more sustainable aircraft. In addition, the exchange between the research groups will expose the students to different tools and methods at Concordia University in a multicultural and multidisciplinary context, essential skills for solving the global challenges of the future aerospace industry.

Computational modelling of advanced electrocatalysts for CO2 electroreduction using DFT and machine learning.

Carbon capture and utilization (CCU), primarily the CO2 electroreduction technology, can convert CO2 into a variety of valuable products, using renewable electricity. However, the path to widespread adoption of the CO2 electroreduction technology in industrial settings is met with several challenges primarily the cost of electricity, efficiency and selectivity of the desired product.
One of the key factors that can help address these challenges and promote industrial application of this process is the choice of a proper electrocatalyst used in the CO2 electrolyzer. The role of electrocatalysts is pivotal, and their design is central to making the CO2 electroreduction technology both sustainable and economically viable. In light of these issues, this proposal focuses on the development of an innovative approach towards the design of efficient electrocatalysts which offer higher selectivity, lower consumption of electricity and increased efficiency by using Density Functional Theory (thermodynamic and microkinetic approach) and machine learning techniques.
This project will serve as the first step in initiating student exchange and research cooperation between Concordia University and Technical University of Denmark. By focusing on sustainability, the research aligns with sustainability science, addressing environmental concerns and guiding eco-friendly technology development in both Canada and Denmark.

Internationalization Strategies for Sport Industry SMEs in Quebec

It is widely accepted in Canada that small and medium-sized enterprises (SMEs) are the engine of the national economy responsible for 98% of businesses and employing more than 10 million individuals. To continue growing and supporting the national economy, SMEs can internationalize to seek increased profits and gain a competitive edge. One industry that can benefit from internationalization is sports businesses. Although technological advancements have created opportunities for sports organizations to enter international markets, sports SMEs still face challenges entering and competing in the global market due to their limited resources and barriers operating internationally. Studies on the internationalization of sports SMEs have shown it has the potential to expand sales and customer base, build competitive advantage, and establish an international position. This is particularly relevant in Quebec, which according to the Government of Canada, is among the provinces with the highest potential to expand into international markets. However, there is a lack of empirical knowledge connecting sports entrepreneurship and international business. This impacts the fulfillment of the potential of Quebec-based sports SMEs. Thus, the purpose of this project is to address this gap gaining a better understanding of the internationalization process and its benefits for sports SMEs in Quebec.

Learning from the river: improving the resilience and ecological value of tidal marsh creation projects in Pacific Northwest estuaries

The tidal marshes of the Fraser River Estuary support numerous species, including juvenile salmon, and offer benefits to nearby communities. To counteract marsh losses, over 100 tidal marshes have been constructed in the estuary from the 1980s to present. Yet after 40 years, key knowledge gaps still limit our confidence in their ability to support juvenile salmon and persist in the long-term. This study aims to address these knowledge gaps by (1) measuring and comparing the abundance of juvenile salmon and their invertebrate prey between created and natural marshes, (2) studying and learning from areas of ongoing “natural” marsh expansion in the estuary, and (3) applying and testing innovative marsh creation approaches via a marsh creation pilot project. This research will inform and improve how tidal marshes are constructed and restored in the estuary, with wide-reaching benefits to both industry, governments, and ENGOs.

A Study of Syntactic Features of Code-Switching Between Mongolian And English Based on Recursive Object Model

Taking Mongolian-English code-switching as an entry point, this research will adopts quantitative linguistic approach, attempts to study the syntactic features of
Mongolian-English code-switching based on Recursive Object Model (ROM).

Geomechanical characterization of using basalt fiber-treated clay soils for improving ground resilience against freeze-thaw actions

The degradation of soil under freeze-thaw cycles will pose a major threat to newly constructed infrastructure in cold regions. The soil ground forms an essential part of the built environment, thus actions should be taken to improve the ground’s resilience. We are collaborating with our industry partner SFTec Inc. to propose a sustainable value-added strategy that reinforces the soft clay soils using a basalt fiber-stabilized approach. The addition of basalt fiber is a clean technology to improve the ground’s resilience against freeze-thaw actions since the fiber is produced from natural basalt rock. Laboratory tests will be conducted to measure the strengths of fiber-stabilized clay soils under freeze-thaw cycles. Numerical homogenization will be conducted for determining the optimized mixing scheme. The successful completion of this project will directly provide guidance on the optimum fiber/clay ratio determination for potential projects at cold regions. SFTec’s R&D product will benefit from the collaboration since it will create a strategic direction.

A data-driven control framework for constrained nonlinear systems with application to unmanned ground vehicles

The objective of this project is the development of a novel data-driven control framework for nonlinear dynamical systems. The proposed solution will be validated and tested on the autonomous ground vehicles available at Concordia University. To achieve the desired goal, the research intern, with the supervision and help of the host and home supervisor, will take a three-step approach. In the first step, the intern will jointly develop a reinforcement learning algorithm and behavioural approach to obtain a data-driven outer and convex differential inclusion of the dynamics of the nonlinear system. Then, in the second part, it will use the developed data-driven model to design a data-driven approach inspired by an existing model-based counterpart. Finally, in the last step, the proposed control architecture will be tested on autonomous ground vehicles and contrasted with other model-based and data-driven alternative schemes.

The conducted research is anticipated to lead to a prestigious peer-reviewed conference/journal publication co-authored by all the participants of this project (student, home supervisor, host supervisor). In general, the intended research and collaboration are believed to contribute to increasing and improving the research and innovation capacities of both host and home institutions in the field of data-driven control.

Evaluation of Human Resource Efficiency Components in Northwood Care Employees Based on the ACHIEVE Model

The evaluation at Northwood Care, using the ACHIEVE model, aims to determine what factors make employees efficient. This model assesses seven key elements: Ability, Clarity, Help, Incentive, Evaluation, Validity, and Environment. Managers use it to identify and address performance issues affecting employee efficiency. The evaluation is crucial because employees are a significant driver of organizational success, particularly in healthcare, where challenges like caring for the sick and emergency preparedness are critical. Measuring employee efficiency in healthcare is complex due to its unique nature. Therefore, identifying the factors that improve efficiency becomes vital to enhance the organization. Studies have highlighted factors like leadership, motivation, experience, creativity, and training that influence employee efficiency. By applying the ACHIEVE model at Northwood Care, managers can pinpoint areas for improvement and strengthen existing strengths. This evaluation ultimately helps the organization become more efficient, retain valuable employees, and achieve greater success.