Projets novateurs réalisés

Data-Driven and Synthesis-Guided Generalization in CHC-Solving

The intern will be working on a research project aimed improving scalability and applicability of automated reasoning tool called SPACER by improving the way it deals with special data types. Overall, the goal of this project is to make SPACER a more effective and reliable tool for verifying program correctness.

Mitigation of Fouling of Tertiary UF Membranes at Low Temperatures

Membranes that are used in wastewater treatment have been found to clog more rapidly at cold temperatures. This study will examine alternative operating strategies that will reduce clogging and thereby reduce the needs for extra energy and chemical consumption under these operating conditions.

Evaluating the effects of motion cues in virtual truck driver training

The use of virtual reality (VR) and realistic real-time graphics has become a critical component in various industries, including aviation. However, the benefits and necessity of full-motion simulators in training remain unclear, especially when considering the fundamental differences between tasks such as flying and driving. This research aims to address the knowledge gap in understanding the effects of motion cues in virtual truck driver training. While studies have been conducted on motion cueing and its effects on training in simulators for different tasks, there is a scarcity of research exploring the impact of motion stimuli on VR-based truck driver training.

Assessing the effect of mowing practices on stem-dwelling arthropod assemblages in an urban conservation project

This project aims to evaluate the effect of mowing on the insect community living in meadow plant stems. Mowing is a necessary practice in meadow restoration in cities due to the need to control invasive plant species. However, past studies have shown that mowing can also contribute to increased mortality rates in a wide variety of animal groups. This presents a potential trade-off between goals for managing the plants in meadows and goals for managing animals. This project will examine the effect of mowing on insects. Many insects rely on their host plant’s structural integrity to complete their life cycles and may thus be negatively impacted by mowing. A better understanding of the potential trade-off between plant and animal conservation management will allow practitioners to take into account a larger breadth of living organisms and to better inform future mowing practices in urban meadow restoration projects.

Vortex Identification Using Machine Learning

Many fluid flows are dominated by the dynamics of vortex formation and convection. Examples of practical importance include flows over aircraft wings/wind turbine blades and environmental flows. Knowledge of vortex parameters such as position, radius, circulation, and convective velocity are needed to understand and predict the influence of vortices on flow development. Although a vortex is intuitively understood as a region of fluid with a coherent rotational motion, there is no universally accepted method of defining and identifying vortices in a fluid flow. Furthermore, reliable vortex identification in turbulent flows is made difficult by the presence of random velocity and pressure field fluctuations. Previous work on vortex identification has attempted to overcome these challenges through the application of machine learning techniques to identify and track vortices in experimental data and numerical simulations. The goal of this project is to develop a robust method for vortex identification, quantification, and tracking based on machine learning techniques.

Detecting Phishing Websites using Machine Learning Techniques

The project “Detecting Phishing Websites using Machine Learning Techniques” aims to develop a method that can accurately identify and block malicious websites. Machine Learning algorithms will be used to analyze various website features, such as URL, page content, rank and other indicators to determine if it is a phishing site or not. By identifying and blocking these websites before they can cause harm, the system will save time and resources while also preventing the loss of sensitive information. With the increasing threat of cyber attacks, this project is essential for ensuring enhanced safety of browsing. Leveraging the power of Machine Learning, the research has the potential to make a significant impact in the fight against phishing.

The expected outcome of this project is a report describing a method that can later be used to develop reliable and efficient tool to help individuals, organizations and state agencies protect themselves from falling victim to phishing attacks.

Analysis of the error propagation dynamics of PIV-pressure

This study aims to analyze the dynamics of error propagation in the PIV-pressure analysis and suggests two approaches: I) a formal analysis and II) corresponding algorithms for optimal sensor placement that minimize the error propagation from the measured data to the computed pressure field. By analyzing the Poisson problem together with boundary conditions, the optimal sensor placement can be determined in advance, which would minimize the error in measurements. However, since there may be multiple locations that could be considered as good options, this task can be treated as an optimization problem. Therefore, a machine learning algorithm will be utilized to narrow down the possible cases and identify the best location for the sensor placement. Based on the formal analysis from I), we design algorithms to determine the optimal sensor placement that works for engineering and applications purposes. As a final goal, we want to estimate the optimal number and optimal location of sensors to fully describe the pressure field.

Quantifying Cortical Resilience Using CCaRT

Although brain resilience has long been thought to be important, only recently have methods been developed to measure it directly. In the proposed research, we will use one such method–known as the cortical challenge and recovery test (CCaRT)–to see if we can identify signs of concussion at the level of brain resilience. We will have 24 people with and without concussion history undergo the CCaRT testing method, and measure resilience metrics that it generates. Among those with concussion, we will select 12 with a minimal concussion history as well as 12 with a more extensive concussion history. We believe that those with a concussion history will show lower levels of brain resilience than those who have no concussion, or only a minimal concussion history. This substudy will be part of a larger study using the same paradigm to compare CCaRT performance between controls and two other important brain conditions, PTSD and long-COVID.

Who are the ‘good Russians’ for Ukrainians during the full-scale war? Attitudes of the Ukrainian society to the Russian opposition in the social media space

The research project aims to examine the attitudes of Ukrainians towards Russians, particularly about the concept of ‘good Russians’ during the ongoing full-scale war. The study will include the exploration of the socio-historical background of Russian-Ukrainian relations, as well as the analysis of the changes in attitudes before and after the Russian aggression in 2014. Then, it will focus on the formation and interpretation of the ‘good Russians’ concept, which is crucial to discover in the Ukrainian discourse to show up its truthful sense in terms of the modern war. With the usage of data scraping methods, the social media data will be collected and analyzed to uncover public sentiment around the ‘good Russians’ and compare such perceptions among demographic, regional, and differently educated groups within Ukrainian society. The research strives to gain an understanding of the war-time dynamics of attitudes, measure the tolerance to Russians, as well as expand this field of academic studies and offer insights into how social media both fosters constructive dialogue and perpetuates divisions within society.

Developing Standard Test for Evaluating Back-Support Exoskeleton Performance for Rebar Workers

Back-support exoskeletons (BSEs) are wearable devices designed to assist and enable human motion for workers in various industries ranging from manufacturing to construction. As with workers in other industries, the opportunity provided by BSEs for construction workers is to reduce injury rates for the benefit of worker health and productivity. Potential risks also exist, including discomfort, compromised balance, snags, and increased stress in the unassisted regions of the body. The challenge comes in finding effective BSEs for specific construction trades working on specific project types. To meet this challenge, this project aims to design and execute a standard test course to evaluate the performance of back-support exoskeletons for rebar workers. An experiment will be performed according to the designed standard test course. The results of this experiment will inform future iterations of a standardized BSE efficacy evaluation framework for rebar workers. Laboratory studies can lead to field studies that may give evidence for practical BSE regulations, guidelines, and ergonomic risk indices for the construction industry. The evaluation framework will include the assessment of a BSE’s effects on safety, productivity, and acceptability, including a tool for estimating a BSE’s Return on Investment (ROI).

Évaluation préliminaire d’une intervention axée sur les traumas auprès d’enfants et de jeunes placés en centres de réadaptation : Une collaboration Québec-France

Les jeunes placés en centres de réadaptation ont pour la plupart vécu différentes formes de maltraitance – par exemple la négligence ou l’abus physique – ce qui nécessite un placement hors de leur milieu familial pour quelques mois, voire plusieurs années. Ces expériences traumatiques les placent à risque de développer des problèmes psychologiques, psychiatriques, scolaires, comportementaux et relationnels. Depuis 2017, Boscoville et le Consortium canadien sur le trauma chez les enfants et les adolescents ont développé et expérimenté des programmes axés sur le trauma qui ont pour but de soutenir les équipes éducatives qui œuvrent auprès d’une clientèle hébergée en centre de réadaptation. Ces pratiques innovantes ont trouvé écho chez des partenaires français, la Fondation Les Apprentis d’Auteuil, qui ont collaboré avec Boscoville pour adapter ces programmes et les implanter. Basée sur une méthodologie mixte, ce projet permettra à l’étudiante de collaborer étroitement avec Boscoville, sur une période de quatre mois, afin d’analyser des données provenant d’entrevues et de questionnaires colligés en France.

Preclinical studies of Mediphage Bioceuticals ministring DNA gene delivery system for the treatment of Dravet Syndrome

This research project will advance the development of a possible treatment for Dravet Syndrome, a rare form of childhood epilepsy caused by a genetic mutation. In Dravet syndrome, an important protein responsible for sending signals along the nerve fibers of the brain does not function, which results in overactivity and triggers debilitating and recurring seizures. MBI’s gene therapy platform (DNA ministrings) is being developed into a precision medicine that would replace the dysfunctional protein with a functional version. This project will assess the potential safety and effectiveness of this novel gene therapy using advanced human induced pluripotent stem cells. This project will advance the mission of the stem cell network by simultaneously developing new stem cell models and a novel potentially life-saving therapy for an uncurable disease.