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Project Director :

Project Overview : The aim of this research program is to understand how the pancreatic beta cell functions under normal conditions in order to identify the disturbed mechanisms that lead to its dysfunction and death during the development of diabetes. We have already identified a new harmful mechanism that is amplified by diabetogenic stress factors. Our challenge is to deepen our understanding of this mechanism to better understand how it damages pancreatic beta cells and to design potential therapies targeting this mechanism in order to protect these cells. The ultimate aim of this research is to use our findings to develop effective treatment strategies for people at risk of or already suffering from diabetes.

Project Director :

Project Overview : This project explores the mechanisms of citizen engagement in Québec City and Edmonton, focusing on the impact of citizen participation on the legitimacy of public policy. The aim of the program is to train students to meet the challenges of citizen participation, governance, and public policy. It will also enable them to perfect their mastery of qualitative (interviews, focus groups) and quantitative methodologies, while strengthening their ability to analyse data using software such as NVivo or SPSS.

Project Director : 

Project Overview : 

In the numerical treatment of scientific and engineering problems, oscillatory integrals occur frequently. They arise from approximation procedures dependent on a parameter, iterative methods, and perturbation techniques. The use of these oscillatory integrals presents significant numerical and computational difficulties. This is why nonlinear transformation methods to improve the convergence of oscillatory integrals have been studied and applied in various situations.

The present proposal concerns the development of fast algorithms for the accurate numerical evaluation of a class of oscillatory integrals involving Bessel functions, with molecular integrals being among the most challenging types. The computation of these molecular integrals is a major task in molecular structure calculations; they occur in the millions, even for small molecules, and are extremely difficult to evaluate accurately and efficiently. Improving the computational methods for molecular integrals is crucial for further advances in the computational study of large molecular systems.

The planned methodology will strategically combine nonlinear transformations with sequence transformations and asymptotic expansions. This integrated approach is designed to significantly enhance both the efficiency and accuracy of numerically evaluating the targeted oscillatory integrals.

Project Director :  in collaboration with Dr Idriss Sekkak

Project Overview : 

Epidemic models are systems of differential equations that play a critical role in understanding disease transmission dynamics. In order to describe real life scenarios, these models can be significantly improved by incorporating delay terms, using discrete (fixed time lags) or distributed (continuous memory effects), to account for biological processes such as incubation periods, waning immunity, or vaccine efficacy decay. However, delayed differential equations introduce substantial challenges in numerical approximation and stability analysis due to their dependence on past states and demand specialized computational techniques, as standard ODE solvers may suffer from instability, step-size sensitivity, and high memory requirements when handling historical dependencies. Furthermore, the infinite-dimensional nature of DDEs complicates stability analysis, often leading to complex dynamics and sustained oscillations that are not observed in their non-delayed counterparts. Therefore, we investigate the impact of delays on numerical solutions of epidemic models, such as delayed SEIR or SIR frameworks, compare the performance of existing numerical methods and analyze how delays influence long-term behavior, including equilibrium stability and outbreak periodicity and delay impact on peak time and basic reproduction number. By developing robust computational strategies and stability criteria for delay-driven epidemic systems, we aim to explore the application of DDE analysis into epidemiological modeling, ultimately building more reliable predictions for public health decision-making.

Project Director :  in collaboration with Dr Idriss Sekkak

Project Overview : 

Air quality is a critical factor influencing public health, particularly in respiratory disease dynamics. In Canada, the National Forestry Database reports over 8,000 wildfires annually, burning an average of 2.1 million hectares and significantly degrading air quality through pollutants like PM2.5 and ozone. These environmental stressors can lead to respiratory infections, weaken immune responses, and potentially increase disease transmission rates. This research proposes a novel set of dynamical systems approach to model the coupling between air quality indices (AQI) and epidemic spread using differential equations. The model will integrate key variables such as pollution levels, population health status, and disease transmission rates to explore how deteriorating air quality influences infection dynamics. Besides, a stability analysis will be conducted to identify equilibrium points, thresholds for disease outbreaks, and the impact of air pollution on long-term epidemic behavior. Also, computational tools will be employed for numerical simulations. In addition to a sensitivity analysis to assess parameter influence, statistical methods to estimate the parameters, and data fitting to visualize system dynamics. Hence, by linking environmental data with epidemiological modeling, we aim to provide alerts for the public health situation, and key performance indicators for public health strategies, such as targeted air quality interventions during disease outbreaks and predictive assessments of health risks under environmental stress.