The American University of Vietnam

Identifying water pollution in complex river systems poses significant challenges due to sparse monitoring infrastructure, heterogeneous data sources, and measurement uncertainties. Traditional deterministic approaches struggle to provide reliable source attribution when faced with incomplete datasets and dynamic spatiotemporal dependencies. This paper presents a Bayesian network-assisted framework for pollutant source identification that integrates multi-source data streams, including insitu water quality measurements, hydrological simulations, and remote sensing observations into a unified probabilistic model. The proposed methodology explicitly incorporates river network topology and temporal evolution dynamics while handling uncertainty through principled Bayesian inference. The framework employs a linear advection-decay model to simulate pollutant transport, combined with maximum a posteriori estimation to identify the most probable source configurations. Feature selection incorporates temporal derivatives, upstream influence metrics, and lagged cross-correlations to capture spatiotemporal dependencies effectively. Experimental validation on synthetic river networks demonstrates the framework's capability to identify pollution sources even under noisy measurement conditions accurately. The probabilistic approach provides interpretable uncertainty quantification, enabling robust decision support for water quality managers. Results show superior performance compared to traditional deterministic methods, particularly in scenarios with limited sensor coverage. The framework offers a scalable, interpretable solution for environmental monitoring and pollution control, providing actionable insights for timely remediation strategies and evidence-based policy formulation in water resource management.

<p>Les enfants construisent des représentations des concepts et des phénomènes physiques et ces représentations sont essentielles à l'éducation. Pour le physicien, le changement d'état est une transformation de la matière due à un transfert d'énergie sous forme de chaleur. Dans plusieurs études portant sur les représentations des enfants, est constaté que ces représentations sont souvent incompatibles avec le modèle scientifique. Dans cette recherche, où nous avons étudié les représentations de la fusion de la glace, 96 enfants âgés de 6 et 11 ont été interviewé. Les résultats ont mis en évidence les difficultés des enfants et les différences entre les enfants des deux âges.</p><p> </p>Children construct representations of physical concepts and phenomena, and these representations are essential to education. For the physicist, the change of state is a transformation of matter due to a transfer of energy in the form of heat. In several studies on the representations of children, it is found that these representations are often incompatible with the scientific model. In this research, where we studied the representations of ice fusion, 96 children aged 6 and 11 were interviewed. The results highlighted the children's difficulties and the differences between the children of both ages.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0955/a.php" alt="Hit counter" /></p>