Vol. 5 No. 1 (2025): Issue 5
Articles

Capillary transport modelling through Dynamic Bayesian Networks

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  • Zainab Ibrahim,
  • Siti Nurhaliza,
  • Amirul Haziq
Zainab Ibrahim
Faculty of Applied Sciences, Universiti Teknologi MARA, Tapah Campus, 35000 Tapah, Malaysia
Siti Nurhaliza
nstitute of Computational Intelligence, Universiti Malaysia Pahang, 26300 Gambang, Malaysia
Amirul Haziq
Center for Complex Networks and Systems, Universiti Utara Malaysia, 06010 Sintok, Malaysia

Published 2025-01-17

Keywords

  • Capillary Transport,
  • Microfluidics,
  • Porous Materials,
  • Dynamic Bayesian Networks,
  • Flow Behavior

How to Cite

Ibrahim, Z., Nurhaliza, S., & Haziq, A. (2025). Capillary transport modelling through Dynamic Bayesian Networks. Journal of Computational Biology and Medicine, 5(1). Retrieved from https://journal.mri-pub.com/index.php/JCBM/article/view/66

Abstract

Capillary transport is a critical process in various scientific and engineering applications, such as microfluidics and porous materials characterization. Understanding and accurately modeling capillary transport phenomena are essential for optimizing these applications. However, the current state of research in this field faces challenges due to the complex and dynamic nature of capillary flow behavior. This paper addresses these challenges by proposing a novel approach based on Dynamic Bayesian Networks (DBNs) to model capillary transport. The innovative aspect of this work lies in the integration of DBNs with capillary flow mechanisms, providing a more comprehensive and accurate representation of the transport process. The study presents a detailed analysis of the methodology and its application in predicting capillary flow behavior. Overall, this research contributes to advancing the understanding and modeling of capillary transport processes, offering valuable insights for future studies in this area.

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