diff --git a/_pages/about.md b/_pages/about.md
index 98b2500..876b920 100644
--- a/_pages/about.md
+++ b/_pages/about.md
@@ -18,4 +18,4 @@ During my PhD at KU Leuven (Belgium), I developed a wide range of interests and
 If any of these topics piqued your interest, please check out my publications.
 
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-_This website was last updated on 22 April 2024._
+_This website was last updated on 3 May 2024._
diff --git a/_publications/2024-01-neuralnetwork.md b/_publications/2024-01-neuralnetwork.md
index 3ef4438..65318ba 100644
--- a/_publications/2024-01-neuralnetwork.md
+++ b/_publications/2024-01-neuralnetwork.md
@@ -6,7 +6,7 @@ excerpt: "Preprint - [arXiv:2312.08490](https://arxiv.org/abs/2312.08490)"
 date: 2024-01-16
 venue: 'Neural Computing and Applications'
 paperurl: 'http://doi.org/10.1007/s00521-023-09403-1'
-citation: 'De Jonghe, J. and Kuczyński, M. D. (2024). &quot;Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas.&quot; <i>Neural Comput. Appl</i>.'
+citation: 'De Jonghe, J. and Kuczyński, M. D. (2024). &quot;Neural network classification of eigenmodes in the magnetohydrodynamic spectroscopy code Legolas.&quot; <i>Neural Comput. Appl</i>. 36, 5955–5964.'
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 __Abstract.__ A neural network is employed to address a non-binary classification problem of plasma instabilities in astrophysical jets, calculated with the Legolas code. The trained models exhibit reliable performance in the identification of the two instability types supported by these jets. We also discuss the generation of artificial data and refinement of predictions in general eigenfunction classification problems.