some changes to see

_projects/mcba.md

@@ -150,7 +150,7 @@ The dataset captured brainwave signals corresponding to the following activities
 Feature extraction and activity classification were performed using **transfer learning** with **YamNet** <d-cite key="yamnet_github"></d-cite>, a deep neural network model.
 * **Audio Representation**: Audio files were imported into **MATLAB** using an **Audio Datastore** [6]. Mel-spectrograms, a time-frequency representation of the audio signals, were extracted using the yamnetPreprocess <d-cite key="yamnetpreprocess"></d-cite> function <d-cite key="transferlearning_matlab"></d-cite>.
 * Dataset Split: The data was divided into **training (70%)**, **validation (20%)**, and **testing (10%)** sets.
-Transfer Learning with YamNet <d-cite key="yamnet_github"></d-cite> <d-cite key="transferlearning_matlab"></d-cite>:
+Transfer Learning with YamNet <d-cite key="yamnet_github, transferlearning_matlab"></d-cite>:
 - The **pre-trained YamNet model** (86 layers) <d-cite key="yamnet_github"></d-cite> was adapted for an 8-class classification task:
     + The initial layers of YamNet <d-cite key="yamnet_github"></d-cite> were **frozen** to retain previously learned representations <d-cite key="transferlearning_matlab"></d-cite>.
     + A **new classification layer** was added to the model <d-cite key="transferlearning_matlab"></d-cite>.

assets/bibliography/papers.bib

@@ -1,5 +1,3 @@
----
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 @article{nprnews,
   url={https://www.npr.org/sections/health-shots/2021/05/20/998725924/a-sense-of-touch-boosts-speed-accuracy-of-mind-controlled-robotic-arm},
   journal={NPR},