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--- example [2026/02/25 16:35] – epsatisep
+++ example [2026/03/18 15:59] (current) – [Refnotes Plugin Usage Examples] team4
@@ Line 4: / Line 4: @@
 ==== Refnotes Plugin Usage Examples ====
-These are examples of citations to books [(Thapa2019)] and [(Obayashi2020)], a chapter on a smart companion [(Reis2020)], articles on IoT [(Hamza2023)], blimps [(Mahn2006)], cloud computing [(Llorente2009)], gas emissions [(Ranjith2020)], pedestrian crossing behaviour [(Sobhani2018)], smart bicycles [(Lee2018)] and diaper moisture [(Khan2018)], and to Web resources [(android41)], [(cloudexpo2008)] and [(gartner2021)].
+These are examples of citations to books [(ALYAARI2026)] and [(Obayashi2020)], a chapter on a smart companion [(Reis2020)], articles on IoT [(Hamza2023)], blimps [(Mahn2006)], cloud computing [(Llorente2009)], gas emissions [(Ranjith2020)], pedestrian crossing behaviour [(Sobhani2018)], smart bicycles [(Lee2018)] and diaper moisture [(Khan2018)], and to Web resources [(android41)], [(cloudexpo2008)] and [(gartner2021)].
 Please use superscript and subscript annotations to write powers and indexes, //e.g.//, m<sup>2</sup> and CO<sub>2</sub>.
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 <WRAP centeralign>
 <figure flabel3>
-{{ :eps_logo.jpg?200 |}}
+{{ :logo.png?200 |}}
 <caption>EPS logo</caption>
 </figure>
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 ==== MathJax Plugin Usage Example ====
-Equation \ref{eq:cosinesimilarity} represents the cosine similarity between two vectors of features. This similarity measurement takes values in the range of $[0,1]$ [(bandyopadhyay2013unsupervised)].
+Equation \ref{eq:cosinesimilarity} represents the cosine similarity between two vectors of features. This similarity measurement takes values in the range of $[0,1]$ [(Bandyopadhyay2013)].
 This is an in line expression $a+b=c$.
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 </WRAP>
-As [(bandyopadhyay2013unsupervised)] states, the most popular similarity metrics are the distance and the cosine similarity. The distance-based metrics include the Euclidean distance, the Hamming distance or the Chebyshev distance, among others.
+As [(Bandyopadhyay2013)] states, the most popular similarity metrics are the distance and the cosine similarity. The distance-based metrics include the Euclidean distance, the Hamming distance or the Chebyshev distance, among others.
 Equation \ref{eq:euclideandistance} displays the Euclidean distance formula.