Link.

src/about.py

@@ -61,7 +61,7 @@ WHAT_IS_F1_HTML_BOTTOM_A_BEFORE_TABS = """
 WHAT_IS_F1_HTML_BOTTOM_A_AFTER_TABS = """
     <p class="mb-4 f1-p">While the problems are often natural to state, their solutions are far from obvious. The solvability of this vast class of problems is guaranteed by an algorithmic <strong>meta-theorem</strong> due to <a href="https://en.wikipedia.org/wiki/Courcelle%27s_theorem" target="_blank" rel="noopener noreferrer" class="f1-a">Courcelle</a>, which broadly states:</p>
     <blockquote class="my-6 f1-blockquote">
-        “For every sufficiently tree-like graph, any problem definable in an expressive formal logic — Monadic Second-Order (MSO) logic — can be solved by a dynamic programming algorithm that operates in time linear in the order of the graph.”
+        “For every sufficiently tree-like graph, any problem definable in an expressive formal logic — <a href="https://en.wikipedia.org/wiki/Monadic_second-order_logic">Monadic Second-Order (MSO) logic</a> — can be solved by a dynamic programming algorithm that operates in time linear in the order of the graph.”
     </blockquote>
     <p class="f1-p">The key is to use a structure known as a tree decomposition, which organises the graph's vertices into a series of overlapping sets, or “bags”, that are themselves arranged in a tree.</p>
     <p class="mb-4 f1-p">An algorithm can then traverse this tree of bags, solving the problem piece by piece using dynamic programming. This process involves designing a “state” that summarises all necessary information about the partial solution within a bag, and then defining how this state transforms as vertices are introduced, forgotten, or bags are merged.</p>