refs

paper/paper.bib

@@ -202,7 +202,7 @@ @article{GMSH
 @article{vandenheuvel2023computational,
 author = {Daniel Vanden{H}euvel and Brenna Devlin and Pascal Buenzli and Maria Woodruff and Matthew Simpson},
 title = {New computational tools and experiments reveal how geometry affects tissue growth in {3D} printed scaffolds},
-year = {20223},
+year = {2023},
 journal = {Chemical Engineering Journal},
 volume = {475},
 pages = {145776},

paper/paper.md

@@ -32,7 +32,7 @@ Delaunay triangulations and Voronoi tessellations have applications in a myriad
 Several software packages with support for computing Delaunay triangulations and Voronoi tessellations in two dimensions already exist, such as _Triangle_ [@shewchuk1996triangle], _MATLAB_ [@MATLAB], _SciPy_ [@SciPy], _CGAL_ [@CGAL], and _Gmsh_ [@GMSH]. DelaunayTriangulation.jl is the most feature-rich of these and benefits from the high-performance of Julia to efficiently support many operations. Julia's multiple dispatch [@bezanson2017julia] 
 is leveraged to allow for complete customisation in how a user wishes to represent geometric primitives such as points and domain boundaries, a useful feature for allowing users to represent primitives in a way that suits their application without needing to sacrfice performance. The [documentation](https://juliageometry.github.io/DelaunayTriangulation.jl/stable/) lists many more features, including its ability to represent a wide range of domains, even those that are disjoint and with holes. 
 
-DelaunayTriangulation.jl has already seen use in several areas. DelaunayTriangulation.jl was used for mesh generation in [@vandenheuvel2023computational] and is used for the `tricontourf`, `triplot`, and `voronoiplot` routines inside Makie.jl [@danisch2021makie]. The packages [FiniteVolumeMethod.jl](https://github.com/SciML/FiniteVolumeMethod.jl) [@vandenheuvel2024finite] and [NaturalNeighbours.jl](https://github.com/DanielVandH/NaturalNeighbours.jl) [@vandenheuvel2024natural] are also built directly on top of DelaunayTriangulation.jl. The design of boundaries in DelaunayTriangulation.jl has been motivated especially for the efficient representation of boundary conditions along different parts of a boundary for solving differential equations, and this is heavily utilised by FiniteVolumeMethod.jl.  
+DelaunayTriangulation.jl has already seen use in several areas. DelaunayTriangulation.jl was used for mesh generation in @vandenheuvel2023computational and is used for the `tricontourf`, `triplot`, and `voronoiplot` routines inside Makie.jl [@danisch2021makie]. The packages [FiniteVolumeMethod.jl](https://github.com/SciML/FiniteVolumeMethod.jl) [@vandenheuvel2024finite] and [NaturalNeighbours.jl](https://github.com/DanielVandH/NaturalNeighbours.jl) [@vandenheuvel2024natural] are also built directly on top of DelaunayTriangulation.jl. The design of boundaries in DelaunayTriangulation.jl has been motivated especially for the efficient representation of boundary conditions along different parts of a boundary for solving differential equations, and this is heavily utilised by FiniteVolumeMethod.jl.  
 
 # Examples
 
@@ -80,7 +80,7 @@ refine!(tri; max_area=1e-3get_area(tri))
 
 We now give an example using Voronoi tessellations. Our example is motivated from Lloyd's algorithm for $k$-means clustering [@du1999centroidal]. We generate $k$ random points and compute their centroidal Voronoi tessellation. We then generate data and label them according to which Voronoi cell they belong to.[^1] The code is given below, and the resulting plot is given in \autoref{fig:2}.
 
-[^1]: This example is somewhat contrived. Our procedure only corresponds to $k$-means clustering when the data set is dense [@kanugo2002efficient; @du1999centroidal]. To exactly obtain $k$-means clustering, the density function used for computing the centroids would need to be adjusted. See Section 2.3 of [@du1999centroidal] for more details.
+[^1]: This example is somewhat contrived. Our procedure only corresponds to $k$-means clustering when the data set is dense [@kanugo2002efficient; @du1999centroidal]. To exactly obtain $k$-means clustering, the density function used for computing the centroids would need to be adjusted. See Section 2.3 of @du1999centroidal for more details.
 
 ```julia
 using Random