diff --git a/assets/mzn/back_knapsack.mzn b/assets/mzn/back_knapsack.mzn
index 235f113..2d1daea 100644
--- a/assets/mzn/back_knapsack.mzn
+++ b/assets/mzn/back_knapsack.mzn
@@ -1,15 +1,15 @@
 % Problem parameters
-enum TOYS;@\Vlabel{line:back-knap-toys}@
-array[TOYS] of int: toy_joy;@\Vlabel{line:back-knap-joy}@
-array[TOYS] of int: toy_space;@\Vlabel{line:back-knap-space}@
-int: space_left;@\Vlabel{line:back-knap-left}@
+enum TOYS;@\Vlabel{line:back:knap:toys}@
+array[TOYS] of int: toy_joy;@\Vlabel{line:back:knap:joy}@
+array[TOYS] of int: toy_space;@\Vlabel{line:back:knap:space}@
+int: space_left;@\Vlabel{line:back:knap:left}@
 
 % Decision variables
-var set of TOYS: selection;@\Vlabel{line:back-knap-sel}@
-var int: total_joy = sum(toy in selection)(toy_joy[toy]);@\Vlabel{line:back-knap-tj}@
+var set of TOYS: selection;@\Vlabel{line:back:knap:sel}@
+var int: total_joy = sum(toy in selection)(toy_joy[toy]);@\Vlabel{line:back:knap:tj}@
 
 % Constraints
-constraint sum(toy in selection)(toy_space[toy]) < space_left;@\Vlabel{line:back-knap-con}@
+constraint sum(toy in selection)(toy_space[toy]) < space_left;@\Vlabel{line:back:knap:con}@
 
 % Goal
-solve maximize total_joy;@\Vlabel{line:back-knap-obj}@
+solve maximize total_joy;@\Vlabel{line:back:knap:obj}@
diff --git a/assets/packages.tex b/assets/packages.tex
index a2b7612..73ca3f7 100644
--- a/assets/packages.tex
+++ b/assets/packages.tex
@@ -82,6 +82,7 @@ style=apa,
 
 \newcommand{\Vlabel}[1]{\label[line]{#1}\hypertarget{#1}{}}
 \newcommand{\lref}[1]{\hyperlink{#1}{\FancyVerbLineautorefname~\ref*{#1}}}
+\newcommand{\Lref}[1]{\hyperlink{#1}{\FancyVerbLineautorefname~\ref*{#1}}}
 \newcommand{\lrefrange}[2]{\FancyVerbLineautorefname{}s~\hyperlink{#1}{\ref*{#1}}--\hyperlink{#2}{\ref*{#2}}}
 \newcommand{\Lrefrange}[2]{Lines~\hyperlink{#1}{\ref*{#1}}--\hyperlink{#2}{\ref*{#2}}}
 
diff --git a/chapters/2_background.tex b/chapters/2_background.tex
index 0f81ecf..f63ba3a 100644
--- a/chapters/2_background.tex
+++ b/chapters/2_background.tex
@@ -130,20 +130,20 @@ Let us introduce the language by modelling the problem from
 \end{listing}
 
 The model starts with the declaration of the \glspl{parameter}.
-\Lref{line:back-knap-toys} declares an enumerated type that represents all
+\Lref{line:back:knap:toys} declares an enumerated type that represents all
 possible toys, \(T\) in the mathematical model in the example.
-\Lref{line:back-knap-joy,line:back-knap-space} declare arrays mapping from toys
+\Lref{line:back:knap:joy,line:back:knap:space} declare arrays mapping from toys
 to integer values, these represent the functional mappings \(joy\) and
-\(space\). Finally, \lref{line:back-knap-left} declares an integer
+\(space\). Finally, \lref{line:back:knap:left} declares an integer
 \gls{parameter} to represent the car capacity as an equivalent to \(C\).
 
-The model then declares its \glspl{variable}. \Lref{line:back-knap-sel} declares
+The model then declares its \glspl{variable}. \Lref{line:back:knap:sel} declares
 the main \gls{variable} \mzninline{selection}, which represents the selection of
 toys to be packed. \(S\) in our earlier model. We also declare the variable
-\mzninline{total_joy}, on \lref{line:back-knap-tj}, which is functionally
+\mzninline{total_joy}, on \lref{line:back:knap:tj}, which is functionally
 defined to be the summation of all the joy for the toy picked in our selection.
 
-Finally, the model contains a constraint, on \lref{line:back-knap-con}, to
+Finally, the model contains a constraint, on \lref{line:back:knap:con}, to
 ensure we do not exceed the given capacity and states the goal for the solver:
 to maximise the value of the variable \mzninline{total_joy}.
 
@@ -157,30 +157,30 @@ primitive constraints.
 Given the assignments
 
 \begin{mzn}
-TOYS = {football, tennisball, stuffed_lama, stuffed_elephant};
-toy_joy = [63, 12, 50, 100];
-toy_space = [32, 8, 16, 40];
-space_left = 64;
+TOYS = {football, tennisball, stuffed_elephant};
+toy_joy = [63, 12, 100];
+toy_space = [32, 8, 40];
+space_left = 44;
 \end{mzn}
 
-is the result of flattening for \mzninline{n=2}:
+the following model is the result of flattening:
 
 \begin{mzn}
-var 1..2: x_1_1;
-var 1..2: x_1_2;
-var 1..2: x_2_1;
-var 1..2: x_2_2;
-constraint all_different([x_1_1, x_1_2]);
-constraint all_different([x_2_1, x_2_2]);
-constraint all_different([x_1_1, x_2_1]);
-constraint all_different([x_1_2, x_2_2]);
+var 0..1: selection_0;
+var 0..1: selection_1;
+var 0..1: selection_2;
+var 0..175: total_joy:: is_defined_var;
+constraint int_lin_le([32,8,40],[selection_0,selection_1,selection_2],44);
+constraint int_lin_eq([63,12,100,-1],[selection_0,selection_1,selection_2,total_joy],0):: defines_var(total_joy);
+solve maximize total_joy;
 \end{mzn}
 
 This \emph{flat} problem will be passed to some \gls{solver}, which will attempt
-to determine an assignment to each decision variable \verb|x_i_j| that satisfies
-all constraints, or report that there is no such assignment.
+to determine an assignment to each decision variable \mzninline{solection_i} and
+\mzninline{total_joy} that satisfies all constraints and maximises
+\mzninline{total_joy}, or report that there is no such assignment.
 
-\section{The current \glsentrytext{minizinc} interpreter}%
+\section{The Current \glsentrytext{minizinc} Interpreter}%
 \label{sec:back-mzn-interpreter}
 
 \section{Other Constraint Modelling Languages}%
@@ -191,5 +191,3 @@ all constraints, or report that there is no such assignment.
 
 \section{Constraint Logic Programming}%
 \label{sec:back-clp}
-
-