Remove commented out text

paper.tex

@@ -126,8 +126,6 @@ Effect of the Choice of Hydration Strategy on Average Academic Performance}
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 %
 
-% \vspace*{-10mm}
-
 \begin{abstract}
     We evaluate the relationship between hydration strategy and
     academic performance and project that by using the right button of
@@ -179,14 +177,6 @@ and $S_\text{B}$ pressing both buttons.
 For the system measurement $10$ datapoints were recorded for each strategy,
 for the behavioural measurement $113$ in total.
 
-% As is always the case with measurements, care must be taken not to alter
-% quantities by measuring them. To this end, we made sure only to take system
-% measurements in the absence of participants and to only record data on the
-% behaviour of participants discreetly.
-
-% TODO: Describe the actual measurement setup? (e.g., filling up a 0.7l bottle
-% and timing with a standard smartphone timer)
-
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 \section{Experimental Results}
 
@@ -235,9 +225,6 @@ Whitney U test. We found that $S _\text{L}$ is faster than $S_\text{R}$ with a
 significance of $p < 0.0001$, while no significant statement could be made
 about $S_\text{R}$ and $S_\text{B}$.
 Fig. \ref{fig:Behavior} shows the results of the behavioural measurement.
-% During this part of the experiment, we also measured the time each participant
-% needed to fill up their bottle. Using the measured flowrates we calculated
-% the mean refill volume to be $\SI{673.92}{\milli\liter}$.
 
 \begin{figure}[H]
     \centering
@@ -289,42 +276,6 @@ S \mright\}$ the system utilization. Using our
 experimental data we can approximate all parameters and obtain
 $W \approx \SI{23.3}{\second}$. The difference to always using
 the fastest strategy amounts to $\SI{4.14}{\second}$.
-% We examine the effects of the choice of hydration strategy. To
-% this end, we start by estimating the potential time savings possible by always
-% choosing the fastest strategy:%
-% %
-% % We can model the time needed for one person to refill their
-% bottle as a random
-% % variable (RV) $T_1 = V/R$ and the time saved by choosing the
-% fastest strategy
-% % as $\Delta T_1 = T_1 - V/\max r$, where $V$ and $R$ are RVs representing the
-% % bottle volume and flowrate. The potential time saving for the
-% last person in a
-% % queue of $N$ people is thus $\Delta T_N = N\cdot\Delta T_1$. We
-% can then model
-% % the total time savings as $\Delta T_\text{tot} = \sum_{n=1}^{N} \Delta T_n$,
-% % where N is an RV describing the queue length. Assuming the
-% independence of all
-% % RVs we can compute the mean total time savings as
-% %
-% \begin{gather*}
-%     T_1 = V/R, \hspace{3mm} \Delta T_1 = T_1 - V/\max R,
-% \hspace{3mm} \Delta T_n = n \cdot \Delta T_1 \\
-%     \Delta T_\text{tot} = \sum_{n=1}^{N} \Delta T_\text{n} =
-% \sum_{n=1}^{N} n \cdot \Delta T_1 = \Delta T_1 \frac{N\mleft( N+1
-% \mright)}{2} \\
-%     \Delta t := E\mleft\{ \Delta T_\text{tot} \mright\} = E\mleft\{
-% \Delta T_1 \mright\} \cdot \mleft[ E\mleft\{ N^2 \mright\} +
-% E\mleft\{ N \mright\} \mright]/2
-%     ,%
-% \end{gather*}
-% %
-% where $V$ and $R$ are random variables (RVs) representing the volume of a
-% bottle and the flowrate, $\Delta T_n$ describes the time the last of $n$
-% people saves, $\Delta T_\text{tot}$ the total time savings and $N$ the length
-% of the queue. It is plausible to assume independence of $R,V$ and $N$.
-% Using our experimental measurements we estimate $\todo{\Delta t =
-% \SI{20}{\second}}$
 
 Strangely, it is the consensus of current research that there is only
 a weak relationship between academic performance and hours studied
@@ -342,18 +293,6 @@ arrival process and the relationship between the response time gain
 the grade gain. Nevertheless, we believe this work serves as a solid
 first step on the path towards achieving optimal study behaviour.
 
-% Many attempts have been made in the literature to relate
-% the time spent studying to academic achievement -  see, e.g.
-% \cite{schuman_effort_1985, zulauf_use_1999, michaels_academic_1989,
-% dickinson_effect_1990}.
-% The overwhelming consensus is that there is a significant relationship,
-% though it is a weak one.
-%
-%Many of the studies were only performed over
-% a period of one week or even day, so we believe care should be taken when
-% generlizing these results. Nevertheless, the overwhelming consensus in the
-% literature is that a significant relationship exists, though it is a weak one.
-
 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % \section{Conclusion}
 
@@ -365,15 +304,6 @@ improvement of up to $0.00103$ levels. We thus propose a novel and
 broadly applicable strategy to boost the average academic performance
 of KIT students: always using the right button.
 
-% Further research is needed to develop a better model of how the choice of
-% hydration strategy is related to academic performance. We
-% suspect that there is a compounding effect that leads to $S_\text{L}$ being an
-% even worse choice of hydration strategy: When the queue is long, students are
-% less likely to refill their empty water bottles, leading to reduced mental
-% ability. Nevertheless, we believe that with this work we have laid a solid
-% foundation and hope that our results will find widespread acceptance among the
-% local student population.
-
 %
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 % Bibliography
@@ -382,75 +312,4 @@ of KIT students: always using the right button.
 
 \printbibliography
 
-% \appendix
-%
-% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% \section{Derivation of Service Time}
-% \label{sec:Derivation of Service Time}
-%
-%
-% We want to compute the response time of our queueing system, i.e.,
-% \cite[Section 14.3]{stewart_probability_2009}
-% \begin{align*}
-%     W = E\mleft\{ S \mright\} + \frac{\lambda E\mleft\{ S^2
-% \mright\}}{2\mleft( 1-\rho \mright)}
-%     .%
-% \end{align*}%
-% We start by modelling the service time and subsequently calculate $\lambda$
-% and $\rho$.
-%
-% Let $S, V$ and $R$ be random variables denoting the service time,
-% refill volume
-% and refill rate, respectively. Assuming that $V$ and $R$ are independent, we
-% have
-% \begin{gather*}
-%     S = \frac{V}{R} \hspace{5mm} \text{and} \hspace{5mm}
-%     P_R(r) = \left\{\begin{array}{rl}
-%         P(S_\text{L}), & r = r_{S_\text{L}} \\
-%         1-P(S_\text{L}), & r = r_{S_\text{R}}
-%     \end{array}\right.
-% \end{gather*}%
-% \begin{align*}
-%     E\mleft\{ S^2 \mright\} &= E\mleft\{ V^2 \mright\}E\mleft\{ 1 /
-% R^2  \mright\} \\
-%     & = E\mleft\{ V^2 \mright\} \mleft(
-% P(S_\text{L})\frac{1}{r^2_{S_\text{L}}} + \mleft( 1 - P(S_\text{L})
-% \mright)\frac{1}{r^2_{S_\text{R}}}  \mright)
-%     .%
-% \end{align*}
-% We now use our experimental data (having calculated $v_n, n\in [1:N]$ from the
-% measured fill times and flow rates) to compute
-% \begin{align*}
-%     \left.
-%     \begin{array}{r}
-%         E\mleft\{ V^2 \mright\} \approx \frac{1}{N+1}
-% \sum_{n=1}^{N} v_n^2 = \todo{15}\\
-%         P(S_\text{L}) \approx \frac{\text{\# times $S_\text{L}$ was
-% chosen}}{N} = \todo{123} \\
-%         r^2_{S_\text{L}} \approx \todo{125} \\
-%         r^2_{S_\text{R}} \approx \todo{250}
-%     \end{array}
-%     \right\} \Rightarrow
-%     \left\{
-%     \begin{array}{l}
-%         E\mleft\{ S \mright\} \approx \todo{678} \\
-%         E\mleft\{ S^2 \mright\} \approx \todo{123}
-%     \end{array}
-%     \right.
-%     .%
-% \end{align*}
-%
-% $\lambda$ is the mean arrival time.
-%
-% \todo{
-%     \textbf{TODOs:}
-%     \begin{itemize}
-%         \item Complete text describing / obtaining $\rho$ and $\lambda$
-%         \item Move model derivation to method section
-%         \item Move calculations with model to results section
-%         \item Add grade gain derivation
-%         \item Idea: Make the whole thing 2 pages and print on A3
-%     \end{itemize}
-% }
-
 \end{document}