Fix stray asterisk and punctuation

discussion

.gitignore

@@ -0,0 +1 @@
+build/

.latexmkrc

@@ -0,0 +1,3 @@
+$pdflatex="pdflatex -shell-escape -interaction=nonstopmode -synctex=1 %O %S";
+$out_dir = 'build';
+$pdf_mode = 1;

Dockerfile

@@ -0,0 +1,14 @@
+FROM alpine:3.19
+
+RUN apk update && apk upgrade
+RUN apk add make texlive texmf-dist-pictures
+RUN apk add texmf-dist-publishers texmf-dist-science texmf-dist-fontsextra texmf-dist-latexextra
+RUN apk add biber texmf-dist-bibtexextra
+
+# The 'bbm' package insists on generating stuff in the home directory. In
+# order to guarantee access to the home directory no matter the user the
+# docker container is run with, create a temporary one anyone can write to
+RUN mkdir /tmp/home
+RUN chmod -R 777 /tmp/home
+ENV HOME=/tmp/home
+

Makefile

@@ -0,0 +1,11 @@
+all: build_folder paper.pdf
+
+build_folder:
+	mkdir -p build/build
+
+paper.pdf: paper.tex
+	latexmk -pdf paper.tex
+
+clean:
+	rm -rf build
+

README.md

@@ -0,0 +1,23 @@
+# KIT Library Hydration Study
+
+Latex sources for a paper on the behaviour of students at the KIT library with
+regard to their water bottle refilling habits.
+
+## Build
+
+### Build manually
+
+```bash
+$ make
+```
+
+### Build using docker
+
+1. Build docker image
+   ```bash
+   $ docker build -f Dockerfile . -t bib-paper
+   ```
+2. Build document
+   ```bash
+   $ docker run --rm -v $PWD:$PWD -w $PWD -u `id -u`:`id -g` bib-paper make
+   ```

common.tex

@@ -0,0 +1,316 @@
+% Author: Andreas Tsouchlos
+%
+% Collection of useful commands and definitions
+%
+% ||====================================================================||
+% ||                               WARNING                              ||
+% ||====================================================================||
+% || The following packages have to be included before using this file: ||
+% ||      amsmath                                                       ||
+% ||      pgfplots                                                      ||
+% ||====================================================================||
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Math Symbols %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+\DeclareMathOperator*{\argmin}{\arg\!\min}
+\DeclareMathOperator*{\argmax}{\arg\!\max}
+\DeclareMathOperator\sign{sign}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Data Manipulation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+%
+% Filters for Pgfplots
+% Source: https://tex.stackexchange.com/a/58563 (modified)
+%
+
+\pgfplotsset{
+    discard if/.style 2 args={
+        x filter/.append code={
+            \edef\tempa{\thisrow{#1}}
+            \edef\tempb{#2}
+            \ifx\tempa\tempb
+                \def\pgfmathresult{inf}
+            \fi
+        }
+    },
+    discard if not/.style 2 args={
+        x filter/.append code={
+            \edef\tempa{\thisrow{#1}}
+            \edef\tempb{#2}
+            \ifx\tempa\tempb
+            \else
+                \def\pgfmathresult{inf}
+            \fi
+        }
+    },
+    discard if gt/.style 2 args={
+        x filter/.append code={
+            \edef\tempa{\thisrow{#1}}
+            \edef\tempb{#2}
+            \ifdim\tempa pt > \tempb pt
+                \def\pgfmathresult{inf}
+            \fi
+        }
+    },
+    discard if lt/.style 2 args={
+        x filter/.append code={
+            \edef\tempa{\thisrow{#1}}
+            \edef\tempb{#2}
+            \ifdim\tempa pt < \tempb pt
+                \def\pgfmathresult{inf}
+            \fi
+        }
+    }
+}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Graphics & Plotting %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%
+% Colors
+%
+
+% KIT Colors
+
+\definecolor{kit-green100}{rgb}{0,.59,.51}
+\definecolor{kit-green70}{rgb}{.3,.71,.65}
+\definecolor{kit-green50}{rgb}{.50,.79,.75}
+\definecolor{kit-green30}{rgb}{.69,.87,.85}
+\definecolor{kit-green15}{rgb}{.85,.93,.93}
+\definecolor{KITgreen}{rgb}{0,.59,.51}
+
+\definecolor{KITpalegreen}{RGB}{130,190,60}
+\colorlet{kit-maigreen100}{KITpalegreen}
+\colorlet{kit-maigreen70}{KITpalegreen!70}
+\colorlet{kit-maigreen50}{KITpalegreen!50}
+\colorlet{kit-maigreen30}{KITpalegreen!30}
+\colorlet{kit-maigreen15}{KITpalegreen!15}
+
+\definecolor{KITblue}{rgb}{.27,.39,.66}
+\definecolor{kit-blue100}{rgb}{.27,.39,.67}
+\definecolor{kit-blue70}{rgb}{.49,.57,.76}
+\definecolor{kit-blue50}{rgb}{.64,.69,.83}
+\definecolor{kit-blue30}{rgb}{.78,.82,.9}
+\definecolor{kit-blue15}{rgb}{.89,.91,.95}
+
+\definecolor{KITyellow}{rgb}{.98,.89,0}
+\definecolor{kit-yellow100}{cmyk}{0,.05,1,0}
+\definecolor{kit-yellow70}{cmyk}{0,.035,.7,0}
+\definecolor{kit-yellow50}{cmyk}{0,.025,.5,0}
+\definecolor{kit-yellow30}{cmyk}{0,.015,.3,0}
+\definecolor{kit-yellow15}{cmyk}{0,.0075,.15,0}
+
+\definecolor{KITorange}{rgb}{.87,.60,.10}
+\definecolor{kit-orange100}{cmyk}{0,.45,1,0}
+\definecolor{kit-orange70}{cmyk}{0,.315,.7,0}
+\definecolor{kit-orange50}{cmyk}{0,.225,.5,0}
+\definecolor{kit-orange30}{cmyk}{0,.135,.3,0}
+\definecolor{kit-orange15}{cmyk}{0,.0675,.15,0}
+
+\definecolor{KITred}{rgb}{.63,.13,.13}
+\definecolor{kit-red100}{cmyk}{.25,1,1,0}
+\definecolor{kit-red70}{cmyk}{.175,.7,.7,0}
+\definecolor{kit-red50}{cmyk}{.125,.5,.5,0}
+\definecolor{kit-red30}{cmyk}{.075,.3,.3,0}
+\definecolor{kit-red15}{cmyk}{.0375,.15,.15,0}
+
+\definecolor{KITpurple}{RGB}{160,0,120}
+\colorlet{kit-purple100}{KITpurple}
+\colorlet{kit-purple70}{KITpurple!70}
+\colorlet{kit-purple50}{KITpurple!50}
+\colorlet{kit-purple30}{KITpurple!30}
+\colorlet{kit-purple15}{KITpurple!15}
+
+\definecolor{KITcyanblue}{RGB}{80,170,230}
+\colorlet{kit-cyanblue100}{KITcyanblue}
+\colorlet{kit-cyanblue70}{KITcyanblue!70}
+\colorlet{kit-cyanblue50}{KITcyanblue!50}
+\colorlet{kit-cyanblue30}{KITcyanblue!30}
+\colorlet{kit-cyanblue15}{KITcyanblue!15}
+
+% Matplotlib Colors
+
+\definecolor{Mpl1}{HTML}{1f77b4}
+\definecolor{Mpl2}{HTML}{ff7f0e}
+\definecolor{Mpl3}{HTML}{2ca02c}
+\definecolor{Mpl4}{HTML}{d62728}
+\definecolor{Mpl5}{HTML}{9467bd}
+\definecolor{Mpl6}{HTML}{8c564b}
+\definecolor{Mpl7}{HTML}{e377c2}
+\definecolor{Mpl8}{HTML}{7f7f7f}
+\definecolor{Mpl9}{HTML}{bcbd22}
+\definecolor{Mpl10}{HTML}{17becf}
+
+%
+% Color Schemes
+%
+
+% Define colormaps
+
+\pgfplotsset{
+	colormap={mako}{
+		rgb=(0.18195582, 0.11955283, 0.23136943)
+		rgb=(0.25307401, 0.23772973, 0.48316271)
+		rgb=(0.21607792, 0.39736958, 0.61948028)
+		rgb=(0.20344718, 0.56074869, 0.65649508)
+		rgb=(0.25187832, 0.71827158, 0.67872193)
+		rgb=(0.54578602, 0.8544913, 0.69848331)
+	},
+	colormap={rocket}{
+		rgb=(0.20973515, 0.09747934, 0.24238489)
+		rgb=(0.43860848, 0.12177004, 0.34119475)
+		rgb=(0.67824099, 0.09192342, 0.3504148)
+		rgb=(0.8833417, 0.19830556, 0.26014181)
+		rgb=(0.95381595, 0.46373781, 0.31769923)
+		rgb=(0.96516917, 0.70776351, 0.5606593)
+	},
+	colormap={cividis}{
+		rgb=(0.130669, 0.231458, 0.43284)
+		rgb=(0.298421, 0.332247, 0.423973)
+		rgb=(0.42512, 0.431334, 0.447692)
+		rgb=(0.555393, 0.537807, 0.471147)
+		rgb=(0.695985, 0.648334, 0.440072)
+		rgb=(0.849223, 0.771947, 0.359729)
+	},
+	colormap={cel}{
+		color=(KITred!90!black);
+		color=(kit-blue100);
+ 		color=(kit-green70);
+		color=(kit-yellow70!80!kit-orange70);
+	},
+	colormap={matplotlib}{
+		% Source: https://github.com/matplotlib/matplotlib/blob/e5a85f960b2d47eac371cff709b830d52c36d267/lib/matplotlib/_cm.py#L1114
+        rgb=(0.2298057,   0.298717966, 0.753683153)
+        rgb=(0.26623388,  0.353094838, 0.801466763)
+        rgb=(0.30386891,  0.406535296, 0.84495867 )
+        rgb=(0.342804478, 0.458757618, 0.883725899)
+        rgb=(0.38301334,  0.50941904,  0.917387822)
+        rgb=(0.424369608, 0.558148092, 0.945619588)
+        rgb=(0.46666708,  0.604562568, 0.968154911)
+        rgb=(0.509635204, 0.648280772, 0.98478814 )
+        rgb=(0.552953156, 0.688929332, 0.995375608)
+        rgb=(0.596262162, 0.726149107, 0.999836203)
+        rgb=(0.639176211, 0.759599947, 0.998151185)
+        rgb=(0.681291281, 0.788964712, 0.990363227)
+        rgb=(0.722193294, 0.813952739, 0.976574709)
+        rgb=(0.761464949, 0.834302879, 0.956945269)
+        rgb=(0.798691636, 0.849786142, 0.931688648)
+        rgb=(0.833466556, 0.860207984, 0.901068838)
+        rgb=(0.865395197, 0.86541021,  0.865395561)
+        rgb=(0.897787179, 0.848937047, 0.820880546)
+        rgb=(0.924127593, 0.827384882, 0.774508472)
+        rgb=(0.944468518, 0.800927443, 0.726736146)
+        rgb=(0.958852946, 0.769767752, 0.678007945)
+        rgb=(0.96732803,  0.734132809, 0.628751763)
+        rgb=(0.969954137, 0.694266682, 0.579375448)
+        rgb=(0.966811177, 0.650421156, 0.530263762)
+        rgb=(0.958003065, 0.602842431, 0.481775914)
+        rgb=(0.943660866, 0.551750968, 0.434243684)
+        rgb=(0.923944917, 0.49730856,  0.387970225)
+        rgb=(0.89904617,  0.439559467, 0.343229596)
+        rgb=(0.869186849, 0.378313092, 0.300267182)
+        rgb=(0.834620542, 0.312874446, 0.259301199)
+        rgb=(0.795631745, 0.24128379,  0.220525627)
+        rgb=(0.752534934, 0.157246067, 0.184115123)
+        rgb=(0.705673158, 0.01555616,  0.150232812)
+	}
+}
+
+% Define cycle lists
+
+\pgfplotscreateplotcyclelist{mako}{%
+	[samples of colormap={4} of mako]%
+}
+\pgfplotscreateplotcyclelist{rocket}{%
+	[samples of colormap={4} of rocket]%
+}
+\pgfplotscreateplotcyclelist{cividis}{%
+	[samples of colormap={4} of cividis]%
+}
+\pgfplotscreateplotcyclelist{viridis}{%
+	[samples of colormap={4} of viridis]%
+}
+\pgfplotscreateplotcyclelist{cel}{%
+	[samples of colormap={4} of cel]%
+}
+\pgfplotscreateplotcyclelist{matplotlib}{%
+	{Mpl1},{Mpl2},{Mpl3},{Mpl4}
+}
+
+% Define 'scolX' colors
+
+\makeatletter
+
+\def\extractcolormapcolor#1#2{%
+	\expandafter\pgfplotscolormapaccess\expandafter[\pgfplotspointmetatransformedrange]%
+		[1.0]%
+		{#2}%
+		{\pgfkeysvalueof{/pgfplots/colormap name}}%
+	\def\pgfplots@loc@TMPb{\pgfutil@definecolor{#1}{\csname pgfpl@cm@\pgfkeysvalueof{/pgfplots/colormap name}@colspace\endcsname}}%
+	\expandafter\pgfplots@loc@TMPb\expandafter{\pgfmathresult}%
+}%
+
+\def\getcolorbyvalue#1{
+	\csname pgfpl@cm@\pgfkeysvalueof{/pgfplots/colormap name}@colspace\endcsname
+}
+
+\makeatother
+
+\def\setschemecolorsfrommap{
+	\extractcolormapcolor{scol0}{0}
+	\extractcolormapcolor{scol1}{333}
+	\extractcolormapcolor{scol2}{666}
+	\extractcolormapcolor{scol3}{1000}
+}
+
+\newcommand{\setschemecolorsmanually}[4]{
+	\colorlet{scol0}{#1}
+	\colorlet{scol1}{#2}
+	\colorlet{scol2}{#3}
+	\colorlet{scol3}{#4}
+}
+
+% Define color schemes
+
+\pgfplotsset{
+	/pgfplots/colorscheme/cel/.style={
+		colormap name={cel},
+		cycle list name={cel},
+		/utils/exec={\setschemecolorsfrommap},
+	},
+	/pgfplots/colorscheme/rocket/.style={
+		colormap name={rocket},
+		cycle list name={rocket},
+		/utils/exec={\setschemecolorsfrommap},
+	},
+	/pgfplots/colorscheme/viridis/.style={
+		colormap name={viridis},
+		cycle list name={viridis},
+		/utils/exec={\setschemecolorsfrommap},
+	},
+	/pgfplots/colorscheme/mako/.style={
+		colormap name={mako},
+		cycle list name={mako},
+		/utils/exec={\setschemecolorsfrommap},
+	},
+	/pgfplots/colorscheme/cividis/.style={
+		colormap name={cividis},
+		cycle list name={cividis},
+		/utils/exec={\setschemecolorsfrommap},
+	},
+	/pgfplots/colorscheme/matplotlib/.style={
+		colormap name={matplotlib},
+		cycle list name={matplotlib},
+		/utils/exec={\setschemecolorsmanually{Mpl1}{Mpl2}{Mpl3}{Mpl4}},
+	},
+}

paper.bib

@@ -0,0 +1,345 @@
+@book{gertner_idea_2012,
+  title={The idea factory: Bell Labs and the great age of American innovation},
+  author={Gertner, Jon},
+  year={2012},
+  publisher={Penguin}
+}
+
+@article{chen_homotopy_2015,
+	title = {Homotopy continuation method for solving systems of nonlinear and polynomial equations},
+	volume = {15},
+	issn = {15267555, 21634548},
+	url = {https://link.intlpress.com/JDetail/1805790889102491649},
+	doi = {10.4310/CIS.2015.v15.n2.a1},
+	language = {en},
+	number = {2},
+	urldate = {2025-02-24},
+	journal = {Communications in Information and Systems},
+	author = {Chen, Tianran and Li, Tien-Yien},
+	year = {2015},
+	keywords = {/unread},
+	pages = {119--307},
+	file = {PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/AM6X2GG5/Chen and Li - 2015 - Homotopy continuation method for solving systems of nonlinear and polynomial equations.pdf:application/pdf},
+}
+
+@misc{reichel_numerical_2023,
+	title = {Numerical {Methods} for {Electrical} {Engineering}, {Meteorology}, {Remote} {Sensing}, and {Geoinformatics}},
+	shorttitle = {Numerical {Methods}},
+	language = {en},
+	author = {Reichel, Wolfgang},
+	year = {2023},
+	keywords = {/unread},
+	file = {PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/VPCNXBJJ/Reichel - for Electrical Engineering, Meteorology, Remote Sensing, and Geoinformatics.pdf:application/pdf},
+}
+
+@book{golub_matrix_2013,
+	edition = {fourth},
+	title = {Matrix {Computations}},
+	isbn = {1-4214-0794-9 978-1-4214-0794-4},
+	url = {http://www.cs.cornell.edu/cv/GVL4/golubandvanloan.htm},
+	publisher = {JHU Press},
+	author = {Golub, Gene Howard and Van Loan, Charles Francis},
+	year = {2013},
+	keywords = {/unread},
+	file = {PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/ECTUSDB6/Golub and Van Loan - 2013 - Matrix Computations.pdf:application/pdf},
+}
+
+@book{allgower_introduction_2003,
+	address = {Philadelphia, Pa},
+	series = {Classics in applied mathematics},
+	title = {Introduction to numerical continuation methods},
+	isbn = {978-0-89871-544-6 978-0-89871-915-4},
+	abstract = {Numerical continuation methods have provided important contributions toward the numerical solution of nonlinear systems of equations for many years. The methods may be used not only to compute solutions, which might otherwise be hard to obtain, but also to gain insight into qualitative properties of the solutions. Introduction to Numerical Continuation Methods, originally published in 1979, was the first book to provide easy access to the numerical aspects of predictor corrector continuation and piecewise linear continuation methods. Not only do these seemingly distinct methods share many common features and general principles, they can be numerically implemented in similar ways. The book also features the piecewise linear approximation of implicitly defined surfaces, the algorithms of which are frequently used in computer graphics, mesh generation, and the evaluation of surface integrals. To help potential users of numerical continuation methods create programs adapted to their particular needs, this book presents pseudo-codes and Fortran codes as illustrations. Since it first appeared, many specialized packages for treating such varied problems as bifurcation, polynomial systems, eigenvalues, economic equilibria, optimization, and the approximation of manifolds have been written. The original extensive bibliography has been updated in the SIAM Classics edition to include more recent references and several URLs so users can look for codes to suit their needs. Audience: this book continues to be useful for researchers and graduate students in mathematics, sciences, engineering, economics, and business. A background in elementary analysis and linear algebra are adequate prerequisites for reading this book; some knowledge from a first course in numerical analysis may also be helpful},
+	language = {eng},
+	number = {45},
+	publisher = {Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104)},
+	author = {Allgower, Eugene L. and Georg, Kurt},
+	collaborator = {{Society for Industrial and Applied Mathematics}},
+	year = {2003},
+	doi = {10.1137/1.9780898719154},
+	keywords = {Continuation methods, Euler-Newton method, /unread},
+	file = {PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/LQGFJ4LJ/Allgower and Georg - 2003 - Introduction to numerical continuation methods.pdf:application/pdf},
+}
+
+@book{higham_functions_2008,
+	title = {Functions of {Matrices}: {Theory} and {Computation}},
+	isbn = {978-0-89871-646-7},
+	shorttitle = {Functions of {Matrices}},
+	abstract = {A thorough and elegant treatment of the theory of matrix functions and numerical methods for computing them, including an overview of applications, new and unpublished research results, and improved algorithms. Key features include a detailed treatment of the matrix sign function and matrix roots; a development of the theory of conditioning and properties of the FrŸ?chet derivative; Schur decomposition; block Parlett recurrence; a thorough analysis of the accuracy, stability, and computational cost of numerical methods; general results on convergence and stability of matrix iterations; and a chapter devoted to the f(A)b problem. Ideal for advanced courses and for self-study, its broad content, references and appendix also make this book a convenient general reference. Contains an extensive collection of problems with solutions and MATLAB? implementations of key algorithms.},
+	language = {en},
+	publisher = {SIAM},
+	author = {Higham, Nicholas J.},
+	month = sep,
+	year = {2008},
+	note = {Google-Books-ID: 2Wz\_zVUEwPkC},
+	keywords = {Mathematics / Algebra / Linear, Mathematics / Applied, Mathematics / Mathematical Analysis, Mathematics / Matrices, Mathematics / Numerical Analysis, /unread},
+	file = {PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/W3DMMA3P/Higham - 2008 - Functions of Matrices Theory and Computation.pdf:application/pdf},
+}
+
+@article{james_w_michaels_academic_1989,
+	title = {Academic effort and college grades},
+	volume = {68},
+	journal = {Social Forces},
+	author = {{James W Michaels} and {Terance D Miethe}},
+	year = {1989},
+	keywords = {/unread},
+	pages = {309--319},
+}
+
+@article{michaels_academic_1989,
+	title = {Academic {Effort} and {College} {Grades}*},
+	volume = {68},
+	issn = {0037-7732},
+	url = {https://doi.org/10.1093/sf/68.1.309},
+	doi = {10.1093/sf/68.1.309},
+	abstract = {This study examines the possibility that specification errors contribute to the Schuman et al (1985) findings of a weak relationship between study time and college grades. Our analyses investigate both main and interactive effects, measures of quantity and quality of study, and various context-specific models of college grades. In contrast to previous findings, we observe significant main and interactive effects of academic effort on college grades.},
+	number = {1},
+	urldate = {2025-03-07},
+	journal = {Social Forces},
+	author = {Michaels, James W. and Miethe, Terance D.},
+	month = sep,
+	year = {1989},
+	keywords = {/unread, ⭐⭐⭐},
+	pages = {309--319},
+	file = {Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/8UXRFWEC/Michaels and Miethe - 1989 - Academic Effort and College Grades.pdf:application/pdf;Snapshot:/home/andreas/workspace/work/hiwi/Zotero/storage/2JBVD4TS/2232194.html:text/html},
+}
+
+@article{dickinson_effect_1990,
+	title = {Effect of {Quality} and {Quantity} of {Study} on {Student} {Grades}},
+	volume = {83},
+	issn = {0022-0671},
+	url = {https://www.jstor.org/stable/27540388},
+	abstract = {In this study we investigated the relationship between study time and test scores in a course on learning principles for college education majors. The students were required to keep a continuous log of the amount of time that they spent reading, reviewing, and organizing for the course. Weak relationships with test scores were found for total study time and time spent reviewing. A much stronger relationship was found for time spent organizing the course content. An extreme-groups analysis revealed that students with high test scores spent 40 min per week organizing compared with 10 min per week for students with low test scores. The results support the importance that information-processing theorists attribute to active learning strategies.},
+	number = {4},
+	urldate = {2025-03-07},
+	journal = {The Journal of Educational Research},
+	author = {Dickinson, Donald J. and O'Connell, Debra Q.},
+	year = {1990},
+	note = {Publisher: Taylor \& Francis, Ltd.},
+	keywords = {/unread},
+	pages = {227--231},
+	file = {JSTOR Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/8HZQXEEU/Dickinson and O'Connell - 1990 - Effect of Quality and Quantity of Study on Student Grades.pdf:application/pdf},
+}
+
+@article{zulauf_use_1999,
+	series = {Selected {Paper}},
+	title = {{USE} {OF} {TIME} {AND} {ACADEMIC} {PERFORMANCE} {OF} {COLLEGE} {STUDENTS}: {DOES} {STUDYING} {MATTER}?},
+	shorttitle = {{USE} {OF} {TIME} {AND} {ACADEMIC} {PERFORMANCE} {OF} {COLLEGE} {STUDENTS}},
+	doi = {10.22004/ag.econ.21547},
+	abstract = {Recursive regression analysis revealed time management skills and study time were positively related with quarter GPA for 93 students in three agricultural economics courses at Ohio State University. GPA increased only 0.04 points [4.0 scale] per additional study hour, suggesting substantial improvements in GPA require substantial increases in study time},
+	language = {eng},
+	editor = {Zulauf, Carl R. and Gortner, Amy K.},
+	year = {1999},
+	note = {Num Pages: 16},
+	keywords = {academic performance, study time, Teaching/Communication/Extension/Profession, time management, /unread, ⭐⭐⭐⭐⭐},
+	file = {PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/PM7CTJ7G/Zulauf and Gortner - 1999 - USE OF TIME AND ACADEMIC PERFORMANCE OF COLLEGE STUDENTS DOES STUDYING MATTER.pdf:application/pdf},
+}
+
+@article{rooney_use_1985,
+	title = {The {Use} of {Self}-{Monitoring} {Procedures} {With} {Low} {IQ} {Learning} {Disabled} {Students}},
+	volume = {18},
+	issn = {0022-2194},
+	url = {https://doi.org/10.1177/002221948501800703},
+	doi = {10.1177/002221948501800703},
+	abstract = {Given the changes in the population served in programs for the learning-disabled, there is a continuing need to verify the effectiveness of teaching methods used with D students. This research investigated the efficacy of two cognitive behavior modification procedures—self-monitoring of attention and self-monitoring of academic accuracy—with a group of low functioning students in a LD self-contained class. Data are presented which indicate that the combination of both procedures was effective for all four students in improving attention-to-task and for three of the four children in percentage of accurate responses in an arithmetic task. The discussion highlights the importance of possible modification of CBM methods and training procedures in order to develop successsful intervention programs for LD students whose cognitive functioning levels are below average.},
+	language = {en},
+	number = {7},
+	urldate = {2025-03-07},
+	journal = {Journal of Learning Disabilities},
+	author = {Rooney, Karen and Polloway, Edward A. and Hallahan, Daniel P.},
+	month = aug,
+	year = {1985},
+	note = {Publisher: SAGE Publications Inc
+TLDR: Investigation of the efficacy of two cognitive behavior modification procedures with a group of low functioning students in a LD self-contained class indicates that the combination was effective for all four students in improving attention-to-task and for three of the four children in percentage of accurate responses in an arithmetic task.},
+	keywords = {/unread, ⭐},
+	pages = {384--389},
+	file = {SAGE PDF Full Text:/home/andreas/workspace/work/hiwi/Zotero/storage/ETX97WRA/Rooney et al. - 1985 - The Use of Self-Monitoring Procedures With Low IQ Learning Disabled Students.pdf:application/pdf},
+}
+
+@article{napoles_role_2023,
+	title = {The {Role} of {Time} {Management} to the {Academic} {Performance} of the {College} {Students} {During} {Pandemic}},
+	volume = {10},
+	copyright = {Copyright (c) 2023 International Journal of Social Sciences and Humanities Invention},
+	issn = {2349-2031},
+	url = {https://valleyinternational.net/index.php/theijsshi/article/view/3829},
+	doi = {10.18535/ijsshi/v10i02.05},
+	abstract = {This study was undertaken to determine a statistical correlation between the role of time management and the academic performance of the college students during a pandemic. The population of the research consisted of distance learning students from the\  Department of Technology Teacher Education of Mindanao State University-Iligan Institute of Technology. One hundred eighty-two participants belonging to different courses who were selected randomly, ranging from first year to fourth year, participated conveniently in this study. Survey questionnaires regarding time management on a five-point likert scale were used to collect data from respondents and were disseminated through Google forms. Before utilizing scales, professionals in the field review them for validity. The variables' previous GPA correlated with .139 and a significance of .061 indicated that they have no significant relationship with how students manage their time. It is concluded that both variables have no significant relationship with each other and time management skills do not affect students' school performance significantly. However, students should be aware of time wastage and take responsibility for enhancing their time management skills and maintaining their grade point average.},
+	language = {en},
+	number = {02},
+	urldate = {2025-03-07},
+	journal = {International Journal of Social Sciences and Humanities Invention},
+	author = {Napoles, Michael Art and Altubar, Jepril Ann B. and Anding, Hannah Kris T.},
+	month = feb,
+	year = {2023},
+	note = {Number: 02},
+	keywords = {Distance Learning, Online Learning, Time Management, /unread},
+	pages = {7731--7741},
+	file = {Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/FST4M46S/Napoles et al. - 2023 - The Role of Time Management to the Academic Performance of the College Students During Pandemic.pdf:application/pdf},
+}
+
+@article{masui_diligent_2014,
+	title = {Do diligent students perform better? {Complex} relations between student and course characteristics, study time, and academic performance in higher education},
+	volume = {39},
+	issn = {0307-5079},
+	shorttitle = {Do diligent students perform better?},
+	url = {https://doi.org/10.1080/03075079.2012.721350},
+	doi = {10.1080/03075079.2012.721350},
+	abstract = {Research has reported equivocal results regarding the relationship between study time investment and academic performance in higher education. In the setting of the active, assignment-based teaching approach at Hasselt University (Belgium), the present study aimed (a) to further clarify the role of study time in academic performance, while taking into account student characteristics (e.g. gender, prior domain knowledge), and (b) to examine the relation between a number of student and course characteristics and study time. Data included course-specific study time recordings across the entire term, grades for 14 courses, expert ratings of six course characteristics, and other data from the records of 168 freshmen in business economics. For most courses, study time predicted grades, even beyond student characteristics. However, there were differential results depending on the course considered, stressing the importance of examining relations at course level instead of globally across courses. As to study time, course characteristics were strong predictors.},
+	number = {4},
+	urldate = {2025-03-07},
+	journal = {Studies in Higher Education},
+	author = {Masui, Chris and Broeckmans, Jan and Doumen, Sarah and Groenen, Anne and Molenberghs, Geert},
+	month = apr,
+	year = {2014},
+	note = {Publisher: SRHE Website
+\_eprint: https://doi.org/10.1080/03075079.2012.721350},
+	keywords = {academic performance, study time, higher education, learning environment, self-regulated learning, student characteristics, /unread, ⭐⭐⭐⭐},
+	pages = {621--643},
+	file = {Submitted Version:/home/andreas/workspace/work/hiwi/Zotero/storage/S7W5636G/Masui et al. - 2014 - Do diligent students perform better Complex relations between student and course characteristics, s.pdf:application/pdf},
+}
+
+@article{plant_why_2005,
+	title = {Why study time does not predict grade point average across college students: {Implications} of deliberate practice for academic performance},
+	volume = {30},
+	shorttitle = {Why study time does not predict grade point average across college students},
+	abstract = {The current work draws upon the theoretical framework of deliberate practice in order to clarify why the amount of study by college students is a poor predictor of academic performance. A model was proposed where performance in college, both cumulatively and for a current semester, was jointly determined by previous knowledge and skills as well as factors indicating quality (e.g., study environment) and quantity of study. The findings support the proposed model and indicate that the amount of study only emerged as a significant predictor of cumulative GPA when the quality of study and previously attained performance were taken into consideration. The findings are discussed in terms of the insights provided by applying the framework of deliberate practice to academic performance in a university setting.},
+	number = {1},
+	journal = {Contemporary Educational Psychology},
+	author = {Plant, E. Ashby and Ericsson, K. Anders and Hill, Len and Asberg, Kia},
+	month = jan,
+	year = {2005},
+	keywords = {Academic performance, Deliberate practice, Grade point average, Study habits, Study time, /unread, ⭐⭐⭐},
+	pages = {96--116},
+	file = {ScienceDirect Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/6CLRGKBL/Plant et al. - 2005 - Why study time does not predict grade point average across college students Implications of deliber.pdf:application/pdf;ScienceDirect Snapshot:/home/andreas/workspace/work/hiwi/Zotero/storage/I7AWASSH/S0361476X04000384.html:text/html},
+}
+
+@article{schuman_effort_1985,
+	title = {Effort and {Reward}: {The} {Assumption} that {College} {Grades} {Are} {Affected} by {Quantity} of {Study}},
+	volume = {63},
+	shorttitle = {Effort and {Reward}},
+	abstract = {The relation between college grades and self-reported amount of effort was examined in four major and several minor investigations of undergraduates in a large state university. Grades were operationalized mainly by using grade point average (GPA), though in one investigation grades in a particular course were the focus. Effort was measured in several different ways, ranging from student estimates of typical study over the term to reports of study on specific days. Despite evidence that these self-reports provide meaningful estimates of actual studying, there is at best only a very small relation between amount of studying and grades, as compared to the considerably stronger and more monotonic relations between grades and both aptitude measures and self-reported class attendance. The plausible assumption that college grades reflect student effort to an important extent does not receive much support from these investigations. This raises a larger question about the extent to which rewards are linked to effort in other areas of life—a connection often assumed but seldom investigated.},
+	number = {4},
+	journal = {Social Forces},
+	author = {Schuman, Howard and Walsh, Edward and Olson, Camille and Etheridge, Barbara},
+	month = jun,
+	year = {1985},
+	keywords = {/unread, ⭐⭐⭐⭐⭐},
+	pages = {945--966},
+	file = {Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/ST8F6X85/Schuman et al. - 1985 - Effort and Reward The Assumption that College Grades Are Affected by Quantity of Study.pdf:application/pdf;Snapshot:/home/andreas/workspace/work/hiwi/Zotero/storage/6ZFY7KPK/2232109.html:text/html},
+}
+
+@misc{e_g_williamson_relationship_1935,
+	title = {The relationship of number of hours of study to scholarship.},
+	url = {https://psycnet.apa.org/record/1936-02704-001},
+	abstract = {A study based on the reported hours of study of 257 freshmen at the University of Minnesota during the week just prior to mid-quarter examinations. The mean hours of study were 27.09; the {\textless}em{\textgreater}r{\textless}/em{\textgreater} between scholarship and hours of study was-.06; the {\textless}em{\textgreater}r{\textless}/em{\textgreater} between scores on the Minnesota college aptitude test and hours of study was -.20. A comparison of these results with other studies is given, and it is concluded that the location of the week used in this study probably makes the new data closer approximations to the true relationships. The student of low ability must study more than the student of high ability, but the increase will not necessarily result in much higher scholarship. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
+	language = {en-US},
+	urldate = {2025-03-07},
+	journal = {APA PsycNET},
+	author = {{E. G. Williamson}},
+	year = {1935},
+	doi = {10.1037/h0056481},
+	keywords = {/unread, ⭐⭐},
+	file = {PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/IIVGJSV8/The relationship of number of hours of study to scholarship..pdf:application/pdf;Snapshot:/home/andreas/workspace/work/hiwi/Zotero/storage/F4ZN6958/1936-02704-001.html:text/html},
+}
+
+@article{kember_learning_1995,
+	title = {Learning approaches, study time and academic performance},
+	volume = {29},
+	issn = {1573-174X},
+	url = {https://doi.org/10.1007/BF01384497},
+	doi = {10.1007/BF01384497},
+	abstract = {This study investigated the relationship between learning approach, time spent studying and grades awarded. A class of mechanical engineering students (N=34; male) were asked to keep an hour-by-hour study diary for one week. The Biggs' Study Process Questionnaire (SPQ) provided measures of these students' approach to study tasks. Use of a surface approach to learning was found to be positively correlated with both high attendance in class and greater hours of independent study time. The former is explained by the surface learner's need for the lecturer to define the course; the latter by the inefficiency of a surface approach. Poor grades in spite of long study hours mirror an inefficient surface approach. This finding suggests the need for individual study counselling. Case studies show that the use of a deep approach does not result in good grades unless accompanied by sufficient work. The diary method in conjunction with the SPQ appears to be a promising method for researching workload, study times and other related variables.},
+	language = {en},
+	number = {3},
+	urldate = {2025-03-07},
+	journal = {Higher Education},
+	author = {Kember, David and Jamieson, Qun Wang and Pomfret, Mike and Wong, Eric T. T.},
+	month = apr,
+	year = {1995},
+	keywords = {Academic Performance, Learn Approach, Mechanical Engineering, Promising Method, Study Time, /unread, ⭐⭐},
+	pages = {329--343},
+	file = {Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/G8WUZWIJ/Kember et al. - 1995 - Learning approaches, study time and academic performance.pdf:application/pdf},
+}
+
+@article{schuman_comment_2001,
+	title = {Comment: {Students}' {Effort} and {Reward} in {College} {Settings}},
+	volume = {74},
+	issn = {0038-0407},
+	shorttitle = {Comment},
+	url = {https://www.jstor.org/stable/2673146},
+	doi = {10.2307/2673146},
+	number = {1},
+	urldate = {2025-03-08},
+	journal = {Sociology of Education},
+	author = {Schuman, Howard},
+	year = {2001},
+	note = {Publisher: [Sage Publications, Inc., American Sociological Association]},
+	keywords = {/unread, ⭐⭐⭐},
+	pages = {73--74},
+	file = {JSTOR Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/AYBTRUSF/Schuman - 2001 - Comment Students' Effort and Reward in College Settings.pdf:application/pdf},
+}
+
+@article{hill_effort_1990,
+	title = {Effort and {Reward} in {College}: {A} {Replication} of {Some} {Puzzling} {Findings}},
+	volume = {5},
+	issn = {0886-1641},
+	shorttitle = {Effort and {Reward} in {College}},
+	url = {https://www.proquest.com/docview/1292260741/citation/16AEB9E8A9FF44E0PQ/1},
+	language = {English},
+	number = {4},
+	urldate = {2025-03-08},
+	journal = {Journal of Social Behavior and Personality},
+	author = {Hill, Lester},
+	year = {1990},
+	note = {Num Pages: 11
+Place: Corte Madera, CA, United States
+Publisher: Select Press},
+	keywords = {Psychology, Social Sciences (General), Sociology, /unread, ⭐⭐⭐},
+	pages = {151--161},
+	file = {Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/X69I8QXV/Hill - 1990 - Effort and Reward in College A Replication of Some Puzzling Findings.pdf:application/pdf},
+}
+
+@article{rau_academic_2000,
+	title = {The {Academic} {Ethic} and {College} {Grades}: {Does} {Hard} {Work} {Help} {Students} to "{Make} the {Grade}"?},
+	volume = {73},
+	issn = {0038-0407},
+	shorttitle = {The {Academic} {Ethic} and {College} {Grades}},
+	url = {https://www.jstor.org/stable/2673197},
+	doi = {10.2307/2673197},
+	abstract = {Most scholars and teachers accept, as part of the natural order of the universe, a strong relationship between study efforts and students' academic performance. Yet, the only systematic investigation of this relationship, a 12-year project at the University of Michigan, repeatedly found little to no correlation between hours studied and grades. The study presented here replicated parts of this project but did so with a different conceptualization of effort. This new perspective views effort as the outcome of an "academic ethic," a student worldview that emphasizes diligent, daily, and sober study. This article shows how this concept can be operationalized and measured and provides evidence for its existence among some students at Illinois State University. It then shows a significant and meaningful relationship between methodical, disciplined study and academic performance. It closes by considering how the selectivity of colleges and universities would affect the findings and suggests some new directions for research.},
+	number = {1},
+	urldate = {2025-03-08},
+	journal = {Sociology of Education},
+	author = {Rau, William and Durand, Ann},
+	year = {2000},
+	note = {Publisher: [Sage Publications, Inc., American Sociological Association]},
+	keywords = {/unread, ⭐⭐⭐},
+	pages = {19--38},
+	file = {JSTOR Full Text PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/H58YHZEJ/Rau and Durand - 2000 - The Academic Ethic and College Grades Does Hard Work Help Students to Make the Grade.pdf:application/pdf},
+}
+
+@book{gross_fundamentals_2008,
+	address = {Hoboken, NJ},
+	edition = {4. ed},
+	series = {Wiley series in probability and statistics},
+	title = {Fundamentals of queueing theory},
+	isbn = {978-0-471-79127-0},
+	language = {eng},
+	publisher = {Wiley},
+	author = {Gross, Donald and Shortle, John F. and Thompson, James M. and Harris, Carl M.},
+	year = {2008},
+	keywords = {/unread, Queuing theory, Warteschlangentheorie},
+	file = {Ebook:/home/andreas/workspace/work/hiwi/Zotero/storage/ZGBENDHH/Gross et al. - 2008 - Fundamentals of queueing theory.epub:application/epub+zip},
+}
+
+@book{stewart_probability_2009,
+	title = {Probability, {Markov} {Chains}, {Queues}, and {Simulation}: {The} {Mathematical} {Basis} of {Performance} {Modeling}},
+	shorttitle = {Probability, {Markov} {Chains}, {Queues}, and {Simulation}},
+	abstract = {Probability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling. The detailed explanations of mathematical derivations and numerous illustrative examples make this textbook readily accessible to graduate and advanced undergraduate students taking courses in which stochastic processes play a fundamental role. The textbook is relevant to a wide variety of fields, including computer science, engineering, operations research, statistics, and mathematics.The textbook looks at the fundamentals of probability theory, from the basic concepts of set-based probability, through probability distributions, to bounds, limit theorems, and the laws of large numbers. Discrete and continuous-time Markov chains are analyzed from a theoretical and computational point of view. Topics include the Chapman-Kolmogorov equations; irreducibility; the potential, fundamental, and reachability matrices; random walk problems; reversibility; renewal processes; and the numerical computation of stationary and transient distributions. The M/M/1 queue and its extensions to more general birth-death processes are analyzed in detail, as are queues with phase-type arrival and service processes. The M/G/1 and G/M/1 queues are solved using embedded Markov chains; the busy period, residual service time, and priority scheduling are treated. Open and closed queueing networks are analyzed. The final part of the book addresses the mathematical basis of simulation.Each chapter of the textbook concludes with an extensive set of exercises. An instructor's solution manual, in which all exercises are completely worked out, is also available (to professors only).Numerous examples illuminate the mathematical theoriesCarefully detailed explanations of mathematical derivations guarantee a valuable pedagogical approachEach chapter concludes with an extensive set of exercises},
+	publisher = {Princeton University Press},
+	author = {Stewart, William J.},
+	month = jul,
+	year = {2009},
+	keywords = {Mathematics / Applied, /unread, Computers / Data Science / Data Modeling \& Design, Mathematics / Probability \& Statistics / General, Technology \& Engineering / Engineering (General)},
+	file = {PDF:/home/andreas/workspace/work/hiwi/Zotero/storage/L2FEI8HG/Stewart - 2009 - Probability, Markov Chains, Queues, and Simulation The Mathematical Basis of Performance Modeling.pdf:application/pdf},
+}

paper.tex

@@ -0,0 +1,305 @@
+\documentclass[journal]{IEEEtran}
+
+\usepackage{amsmath,amsfonts}
+\usepackage{siunitx}
+\usepackage{mleftright}
+\usepackage{float}
+\usepackage{titlesec}
+\usepackage[
+    backend=biber,
+    style=ieee,
+    sorting=nty,
+]{biblatex}
+
+\usepackage{pgfplots}
+\pgfplotsset{compat=newest}
+\usepgfplotslibrary{statistics}
+
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Template modifications
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+
+\titlespacing*{\section}{0mm}{3mm}{1mm}
+
+\makeatletter
+\def\@maketitle{%
+    \newpage
+    \null
+    \vspace*{-4mm}
+    \begin{center}%
+        {\Huge \linespread{0.9}\selectfont \@title \par}%
+        {\large \lineskip .5em%
+            \begin{tabular}[t]{c}%
+                \@author
+            \end{tabular}
+        \par}%
+    \end{center}%
+    \vspace*{-8mm}
+}
+\makeatother
+
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Inputs & Global Options
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+
+%
+% Figures
+%
+
+\input{common.tex}
+\pgfplotsset{colorscheme/rocket}
+
+\newcommand{\figwidth}{\columnwidth}
+\newcommand{\figheight}{0.5\columnwidth}
+
+\pgfplotsset{
+    FERPlot/.style={
+        line width=1pt,
+        densely dashed,
+    },
+    BERPlot/.style={
+        line width=1pt,
+    },
+    DFRPlot/.style={
+        only marks,
+    },
+}
+
+%
+% Bibliography
+%
+
+\addbibresource{paper.bib}
+\AtBeginBibliography{\footnotesize}
+
+%
+% Custom commands
+%
+
+\newcommand\todo[1]{\textcolor{red}{#1}}
+
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Title, Header, Footer, etc.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+
+\begin{document}
+
+\title{\vspace{-3mm}The Effect of the Choice of Hydration Strategy on
+    Average Academic
+Performance}
+
+\author{Some concerned fellow students%
+    \thanks{The authors would like to thank their hard-working peers as
+        well as the staff of the KIT library for their unknowing - but vital
+- participation.}}
+
+\markboth{Journal of the Association of KIT Bibliophiles}{The Effect
+of the Choice of Hydration Strategy on Average Academic Performance}
+
+\maketitle
+
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Abstract & Index Terms
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+
+\begin{abstract}
+    We evaluate the relationship between hydration strategy and
+    academic performance and project that by using the right button
+    of the water dispenser to fill up their water bottles, students
+    can potentially gain up to \SI{4.14}{\second} of study time per
+    refill, which amounts to raising their grades by up to
+    $0.0003$ points.
+\end{abstract}
+
+\begin{IEEEkeywords}
+    KIT Library, Academic Performance, Hydration
+\end{IEEEkeywords}
+
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Content
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+
+\vspace*{-5mm}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Introduction}
+
+% TODO: "The right strategy" pun?
+
+\IEEEPARstart{T}{he} concepts of hydration and study have always been
+tightly interwoven. As an example, an investigation was once
+conducted by Bell Labs into the productivity of their employees, that
+found that ``workers with the most patents often shared lunch or
+breakfast with a Bell Labs electrical engineer named Harry Nyquist''
+\cite{gertner_idea_2012}, and we presume that they also paired their
+food with something to drink. We can see that intellectual
+achievement and fluid consumption are related even for the most
+prestigious research institutions.
+
+In this work, we quantify this relationship in the context of
+studying at the KIT library and subsequently develop a novel and
+broadly applicable strategy to leverage it to improve the academic
+performance of KIT students.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Experimental Setup}
+
+Over a period of one week, we monitored the use of the water
+dispenser on the ground floor of the KIT library at random times
+during the day. The experiment comprised two parts: a system
+measurement to determine the flowrate of the water dispenser, and a
+behavioural measurement, i.e., a record of participants' chosen
+hydration strategies: $S_\text{L}$ denotes pressing the left
+button of the water dispenser, $S_\text{R}$ the right one, 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.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Experimental Results}
+
+\begin{figure}[H]
+    \centering
+
+    \vspace*{-4mm}
+    \begin{tikzpicture}
+        \begin{axis}[
+                width=0.8\columnwidth,
+                height=0.35\columnwidth,
+                boxplot/draw direction = x,
+                grid,
+                ytick = {1, 2, 3},
+                yticklabels = {$S_\text{B}$ (Both buttons),
+                $S_\text{R}$ (Right button), $S_\text{L}$ (Left button)},
+                xlabel = {Flowrate (\si{\milli\litre\per\second})},
+            ]
+            \addplot[boxplot, fill, scol1, draw=black]
+            table[col sep=comma, x=flowrate]
+            {res/flowrate_both.csv};
+
+            \addplot[boxplot, fill, scol2, draw=black]
+            table[col sep=comma, x=flowrate]
+            {res/flowrate_right.csv};
+
+            \addplot[boxplot, fill, scol3, draw=black]
+            table[col sep=comma, x=flowrate]
+            {res/flowrate_left.csv};
+        \end{axis}
+    \end{tikzpicture}
+
+    \vspace*{-3mm}
+
+    \caption{Flow rate of the water dispenser depending on the
+    hydration strategy.}
+    \label{fig:System}
+    \vspace*{-2mm}
+\end{figure}
+
+Fig. \ref{fig:System} shows the results of the system measurement.
+To investigate the difference in flowrate between strategies, we used
+a Mann Whitney U test, because of its nonparametric nature.
+We found that $S _\text{L}$ was slower than
+$S_\text{R}$ with a significance of $p < 0.01$, while no
+statistically significant difference was found between $S_\text{R}$ and
+$S_\text{B}$. The results of the behavioural measurement are shown in
+Fig. \ref{fig:Behavior}.
+
+\begin{figure}[H]
+    \centering
+
+    \vspace*{-2mm}
+    \begin{tikzpicture}
+        \begin{axis}[
+                ybar,
+                bar width=15mm,
+                width=\columnwidth,
+                height=0.35\columnwidth,
+                area style,
+                xtick = {0, 1, 2},
+                grid,
+                ymin = 0,
+                enlarge x limits=0.3,
+                xticklabels = {\footnotesize{$S_\text{L}$ (Left
+                    button)}, \footnotesize{$S_\text{R}$  (Right
+                button)}, \footnotesize{$S_\text{B}$}  (Both buttons)},
+                ylabel = {No. chosen},
+            ]
+            \addplot+[ybar,mark=no,fill=scol1] table[skip first n=1,
+            col sep=comma, x=button, y=count]
+            {res/left_right_distribution.csv};
+        \end{axis}
+    \end{tikzpicture}
+
+    \vspace*{-3mm}
+
+    \caption{Distribution of the choice of hydration strategy.}
+    \label{fig:Behavior}
+\end{figure}
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Modelling the grade improvement}
+
+We can consider the water dispenser and students as comprising a
+queueing system, specifically an M/G/1 queue
+\cite{stewart_probability_2009}. The expected response time, i.e.,
+the time spent waiting as well as the time dispensing water, is
+\cite[Section 14.3]{stewart_probability_2009}%
+%
+\begin{align*}
+    W = E\mleft\{ S \mright\} + \frac{\lambda \cdot E\mleft\{ S^2
+    \mright\}}{2\mleft( 1-\rho \mright)}
+    ,%
+\end{align*}%
+%
+where $S$ denotes the service time (i.e., the time spent refilling a
+bottle), $\lambda$ the mean arrival rate, and $\rho = \lambda \cdot
+E\mleft\{ S \mright\}$ the system utilisation. 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 $\SI{4.14}{\second}$.
+
+Strangely, it is the consensus of current research that there is only
+a weak relationship between academic performance and hours studied
+\cite{plant_why_2005}. Observing Figure 1 in
+\cite[p. 950]{schuman_effort_1985} and performing a linear regression,
+we quantified the grade gain per additional hour studied as
+$\SI{0.054}{points/hour}$. Using an estimate of 5 refills per day, we
+thus predict a possible gain of up to $0.0003$ points.
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\section{Discussion and Conclusion}
+
+Further research is needed, particularly on the modelling of the
+arrival process and the relationship between the response time and
+the grade gain. Nevertheless, we believe this work serves as a solid
+first step on the path towards achieving optimal study behaviour.
+
+In this study, we investigated how the choice of hydration strategy
+affects average academic performance. We found that always choosing
+to press the right button leads to an average time gain of
+\SI{4.14}{\second} per refill, which translates into a grade
+improvement of up to $0.0003$ points. We thus propose a novel and
+broadly applicable strategy to boost the average academic performance
+of KIT students: always using the right button.
+
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Bibliography
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+
+\printbibliography
+
+\end{document}
+

res/flowrate_both.csv

@@ -0,0 +1,11 @@
+index,flowrate
+0,42.47104247
+1,46.41350211
+2,42.80155642
+3,43.8247012
+4,42.63565891
+5,40
+6,43.8247012
+7,44
+8,43.8247012
+9,46.21848739

res/flowrate_left.csv

@@ -0,0 +1,11 @@
+index,flowrate
+0,25.46296296
+1,24.49888641
+2,25.88235294
+3,25.17162471
+4,25.94339623
+5,26.5060241
+6,25.17162471
+7,27.29528536
+8,27.5
+9,24.88687783

res/flowrate_right.csv

@@ -0,0 +1,11 @@
+index,flowrate
+0,42.47104247
+1,42.14559387
+2,47.00854701
+3,37.03703704
+4,43.1372549 
+5,39.14590747
+6,37.93103448
+7,38.32752613
+8,37.54266212
+9,38.19444444

res/full_participant_measurement.csv

@@ -0,0 +1,114 @@
+time,button
+28,left
+22,left
+17,left
+40,left
+24,left
+41,left
+11,left
+11,left
+26.56,left
+37,left
+30,left
+30,left
+8,left
+21,left
+20,left
+19,left
+28,left
+20,left
+21,left
+16.43,left
+16,left
+29,left
+20,left
+24,left
+22,left
+15,left
+13,left
+22,left
+23,left
+40,left
+19.8,left
+35.38,left
+21,left
+16.3,left
+29.3,left
+30.3,left
+30.2,left
+25,left
+14,left
+14.1,left
+40,left
+24.4,left
+5.2,left
+50,left
+29.7,left
+39,left
+17,left
+40.7,left
+27.3,left
+19.8,left
+7.55,right
+14,right
+9,right
+13,right
+5,right
+13,right
+13.58,right
+15.58,right
+25,right
+20,right
+14,right
+13,right
+14,right
+13.3,right
+19,right
+13,right
+10,right
+15,right
+14,right
+19.4,right
+12.8,right
+13.5,right
+19.31,right
+27.5,right
+13.1,right
+23.6,right
+15,right
+18.7,right
+18,right
+12.7,right
+40.3,right
+12.86,right
+22.9,right
+10,right
+20,right
+12,right
+19,right
+39.8,right
+20,both
+20,both
+15,both
+19,both
+13,both
+7,both
+15,both
+17.3,both
+12,both
+23,both
+11.26,both
+35.66,both
+13.54,both
+27.81,both
+16.83,both
+17.13,both
+17.8,both
+39,both
+11,both
+13.6,both
+21.7,both
+14.25,both
+12,both
+12.9,both
+12.35,both

res/left_right_distribution.csv

@@ -0,0 +1,5 @@
+# 0=left, 1=right, 2=both
+button,count
+0,50
+1,38
+2,25

scripts/approximate_response_time.py

@@ -0,0 +1,88 @@
+import pandas as pd
+import numpy as np
+
+
+filename_participants = "res/full_participant_measurement.csv"
+
+filename_left = "res/flowrate_left.csv"
+filename_right = "res/flowrate_right.csv"
+filename_both = "res/flowrate_both.csv"
+
+arrival_rate = 1 / 36.66  # Measured
+
+
+def get_response_time_and_utilization(S, arrival_rate):
+    df = pd.read_csv(filename_participants)
+
+    E_S = S.mean()
+    E_S2 = (S**2).mean()
+
+    utilization = arrival_rate * E_S
+
+    W = E_S + (arrival_rate * E_S2) / 2*(1 - utilization)
+
+    return W, utilization
+
+
+def print_response_time():
+    df = pd.read_csv(filename_participants)
+    S = df["time"]
+
+    W, rho = get_response_time_and_utilization(S, arrival_rate)
+
+    print(f"//")
+    print(f"// Response time")
+    print(f"// ")
+    print(f"    E{{S}} = {S.mean():.3f} s")
+    print(f"1/lambda = {1/arrival_rate:.3f} s")
+    print(f"     rho = {rho:.3f}")
+    print(f"       W = {W:.3f} s")
+
+
+def print_best_achievable_response_time():
+    # Get mean flowrates
+
+    df_left = pd.read_csv(filename_left)
+    df_right = pd.read_csv(filename_right)
+    df_both = pd.read_csv(filename_both)
+
+    flowrate_left = np.mean(np.array(df_left["flowrate"]))
+    flowrate_right = np.mean(np.array(df_right["flowrate"]))
+    flowrate_both = np.mean(np.array(df_both["flowrate"]))
+
+    # Convert service times to what they would be with the best strategy
+
+    df_part = pd.read_csv(filename_participants)
+
+    times_left = np.array(df_part[df_part["button"] == "left"]["time"])
+    times_right = np.array(df_part[df_part["button"] == "right"]["time"])
+    times_both = np.array(df_part[df_part["button"] == "both"]["time"])
+
+    sizes_left = times_left * flowrate_left
+    sizes_right = times_right * flowrate_right
+    sizes_both = times_both * flowrate_both
+
+    sizes = np.concatenate([sizes_left, sizes_right, sizes_both])
+
+    S = sizes / flowrate_right
+
+    # Calculate response time
+
+    W, rho = get_response_time_and_utilization(S, arrival_rate)
+
+    print(f"//")
+    print(f"// Best possible response time")
+    print(f"// ")
+    print(f"    E{{S}} = {S.mean():.3f} s")
+    print(f"1/lambda = {1/arrival_rate:.3f} s")
+    print(f"     rho = {rho:.3f}")
+    print(f"       W = {W:.3f} s")
+
+
+def main():
+    print_response_time()
+    print_best_achievable_response_time()
+
+
+if __name__ == "__main__":
+    main()

scripts/calculate_mean_bottle_size.py

@@ -0,0 +1,45 @@
+import numpy as np
+import pandas as pd
+
+
+filename_participants = "res/full_participant_measurement.csv"
+
+filename_left = "res/flowrate_left.csv"
+filename_right = "res/flowrate_right.csv"
+filename_both = "res/flowrate_both.csv"
+
+
+def main():
+    # Get bottle fillup times
+
+    df_part = pd.read_csv(filename_participants)
+
+    times_left = np.array(df_part[df_part["button"] == "left"]["time"])
+    times_right = np.array(df_part[df_part["button"] == "right"]["time"])
+    times_both = np.array(df_part[df_part["button"] == "both"]["time"])
+
+    # Get mean flowrates
+
+    df_left = pd.read_csv(filename_left)
+    df_right = pd.read_csv(filename_right)
+    df_both = pd.read_csv(filename_both)
+
+    flowrate_left = np.mean(np.array(df_left["flowrate"]))
+    flowrate_right = np.mean(np.array(df_right["flowrate"]))
+    flowrate_both = np.mean(np.array(df_both["flowrate"]))
+
+    # Calculate mean bottle size
+
+    sizes_left = times_left * flowrate_left
+    sizes_right = times_right * flowrate_right
+    sizes_both = times_both * flowrate_both
+
+    sizes = np.concatenate([sizes_left, sizes_right, sizes_both])
+
+    mean_size = np.mean(sizes)
+
+    print(f"Mean bottle size: {mean_size}")
+
+
+if __name__ == "__main__":
+    main()

scripts/find_grade_gain.py

@@ -0,0 +1,51 @@
+import matplotlib.pyplot as plt
+from scipy import stats
+import numpy as np
+import argparse
+
+
+def main():
+    """
+    [1] H. Schuman, E. Walsh, C. Olson, and B. Etheridge, “Effort and Reward:
+        The Assumption that College Grades Are Affected by Quantity of Study*,”
+        Social Forces, vol. 63, no. 4, pp. 945–966, June 1985.
+    """
+    # [1, p. 950]
+    hours_studied = np.array([1, 2.5, 3.5, 4.5, 5.5, 6.5])
+    gpa = np.array([2.94, 2.91, 2.97, 2.86, 3.25, 3.18])
+
+    # Parse command line arguments
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--plot", action="store_true")
+
+    args = parser.parse_args()
+
+    # Compute Spearman rank order correlation
+
+    corr, p = stats.spearmanr(hours_studied, gpa)
+
+    print("======== Spearman rank order correlation ========")
+    print(f"Correlation: {corr}")
+    print(f"p-value: {p}")
+
+    # Perform linear regression
+
+    slope, intercept, r, p, std_err = stats.linregress(hours_studied, gpa)
+
+    print("======== Linear regression ========")
+    print(f"slope: {slope:.8f} points/hour = {slope / (60 * 60):.8f} points/second")
+    # Printing the p-value here doesn't make much sense, because we don't know
+    # whether the assumptions for the test are satisfied
+
+    if args.plot:
+        plt.plot(hours_studied, gpa, label="Plot from publication")
+        plt.plot(hours_studied, slope * hours_studied + intercept, label="Best fit")
+        plt.xlabel("Hours studied")
+        plt.ylabel("GPA")
+        plt.legend()
+        plt.show()
+
+
+if __name__ == "__main__":
+    main()

scripts/investigate_flowrate_difference.py

@@ -0,0 +1,28 @@
+from scipy.stats import mannwhitneyu
+import pandas as pd
+import numpy as np
+
+
+filename_left = "res/flowrate_left.csv"
+filename_right = "res/flowrate_right.csv"
+filename_both = "res/flowrate_both.csv"
+
+
+def main():
+    df_left = pd.read_csv(filename_left)
+    flowrate_left = np.array(df_left["flowrate"])
+
+    df_right = pd.read_csv(filename_right)
+    flowrate_right = np.array(df_right["flowrate"])
+
+    df_both = pd.read_csv(filename_both)
+    flowrate_both = np.array(df_both["flowrate"])
+    
+    U_lr, p_lr = mannwhitneyu(flowrate_left, flowrate_both, method="exact")
+    U_rb, p_rb = mannwhitneyu(flowrate_right, flowrate_both, method="exact")
+    
+    print(f"Left-Right: p = {p_lr}")
+    print(f"Right-Both: p = {p_rb}")
+
+if __name__ == "__main__":
+    main()

tex-fmt.toml

@@ -0,0 +1 @@
+tabsize = 4