chengyewang/TexOCR-eval huggingface dataset:
\section{Exhibitor List}

\begin{table}[h]
\centering
\begin{tabular}{|c|l|l|l|}
\hline
Booth \# & Company/Organization & Contact & Website \\
\hline
\multicolumn{4}{|l|}{Atrium} \\
\hline
100 & Angle Park, Inc. & Martin Baumgaertner & anglepark.com \\
101 & Bluewater Studio & Erich Zuern & bluewater.studio \\
102 & Curate One & Dan Jecha & curateone.com \\
103 & Guardian Fine Art Services & Christine Anderson & guardianfineart.com \\
104 & Mutual of America & Paul Wierzba & mutualofamerica.com \\
105 & Tour-Mate Systems Ltd. & Lynn Taylor & tourmate.com \\
106 & Taylor Studios, Inc. & Danielle Rice & taylorstudios.com \\
107 & The Kubala Washatko Architects & Wayne Reckard & tkunwa.com \\
108 & Midway Spark & Eli Ball & midwayspark.com \\
109 &  &  &  \\
110 & Solid Light, Inc. & Beth Geiser & solidlight-inc.com \\
111 & TimeLooper Inc. & Andrew Feinberg & timelooper.com \\
\hline
\multicolumn{4}{|l|}{Milwaukee Foyer} \\
\hline
200 &  &  &  \\
201 &  &  &  \\
202 &  &  &  \\
203 &  &  &  \\
\hline
\end{tabular}
\label{table_1}
\end{table}
\begin{table}[h]
\centering
\caption{Statistical results of the Shapiro (W), Pearson correlation (r) and holm Bonferroni tests, with their p values, applied on our dataset of metal concentrations in the different bat tissues and soil concentrations (NSRI dataset).}
\label{table_S1}
\begin{tabular}{llllllll}
Metal & Statistical test & Kidneys & Liver & Stomach & Bones & Soil & Fur \\
Cd & Shapiro W & 0.46 & 0.15 & 0.27 & 0.13 & 0.46 & 0.05 \\
 & P value & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 \\
 & Pearson r & 0.37 & 0.23 & 0.54 & 0.53 & 0.10 & - \\
 & p value & 4.058e-07 & 0.002257 & 5.358e-13 & 2.887e-14 & 0.1846 & - \\
 & Bonferroni Adjusted p value & 1.2174e-06 & 4.5140e-03 & 2.1432e-12 & 1.4435e-13 & 1.8460e-01 & - \\
Cu & Shapiro W & 0.56 & 0.63 & 0.40 & 0.63 & 0.40 & 0.68 \\
 & P value & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 \\
 & Pearson r & 0.39 & 0.24 & 0.41 & 0.25 & 0.09 & - \\
 & p value & 2.066e-08 & 0.0009522 & 5.346e-08 & 0.0004402 & 0.2359 & - \\
 & Bonferroni Adjusted p value & 1.0330e-07 & 1.9044e-03 & 2.1384e-07 & 1.3206e-03 & 2.3590e-01 & - \\
Pb & Shapiro W & 0.35 & 0.13 & 0.33 & 0.73 & 0.41 & 0.17 \\
 & P value & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 \\
 & Pearson r & 0.42 & 0.10 & 0.75 & 0.72 & 0.04 & - \\
 & p value & 1.367e-09 & 0.1934 & $<$2.2e-16 & $<$2.2e-16 & 0.5742 & - \\
 & Bonferroni Adjusted p value & 4.101e-09 & 3.868e-01 & 1.100e-15 & 1.100e-15 & 5.742e-01 & - \\
Zn & Shapiro W & 0.55 & 0.21 & 0.19 & 0.88 & 0.37 & 0.66 \\
 & P value & $<$2.2e-16 & $<$2.2e-16 & $<$2.2e-16 & 3.833e-11 & $<$2.2e-16 & $<$2.2e-16 \\
 & Pearson r & 0.19 & -0.07 & 0.39 & 0.36 & 0.04 & - \\
 & p value & 0.007064 & 0.346 & 2.301e-07 & 2.24e-07 & 0.5378 & - \\
 & Bonferroni Adjusted p value & 0.02119200 & 0.69200000 & 0.00000112 & 0.00000112 & 0.69200000 & - \\
\end{tabular}
\end{table}
\section{Slaapkwaliteit}

\begin{table}[h]
\centering
\begin{tabular}{lccccc}
 & N & Minimum & Maximum & Gemiddelde & Sd \\
Slaapkwaliteit tijdens gewone nachten & 2017 & 1,0 & 6,0 & 3,9 & 1,0 \\
Slaapkwaliteit tijdens consignatiediensten & 1981\footnote{De resterende respondenten kozen het antwoord ``niet van toepassing''.} & 1,0 & 6,0 & 2,8 & 1,2 \\
\end{tabular}
\label{table_1}
\end{table}

Hieronder is de verdeling van de scores te zien.

\begin{figure}[h]
\centering
\includegraphics{figure_1.pdf}
\caption{Hoe zou je over het algemeen je slaapkwaliteit tijdens gewone nachten (nachten zonder consignatie of nachtdienst) beoordelen?}
\label{figure_1}
\end{figure}

\begin{figure}[h]
\centering
\includegraphics{figure_2.pdf}
\caption{Hoe zou je over het algemeen je slaapkwaliteit tijdens consignatie-nachten beoordelen? (Als je wordt opgeroepen slaap je uiteraard slechter. Beantwoord de vraag daarom aub met betrekking tot consignatie nachten zonder oproep!)}
\label{figure_2}
\end{figure}
Summary Report -- Sample ID: 20134208

\begin{table}[h]
\centering
\begin{tabular}{llll}
Nuclide Name & Activity (pCi/gram) & 2-sigma Error & MDA (pCi/gram) \\
AG-108m & Not Detected & ----- & 2.98E-002 \\
AG-110m & Not Detected & ----- & 2.45E-002 \\
BA-133 & Not Detected & ----- & 4.21E-002 \\
BE-7 & Not Detected & ----- & 2.05E-001 \\
CD-115 & Not Detected & ----- & 1.12E-001 \\
CE-139 & Not Detected & ----- & 2.48E-002 \\
CE-141 & Not Detected & ----- & 4.59E-002 \\
CE-144 & Not Detected & ----- & 2.03E-001 \\
CM-243 & Not Detected & ----- & 1.53E-001 \\
CO-56 & Not Detected & ----- & 2.74E-002 \\
CO-57 & Not Detected & ----- & 2.66E-002 \\
CO-58 & Not Detected & ----- & 2.67E-002 \\
CO-60 & Not Detected & ----- & 3.14E-002 \\
CR-51 & Not Detected & ----- & 2.04E-001 \\
CS-134 & Not Detected & ----- & 3.50E-002 \\
CS-137 & Not Detected & ----- & 2.56E-002 \\
EU-152 & Not Detected & ----- & 7.96E-002 \\
EU-154 & Not Detected & ----- & 1.37E-001 \\
EU-155 & Not Detected & ----- & 1.21E-001 \\
FE-59 & Not Detected & ----- & 6.26E-002 \\
GD-153 & Not Detected & ----- & 8.93E-002 \\
HG-203 & Not Detected & ----- & 2.87E-002 \\
I-131 & Not Detected & ----- & 2.86E-002 \\
IR-192 & Not Detected & ----- & 2.32E-002 \\
K-40 & 2.35E+001 & 3.11E+000 & 2.25E-001 \\
MN-52 & Not Detected & ----- & 3.25E-002 \\
MN-54 & Not Detected & ----- & 2.88E-002 \\
MO-99 & Not Detected & ----- & 3.28E-001 \\
NA-22 & Not Detected & ----- & 3.56E-002 \\
NA-24 & Not Detected & ----- & 2.77E-001 \\
ND-147 & Not Detected & ----- & 1.85E-001 \\
NI-57 & Not Detected & ----- & 6.93E-002 \\
RU-103 & Not Detected & ----- & 2.41E-002 \\
RU-106 & Not Detected & ----- & 2.32E-001 \\
SB-122 & Not Detected & ----- & 5.73E-002 \\
SB-124 & Not Detected & ----- & 2.48E-002 \\
SB-125 & Not Detected & ----- & 7.09E-002 \\
SN-113 & Not Detected & ----- & 3.07E-002 \\
SR-85 & Not Detected & ----- & 3.06E-002 \\
TA-182 & Not Detected & ----- & 1.27E-001 \\
TA-183 & Not Detected & ----- & 4.28E-001 \\
TL-201 & Not Detected & ----- & 2.30E-001 \\
Y-88 & Not Detected & ----- & 2.14E-002 \\
ZN-65 & Not Detected & ----- & 8.24E-002 \\
ZR-95 & Not Detected & ----- & 4.74E-002 \\
\end{tabular}
\end{table}
Communication between the students and the teaching staff, and between the students themselves.

\begin{itemize}
\item Completion of activities and access to the resources proposed by the teaching staff.
\end{itemize}

Lecturer assessment of students.

It should be borne in mind that if it is planned to process the personal data for a purpose other than the one that led to the collection, before the post processing the interested party must be provided with information on this other purpose and any additional pertinent information, for so that you can exercise your rights, among others and, where appropriate, that of not giving consent for this new purpose.

\section{8. Information to be provided to students}

The interested parties will have to be informed in two specific moments:

a) At the time of sending them an email with the link of the session.

b) At the time of login.

The basic data protection information to be provided is as follows:

\begin{table}[h]
\centering
\begin{tabular}{|p{5cm}|p{10cm}|}
\hline
Responsible for the treatment & Universitat Polit\`ecnica de Catalunya \\
& Name of the center or unit with a link to the web page to contact. \\
& E-mail address of the subject coordinator (or list of teachers' addresses assigned to each specific group or test) \\
\hline
Contact data of the data protection delegate & Universitat Polit\`ecnica de Catalunya. (https://www.upc.edu/normatives/ca/proteccio-de-dades/normativa-europea-de-proteccio-de-dades/dades-de-contacte-del-delegat-de-proteccio-de-dades) \\
\hline
Goal of the treatment & F03.21 Virtual campus management. (https://rat.upc.edu/ca/registre-de-tractaments-de-dades-personals/F03.21) Sessio de classe / d'avaluaci\'o en remot (o sessions de classe / avaluacions en remot de l'assignatura XXX) del centre XXX any 2020 \\
\hline
Legal status & Based on the fulfillment of a mission carried out in the public interest (Organic Law 6/2001, of December 21, on Universities) \\
\hline
Recipients & Your first and last name or ID in the session will be visible to other participants when you attend the session. \\
\hline
\end{tabular}
\label{table_1}
\end{table}
\section{IV: Results}

\begin{figure}
\centering
\caption{Figure 6: Regressions. Regressions on the determinants of change in the dependent variable, expected return annualized for investors. \textit{Development} is a dummy variable which takes the value of one if the investment profile of the real estate asset is either value-add or opportunistic. \textit{Size} indicates the size of the commercial real estate property in terms of ``as-is'' appraised value or renovated/repaired value. \textit{Multifamily} is a dummy variable that takes the value of one if the property-type is an apartment complex with 5+ units. \textit{Term} indicates the targeted investment hold period in years. \textit{Minimum Investment} is the minimum dollar amount requirement for each deal available to investors. \textit{Leverage} is the ratio of debt financing to estimated value (size) of the property. \textit{Crowdfunded Ratio} is the ratio of crowdfunding utilized to finance the total transaction divided by the estimated value (size) of the property.\footnote{Each regression has a slightly different sample size because Stata, the statistical software employed to run this analysis, omits observations that are missing data on at least one of the independent variables.}}

\begin{tabular}{lcccc}
\hline
\multicolumn{5}{c}{Dependent Variable: Expected Return} \\
 & (1) & (2) & (3) & (4) \\
\hline
\textit{Property Characteristics} \\
Development & 0.039*** & 0.041*** & 0.040*** & 0.038** \\
 & (3.78) & (3.26) & (2.97) & (2.61) \\
Size & .004 & .009 & .014 & .012 \\
 & (.37) & (.67) & (.89) & (.72) \\
Multifamily & .011 & .012 & -.0001 & -.001 \\
 & (1.18) & (1.23) & (-0.09) & (-.11) \\
\hline
\textit{Campaign Characteristics} \\
Term & - & -.006** & - & -.006** \\
 &  & (-2.58) &  & (-2.36) \\
Minimum Investment & - & .004 & - & .041* \\
 &  & (.819) &  & (1.78) \\
\hline
\textit{Financing Characteristics} \\
Leverage & - & - & .111 & .095 \\
 &  &  & (1.39) & (1.10) \\
Crowdfunded Ratio & - & - & -.043 & -.075** \\
 &  &  & (-1.42) & (-2.28) \\
\hline
Constant & 0.122 & .098 & -.011 & -.119 \\
 & (1.55) & (1.07) & (-.09) & (-.92) \\
\hline
Observations & 93 & 78 & 53 & 49 \\
R-squared & 0.168 & 0.269 & 0.219 & 0.337 \\
\hline
\multicolumn{5}{l}{Coefficient, t-statistic in parentheses} \\
\multicolumn{5}{l}{*** p$<$0.01, ** p$<$0.05, * p$<$0.10} \\
\hline
\end{tabular}
\label{figure_6}
\end{figure}
Step3. Divide the region into $10^d$ equal small regions.

Step4. Label the small regions by $1, 2, \ldots, m$ according to the inside samples' class.

Step5. Remerge the frontiers of the same class regions in where is the same class then save it as a link table.

Step6. For any regions where there are training samples from different classes in the same unit, go to Step2.

Step7. Repeat the above until there is no region where there are different classes.

At the end, some separating hyper surfaces, which are described by the link tables, are obtained.

After learning from the training sample, the explorationist's classification experience is collected in the hyper surface, i.e. link table. Using hyper surface the absent data can be estimated or predicted from data such as well logging. The steps are as following.

Step1. Input a testing sample and make a radial from the sample.

Step2. Input all link tables of class $k$ $(k = 1,2,3,\ldots,m)$ obtained by the above training algorithm.

Step3. Count the intersecting number of the sample with the above link table.

Step4. If the intersecting number of the sample with the above link tables is odd then label the sample by $k$. It is mean that the prediction value is the $k$th decision value, otherwise go to next step.

Step5. Input all link tables of class $k + 1$ obtained by the above training algorithm. Do step3-4 until $k = m$.

Step6. Calculate the classifying accuracy rate.

This is a universal prediction method for large nonlinear data bases. In fact, for large data sets ($10^7$) (see Table \ref{table_1} and Table \ref{table_2}), the speed of HSC is very fast. The reason is that the time of saving and extracting hyper surfaces, which is not the advantage of SVM. Another reason is that the decision process is very easy by using the Jordan Curve Theorem.

\begin{table}[h]
\centering
\caption{Training results}
\label{table_1}
\begin{tabular}{llll}
Training Samples & Training Time & Recall Time & The Rate of Recall (\%) \\
3,314 & 1s & 2s & 100.00 \\
6,677 & 3s & 4s & 100.00 \\
9,525 & 4s & 7s & 100.00 \\
9,530 & 6s & 7s & 100.00 \\
17,919 & 8s & 12s & 100.00 \\
43,217 & 18s & 31s & 100.00 \\
106,344 & 48s & 1m 17s & 100.00 \\
1,053,125 & 9m 0s & 12m 29s & 100.00 \\
\end{tabular}
\end{table}
\begin{figure}
\centering
\includegraphics{figure_11.pdf}
\caption{Our Replica Electromagnet}
\label{fig:figure_11}
\end{figure}

To confirm some of the suggestions made about Henry's experiments, we constructed a replica of Henry's Albany magnet, shown in Figure~\ref{fig:figure_11} above. Each coil was wound in the same way as experiments 8--16. There are a couple of main differences between our magnet and Henry's: 1) Our magnet was forged from a mild steel, which has a higher carbon content than wrought iron, giving it some residual magnetism after the external field is removed. Mild steel also takes much longer to reach saturation, and has a smaller initial permeability; 2) We used 9, 60 ft strands of high purity enameled copper wire, which has a much thinner insulation than Henry's silk, allowing for a greater number of turns, and has a lower resistivity than the copper wire used by Henry. These two differences, especially using a different magnetic material, can be shown to have a large effect on our results. Nevertheless, all the data collected below should be consistent enough to establish certain comparisons with Henry's results.

When measuring the B-field at different areas on the poles, it was observed that the B-Field increased when the probe was brought towards the inner edge of the pole. Furthermore, the B-field was at a maximum at the inner corners of each pole (about double that of the center). Even though the reading was higher at the corners, it was difficult to get consistent measurements there, so these results were discarded.

To test the idea of flux leakage though a magnetic circuit, each coil was attached to a constant current of 5 Amps and the B-Field was measured at the center of each pole. The results are shown below in Table~\ref{tab:table_1}.

\begin{table}
\centering
\begin{tabular}{c c c c}
Coil & Right side B-Field (Gauss) & Left Side B-Field (Gauss) & Total B-Field (Gauss) \\
1 & 140 & 29 & 169 \\
2 & 117 & 49 & 166 \\
3 & 98 & 53 & 151 \\
4 & 87 & 68 & 155 \\
5 & 78 & 78 & 156 \\
6 & 58 & 97 & 155 \\
7 & 48 & 107 & 155 \\
8 & 39 & 112 & 151 \\
9 & 29 & 136 & 165 \\
\end{tabular}
\caption{B-Field measurements taken for each coil supplied with a constant current}
\label{tab:table_1}
\end{table}
Observando a tabela \ref{tab:table_6}, é possível verificar que a conservação dos solos com dados disponíveis após 1975, somente 6 propriedades utilizavam essa prática, já em 1980, houve um aumento de 1364 propriedades e para o ano de 1985 comparando-o com o ano de 1980, o aumento foi de 434 propriedades que praticam a conservação dos solos.

A prática mais utilizada é sem dúvida as curvas em nível que auxiliam o combate à erosão. Órgãos do governo estadual como a EMATER, tem auxiliado bastante o produtor tanto no manejo do solo como na construção de curvas em nível.

"As minhas terras não tinha curvas de nível, mas a técnica da EMATER, falou que eu tinha que fazer, porque a erosão já tava levando muita terra embora, então eu fiz porque com as curvas de nível até os rios ficam protegidos".

O depoimento do agricultor José Beteto da Comunidade de Guairacá expressa a importância da assistência técnica na orientação do produtor sobre a melhor conservação da propriedade.

Portanto, acreditamos que este processo de modernização/ industrialização apenas aumentou ainda mais o problema agrário no Brasil, já que ocasionou uma diminuição da pequena propriedade. Porém, ainda podemos encontrar nestes espaços contemplados pelo processo de modernização, pequenos produtores que continuam a produzir. Este processo que aparentemente atingiu todas as áreas do Norte Paranaense, na verdade ocorreu de forma diferenciada no caráter geográfico, econômico, técnico e social, que produziu desigualdades regionais e locais.

\begin{table}[h]
\centering
\caption{Uso de Insumo e Conservação do Solo do Município de Londrina}
\label{table_6}
\begin{tabular}{lccccc}
Ano & estabelecimentos & Adubos (químicos e orgânicos) & calcário & defensivos & Prática de conservação do solo \\
1970 & 5.580 & 1.153 & - & - & - \\
1975 & 4.146 & 1.665 & 7 & 319 & 6 \\
1980 & 3.654 & 2.072 & 615 & 2.821 & 1.370 \\
1985 & 4.151 & 2.532 & 834 & 2.937 & 1.804 \\
1995/96 & 3.119 & 2944 & 990 & - & - \\
\end{tabular}

Fonte: IBGE. Censos Agropecuários do Paraná: 1970, 1975, 1980, 1985, 1995/96.
\end{table}
\begin{table}[htbp]
\centering
\caption{Logistic regression analysis of the association of individual somatic depressive symptoms (dichotomous) with serum pyridoxal 5'-phosphate in women (n=1,489)}
\label{tab:table_14}
\begin{tabular}{lllllllll}
\hline
 & \multicolumn{2}{c}{Sleeping problems} & \multicolumn{2}{c}{Fatigue} & \multicolumn{2}{c}{Abnormal appetite} & \multicolumn{2}{c}{Psychomotor abnormalities} \\
 & OR (95\% CI) & p-value & OR (95\% CI) & p-value & OR (95\% CI) & p-value & OR (95\% CI) & p-value \\
\hline
\multicolumn{9}{l}{Unadjusted Model} \\
PLP $<$20 nmol/L & 2.82 (1.68, 4.72) & $<$0.001 & 1.96 (1.33, 2.90) & 0.001 & 2.94 (1.68, 5.13) & $<$0.001 & 2.63 (1.04, 6.65) & 0.04 \\
PLP 20--29.9 & 1.45 (0.89, 2.37) & 0.13 & 1.24 (0.73, 2.11) & 0.42 & 1.53 (0.86, 2.72) & 0.14 & 0.71 (0.27, 1.83) & 0.46 \\
PLP $\geq$30 nmol/L & ref &  & ref &  & ref &  & ref &  \\
\multicolumn{9}{l}{Adjusted Model} \\
PLP $<$20 nmol/L & 1.60 (0.52, 4.90) & 0.40 & 0.96 (0.58, 1.58) & 0.85 & 0.79 (0.25, 2.57) & 0.69 & 2.02 (0.45, 9.09) & 0.35 \\
PLP 20--29.9 & 1.10 (0.63, 1.94) & 0.73 & 0.68 (0.31, 1.48) & 0.32 & 1.11 (0.60, 2.07) & 0.73 & 0.21 (0.03, 1.34) & 0.10 \\
PLP $\geq$30 nmol/L & ref &  & ref &  & ref &  & ref &  \\
\hline
\end{tabular}

Adjusted model includes age, ethnicity, smoking, oral contraceptive use, sleep duration, physical activity, body mass index, CRP and mutually adjusted for the sum of the remaining depression items.
\end{table}
\begin{table}[h]
\centering
\caption{Adverse sanctions in year to September, 1999--2013}
\label{table_1}
\begin{tabular}{lll}
\hline
Year to September & Adverse sanction decisions, JSA claimants (thousands) & Adverse JSA sanctions as proportion of JSA claimant count \\
\hline
2001 & 280 & 2.6\% \\
2002 & 270 & 2.7\% \\
2003 & 260 & 2.6\% \\
2004 & 230 & 2.5\% \\
2005 & 240 & 2.6\% \\
2006 & 240 & 2.4\% \\
2007 & 320 & 3.3\% \\
2008 & 340 & 3.9\% \\
2009 & 400 & 2.7\% \\
2010 & 610 & 3.7\% \\
2011 & 710 & 4.5\% \\
2012 & 750 & 4.4\% \\
2013 & 870 & 5.5\% \\
\hline
\end{tabular}

Source: Social Security Advisory Committee, 2014, p.13
}
\begin{table}[h]
\centering
\caption{Norm Types}
\label{table_1}
\begin{tabular}{lllll}
Norm Type & Examples & Level & Moral Reach & Contestation \\
Fundamental Type 1 & Human rights; Rule of law; Democracy & Macro & Wide & More \\
Organizing Principle Type 2 & Responsibility to protect (UN); Rule of law mechanism (EU); Qualified majority voting (EU) & Meso & Medium & Medium \\
Standardized Procedures, Regulations Type 3 & Responsibility to protect pillars; Specific rule of law implementation; Electoral rules & Micro & Narrow & Less \\
\end{tabular}

Source: Adapted from Wiener, \textit{Invisible Constitution of Politics}, 66, Table 4.2 (see note 1 to the text of this article).
\end{table}
follow the steps below to navigate to both your Pay Advice and Annual Leave Certification.

\section{Open your Pay Advice}
\begin{enumerate}
\item Log into your employee portal.
\item Select the CU Resources tab. (CU System employees will skip this step.)
\item Open the CU Resources Home drop-down menu at the top, then select My Info and Pay.
\item Click the Paychecks tile and choose your last July paycheck.
\end{enumerate}

\section{Open Annual Leave Certification}
\begin{enumerate}
\item Open your portal again in a new window and select the CU Resources tab. (CU System employees will skip this step.)
\item Select the CU Resources Home drop-down menu at the top, then click My Info and Pay.
\item Click the Annual Leave Certification tile.
\end{enumerate}

To begin, subtract 14.67 from the Paid Time Off column end balance and subtract 10 hours from the Sick Leave end balance on your Pay Advice.

Next, look at the Ending Balance column on the Annual Leave Certification to verify that the numbers are similar. The numbers in your Pay Advice are rounded and the Annual Leave Certification is an exact amount, so the numbers might be slightly out of balance due to rounding differences. Note: Accrued time amount will vary for classified employees based on length of service.

After you verify that the numbers are close, select the radio button on the Annual Leave Certification and click Submit to certify your leave.
\section{Results}

\subsection{Content Validity}
The minimum I-CVI and the computed S-CVI for the study module were 0.78 and 0.93, respectively (Table~\ref{tab:table_2}). The minimum value of I-CVI was given to items 5, 10 and 11 (0.78) and Items 1--4 and 12--16 had the complete value (1.00) (Table~\ref{tab:table_2}).

\begin{table}[h]
\centering
\caption{The ranking of the questionnaire's items according to the experts (n=9)}
\label{tab:table_2}
\begin{tabular}{ccc}
Item & Number of Agreement (ranked 3 or 4) & I-CVI \\
1 & 9 & 1.00 \\
2 & 9 & 1.00 \\
3 & 9 & 1.00 \\
4 & 9 & 1.00 \\
5 & 7 & 0.78 \\
6 & 8 & 0.89 \\
7 & 8 & 0.89 \\
8 & 8 & 0.89 \\
9 & 8 & 0.89 \\
10 & 7 & 0.78 \\
11 & 7 & 0.78 \\
12 & 8 & 1.00 \\
13 & 9 & 1.00 \\
14 & 9 & 1.00 \\
15 & 9 & 1.00 \\
16 & 9 & 1.00 \\
\end{tabular}

S-CVI (Based on mean): 0.93
\end{table}

Therefore, the results of the assessments showed that the majority of the experts believed that the module was valid according to the study population and objectives. However, the essential changes were applied based on the experts' recommendation such as improving the activities details and steps, adding picture to the module as well as highlighting the target life skill(s) in each session.

\subsection{Face Validity}
Most of the participants in both groups were female (19 females vs. 11 males) with Malay ethnicity (14 Malay vs. 7 Chinese and 9 Indian). Although the participants did not live with their parents, 40.2\% of them had at least one living parent.

Using Paired Samples t-Test, the results showed that the mean score of 6 coping methods including active coping (t=-5.85, P<0.001), using of emotional supports (t=-4.80, P<0.001), using of instrumental support (t=-4.89, P<0.001), positive reframing (t=-3.89, P<0.001), planning (t=-4.91, P<0.001) and acceptance (t=-5.98, P<0.001) methods significantly increased after interventional sessions. Meanwhile, the mean score of 3 coping methods including self-distraction (t=5.43, P<0.001), denial (t=2.80, P<0.02) and self-blame (t=7.24, P<0.001) significantly decreased in post-test. No significant change was found in mean score of substance use, behavior disengagement, venting, humor and religion (p>0.05).