Supertabular* 两栏文章模板

Question

因此，我将对我的问题发表一个答案，虽然对我来说并不理想，但我使用了“cuted”包中的“strip”环境，并使用 supertabular 包中的 \shrinkheight{} 相应地修改了表格的第一次分割。解决方案归功于这个帖子，尤其是 Bernard 使用 xtab（而不是 supertabular）的回答。如果有人可以让 supertabular* 环境工作，请告诉我，我很想了解这个问题。

\documentclass[twocolumn]{article}
    \usepackage{supertabular, booktabs}
\newcommand{\tabitem}{~~\llap{\textbullet}~~}
\usepackage{cuted}
\usepackage{array} %% Array m option in particular (center and sized)
\usepackage{diagbox} %% Array diagonal box
\usepackage{multirow} %for multi row in table
%\usepackage{xtab, booktabs}
\usepackage{blindtext}

\begin{document}

\blindtext[4]


%\begin{center}
 \begin{strip}
 \centering
 %\setlength{\extrarowheight}{2pt}
\tablefirsthead{%
\toprule
AI/ML Models & Prediction & Optimisation/ Automation & Analysis & Modelling/
Simulation \\ \midrule \\
}
\tablehead{%
%\hline\multicolumn{4}{|l|}{\small\sl continued from previous page}\\
\toprule
AI/ML Models & Prediction & Optimisation/ Automation & Analysis & Modelling/
Simulation \\ \midrule \\
}
\tabletail{%
\bottomrule
\multicolumn{5}{c}{\small continued on next page} \\
\bottomrule \\
}
\tablelasttail{%
\bottomrule \\
}

\tablecaption{Publication Distribution with the Design Space}
\label{tab: designSpace} 

\begin{supertabular}{@{}p{\dimexpr 0.20\textwidth-\tabcolsep \relax}p{\dimexpr 0.22\textwidth-2\tabcolsep \relax}p{\dimexpr 0.20\textwidth-2\tabcolsep \relax}p{\dimexpr 0.18\textwidth-2\tabcolsep \relax}p{\dimexpr 0.20\textwidth-\tabcolsep\relax }@{}}
%\begin{supertabular*}{p{0.20\textwidth}p{0.22\textwidth}p{0.20\textwidth}p{0.18\textwidth}p{ 0.20\textwidth}}

Multi-Agent System (MAS) 
&
& 
&  
& \\  
\shrinkheight{-60ex}

Evolutionary Algorithm (EA) 
&  
& 
& 
& \\

Simulated Annealing (SA) 
& 
& 
& 
& \\  

Tabu search 
& 
&  
& 
& \\  

Particle Swarm Optimisation 
& 
&  
& 
& \\  

Ant Colony Algorithm 
& 
&  
& 
& \\  

Bee Colony Algorithm
&
& 
&
& \\


Neural Network (NN) 
& 
& 
& 
& \\  

Deep Deterministic Policy Gradient (DDPG) 
& 
& 
& 
& \\  

Convolutional Neural Networks (CNN)
& 
&
&
& \\

Deep Q-Network (DQN) 
& 
&  
& 
& \\  

Deep Learning
& 
&
&
& \\
Autoencoder
& 
&
&
&  \\

Random Forest (RF) 
& 
&
& 
& \\  

Quantile Regression Forest 
&  
& 
& 
& \\   

Gradient Boosting Machine (GBM) 
& 
& 
& 
& \\  

Support Vector Machine (SVM) 
& 
&  
& 
& \\  

Decision Tree 
& 
& 
& 
&  \\  

Fuzzy Logic 
&  
& 
& 
& \\  

Linear Regression 
& 
&  
& 
&  \\  

Linear Functional Regression 
& 
& 
& 
& \\  

Linear Discriminant Analysis (LDA)
&  
& 
& 
& \\

Quadratic Discriminant Analysis (QDA)
&  
& 
& 
& \\

Logistic regression
& 
& 
& 
& \\  

Binary logistic regression models 
& 
& 
&  
& \\  

Gaussian Mixture Model 
& 
& 
& 
& \\  

Bayesian Network 
& 
& 
& 
&  \\  

Recursive Bayesian estimation 
& 
&  
& 
& \\  

k-nearest neighbor (k-NN)
& 
&
&
&\\

Genetic algorithm 
&
& 
&
& \\

Hierarchical clustering
&
&
&
& \\

Dynamic Bayesian Belief Network 
&  
& 
& 
&  \\  

BIRCH 
& 
& 
&  
& \\  

DBSCAN
& 
& 
&  
& \\  

OPTIC 
& 
& 
&  
& \\  

K-means 
& 
& 
&   
& \\  

Non Negative Matrix Factorisation (NMF) 
& 
&  
& 
& \\  

A* 
& 
& 
& 
& \\  

Multi-Layer Perceptron (MLP)
& 
& 
& 
& \\ 

Deep Reinforcement Learning
& 
& 
& 
& \\ 

Recurrent neural network \& LSTM
& 
&
&
& \\

Reinforcement Learning
& 
& 
& 
& \\ 

k-nearest neighbours (kNN)
& 
& 
& 
& \\ 

Principal Component Analysis (PCA)
& 
&
&
& \\

OPTICS clustering
&
&
&
& \\

Not referenced 
&  
&  
& 
& \\  


\end{supertabular}
%\end{center}
\end{strip}

\blindtext[4]

\end{document}

Answer 1

因此，我将对我的问题发表一个答案，虽然对我来说并不理想，但我使用了“cuted”包中的“strip”环境，并使用 supertabular 包中的 \shrinkheight{} 相应地修改了表格的第一次分割。解决方案归功于这个帖子，尤其是 Bernard 使用 xtab（而不是 supertabular）的回答。如果有人可以让 supertabular* 环境工作，请告诉我，我很想了解这个问题。

\documentclass[twocolumn]{article}
    \usepackage{supertabular, booktabs}
\newcommand{\tabitem}{~~\llap{\textbullet}~~}
\usepackage{cuted}
\usepackage{array} %% Array m option in particular (center and sized)
\usepackage{diagbox} %% Array diagonal box
\usepackage{multirow} %for multi row in table
%\usepackage{xtab, booktabs}
\usepackage{blindtext}

\begin{document}

\blindtext[4]


%\begin{center}
 \begin{strip}
 \centering
 %\setlength{\extrarowheight}{2pt}
\tablefirsthead{%
\toprule
AI/ML Models & Prediction & Optimisation/ Automation & Analysis & Modelling/
Simulation \\ \midrule \\
}
\tablehead{%
%\hline\multicolumn{4}{|l|}{\small\sl continued from previous page}\\
\toprule
AI/ML Models & Prediction & Optimisation/ Automation & Analysis & Modelling/
Simulation \\ \midrule \\
}
\tabletail{%
\bottomrule
\multicolumn{5}{c}{\small continued on next page} \\
\bottomrule \\
}
\tablelasttail{%
\bottomrule \\
}

\tablecaption{Publication Distribution with the Design Space}
\label{tab: designSpace} 

\begin{supertabular}{@{}p{\dimexpr 0.20\textwidth-\tabcolsep \relax}p{\dimexpr 0.22\textwidth-2\tabcolsep \relax}p{\dimexpr 0.20\textwidth-2\tabcolsep \relax}p{\dimexpr 0.18\textwidth-2\tabcolsep \relax}p{\dimexpr 0.20\textwidth-\tabcolsep\relax }@{}}
%\begin{supertabular*}{p{0.20\textwidth}p{0.22\textwidth}p{0.20\textwidth}p{0.18\textwidth}p{ 0.20\textwidth}}

Multi-Agent System (MAS) 
&
& 
&  
& \\  
\shrinkheight{-60ex}

Evolutionary Algorithm (EA) 
&  
& 
& 
& \\

Simulated Annealing (SA) 
& 
& 
& 
& \\  

Tabu search 
& 
&  
& 
& \\  

Particle Swarm Optimisation 
& 
&  
& 
& \\  

Ant Colony Algorithm 
& 
&  
& 
& \\  

Bee Colony Algorithm
&
& 
&
& \\


Neural Network (NN) 
& 
& 
& 
& \\  

Deep Deterministic Policy Gradient (DDPG) 
& 
& 
& 
& \\  

Convolutional Neural Networks (CNN)
& 
&
&
& \\

Deep Q-Network (DQN) 
& 
&  
& 
& \\  

Deep Learning
& 
&
&
& \\
Autoencoder
& 
&
&
&  \\

Random Forest (RF) 
& 
&
& 
& \\  

Quantile Regression Forest 
&  
& 
& 
& \\   

Gradient Boosting Machine (GBM) 
& 
& 
& 
& \\  

Support Vector Machine (SVM) 
& 
&  
& 
& \\  

Decision Tree 
& 
& 
& 
&  \\  

Fuzzy Logic 
&  
& 
& 
& \\  

Linear Regression 
& 
&  
& 
&  \\  

Linear Functional Regression 
& 
& 
& 
& \\  

Linear Discriminant Analysis (LDA)
&  
& 
& 
& \\

Quadratic Discriminant Analysis (QDA)
&  
& 
& 
& \\

Logistic regression
& 
& 
& 
& \\  

Binary logistic regression models 
& 
& 
&  
& \\  

Gaussian Mixture Model 
& 
& 
& 
& \\  

Bayesian Network 
& 
& 
& 
&  \\  

Recursive Bayesian estimation 
& 
&  
& 
& \\  

k-nearest neighbor (k-NN)
& 
&
&
&\\

Genetic algorithm 
&
& 
&
& \\

Hierarchical clustering
&
&
&
& \\

Dynamic Bayesian Belief Network 
&  
& 
& 
&  \\  

BIRCH 
& 
& 
&  
& \\  

DBSCAN
& 
& 
&  
& \\  

OPTIC 
& 
& 
&  
& \\  

K-means 
& 
& 
&   
& \\  

Non Negative Matrix Factorisation (NMF) 
& 
&  
& 
& \\  

A* 
& 
& 
& 
& \\  

Multi-Layer Perceptron (MLP)
& 
& 
& 
& \\ 

Deep Reinforcement Learning
& 
& 
& 
& \\ 

Recurrent neural network \& LSTM
& 
&
&
& \\

Reinforcement Learning
& 
& 
& 
& \\ 

k-nearest neighbours (kNN)
& 
& 
& 
& \\ 

Principal Component Analysis (PCA)
& 
&
&
& \\

OPTICS clustering
&
&
&
& \\

Not referenced 
&  
&  
& 
& \\  


\end{supertabular}
%\end{center}
\end{strip}

\blindtext[4]

\end{document}

Supertabular* 两栏文章模板

答案1

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