我正在写一篇论文，要使用类 (elsarticle.cls) 发送给 Elsevier 期刊。我对电子邮件中的下划线“_”有问题
\ead{[email protected]}
这不是真正的电子邮件，它只是一个例子，但是我的电子邮件和主管电子邮件包含下划线“_”，当我编译代码时出现错误： ! 缺少 $ 插入.我怎样才能克服这个问题？
\documentclass[5p,twocolumn]{elsarticle}
\usepackage{amssymb}
\usepackage[figuresright]{rotating}
\usepackage{mathptmx}
\usepackage{lipsum} % for mock text
\newcounter{resume}
\usepackage{float}
\usepackage{stfloats}
%end
% declarations for front matter
\begin{document}
\begin{frontmatter}
\author[rvt]{Author1\corref{cor1}}
\ead{[email protected]}
\author[rvt]{Author2}
\ead{[email protected]}
%% \fntext[label2]{}
\cortext[cor1]{Coresponding author. Tel.: +225 095243621.}
\address[rvt]{Non Destructive Testing Laboratory (NDT Lab), Automatic Department, Sciences and Technology Faculty, University , BP 98 street, 18000, City, country}
\title{Title of the paper}
%% use optional labels to link authors explicitly to addresses:
%% \author[label1,label2]{<author name>}
%% \address[label1]{<address>}
%% \address[label2]{<address>}
%\author{}
%\address{}
\begin{abstract}
Based on clinical data collected using different brain imaging and recording techniques, brain researchers built mathematical models of the activity in the human brain. To test these models they simulate them by performing on those models a virtual brain experiment and compare the outputs from those with the real brain activity recordings. The models can be a basis for understanding what goes wrong in brain diseases and brain disorders and potentially help to create new drugs for these conditions. These models are often formulated in a continuous-discrete state space form. To fit these models to actual data, this require having suitable techniques that permits us to estimate both the hidden states and parameters of such models. The method proposed in this paper is a combination between the Square Root Cubature Kalman Filter (SCKF) and Maximum Likelihood Estimation (MLE). It uses gradient based optimization algorithms, for minimizing-maximizing the objective function. In the proposed method, it will be explained how the gradient can be calculated with a SCKF-like recursion. Numerical results obtained with simulated data are presented and discussed.
\end{abstract}
\begin{keyword}
FMRI \sep Biophysical model\sep Stochastic Metabolic Hemodynamic Model\sep Maximum likelihood estimation \sep Square-root Cubature Kalman Filter
%% PACS codes here, in the form: \PACS code \sep code
%% MSC codes here, in the form: \MSC code \sep code
%% or \MSC[2008] code \sep code (2000 is the default)
\end{keyword}
\end{frontmatter}
%%
%% Start line numbering here if you want
%%
% \linenumbers
%% main text
\section{Introduction}
\label{intro}
Functional magnetic resonance imaging (fMRI) represents one of the most powerful and noninvasive tools that has ever been developed, by virtue of its capability to image human brain function. The goal of research interest in fMRI is to understand the neural mechanism behind how we see, hear, think, feel and move. One of the most promising fields in which the fMRI was extensively used is the Cognitive Neuroscience, which focuses on the study of working memory, decision making, perception, sensation, reasoning, acquisition of knowledge and behavior.
\end{document}
答案1
只需写\_而不是_。
请参阅以下 MWE：
\documentclass[5p,twocolumn]{elsarticle}
\usepackage{amssymb}
\usepackage[figuresright]{rotating}
\usepackage{mathptmx}
\usepackage{lipsum} % for mock text
\newcounter{resume}
\usepackage{float}
\usepackage{stfloats}
%end
% declarations for front matter
\begin{document}
\begin{frontmatter}
\author[rvt]{Author1\corref{cor1}}
\ead{author1\[email protected]} % <==================================
\author[rvt]{Author2}
\ead{author2\[email protected]} % <==================================
%% \fntext[label2]{}
\cortext[cor1]{Coresponding author. Tel.: +225 095243621.}
\address[rvt]{Non Destructive Testing Laboratory (NDT Lab), Automatic Department, Sciences and Technology Faculty, University , BP 98 street, 18000, City, country}
\title{Title of the paper}
%% use optional labels to link authors explicitly to addresses:
%% \author[label1,label2]{<author name>}
%% \address[label1]{<address>}
%% \address[label2]{<address>}
%\author{}
%\address{}
\begin{abstract}
Based on clinical data collected using different brain imaging and recording techniques, brain researchers built mathematical models of the activity in the human brain. To test these models they simulate them by performing on those models a virtual brain experiment and compare the outputs from those with the real brain activity recordings. The models can be a basis for understanding what goes wrong in brain diseases and brain disorders and potentially help to create new drugs for these conditions. These models are often formulated in a continuous-discrete state space form. To fit these models to actual data, this require having suitable techniques that permits us to estimate both the hidden states and parameters of such models. The method proposed in this paper is a combination between the Square Root Cubature Kalman Filter (SCKF) and Maximum Likelihood Estimation (MLE). It uses gradient based optimization algorithms, for minimizing-maximizing the objective function. In the proposed method, it will be explained how the gradient can be calculated with a SCKF-like recursion. Numerical results obtained with simulated data are presented and discussed.
\end{abstract}
\begin{keyword}
FMRI \sep Biophysical model\sep Stochastic Metabolic Hemodynamic Model\sep Maximum likelihood estimation \sep Square-root Cubature Kalman Filter
%% PACS codes here, in the form: \PACS code \sep code
%% MSC codes here, in the form: \MSC code \sep code
%% or \MSC[2008] code \sep code (2000 is the default)
\end{keyword}
\end{frontmatter}
%%
%% Start line numbering here if you want
%%
% \linenumbers
%% main text
\section{Introduction}
\label{intro}
Functional magnetic resonance imaging (fMRI) represents one of the most powerful and noninvasive tools that has ever been developed, by virtue of its capability to image human brain function. The goal of research interest in fMRI is to understand the neural mechanism behind how we see, hear, think, feel and move. One of the most promising fields in which the fMRI was extensively used is the Cognitive Neuroscience, which focuses on the study of working memory, decision making, perception, sensation, reasoning, acquisition of knowledge and behavior.
\end{document}