使用 Elsevier 文章类别 (elsarticle.cls) 对方程式进行左对齐

Question

我正在撰写一篇论文，将使用 Elsevier 文章类 (elsarticle.cls) 发送至 Elsevier 期刊。对于左对齐方程，我使用了fleqn但方程并不完全像 Elsevier 发布的版本那样左对齐 (图片)。

矿：

有什么建议么？

\documentclass[5p,twocolumn]{elsarticle}

\usepackage[fleqn]{amsmath}
\usepackage{mathptmx}

% 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.: [phone].}
\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}


\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

\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.

\begin{equation}
\mathbf{x}(t+\delta) = \mathbf{x}(t)+\delta \mathbf{f}(t,\mathbf{x}(t),\mathbf{u}(t),\theta)+\sqrt{\mathbf{Q}}\mathbf{w}
\label{eq:Equat_4}
\end{equation}

\begin{equation}
\mathbf{x}(t+\delta) = \mathbf{x}(t)+ \mathbf{J_x}^{-1}[\exp(\delta \mathbf{J_x}) - \mathbf{I}]\mathbf{f}(t,x(t),u(t),\theta)
\label{eq:Equat_7}
\end{equation} 

\end{document}

Answer 1

插入\setlength{\mathindent}{0cm}序言中。