本章节，我们将以科技论文的结构框架主线，从题目、摘要、引言、文献综述、模型方法、结果展示、讨论、致谢、参考文献等9个模块分别开展叙述。在每个模块中会详细解释它的作用、注意事项、叙述套路、典例分析和语句摘录，让读者对科技论文的行文结构有一个非常好的把控。

Abstract

“我强烈地感觉到，正是那些充斥着‘术语’和废话的拙劣摘要严重地阻碍了科技交流”

——Sheila M.McNab

非常认同这句话，我用它来开篇引出摘要的写法叙述。科技论文的本质是：用论文记录你的有意义的研究，并通过期刊平台分享交流，促进学科的发展；同时，因为论文的发表，学者在该领域的影响会提升。

所以，做为开场白的摘要，它不是金庸小说充满情节的"飞雪连天射白鹿,笑书神侠倚碧鸳"，更不是琼瑶言情剧翻来覆去的“情深深雨蒙蒙”。可能把它比喻成小学生的日记会比较贴切吧！

精准到位，以便交流，这是摘要的核心定位。它是总体文章的缩影，阅读者看完就把握了文章的框架。如果这个框架内容是阅读者寻找的，那么才有继续阅读细节的必要性。

要点

摘要部分的写法是有套路和模板的，也就是【1+1+n+1+1】的写作套路，但也不绝对固定，可供参考：

一句简要的背景介绍。

一句进行本研究的原因，即motivation（动机）。

n句提出本文的研究内容，并简要介绍内容，n = 3-5句话。

一句吹嘘结论（结果），抓住读者和眼球，总结结论时态要用一般现在时。

一句强调研究意义。

典型实例

【参考文献】

Inferring trip purposes and uncovering travel patterns from taxi trajectory data

Li Gong, Xi Liu, Lun Wu and Yu Liu

ABSTRACT

Global positioning system-enabled vehicles provide an efficient way to obtain large quantities of movement data for individuals（GPS在出行数据收集中的重要性）. However, the raw data usually lack activity information, which is highly valuable for a range of applications and services（转折，存在不足，需要改进）. This study provides a novel and practical framework for inferring the trip purposes of taxi passengers such that the semantics of taxi trajectory data can be enriched（立马提出本文做了什么及意义）. The probability of points of interest to be visited is modeled by Bayes’ rules, which take both spatial and temporal constraints into consideration. Combining this approach with Monte Carlo simulations, we conduct a study on Shanghai taxi trajectory data（这两句具体解释了前面提出的approach）. Our results closely approximate the residents’ travel survey data in Shanghai（吹嘘一下结果很好，吸引眼球）. Furthermore, we reveal the spatiotemporal characteristics of nine daily activity types based on inference results, including their temporal regularities, spatial dynamics, and distributions of trip lengths and directions（用furthermore来引导，用该方法做了哪些后续的内容）. In the era of big data, we encounter the dilemma of “trajectory data rich but activity information poor” when investigating human movements from various data sources. This study presents a promising step toward mining abundant activity information from individuals’ trajectories（站在宏观的角度，总叙下本研究的意义所在）.

一句背景介绍：说明GPS在出行数据收集中的重要性。

一句进行本研究的原因：原始数据存在活动信息，阻碍了研究，需要改进。

n句提出本文的研究内容，并简要介绍内容：立马提出本文做了什么、意义，即提出出行目的地判断的方法，并简要介绍怎么做的。

一句吹嘘结论（结果）：由这个方法得到的结合和上海市的出行调查数据高度吻合。

用furthermore来引导，用该方法做了哪些后续的内容。

一句强调研究意义：为轨迹数据挖掘出行活动信息提供了有发展前景的方法（promising）。

一句进行本研究的原因，即motivation（动机）。

经典语句摘录

一句进行本研究的原因，即motivation（动机）

However, the raw data usually lack activity information, which is highly valuable for a range of applications and service.

We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment. (用“要解决什么问题”的方式来反向说明存在的问题)

In this paper we develop a spatial decision support system that assists free-floating carsharing providers in countering imbalances between vehicle supply and customer demand in existing business areas and reduces the risk of imbalance when expanding the carsharing business to a new city. (用“要解决什么问题”的方式来反向说明存在的问题)

n句提出本文的研究内容，并简要介绍内容，n = 3-5句话;

This study provides a novel and practical framework for inferring the trip purposes of taxi passengers such that the semantics of taxi trajectory data can be enriched…

We present a novel method that provides strategic and operational decision support to companies maneuvering this competitive and constantly changing market environment…

In this paper we develop a spatial decision support system that…

一句吹嘘结论（结果），抓住读者和眼球，总结结论时态要用一般现在时;

Our results closely approximate the residents’ travel survey data in Shanghai

The results indicate that predicted and actual usage patterns are very similar.

We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities.

一句强调研究意义。

This study presents a promising step toward mining abundant activity information from individuals’ trajectories.

Hence, our approach can be used to define new business areas when expanding to new cities to include high demand areas and exclude low demand areas, thereby reducing the risk of supply-demand imbalance.

We find that our approach correctly identifies areas with a high carsharing activity and can be easily adapted to other cities.