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邏輯清晰的科技論文是引人入勝的，讀者可以興趣盎然地讀完，感覺不累。語言結(jié)構(gòu)的準(zhǔn)確表達(dá)是文章有邏輯的前提，這樣的語言結(jié)構(gòu)主要體現(xiàn)在句子間的聯(lián)系和段落間的聯(lián)系。本推送分為兩個章節(jié)，分兩次推送，分別介紹如何在句子間和段落間下功夫，使得文章富有結(jié)構(gòu)邏輯。

（一）內(nèi)容上的邏輯

這里選取最近閱讀到的一篇交通sci論文的abstract做個邏輯訓(xùn)練，每句話所要表達(dá)的意思我都用中文進(jìn)行了標(biāo)注

【參考文獻(xiàn)】

Inferring trip purposes and uncovering travel patternsfrom taxi trajectory data

Li Gong, Xi Liu, Lun Wu and Yu Liu

ABSTRACT

Global positioning system-enabled vehiclesprovide an efficient way to obtain large quantities of

movement data for individuals（GPS在出行數(shù)據(jù)收集中的重要性）. However, the raw data usually lack activity information, which is highlyvaluable for a range of applications and services（轉(zhuǎn)折，存在不足，需要改進(jìn)）. This study provides a novel and practical framework for inferringthe trip purposes of taxi passengers such that the semantics of taxi trajectorydata can be enriched（立馬提出本文做了什么、意義）. Theprobability of points of interest to be visited is modeled by Bayes’ rules,which take both spatial and temporal constraints into consideration. Combiningthis approach with Monte Carlo simulations, we conduct a study on Shanghai taxitrajectory data（這兩句具體解釋了前面提出的approach）. Our results closely approximate the residents’ travel survey datain Shanghai（吹噓一下結(jié)果很好，吸引眼球）. Furthermore, we reveal thespatiotemporal characteristics of nine daily activity types based on inferenceresults, including their temporal regularities, spatial dynamics, anddistributions of trip lengths and directions（用furthermore來引導(dǎo)，用該方法做了哪些后續(xù)的內(nèi)容）. In the era of bigdata, we encounter the dilemma of “trajectory data rich but activityinformation poor” when investigating human movements from various data sources.This study presents a promising step toward mining abundant activityinformation from individuals’ trajectories（站在宏觀的角度，總敘下本研究的意義所在）.

首先，說明GPS在出行數(shù)據(jù)收集中的重要性

轉(zhuǎn)折，存在不足，需要改進(jìn)

立馬提出本文做了什么、意義

這兩句具體解釋了前面提出的approach

吹噓一下結(jié)果很好，吸引眼球

用furthermore來引導(dǎo)，用該方法做了哪些后續(xù)的內(nèi)容

站在宏觀的角度，總敘下本研究的意義所在

這里的寫作邏輯非常緊湊，在內(nèi)容上環(huán)環(huán)相扣，用最少的語言表達(dá)完作者的意思。多一句少一句都不夠美，這體現(xiàn)了科技論文寫作中簡潔、精煉敘述的原則。

（二）結(jié)構(gòu)上的邏輯

當(dāng)敘述在內(nèi)容上富有邏輯的同時，結(jié)構(gòu)邏輯（起懲轉(zhuǎn)折）也是不可或缺的。這里非常重要的是連接詞的使用。下面簡單列舉一下一些基本的連接詞：

表示遞進(jìn)的：then, subsequently, in addition, besides, whatis more, moreover, furthermore, in order to...further, 等等；

表示轉(zhuǎn)折和讓步關(guān)系的：but, however, nevertheless, nonetheless, onthe contrary, on the other hand, in contrast, instead of, even so, though,although, despite, regardless of, in spite of, as opposed to等等；

表示層次關(guān)系的：firstly, first of all, to begin with,secondly...finally, last but not least, afterwards,simultaneously, at themeantime, meanwhile, eventually等等；

表示因果關(guān)系的：because, because of this, since, for thisreason, thanks to, due to, owing to, seeing that, on account of, therefore，as a result, hence,consequently,  accordingly等等；

表示歸納總結(jié)的：in conclusion, in summary, in sum, in short，overall,等等;

表示條件關(guān)系的：unless, otherwise, only if, if only, supposethat, as soon as, in case that, providing that, given that等等。

這里依舊將剛才的那篇摘要拿來做個分析，轉(zhuǎn)折銜接的標(biāo)注如下，體會結(jié)構(gòu)中如何連接一個個句子，使文章表達(dá)清晰：

Global positioning system-enabled vehiclesprovide an efficient way to obtain large quantities of movement data forindividuals.However, the raw data usually lack activity information, which is highlyvaluable for a range of applications and services. This study provides a novel andpractical framework for inferring the trip purposes of taxi passengers such thatthe semantics of taxi trajectory data can be enriched. The probability ofpoints of interest to be visited is modeled by Bayes’ rules, which take bothspatial and temporal constraints into consideration. Combining this approach withMonte Carlo simulations, we conduct a study on Shanghai taxi trajectory data. Our resultsclosely approximate the residents’ travel survey data in Shanghai. Furthermore,we reveal the spatiotemporal characteristics of nine daily activity types basedon inference results, including their temporal regularities, spatialdynamics, and distributions of trip lengths and directions. In the era of bigdata, we encounter the dilemma of “trajectory data rich but activityinformation poor” when investigating human movements from various data sources.This studypresents a promising step toward mining abundant activity information fromindividuals’ trajectories.