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科技论文写作,让文章富有结构逻辑(一)
作者:锤子简历 2019/07/30 23:30:00
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逻辑清晰的科技论文是引人入胜的,读者可以兴趣盎然地读完,感觉不累。语言结构的准确表达是文章有逻辑的前提,这样的语言结构主要体现在句子间的联系和段落间的联系。本推送分为两个章节,分两次推送,分别介绍如何在句子间和段落间下功夫,使得文章富有结构逻辑。


科技论文写作,让文章富有结构逻辑.png


(一)内容上的逻辑

这里选取最近阅读到的一篇交通sci论文的abstract做个逻辑训练,每句话所要表达的意思我都用中文进行了标注

【参考文献】


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在出行数据收集中的重要性). However, the raw data usually lack activity information, which is highlyvaluable for a range of applications and services(转折,存在不足,需要改进). 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(立马提出本文做了什么、意义). 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(这两句具体解释了前面提出的approach). Our results closely approximate the residents’ travel survey datain Shanghai(吹嘘一下结果很好,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来引导,用该方法做了哪些后续的内容). 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(站在宏观的角度,总叙下本研究的意义所在).


首先,说明GPS在出行数据收集中的重要性


转折,存在不足,需要改进


立马提出本文做了什么、意义


这两句具体解释了前面提出的approach


吹嘘一下结果很好,吸引眼球


用furthermore来引导,用该方法做了哪些后续的内容


站在宏观的角度,总叙下本研究的意义所在


这里的写作逻辑非常紧凑,在内容上环环相扣,用最少的语言表达完作者的意思。多一句少一句都不够美,这体现了科技论文写作中简洁、精炼叙述的原则。


(二)结构上的逻辑

当叙述在内容上富有逻辑的同时,结构逻辑(起惩转折)也是不可或缺的。这里非常重要的是连接词的使用。下面简单列举一下一些基本的连接词:


表示递进的:then, subsequently, in addition, besides, whatis more, moreover, furthermore, in order to...further, 等等;


表示转折和让步关系的: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;


表示层次关系的:firstly, first of all, to begin with,secondly...finally, last but not least, afterwards,simultaneously, at themeantime, meanwhile, eventually等等;


表示因果关系的: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;


表示归纳总结的:in conclusion, in summary, in sum, in short,overall,等等;


表示条件关系的:unless, otherwise, only if, if only, supposethat, as soon as, in case that, providing that, given that等等。


这里依旧将刚才的那篇摘要拿来做个分析,转折衔接的标注如下,体会结构中如何连接一个个句子,使文章表达清晰:


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.

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