电动汽车、氢能源汽车等新能源产业的快速发展为国家碳达峰目标的实现奠定了深厚的基础,是国家战略布局的核心产业。为了全方位动态展示新能源汽车产业的研究前沿热点与科研合作态势。文章利用Citespace软件,基于中国知网数据库中新能

论文高被引和低被引是科学界普遍存在和对立统一的两类现象。前者因其强大的正面导向作用受到国内外学者的广泛关注,而后者对科研质量和影响力的反面警示作用也不可忽视。本文首先阐释了科技文献低被引现象的背景、内涵和研究意义,然后通过全面研读和解析低被引及其相关研究成果的研究内容,将科技文献低被引研究内容归纳为四大主题:低被引阈值及其比例研究、低被引率演变规律研究、文献低被引影响因素及未来价值预测研究、低被引指标在科研评价中的应用研究。最后从科技文献发表时间演化分析的角度分别评述了四大研究主题的发展动态及存在的问题,并针对科技文献低被引研究中存在的问题,提出未来的研究展望和解决思路,希望借此为国内低被引及其相关领域研究人员提供一定的借鉴和启示。

区块链是目前国际新兴前沿技术,涉及技术较多且应用领域广泛,需要进行全面解读。本文首先比较分析了五种区块链技术及其之间的异同,以及传统技术与区块链技术之间的差异。然后对国内外代表性区块链企业、区块链技术平台及其典型应用进行详细分析和比较,以期为国内外相关研究人员及从业者提供一定的指导。通过比较分析,我们发现:（1）区块链技术主要由区块结构、去中心化且开源的协议机制、加密解密算法、P2P分布式网络、公有链和联盟链组成。（2）与传统技术相比,区块链技术的优势在于通过技术实现的去中心化交易机制,数学加密、共识和信任算法,实现数据不可篡改功能。（3）目前的区块链应用平台数量众多,广泛应用于社会公共事业、跨境支付、保险理赔、产品交易保障、金融和证券交易、贷款交易和管理、供应链管理、公益慈善、医疗、物流、商品溯源等领域。（4）世界顶级公司,如:高盛、微软、阿里巴巴、腾讯、百度和360等已经全面介入区块链技术,并针对不同场景设计和应用了区块链平台和产品。

It is essential to enhance the ability of wind speed forecasting for wind farm managers. The contribution of this research is to develop a novel hybrid method for multi-step ahead wind speed forecasting including empirical wavelet transformation-Kullback–Leibler divergence, autoregressive fractionally integrated moving average and improved back-propagation neural network. The empirical wavelet transformation-Kullback–Leibler divergence is used to extract these valuable features of wind speed fluctuations. The autoregressive fractionally integrated moving average is utilized to extract the long memory characteristics and capture the linear fluctuations of wind speed. The improved back propagation neural network is established to capture the corresponding nonlinear fluctuations, its inputs and outputs are determined by phase space reconstruction and its weights and thresholds are optimized by a modified bat algorithm with cognition strategy. Three prediction cases are employed to test the developed model. The simulation results demonstrate that the developed model outperforms several common benchmark models.
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Patterns of co-authorship provide an effective means of probing the structures of research communities. In this paper, we use the CiteSpace social network tool and co-authorship data from the Web of Science to analyse two such types of community. The first type is based on the cited publications of a group of highly productive authors in a particular discipline, and the second on the uncited publications of those highly productive authors. These pairs of communities were generated for three different countriesthe People's Republic of China (PRC), the United Kingdom (UK) and the United States of America (USA)and for four different disciplines (as denoted by Web of Science subject categories)Chemistry Organic, Engineering Environmental, Economics, and Management. In the case of the UK and USA, the structures of the cited and uncited communities in each of the four disciplines were markedly different from each other; in the case of the PRC, conversely, the cited and uncited PRC communities had broadly similar structures that were characterised by large groups of connected authors. We suggest that this may arise from a greater degree of guest or honorary authorship in the PRC than in the UK or the USA.

以可视化图谱的方式全面直观展示当前新兴的热点主题——大数据的国内外研究全貌,为领域研究者和从业者提供一定的指引。因此,本文融合Web of Science和中国知网中"大数据"研究文献数据,借助CiteSpaceⅡ可视化分析软件绘制出国内外大数据领域的科学知识图谱——作者、国家和机构的科研合作网络,关键词共现聚类网络,文献共被引聚类网络,揭示出国内外大数据研究概貌。研究发现,国内大数据方面科研社区较多,但规模较小,而国际大数据科研社区较少,但规模较大,规模最大研究社区,达到30多人。国家和机构之间的科研合作极少,国际TOP30科研机构中,大部分为美国高校,中国机构较少。国内外大数据研究都涉及的热点有:基于大数据的云计算;基于MapReduce和Hadoop的海量数据分布式处理研究;大数据在数据挖掘、社会网络、互联网金融、识别与预测等领域的应用研究。而国内涉及较少的研究热点有:大数据模型和算法研究、大数据分类研究和大数据相关系统研究。

The term "non-citation factor" refers to the percentage of never-cited papers in a citation time window, a common phenomenon in the science world. Some scholars have qualitatively explored the reasons for not citing a publication, and quantitatively analyzed the mathematical functional relations between the "non-citation factor" and "impact factor of a journal." This study simultaneously considers the mutual relations and closeness degree between the "non-citation factor" and different influencing factors from a novel perspective-that of using a more structuralized panel data model. The analysis revealed that the determinants, including "impact factor of journal," "age of journal," "average number of references per paper in journal," and "issues of journal," exerted an extremely small but positive influence (< 0.025) on the decline of "percentage of never-cited papers in the citation time window of publication year or 3 years." That means the improvement of these determinants can decrease the percentage of never-cited papers. The "impact factor of the journal" always had the biggest positive influence, while the "average number of references per paper in journal" always had the smallest positive influence. In wider citation time windows of 3 or 6 years, two determinants-"number of publications in journal" and "amount of interdisciplinarity in journal"-began to exert a negative effect with a positive correlation coefficient on the decline of the "non-citation factor." That means the improvement of these two determinants cannot decrease the value of the "non-citation factor," even though they can increase its value. It is worth noting that the "impact factor of the journal" had a positive influence on the decline of the percentages of never-cited papers in the citation time window of publication year or 3 years, and began to play a negative role in the decline of percentage of never-cited papers in the citation time window of 6 years. Finally, three variables-"average number of authors per paper in journal," "average number of references per paper in journal," and "issues of journal"-no longer exerted an influence on the decline of percentages of never-cited papers in the citation time window of 6 years, while "age of journal" and "average number of pages per paper in journal" still made a positive contribution. Our findings could help research institutions, researchers, editors, and publishers understand the positively or negatively influential factors that lead to non-citation, thus improving the chance of papers being cited and having some academic influence.

Although bibliometric approach has been frequently utilized to analyse reasons behind non-citation and show relations between uncitedness and impact factors, the survey-based structural equation model approach is not usually used. Therefore, a Likert scale questionnaire was designed to collect data on noncitation and its various types of determinants. The survey-based structural equation model was used to analyse mutual relations and correlation degrees between non-citation rate and its various determinants. As a result, the categories 'academic status of journal' and 'personal features of papers' were found to be two extremely significant determinants of non-citation rate. Their path coefficients reached 0.83 and 0.43 respectively. Accordingly, the category 'contents and topics of papers' was shown to have extremely significant indirect influence on non-citation rate through `academic status of journal'. The three observed variables of 'academic status of journal' including `public praise of journal', 'impact factor of journal', and 'member of SCI, EI and Scopus Journals', showed the highest values of indirect effect on the non-citation rate. Furthermore, there were weaker correlations among 'academic status of journal', 'personal features of papers', 'contents and topics of papers' and 'publicity and recommendation' except between 'contents and topics of papers' and 'academic status of journal'. Meanwhile, the six observed variables of 'publicity and recommendation' and 'contents and topics of papers' show the smaller values at .0.12 of indirect effect on non-citation rate. Our empirical results suggest some significant determinants of non-citation rate that might enlighten researchers on how to improve the chance of having their works cited, and assist them in expanding their research impact. These findings can also help journal editors to identify contributions with high-citation potential.

Stock price index (SPI) prediction is essential to investors when they make investment decisions. However, due to the intrinsic complexity and non-stationary of SPI, accurate SPI prediction has been a challenge task for a longtime. Compared to a quantity of single algorithms, hybrid methods always have higher prediction accuracy. Based on the theories of genetic algorithm (GA), grey model (GM), back-propagation neural network (BPNN) and support vector regression (SVR), in this paper, two hybrid models named GA-SVR-GM and GABPNN-GM are proposed for improving the prediction accuracy of Composite Index in Shanghai Stock Exchange (SHSE) and Component Index in Shenzhen Stock Exchange (SZSE). The SVR (or BPNN) is first used to forecast SPI and then with the obtained errors GM is constructed taking into account the different patterns that the SVR (or BPNN) cannot identify, reducing with the final errors. Moreover, in order to obtain the better forecasting results of SVR (or BPNN), the model parameters are optimized by GA. Two prediction cases of SPI are used to test the performance of the proposed hybrid models. Compared with the random walk (RW), SVR, and BPNN, the simulation results show that the GA-SVR-GM and GA-BPNN-GM consistently have the minimum statistical error of the root mean square error (RAISE), mean absolute error (NAE), and mean absolute percentage error (NAPE). Thus, it can be concluded that such improving methods are effective.

论文零被引率是指一个国家、机构、期刊或个人某年出版的文档集合中,在出版后的某个特定引用时间窗口中未被引文档的数量或比例。它是科学出版领域中普遍存在的一种现象,然而当前的引用分布研究文献过多地关注至少被引一次论文的引用分布情况,而对论文零被引率的演变规律关注甚少。为此,本文从国内外图书情报学领域期刊中,选取6本不同影响力期刊为研究样本,实证分析了6本期刊1990—1993各年出版的论文在出版后0~19年不同引用时间窗口中论文零被引率的时间演变规律,以及1990—2012各年论文出版后当年、三年和六年零被引率的年度变化规律。实证分析发现:（1）除日本多语言期刊《图书与情报》（LIS）之外,其他5本期刊论文零被引率的时间演化规律能够很好地被一个三参数负指数模型:P（Xt=0）=K+Ae–S×t拟合,拟合优度（R~2）都达到96%以上;（2）除日本《图书与情报》（LIS）期刊外,其他5本期刊论文零被引率随引用时间窗口变大而形成一系列具有"开始下降较快,随后非常缓慢地下降,直至一个非常低且稳定的水平线"特征的凹形曲线;并且此5本期刊睡眠系数S区间值大小与它们的下降幅度区间值大小的方向一致,期刊的下降幅度越大,越易于较快达到一个稳定的、变化非常少的水平,从而导致一些未被引的文献在此后的长时间内处于睡眠状态;（3）6本期刊论文零被引率的年度变化规律显示,随着时代的发展,图书情报学领域期刊论文的传播与利用速度越来越快。除日本《图书与情报》（LIS）论文零被引率较高,且历年变化较小外,其他5本期刊论文零被引率,尽管历年变化较大,但总体呈现下降趋势。