Xianwen Wang

Yingyuan Yang, Guoyuan Liang, Xianwen Wang et al.

To satisfy the rigorous requirements of precise edge detection in critical high-accuracy measurements, this article proposes a series of efficient approaches for localizing subpixel edge. In contrast to the fitting based methods, which consider pixel intensity as a sample value derived from a specific model. We take an innovative perspective by assuming that the intensity at the pixel level can be interpreted as a local integral mapping in the intensity model for subpixel localization. Consequently, we propose a straightforward subpixel edge localization method called Converted Intensity Summation (CIS). To address the limited robustness associated with focusing solely on the localization of individual edge points, a Stable Edge Region (SER) based algorithm is presented to alleviate local interference near edges. Given the observation that the consistency of edge statistics exists in the local region, the algorithm seeks correlated stable regions in the vicinity of edges to facilitate the acquisition of robust parameters and achieve higher precision positioning. In addition, an edge complement method based on extension-adjustment is also introduced to rectify the irregular edges through the efficient migration of SERs. A large number of experiments are conducted on both synthetic and real image datasets which cover common edge patterns as well as various real scenarios such as industrial PCB images, remote sensing and medical images. It is verified that CIS can achieve higher accuracy than the state-of-the-art method, while requiring less execution time. Moreover, by integrating SER into CIS, the proposed algorithm demonstrates excellent performance in further improving the anti-interference capability and positioning accuracy.

Xianwen Wang, Zhichao Fang

In this study, following the idea of our previous paper (Wang, et al., 2013a), we improve the method to detect and track hot topics in a specific field by using the real-time article usage data. With the "usage count" data provided by Web of Science, we take the field of computational neuroscience as an example to make analysis. About 10 thousand articles in the field of Computational Neuroscience are queried in Web of Science, when the records, including the usage count data of each paper, have been harvested and updated weekly from October 19, 2015 to March 21, 2016. The hot topics are defined by the most frequently used keywords aggregated from the articles. The analysis reveals that hot topics in Computational Neuroscience are related to the key technologies, like "fmri", "eeg", "erp", etc. Furthermore, using the weekly updated data, we track the dynamical changes of the topics. The characteristic of immediacy of usage data makes it possible to track the "heat" of hot topics timely and dynamically.

Xianwen Wang, Shenmeng Xu, Zhichao Fang

In this study, we propose a novel way to explore the patterns of people's visits to academic articles. About 3.4 million links to referral source of visitors of 1432 papers published in the journal of PeerJ are collected and analyzed. We find that at least 57% visits are from external referral sources, among which General Search Engine, Social Network, and News & Blog are the top three categories of referrals. Academic Resource, including academic search engines and academic publishers' sites, is the fourth largest category of referral sources. In addition, our results show that Google contributes significantly the most in directing people to scholarly articles. This encompasses the usage of Google the search engine, Google Scholar the academic search engine, and diverse specific country domains of them. Focusing on similar disciplines to PeerJ's publication scope, NCBI is the academic search engine on which people are the most frequently directed to PeerJ. Correlation analysis and regression analysis indicates that papers with more mentions are expected to have more visitors, and Facebook, Twitter and Reddit are the most commonly used social networking tools that refer people to PeerJ.

Xianwen Wang, Chen Liu, Wenli Mao et al.

In this study, we compare the difference in the impact between open access (OA) and non-open access (non-OA) articles. 1761 Nature Communications articles published from 1 Jan. 2012 to 31 Aug. 2013 are selected as our research objects, including 587 OA articles and 1174 non-OA articles. Citation data and daily updated article-level metrics data are harvested directly from the platform of nature.com. Data is analyzed from the static versus temporal-dynamic perspectives. The OA citation advantage is confirmed, and the OA advantage is also applicable when extending the comparing from citation to article views and social media attention. More important, we find that OA papers not only have the great advantage of total downloads, but also have the feature of keeping sustained and steady downloads for a long time. For article downloads, non-OA papers only have a short period of attention, when the advantage of OA papers exists for a much longer time.

Xianwen Wang, Lian Peng, Chunbo Zhang et al.

In our previous study (Wang et al., 2012), we analyzed scientists' working timetable of 3 countries, using realtime downloading data of scientific literatures. In this paper, we make a through analysis about global scientists' working habits. Top 30 countries/territories from Europe, Asia, Australia, North America, Latin America and Africa are selected as representatives and analyzed in detail. Regional differences for scientists' working habits exists in different countries. Besides different working cultures, social factors could affect scientists' research activities and working patterns. Nevertheless, a common conclusion is that scientists today are often working overtime. Although scientists may feel engaged and fulfilled about their hard working, working too much still warns us to reconsider the work - life balance.