PurposeThis article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining.Design/methodology/approachThe literatures on single cell research were extracted from Clarivate Analytic's Web of Science Core Collection between 2009 and 2019. Firstly, bibliometric analyses were performed with Thomson Data Analyzer (TDA). Secondly, topic identification and evolution trends of single cell research was conducted through the LDA topic model. Thirdly, taking the post-discretized method which is used for topic evolution analysis for reference, the topics were also be dispersed to countries to detect the spatial distribution.FindingsThe publication of single cell research shows significantly increasing tendency in the last decade. The topics of single cell research field can be divided into three categories, which respectively refers to single cell research methods, mechanism of biological process, and clinical application of single cell technologies. The different trends of these categories indicate that technological innovation drives the development of applied research. The continuous and rapid growth of the topic strength in the field of cancer diagnosis and treatment indicates that this research topic has received extensive attention in recent years. The topic distributions of some countries are relatively balanced, while for the other countries, several topics show significant superiority.Research limitationsThe analyzed data of this study only contain those were included in the Web of Science Core Collection.Practical implicationsThis study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges. The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension.Originality/valueThis paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field. The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.
The sections in this article are History and Development Visualization Process Visualization Methods Dataflow Visualization Systems Data Representations Information Visualization Terrain Visualization Current and Future Directions
Although the chord height error might be ensured, the adaptive algorithm of the NURBS curve circular interpolation feed rate is still likely to result in the speed fluctuation which is not in accordance with given acceleration or deceleration rules. On the foundation of the analytic principle of the NURBS curve circular interpolation, a kind of interpolating algorithm based on the NURBS curve continuous small segments of S-shaped acceleration and deceleration is presented. And considering the chord height error, the variable step and feed rate can be effectively combined to make a better estimation. The simulation results on real examples show that the method not only simplifies the complicated calculation of curve interpolation process, simultaneously improves the speed of Motion Smoothing Implementation for NURBS curve interpolation, but also ensures the interpolation precision. The algorithm can be commonly applied in real manufacturing for high-speed and high-precision curve process.
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