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A visibility graph transforms time series into graphs, facilitating signal processing by advanced graph data mining algorithms. In this paper, based on the classic limited penetrable visibility graph method, we propose a novel mapping method named circular limited penetrable visibility graph, which replaces the linear visibility line in limited penetrable visibility graph with nonlinear visibility arc for pursuing more flexible and reasonable mapping of time series. Tests on degree distribution and some common network features of the generated graphs from typical time series demonstrate that our circular limited penetrable visibility graph can effectively capture the important features of time series and show higher robust classification performance than the traditional limited penetrable visibility graph in the presence of noise. The experiments on real-world time-series datasets of radio and electroencephalogram signals also suggest that the structural features provided by a circular limited penetrable visibility graph, rather than a limited penetrable visibility graph, are more useful for time-series classification, leading to higher accuracy. This classification performance can be further enhanced through structural feature expansion by adopting subgraph networks. All of these results demonstrate the effectiveness of our circular limited penetrable visibility graph model.

Viola kunawarensis Royle is a precious Uygur medicinal material that has anti-fever and detoxifying effects. This study reports the complete chloroplast genome sequence of V. kunawarensis based on Illumina NovaSeq-PE150 platform sequencing reads. The genome is 156,837 bp long and contains a small single-copy (SSC) region of 17,059 bp and a large single-copy (LSC) region of 86,194 bp, separated by two inverted repeats (IRs) of 26,792 bp each. There are 111 unique genes in the chloroplast genome. In this study, V. kunawarensis was confirmed to be most closely related to all comprising Viola taxa except Viola mirabilis and Viola websteri.