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Fault-related monitoring variables selection is a process of obtaining a subset of variables from the original set, which is of great significance for reducing information redundancy and improving the performance of the fault diagnosis models. This paper aims to propose a novel variables selection approach based on complex networks.

Firstly, a dual-layer correlation networks (DlCN) which consists of mechanism-oriented correlation sub-network (MoCSN) and data-oriented correlation sub-network (DoCSN) is constructed. Secondly, an algorithm for identifying critical fault-related monitoring variables based on dual correlations is introduced. In the algorithm, the topological attributes of the MoCSN and correlation threshold of the DoCSN are used successively.

In the experiments of vertical elevator fault diagnosis, the critical fault-related monitoring variables selected by the DlCN-based approach is more effective than the traditional approaches. It indicates that fusion mechanism-oriented correlation can enhance the comprehensiveness of variable correlation analysis. Moreover, the approach has been proved to be adaptable to different fault diagnosis models.

In the DlCN-based variables selection approach, the mechanism-oriented correlation and data-oriented correlation are comprehensively considered. It improves the precision of variables selection. Meanwhile, it is an unsupervised and model-agnostic approach which addresses the shortcomings of some conventional approaches that require data labels and have insufficient adaptability for fault diagnosis models.

Discussions about digital tourism continue to increase among scholars as Information Communication and Technology (ICT) infrastructure develops. Dynamic changes due to technological aspects have given rise to various developments in the tourism industry. Therefore, this study aims to evaluate the scientific structure of the development of digital tourism topics through a bibliometric analysis approach. In total, 102 publications from research on digital tourism were taken from Scopus database between 2001 and 2021, for further bibliometric analysis using the VOSviewer application. Interesting findings describe the most cited digital tourism publications, the contribution of digital tourism by various authors, institutions, countries, co-citation analysis, bibliographic coupling, and co-occurrence for the main trends of digital tourism. This study compiles a detailed review of digital tourism research. This article adds substantial value to the digital tourism topic by analyzing bibliometric data. It provided scientific information regarding digital tourism for other researchers and future research.