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The fault diagnosis of a marine turbocharger system is very crucial for realizing intelligent operation and maintenance in a big data analysis context. In order to improve the diagnostic rate of faults in engineering applications, in this paper, a new unsupervised machine learning algorithm, which is based on one-class support vector machine (OSVM), affinity propagation (AP) and Gaussian mixture model (GMM), called OAGFD is proposed for fault diagnosis. OSVM was firstly used to divide samples of marine turbocharger system into normal and fault samples, and only the fault samples are used in following steps to identify specific fault types. The AP was adopted automatically to provide an initial value for expectation maximization, which can obtain the maximum value of iteration parameters. The GMM is used to classify faults of marine turbocharger system and output the fault diagnosis results. Finally, the OAGFD is validated by actual data. The experiment results show that OAGFD can quickly and accurately be trained. The OAGFD method can achieve higher identification accuracy for multi-faults of marine turbocharger system and takes on faster operation speed and stronger generalization ability than tradition methods. It is an efficient and unsupervised fault diagnosis technique and has both theoretical and practical value. This research provides a new method for automatic fault diagnosis of the marine turbocharger system.

This paper uses the mediation effect and a spatial panel model using panel data from 30 provinces in China from 2011 to 2019 to study the relationship between the digital economy, industrial structure, and carbon emission. The research results show that the development of digital economy can effectively promote the reduction of carbon emissions. The development of the digital economy has a significant role in promoting the rationalization of the industrial structure. The digital economy not only directly suppresses carbon emissions, but also indirectly has a significant inhibitory effect on carbon emissions by promoting the rationalization and improvement of the industrial structure. The development of the digital economy suppresses the optimization of the industrial structure. The improvement of industrialization has hindered the industrialization process. It is necessary to strengthen research and development into digital technology and enhance the capacity of the digital economy to promote carbon emissions reduction.

In this paper, we examine the role of peer‐to‐peer (P2P) platforms in enhancing financial inclusion from the borrowers’ point of view across the rural–urban dimension. We show that when number of bank branches decrease in a rural community, the P2P loan requests increase if there is at least one bank branch in the community allowing people to participate in the P2P market. We also find that the number of P2P loan requests from urban areas is higher when such areas have fewer pawnshops per capita. Our results suggest that P2P enhances financial inclusion of those lacking traditional institutions in rural communities and offers an alternative to those with fewer fringe banks in urban communities.

ABSTRACTIn the last couple of decades, China’s software and services industry has emerged as the driving power of economic growth playing a significant role in leading innovation in China. Research on inter-city network structure, drawing on patent cooperation among cities, helps understand the positioning of cities in terms of software and service-related innovation. This helps guide urban- or regional-development strategy. In this paper, we investigate 22,316 patents of software and service-listed firms. We examine the number of patent applications, technological innovation and development, and the geographical distribution of companies owning patents using Social Network Analysis (SNA). Our results show that: (1) Chinese software and service-related innovation has a notable agglomeration in a few geographically dispersed mega-cities such as Beijing, Shanghai, and Shenzhen; (2) the technological innovation and development of the software and services industry in China is deeply dependent on importing technologies from foreign countries, particularly from the United States and Japan; and (3) the “Industry-University-Research” combination effect of innovation in China is still relatively low. Our policy recommendations include encouragement of cooperation, innovation of investment mechanism, preferential tax treatment of enterprise income tax, and sustainable and improved learning programs for nurturing highly qualified professionals to create an environment for independent innovation and entrepreneurship.