Search results

This study explores the characteristics of high-speed rail (HSR) and air transportation networks in China based on the weighted complex network approach. Previous related studies have largely implemented unweighted (binary) network analysis, or have constructed a weighted network, limited by unweighted centrality measures. This study applies weighted centrality measures (mean association [MA], triangle betweenness centrality [TBC], and weighted harmonic centrality [WHC]) to represent traffic dynamics in HSR and air transportation weighted networks, where nodes represent cities and links represent passenger traffic. The spatial distribution of centrality results is visualized by using ArcGIS 10.2. Moreover, we analyze the network robustness of HSR, air transportation, and multimodal networks by measuring weighted efficiency (WE) subjected to the highest weighted centrality node attacks. In the HSR network, centrality results show that cities with a higher MA are concentrated in the Yangtze River Delta and the Pearl River Delta; cities with a higher TBC are mostly provincial capitals or regional centers; and cities with a higher WHC are grouped in eastern and central regions. Furthermore, spatial differentiation of centrality results is found between HSR and air transportation networks. There is a little bit of difference in eastern cities; cities in the central region have complementary roles in HSR and air transportation networks, but air transport is still dominant in western cities. The robustness analysis results show that the multimodal network, which includes both airports and high-speed rail stations, has the best connectivity and shows robustness.

This paper aims to study the influence of aerodynamics force of trains passing each other on the dynamic response of vehicle bridge coupling system based on numerical simulation and multi-body dynamics and put forward the speed threshold for safe running of train under different crosswind speeds.

The computational fluid dynamics method is adopted to simulate the aerodynamic force in the whole process of train passing each other by using dynamic grid technology. The dynamic model of vehicle-bridge coupling system is established considering the effects of aerodynamic force of train passing each other under crosswind, the dynamic response of train intersection on the bridge under crosswind is computed and the running safety of the train is evaluated.

The aerodynamic force of trains' intersection has little effects on the derailment factor, lateral wheel-rail force and vertical acceleration of train, but it increases the offload factor of train and significantly increases the lateral acceleration of train. The crosswind has a significant effect on increasing the derailment factor, lateral wheel-rail force and offload factor of train. The offload factor of train is the key factor to control the threshold of train speed. The impact of the aerodynamic force of trains' intersection on running safety cannot be ignored. When the extreme values of crosswind wind speed are 15 m·s⁻¹, 20 m·s⁻¹ and 25 m·s⁻¹, respectively, the corresponding speed thresholds for safe running of train are 350 km·h⁻¹, 275 km·h⁻¹ and 200 km·h⁻¹, respectively.

The research can provide a more precise numerical method to study the running safety of high-speed trains under the aerodynamic effect of trains passing each other on bridge in crosswind.

The purpose of this study is to quantify port efficiency assessment indicators to analyze the impact of COVID-19 on Chinese One Belt One Road (OBOR) ports.

This study utilized a grey prediction model GM(1,1) to forecast five relevant indicators for each of the 17 OBOR ports both with and without COVID-19 background conditions. Additionally, the data envelopment analysis (DEA) efficiency assessment approach was used to analyze the impact of COVID-19 on port efficiency.

The results indicate that cargo and container throughput growth rates during the COVID-19 pandemic are reduced by 1.7 and 2.1%, respectively. There was also a noticeable reduction in technological efficiency (TE) as well as pure technological efficiency (PTE), while scale efficiency (SE) remained largely unaffected. Furthermore, the dynamic efficiency MI was mainly negatively impacted by changes in overall efficiency change (EFFCH), where pure efficiency change (PECH) less than one contributed significantly towards overall regression of port efficiencies during this period.

This paper is unique in its use of a combination of the grey prediction model and DEA efficiency assessment to quantify changes in important indicators during pandemic periods. This approach not only provides a quantitative understanding of the impact on port-level efficiency through numerical quantification but also offers readers an intuitive understanding.

This study aims to investigate whether firms purchase directors' and officers' liability (D&O) insurance when the country-level economic policy uncertainty (EPU) is high.

This study uses D&O insurance data from Chinese listed firms between 2003 and 2019 to conduct regression analyses to examine the association between D&O insurance and EPU.

The results show that government EPU, despite being an exogenous factor, increases the likelihood of firms' purchasing D&O insurance, and this effect is more pronounced when firms are exposed to great share price crash risk and high litigation risk, suggesting that firms intend to purchase D&O insurance possibly due to the accentuated stock price crash risk and litigation risk associated with EPU. In addition, the results indicate that the effect of EPU on the D&O insurance purchase decision is moderated by the provincial capital market development and internal control quality.

The study highlights the role of uncertain economic policies in shareholder approval of D&O insurance purchases.

The study enriches the literature on the determinants of D&O insurance purchases by documenting novel evidence that country-level EPU is a key institutional factor shaping firms' decisions to purchase D&O insurance.

Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.

This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.

Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.

This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

Aiming at the lack of explanatory power of traditional industrial organization theory in cross-border competition, by introducing the idea of ecological niche, the authors aim to explore the competitive situation of platform-based enterprises when they operate in multiple fields.

With the help of ecological niche theory, construct the niche width and niche overlap index of typical enterprises in the platform economy, and find out the advantages and the intensity of competition through comparative analysis.

In an environment of cross-border competition, large enterprises have significant competitive advantages, and the fierce competition is concentrated among medium-sized enterprises.

The conclusions of this paper not only provide new insights for explaining the phenomenon of cross-border competition in the platform economy, but also provide theoretical reference for the anti-trust enforcement practice in the platform economy.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

This paper summarizes the severity of global warming, collaboration and endeavor within international government and the trend of international development for “energy-saving and emission reduction.” The Chinese government is enduring high pressure under the environment of “global warming” and “energy-saving and emission reduction” and it has made a policy for “energy-saving and emission reduction.” Based on this, we analyzed the possibility and feasibility for our logistics to “energy-saving and emission reduction,” then propose some solutions for our logistics industry to development and “energy-saving and emission reduction.”

This paper aims to clear whether the monopoly structure of the internet industry has produced market power and discussed the welfare change of the internet industry monopoly.

By using new empirical industrial organization methods and taking the e-commerce market as an example, the authors measured market power and economies of scale of the internet platform companies.

Internet platform enterprises have formed scale economy, but it has not had market power, and the industry still maintains high levels of competition; also, the emergence of large enterprises may increase the welfare of consumers.

The conclusion of this paper clarified actual competition status of internet industry and provided a new foothold for regulation and ideas for the traditional industry to crack the Marshall Conflict.

Library intelligence institutions, which are a kind of traditional knowledge management organization, are at the frontline of the big data revolution, in which the use of unstructured data has become a modern knowledge management resource. The paper aims to discuss this issue.

This research combined theme logic structure (TLS), artificial neural network (ANN), and ensemble empirical mode decomposition (EEMD) to transform unstructured data into a signal-wave to examine the research characteristics.

Research characteristics have a vital effect on knowledge management activities and management behavior through concentration and relaxation, and ultimately form a quasi-periodic evolution. Knowledge management should actively control the evolution of the research characteristics because the natural development of six to nine years was found to be difficult to plot.

Periodic evaluation using TLS-ANN-EEMD gives insights into journal evolution and allows journal managers and contributors to follow the intrinsic mode functions and predict the journal research characteristics tendencies.