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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.

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 study aims to propose a semiempirical and semitheoretical cyclic compaction constitutive model of coarse-grained soil filler for the high-speed railway (HSR) subgrade under cyclic load.

According to the basic framework of critical state soil mechanics and in view of the characteristics of the coarse-grained soil filler for the HSR subgrade to bear the train vibration load repeatedly for a long time, the hyperbolic empirical relationship between particle breakage and plastic work was derived. Considering the influence of cyclic vibration time and stress ratio, the particle breakage correction function of coarse-grained soil filler for the HSR subgrade under cyclic load was proposed. According to the classical theory of plastic mechanics, the shearing dilatation equation of the coarse-grained soil filler for the HSR subgrade considering particle breakage was modified and obtained. A semiempirical and semitheoretical cyclic compaction constitutive model of coarse-grained soil filler for the HSR subgrade under cyclic load was further established. The backward Euler method was used to discretize the constitutive equation, build a numerical algorithm of “elastic prediction and plastic modification” and make a secondary development of the program to solve the cyclic compaction model.

Through the comparison with the result of laboratory triaxial test under the cyclic loading of coarse-grained soil filler for the HSR subgrade, the accuracy and applicability of the cyclic compaction model were verified. Results show that the model can accurately predict the cumulative deformation characteristics of coarse-grained soil filler for the HSR subgrade under the train vibration loading repeatedly for a long time. It considers the effects of particle breakage and stress ratio, which can be used to calculate and analyze the stress and deformation evolution law of the subgrade structure for HSR.

The research can provide a simple and practical method for calculating deformation of railway under cyclic loading.

With the yearly increase of mileage and passenger volume in China's high-speed railway, the problems of traditional paper railway tickets have become increasingly prominent, including complexity of business handling process, low efficiency of ticket inspection and high cost of usage and management. This paper aims to make extensive references to successful experiences of electronic ticket applications both domestically and internationally. The research on key technologies and system implementation of railway electronic ticket with Chinese characteristics has been carried out.

Research in key technologies is conducted including synchronization technique in distributed heterogeneous database system, the grid-oriented passenger service record (PSR) data storage model, efficient access to massive PSR data under high concurrency condition, the linkage between face recognition service platforms and various terminals in large scenarios, and two-factor authentication of the e-ticket identification code based on the key and the user identity information. Focusing on the key technologies and architecture the of existing ticketing system, multiple service resources are expanded and developed such as electronic ticket clusters, PSR clusters, face recognition clusters and electronic ticket identification code clusters.

The proportion of paper ticket printed has dropped to 20%, saving more than 2 billion tickets annually since the launch of the application of E-ticketing nationwide. The average time for passengers to pass through the automatic ticket gates has decreased from 3 seconds to 1.3 seconds, significantly improving the efficiency of passenger transport organization. Meanwhile, problems of paper ticket counterfeiting, reselling and loss have been generally eliminated.

E-ticketing has laid a technical foundation for the further development of railway passenger transport services in the direction of digitalization and intelligence.

This paper aims to propose an enhanced densely dehazing network to suit railway scenes’ features and improve the visual quality degraded by haze and fog.

It is an end-to-end network based on DenseNet. The authors design enhanced dense blocks and fuse them in a pyramid pooling module for visual data’s local and global features. Multiple ablation studies have been conducted to show the effects of each module proposed in this paper.

The authors have compared dehazed results on real hazy images and railway hazy images of state-of-the-art dehazing networks with the dehazed results in data quality. Finally, an object-detection test is taken to judge the edge information preservation after haze removal. All results demonstrate that the proposed dehazing network performs better under railway scenes in detail.

This study provides a new method for image enhancing in the railway monitoring system.

The purpose of this paper is to investigate the effect of round pits arrangement patterns on tribological properties of journal bearing. In this paper, the tribological behaviors of journal bearing with different arrangement patterns under lubrication condition were studied based on M-2000 friction and wear tester.

The friction and wear of journal bearing contact surface were simulated by ANSYS. The wear mechanism of bearing contact surfaces was investigated by the means of energy dispersive spectrum analysis on the surface morphology and friction and wear status of the journal bearing specimens by Scanning Electron Microscopy (SEM) and Energy Dispersive Spectrometer (EDS). Besides, the wearing capacity of the textured bearing was predicted by using the GM (1,1) and Grey–Markov model.

As the loads increase, the friction coefficient of journal bearing specimens decrease first and then increase slowly. The higher rotation speed, the lower friction coefficient and the faster temperature build-up. The main friction method of the bearing sample is three-body friction. The existence of texture can effectively reduce friction and wear. In many arrangement patterns, the best is 4# bearing with round pits cross-arrangement pattern. Its texturing diameters are 60 µm and 125 µm, and the spacing and depth are 200 µm and 25 µm, respectively. In addition, the Grey–Markov model prediction result is more accurate and fit the experimental value better.

The friction and wear mechanism is helpful for scientific research and engineers to understand the tribological behaviors and engineering applications of textured bearing. The wear capacity of textured bearing is predicted by using the Grey–Markov model, which provides technical help and theoretical guidance for the service life and reliability of textured bearing.

The theory of complex adaptive systems (CASs) represents an interesting perspective to study the characteristics of circular supply chains (CSCs). In this regard, the current literature lacks evidence regarding coordination and integration mechanisms, characteristics of the environment and emerging system properties of CSCs. This paper aims to fill this gap and focuses on how and why companies design (i.e. configure and coordinate) their CSCs and what value these design choices help to create across different industries.

The authors use a multiple case study approach and analyze data collected from a sample of five sustainable start-ups operating in the fashion and construction industries in Italy to better understand how these companies design (i.e. configure and coordinate) their CSCs.

Results reveal that in the two industries under investigation, the design of CSCs built around open and closed–loop logic is triggered by the intention to solve a negative sustainability impact. The sustainability impact determines whether the value is restored within the same supply chain, in another, or inside or outside the same industry. Interestingly, start-ups appear to coordinate other CSC actors with three leading roles: (1) orchestrator, (2) integrated orchestrator and (3) circular manufacturer. The coordination role of the start-ups differs in each supply chain configuration based on the level of vertical integration of manufacturing activities.

From a theoretical perspective, the authors' results expand previous supply chain management (SCM) literature by presenting an empirical analysis of the configuration and coordination of CSCs, and discussing the drivers for creating such circularity from a CAS perspective. From a managerial perspective, the authors offer a practical experience to entrepreneurs on how to transform circular and sustainable business model aspirations into CSC practices.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.