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The traction cable is paralleled with the existing traction network of electrified railway through transverse connecting line to form the scheme of long distance power supply for the traction network. This paper aims to study the scheme composition and power supply distance (PSD) of the scheme.

Based on the structure of parallel traction network (referred to as “cable traction network (CTN)”), the power supply modes (PSMs) are divided into cable + direct PSM and cable + autotransformer (AT) PSM (including Japanese mode, French mode and new mode). Taking cable + Japanese AT PSM as an example, the scheme of long distance power supply for CTN under the PSMs of co-phase and out-of-phase power supply are designed. On the basis of establishing the equivalent circuit model and the chain circuit model of CTN, taking the train working voltage as the constraint condition, and based on the power flow calculation of multiple train loads, the calculation formula and process for determining the PSD of CTN are given. The impedance and PSD of CTN under the cable + AT PSM are simulated and analyzed, and a certain line is taken as an example to compare the scheme design.

Results show that the equivalent impedance of CTN under the cable + AT PSM is smaller, and the PSD is about 2.5 times of that under the AT PSM, which can effectively increase the PSD and the flexibility of external power supply location.

The research content can effectively improve the PSD of traction power supply system and has important reference value for the engineering application of the scheme.

Intuitionistic linguistic fuzzy information (ILFI), characterized by linguistic terms and intuitionistic fuzzy sets (IFSs), can easily express the fuzzy information in the process of muticriteria decision making (MCDM) and muticriteria group decision making (MCGDM) problems. The purpose of this paper is to provide an overview of aggregation operators (AOs) and applications of ILFI.

First, some meaningful AOs for ILFI are summarized, and some extended MCDM approaches for intuitionistic uncertain linguistic variables (IULVs), such as extended TOPSIS, extended TODIM, extended VIKOR, are discussed. Then, the authors summarize and analyze the applications about the AOs of IULVs.

IULVs, characterized by linguistic terms and IFSs, can more detailed and comprehensively express the criteria values in the process of MCDM and MCGDM. Therefore, lots of researchers pay more and more attention to the MCDM or MCGDM methods with IULVs.

The authors summarize and analyze the applications about the AOs of IULVs Finally, the authors point out some possible directions for future research.

The purpose of this paper is to detect abnormal data of complex and sophisticated industrial equipment with sensors quickly and accurately. Due to the rapid development of the Internet of Things, more and more equipment is equipped with sensors, especially more complex and sophisticated industrial equipment is installed with a large number of sensors. A large amount of monitoring data is quickly collected to monitor the operation of the equipment. How to detect abnormal data quickly and accurately has become a challenge.

In this paper, the authors propose an approach called Multiple Group Correlation-based Anomaly Detection (MGCAD), which can detect equipment anomaly quickly and accurately. The single-point anomaly degree of equipment and the correlation of each kind of data sequence are modeled by using multi-group correlation probability model (a probability distribution model which is helpful to the anomaly detection of equipment), and the anomaly detection of equipment is realized.

The simulation data set experiments based on real data show that MGCAD has better performance than existing methods in processing multiple monitoring data sequences.

The MGCAD method can detect abnormal data quickly and accurately, promote the intelligent level of smart articles and ultimately help to project the real world into cyber space in CrowdIntell Network.

This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

This study investigates the relationship between the strength of innovative entrepreneurial ecosystems and subjective well-being in 43 European smart cities. Subjective well-being is operationalized by a Quality of Life (QOL) survey that references the level of multidimensional satisfaction or happiness expressed by residents at the city level. The entrepreneurial ecosystem concept depicted here highlights actor interdependence that creates new value in a specific community by undertaking innovative entrepreneurial activities. The research uses objective and subjective variables to analyze the relationships between the entrepreneurial ecosystem and subjective well-being.

The authors conducted a cluster analysis with a nonaggregative quantitative approach based on the theory of the partially ordered set (poset); the objective was to find significant smart city level relationships between the entrepreneurial ecosystem and subjective well-being.

The strength of the entrepreneurial ecosystem is positively related to subjective well-being only in large cities. This result confirms a strong interdependency between the creation of innovative entrepreneurial activities and subjective well-being in large cities. The smart cities QOL dimensions showing higher correlations with the entrepreneurial ecosystem include urban welfare, economic well-being and environmental quality, such as information and communications technology (ICT) and mobility.

Despite the main implications being properly referred to large cities, the governments of smart cities should encourage and promote programs to improve citizens' subjective well-being and to create a conducive entrepreneurship environment.

This study is one of the few contributions focused on the relationship between the entrepreneurial smart city ecosystem and subjective well-being in the urban environment.

The National Pension Service (NPS) of Korea suddenly announced that they would suspend their stock lending business from October 22, 2018. Using this ideal setting, the authors investigate the effects of this suspension on market quality and short-selling activities. The authors find that stock return does not increase after the suspension of stock lending for both the KOSPI and KOSDAQ markets. However, the returns of stocks with NPS ownership decline less than those without NPS ownership. The authors also find that the institutional and foreign investors' short sales did not increase in both markets after the lending business suspension by the NPS. In addition, the effect of suspension of stock lending on market quality is mixed, so the authors cannot conclude that market quality has improved. Overall, the authors’ results indicate that the stock market, especially for short-sales activity, has not been affected by the suspension of the stock lending service by the NPS.

Corporate social responsibility (CSR) is significant in the financial market. Despite plenty of existing research on CSR, few studies have quantified the fine-grained aspects of CSR and examined how diverse CSR aspects are associated with firms' trade credit. Based on the released CSR reports, this paper strives to measure the CSR fulfillment of firms and examine the relationships between CSR and trade credit in terms of textual features presented in these reports.

This research proposes a natural language processing-based framework to extract the overall readability and the sentiment of fine-grained aspects from CSR reports, which can signal the performance of firms' CSR in diverse aspects. Furthermore, this paper explores how the textual features are associated with trade credit through partial dependence plots (PDPs), and PDPs can generate both linear and nonlinear relationships.

The study’s results reveal that the overall readability of the reports is positively associated with trade credit, while the performance of the fine-grained CSR aspects mentioned in the CSR reports matters differently. The performance of the environment has a positive impact on trade credit; the performance of creditors, suppliers and information disclosure, shows a U-shaped influence on trade credit; while the performance of the government and customers is negatively associated with trade credit.

This study expands the scope of research on CSR and trade credit by investigating fine-grained aspects covered in CSR reports. It also offers some managerial implications in the allocation of CSR resources and the presentation of CSR reports.

Recently, deep learning (DL) has been widely applied in various aspects of human endeavors. However, studies have shown that DL models may also be a primary cause of data leakage, which raises new data privacy concerns. Membership inference attacks (MIAs) are prominent threats to user privacy from DL model training data, as attackers investigate whether specific data samples exist in the training data of a target model. Therefore, the aim of this study is to develop a method for defending against MIAs and protecting data privacy.

One possible solution is to propose an MIA defense method that involves adjusting the model’s output by mapping the output to a distribution with equal probability density. This approach effectively preserves the accuracy of classification predictions while simultaneously preventing attackers from identifying the training data.

Experiments demonstrate that the proposed defense method is effective in reducing the classification accuracy of MIAs to below 50%. Because MIAs are viewed as a binary classification model, the proposed method effectively prevents privacy leakage and improves data privacy protection.

The method is only designed to defend against MIA in black-box classification models.

The proposed MIA defense method is effective and has a low cost. Therefore, the method enables us to protect data privacy without incurring significant additional expenses.

Information practices become highly complex in biodiversity citizen science projects due to the projects’ large scale, distributed setting and vast inclusion of participants. This study aims to contribute to knowledge concerning what variations of information practices can be found in biodiversity citizen science and what these practices may mean for the overall collaborative biodiversity data production in such projects.

Fifteen semi-structured interviews were carried out with participants engaged with the Swedish biodiversity citizen science information system Artportalen. The empirical data were analysed through a practice-theoretical lens investigating information practices in general and variations of practices in particular.

The analysis shows that the nexus of biodiversity citizen science information practices consists of observing, identifying, reporting, collecting, curating and validating species as well as decision-making. Information practices vary depending on participants’ technical know-how; knowledge production and learning; and preservation motivations. The study also found that reporting tools and field guides are significant for the formation of information practices. Competition was found to provide data quantity and knowledge growth but may inflict data bias. Finally, a discrepancy between practices of validating and decision-making have been noted, which could be mitigated by involving intermediary participants for mutual understandings of data.

The study places an empirically grounded information practice-theoretical perspective on citizen science participation, extending previous research seeking to model participant activities. Furthermore, the study nuances previous practice-oriented perspectives on citizen science by emphasising variations of practices.

The purpose of this study was to conduct a comprehensive analysis of the impact and its mechanism on the transfer of agricultural labor forces in the surrounding areas resulting from the establishment of a natural reserve, which holds great significance. The significance of this analysis is on the ecological protection of the natural reserve and the coordinated development of local social economy.

This study first performs an analysis on the impact and its mechanism on the establishment of the natural reserve on the transfer of agricultural labor forces from two aspects, which are push and pull factors. Then, based on county panel data in Jiangxi Province from 1995 to 2012, this study builds a generalized difference-in-difference model and performs an empirical study on the impact, heterogeneity and its mechanism on the establishment of the natural reserve on the transfer of agricultural labor forces.

The empirical analysis reveals that the establishment of natural reserves would significantly promote the transfer of agricultural labor forces to non-agricultural sectors. The robust test and placebo test with changed estimation methods verify the robust of the result. The result passes the parallel trend test and shows that the impact is most significant within one year after the implementation of the policy. From the mechanism analysis, the impact mainly comes from the “push” effect brought by the restricted development of agricultural production and primary industry on agricultural labor forces, and the “pull” effect brought by the development of local tertiary industry.

The conclusion of this study enriches the understanding of the internal mechanism between the establishment of natural reserves and the transfer of agricultural labor forces from the push and pull factors, and can provide reference for formulating policies to promote the coordinated development of natural reserve construction and regional social economy.