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This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.

The purpose of this paper is to examine the common stock price reaction and the changes to the risk exposure of the cross-listing for real estate investment trusts (REITs).

The paper adopts the event study methodology to assess the abnormal returns (ARs). Pre- and post-cross-listing changes in the risk exposure for the domestic and foreign markets are examined, via a modified two-factor international asset pricing model. A comparison is made for two broad cross-listings, namely, the depositary receipts and the dual ordinary listings, to examine the impacts from institutional differences.

Cross-listed REITs generally experience positive and significant ARs throughout the event window, implying significant superior returns associated with the cross-listing for REITs. On systematic risks, REITs exhibit significant decline in their domestic market β coefficients after the cross-listing. However, the foreign market β coefficients do not yield conclusive evidence when compared across the sample.

Results are consistent with prudential asset allocation for potential diversification gains from the cross-listing, as the reduction from the domestic market beta is more significant than changes in the foreign market beta.

The results and findings should incentivise REIT managers to explore viable cross-listing.

Such cross-listing for REITs should enhance risk diversification.

This is a pioneer study on cross-listing of REITs. It provides a basis for investment decision making, and could provoke further research and discussion.

The purpose of this paper is to present the terms of grey numbers and its operations.

The definitions of elementary terms about grey numbers and its operations are presented one by one.

The reader could know the basic explanation about the important terms about grey numbers and its operations from this paper.

Many of the colleagues thought that unified definitions of key terms would be beneficial for both the readers and the authors.

It is a fundamental work to standardise all the definitions of terms for a new discipline. It is also propitious to spread the universal principles of grey system theory.

In the presented research, the authors have conducted the literature review and organised real interviews of fruit retailers (FRs) to construct the advanced hierarchical structural (AHS) chain of macro-micro parameters for measuring the performances of defined fruit supply bazaars (FSBs). Apart from this, the purpose of this paper is to develop the grey set-based scorecard model for solving the proposed AHS chain of macro-micro parameters.

The performance of FSBs is linked with the supply of fruits towards clients under a feasible rate, which circuitously depends upon the evaluation of the economic locality of FSBs. The authors developed an advanced hierarchical structure of macro-micro parameters via a literature survey and considered these parameters based on the sampling score of FRs corresponding to select feasible FSBs/alternatives. Furthermore, the authors developed a grey set-based scorecard model for undertaking the incomplete information of FRs against the hierarchical structure.

It is found that the work is well suited for FRs as they can measure the performances of defined FSBs in accordance with their own opinions under the proposed AHS of macro-micro parameters. Apart from this, the work is useful for benchmarking the vegetable supply bazaars (VSBs) on the replacement of AHS. The proposed hierarchical structure with a grey-based scorecard model is flexible in its nature and can undertake more than 1,000 macro-micro parameters and FRs to access potential decision.

The conducted research work has a precise value for evaluating the economic FSB locality. The overall performance scores of considered FSB localities are computed as (∂₁)=1.991, (∂₂)=2.567 and (∂₃)=2.855, where (∂₃) is found to be more significant than available FSBs. This work can be used for opting the economic locality of VSB too.

The purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning.

A fuzzy Bayesian least squares support vector machine (LS_SVM) model is established in this paper, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established.

The fuzzy Bayesian LS_SVM model established in this paper is an effective artificial intelligence method for risk comprehensive evaluation in urban network planning through empirical study.

The paper breaks new ground in using artificial intelligence to evaluate urban power network planning risks.

Firm growth in industry clusters is a complex issue. On the one hand, industrial clusters can promote firm growth. On the other hand, they can restrict the growth of a firm in some aspects. Their various effects have to be analyzed in detail. The purpose of this paper is to examine these effects and the law of enterprise growth in electronic information industry clusters of China.

This paper makes use of the panel data of the Chinese manufacturing industry with the intention of testing Gibrat's law. It carries out an empirical analysis on the influence of Chinese electronic information industry clusters on firm growth.

The result of the present research indicates that industry clusters definitely have a positive impact on firm growth, profit and longevity. However, in regard to the firms' data of China 2006 to 2007, the electronic information industry clusters have negative effects on scale of business growth of small and medium‐sized companies but not big companies. Moreover, the innovation of companies inside a cluster could not catch up with that of companies outside the cluster.

For the electronic information enterprises, growth rate is positively correlated with the enterprise age. Gibrat's law is tenable, that is, firm growth mainly depends on firm age. In Chinese electronic information industry clusters, R&D has only a weak influence on enterprise growth. In contrast, the economic soundness of the region where the electronic information industry clusters are located is more beneficial to the growth of enterprises in the cluster.

In this work, the authors used reversible addition-fragmentation transfer (RAFT) polymerization to develop a new cationic acrylate modified epoxy resin emulsion for water-borne inkjet which have the advantages of both polyacrylate and epoxy resin. The emulsion was successfully used in the canvas coating for inkjet printing. This paper aims to contribute to the development of novel cationic emulsions for inkjet printing industry.

In this work, the epoxy acrylate was synthesized from RAFT agent and epoxy resin firstly. Cationic macromolecular emulsifier was prepared by RAFT polymerization, using 2,2’-Azobisisobutyronitrile as initiator, 2-(dimethylamino)ethyl methacrylate and styrene as monomer, which was directly used to prepare the emulsion. The influences of the amount of 2-(dimethylamino)ethyl methacrylate on particle size, zeta potential and water contact angle were studied. Finally, the cationic emulsion was used to print images by inkjet printing.

The emulsion has the smallest particle size, the highest potential and the highest water contact angle when the DM content is 13 Wt.%. The transmission electron microscopy analysis reveals the latex particles is core-shell sphere with the diameters in the range 120–200 nm. The emulsion was successfully used in the canvas coating for inkjet printing. This work will contribute to the development of novel cationic emulsions for inkjet printing industry.

The emulsion was successfully used in the canvas coating for inkjet printing. This work will contribute to the development of novel cationic emulsions for inkjet printing industry.

China has a new rural revitalization strategy to stimulate rural transformation through modernizing rural areas and resolving their social contradictions. While social capital is recognized as an important element to rural revitalization and entrepreneurship, research into the role of psychological capital is less developed. Therefore, this paper assesses the impact of both social and psychological capital on entrepreneurial performance of Chinese new-generation rural migrant entrepreneurs (NGRMEs) who have returned to their homes to develop businesses as part of the rural revitalization revolution.

Based on a survey, data were collected from 525 NGRMEs in Shaanxi province. This paper uses factor analysis to determine variables for a multiple linear regression model to investigate the impacts of dimensions of both social capital and psychological capital on NGRMEs’ entrepreneurial performance.

Through the factor analysis, social capital of these entrepreneurs consists of five dimensions (reputation, participation, networks, trust and support), psychological capital has three dimensions (innovation and risk-taking, self-efficacy and entrepreneurial happiness) and entrepreneurial performance contains four dimensions (financial, customer, learning and growth, and internal business process). Furthermore, the multiple linear regression model empirically verifies that both social capital and psychological capital significantly influence and positively correlate with NGRMEs' entrepreneurial performance.

This study shows the importance of how a mixture of interrelated social and psychological dimensions influence entrepreneurial performance that may contribute to the success of the Chinese rural revitalization strategy. This has serious implications when attempting to improve the lives of over 100 million rural Chinese citizens.

Fiscal fund is the key support of carbon emissions control for local governments. This paper aims to analyze the impact of fiscal decentralization on carbon emissions by spatial Durbin model (SDM), and verify the existence of “free-riding” phenomenon to reveal the behavior of local governments in carbon emissions control.

Based on the provincial data of carbon emissions from 2005 to 2016 in China, this paper uses spatial exploratory data analysis technology to analyze the spatial correlation characteristics and constructs SDM to test the impact of fiscal decentralization on carbon emissions.

The results show that carbon emissions exhibits significant spatial autocorrelation in China, and the increasing of fiscal decentralization in the region will increase carbon emissions in surrounding areas and on the whole. Then, by comparing the impact of fiscal decentralization on carbon emissions and industrial solid waste, it is found that “free-riding” phenomenon of carbon emissions control exists in China.

Based on the spatial cluster characteristics of China’s provincial carbon emissions, carbon emissions control regions can be divided into regions and different carbon emission control policies can be formulated for different cluster regions. Carbon emissions indicators should be included in the government performance appraisal policy, and carbon emissions producer survey should be increased in environmental policies to avoid “free-riding” behaviors of local government in carbon emissions control in China.

This paper contributes to fill this gap and fully considers the spatial spillover characteristics of carbon emissions by introducing spatial exploratory data analysis technology, constructs SDM to test the impact of fiscal decentralization on carbon emissions in the perspective of space econometrics, and tests the existence of “free-riding” phenomenon in carbon emissions control for local governments in China.