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This paper aims to introduce a novel four-dimensional hyper-chaotic system with different hyper-chaotic attractors as certain parameters vary. The typical dynamical behaviors of the new hyper-chaotic system are discussed in detail. The control problem of these hyper-chaotic attractors is also investigated analytically and numerically. Then, two novel electronic circuits of the proposed hyper-chaotic system with different parameters are presented and realized using physical components.

The adaptive control method is derived to achieve chaotic synchronization and anti-synchronization of the novel hyper-chaotic system with unknown parameters by making the synchronization and anti-synchronization error systems asymptotically stable at the origin based on Lyapunov stability theory. Then, two novel electronic circuits of the proposed hyper-chaotic system with different parameters are presented and realized using physical components. Multisim simulations and electronic circuit experiments are consistent with MATLAB simulation results and they verify the existence of these hyper-chaotic attractors.

Comparisons among MATLAB simulations, Multisim simulation results and physical experimental results show that they are consistent with each other and demonstrate that changing attractors of the hyper-chaotic system exist.

The goal of this paper is to construct a new four-dimensional hyper-chaotic system with different attractors as certain parameters vary. The adaptive synchronization and anti-synchronization laws of the novel hyper-chaotic system are established based on Lyapunov stability theory. The corresponding electronic circuits for the novel hyper-chaotic system with different attractors are also implemented to illustrate the accuracy and efficiency of chaotic circuit design.

The purpose of this paper is to explore the actions of different senses on visitors’ embodied experience in dark tourism “field,” including embodied emotions/cognitions.

This research uses qualitative analysis by applying tourists’ reviews from two main Chinese tourism websites and the software of MAXQDA. It identifies the senses applied in the embodiment process in dark tourism “field” and matches these senses to the specific types of embodied emotions/cognitions.

This research identifies four main senses. The visual sense has the greatest influence on 27 embodied emotions and 7 embodied cognitions. Auditory and temperature sense create particular emotions. This research also points out the phenomenon of “banned behavior.” At last, to achieve accessibility/acceptability, Nanjing Memorial Hall applies two strategies to distance the extreme historical events from visitors: the construction of aesthetic elements and the way it shows historical objects.

It uses both qualitative and quantitative data to identify the classifications and degrees of senses, emotions and cognitions as well as the relations between them. However, there are difficulties in the coding process because of the language differences, which requires a good understanding of the context of the tourism experience.

The research results could be used as a psychological reference and in the design of dark tourism product.

It provides a specific understanding of the way in which visitors interact with dark tourism objects and environment.

This is the first research that explains the dark tourism experience from the perspective of embodiment. It provides conceptual as well as empirical reference for a new research topic.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

The purpose of this study is to present a multi-scale analysis methodology for calculating the effective stiffnesses and the micro stresses of helically wound structures efficiently and accurately. The helically wound structure is widely applied in ocean and civil engineering as load-bearing structures with high flexibility, such as wire ropes, umbilical cables and flexible risers. Their structures are usually composed of a number of twisted subcomponents with relatively large slender ratio and have the one-dimensional periodic characteristic in the axial direction. As the huge difference between the axial length and the cross-section size of this type of structures, the finite element modeling and theoretical analysis based on some assumption are usually unavailable leading to the reduction of computability; even the optimization design becomes infeasible.

Based on the asymptotic homogenization theory, the one-dimensional periodic helically wound structure is equivalent to the one-dimensional homogeneous beam. A novel implementation of the homogenization is derived for the analysis of the effective mechanical properties of the helically wound structure, and the tensile, bending, torsional and coupling stiffness properties of the effective beam model are obtained. On this basis, a downscaling analysis formation for the micro-component stress in the one-dimensional periodic wound structure is constructed. The stress of micro-components in the specified geometry position of the helically wound structure is obtained basing on the asymptotic homogenization theory simultaneously.

By comparing with the result from finite element established accurately, the established multi-scale calculation method of the one-dimensional periodic helically wound structure is verified. The influence of size effects on the macro effective performance and the micro-component stress is discussed.

This paper will provide the theoretical basis for the efficient elastoplastic analysis of the helically wound structure, even the fatigue analysis. In addition, it is necessary to point out that the axial length of the helically wound structure in the general engineering problems that such as deep-sea risers and submarine cables.

This paper aims to examine how the law may play a role in mediation by paying special attention to how the law is excluded from and included in the process of court mediation in China.

Hundred model court mediation cases selected by the Supreme People’s Court of China were analysed and reviewed.

The law is relevant in Chinese-style court mediation in four ways: first, judge-mediators are intended to use mediation to avoid resolving legal difficulties or challenges; second, judge-mediators consult the law to anticipate the losing party and the potential negative effects that might result from the adjudication; third, judge-mediators refer to the law to propose a mediation scheme or plan to guide the parties to settle; and fourth, judge-mediators would use the law as a bargaining chip in various ways to induce the parties to settle.

Standards should be set out for the use of law in the mediation process to standardise judge-mediators’ actions, to ensure that the law is not used coercively to push settlement, which would undermine the parties’ self-determination in mediation.

This paper provides an original understanding of how law affects the process, the outcomes and, ultimately, the nature of settlements that parties achieve through court mediation in China. This study contributes to the literature that argues that ethical norms and legal standards should be set to direct those legal evaluations.

The purpose of this paper is to present an algorithm for detecting communities in social networks.

The majority of existing methods of community detection in social networks are based on structural information, and they neglect the content information. In this paper, the authors propose a novel approach that combines the content and structure information to discover more meaningful communities in social networks. To integrate the content information in the process of community detection, the authors propose to exploit the texts involved in social networks to identify the users’ topics of interest. These topics are detected based on the statistical and semantic measures, which allow us to divide the users into different groups so that each group represents a distinct topic. Then, the authors perform links analysis in each group to discover the users who are highly interconnected (communities).

To validate the performance of the approach, the authors carried out a set of experiments on four real life data sets, and they compared their method with classical methods that ignore the content information.

The experimental results demonstrate that the quality of community structure is improved when we take into account the content and structure information during the procedure of community detection.