Search results

This paper aims to put forward a kind of new method of intelligent trust engineering metrics for application of mobile ad hoc network (MANET).

The new method calculates the communication trust by using the number of data packets among the nodes of MANET, predicts intelligently the trust and calculates the comprehensive trust based on the historical trust; then calculates the energy trust based on the residual energy of the nodes of MANET, calculates the direct trust based on the communication trust and energy trust. The new method calculates the recommendation trust based on the recommendation reliability; adopts the adaptive weighting to calculate the integrated direct trust by considering the direct trust with recommendation trust.

Based on the integrated direct trust and the factor of trust propagation distance, the indirect trust among the nodes of MANET is calculated. The above process can be optimized based on the dynamic machine learning presented in this study. The advantage of the new method is its intelligent ability to discover malicious nodes.

The advantage of the new method is its intelligent ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and proceeds to protect the network. The authors have done the experiments based on the tool kits such as NS3, QualNet, OMNET++ and MATLAB. The experimental results show that this study’s approach can effectively avoid the attacks of malicious nodes, and more conformable to the actual engineering application of MANET.

The communication link in the engineering of Internet of Vehicle (IOV) is more frequent than the communication link in the Mobile ad hoc Network (MANET). Therefore, the highly dynamic network routing reliability problem is a research hotspot to be solved.

The graph theory is used to model the MANET communication diagram on the highway and propose a new reliable routing method for internet of vehicles based on graph theory.

The expanded graph theory can help capture the evolution characteristics of the network topology and predetermine the reliable route to promote quality of service (QoS) in the routing process. The program can find the most reliable route from source to the destination from the MANET graph theory.

The good performance of the proposed method is verified and compared with the related algorithms of the literature.

The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.

This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.

Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.

The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

This study aims to investigate the impact of ChatGPT on users’ continuous knowledge contributions in online question-and-answer (Q&A) communities based on social exchange theory and stimulus-organism-response (SOR) theory.

Data were collected from a Chinese online Q&A community, and the difference-in-differences (DID) model was employed to verify the proposed hypotheses.

The results show that ChatGPT negatively impacts users’ continuous knowledge contributions in online Q&A communities, with variations observed across different knowledge domains.

This study is the first attempt to examine the impact of ChatGPT on users’ continuous knowledge contributions in online Q&A communities. The findings provide valuable insights for community managers to develop strategies for mitigating the effects of ChatGPT on online Q&A communities.

This study aims to examine the risk-resistant role of environmental, social and governance (ESG) performance in the capital market, focusing on an organizational standpoint. Furthermore, it aims to offer management decision advice to companies seeking protection against stock market risks. Conclusions obtained through this research have the potential to enrich the economic consequences of ESG performance, provide practical implications for enhancing corporate ESG performance, improving corporate information quality and stabilizing capital market development.

Based on the data of Chinese A-share listed companies from 2009 to 2020, this study examines the risk-resistant function of ESG performance in the capital market. The impact of ESG performance on management behavior is analyzed from the perspective of organizational management and the three mechanisms of pre-event, during the event and post-event.

This paper demonstrates that companies that effectively implement ESG practices are capable of effectively mitigating risks associated with stock price crashes. Heterogeneity analysis reveals that the inhibitory effect of ESG performance on stock price crash risk is more pronounced in nonstate-owned enterprises and enterprises with higher levels of marketization. After controlling for issues such as endogeneity, the conclusions of this paper are still valid. The mechanism analysis indicates that ESG performance reduces the risk of stock price crash through three paths of organizational management: pre-event, during the event and post-event. That is, ESG performance plays the role of restraining managers’ opportunistic behavior, reducing information asymmetry and boosting investor sentiment.

This paper provides new insights into the relationship between ESG performance and stock price crash risk from an organizational management perspective. This study establishes three impact mechanisms (governance effect, information effect and insurance effect), offering a theoretical basis for strategic corporate decisions of risk management. Additionally, it comprehensively examines the contextual differences in the role of ESG performance, shedding light on the specific domains where ESG practices are influential. These findings offer valuable insights for promoting stable development in the capital market and fostering the healthy growth of the real economy.

The purpose of this paper is to design a simple and accurate a-posteriori Lagrangian-based error estimator is developed for the class of backward differentiation formula (BDF) algorithms with variable time step size, and the adaptive time-stepping in BDF algorithms is demonstrated for efficient time-dependent simulations in fluid flow and heat transfer.

The Lagrange interpolation polynomial is used to predict the time derivative, and then the accurate primary result is obtained by the Gauss integral, which is applied to evaluate the local error. Not only the generalized formula of the proposed error estimator is presented but also the specific expression for the widely applied BDF1/2/3 is illustrated. Two essential executable MATLAB functions to implement the proposed error estimator are appended for practical applications. Then, the adaptive time-stepping is demonstrated based on the newly proposed error estimator for BDF algorithms.

The validation tests show that the newly proposed error estimator is accurate such that the effectivity index is always close to unity for both linear and nonlinear problems, and it avoids under/overestimation of the exact local error. The applications for fluid dynamics and coupled fluid flow and heat transfer problems depict the advantage of adaptive time-stepping based on the proposed error estimator for time-dependent simulations.

In contrast to existing error estimators for BDF algorithms, the present work is more accurate for the local error estimation, and it can be readily extended to practical applications in engineering with a few changes to existing codes, contributing to efficient time-dependent simulations in fluid flow and heat transfer.

This chapter reviews the history of regulation and deregulation in international air transport and discusses the positive impacts of deregulation and open skies on the tourism sector in the Asia Pacific region. The Hong Kong–Bangkok market was examined, which shows that the granting of the fifth freedom rights has given the two places sufficient air service provisions to build tourism. Future reforms in air transport such as relaxing ownership restrictions and expanding air freedoms rights are explored.

With significant changes in the aviation industry, various airport–airline arrangements have been formed to achieve alternative objectives. However, no consensus has been reached on such arrangements’ economic effects and the associated optimal public policy. This chapter aims to provide an interpretive review of the common types of airport–airline arrangements, the different modeling approaches used and key conclusions reached by recent studies. Our review suggests that airport–airline arrangements can take diverse forms and have been widely used in the industry. They may allow the airport and its airlines to internalize demand externality, increase traffic volume, reduce airport investment risks and costs, promote capacity investment, enhance service quality, or simply are a response to the competition from other airport–airline chains. On the other hand, such vertical arrangements, especially for those exclusively between airports and selected airlines, could lead to collusive outcomes at the expenses of non-participating organizations. The effects of such arrangements are also significantly influenced by the contract type, market structure and bargaining power between the airport and airline sectors. While case by case investigations are often needed for important economic decisions, we recommend policy-makers to promote competition in the airline and airport segments whenever possible, and demand more transparency or regulatory reporting of such arrangements. Policy debates and economic studies should be carried out first, before intrusive regulations are introduced.