Impact of Corporate Social Responsibility on Operations of a Live-Streaming Supply Chain
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Discrete Dynamics in Nature and Society publishes research that links basic and applied research relating to discrete dynamics of complex systems encountered in the natural and social sciences.
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Chief Editor, Dr Renna, is an associate professor at the University of Basilicata, Italy. His research interests include manufacturing systems, production planning and enterprise networks.
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More articlesNumerical Analysis and Computation of a Subgrid-Sparse-Grad-Div Stabilization Method for Incompressible Flow Problems
In this article, a subgrid-sparse-grad-div method for incompressible flow problem was proposed, which is a combination of the subgrid stabilization method and the recently proposed sparse-grad-div method. The method maintains the advantage of both methods: (i) It is robust for solving incompressible flow problem with dominance of the convection, especially when the viscosity is too small. (ii) It can keep mass conservation. Therefore, the method is very efficient for solving incompressible flow. Moreover, based on the Crank–Nicolson extrapolated scheme for temporal discretization, and mixed finite element in spatial discretization, we derive the unconditional stability and optimal convergence of the method. Finally, numerical experiments are proposed to validate the theoretical predictions and demonstrate the efficiency of the method on a test problem for incompressible flow.
Deep Reinforcement Learning and Auto-Differential Evolution Co-Guided Coal Washing
Background. Coal washing is a complicated process and difficult to control, which has many controlling parameters with strong coupling relationship. It is still a challenge to realize the self-perception, self-adjustment, and self-evaluation of coal washing machine, improve the quality of coal washing, ensure production safety, and reduce labor cost. Methods. Through the intelligent transformation of jig, this paper proposes an intelligent washing method with cooperated deep reinforcement learning and evolutionary computation. First, it designs a fault warning method based on statistical analysis, helping to recover the normal running state of jig with manual maintenance. Then, it constructs a regulation strategy generation method with deep reinforcement learning supported by the fusion of artificial experience and historical data. Last, for the lack of monitoring data caused by poor communication quality and environment, the regulation strategy prediction method with evolutionary computation and surrogate model is proposed. Results. In practice, this method shows accurate fault warning accuracy and rapid cleaned coal ash adjustment response ability. Conclusions. This shows that the method proposed in this paper is of great significance for intelligent washing and can better cope with the special situation when the washing equipment sensing data are missing.
Quantum Game-Based Study on the Incentive Mechanism for the Cooperative Distribution of E-Commerce Logistics Alliance
Motivating active participation in e-commerce logistics alliances to enhance delivery efficiency and customer satisfaction has long been a societal interest. Leveraging the quantum game theory, this paper develops a model for incentivizing collaboration within these alliances. This model enables theoretical and numerical analysis of members’ strategies and entanglement levels. The findings show that quantum strategies increase members’ profits, achieving Nash equilibriums and Pareto optimal outcomes, outperforming the classical game theory. In addition, the size of quantum entanglement emerges as a critical determinant influencing members’ active participation in collaborative distribution. Strengthening information sharing and aligning interests can enhance entanglement levels among members, making them more inclined to adopt strategies promoting active involvement in collaborative distribution. Moreover, members can adapt their strategies based on the initial entanglement in collaborative distribution, thereby incentivizing participation and reducing ethical risks. In conclusion, through numerical analysis, we present relevant strategies and recommendations for incentivizing collaborative distribution within e-commerce logistics alliances.
Multiple Solutions of a Nonlocal Problem with Nonlinear Boundary Conditions
In this article, we consider a class of nonlocal p(x)-Laplace equations with nonlinear boundary conditions. When the nonlinear boundary involves critical exponents, using the concentration compactness principle, mountain pass lemma, and fountain theorem, we can prove the existence and multiplicity of solutions.
Intellectual Structure of Global Value Chain Research: Visualization and Bibliometric Analysis Based on VOSviewer
Over the past few decades, numerous scholars have conducted research on global value chains, and the amount of literature in this field has grown rapidly to become “big data.” In order to deeply analyze and explore the current research status and development trends in the field of global value chains, this article conducted a systematic and quantitative study on the global value chain. Based on the Web of Science Core Collection SCIE and SSCI databases, this article searched 8273 articles published between 2001 and 2021 with the theme of “Global Value Chain” and used visualization and bibliometric analysis methods. The research field was analyzed using VOSviewer software. Research has shown that the number of papers published in the global value chain field has been continuously increasing over time and has entered a period of rapid development since 2016. The main disciplines are economics and management. A few researchers and research institutions have a strong influence, but the cooperation between scholars and institutions is relatively weak. The United States, the United Kingdom, and China dominate the research field. The complex impact of the natural environment and public health on the global value chain, and the impact of big data and artificial intelligence on the global value chain, has become a new direction for future research. Countries should actively promote the development of the digital economy and green economy, promote the effective allocation of global value chain resources by enterprises, and adhere to the development of good bilateral political relations to reduce the uncertainty of enterprise participation in the global value chain caused by political risks.
Novel Complex Intuitionistic Hesitant Fuzzy Distance Measures for Solving Decision-Support Problems
The key objective of this article is to introduce the innovative idea of a complex intuitionistic hesitant fuzzy set (CIHFS), which blends the intuitionistic hesitant fuzzy set with the complex fuzzy set to address the uncertain information in real-life complex problems. In CIHFS, the range of the membership functions is extended from the subset of the real number to the unit disc under the hesitant environment. To determine how well the CIHFSs can be distinguished from one another, we first propose generalized distance measures and weighted generalized distance measures based on the Hamming, Euclidean, and Hausdorff metrics. Some interesting properties and their relationships are thoroughly discussed. Furthermore, a decision-making framework for selecting the optimal option from the feasible set has been proposed, which is grounded in these distance metrics. For the purpose of proving the method’s efficacy, we included examples from pattern recognition and medical diagnostics.