With the in-depth development of intelligent media technology, online and offline fusion, reality and virtual entanglement, information content generalization, the boundary between positive and negative information is blurred, all kinds of misinformation in the social network fission spread, and cyberspace governance has become a global consensus. In this article, we comprehensively consider the spread of misinformation in location-based interpersonal social network and online social network, and systematically tackle the novel problem of minimizing the influence of misinformation under individual protection strategies. We first analyze the complexity and modularity of the problem. Then, we leverage the Lovász extension to devise a nonsubmodular set function continuity approximate convex relaxation method, and develop an approximate projected subgradient procedure to obtain a solution with a factor approximate guarantee. Finally, experiments on three assembled real-world datasets demonstrate the effectiveness and feasibility of our designed method and developed the algorithm.

The long-term production capacity planning of semiconductor supply chain manufacturers has a series of characteristics, such as large capital investment, fast technology upgrading, long lead-time of manufacturing equipment, and unstable market environment, which leads to the uncertainty of demand. In addition, blockchain technology is widely used to build consortium chains for information sharing across upstream and downstream enterprises, thus having the potential ability to help finance semiconductor manufacturers. This paper combines two uncertainty-oriented methods (stochastic programming and robust optimization) to examine the conversion of capacity between different product types and construct a two-stage mathematical planning model maximizing the net profit of manufacturers. Through the introduction of blockchain technology and information sharing among enterprises, we improve the effectiveness of our model to realize the optimal allocation of long-term capacity planning. Finally, we reformulate the model into a tractable MINLP, construct numerical examples to verify the solvability, and carry out sensitivity analysis.