Logistics information sharing plays a pivotal role in fostering efficient collaboration within the smart supply chain. With the deepening integration of digital technologies such as the Internet of Things (IoT), blockchain, and Artificial Intelligence (AI), inter-enterprise information sharing strategies exhibit novel and dynamic evolutionary patterns. From the perspective of multi-technology collaboration, this paper develops a novel evolutionary game model between manufacturers and logistics service providers. Digital maturity is employed as a comprehensive evaluation index for technology integration. Four technological dimensions—synergy amplification, cost reduction, risk mitigation, and capability enhancement—are introduced as parameters to characterize the synergy effects of technologies. Through dynamic analysis of the model, we obtain the following findings: First, there exists a critical threshold of digital maturity in the strategic evolution of both players. When the overall digital maturity surpasses this threshold, the system shifts from a non-cooperative monostable equilibrium to a bistable equilibrium, where both information sharing and non-sharing coexist. Second, the technological parameters exhibit differentiated synergy effects. Specifically, synergy amplification and capability enhancement demonstrate a multiplier effect that significantly lowers the threshold, whereas cost reduction and risk mitigation exert influence through linear synergy. Third, sensitivity analysis reveals that technological input positively influences cooperative strategies. However, inputs associated with synergy amplification and capability enhancement are subject to diminishing marginal returns. Finally, numerical simulations validate the analytical results and further suggest that phased technological investments and cross-technology collaboration are optimal strategies for promoting information sharing and fostering sustainable cooperation within the supply chain.

In this work, we propose a new hybrid Multi‐Objective Evolutionary Algorithm (hMOEA) specifically designed for semantic sensor data integration, targeting talent development within the burgeoning field of the Semantic Internet of Things (SIoT). Our approach synergizes the capabilities of Multi‐Objective Particle Swarm Optimization and Genetic Algorithms to tackle the sophisticated challenges inherent in Sensor Ontology Matching (SOM). This innovative hMOEA framework is adapt at discerning precise semantic correlations among diverse ontologies, thereby facilitating seamless interoperability and enhancing the functionality of IoT applications. Central to our contributions are the development of an advanced multi‐objective optimization model that underpins the SOM process, the implementation of the hMOEA framework which sets a new benchmark for accurate semantic sensor data integration, and the rigorous validation of hMOEA's superiority through extensive testing in varied real‐world SOM scenarios. This research not only marks a significant advancement in SOM but also highlights the critical role of cutting‐edge SOM methodologies in educational curricula, for example, the new business subject education proposed by China in recent years, aimed at equipping future professionals with the necessary skills to innovate and lead in the SIoT and SW domains.

In this study, the authors first develop a direct method used to solve the linear nonhomogeneous time-invariant difference equation with the same number for inputs and outputs. Economic cybernetics is the crystallization for the integration of economics and cybernetics. It analyzes the stability, controllability, and observability of the economic system by establishing a system model and enables people to better understand the characteristics of the economic system and solve economic optimization problems. The economic model generally applies the discrete recurrence difference equation. The significant analytic approach for the difference equation is the z-transformation technique. The z-transformation state of the economic cybernetics state-space difference equation generally is a rational function with the same power for the numerator and the denominator. The proposed approach will take the place of the traditional methods without all annoying procedures involving the long division of some complicated polynomials, the expanded multiplication of many polynomial factors, the differentiation of some complicated polynomials, and the complex derivations of all partial fraction parameters. To highlight the novelty of this research, this study especially applies the proposed theorems originally belonging to engineering to the field of economic applications.

In order to resist quantum attacks, a key exchange protocol based on infinite non-abelian groups is proposed in this paper. For the purpose, by the composition of twice the operation of a semidirect product, we construct a shared secret key which contains two hard problems of equivalent decomposition problem (EDP) and discrete logarithm problem (DLP). Then, two methods，algebra attack and brute force attack, were employed to verify the antiattack for the proposed protocol. By a sound mathematical inference, it demonstrates that the proposed protocol possesses security positively. Finally, we analyzed the computational complexity and bit complexity when the protocol being implemented on braid groups, and furthermore, the complexity data confirm the feasibility of establishing the key exchange protocol there. Thus, in any case, security or complexity, the actual use of the proposed protocol means achievable in practice.

Consumer Electronics Data Fusion (CEDF) integrates diverse and heterogeneous consumer-generated data, crucial for advancing innovation, user experience, and decision-making in Consumer Electronics (CE). However, the challenges of multi-modal data diversity and sparse data structures complicate the fusion process. This paper proposes a novel Multi-modal Multi-objective Evolutionary Algorithm (MMOEA) that emphasizes the construction and integration of Large Models (LMs) for adaptive CEDF. First, two metrics are designed to balance alignment accuracy and conservativity, forming a robust multi-objective optimization model. Second, we integrate LMs into the MMOEA, dynamically constructing task-specific LM ensembles to enhance semantic comprehension and scalability. Third, Auxiliary Matrices (AMs) are incorporated to guide the search process, ensuring convergence and diversity in solution spaces. The experiments on the OAEI conference dataset and ten real-world CEDF task pairs validate the MMOEA’s effectiveness in producing high-quality, diverse solutions and offering decision-makers robust fusion strategies for complex CE systems.

This paper aims at the dynamic properties of the proposed globally planned economic systems named after CPE proposed by Loo-Keng Hua who is one of the worldwide famous Chinese mathematicians. First, we give new existence conditions of growth balanced solution to the model. Second, we lead into the concept of stability for balanced output and carry out a theorem that deals with some equivalent conditions for judging a solution of output starting from the fact that any initial input can whether approach the existing balanced solution or not. Third, a new dynamic price system related to interest factors is proposed here and it is demonstrated that the new price equation is a much generalized one in comparison with the original price one which is only a special case of this new price equation. Also, relationships of the balanced solutions between the price and the output equation are investigated and the stability analysis is studied as well for the new price system. Finally, two examples are employed to illustrate the technical operation of input-output method and some new contributions of this article.