Focusing on higher organisms, this paper analyzes and develops a comprehensive review of the problems in bionic computing and also proposes and expounds some new views and insights, from the perspective of collective intelligence as a whole, which includes swarm intelligence and crowd intelligence. On the basis of an overview on the research progress of bionic computation in higher organisms (including fundamental higher organisms, regular higher organisms and quasi-man organisms), the reflux phenomenon in the research on the trend of making algorithms marked by “zoo algorithm” in swarm intelligence optimization is found. A reasonable interpretation of the reasons for the formation of the trend of making algorithms from both the bionic-computational dimension and the problem-method dimension. Furthermore, the overall idea of problem solving is given, and the two main development directions of bionic computing for collective intelligence are refined and formed. Emphasis on the expansion of bionic behavior towards cooperative behavior is dominant in the direction of collective intelligence bionic computing development. Aiming at the difficulties existing in the research of swarm intelligence optimization, five bottlenecks that need to be focused on to achieve breakthroughs are proposed. Based on the overall view of “metaphorical bionic computing-normative bionic computing-complex bionic computing”, the new paradigm of intelligent computing of complex bionic computing is advocated, which can guide the direction for higher organism bionic computing.

Opinion dynamics has emerged as a research hotspot in many fields such as network science, control theory and sociology, focusing on the analysis and interventions of opinion evolution in complex networks. This paper reviews two research directions developed from the Friedkin-Johnsen model. First, the co-evolution of implicit opinions and explicit opinions under the social pressure is presented, with an emphasis on recent advances in conformity behavior and opinion polarization. Second, the opinion intervention based on opinion maximization problem is introduced and the recent progress is summarized from perspective of intervention strategies such as node selection and timing selection. Finally, future research directions of opinion dynamics under the intersection of multiple fields are discussed.

A data-driven control scheme based on dynamic event triggered strategy is proposed for bipartite consensus control of complex nonlinear MASs with unmeasurable external disturbance. The dynamic model of MASs with external disturbances is transformed into an equivalent data model using pseudo partial derivative, and radial basis function neural network is used to estimate the variation of unmeasured external disturbance. Based on the above data model, utilizing model free adaptive iterative learning algorithm combined with dynamic event triggered strategy, a dynamic event triggered based model-free iterative bipartite consensus control scheme is proposed. Then, the stability analysis of the proposed control scheme is given. The effectiveness of this control scheme is verified by simulation.

Distributed cooperative localization is a key process in many cooperative tasks for multi-agent systems. Distributed cooperative localization aims to enable each agent to determine their own locations by using the available locations of anchors, the relative measurements to their neighbors, and the distributed network communication. This paper summarizes the advances in the distributed cooperative localization for multi-agent systems. First, according to the types of relative measurements, the distributed cooperative localization methods are classified into three types, i.e., distance-based, bearing-based, and mixed-measurement-based distributed cooperative localization methods. Then, by characterizing the constraint relationship between the measurement and the agents’ locations, the design schemes of the above distributed cooperative localization methods are elaborated in detail, and the advantages and disadvantages of the methods are compared. Moreover, this paper introduces the research status of reliable distributed cooperative localization methods for multi-agent systems with malicious measurements. Finally, the ongoing challenges in distributed cooperative localization are anticipated, and the potential directions for resolution are proposed.

In order to explore new chaos phenomenon and its producing mechanism, it is convenient for us to understand and apply chaos. A new kind of chaotic system is constructed by using chaos theory and we find that this system has rich nonlinear dynamic properties. Using nonlinear system theory and numerical simulation techniques, we study the attractors of this system and their dimensions, Lyapunov exponent, initial sensitivity, dissipation, bifurcation plot and global attraction domain and this paper enriches and develops chaos theory. This chaotic system has potential applications and can be used to protect data security and image encryption.

To explore the factors affecting collaborative innovation between military and civilian enterprises, a four stage game model for technological innovation cooperation between military and civilian enterprises was constructed based on the Cournot model and extended game theory, and numerical simulation analysis was conducted. The results indicate that in cooperative research and development, both technology spillover and R&D efficiency improvement will increase the investment and revenue of both companies, while in independent research and development, technology spillover and R&D efficiency improvement will reduce the R&D investment and revenue of the enterprise; There is heterogeneity between military and civilian enterprises. Therefore, implementing targeted measures to improve research and development efficiency and technology spillover level can promote research and development cooperation and enhance the efficiency of military civilian integration technology innovation cooperation.

In order to promote the standardized development of various industries, corresponding standardizing documents need to be formulated in various fields, such as national standard and industry standard. These standardizing documents not only provide a unified operating standard for the industry, but also provide a clear guidance basis for relevant parties. The Central Committee of the CPC and the State Council clearly pointed out in the "the Outlines for the Development of National Standardization" that promoting the digitalization process of standard is an important measure to realize the modernization of the industry. Therefore, it is particularly important to carry out research on the automatic generation of standardizing documents. With the rapid development of artificial intelligence technology, especially the outstanding performance of large language models in text generation tasks, it is possible to use these advanced technologies to realize the automatic generation of standardizing documents. Based on this background, this paper proposes a two-stage scheme for generating standardizing documents. The scheme first generates the outline of the standardizing document through the large model, and then expands to generate the complete document content on this basis. By combining in-context learning and retrieval augmented generation techniques, this method can not only generate high-quality text, but also significantly improve the accuracy and professionalism of the generated content. In order to verify the feasibility of the scheme, we conducted a series of experiments on our self-built dataset, and the results show that the method can effectively generate documents that meet industry standards, and has good practicability and promotion potential.

Agricultural product trade is an important channel to achieve the global "zero" hunger sustainable development goal. In the accelerated evolution stage of world changes, there is an urgent need to conduct research on the layout of trade networks and the influence of node countries. This article studies the evolution of agricultural product trade based on complex network theory and measures a country's influence through network efficiency. The results show that: 1) The agricultural product trade network is becoming increasingly efficient and close. 2) The influence of a node country is closely related to its comprehensive strength; countries with great influence have strong import and export intensity, and there is a positive correlation between a country's export intensity and its import intensity. 3) Agricultural product trade is mainly dominated by a few large countries, which will exacerbate the vulnerability of global agricultural product supply under unstable factors such as natural disasters or geopolitics.

This study aims to address the problem of vital nodes identification in complex networks using graph attention mechanism. This paper integrates both node′s virus transmissibility and structural impact, constructing training labels on the generated network to learn node importance through a graph attention network model. Experimental results demonstrate the excellence of this algorithm in two critical tasks: influence maximization and immune isolation.

Human motion generation aims to generate realistic, high-quality human motion. Aiming to summarize the recent advances in text-driven human motion generation technology, through extensively investigating relevant research and literature, this paper systematically reviews the development process and research status of the text-driven human motion generation task. It comprehensively summarizes the model methods related to the task by classifying the generation models and further analyzes the research progress of key technical issues. It summarizes the commonly used datasets and evaluation methods and deeply discusses the unresolved problems and possible future research directions in this field.