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.

To scientifically evaluate the resilience of China′s industrial chain and promote high-quality economic development, this paper employs complex network theory and utilizes data from China′s multi-regional input-output table to construct a simulated attack model of the industrial chain network, thereby measuring the resilience of the industrial chain. The results show that the industrial chain layout of 31 provinces in China exhibits obvious local correlation attributes, and presents a collaborative development characteristic led by key regions and industries; The overall resilience of China′s industrial chain is strong, and during the inspection period, the overall resilience index of China′s industrial chain shows an upward trend; There is a significant gap in the resilience of the industrial chain between regions and industries in China.

To improve the ability to predict the evolution trend of public opinion, a public opinion evolution trend prediction model based on an improved wolf pack algorithm and optimized long-short term memory neural network is proposed. Use Halton Sequence to initialization to improve population diversity. Design step factor to perform Gauss-Sine perturbation transformation to improve wolf group exploration and development capabilities. Combine with the spiral in the whale optimization algorithm to improve the siege mechanism to enhance the local search ability of wolves. The bidirectional memory population is used to increase the cooperative ability of the wolf pack. The improved wolf pack algorithm (IWPA) is applied to the hyperparameter prediction of the LSTM neural network. Using keywords such as “COVID-19” and “Food Safety”, the experiment proves that the IWPA-LSTM neural network public opinion evolution prediction model has good accuracy and generality. The model is suitable for the prediction of various public opinion evolution trends.

In order to ensure that urban emergency supplies can be delivered to the demand point in a timely and accurate manner, especially for special emergency supplies with a short life cycle′ perishable goods, its timeliness requirements are higher. Based on the emergency scenario of sudden public health events, this paper establishes a multi-objective location model of urban perishable goods distribution center with the goal of minimizing transportation time and transportation cost and maximizing relative coverage area. The Harris Hawk optimization algorithm (HHO) is improved to achieve an effective solution to the multi-objective location problem of perishable goods distribution center. In order to verify the effectiveness of the model, a district in Shanghai is selected as a research example. The results show that the improved HHO algorithm can solve the location model of perishable goods distribution center under actual urban road conditions, and can provide an intuitive multi-objective location optimization scheme.

Urban Metro and conventional bus carry a significant portion of residents' daily travel services, and their disruption due to sudden incidents often results in widespread and profound impacts. To ensure safe and efficient operation of public transportation, based on complex network theory, a method is proposed from the perspective of structural resilience for identifying key stations and routes of urban metro and conventional bus networks through importance, that is the resilience-based mean square deviation-TOPSIS comprehensive evaluation method. The reliability of this method is respectively verified through the monotonicity of the importance evaluation results, the robustness analysis of different attack strategies, and the comparative analysis of construction timelines. The case study results show that this method can well differentiate each station in the network; when conducting robustness analysis, it can also reflect the characteristic that key stations with greater importance have a larger impact on the overall network performance; the K-means clustering results of the importance of Shenzhen metro lines are generally consistent with the construction timeline. The reliability of this method in identifying key stations and routes is verified comprehensively.

In epidemic prevention and control efforts, the rational allocation of medical resources has consistently been a focal point of attention for professionals in the field. In order to investigate the practical effectiveness of various immune measures in epidemic prevention during the process of pandemic spread, this study introduces an infectious disease model within complex networks that considers bidirectional immune interventions. Through theoretical analysis and numerical simulations of the model, we delve into a detailed discussion on the impact of immune measures targeted at different population groups on the transmission of the virus. In the theoretical analysis, the stability of the disease-free equilibrium point in the model is examined through the incorporation of the basic reproduction number analysis. In numerical simulations, the impact of bidirectional immunization and population mobility on the spread of infectious diseases is scrutinized through Monte Carlo simulations within the context of complex networks. Simulation results indicate that, compared to enhancing the recovery rate of infected individuals, increasing the immunization rate among susceptible individuals can more effectively reduce the scale of infectious diseases.

Mission of this research is to better capture the complexity of large-scale financial systems and overcome the insufficiency of equilibrium based neoclassical finance and behavioral finance in revealing the mechanism of financial crisis and the emergence of order. This study started from the dilemma of existing theories and the motivation of introducting complexity science. Then advances of multi-agent simulation and complex network analysis are summarized and discussed as two fundimental instruments of complexity science. Research trends of complexity science in financial research, are proposed as the result of the discussions. This study provides methodology reference and analytical tools for the theoretical research and practical applications of complex financial systems in the new era.

Complex criminal cases today present systematic characteristics of multi-factor coupling and dynamic evolution, and their investigation process faces the challenge of nonlinear information integration. Criminal suspects use anti-detection methods such as changing clothes and shoes, facial obstruction, and posture camouflage, combined with complex environmental interference, which significantly reduces the practical effectiveness of single technical means such as face recognition and video structuring. In order to resolve this problem, this article focuses on the actual needs of suspect identification and tracking, breaks through the recognition bottleneck of a single modality, systematically integrates multi-information such as video structuring, face recognition, and gait recognition, and proposes a multi-information fusion video investigation system with gait recognition as the core, which realizes the dual characterization of suspect behavior patterns and identity characteristics, and provides a new technical path for improving identity recognition capabilities and the efficiency of solving complex cases.

In order to discover new chaotic systems, the generalized Lorenz-Stenflo chaotic system is constructed based on the Lorenz-Stenflo chaotic system by adding the disturbance parameters. The basic chaotic characteristics of this system are analyzed by means of the dissipation, equilibrium point and stability, bifurcation diagram and Lyapunov exponential spectrum. Based on Lyapunov stability theory, the analytical expression of the global exponential attractive set of the system is given. The global exponential synchronization is achieved by adding linear feedback control to two generalized Lorenz-Stenflo systems by using the estimator of system solution boundness. Finally, the numerical simulation of the synchronization process is carried out, and the computer simulation results confirm the theoretical feasibility of global exponential synchronization.

To obtain a better topology and identify the key nodes and lines of distribution networks, this paper presents a multi-attribute method for optimizing network structure and identifying key nodes and lines. Firstly, multiple distribution network topologies are obtained by the branch-exchange algorithm. Secondly, the invulnerability indices are constructed based on complex network theory. Then, the improved TOPSIS method is used to find the optimal network. Finally, the importance indices are constructed to identify key nodes and lines. Taking the 33-bus and 69-bus systems as examples, it verified that the optimized distribution network has stronger anti-attack ability through random attack. Random attacks and deliberate attacks are used on the distribution network. The results show that the network parameters significantly decrease under deliberate attacks, which proves that this method can effectively identify the key nodes and lines.

Aiming at the problem of conflicting lane changing behaviors of intelligent connected vehicles, a vehicle lane changing decision model based on the game theory is proposed. Firstly, the scenarios of major conflicts occurring in the process of vehicle lane changing are classified, and the lane changing game model is proposed. Then the time difference to collision (TDTC) is introduced, and the speed gains, safety gains and efficiency gains are established as the game payoff function. Besides, the decision-making mechanism is established to improve the efficiency of vehicle lane-changing decision-making. Simulation experiments and comparative analysis of the safety and feasibility of two decision models in conflict situations are conducted using MATLAB software, which includes the proposed lane-changing game decision-making model and the mixed-strategy gaming decision making model. The results show that the proposed lane-changing game decision-making model has higher decision-making efficiency and safety.

For the two-player game problem, on the basis of Q-learning algorithm, the state-value function is updated by using neural network parameter approximation, the adaptive gradient optimization algorithm is selected for parameter updating, and the behaviors of the two agents are regulated by the Nash equilibrium idea. At the same time, in order to improve the protection effect of the model, differential privacy protection is added to the results to ensure the security of the data in the process of the two-player games. Finally, the experimental results verify the usability of the algorithm, which is able to train two agents to reach their respective target points stably after multiple rounds.

In order to explore the dynamic evolution of innovator collaborative innovation network, networks are constructed based on the data of joint invention patent in electronic information industry. The paper reveals the characteristics of networks evolution, and constructs random actor models to identify the influencing factors of networks evolution. The results show that the relationship between innovators and innovation in electronic information industry increases in the early stage and becomes stable in the later stage. The network scale tends to be stable and the connections between nodes become closer. Network evolution is influenced by network structure characteristics, innovator characteristics and proximity mechanism. Among them, cooperation breadth and geographical proximity have the greatest impact.

This study proposes a new method to improve short-term load forecasting accuracy. The method is based on Variational Modal Decomposition (VMD) with consideration of VMD residuals and an Improved Northern Eagle Algorithm (INGO) optimized Bi-directional Long Short Term Memory (BiLSTM) network. The VMD is used to decompose historical load data into multiple eigenmode components (IMFs) and a residual quantity. The BiLSTM model is then constructed separately for each IMF and residual, as well as the associated meteorological parameters. To avoid the impact of poorly selected hyperparameters on prediction accuracy, the INGO algorithm optimizes the implied layer nodes, training times, and learning rates of the BiLSTM. Last but not least, the prediction results are superimposed to obtain the final results. By analyzing specific cases, this paper′s method has demonstrated a higher prediction precision when compared to alternative methods. This validation confirms the effectiveness of the method presented in this article.

According to the complex correlation characteristics of the actual interdependent network and the important evaluation indicators in the network, five different coupling methods are proposed, and nine interdependent network models are established. Considering the information transmission, redistribution and cascading failures in the network, a cascading failure model based on betweenness artificial flow models is established. We based on the game theory, attack-defense game problems of critical infrastructure are analyzed from the perspective of complex network, and the robustness of various interdependent networks is analyzed. We discovered the preferences of game participants in the interdependent networks, providing decision support for the protection of infrastructure networks.

To promote the coordinated and high-quality development of China's economy, this study combines the theory of flow-based economy and multiplex network analysis methods to construct a network model of material, capital, and technology information flow among cities. The entropy-based multi-attribute node importance measurement method is applied to propose a new framework for measuring the level of urban economy. Research shows that urban network structure is closely related to changes in macroeconomic conditions. The output results of the constructed economic level measurement model are consistent with authoritative survey reports, thus proving its effectiveness. Further studies reveal notable urban economic disparities across China's regions, with the east and the Yangtze River Delta in the lead. Positive trends emerged in the west and northeast due to policy guidance, but the central and Bohai Economic Circle still need more policy support. The flow of technological information is crucial for the upgrading of economic levels in economically developed regions. In contrast, the growth of levels in underdeveloped regions relies more on new capital investment.

Rapid and correct identification of vulnerable lines is of great significance to maintaining the safety of the power grid and avoiding large-scale power outages. Based on this, this paper proposes an analysis scheme to establish a multi-link accident chain model based on new indicators and generate a chain fault diagram. The model is calculated and analyzed based on the complex network theory, and the line vulnerability is measured based on the system load loss rate. Finally, the IEEE39-bus system is taken as an example to verify the effectiveness of the method. The method breaks through the limitation of a single pair of single lines in the process of establishing an accident chain model by the traditional method and effectively realizes the identification of fragile lines, so as to maintain the safety of the power grid and improve the stability of the power grid.

To study the evolutionary characteristics of cultural tourism information dissemination in the new media environment, analyze the impact of blogger promotion and host marketing on potential tourists, apply prospect theory to depict the profit and loss perception of bloggers and hosts in the process of cultural tourism information dissemination, comprehensively use SEIR and evolutionary game theory to construct a cultural tourism information dissemination model, and conduct numerical simulation with scenario cases in Xishuangbanna, Yunnan and Gaochun Old Street, Jiangsu. The research results indicate that, under a single information dissemination, the number of tourists follows an inverted U-shaped curve with the dissemination of cultural tourism information; The dissemination effect of cultural tourism information is positively correlated with the proportion of bloggers who choose communication strategies, the proportion of broadcasters who choose sales strategies, the number of contacts with communicators, the proportion of bloggers in the population, and the number of "fans" of bloggers, but negatively correlated with the duration of dissemination and natural attenuation parameters.

In order to identify the important weighted motifs in the directed weighted networks, the directed weighted networks are transformed into label networks and the simple motifs are expanded to label motifs by defining the edge weights as strong and weak labels. For the label motifs of the three nodes, the time-consuming procedure of subgraph traversal is replaced by the estimated probability of the corresponding number of the motifs appear in the random networks, and the important label motifs in the directed weighted networks are identified by introducing a dynamic indicator associated with the label motif type. It is applied to the passing networks of Guangdong team and Liaoning team in the 2019—2020 finals of China Basketball Association (CBA). The important passing modes of the teams in the games and the important players in the corresponding modes are obtained. The important label motifs play a significant role in mining the important construction patterns and key nodes of the directed weighted networks.

In order to clarify the risk factors of food safety issues in the traditional catering business and find effective measures to address issues from the source. The fault tree is constructed to identify risk factors, and qualitative analysis of risk factors is done by using the minimum cut set and structural importance. Moreover, the fault tree model is transformed into a Bayesian network, and final BN model is finished by structure learning and parameter learning based on case data. The two-way reasoning ability of BN is used to conduct quantitative analysis, and sensitive factors of various types of problems from the perspective of catering operators are finally obtained. The results indicate that personnel management is most likely to cause problems. Insufficient training is an important reason for food safety issues.

In order to study the influence of higher-order structures on the evolution of cooperative behavior on scale-free networks, a network game model based on the Prisoner's Dilemma game is constructed. A second-order higher-order structure is introduced on the scale-free network, a triangular face game containing pairwise games is defined, and the higher-order structure parameters are used to link the pairwise game payoffs with the face game payoffs, and the influence of the higher-order structure on the evolution of cooperative behavior is analyzed through simulation experiments. The results show that when individuals with high connectivity prioritize cooperation and obtain high payoffs, other individuals with high connectivity will be prompted to choose cooperation, and once a stable "all-cooperative" triangular strategy structure is formed among individuals, the payoffs of each cooperator can be significantly increased, which in turn promotes the emergence of cooperative behaviors.

The advance-selling mode has prolonged the overall sales duration of products, which brings new problems to the decision-making of product ordering time for e-commerce platforms. In order to study and compare the product deposit, product price and e-commerce platform profit under the influence of two kinds of ordering time strategies, a two-stage leader-follower game model is established to analyze the influence of deposit sensitivity, balance sensitivity and manufacturing cost on ordering time selection and product deposit decision. The results show that the consumer’s sensitivity to the product deposit and the product balance will affect the wholesale price of the products in the delayed order model, while the wholesale price in the early order model is only affected by the sensitivity of the product deposit. Compared with the early order model, the delayed order model can be applied to consumers with more stringent product price sensitivity. The E-commerce platform’s advance-selling coupon/deposit strategy depends on the consumers’ sensitivity to the product balance.

The current research on supply chain risk propagation has limitations in the description of enterprise status setting and the relationship between enterprises. A cellular space is mapped to represent the supply chain network, fully considering the differences in operating capabilities of node enterprises under interruption risks and the mutual influence of neighboring cells. The improved risk transmission rule and a simulation model based on cellular automata and SEIRD infectious disease models are constructed. According to the results of multi-scenario simulation and research expansion, the potential reasons for the advancement of supply chain network disruption risk propagation process are analyzed, as well as the influence of market heat, market competition degree, risk early warning and control, and government macro-control on the propagation trend. The research conclusions can provide strategic reference for supply chain disruption risk management decision-making.

In order to enrich the chaotic system model, a four-dimensional hyperchaotic system is constructed based on the Chen chaotic system. By analyzing the dissipation, equilibrium points, stability, sensitivity to initial values, bifurcation diagram, Lyapunov exponent spectra, LE dimension, power spectra and poincaré mapping of the new chaotic system, the rich and complex dynamic characteristics of the new chaotic system are revealed. In order to further enhance the security of information in secure communication, the new chaotic system and Qi hyperchaotic system are synchronized by using the transform modified function projective synchronization method, and a synchronization controller is designed to realize fast synchronization between hyperchaotic systems with different structures. The theoretical analysis and simulation results are consistent. Compared with the adaptive synchronous controller, the time required for the drive system and the response system to achieve synchronization is obviously shortened, which reflects the superiority of the method proposed in this paper.

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.

In order to get more complex dynamical properties, a chaotic system with cubic polynomial taking absolute value function is designed. The theoretical model of the chaotic system is described by a set of nonlinear equations with three state variables. The basic properties and the dynamic characteristics of the system like phase diagram, time domain waveform, Lyapunov exponent, Poincaré map and bifurcation diagram are analyzed. Under certain parameter conditions, the system has periodic and chaotic properties, and there are coexistence attractors or aggregation attractors when the initial values are symmetrical. In addition, when some system parameters change, the system has constant dynamic characteristics, when the initial value of the state variable changes, the dynamic characteristics of the system also remain unchanged. The correctness of the theory is verified by circuit simulation. Based on the newly designed chaotic system, an encryption scheme is designed, and the encryption performance is analyzed, which shows the effectiveness of the encryption scheme.

In order to explore the impact of the Covid-19 epidemic on China's stock market, the Granger causality test is used to construct a complex network model of three stages before the outbreak, the outbreak period and the normalized control of the epidemic. The comparative analysis is made from the network topology, survivability and node importance. It is found that the impact of the epidemic has significantly changed the structural relationship of the Shanghai Stock Exchange industry sectors, and the linkage effect between industry indices has weakened; the network attack simulation experiment shows that in the face of more destructive deliberate attacks, the third stage has a more durable resistance, followed by the first, second-stage network. Before and after the epidemic, the ranking of the importance of industry stocks has changed significantly. The electronics, social services, comprehensive and commercial retail industries have gradually taken an important position in the stock market; the computer, household appliances and communication industries have become important control nodes for information transmission efficiency in the stock market network. During the attack of the epidemic, the positive impact of medical biology was obvious, and it became a buffer for risk contagion in the market.

The coal mill group is one of the important equipment in thermal power plants. To ensure the safety and stability of the production process, a fault diagnosis method named clustering coarsening graph convolution neural network (CC-GCN) is proposed in this paper. Firstly, the graph structure is established between the original fault samples by KNN algorithm and converted into graph samples. Then, spectral clustering is used to compress the graph samples into multi-level coarsening graphs, and convolution operations and feature fusion are performed for each level of coarsening graph respectively. Finally, fault diagnosis is performed on the fault samples based on the graph classification method. Simulation experiments are carried out on two sets of data sets with different operation conditions of the coal mill group, and the results show that this method can not only effectively improve the accuracy of fault diagnosis, but also significantly reduce the running time of the model.

Most current predation models describe the stochastic nature of the environment in terms of white noise, for the problem that the parameters in the model may satisfy the Ornstein-Uhlenbeck process in a real situation, a stochastic three-species predation model with refuge and Ornstein-Uhlenbeck process is proposed. The dynamic behavior of the model is analyzed by its formula, differential inequality and stochastic analysis theory. The existence and uniqueness of the global positive solution of the model are proved, sufficient conditions for the average persistence and extinction of each population were obtained separately, finally, python is used to carry out numerical simulation to verify the conclusions obtained in the theorem, and further study the impact of refuge on population size.

In order to identify important nodes in temporal networks, a node importance evaluation method is proposed in based on inter-layer neighborhood information entropy. Inspired by the directed flows model of temporal networks, the method introduces the parameter ω to fuse the inter-layer neighborhood topology information of node at adjacent snapshots, uses information entropy to describe the complexity of network structure, and also takes into account the global topological information. The effectiveness and applicability of the method is proved by using the SIR propagation model, Kendall correlation coefficient, Top-k metrics, and the proposed method is compared with six evaluation methods on six real datasets. The experimental results demonstrate that the method can more effectively identify the important nodes in the temporal network. Meanwhile, the identification of the nodes of with high importance is more accurate. In addition, the parameter ω can be adjusted to improve the evaluation effect of this method according to the topology of the temporal network. Last but not least, the time complexity of this method is O(mn), which is suitable for large-scale temporal networks.

In order to better understand the network, based on the similarity, let the nodes select multiple similar nodes to form similar node pairs. Through the Monte Carlo simulation results, a pairing algorithm based on the maximum node similarity and degree is proposed to discover the overlapping community structure of the network. Using multi-level most similarity to continue to optimize the community structure, find out the deep overlapping structure and sub-community structure of the network community. The proposed algorithm discovers the overlapping structure of the network based on the reason why the real network forms a community, and further optimizes the community structure, discovering the deep overlapping community structure of the network and its sub-community structure.

In order to study the property of self-oscillating gels, dissipative structure theory is applied to analyze the oscillating dynamics of the Belousov-Zhabotinsky (BZ) reaction, and Andronov-Hopf bifurcation is adopted for the BZ reaction model to study the occurrence and energy status and periodic change between chemical and mechanical potential; and its complex nonlinear characteristics are the key to the force-chemical coupling behavior of self-oscillating gels. From this study, we find that BZ reaction a typical system that is far from thermodynamic equilibrium, and the extra potential provided from the inputs might cause the system to destabilize, and with the onset of fluctuations, periodic deformation between swelling and deswelling takes place. The study of BZ rection dynamically could aid exploration on adjusting smart/ soft material that exhibits heartbeat-like behaviors

In order to explore the influence of driver visual angle on manual car-following decision-making, a car-following model considering the expected visual angle is proposed based on the visual angle model. Considering the vehicle width, speed and driver risk perception, the expected visual angle function is constructed, and the improved VA model is established by using the expected visual angle as the direct feedback control term. Newton iteration method is used to solve the condition of linear stability of traffic flow. Numerical simulation and parameter calibration verify the improvement effect of the model. The results show that the traffic flow stability of the model in this paper is higher than that of the VA model. Considering the expected visual angle and collision risk perception is beneficial to enhance the stability and safety of traffic flow and improve the fitting accuracy of the model.

This paper tries address the problem of regulatory capture in drug safety governance resulting from information asymmetry and hidden behavior of subjects. To achieve this, this paper employs evolutionary game theory to construct a three-party evolutionary game model that includes the State Drug Administration, the local Drug Administration, and the drug manufacturer. Then the Evolutionary Stability Strategies (ESS) are obtained, and a simulation analysis is conducted to analyze the impact of each parameter on the regulatory capture of the drug administrations. In addition, this paper focuses on the effects of the penalty intensity of the State Drug Administration on the local Drug Administration and drug manufacturers. Based on the findings, recommendations are provided for the government.

In order to analyze the competitive pattern and factors influencing the competitive advantage of China's major domestic passenger airlines during the epidemic period, we use the TOPSIS-Entropy method and a modified Huff model to quantify the competitive advantage strength of airlines on routes, construct a weighted bipartite network of airlines-routes empowerment based on advantage strength, and study the market segmentation of airlines' competitive advantage and its static characteristics from the perspective of network. The Tobit regression model is used to analyze the factors influencing competitive advantage. The study shows that during the epidemic period, the airlines have a low degree of multi-market exposure, and the three major airlines have obvious competitive advantages, which vary greatly in city distribution and are mainly concentrated in their base cities; HU, 3U and other airlines have less obvious advantages in market competition and are more competitive and mainly around the second-tier coastal cities. The competitive advantage of airlines is constrained by a combination of factors, and the revenue during the epidemic period is more indicative of the ability to control cost.

To explore how to reasonably and effectively select potential partners in the process of constructing innovation consortia, maximizing the strategic role of innovation consortia in achieving high-level scientific and technoligical self-reliance and strength, this paper introduces a method for computing the technical background similarity of innovative entities based on patent heterogeneity networks and the SimRank algorithm. By using visualization techniques, we display potential collaboration networks and leverage the spectral clustering algorithm to categorize potential partners for the innovation consortium. Through empirical analysis in the field of polymer materials, the results effectively showcase potential collaborative relationships and clusters of innovative entities with highly similar technical backgrounds in this field. This confirms the validity of our theoretical approach and methodology, offering guidance for the selection of partners in innovation consortiums.

In a mixed intersection composed of autonomous and manual vehicles, the traditional traffic strategy leads to slow traffic, long waiting time, and low efficiency. To solve the problem, in this paper, platoons of vehicles are formed based on the distance between vehicles. The platoons with autonomous and manual vehicles are controlled by different traffic strategies and priorities, which effectively prevents the traffic efficiency reduction of autonomous vehicles and ensures the fairness of traffic at intersections. Simulations show that with the increase of penetration rate, the proposed strategy is superior to the traditional strategies in terms of the average speed, the average waiting time, and the average fuel consumption.

With the rapid development of the cold chain logistics industry, enterprises are paying more attention to the cost, quality of logistics services and carbon emissions generated by transportation. In order to solve this problem, a multi- depot half-open cold chain logistics routing optimization model is constructed, and various dynamic changes are integrated into the model. An improved NSGA-II algorithm is designed and the standard test function is used to prove that the algorithm has good convergence and diversity. The results show that, compared with the scheme without considering carbon emission, the distribution cost of the scheme with considering carbon emission increases by 19.00%, the transportation distance decreases by 5.16%, and the carbon emission cost decreases by 13.03%. Compared with the initial distribution scheme, the distribution cost of the real-time optimization scheme decreased by 29.78%, the carbon emission cost decreased by 23.19%, and the customer satisfaction increased by 14.49%. Compared with the single distribution center mode, the distribution cost of the multi-depot mode is reduced by 27.30%, the carbon emission cost is reduced by 48.89%, and the customer satisfaction is increased by 13.11%, which brings certain management enlightenment for cold chain logistics enterprises.

In order to reveal the key factors of hazardous materials transportation accidents and the structural relationship between factors, a new node system is established with agent, behavior, consequence, organization, environment and event as elements, using the meta-network model, combined with the characteristics of hazardous materials road transportation. Taking the major transportation explosion accident of ‘6.13’liquefied petroleum gas tank truck in Wenling, Zhejiang Province as an example, constructed the meta-network of hazardous materials transportation accidents and analyzed the structural characteristics and strike strategies of the network. It is found that the network of hazardous materials transportation accidents presents the characteristics of sparse network. The key strike effect with the closeness centrality is the best.