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.

The conditions for the spontaneous emergence of cooperation under the complex human behavior model have become the focus of many disciplines. Exploring the conditions for cooperation has both important scientific significance and theoretical value for understanding the institutional arrangements in human society. The social dilemma games provide a theoretical prototype for studying cooperation issues between multiple individuals. As a dynamic analysis method that can describe individuals′ learning and strategy adjustment processes, evolutionary game theory has been one of the most effective frameworks for studying the evolution of cooperation. This review article systematically summarizes the research progress of using the evolutionary game method to study the issues of group cooperation in social dilemma games. Specifically, the following topics are included: research progress of (1) social dilemma game models, evolutionary game theory and equilibrium analysis methods, (2) social dilemma games and the evolution of cooperation under reward/punishment mechanism and reputation mechanism, (3) social dilemma games and the evolution of cooperation with separation strategy and extortion strategy, and (4) social dilemma games and the evolution of cooperation under the network reciprocity. Finally, prospects for further research issues in this area are presented.

Opinion reversion is an important phenomenon in network emergencies and hot events. In order to explore the internal mechanism and the evolution law of public opinion reversion, we propose an opinion evolution model based on opinion leaders from the perspective of opinion dynamics. The model is applied to simulate the evolution process of public opinion under the guidance of opinion leaders. We take opinion leaders into account, improve the HK model and use social network analysis method to identify the opinion leaders based on scale-free network structure. To verify the simulation results we select the reversal case on sina twitter. The results show that opinion leaders play a key role in the evolution of opinion, which can guide the public opinion to reverse. The results also indicate that the improved model can simulate the evolution process of network opinion reversion.

In the majority of cases, networks are beneficial. However, many times it may also be harmful, such as terrorist networks and disease spreading networks. It has become an urgent challenging problem to disintegrate these harmful networks by various methods such as immunization, block, isolation, disturbance, and attack. The core task of network disintegration is to identify the “critical nodes (edges)”. This survey firstly gives the mathematical description of network disintegration. On this basis, this survey then reviews the status of network disintegration study in the fields of operations research, network science, and computer science based on mathematical programming, the centrality metrics, the heuristic algorithms, evolutionary computation, and machine learning, respectively. Lastly, this survey presents the prospects of network disintegration study from the aspects of the target network, disintegration model, and algorithm.

To explore the behavioral characteristics of massive crowds under the active interaction of millions of streamers and viewers in the field of live streaming, this paper summarized the temporal patterns of live streaming workload and user behavior characteristics of the live streaming platform, taking Douyu and Huya live streaming platforms as examples, a statistical analysis of 123 consecutive days, involving more than 2.4 million anchors, and more than 726 million live streaming data. The live streaming workload has obvious intra-day and intra-week effect. Different live streaming modes have significant differences in live streaming characteristics such as the average number of viewers and followers. The lifetime of streamers and the number of viewers conform to a power law distribution. With the development of the platform, there is a strong linear correlation between the number of streamers and viewers, but its volatility is gradually increasing, reflecting the increasingly strong heterogeneity and non-uniformity of the system. It is of great significance for understanding user behavior patterns in complex systems of live streaming, mining user distribution laws and changing trends, and designing business models such as personalized recommendations.

The topological characteristics and resilience analysis of public transportation systems are of great significance in urban management to ensure its safe and sustainable operation. This paper constructs a bus-metro interdependent network model based on the passenger transfer relationship and uses deep learning to identify their network topology attributes. A comprehensive importance indicator of the nodes is established by entropy weight-technique for order preference by similarity to ideal solution (EWM-TOPSIS), and the resilience of the network under different recovery strategies are analyzed. In order to verify the applicability and accuracy of the method, this study takes Wuhan's public transportation network as an example, which has practical guiding significance for the post-disaster recovery and operation management of the urban public transportation system.

Invulnerability analysis is one of the core contents of power grid security research. Traditional analysis methods cannot effectively analyze the process of failure generation, and have limitations in the research of invulnerability analysis. This paper studies the invulnerability analysis of power networks based on complex network theory, and conducts an empirical analysis using Chongming District of Shanghai as an example. For random attacks and selective attacks on the power network, the changes in the network efficiency and the maximum number of connected subgraphs after the attack are obtained, and the network efficiency change rate is proposed as a parameter to evaluate the invulnerability. According to the simulation results, a segmented protection scheme based on real-time centrality closeness priority attack strategy is proposed to improve the invulnerability of the power network and ensure the safety of the power grid.

In order to promote the development of autonomous driving technology, this paper focuses on the lane-changing decision-making behavior of autonomous vehicles. First, the lane-changing intention is quantified objectively, and then the lane-changing collision probability and the lane-changing dynamic risky coefficient are introduced. Based on the game theory, the decision-making behavior model of the lane-changing game for autonomous vehicles is established. Besides, speed gains are considered as the objective of game gains. Therefore, autonomous vehicles can change lanes in a coordinated, safe and reasonable manner. Finally, with SUMO software, the traditional LC2013 lane-changing model and the decision-making behavior model of the lane-changing game are used for simulation experiments and comparative analysis. The results show that the decision-making behavior model of the lane-changing game has higher stability, reliability, safety and lane utilization.

Aiming at the problems of inaccurate keywords extraction and only considering single correlation in subject words extraction, a subject words extraction algorithm combining integration idea with complex network is proposed. Firstly, the keywords of topic data are extracted through the integration algorithm to improve the accuracy of keywords extraction. Secondly, the traditional word co-occurrence formula is improved to calculate the co-occurrence degree of keywords, and a keywords co-occurrence network is established. Based on the network, the optimal connected subgraph is obtained. At the same time, the importance of keywords is measured by taking the centrality of node degree as the weight, and the subject words are mapped. Finally, the micro-blog topic data set is used to verify the example, which proves that the algorithm is effective and better than the traditional word co-occurrence algorithm, and it is applied in the Qingdao community topic data set.

In this paper, we use generative adversarial network (GAN) to improve semantic segmentation of images. The model is composed of a semantic segmentation network and a discriminant network, where the segmentation network responses for generating semantic segmentation result while the discriminant network responses for detecting the difference between the generated result and the labels on the global structure level and improving the segmentation effect. In order to extract context information, we adopt the spatial pyramid pooling module in the segmentation network, which could perform pooling operation on multiple levels of sub-regions. Meanwhile, in order to solve the problem of a large number of manual annotations needed in the semantic segmentation data set, we use the discriminant network to generate pseudo labels and realize semi-supervision in the training of the segmentation network. The model has been tested using PASCAL VOC2012 dataset, and the results show that supervised and semi-supervised approaches proposed in this paper are superior to the existing methods.

After the outbreak of COVID-19, it is of great significance to find an appropriate dynamic model of COVID-19 epidemic in order to master its transmission law, predict its development trend, and provide corresponding prevention and control basis. In this paper, the SEIRV chamber model is adopted, and the dynamics model of infectious disease is established by combining the fractional derivative of Conformable. The fractional derivative differential equation of Conformable is discretized by numerical method and its numerical solution is obtained. In addition, numerical simulation was carried out on the confirmed data of Wuhan city from January 23, 2020 to February 11, 2020. At the same time, consider that the Wuhan municipal government revised the epidemic data on February 12, 2020, adding nearly 14,000 people. The order α value of SEIRV model is modified, and then the revised data is simulated. The simulation results are in good agreement with the published data. The results show that compared with the traditional integer order model, the fractional order model can simulate the modified data. This reflects the advantages of fractional infectious disease dynamics model, and can provide certain reference value for the prediction of COVID-19 model.

Reviews and prospects the existing academic literature in terms of research paradigms, research methods and research tools on the complexity of international capital flow network. Studies show that the research paradigm ofcomplexity science based on discrete spatial network is pretty suitablefor the research on the complexity of international capital flow network. And the procedure, consisting of "construction of complex discrete network—system evolution—relationship analysis on input and output—proposals fordecision making ", can work quite well while doing this research. Besides, the construction and topological structure analysis of international trade capital flow network, which also belongs to international fund interactive network, could be applied to the research on the complexity of international capital flow network in the context of increasing data availability. And after the expansion on network node and neighbor mode, the newly defined generalized cellular automaton model could be used as a powerful tool for evolution and parameter calculation of international capital flow network.

To tackle the stochastic multi-depot community home health care problem, this work formulates a stochastic programming model with chance constraint to minimize the total operation time. Moreover, an improved shuffled shepherd optimization algorithm is designed by applying crossover operation instead of the basic step updating method. Therefore, the designed approach can suitably solve the community home health care problem with discrete characteristic. Extensive experiments are carried out and the results show that the proposed approach has better performance in solving the considered problem compared with two well-known meta-heuristic methods.

This paper introduces the derivation of basic reproduction numbers for several epidemic models. The basic reproduction number plays an important role in describing the dynamic behavior of infectious disease models,which is an important indicator to determine the prevalence of diseases. Therefore, the basic reproductive number is a significant reference for the prevention and control of diseases and the immunization strategy. The basic reproduction numbers can be derived by means of the definition, the monotonicity of the infected individuals at the initial moment, the existence of the positive equilibrium and the local stability of the disease-free equilibrium, the numerical simulation,respectively. This paper introduces many epidemic models especially network model and calculates their basic reproduction numbers. Finally, we analyze the changes in the basic reproduction numbers during different periods.

In order to explore the enabling mechanism, as well as the mechanism of government classification regulation on smart platforms and pharmaceutical enterprises' CSR strategies, a tripartite evolutionary game model for the government, smart platforms, and pharmaceutical enterprise is constructed. Through the analysis of the critical condition, stability and evolution path of the evolution of the tripartite strategy, it reveals the action mechanism of each factor and the evolution law of the tripartite game, and analyzes the influence of key factors such as the equilibrium state of system evolution and regulatory policies based on Anylogic simulation. The research shows that: Government classification regulations significantly affect the CSR performance decision and intelligent supply chain construction of pharmaceutical enterprises and intelligent platform, and the regulatory effect is affected by the sensitivity of the policies of both parties; Increased intelligence can strengthen the risk flexibility and autonomy of intelligent platform and pharmaceutical enterprises, but there are limitations in the space and time for pharmaceutical enterprises to implement strategies; When the reward coefficient is moderate, the evolution speed of smart platforms and pharmaceutical enterprise is significantly positively correlated with the reward coefficient. Based on this, it proposes measures to improve the efficiency of government CSR governance and solve the dilemma of CSR implementation in the pharmaceutical industry.

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.

In this paper, we mainly investigated the estimation of networked control systems (NCSs) with packet losses. Firstly, we introduced the classical Kalman filter estimator and the covariance matrix. When the measurement equation is with noise, we give the optimal estimator. The optimal estimator is strictly calculated by the recursive method. Moreover, according to whether the packet loss process can be observed, we discussed the estimation problem. Finally, for the application, a simple sub-optimal approximation estimator was developed. It will be helpful to study the NCSs with packet losses in large finite horizon, and provides the research direction for further analyzing the problem of NCSs.

The optimization of platform regulation strategy is the key to ensure the compliance and legality of suppliers and anchors in live broadcast e-commerce. Aiming at the interest relationship, behavior strategy and game relationship of suppliers, live broadcasting platform and anchor, this paper constructs the behavior strategy evolution game model of the three, reveals the behavior strategy evolution law of live broadcasting e-commerce under the platform regulation, and uses Netlogo to simulate the strategy evolution process. The research shows that the appropriate punishment and restraint of the platform is helpful to standardize the behavior of suppliers and anchors; Higher platform subsidies can encourage suppliers and anchors to standardize their behavior, but at the expense of their own interests, the system is difficult to stabilize; The illegal income factor is an important factor affecting the supplier's supply strategy and anchor behavior choice. Accordingly, the basic strategies and specific measures to standardize the behavior of suppliers and anchors are put forward.

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 explore the impact of network structure and knowledge base on enterprise innovation performance, the cooperative innovation network of industry-university-research institute is constructed from the perspective of social network, and the negative binomial regression model is used for empirical analysis. The results show that the industry-university-research innovation network centrality has a significant positive impact on enterprise innovation performance, network structure hole does not show a significant inverted U-shaped relationship with enterprise innovation performance, knowledge base has a significant positive impact on enterprise innovation performance, and the interaction between knowledge base and network centrality has a significant negative impact on enterprise innovation performance, the interaction between knowledge base and structure hole has a positive effect on enterprise innovation performance, but it is not significant.

In order to better formulate policies for the development of artificial intelligence, this paper analyzes the technology convergence mechanism in the field of artificial intelligence by constructing the technology convergence network model. Based on the patent data from 2010—2019 of artificial intelligence field, combined with technology and organization dimension, this paper tries to analyzes from three aspects of technical characteristics, organization technical characteristics and organization relationship characteristics. The results show that: In the field of artificial intelligence, organization cooperation is sparse, fusion technology is relatively scattered, organization and technology have obvious core-edge structure characteristics. On the level of technical characteristics, similar technologies are easier to be converged, and technologies that have already been converged will promote new convergence; On the level of organizational technical characteristics, the common technologies owned by the orgnization will negatively affect the occurrence of convergence with other technologies; On the level of organizational relationship characteristics, the effect of cooperation between organizations on technological convergence is closely related to the development stage of the field, and the "circle of buddies" inhibits technology convergence.

Complex networks nodes can have multiple attributes, and different attributes or attribute sets will lead to different connections between nodes, thus the network would have different functions.Aiming at the problem that existing complex network models can not construct networks with different functions according to the selected attributes of nodes, a multifunctional complex network model is proposed.The network model is represented only by nodes and their attribute sets. Different network topology and network functions are determined by selecting the attributes of nodes and defining the mapping rules of nodes under the corresponding attributes.By establishing and analyzing a missile defense network, the availability and effectiveness of the multi-functional complex network model are verified

In this study, behavioral attitudes, subjective norms and perceived behavioral control are used as mediators to explore the role of consumption motivation and cognitive level in shaping consumers' purchase behavior for green agricultural products. A survey of 432 supermarket consumers in Wuhan shows that they have a high willingness to buy safe agricultural products. The consumption motivation and cognitive level of agricultural products' quality and safety level have a positive impact on consumers' purchase behavior through behavior attitudes, subjective norms and perceived behavioral control.Consumer behavior and attitude affects the subjective norms. However, the mediating effects of behavior attitude and subjective norms are independent among consumers' consumption motivation, cognitive level and purchase behavior. Consumer behavior and attitude do not transfer to specific purchase intention and behavior through subjective norms, which provides decision-making reference for government in the supervision of agricultural product quality and safety.

In order to improve the accuracy of gender recognition for social network users, the text features and emoticon features of a single user are fused to identify the user's gender, and then the interactive feature information of multiple users is extracted to further improve the accuracy of gender recognition. The experimental results show that the accuracy of user gender recognition is improved by 6.8% after the fusion of multi-user interaction features. It shows that emoticons and multi-user interaction features are very helpful to improve the accuracy of user gender identification, and improve the accuracy of gender information identification of social network users.

This paper studied a bi-objective vehicle routing problem with time windows, variable speed, multiple path choice, and capacity constraints (BOVRPTWVDPC) in a complex road network aiming at minimizing the total cost of distribution and maximizing the overall customer satisfaction. In modelling of customer’s satisfaction, the factors of distribution time window and customer priority were taken into account, and in the description of vehicle speed, the traffic period and road conditions were considered. A bi-objective mixed integer programming model was established, and an improved ant colony algorithm was designed to solve the problem. The simulation results show that the proposed model and the improved algorithm are effective and have certain reference value for vehicle distribution path planning under complex road conditions.

The frequency of bin entry and exit is one of the key factors affecting the efficiency of the “part-to-picker” picking system based on the shuttle storage system. In case of sufficient goods in the bin, the bins of a certain kind of goods required by the same batch of orders only need to be shipped out once. To allocate similar orders to one batch and to reduce the number of bins in and out of the warehouse can improve the picking efficiency of the system. Taking the minimum number of bins out of the warehouse as the objective function, an order batching optimization model is established. According to the characteristics of the model, an improved genetic algorithm is designed. A hybrid crossover strategy is proposed. On the basis of elite retention, partial search is performed on part of the elite chromosomes of each generation with a certain probability to improve the convergence speed and solution accuracy of genetic algorithm. The simulation results show that the total number of outgoing of bins is reduced after optimization, and the picking efficiency of the system is improved and the approach proposed is valid.

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.

Evolutionary games on complex networks is a new interdisciplinary research field at the cross-point of complex networks and evolutionary game. With a complex network and and an evolutionary game dynamics representing the interaction structure among agents and the decision paradigm respectively, evolutionary games on complex networks provides a systematic framework for analyzing and predicting the collective decision-making behaviors of complex interactive populations. This review aims to give a brief survey of evolutionary game dynamics on complex networks from a computational perspective. In detail, we will firstly present a mathematical formulation of the model of evolutionary game dynamics on complex networks, and then analyze the computational complexity of these networked game dynamics, and finally outline some main analytical results about evolutionary game dynamics on complex networks. This computational survey will be a well complement to those simulation results in evolutionary game dynamics on complex networks.

In order to better reveal the transmission mechanism of COVID-19, this paper proposes the SEAIHR dynamic model by analyzing the transmission characteristics of COVID-19, considering the self-healing of the hidden lurks and the early isolation of the lurks, introducing “h hospitalization isolation”, “recessive cure”, considering the change of prevention and control intensity, and introducing “morbidity status”. Using the real epidemic data and considering the changes of parameters in different stages, a multi model comparative test was conducted. The experimental results showed that the fitting and prediction accuracy of the SEAIHR model was significantly improved, and the fitting error was 34.4%~72.8% lower than that of the classical model in the early and middle stages of the epidemic, providing reference and guidance for epidemic prevention and control.

In order to make full use of emergency supplies and improve emergency response capacity, a multi-objective optimization model was established to minimize the average waiting time of disaster sites and the dispatching cost of emergency supplies, taking into account the constraints of road passage and transportation capacity in the initial stage of disaster. Population entropy and Gaussian function were introduced by the improved NSGA-II, based on adaptive mechanism, to dynamically adjust mutation and crossover probability, and combined mutation and crossover process with horizontal and vertical information of evolution to guide population evolution and improve evolution speed. A random variable neighborhood search operator based on greedy idea was designed to fully explore the solution space. The influence of the same on evolution in Pareto frontier was eliminated by adjusting the strategy. An example was given to verify the proposed algorithm, and the results shown that the proposed algorithm was superior to the traditional NSGA-II algorithm and the known literature algorithm, and maintained good convergence and obtained better diversity.

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.

The outbreak of COVID19 has turned many areas into disaster areas. In order to provide timely relief to the disaster areas, accurate supply of post-disaster emergency resources has become the primary factor to ensure the safety of the people in the disaster areas. In this paper, SEIR was used to predict the number of infected people in each disaster area at the decision-making moment, and then the weight of urgency degree and material demand in the disaster area were calculated. Based on the degree of urgency, a multi-objective optimization model of emergency resource scheduling was constructed to maximize the satisfaction of the victims, minimize the total cost and consider the fairness of distribution. A multi-objective artificial bee colony algorithm is proposed. Aiming at the disadvantages of artificial bee colony algorithm such as precocity, the dynamic parameter and Pareto solution set are used to define the new bee colony location updating formula, and the teaching optimization is used to disturb the bee colony location, so as to avoid the algorithm falling into local extremum. The simulation results show that the proposed model and algorithm can effectively solve the problem of optimal allocation of emergency resources at multiple disaster points under epidemic events, and the improved algorithm has better performance.

In order to explore the evolution law of innovation in electronic information industry, the collaborative innovation network is constructed from the perspective of social network. Based on the patent data from 1985—2017, the evolution path and spatial pattern trend of collaborative innovation is analyzed. The results show that electronic information industry has entered a stage of rapid development with enhanced network connectivity and obvious core-edge structure. The cooperative mode has gradually changed from institute-oriented to enterprise-oriented collaborative innovation mode with universities and institutes as knowledge partners. The spatial pattern is characterized by unbalanced development, and the inter-regional cooperation shows a radiation-type network form dominated by the core region.

To reveal the characteristics and influencing factors of cross-platform information diffusion in social media, this paper took the Legitimate Defense Case in Kunshan as an example and studied the characteristics and related factors of information diffusion from other platforms to Sina Weibo using statistical inference and regression analysis methods. We found that users are more inclined to the latter when balancing the high amount of information in microblogs with the convenience of obtaining information, and the transmissibility, basic reproductive number and diffusion depth of cross-platform information are significantly lower than those of non-cross-platform information. Information from WeChat official accounts, Weibo videos, Weibo articles and Sina news has more advantages over other types of information in terms of depth and scale of diffusion. Compared with ordinary users, users who are authenticated as media and government administration spread information from news platforms on a larger scale. A comprehensive consideration of the types and source platforms of information can help us to better understand the spreading of sudden social events in the Internet space, thus helping to effectively guide or control the evolution of public opinion.

At present, most ecological competition networks are characterized by integer-order systems, and system behavior is only affected by the current moment. In this paper, a fractional time-delay predator-prey model with independent non-cross propagation is proposed. Time delay is selected as the bifurcation parameter. The stability and bifurcation problems of the model are studied through analyzing the distribution of the roots of the corresponding characteristic equation under the influence of different orders, and the stability conditions and Hopf bifurcation criteria induced by time delay are established. Finally, the accuracy of the theoretical results is verified by numerical simulations.

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.

It is a common operation mode of shuttle-based storage and retrieval systems (SBS/RS) to combine storage and outbound tasks to form a compound operation. Optimizing a reasonable compound operation path is of great significance for improving operation efficiency and reducing operation costs. In order to improve the order picking efficiency of SBS/RSs, the optimization of the system's compound operation path is attributed to a task assignment problem, and an optimization model is established with the goal of minimizing the total time to complete a batch of tasks. An improved discrete particle swarm optimization (IDPSO) algorithm is designed. The position and velocity of particles and the equation of motion are redefined. Cycle crossover and exchange mutation are introduced into the addition of velocity to achieve fast convergence of the algorithm, while maintaining the diversity of the particle swarm through a repulsion operator, reducing the possibility of falling into a local optimum. The simulation results show that the performance of the algorithm is better than genetic algorithm, which effectively shortens the time of compound operations and improves the picking efficiency.

An effective policy impact analysis methods can help the government to understand the impact of the policy on the stakeholders and the market in a timely and accurate manner, which is of great significance for maintaining market stability and social harmony. The paper takes the online review formed after the enactment of the policy as the source data, and based on the LTP, identifying and extracting causal event pairs and succeeding event pairs, using Gephi to construct policy influence event evolution graph, analyzing the impact of policy on stakeholders and related markets, and taking the "317 New Deal" in Beijing as an example to conduct an empirical study. The results show that the policy influence event evolution graph can fully portray the policy's impact on stakeholders and related market.

Synergy is ubiquitous in contagion processes on complex networks. Most existing studies have been focused on the continuous models, yet the discrete models received less attention. Motivated by this, we employ the generating function method to study a two-state (active or inactive) threshold model on complex networks with different synergistic effects. Compared to the case without synergy, the positive synergy enhances prevalence and weakens systematic robustness. The negative synergy, however, plays an opposite role. These effects are strengthened when the network is heterogeneous.

In order to explore the multidimensional influence of directors and leading actors on the film, we establish the director-co-director network, the leading actor-co-director network and the director-leading double model network to analyse the the film market multi-directional from the perspective of network location. The regression results show that the director and the leading style does not affect the film's score, but co-directing can promotes the box growth. Both degree centrality and structure hole of the director and leading actor can promote the increase of film score and box office. However, they do not have superposition effect, the degree centrality has a greater relative influence. Sharing information is better than exclusive information.