Complexity最新文献

Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China’s counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns.

The indexing work of complex products and systems (CoPS) is to determine a comprehensive set of indicators to ensure seamless integration of CoPS modules. Despite its pivotal role as a pioneering phase in CoPS development, indexing work has garnered relatively scant research attention. This paper explores CoPS indexing work through a network game model that considers a main manufacturer and several suppliers. The primary aim is to discern the key influencing factors affecting CoPS indexing work and elucidate the influence mechanism. Several interesting conclusions have been drawn: (1) subjective, environmental, and structural factors as the three key aspects influencing the CoPS indexing work network; (2) Subjective factors directly influence the agencies’ selection of the optimal effort level for collaborative work, whereas environmental and structural factors indirectly impact their choice by affecting their network centrality; (3) the environmental factors within the indexing work network dictate the rate at which interagent interactions diminish with distance. To demonstrate and validate the research’s findings, an examination of the indexing process of the China Lanxin High-speed Railway is conducted. This study offers new insights into CoPS indexing work, providing both theoretical references and practical suggestions for project teams to improve collaborative efficiency.

Population aging has emerged as a social issue in most countries worldwide. Developing education for the elderly is one of the key strategies to combat the challenges of an aging population. Based on the two-dimensional analysis framework of the policy instruments and the type of elderly education, the content analysis method was used to analyze 49 elderly education policy documents developed and issued in China over the last four decades in this study. This study aims to provide a reference for the formulation of education policies for the elderly in China and other countries. The results indicated that the allocation of policy instruments was inconsistent between and within types. It is imperative to issue new programmatic documents, optimize the allocation of policy instruments, and enhance their adaptability, thereby boosting the development of elderly education.

This study utilizes the Takagi–Sugeno fuzzy model to represent a subset of nonlinear systems and presents an innovative adaptive approach for optimal dynamic terminal sliding mode control (TSMC). The systems under consideration encompass bounded uncertainties in parameters and actuators, as well as susceptibility to external disturbances. Performance evaluation entails the design of an adaptive terminal sliding surface through a two-step process. Initially, a state feedback gain and controller are developed using Linear Matrix Inequality (LMI) techniques, grounded on H₂-performance and partial eigenstructure assignment. Dynamic sliding gain is subsequently attained via convex optimization, leveraging the derived state feedback gain and the designed terminal sliding mode (TSM) controller. This approach diverges from conventional methods by incorporating control effort and estimating actuator uncertainty bounds, while also addressing sliding surface and TSM controller design intricacies. The TSM controller is redefined into a strict feedback form, rendering it suitable for addressing output-tracking challenges in nonlinear systems. Comparative simulations validate the effectiveness of the proposed TSM controller, emphasizing its practical applicability.

In this paper, we study the Hyers–Ulam stability of Hadamard fractional Itô–Doob stochastic integral equations by using the Banach fixed point method and some mathematical inequalities. Finally, we exhibit three theoretical examples to apply our theory.

In this paper, two novel methodologies of employing machine learning (here, the type-2 fuzzy system) are presented to control a multiagent system in which the agents are flexible joint robots. In the previous methods, the static mode controller has been investigated, which has little flexibility and cannot measure all the states of the system, but in the method presented in this paper, we can eliminate these disadvantages. The control signal is consisting of feedback from the output and the estimated states of the system. In the first method, the control signal coefficients are calculated from the linear matrix inequality (LMI), followed by a type-2 fuzzy system that adds the compensation signal to the control signal. In the second method, the type-2 fuzzy system is directly used to estimate the control signal coefficients which do not employ LMI. Both methods have their disadvantages and benefits, so in general, one of these two methods cannot be considered superior. To prove the effectiveness of the two proposed methods, a topology with four robots has been considered. Both proposed methods have been evaluated for controlling the angle and speed of the robot link. Also, another simulation was made without using the fuzzy system to verify the importance of our methods. Simulation results indicate the proper efficiency of proposed methods, especially in presence of uncertainty in the system.

With the rapid development of the digital economy, there has been an explosion in the amount of data generated. Data have become a vital resource for nations, just as tangible assets and human capital are crucial factors of production. Consequently, protecting digital security has become paramount. However, the increasing frequency of various data security incidents in recent years has exposed issues such as inadequate platform governance, lack of government regulation, and incomplete digital security governance (DSG) mechanisms. This study aims to create a healthy and open digital ecosystem and proposes a new digital security governance framework. It subdivides traditional government departments into local governments and the central government. Together with third-party platforms, they are analyzed as participants in an evolutionary game. The study examines the evolutionary stability of strategy choices made by each party and explores the relationship between key factors such as negative externalities, data security incident probabilities, and their impacts on strategy selection using numerical simulations. The research findings indicate the following. (1) Key parameters such as implicit benefits, government subsidies, and negative externalities play a significantly positive role in the development of the digital ecosystem. (2) The central government consistently tends towards emergency response, considering the overall societal perspective, as long as it is capable of bearing the costs. (3) Local governments may exhibit free-riding behavior during the governance process. To address this, it is important to increase the willingness of third-party platforms to govern and oversee the participation of local governments, which is an effective way to prevent data crises. The study also identifies different governance models in various environments: (1) a digital security governance model in a stable market environment, involving increased central government intervention and supervision of local governments’ participation; and (2) a digital security governance model in the event of a data crisis, where the central government establishes subsidies that exceed the governance costs of local governments to enhance the willingness of third-party platforms to govern. Finally, recommendations and strategies are presented to enhance the level of digital security governance.

In recent years, in the classic battles and armed conflicts around the world, battlefield environment reconnaissance and the collection and processing of operational information play an increasingly critical role in the victory and defeat of the battlefield. Unmanned equipment, especially UAV equipment, is used by more and more countries in the field of combat reconnaissance. Meanwhile, the types of UAV are gradually diversified with the change of operational requirements. UAVs adapted to different combat environments shine brightly on the battlefield. In terms of naval battle field, due to the limitations and deficiencies of reconnaissance methods such as surface radar, UAVs play a more prominent role in combat reconnaissance. There are more scenarios for UAVs to be used in combat reconnaissance in naval battle field and higher requirements for UAVs’ combat effectiveness. Therefore, this paper takes UAVs’ naval battle reconnaissance missions as the research object. By using PSO as the research method, this paper studies the combat reconnaissance task configuration of UAVs, hoping to contribute to the improvement of UAVs’ combat reconnaissance capability and combat effectiveness.

The heat kernel associated with a discrete graph Laplacian is the basic solution to the heat diffusion equation of a strict graph or network. In addition, this kernel represents the heat transfer that occurs over time across the network edges. Its computation involves exponentiating the Laplacian eigensystem with respect to time. In this paper, we expand upon this concept by considering a novel network-theoretic approach developed in recent years, which involves defining the k-path Laplacian operator for networks. Prior studies have adopted the notion of integrating long-range interactions (LRI) in the transmission of “information” across the nodes and edges of the network. Various methods have been employed to consider long-range interactions. We explore here the incorporation of long-range interactions in network analysis through the use of Mellin and Laplace transforms applied to the k-path Laplacian matrix. The contribution of this paper is the computation of the heat kernel associated with the k-path Laplacian, called the generalized heat kernel (GHK), and its employment as the basis for extracting stable and useful novel versions of invariants for graph characterization. The results presented in this paper demonstrate that the use of LRI improves the results obtained with classical diffusion methods for networks characterization.

This paper is offered as a contribution to understanding historical trade. The method of input-output modelling is utilised to evaluate the evolution of cities (and their associated regions) in England and Wales between 1851 and 1911. The construction of input-output accounts for each city region is based on “heroic” data assumptions, which enable the construction of a demonstration model illustrating a new iterative approach to historical analysis. In its current application, the model enables estimates to be made of mesolevel trade between cities, which enhances our analysis of urban evolution in this period.