Modern service industry agglomeration and its influencing factors: spatial interaction in Chinese cities

From the perspective of spatial interaction, the impact of the modern service industry’s agglomeration on today’s increasingly connected cities is worth studying. This study uses a spatial econometric model to test the development trends and factors influencing the modern service industry’s agglomeration in the Yangtze River Delta city group. The results show that the industry has the highest agglomeration in leasing and business and the lowest in education. The overall concentration of the industry is generally low, implying a more fragmented distribution. Moreover, the agglomeration has a significant positive spatial correlation with economic development, knowledge intensity, and city size. However, it has a negative correlation with information technology level and transportation infrastructure, inconsistent with existing research. This study argues that the development of the information technology level and transportation infrastructure in a city could lead to the ‘virtual agglomeration’ of the modern service industry and gradual decentralisation in geographical distribution. This is a new paradox that city groups may face when improving their infrastructure and developing modern services. This study uses the spatial interaction perspective to propose policy recommendations for promoting the modern service industry’s agglomeration and coordinated regional development

An empirical study on coordinated development of energy consumption structure and green total factor productivity under spatial interaction

Existing studies have found a non-linear relationship between the energy consumption structure (ECS) and the green total factor productivity (GTFP), but their influencing factors are not yet clear. This study examines the spatial impact of existing green development measures on coordinating the ECS and the GTFP using the coupling and spatial econometric models. The research findings are as follows: (1) The coordination between the ECS and the GTFP has increased over time, and the coordination is significantly higher in economically developed cities. (2) The spatial analysis results show a significant spatial auto-correlation between the ECS and the GTFP coordination. Green development approaches such as environmental regulations, technological innovations, and industrial structure significantly contribute to the degree of coordination. Decomposition of the spatial effects shows that technological innovations significantly affect local and neighbouring cities. These conclusions hold after endogeneity and robustness tests. The results suggest that local governments in city clusters should promote environmental regulations, industrial structure, and technological innovations to promote the coordinated development of the ECS and the GTFP of urban agglomeration

Research on IGOA-LSSVM based fault diagnosis of power transformers

Power transformer is an important part of power equipment, and its functionality affects the proper operation of the whole power network. In order to diagnose power transformer faults effectively, the authors propose a fault diagnosis strategy based on an improved locust optimization algorithm for least squares vector machines (IGOA-LSSVM). Firstly, it was required to address the problem that the diagnostic prediction accuracy of the least squares vector machine is reduced due to its parameters. So this paper introduces the locust optimization algorithm with simple algorithm structure and good performance for optimizing the parameters. And at the same time, the authors generate an improved locust optimization algorithm with self-learning factors, proportional weight coefficients and Levy flight strategy. Secondly, the improved locust optimization algorithm is used for optimizing the least squares vector machine parameters. Finally, in the simulation experiments, the results of the benchmark test function illustrate that the IGOA algorithm has better performance, and the test results of a fault samples diagnosis of the power transformer equipment illustrate that the IGOA-LSSVM has good prediction effect and improves the fault identification accuracy compared with ACO-LSSVM and PSO-LSSVM in five types of fault diagnosis