Systematics of Cafius corallicola (Fairmaire) (Coleoptera: Staphylinidae) with a discussion of its phylogenetic relationships

Yoo, In Seol; Ahn, Kee‐Jeong

doi:10.1016/j.aspen.2021.101854

Cited by 2 publications

(2 citation statements)

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“… The agglomeration data of different fields should be based on the agglomeration of specialized villages. The construction time T of the system dynamics model [ 20 ], the calculation effect θ , and the simplification rate of the agglomeration data w I collected by Python should be calculated. The specific formula is as follows:

where G Ij is the number of I industry information of j cluster data, G I is the total amount of I industry information in the cluster database of suburban mountain tourism professional village, G j is the total amount of j cluster data, and w i is the simplification degree of I tourism professional village related information.…”

Section: Research Modementioning

confidence: 99%

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An Optimization Analysis Model of Tourism Specialized Villages Based on Neural Network and System Dynamics

Wang

Sui-ying

et al. 2022

Computational Intelligence and Neuroscience

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With the rapid development of tourism, professional tourism villages emerge one after another, which has become the focus of the tourism industry. At present, there are some problems in tourism professional villages, such as imperfect management and inaccurate prediction of future development, which affect the rational allocation of tourism resources. In order to improve the distribution of tourism resources and better predict the development of tourism professional villages, it is necessary to make comprehensive judgment and analysis, especially the analysis of indicators such as the prediction and development judgment of tourism professional villages. This paper discusses the optimization analysis of the agglomeration of tourism specialized villages by backpropagation (BP) neural network and system dynamics model, analyzes the system structure of the agglomeration factors of tourism specialized villages, and promotes the intelligent integration of the agglomeration factors. The development of clusters of professional villages promotes data integration among resources, economy, society, and other elements and presents the characteristics of big data. As the level of concentration of professional villages increases, the complexity of the associated factors also increases, which increases the difficulty and effectiveness of tourism analysis. In view of this situation, taking mountain tourism as the research object, this paper proposes an improved system dynamics model based on BP, extracts features from cross factor (resource, economic, and social) data, and optimizes the relationship between professional village agglomeration and various factors. The MATLAB simulation results show that based on the improved system dynamics analysis, the simplification rate of (resources, economy, and society) data can be controlled at more than 24%, the degree of agglomeration is more than 95%, and the construction time of the relationship map of related factors is less than 40 s. Therefore, the analysis method proposed in this paper is suitable for the calculation of the agglomeration of tourism professional villages in the mountain area and can meet the needs of the development of tourism professional villages in the mountain area.

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Section: Research Modementioning

confidence: 99%

“…The agglomeration data of different fields should be based on the agglomeration of specialized villages. The construction time T of the system dynamics model [ 20 ], the calculation effect θ , and the simplification rate of the agglomeration data w I collected by Python should be calculated. The specific formula is as follows:

…”

Section: Research Modementioning

confidence: 99%