Eulerian Algorithms for Computing Some Lagrangian Flow Network Quantities

2021 ◽  
pp. 110620
Author(s):  
Guoqiao You ◽  
Shingyu Leung
Keyword(s):  
Flow Network ◽  
Lagrangian Flow

scholarly journals Lagrangian Flow Network approach to an open flow model

2017 ◽  
Vol 226 (9) ◽  
pp. 2057-2068 ◽  
Author(s):  
Enrico Ser-Giacomi ◽  
Víctor Rodríguez-Méndez ◽  
Cristóbal López ◽  
Emilio Hernández-García
Keyword(s):  
Flow Model ◽  
Network Approach ◽  
Flow Network ◽  
Open Flow ◽  
Lagrangian Flow

The impact of China's import ban on global copper scrap flow network and the domestic copper sustainability

2021 ◽  
Vol 169 ◽  
pp. 105525
Author(s):  
Sen Liu ◽  
Wei Liu ◽  
Quanyin Tan ◽  
Jinhui Li ◽  
Wenqing Qin ◽  
...  
Keyword(s):  
Flow Network ◽  
The Impact

Military Human Resource Planning through Flow Network Modeling

2021 ◽  
pp. 1-12
Author(s):  
Oussama Mazari Abdessameud ◽  
Filip Van Utterbeeck ◽  
Marie-Anne Guerry
Keyword(s):  
Human Resource ◽  
Network Modeling ◽  
Resource Planning ◽  
Flow Network ◽  
Human Resource Planning ◽  
Through Flow

scholarly journals Hidden Topological Structure of Flow Network Functionality

2021 ◽  
Vol 126 (2) ◽  
Author(s):  
Jason W. Rocks ◽  
Andrea J. Liu ◽  
Eleni Katifori
Keyword(s):  
Topological Structure ◽  
Flow Network ◽  
Structure Of Flow ◽  
Network Functionality

scholarly journals Study on Tourism Flow Network Patterns on May Day Holiday

2021 ◽  
Vol 13 (2) ◽  
pp. 947
Author(s):  
Shanshan Wu ◽  
Lucang Wang ◽  
Haiyang Liu
Keyword(s):  
Transportation Network ◽  
Edge Structure ◽  
Flow Network ◽  
Factors Affecting ◽  
Spatial Distribution Characteristics ◽  
Transportation Modes ◽  
May Day ◽  
High Level ◽  
Network Patterns ◽  
Main Factor

The development of tourism is based on tourism flow and studying a tourism flow network can help to elucidate its mechanism of operation. Transportation network is the path to realize the spatial displacement of tourism flow. This study used “Tencent migration” big data to explore the spatial distribution characteristics and rules of tourism flow in China, providing suggestions for the development of tourism. The results demonstrate that the 361 cities studied can be divided into three types: destination-oriented, tourist-origin-oriented, and destination-oriented and tourist-origin-oriented. There are significant differences in the quantity of flow, the area of concentration, and the factors affecting the flow in the three types of cities. The larger the flow of tourism between cities, the higher the network level, and the wider the network range. The high-level nodes are closely related, while the peripheral nodes are more widely distributed, with weak attractiveness and inconvenient traffic, forming a “core-edge” structure. Different network patterns are established for different modes of transportation. The degree of response of different types of transportation to distance is the main factor influencing the network patterns of diverse paths. These findings have practical implications for the choice of appropriate travel destinations and transportation modes for tourists.

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