Measuring bullwhip effect is useful for making better controls on production planning and enhancing the supply chain operating efficiency.First,this article establishes the comparative analysis model of bullwhip effec...Measuring bullwhip effect is useful for making better controls on production planning and enhancing the supply chain operating efficiency.First,this article establishes the comparative analysis model of bullwhip effect between(s,S)and Periodic Review(PR)inventory policy based on the quantitative bullwhip effect model under different inventories.Then,the impacts of lead time,inventory review time,autocorrelation coefficient,and the number of samples on the gap of bullwhip effect under(s,S)and PR IP are analyzed.The results show that bullwhip effect in PR is more intense than(s,S)inventory policy.Contractors should pay more attention to control bullwhip effect when adopting PR inventory policy to enhance the total operating efficiency of the engineering project supply chains.展开更多
Rehabilitation engineering aims in the upmost degree to restore the lost functions for those persons with physical disability. Biomechanical modeling has been widely used for different purposes in rehabilitation engin...Rehabilitation engineering aims in the upmost degree to restore the lost functions for those persons with physical disability. Biomechanical modeling has been widely used for different purposes in rehabilitation engineering to understand the bio-展开更多
Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully...Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.展开更多
文摘Measuring bullwhip effect is useful for making better controls on production planning and enhancing the supply chain operating efficiency.First,this article establishes the comparative analysis model of bullwhip effect between(s,S)and Periodic Review(PR)inventory policy based on the quantitative bullwhip effect model under different inventories.Then,the impacts of lead time,inventory review time,autocorrelation coefficient,and the number of samples on the gap of bullwhip effect under(s,S)and PR IP are analyzed.The results show that bullwhip effect in PR is more intense than(s,S)inventory policy.Contractors should pay more attention to control bullwhip effect when adopting PR inventory policy to enhance the total operating efficiency of the engineering project supply chains.
基金Research Grant Council of Hong Kong (GRF Project nos PolyU5331 /07E,PolyU5352 /08E)a grant from Ministry of Sciences and Technology,China (No 2006BAI22B00)
文摘Rehabilitation engineering aims in the upmost degree to restore the lost functions for those persons with physical disability. Biomechanical modeling has been widely used for different purposes in rehabilitation engineering to understand the bio-
基金supported by the National Technology Extension Fund of Forestry,Forest Vegetation Carbon Storage Monitoring Technology Based on Watershed Algorithm ([2019]06)Fundamental Research Funds for the Central Universities (No.PTYX202107).
文摘Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.
