Zobrazeno 1 - 10
of 6 357
pro vyhledávání: '"HE, Fang"'
Autor:
Grundmann, Lena, Müller-Linow, Mark, Kuhn, Arnd, He, Fang, Schlehuber, Dennis, Keuter, Volkmar, Noll, Gundula A., Prüfer, Dirk
Publikováno v:
Julius-Kühn-Archiv, Vol 476, Pp 35-43 (2023)
Die Entwicklung nachhaltiger Anbaumethoden für Medizinalpflanzen zur Deckung des steigenden Bedarfs an qualitativ hochwertiger Rohware ist für die pharmazeutische Industrie von großer Bedeutung. Gleichzeitig gilt es Umweltauswirkungen zu minimiere
Externí odkaz:
https://doaj.org/article/7e7c9d09cc2f408ab33303a46c53c444
Like Graph Neural Networks (GNNs), Signed Graph Neural Networks (SGNNs) are also up against fairness issues from source data and typical aggregation method. In this paper, we are pioneering to make the investigation of fairness in SGNNs expanded from
Externí odkaz:
http://arxiv.org/abs/2408.08508
In the contemporary logistics industry, automation plays a pivotal role in enhancing production efficiency and expanding industrial scale. Autonomous mobile robots, in particular, have become integral to the modernization efforts in warehouses. One n
Externí odkaz:
http://arxiv.org/abs/2404.04832
We address a dynamic pricing problem for airlines aiming to maximize expected revenue from selling cargo space on a single-leg flight. The cargo shipments' weight and volume are uncertain and their precise values remain unavailable at the booking tim
Externí odkaz:
http://arxiv.org/abs/2404.04831
This study proposes an innovative model-based modular approach (MMA) to dynamically optimize order matching and vehicle relocation in a ride-hailing platform. MMA utilizes a two-layer and modular modeling structure. The upper layer determines the spa
Externí odkaz:
http://arxiv.org/abs/2310.09071
The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive
Externí odkaz:
http://arxiv.org/abs/2307.06742
Federated learning (FL) enables geographically dispersed edge devices (i.e., clients) to learn a global model without sharing the local datasets, where each client performs gradient descent with its local data and uploads the gradients to a central s
Externí odkaz:
http://arxiv.org/abs/2212.08272
Autor:
Liao, Chang-sheng1,2 (AUTHOR), He, Fang-zheng1,2 (AUTHOR), Li, Xi-yong1,2 (AUTHOR), Han, Peng-fei1 (AUTHOR) 18003551149@163.com
Publikováno v:
PLoS ONE. 7/9/2024, Vol. 19 Issue 7, p1-18. 18p.
Autor:
He, Fang1 (AUTHOR), Ye, Lingqi2 (AUTHOR), Wang, Leilei1 (AUTHOR), Zhou, Jiping3 (AUTHOR), Shao, Xiaotong4 (AUTHOR), Miao, Pu5 (AUTHOR), Wang, Shan2 (AUTHOR), Li, Hong4 (AUTHOR), Ding, Yao2 (AUTHOR), Wang, Shuang2 (AUTHOR) wangs77@zju.edu.cn
Publikováno v:
CNS Neuroscience & Therapeutics. Jul2024, Vol. 30 Issue 7, p1-8. 8p.
Autor:
Feng, Zhi‐wei1,2,3,4 (AUTHOR), Xiao, He‐fang1,3,4 (AUTHOR), Wang, Xing‐wen1,3,4 (AUTHOR), Niu, Yong‐kang1,3,4 (AUTHOR), Zhao, Da‐cheng1,3,4 (AUTHOR), Tian, Cong1,3,4 (AUTHOR), Wang, Sheng‐hong1,3,4 (AUTHOR), Peng, Bo1,3,4 (AUTHOR), Yang, Fei1,2,3,4 (AUTHOR), Geng, Bin1,3,4 (AUTHOR), Guo, Ming‐gang2 (AUTHOR), Sheng, Xiao‐yun1,3,4 (AUTHOR) shengxy20@lzu.edu.cn, Xia, Ya‐yi1,3,4 (AUTHOR) xiayy@lzu.edu.cn
Publikováno v:
Orthopaedic Surgery. Jun2024, Vol. 16 Issue 6, p1418-1433. 16p.