Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Hanhan, Li"'
Publikováno v:
Frontiers in Environmental Science, Vol 12 (2024)
Herein, the impact of varying proportions of cellulose/lignin in bamboo on the production of hydrothermal biochar was investigated. Different characterization techniques were applied to explore the structure of hydrothermal biochar derived from three
Externí odkaz:
https://doaj.org/article/5b96a32145a24e0698bdcb53258b67e0
Publikováno v:
Ultrasonics Sonochemistry, Vol 111, Iss , Pp 107079- (2024)
Daphne genkwa (D. genkwa) is the dried flower buds of a Chinese medicinal plant with multiple biological activities. Response surface methodology (RSM) combined with artificial neural network (ANN) techniques were utilized to optimize ultrasound-assi
Externí odkaz:
https://doaj.org/article/e01c446464d24da989bb8ac79273c196
Autor:
Hanhan Li, Xiaoqi Wei, Zehui Huang, Haoze Liu, Ronghua Ma, Menghua Wang, Minqi Hu, Lide Jiang, Kun Xue
Publikováno v:
Remote Sensing, Vol 16, Iss 14, p 2611 (2024)
Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (Rrs) and the inversion of surface chlorophyll-a concentration (Chla). I
Externí odkaz:
https://doaj.org/article/f61050549a4a4e57a736b64e642e85e5
Autor:
Huibin Tan, Ye Wang, Yuanliang Jiang, Hanhan Li, Tao You, Tingting Fu, Jiaheng Peng, Yuxi Tan, Ran Lu, Biwen Peng, Wencai Huang, Fei Xiong
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Abstract To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT image
Externí odkaz:
https://doaj.org/article/5cc646e440da4ac9af37baea29722cba
Autor:
Xueting Xing, Mengzhu Kong, Qiaoyu Hou, Jiaqi Li, Wen Qian, Xijing Chen, Hanhan Li, Changqing Yang
Publikováno v:
Pharmaceutical Biology, Vol 60, Iss 1, Pp 1190-1197 (2022)
Context Ginkgo leaf tablet (GLT), a traditional Chinese herbal formula, is often combined with rosiglitazone (ROS) for type 2 diabetes mellitus treatment. However, the drug-drug interaction between GLT and ROS remains unknown.Objective To investigate
Externí odkaz:
https://doaj.org/article/0b1050b763af4633a16e980bca71d764
Autor:
Wei Guo, Xiaohui Yao, Siyuan Lan, Chi Zhang, Hanhan Li, Zhuangzhong Chen, Ling Yu, Guanxian Liu, Yuan Lin, Shan Liu, Hanrui Chen
Publikováno v:
Chinese Medicine, Vol 17, Iss 1, Pp 1-19 (2022)
Abstract Background There is no comprehensive treatment method for hepatocellular carcinoma (HCC); hence, research and development are still focused on systemic therapies, including drugs. Sinikangai fang (SNKAF) decoction, a classic Chinese herbal p
Externí odkaz:
https://doaj.org/article/2c824ea0d91045d19fb713e6318f512a
Publikováno v:
Frontiers in Environmental Science; 2024, p01-11, 11p
Publikováno v:
Remote Sensing, Vol 15, Iss 19, p 4886 (2023)
The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion
Externí odkaz:
https://doaj.org/article/d4ede993be2f4265a8710d959a0ac5ec
Publikováno v:
Frontiers in Pharmacology, Vol 14 (2023)
Background: Cancer of unknown primary (CUP), which accounts for 3%–5% of new cancer cases every year, involves the presence of a type of histologically confirmed metastatic tumors whose primary site cannot be confirmed by conventional diagnostic me
Externí odkaz:
https://doaj.org/article/c832c1974e14424aa19fb1be8d35a561
Publikováno v:
Mathematics, Vol 11, Iss 14, p 3224 (2023)
Analyzing the working conditions of a dam using safety monitoring indices (SMIs) is a relatively intuitive and effective method for dam safety evaluation. Therefore, a reasonable and accurate method for determining the SMIs of a dam is of vital impor
Externí odkaz:
https://doaj.org/article/c10d6a3c3be34bb5b57b0b13b337c594