Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jiachang Guo"'
Autor:
Haidan Lin, Jiachang Guo, Daiyong Yang, Shouxue Li, Dan Liu, Changyan Liu, Zilong Zhang, Bolin Wang, Haifeng Zhang
Publikováno v:
Catalysts, Vol 13, Iss 7, p 1113 (2023)
The detection of dissolved gas in transformer oil is of vital importance to diagnose the early fault and monitor the security and stability of power systems. In this work, distinct Pd/C, Pd/C-R and Pd/NC were synthesized and evaluated. XRD and XPS ch
Externí odkaz:
https://doaj.org/article/5428e76a82eb4cbfaf3761dec93d7658
Autor:
Zehao Niu, Guoxing Wei, Hao Liang, Xin Wang, Wenjuan Yang, Gang Wei, Jiachang Guo, Yingen Chen, Ran Tao, Jun Niu
Publikováno v:
Journal of Burn Care & Research.
Burn injury is a life-threatening disease with a poor prognosis. The immune change and underlying mechanisms remain largely unknown. Thus, this study aims to find potential biomarkers and analyze the immune infiltrates after burn injury. Gene express
Publikováno v:
Sustainability
Volume 15
Issue 4
Pages: 2887
Volume 15
Issue 4
Pages: 2887
The contradiction between water supply and demand has become increasingly prominent due to the large agricultural water consumption and low irrigation water use efficiency (IWUE) in the extremely arid area of Xinjiang, which needs to be solved by eff
Publikováno v:
Sustainability; Volume 15; Issue 10; Pages: 8342
Stomata are essential for regulating the exchange of water and energy between plants and the atmosphere. In the context of climate warming, especially in extremely arid regions, the knowledge of stomatal conductance variation patterns is fundamental
Publikováno v:
Building and Environment. :110423
Publikováno v:
Journal of Physics: Conference Series. 2477:012100
A low-complexity vital sign detection system is proposed. The system uses a phase discriminator as the demodulation module and a phase shifter to solve the null problem in CW radar. This design significantly reduces the complexity. In the subsequent
Publikováno v:
Caries research.
This study aimed to evaluate the validity of a deep learning-based convolutional neural network (CNN) for detecting proximal caries lesions on bitewing radiographs. A total of 978 bitewing radiographs, 10,899 proximal surfaces, were evaluated by two