Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Jiaan ZHANG"'
Publikováno v:
Energy Reports, Vol 11, Iss , Pp 498-509 (2024)
A global consensus has been reached that promotes full electrification in the automotive field to solve environmental problems, and Electric Vehicles (EVs) have developed rapidly in recent years. China has gradually promoted electrification for taxis
Externí odkaz:
https://doaj.org/article/5c92cfa7bf0e41f2bf6bc00d0efea3f9
Publikováno v:
Transport, Vol 39, Iss 1 (2024)
The universal application of the hub-and-spoke maritime network makes feeder line network key to restricting the quality and efficiency of maritime transportation. However, container liner routes in feeder line network are susceptible to the changes
Externí odkaz:
https://doaj.org/article/329a46bfb14645b8888c825571ea3fa6
Publikováno v:
Energies, Vol 17, Iss 17, p 4339 (2024)
Accurate forecasting of electric vehicle (EV) power consumption per unit mileage serves as the cornerstone for determining diurnal variations in EV charging loads. To enhance the prediction accuracy of EV power consumption per unit mileage, this pape
Externí odkaz:
https://doaj.org/article/4c7de60ccfcd465c99711a9cf24fe0c0
Autor:
Xuyue Zhou, Yu Hu, Lingxi Liu, Lihao Liu, Hongying Chen, Dan Huang, Mei Ju, Chao Luan, Kun Chen, Jiaan Zhang
Publikováno v:
Cell Communication and Signaling, Vol 21, Iss 1, Pp 1-12 (2023)
Abstract Background Psoriasis is a chronic inflammatory dermatosis with an unclear pathogenesis. Mast cells (MCs) can serve as a bridge between innate and adaptive immunity and are involved in the regulation of the inflammatory state and immune homeo
Externí odkaz:
https://doaj.org/article/931d90aeb55c45fcba45b0e7d70727c7
Publikováno v:
Journal of Modern Power Systems and Clean Energy, Vol 11, Iss 5, Pp 1462-1479 (2023)
To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system (RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables sta
Externí odkaz:
https://doaj.org/article/cd225ff68b784dc0b4b53bbfd360a657
Autor:
Yu Hu, Xuyue Zhou, Lihao Chen, Rong Li, Shuang Jin, Lingxi Liu, Mei Ju, Chao Luan, Hongying Chen, Ziwei Wang, Dan Huang, Kun Chen, Jiaan Zhang
Publikováno v:
Frontiers in Immunology, Vol 13 (2022)
BackgroundKeloids are a fibroproliferative disease characterized by unsatisfactory therapeutic effects and a high recurrence rate.ObjectiveThis study aimed to investigate keloid-related circulating metabolic signatures.MethodsUntargeted metabolomic a
Externí odkaz:
https://doaj.org/article/3d5e564df29a4f62934081e064880fdb
Publikováno v:
Energies, Vol 16, Iss 7, p 3092 (2023)
Photovoltaic (PV) power shows different fluctuation characteristics under different weather types as well as strong randomness and uncertainty in changeable weather such as sunny to cloudy, cloudy to rain, and so on, resulting in low forecasting accu
Externí odkaz:
https://doaj.org/article/64a121ee780b43e3aa529ce0222019af
The paeonol target gene autophagy-related 5 has a potential therapeutic value in psoriasis treatment
Publikováno v:
PeerJ, Vol 9, p e11278 (2021)
Background Paeonol is a potent therapy for psoriasis. This study aimed to screen out paeonol-targeted genes in psoriasis and validate the potential of using paeonol for the management of psoriasis. Methods Microarray datasets were obtained from the G
Externí odkaz:
https://doaj.org/article/e696bdd0c613472e8d921985497b1e34
Publikováno v:
Energies, Vol 15, Iss 7, p 2633 (2022)
The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional
Externí odkaz:
https://doaj.org/article/d0d976d890d54117a25a0311fe351b77
Publikováno v:
Epigenomics. 15:209-226
Aim: To identify DNA methylation and transcription biomarkers in the psoriatic epidermis. Materials & methods: Gene transcription and DNA methylation datasets of psoriatic epidermal tissue were obtained from the Gene Expression Omnibus. Machine learn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ad107b23425f222e6919bd03c555136
https://doi.org/10.2217/epi-2022-0458
https://doi.org/10.2217/epi-2022-0458