Zobrazeno 1 - 10
of 294
pro vyhledávání: '"Haifeng Pan"'
Publikováno v:
地质科技通报, Vol 43, Iss 4, Pp 181-190 (2024)
Objective The Shengbei Depression is the largest petroliferous depression of the Tuha Basin. With the strong demand for the exploration and development of tight sandstone gas in the Middle Jurassic Sanjianfang Formation, it is urgent to study the sed
Externí odkaz:
https://doaj.org/article/03ef9661494f4943862a2827cc4de7f5
Publikováno v:
Renal Failure, Vol 46, Iss 1 (2024)
Background The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD).Methods Af
Externí odkaz:
https://doaj.org/article/a5fc5cd40a5a4ba4be571c2dbcab9916
Publikováno v:
Systems Science & Control Engineering, Vol 12, Iss 1 (2024)
Global warming, as the main feature of the climate problem, is gradually coming into our field of vision. Under this background, Poisson jump is applied to describe the arrival of natural disasters caused by global warming. Households and firms can d
Externí odkaz:
https://doaj.org/article/a80143e77e224cad81493519a0626bef
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-12 (2024)
Abstract Background This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. Methods Population data regarding intensiv
Externí odkaz:
https://doaj.org/article/c24ca7beff284d5d83dad437bbe6d69b
Autor:
Wenbin Wu, Zeping Shi, Mykhaylo Ozerov, Yuhan Du, Yuxiang Wang, Xiao-Sheng Ni, Xianghao Meng, Xiangyu Jiang, Guangyi Wang, Congming Hao, Xinyi Wang, Pengcheng Zhang, Chunhui Pan, Haifeng Pan, Zhenrong Sun, Run Yang, Yang Xu, Yusheng Hou, Zhongbo Yan, Cheng Zhang, Hai-Zhou Lu, Junhao Chu, Xiang Yuan
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Arising from the extreme/saddle point in electronic bands, Van Hove singularity (VHS) manifests divergent density of states (DOS) and induces various new states of matter such as unconventional superconductivity. VHS is believed to exist in
Externí odkaz:
https://doaj.org/article/1d3a73625ece42bf93935cd3a2bd8536
Autor:
Siyuan Yu, Haifeng Pan, Han Yang, Haoyun Zhuang, Haihui Yang, Xuan Yu, Shiyin Zhang, Mujin Fang, Tingdong Li, Shengxiang Ge, Ningshao Xia
Publikováno v:
iScience, Vol 27, Iss 4, Pp 109464- (2024)
Summary: Non-viral gene delivery systems have received sustained attention as a promising alternative to viral vectors for disease treatment and prevention in recent years. Numerous methods have been developed to enhance gene uptake and delivery in t
Externí odkaz:
https://doaj.org/article/121adc16db6642dbb73b39927c651623
Autor:
Xunliang Li, Jie Zhu, Wenman Zhao, Yuyu Zhu, Li Zhu, Rui Shi, Zhijuan Wang, Haifeng Pan, Deguang Wang
Publikováno v:
Obesity Facts, Pp 1-8 (2023)
Introduction: Observational studies have shown that obesity is a risk factor for various autoimmune diseases. However, the causal relationship between obesity and autoimmune diseases is unclear. Mendelian randomization (MR) was used to investigate th
Externí odkaz:
https://doaj.org/article/79c0614d879649a6bb3d651b5b9c8791
Publikováno v:
Photonics, Vol 11, Iss 8, p 704 (2024)
Synchronous laser beam scanning is a common technique used in single-photon imaging where the spatial resolution is primarily determined by the beam divergence angle. In this context, Bessel beams have been investigated as they can overcome the diffr
Externí odkaz:
https://doaj.org/article/810c280e437f43d79f02cb4b044f9276
Publikováno v:
Journal of Photochemistry and Photobiology, Vol 18, Iss , Pp 100211- (2023)
Epidemiological evidence indicates that damage to DNA/RNA initialized by ultraviolet (UV) radiation is associated with skin cancer. Wavelength dependence of DNA photodamage was proposed as early as 1990s and demonstrated later on. Unraveling the phot
Externí odkaz:
https://doaj.org/article/1ab0b808d4b8475e9813095dfce5c789
Publikováno v:
Renal Failure, Vol 45, Iss 1 (2023)
AbstractBackground This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD).Methods This study collected data on CKD patients from 2008
Externí odkaz:
https://doaj.org/article/d38cbd8f37474953b1dad113a7ddb095