Zobrazeno 1 - 10
of 5 545
pro vyhledávání: '"ZHENG Qiang"'
Publikováno v:
Open Chemistry, Vol 22, Iss 1, Pp 27-36 (2024)
The brain, heart, liver, kidney, and other organs are susceptible to the harmful effects of ischemia-reperfusion injury (IRI), where the excessive production of reactive oxygen species (ROS) following IRI contributes to tissue damage and ensuing infl
Externí odkaz:
https://doaj.org/article/97640520bc8d4614b02b9dd853d88a0a
Publikováno v:
PeerJ, Vol 12, p e16554 (2024)
Glucose-6-phosphate dehydrogenase (G6PD) is a the first and rate-limiting enzyme that plays a critical role in G6PD deficiency, the most common enzyme disorder worldwide, is related to intravascular hemolysis. To determine the clinical enzyme activit
Externí odkaz:
https://doaj.org/article/0b27366d8b42417fba468423164af2d2
Publikováno v:
Zhongliu Fangzhi Yanjiu, Vol 50, Iss 3, Pp 264-270 (2023)
Objective To investigate the predictive value of preoperative fibrinogen/albumin ratio (FAR) and systemic immune inflammation index (SII) on the postoperative prognosis of patients with pancreatic ductal adenocarcinoma. Methods An ROC curve was used
Externí odkaz:
https://doaj.org/article/31ab173938df47fdab62d7ece2e14fc7
The rapid advancement in point cloud processing technologies has significantly increased the demand for efficient and compact models that achieve high-accuracy classification. Knowledge distillation has emerged as a potent model compression technique
Externí odkaz:
http://arxiv.org/abs/2409.02020
Advances in self-supervised learning are essential for enhancing feature extraction and understanding in point cloud processing. This paper introduces PMT-MAE (Point MLP-Transformer Masked Autoencoder), a novel self-supervised learning framework for
Externí odkaz:
http://arxiv.org/abs/2409.02007
This study addresses the computational inefficiencies in point cloud classification by introducing novel MLP-based architectures inspired by recent advances in CNN optimization. Traditional neural networks heavily rely on multiplication operations, w
Externí odkaz:
http://arxiv.org/abs/2409.01998
In recent years, point cloud analysis methods based on the Transformer architecture have made significant progress, particularly in the context of multimedia applications such as 3D modeling, virtual reality, and autonomous systems. However, the high
Externí odkaz:
http://arxiv.org/abs/2408.05508
In the domain of point cloud analysis, despite the significant capabilities of Graph Neural Networks (GNNs) in managing complex 3D datasets, existing approaches encounter challenges like high computational costs and scalability issues with extensive
Externí odkaz:
http://arxiv.org/abs/2407.00921
Autor:
Xu, Xusheng, Cui, Jiangyu, Cui, Zidong, He, Runhong, Li, Qingyu, Li, Xiaowei, Lin, Yanling, Liu, Jiale, Liu, Wuxin, Lu, Jiale, Luo, Maolin, Lyu, Chufan, Pan, Shijie, Pavel, Mosharev, Shu, Runqiu, Tang, Jialiang, Xu, Ruoqian, Xu, Shu, Yang, Kang, Yu, Fan, Zeng, Qingguo, Zhao, Haiying, Zheng, Qiang, Zhou, Junyuan, Zhou, Xu, Zhu, Yikang, Zou, Zuoheng, Bayat, Abolfazl, Cao, Xi, Cui, Wei, Li, Zhendong, Long, Guilu, Su, Zhaofeng, Wang, Xiaoting, Wang, Zizhu, Wei, Shijie, Wu, Re-Bing, Zhang, Pan, Yung, Man-Hong
We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on the design and implementation of noisy intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of MindSpore, an advanced open-
Externí odkaz:
http://arxiv.org/abs/2406.17248
Publikováno v:
Journal of Orthopaedic Surgery and Research, Vol 15, Iss 1, Pp 1-10 (2020)
Abstract Background To explore the epidemiological characteristics, clinical characteristics, treatment strategies, and clinical results of non-dislocated hyperextension tibial plateau fracture. Method A total of 25 cases of non-dislocated hyperexten
Externí odkaz:
https://doaj.org/article/ae572c51a0b0484c932bdf921b5a29cd