Zobrazeno 1 - 10
of 3 305
pro vyhledávání: '"XIA Min"'
Publikováno v:
Indian Journal of Ophthalmology, Vol 72, Iss Suppl 2, Pp S162-S166 (2024)
The purpose of this study is to examine the viability, precision, and consistency of a computer-based optokinetic nystagmus analyzer (nystagmus meter) for diagnosing eyesight in preschoolers. A total of 59 subjects who could pass the log of minimum a
Externí odkaz:
https://doaj.org/article/3d5c32fb00204d13b779ced124263051
Autor:
Yaqian Hao, Yu Fu, Liangliang Sun, Yaying Yu, Xia Min, Qiannan Wei, Shuangjian Huang, Sen Zhao, Li Wang, YuanYuan Wang, Yangyang Li, Xia Zheng, Chenlu Zhang, Hongxia Xu, Xiaoxue Wang, Garrick D. Lee
Publikováno v:
Frontiers in Endocrinology, Vol 14 (2023)
ObjectivesThe aim is to evaluate the effect of a novel 14-day fasting regimen on the balance between skeletal muscle and adipose tissue composition which might associate with inflammatory factors. Our analysis includes basic physical examinations, cl
Externí odkaz:
https://doaj.org/article/c256966fc38143e49a2828c8caee8933
Publikováno v:
Rock and Soil Mechanics, Vol 43, Iss 3, Pp 808-818 (2022)
Compared with traditional small deformation bolt materials, the negative Poisson’s ratio (NPR) bolt/cable material has excellent mechanical properties such as large elongation, high strength, high toughness, high constant resistance, and energy abs
Externí odkaz:
https://doaj.org/article/d7254e3bdad94fa28fb30fd83f967a09
Autor:
Li, Jian, Lu, Weiheng, Fei, Hao, Luo, Meng, Dai, Ming, Xia, Min, Jin, Yizhang, Gan, Zhenye, Qi, Ding, Fu, Chaoyou, Tai, Ying, Yang, Wankou, Wang, Yabiao, Wang, Chengjie
Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning. O
Externí odkaz:
http://arxiv.org/abs/2408.08632
Given the untapped potential for continuous improvement of examinations, quantitative investigations of examinations could guide efforts to considerably improve learning efficiency and evaluation and thus greatly help both learners and educators. How
Externí odkaz:
http://arxiv.org/abs/2407.13161
As a promising framework for resolving partial differential equations (PDEs), Physics-Informed Neural Networks (PINNs) have received widespread attention from industrial and scientific fields. However, lack of expressive ability and initialization pa
Externí odkaz:
http://arxiv.org/abs/2406.04170
Predicting the Remaining Useful Life (RUL) is essential in Prognostic Health Management (PHM) for industrial systems. Although deep learning approaches have achieved considerable success in predicting RUL, challenges such as low prediction accuracy a
Externí odkaz:
http://arxiv.org/abs/2405.12377
Although physics-informed neural networks (PINNs) have shown great potential in dealing with nonlinear partial differential equations (PDEs), it is common that PINNs will suffer from the problem of insufficient precision or obtaining incorrect outcom
Externí odkaz:
http://arxiv.org/abs/2402.04390
Autor:
Xu Aiqun, Yang Ping, Cui Wei, Li Lei, Yu Hui, Wang Haining, Quan Rengui, Song Yuchun, Xia Min
Publikováno v:
Srpski Arhiv za Celokupno Lekarstvo, Vol 145, Iss 7-8, Pp 346-351 (2017)
Introduction/Objective. Preterm birth (PB) is the most important reason of neonatal mortality, and the second most common direct cause of death for children under the age of five years. The aim of this study was to analyze the clinical features and o
Externí odkaz:
https://doaj.org/article/5c31707bf3364350b20e0c3297ce1b96
Autor:
Tao Yibin, Xue Jinhua, Xia Min, Tao Jin, Zhang Qichao, Li Xiao, Liao Qiangqiang, Li Cheng, Tang Haibo
Publikováno v:
E3S Web of Conferences, Vol 194, p 02001 (2020)
Taking the power load of an industrial park in Shanghai as an example in this paper, particle swarm optimization and cost-benefit model are employed to analyse the economy of new lithium-ion batteries, echelon lithium-ion batteries and lead-carbon ba
Externí odkaz:
https://doaj.org/article/c8f128999c164ac2b94c711a32973e1c