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993 results on '"Lin Yuting"'

1. Psychosocial factors affecting pregnancy reactions of primipara in Mianyang

Author

Lin Yuting, Yang Jinghong, and Zhang Li

Subjects
primiparas, pregnancy pressure, depression, factors, Psychology, BF1-990, Psychiatry, RC435-571
Abstract

ObjectiveTo explore the psychosocial factors affecting pregnancy reactions of primipara， so as to alleviate pregnancy reactions in early pregnancy stage.MethodsBasic information scale， Symptom Checklist 90 （SCL-90）， Pregnancy Pressure Scale （PPS） and Pregnancy-unique Quantification of Emesis and Nausea （PUQE） were used to assess the primiparas in a hospital from April to June 2020， thereafter， multiple linear regression was used to screen the main psychosocial factors affecting pregnancy reaction.ResultsA total of 100 pregnant women completed the survey， and the scores of PUQE， PPS and SCL-90 were （4.39±1.14）， （10.89±6.94） and （112.75±22.27）， respectively. Analysis showed that the pregnancy reactions were mild in 93 cases （93%）， moderate in 6 cases （6%） and severe in 1 case （1%）. One-way ANOVA results showed that there was no significant difference in PUQE score among primipara of different age， working status， education levels and average annual family incomes （F=0.57， 0.07， 0.89， 0.51， P>0.05）. Multifactorial regression analysis showed that pregnancy stress， SCL-90 score， obsessive-compulsive symptoms， depression levels， hostility， paranoia and others had statistical significance （P

Published
2021
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2. The research and application of LLC resonant converter in two-stage charger

Author

Lin Yuting, Cao Taiqiang, and Chen Yufeng

Subjects
charger, FHA, Boost PFC, LLC resonant converter, Electronics, TK7800-8360
Abstract

Aiming at the low power density, low charging efficiency and input current harmonic content of traditional electric vehicle charging machine, a new topology structure is adopted in this paper. The former stage adopts two-stage staggered parallel Boost PFC circuit, which can effectively improve the power density of the former converter and reduce the THD value of the input current. The rear stage adopts the semi-bridge LLC resonant circuit to improve the power density and charging efficiency of the rear converter. The working principles of the two-level staggered parallel Boost PFC and the half-bridge LLC resonant converter are analyzed. The fundamental wave analysis method(First Harmonic Approximation，FHA) is used to do LLC resonant network modeling, and on this basis it determines the scope of switching frequency and the optimum working range. The simulation and experiment validate the correctness of the mathematical model and parameter design. Finally, a charger is designed whose input voltage is 175 V～265 V, and maximum output power is 1.5 kW. The experimental results show that the front-end converter power factor is 0.996, the input current THD is 4%, and the machine efficiency can reach 94%.

Published
2018
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7. NLITS: A Natural Language Interface for Time Series Databases

Author

Lin, Yuting, Xu, Jianqiu, Wang, Xieyang, Zhang, Yitong, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Tung, Anthony, editor, Zheng, Zhonglong, editor, Yang, Zhengyi, editor, Wang, Xiaoyang, editor, and Guo, Hongjie, editor

Published
2024
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8. Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks

Author

Gong, Jibing, Wan, Yao, Liu, Ye, Li, Xuewen, Zhao, Yi, Wang, Cheng, Lin, Yuting, Fang, Xiaohan, Feng, Wenzheng, Zhang, Jingyi, and Tang, Jie

Subjects
Computer Science - Information Retrieval, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem of concept recommendation in this paper, which can be viewed as recommending knowledge to users in a fine-grained way. We put forward a novel approach, termed HinCRec-RL, for Concept Recommendation in MOOCs, which is based on Heterogeneous Information Networks and Reinforcement Learning. In particular, we propose to shape the problem of concept recommendation within a reinforcement learning framework to characterize the dynamic interaction between users and knowledge concepts in MOOCs. Furthermore, we propose to form the interactions among users, courses, videos, and concepts into a heterogeneous information network (HIN) to learn the semantic user representations better. We then employ an attentional graph neural network to represent the users in the HIN, based on meta-paths. Extensive experiments are conducted on a real-world dataset collected from a Chinese MOOC platform, XuetangX, to validate the efficacy of our proposed HinCRec-RL. Experimental results and analysis demonstrate that our proposed HinCRec-RL performs well when comparing with several state-of-the-art models.

Published
2022
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23. Prevalence, risk factors, impact and management of pneumonia among preschool children in Chinese seven cities: a cross-sectional study with interrupted time series analysis

Author

Shi, Haonan, Wang, Tingting, Zhao, Zhuohui, Norback, Dan, Wang, Xiaowei, Li, Yongsheng, Deng, Qihong, Lu, Chan, Zhang, Xin, Zheng, Xiaohong, Qian, Hua, Zhang, Ling, Yu, Wei, Shi, Yuqing, Chen, Tianyi, Yu, Huaijiang, Qi, Huizhen, Yang, Ye, Jiang, Lan, Lin, Yuting, Yao, Jian, Lu, Junwen, and Yan, Qi

Published
2023
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28. CCR5 and inflammatory storm

Author

Lin, Yuting, Liu, Shasha, Sun, Yang, Chen, Chen, Yang, Songwei, Pei, Gang, Lin, Meiyu, Yu, Jingbo, Liu, Xuan, Wang, Huiqin, Long, Junpeng, Yan, Qian, Liang, Jinping, Yao, Jiao, Yi, Fan, Meng, Lei, Tan, Yong, Chen, Naihong, Yang, Yantao, and Ai, Qidi

Published
2024
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30. Learning-Based Quality Assessment for Image Super-Resolution

Author

Zhao, Tiesong, Lin, Yuting, Xu, Yiwen, Chen, Weiling, and Wang, Zhou

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images. Quality evaluation metrics play a critical role in comparing and optimizing SR algorithms, but current metrics achieve only limited success, largely due to the lack of large-scale quality databases, which are essential for learning accurate and robust SR quality metrics. In this work, we first build a large-scale SR image database using a novel semi-automatic labeling approach, which allows us to label a large number of images with manageable human workload. The resulting SR Image quality database with Semi-Automatic Ratings (SISAR), so far the largest of SR-IQA database, contains 8,400 images of 100 natural scenes. We train an end-to-end Deep Image SR Quality (DISQ) model by employing two-stream Deep Neural Networks (DNNs) for feature extraction, followed by a feature fusion network for quality prediction. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics and achieves promising generalization performance in cross-database tests. The SISAR database and DISQ model will be made publicly available to facilitate reproducible research.

Published
2020

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