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Yingmei Li on Muck Rack

Yingmei Li

As seen in: Nature, Wiley Online Library, Frontiers in Public Health, MDPI, ACS Publications, Royal Society of Chemistry
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Articles

Factors associated with job burnout among dental nurses: a cross-sectional study

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By Chenxia Xiong, Rong Wang, Yingmei Li, Jinyu Yang, Wenfang Liu, Ying Liu, Juan Liu, Xinchun Zou, Yuesu Huang
| Frontiers in Public Health Verified
Abstract Background: Job burnout is a systemic challenge in healthcare, and dental nurses facing unique occupational stressors that linked to higher burnout symptom levels. Despite the growing recognition of burnout as a public health concern in health care workers, previous studies have paid relatively little attention to dental nurses. Objective: This study aimed to investigate the current status of job burnout among dental nurses and identify its associated factors.
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Endurance exercise intervention reduces vegetable intake which compromises the achievement of glycemic benefit: a randomized controlled, three-period crossover trial

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By Sihan Wang, Hui Gao, Hao Cai, Ling Liu, Yingmei Li, Mingjing Gao, Shuangyan Liu, Wei Wang, Mingjian Fu, Yurong LIU, Yunhua Zi, Xuanwen Mu, Xiaogang Zhang, Kuai Yu, Gaokun Qiu, Yacan Huang
| Nature Verified
Abstract There exists long-lasting concern, but no substantive evidence, that exercise may cause compensatory eating and compromise its glycemic benefits. We performed a randomized controlled, three-period crossover trial of moderate- and vigorous-intensity endurance exercise (MOD-EX and VIG-EX) intervention to fill this knowledge gap, which was also externally validated in the UK Biobank (UKB) cohort.
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A multidimensional reliability-enhanced belief rule base model for fault diagnosis

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By Boyu Liu, Ning Li, Jiaqi Wu, Yingmei Li, Shutong Zhao, Zida Xia
| Nature Verified
Abstract Fault diagnosis is essential for the safe and stable operation of industrial equipment and for improving maintenance efficiency. The belief rule base (BRB) is well suited to fault diagnosis under complex conditions because of its strengths in knowledge representation and uncertainty reasoning.
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