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Guangwei Liu

As seen in: Nature, Wiley Online Library, arXiv, PLOS ONE, BMJ Open, Frontiers, MDPI, Cell Press, Journal of Sensors
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Articles

Machine learning analysis of surface electromyography for quantifying neuromuscular compensatory profiles and associated functional disability in neck pain

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By Changxiao Han, Bochen Peng, Hongtao Li, Jiacheng Zheng, Xu Wang, Zhiwen Li, Jiali Chen, Guangyi Yang, Guangwei Liu, Liguo Zhu, Minshan Feng
| Frontiers
Abstract Background: Neck pain presents significant clinical heterogeneity, while current assessment methods lack systematic approaches for quantifying muscle function. This study established a multi-dimensional surface electromyography (sEMG) framework to quantify the predictive value of muscle function features for clinical outcomes and identify neuromuscular compensatory states.
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Reducing Sulfur Dioxide and Boosting Agricultural Productivity: Evidence From China's Sulfur Dioxide Trading Pilot

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By Qianying Chen, Pengyu Chen, Guangwei Liu
| Wiley Online Library Verified
1 Introduction As industrialization advances, atmospheric pollution has become a global issue. Taking sulfur dioxide as an example, emissions solely from coal-fired power plants lead to over one million deaths annually (Lelieveld et al. 2015). Sulfur dioxide severely damages human lung function (Hazucha and Bates 1975). It also has varying degrees of harmful effects on vegetation (LeBlanc and Rao 1973) and contributes to the generation of more acid rain (Barreca et al. 2021).
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GWKNN: an enhanced k-nearest neighbor algorithm with G metric reconstruction and Grey Wolf Optimizer - Scientific Reports

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By Zhiqing Guo, Guangwei Liu, Wei Liu, Feng Jiang, Zishun Ni
| Nature Verified
Abstract k-nearest neighbors (KNN) is a classic supervised classification method widely used in pattern recognition and data mining. However, traditional KNN may lead to a decline in discriminant performance in high-dimensional feature spaces due to degraded distance measurements. At the same time, when class distributions are uneven, its equal-weight voting mechanism is prone to decision bias, thereby affecting the fairness of classification results.
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