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- 저자 : Huihui Yuan (검색결과 3 건)
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Huihui Zhou,Guijian Liu,Chuncai Zhou,Yu Chen,Muhammad Arif,Mei Sun,Yuan Liu,Hongyang Wang 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.122 No.-
The comprehensive and harmless utilization of copper slag flotation tailing (CSFT) is the key to a wastefreeand sustainable copper industry. Here, the mineralogy and molybdenum micro-dissemination inCSFT were quantified and characterized, and the feasibility of the comprehensive recovery of Mo, Cuand Fe resources from CSFT and hazardous elements fractionation behavior by magnetic separationwas discussed. To investigate the occurrence and abundance of Mo in each phase, CSFT was classified intofour types of phases, including magnetite/hematite (27.26%), silicate associations (43.37%), and metallicsulphides and oxides. Molybdenum distribution is closely related to magnetite/hematite-Fe and S. Themagnetic separation results indicated that Mo and Cu were enhanced in magnetic products by 34–41and 15–21%, respectively. Cleaner non-magnetic residues were found to decrease significantly by 37–44, 58–60, and 11–19% for Cu, As, and Cr, respectively. Mineral fractionation was observed instead ofchemical changes during magnetic separation. Despite a weak separation effect on magnetic and nonmagneticphases due to their close bonds and fine-disseminated minerals, the Fe-silicate associationswithout magnetic phases were well separated into non-magnetic residues. The enrichment of Ca, Mgand separation of an iron-silicate component in non-magnetic residues enhanced the cementitious propertyand allowed the development of more pathways of reutilization.
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Xiaodan Wang,Zhong Zhong,Yijia Hu,Huihui Yuan 한국기상학회 2010 Asia-Pacific Journal of Atmospheric Sciences Vol.46 No.2
The effect of the lateral boundary scheme in regional climate model (RCM) on the track simulation of tropical cyclone (TC) was investigated using RegCM3, for the case of Winnie (1997), which formed in the Western Pacific and landed on China in August 1997. The results show that there is an inevitable simulation error in the track of Winnie, and the narrower buffer zone size (BZS) will make a great error. However, it was demonstrated that a much broader BZS does not allow a better track simulation of Winnie, and the optimal BZS does not reduce the track error substantially. Moreover, the configuration scheme of nudging parameters plays an important role in the track simulation, and different nudging parameter configuration scheme could make the root mean square errors (RMSEs) of simulated track by more than two times. Nevertheless, the optimal configuration scheme can reduce the track error effectively by maintaining the equilibrium between the two additional nudging terms in the prognostic equations in the buffer zone, whereas both the strong nudging scheme and the weak nudging scheme distort the track simulation of the Winnie. It is also found that the simulated weaker west Pacific subtropical high (WPSH), which leads to the turning of the TC ahead of time, is the reason for the track simulation error. A possible approach for reducing track simulation error of TCs is also discussed.
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Lu Yi,Wu Jiachuan,Hu Minhui,Zhong Qinghua,Er Limian,Shi Huihui,Cheng Weihui,Chen Ke,Liu Yuan,Qiu Bingfeng,Xu Qiancheng,Lai Guangshun,Wang Yufeng,Luo Yuxuan,Mu Jinbao,Zhang Wenjie,Zhi Min,Sun Jiachen 거트앤리버 소화기연관학회협의회 2023 Gut and Liver Vol.17 No.6
Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions: We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.
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