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Machine Learning Aided Tracking Analysis of Haze Pollution and Regional Heterogeneity
( Fangfang Gu ),( Keshen Jiang ),( Fangdong Cao ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
Not only can air pollution reduce the overall competitiveness of tourist destinations, but also changes tourists' travel decisions, thereby affecting the tourism flows. The study presents a machine learning method to analyze how the haze pollution puts spatial effect on tourism flows in China from 2001 to 2018, and reveals the regional differences in heterogeneity among eastern, central, and western China. Our investigation reveals three interesting observations. First, the Environmental Kuznets Curve of the impact of haze pollution on tourism flows is not significant. In the eastern and western regions, the interaction between haze pollution and domestic tourism flows as well as inbound tourism flows shows an inverted U-shaped curve respectively. Second, there is an significantly positive spillover effect of tourism flows in all of the eastern, central, and western regions. As to the intensity of spillover, domestic tourism flows is higher than that of the inbound tourism flows. Both of the above figures are greatest in the eastern. Third, the Chinese haze pollution mainly reduces the inbound tourism flows, and only imposes significantly negative direct effects on the domestic tourism flows in the central region. In the central and eastern regions, significantly negative direct effects and spillover effects are exerted on inbound tourism.
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Predicting Urban Tourism Flow with Tourism Digital Footprints Based on Deep Learning
Fangfang Gu,Keshen Jiang,Yu Ding,Xuexiu Fan 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.4
Tourism flow is not only the manifestation of tourists’ special displacement change, but also an important driving mode of regional connection. It has been considered as one of significantly topics in many applications. The existing research on tourism flow prediction based on tourist number or statistical model is not in-depth enough or ignores the nonlinearity and complexity of tourism flow. In this paper, taking Nanjing as an example, we propose a prediction method of urban tourism flow based on deep learning methods using travel diaries of domestic tourists. Our proposed method can extract the spatio-temporal dependence relationship of tourism flow and further forecast the tourism flow to attractions for every day of the year or for every time period of the day. Experimental results show that our proposed method is slightly better than other benchmark models in terms of prediction accuracy, especially in predicting seasonal trends. The proposed method has practical significance in preventing tourists unnecessary crowding and saving a lot of queuing time.
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Pan Xiaoming,Gu Yinger,Zhang Xian,Shi Biwei,Cui Long,Wang Fangfang,Qu Fan 한국한의학연구원 2022 Integrative Medicine Research Vol.11 No.1
Background: Polycystic ovary syndrome (PCOS) is one of the most common causes of anovulatory infertility. Chinese herbal medicine (CHM) has many advantages in treating PCOS. We conducted a retrospective cohort study to investigate the effects of CHM (Bu-Shen-Tian-Jing Formula, BSTJF) on the outcomes of IVF in Chinese patients with PCOS and the potential underlying mechanism. Methods: A total of 111 patients with PCOS who undergone IVF between November 2009 and July 2018 were included. Fifty-four patients received a three-month BSTJF therapy before controlled ovarian hyperstimulation, while the other 57 patients didn't. The data of the PCOS patients was collected. Anti-Müllerian hormone (AMH), growth differentiation factor-8 (GDF-8) levels in the follicular fluid were evaluated. Results: BSTJF helped patients with PCOS to get more retrieved oocytes (P<0.05) and fertilized oocytes (P<0.05). The clinical cumulative pregnancy rate, live birth rate, and term delivery rate were significantly higher in the same stimulated cycle of the PCOS patients with BSTJF treatment (P<0.05). No significant differences existed between the two groups in the rate of fertilization, hospitalization rate of ovarian hyper stimulation syndrome and obstetrical or neonatal complications. BSTJF significantly decreased the AMH levels in the follicular fluids (P<0.05). Conclusion: BSTJF significantly may improve the outcomes of IVF in Chinese patients with PCOS through decreasing AMH levels in follicular fluids. However, the evidence is limited due to the small sample size and the several potential bias. Background: Polycystic ovary syndrome (PCOS) is one of the most common causes of anovulatory infertility. Chinese herbal medicine (CHM) has many advantages in treating PCOS. We conducted a retrospective cohort study to investigate the effects of CHM (Bu-Shen-Tian-Jing Formula, BSTJF) on the outcomes of IVF in Chinese patients with PCOS and the potential underlying mechanism. Methods: A total of 111 patients with PCOS who undergone IVF between November 2009 and July 2018 were included. Fifty-four patients received a three-month BSTJF therapy before controlled ovarian hyperstimulation, while the other 57 patients didn't. The data of the PCOS patients was collected. Anti-Müllerian hormone (AMH), growth differentiation factor-8 (GDF-8) levels in the follicular fluid were evaluated. Results: BSTJF helped patients with PCOS to get more retrieved oocytes (P<0.05) and fertilized oocytes (P<0.05). The clinical cumulative pregnancy rate, live birth rate, and term delivery rate were significantly higher in the same stimulated cycle of the PCOS patients with BSTJF treatment (P<0.05). No significant differences existed between the two groups in the rate of fertilization, hospitalization rate of ovarian hyper stimulation syndrome and obstetrical or neonatal complications. BSTJF significantly decreased the AMH levels in the follicular fluids (P<0.05). Conclusion: BSTJF significantly may improve the outcomes of IVF in Chinese patients with PCOS through decreasing AMH levels in follicular fluids. However, the evidence is limited due to the small sample size and the several potential bias.
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