Shift-share analysis has been confirmed a useful approach in the study of regional economics and many kinds of extended shift-share models have been advanced and put into practice in economic studies, but few have hit...Shift-share analysis has been confirmed a useful approach in the study of regional economics and many kinds of extended shift-share models have been advanced and put into practice in economic studies, but few have hitherto been introduced and applied to the tourism research in China. Moreover understanding the spatially competitive relationship is of paramount importance for marketers, developers, and planners involved in tourism strategy development. Based on international tourism receipts from 1995 to 2004, this study aims at probing into the spatial competitiveness of interna- tional tourism in Jiangsu Province in comparison with its neighbors by applying a spatially extended shift-share model and a modified dynamic shift-share model. The empirical results illustrate that exceptional years may exist in the ap- plication of dynamic shift-share models. To solve this issue, modifications to dynamic shift-share model are put forward. The analytical results are not only presented but also explained by the comparison of background conditions of tourism development between Jiangsu and its key competitors. The conclusions can be drawn that the growth of international tourism receipts in Jiangsu mainly attributes to the national component and the competitive component and Zhejiang is the most important rival to Jiangsu during the period of 1995-2004. In order to upgrade the tourism competitiveness, it is indispensable for Jiangsu to take proper positioning, promoting and marketing strategies and to cooperate and integrate with its main rivals.展开更多
In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and a...In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.展开更多
基金Under the auspices of the National Natural Science Foundation of China (No. 40371030)
文摘Shift-share analysis has been confirmed a useful approach in the study of regional economics and many kinds of extended shift-share models have been advanced and put into practice in economic studies, but few have hitherto been introduced and applied to the tourism research in China. Moreover understanding the spatially competitive relationship is of paramount importance for marketers, developers, and planners involved in tourism strategy development. Based on international tourism receipts from 1995 to 2004, this study aims at probing into the spatial competitiveness of interna- tional tourism in Jiangsu Province in comparison with its neighbors by applying a spatially extended shift-share model and a modified dynamic shift-share model. The empirical results illustrate that exceptional years may exist in the ap- plication of dynamic shift-share models. To solve this issue, modifications to dynamic shift-share model are put forward. The analytical results are not only presented but also explained by the comparison of background conditions of tourism development between Jiangsu and its key competitors. The conclusions can be drawn that the growth of international tourism receipts in Jiangsu mainly attributes to the national component and the competitive component and Zhejiang is the most important rival to Jiangsu during the period of 1995-2004. In order to upgrade the tourism competitiveness, it is indispensable for Jiangsu to take proper positioning, promoting and marketing strategies and to cooperate and integrate with its main rivals.
基金the National Natural Science Foundation of China(Nos.41804047 and 42111540260)Fundamental Research Funds of the Institute of Geophysics,China Earthquake Administration(NO.DQJB19A0114)the Key Research Program of the Institute of Geology and Geophysics,Chinese Academy of Sciences(No.IGGCAS-201904).
文摘In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.
