The vast majority of tourism forecasting studies have centered on tourist arrivals at an aggregated level.Little research has been done of forecasting tourist expenditure at a national level let alone at a regional le...The vast majority of tourism forecasting studies have centered on tourist arrivals at an aggregated level.Little research has been done of forecasting tourist expenditure at a national level let alone at a regional level.This study uses expenditure data to assess the relative economic impact of tourism into regional areas.By comparing five time-series models(the Na?ve,Holt,ARMA and Basic Structural Model(BSM)with and without intervention),and three econometric models(the Vector Autoregressive(VAR)model and the Time Varying Parameter(TVP)with and without intervention),the study sought to find the most accurate model for forecasting tourism expenditure two years ahead for each of the 31 provinces of China's Mainland.The results show that TVP models outperform other time series and econometric models.The research also provides practical management outcomes by providing methods for forecasting tourist expenditure as an indicator of economic growth in China’s provinces.The research concludes with the findings on the most appropriate model for regional forecasting and potential new variables suitable at the regional level.展开更多
Abstract This paper investigates the impact of innovation on export decisions of Chinese high-tech firms during the period of 2005-2007. Using a parametric, instrumental variable approach and a non-parametric matching...Abstract This paper investigates the impact of innovation on export decisions of Chinese high-tech firms during the period of 2005-2007. Using a parametric, instrumental variable approach and a non-parametric matching method, we find that firm-level innovation efforts, measured by R＆D spending and new product output, play only a minor role for domestic exporters. Foreign-invested firms dominate the high-tech exports but do not rely on indigenous innovation activities. These results demonstrate that the success of Chinese high-tech exports does not result from heavy R＆D expenditure and technological progress. Moreover, different types of innovation measures show different impacts on the likelihood of exporting. The impacts of innovation on exporting vary widely across industries and Chinese regions.展开更多
Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D structure.An accurate residue-residue contact map is one of the essenti...Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D structure.An accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure prediction.Recently,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant progress.However,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party programs.In this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial intelligence.We have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical information.The proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.展开更多
文摘The vast majority of tourism forecasting studies have centered on tourist arrivals at an aggregated level.Little research has been done of forecasting tourist expenditure at a national level let alone at a regional level.This study uses expenditure data to assess the relative economic impact of tourism into regional areas.By comparing five time-series models(the Na?ve,Holt,ARMA and Basic Structural Model(BSM)with and without intervention),and three econometric models(the Vector Autoregressive(VAR)model and the Time Varying Parameter(TVP)with and without intervention),the study sought to find the most accurate model for forecasting tourism expenditure two years ahead for each of the 31 provinces of China's Mainland.The results show that TVP models outperform other time series and econometric models.The research also provides practical management outcomes by providing methods for forecasting tourist expenditure as an indicator of economic growth in China’s provinces.The research concludes with the findings on the most appropriate model for regional forecasting and potential new variables suitable at the regional level.
文摘Abstract This paper investigates the impact of innovation on export decisions of Chinese high-tech firms during the period of 2005-2007. Using a parametric, instrumental variable approach and a non-parametric matching method, we find that firm-level innovation efforts, measured by R＆D spending and new product output, play only a minor role for domestic exporters. Foreign-invested firms dominate the high-tech exports but do not rely on indigenous innovation activities. These results demonstrate that the success of Chinese high-tech exports does not result from heavy R＆D expenditure and technological progress. Moreover, different types of innovation measures show different impacts on the likelihood of exporting. The impacts of innovation on exporting vary widely across industries and Chinese regions.
基金supported by the Strategic Priority CAS Project (No. XDB38050100)the National Key Research and Development Program of China (No. 2018YFB0204403)+4 种基金the National Natural Science Foundation of China (No. U1813203)the Shenzhen Basic Research Fund (Nos. RCYX2020071411473419,JCYJ20200109114818703,and JSGG20201102163800001)CAS Key Lab (No. 2011DP173015)Hong Kong Research Grant Council (No. GRF-17208019)the Outstanding Youth Innovation Fund (Doctoral Students) of CAS-SIAT (No. Y9G054)。
文摘Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D structure.An accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure prediction.Recently,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant progress.However,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party programs.In this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial intelligence.We have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical information.The proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.
