The public health and ecological impacts of volatile organic compound(VOCs) pollution have become a serious problem in China,arousing increasing attention to emissions control.In this context,this paper analyses the e...The public health and ecological impacts of volatile organic compound(VOCs) pollution have become a serious problem in China,arousing increasing attention to emissions control.In this context,this paper analyses the effectiveness of VOC reduction policies,namely pollution charges and environmental taxes at the national and industrial sector levels.It uses a computable general equilibrium model,which connects macroeconomic variables with VOC emissions inventory,to simulate the effects of policy scenarios(with 2007 as the reference year).This paper shows that VOC emissions are reduced by 2.2% when a pollution charge equal to the average cost of engineering reduction methods-the traditional approach to regulation in China-is applied.In order to achieve a similar reduction,an 8.9% indirect tax would have to be imposed.It concludes that an environmental tax should be the preferred method of VOC regulation due to its smaller footprint on the macroeconomy.Other policies,such as subsidies,should be used as supplements.展开更多
There is a clear trend in the increase of damages and loss of lives and livelihoods in coastal areas as a result of rapid increase in coastal populations, and overall socio-economic development in coastal regions resu...There is a clear trend in the increase of damages and loss of lives and livelihoods in coastal areas as a result of rapid increase in coastal populations, and overall socio-economic development in coastal regions resulting in an increase in vulnerability of populations exposed to coastal floods and exposed infrastructure. Coastal flooding as a result of i.e. storm surges are difficult to predict and cannot be prevented, however there are means to apply integrated flood risk management approaches aiming to reduce the impact of coastal floods. A measure of the effectiveness of such approaches is the awareness and response of coastal communities to coastal flood risks. The paper introduces best practices and methods to lower coastal flood risk at the level of provinces, districts and the community level. This includes advances in coastal flood forecasting and early warning practices, improvement of institutional preparedness and integrated flood management practices as well as measures at the community level aiming to strengthen their resilience to coastal floods. The paper provides a showcase for the historical development and achievements to pave ways for the eventual implementation of a pilot project on integrated flood risk management in coastal areas in central Viet Nam.展开更多
Relevance Vector Machine(RVM)is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model.Relevance Vector Machine classification suffers from theoretical limitations an...Relevance Vector Machine(RVM)is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model.Relevance Vector Machine classification suffers from theoretical limitations and computational inefficiency mainly because there is no closed-form solution for the posterior of the weight parameters.We propose two advanced Bayesian approaches for RVM classification,namely the Enhanced RVM and the Reinforced RVM,to perfect the theoretic framework of RVM and extend the algorithm to the imbalanced data problem,which has an arresting skew in data size between classes.First,the Enhanced RVM conducts a strict Bayesian sampling process instead of the approximation method in the original one to remedy its theoretic limitations,especially the nonconvergence of the iterations.Secondly,we conjecture that the hierarchical prior makes the Reinforced RVM achieve consistent estimations of the quantities of interest compared with the non-consistent estimations of the original RVM.Consistency is necessary for RVM classification since it makes the model more stable and localises the relevant vectors more accurately in the imbalanced data problem.The two-level prior also renders the Reinforced one competitive in the imbalanced data problem by building the inner connection of parameter dimensions and alloting a more vital relevance to the small class data weight parameter.The theoretic proofs and several numeric studies demonstrate the merits of our two proposed algorithms.展开更多
This paper investigates how international cooperation for patent examination using Patent Prosecution Highway(PPH)agreements has affected the quality of firms'exports.Taking the PPH agreements signed between China...This paper investigates how international cooperation for patent examination using Patent Prosecution Highway(PPH)agreements has affected the quality of firms'exports.Taking the PPH agreements signed between China and the export destinations as a quasi-natural experiment,we established a difference-in-difference-in-differences model.We found that international cooperation for patent examination caused firms to increase export quality to PPH partners in patent-intensive industries to a greater extent.This effect was more profound among PPH partners with stronger intellectual property protection,diferentiated products and core products,and agreements along with the Patent Cooperation Treaty.We also found that PPH agreements increased the number of Chinese patent applications filed in PPH partners,patent applications by PPH partners in China,and the level of innovation,all of which constitute the major channels through which export quality to PPH partners increases.Our findings demonstrate that international patent cooperation has played an important role in promoting international trade quality.展开更多
This study uses data covering 3,914 farm households,collected from Henan province in China,to investigate the links between the price of agricultural mechanization services and farmers'exit from land operation.The...This study uses data covering 3,914 farm households,collected from Henan province in China,to investigate the links between the price of agricultural mechanization services and farmers'exit from land operation.The results indicate that the increasing price of agricultural mechanization services leads tofarmers leaving land operation,especially when the high sunk costs and the long-term breakeven period of self-owned machinery are considered.This effect is intensified by the rapid rural-urban migration in China.Further analysis reveals that the surge in service prices reduced land renting-in and encouraged non-grain production.Our analysis suggests that the agricultural mechanization service market in China tends to work against the survival of smallholder farmers.However,the price of agricultural mechanization services is conducive to eliminating less-productive farmers and cultivating new agricultural operators.展开更多
When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS...When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources.These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models.Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments.For each ship,nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested.Experimental results revealed the benefits of fusing voyage report data,AIS data,and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate.Over the best datasets,the performances of several decision tree-based models are promising,including Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG).With the best datasets,their R^(2) values over the training sets are all above 0.96 and mostly reach the level of 0.99-1.00,while their R^(2) values over the test sets are in the range from 0.75 to 0.90.Fit errors of ET,AB,GB,and XG on daily bunker fuel consumption,measured by RMSE and MAE,are usually between 0.8 and 4.5 ton/day.These results are slightly better than our previous study,which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data,compared with the estimated geographical positions derived from the great circle route,in retrieving weather and sea conditions the ship sails through.展开更多
The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimizatio...The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimization,weather routing,and the virtual arrival policy.The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed,displacement/draft,trim,weather conditions,and sea conditions.Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection.To overcome this issue,this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution.Eleven widelyadopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company.The best datasets found reveal the benefits of fusing voyage report data and meteorological data,as well as the practically acceptable quality of voyage report data.Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG)present the best fit and generalization performances.Their R^(2) values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set,and 0.74 to 0.90 for the test set.Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day.These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.展开更多
Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea cu...Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.展开更多
Background:Systemic sclerosis is characterized by the involvement of organs and the presence of specific antibodies.The objectives of this study were to identify the autoantibodies and to determine their association w...Background:Systemic sclerosis is characterized by the involvement of organs and the presence of specific antibodies.The objectives of this study were to identify the autoantibodies and to determine their association with the selected clinical features of the disease among Bangladeshi systemic sclerosis patients.Methods:This cross-sectional study was performed at the rheumatology outpatient clinic of Bangabandhu Sheikh Mujib Medical University.Autoantibodies against nine systemic sclerosis-specific antigens were tested using an enzyme-linked immunoassay immunoblot kit.Several clinical features of patients with positive and negative autoantibody were examined by χ^(2) or Fisher's exact tests.Results:A total of 71 patients with systemic sclerosis(66;93.0%female)were included.Their mean age at disease onset was 33.2 years.Fifty-seven(80.3%)patients had diffuse cutaneous subtype.Out of nine autoantibodies,four were positive,anti-topoisomerase-I(57.7%),anti-U1 ribonucleic protein(21.1%),anti-RNA polymerase Ⅲ(18.3%),and anticentromere antibodies(4.2%).Eleven(15.5%)patients were negative for any antibodies and 11 patients were positive for at least two autoantibodies.Anti-U3-RNP,anti-PMScl,anti-Ku,and anti-Th/To auto antibodies were absent in all patients.Anti-RNA polymerase III was associated with raised pulmonary arterial systolic pressure(PASP)and anti-U1-RNP with decreased forced vital capacity(FVC).Conclusions:Anti-topoisomerase-I was the commonest autoantibody in patients with systemic sclerosis in Bangladesh.Anti-RNA polymerase III antibody had significant association with raised PASP and anti-U1-RNP with decreased FVC.展开更多
基金supported by the National Basic Research Program(973 Program)of China:[Grant Number2012CB955800]the National Natural Science Foundation(863 Program)of China:[Grant Number 2012 AA063101]the "Strategic Priority Research Program" of the Chinese Academy of Sciences[Grant Number XDB05050200]
文摘The public health and ecological impacts of volatile organic compound(VOCs) pollution have become a serious problem in China,arousing increasing attention to emissions control.In this context,this paper analyses the effectiveness of VOC reduction policies,namely pollution charges and environmental taxes at the national and industrial sector levels.It uses a computable general equilibrium model,which connects macroeconomic variables with VOC emissions inventory,to simulate the effects of policy scenarios(with 2007 as the reference year).This paper shows that VOC emissions are reduced by 2.2% when a pollution charge equal to the average cost of engineering reduction methods-the traditional approach to regulation in China-is applied.In order to achieve a similar reduction,an 8.9% indirect tax would have to be imposed.It concludes that an environmental tax should be the preferred method of VOC regulation due to its smaller footprint on the macroeconomy.Other policies,such as subsidies,should be used as supplements.
文摘There is a clear trend in the increase of damages and loss of lives and livelihoods in coastal areas as a result of rapid increase in coastal populations, and overall socio-economic development in coastal regions resulting in an increase in vulnerability of populations exposed to coastal floods and exposed infrastructure. Coastal flooding as a result of i.e. storm surges are difficult to predict and cannot be prevented, however there are means to apply integrated flood risk management approaches aiming to reduce the impact of coastal floods. A measure of the effectiveness of such approaches is the awareness and response of coastal communities to coastal flood risks. The paper introduces best practices and methods to lower coastal flood risk at the level of provinces, districts and the community level. This includes advances in coastal flood forecasting and early warning practices, improvement of institutional preparedness and integrated flood management practices as well as measures at the community level aiming to strengthen their resilience to coastal floods. The paper provides a showcase for the historical development and achievements to pave ways for the eventual implementation of a pilot project on integrated flood risk management in coastal areas in central Viet Nam.
基金National Statistical Science Research Project of China,Grant/Award Number:2021LY070Association of Fundamental Computing Education in Chinese Universities,Basic Computer Education Teaching Research Project,Grant/Award Number:2022-AFCEC-217。
文摘Relevance Vector Machine(RVM)is a supervised learning algorithm extended from Support Vector Machine based on the Bayesian sparsity model.Relevance Vector Machine classification suffers from theoretical limitations and computational inefficiency mainly because there is no closed-form solution for the posterior of the weight parameters.We propose two advanced Bayesian approaches for RVM classification,namely the Enhanced RVM and the Reinforced RVM,to perfect the theoretic framework of RVM and extend the algorithm to the imbalanced data problem,which has an arresting skew in data size between classes.First,the Enhanced RVM conducts a strict Bayesian sampling process instead of the approximation method in the original one to remedy its theoretic limitations,especially the nonconvergence of the iterations.Secondly,we conjecture that the hierarchical prior makes the Reinforced RVM achieve consistent estimations of the quantities of interest compared with the non-consistent estimations of the original RVM.Consistency is necessary for RVM classification since it makes the model more stable and localises the relevant vectors more accurately in the imbalanced data problem.The two-level prior also renders the Reinforced one competitive in the imbalanced data problem by building the inner connection of parameter dimensions and alloting a more vital relevance to the small class data weight parameter.The theoretic proofs and several numeric studies demonstrate the merits of our two proposed algorithms.
基金the support from the National Natural Science Foundation of China(No.71903003)the National Social Science Fund of China(No.21CTJ015)+1 种基金the Strategic Economy and Civil-Military Integration Interdisciplinarity Program of Beijing Universities Advanced Disciplines Initiative(No.GJ2019163)the Program for Innovation Research in Central Universityof Financeand Economics。
文摘This paper investigates how international cooperation for patent examination using Patent Prosecution Highway(PPH)agreements has affected the quality of firms'exports.Taking the PPH agreements signed between China and the export destinations as a quasi-natural experiment,we established a difference-in-difference-in-differences model.We found that international cooperation for patent examination caused firms to increase export quality to PPH partners in patent-intensive industries to a greater extent.This effect was more profound among PPH partners with stronger intellectual property protection,diferentiated products and core products,and agreements along with the Patent Cooperation Treaty.We also found that PPH agreements increased the number of Chinese patent applications filed in PPH partners,patent applications by PPH partners in China,and the level of innovation,all of which constitute the major channels through which export quality to PPH partners increases.Our findings demonstrate that international patent cooperation has played an important role in promoting international trade quality.
基金Key Project of National Social Science Fund of China(No.20FGLA004).
文摘This study uses data covering 3,914 farm households,collected from Henan province in China,to investigate the links between the price of agricultural mechanization services and farmers'exit from land operation.The results indicate that the increasing price of agricultural mechanization services leads tofarmers leaving land operation,especially when the high sunk costs and the long-term breakeven period of self-owned machinery are considered.This effect is intensified by the rapid rural-urban migration in China.Further analysis reveals that the surge in service prices reduced land renting-in and encouraged non-grain production.Our analysis suggests that the agricultural mechanization service market in China tends to work against the survival of smallholder farmers.However,the price of agricultural mechanization services is conducive to eliminating less-productive farmers and cultivating new agricultural operators.
基金the IAMU(International Association of Maritime Universities)research project titled“Data fusion and machine learning for ship fuel efficiency analysis:a small but essential step towards green shipping through data analytics”(Research Project No.20210205_AMC).
文摘When voyage report data is utilized as the main data source for ship fuel efficiency analysis,its information on weather and sea conditions is often regarded as unreliable.To solve this issue,this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources.These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models.Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments.For each ship,nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested.Experimental results revealed the benefits of fusing voyage report data,AIS data,and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate.Over the best datasets,the performances of several decision tree-based models are promising,including Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG).With the best datasets,their R^(2) values over the training sets are all above 0.96 and mostly reach the level of 0.99-1.00,while their R^(2) values over the test sets are in the range from 0.75 to 0.90.Fit errors of ET,AB,GB,and XG on daily bunker fuel consumption,measured by RMSE and MAE,are usually between 0.8 and 4.5 ton/day.These results are slightly better than our previous study,which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data,compared with the estimated geographical positions derived from the great circle route,in retrieving weather and sea conditions the ship sails through.
基金the IAMU(International Association of Maritime Universities)research project titled“Data fusion and machine learning for ship fuel efficiency analysis:a small but essential step towards green shipping through data analytics”(Research Project No.20210205_AMC).
文摘The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships'bunker fuel consumption and the accompanying emissions,including speed optimization,trim optimization,weather routing,and the virtual arrival policy.The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed,displacement/draft,trim,weather conditions,and sea conditions.Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection.To overcome this issue,this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution.Eleven widelyadopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company.The best datasets found reveal the benefits of fusing voyage report data and meteorological data,as well as the practically acceptable quality of voyage report data.Extremely randomized trees(ET),AdaBoost(AB),Gradient Tree Boosting(GB)and XGBoost(XG)present the best fit and generalization performances.Their R^(2) values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set,and 0.74 to 0.90 for the test set.Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day.These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.
基金the IAMU(International Association of Maritime Universities)research project titled“Data fusion and machine learning for ship fuel efficiency analysis:a small but essential step towards green shipping through data analytics”(Research Project No.20210205_AMC).
文摘Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.
文摘Background:Systemic sclerosis is characterized by the involvement of organs and the presence of specific antibodies.The objectives of this study were to identify the autoantibodies and to determine their association with the selected clinical features of the disease among Bangladeshi systemic sclerosis patients.Methods:This cross-sectional study was performed at the rheumatology outpatient clinic of Bangabandhu Sheikh Mujib Medical University.Autoantibodies against nine systemic sclerosis-specific antigens were tested using an enzyme-linked immunoassay immunoblot kit.Several clinical features of patients with positive and negative autoantibody were examined by χ^(2) or Fisher's exact tests.Results:A total of 71 patients with systemic sclerosis(66;93.0%female)were included.Their mean age at disease onset was 33.2 years.Fifty-seven(80.3%)patients had diffuse cutaneous subtype.Out of nine autoantibodies,four were positive,anti-topoisomerase-I(57.7%),anti-U1 ribonucleic protein(21.1%),anti-RNA polymerase Ⅲ(18.3%),and anticentromere antibodies(4.2%).Eleven(15.5%)patients were negative for any antibodies and 11 patients were positive for at least two autoantibodies.Anti-U3-RNP,anti-PMScl,anti-Ku,and anti-Th/To auto antibodies were absent in all patients.Anti-RNA polymerase III was associated with raised pulmonary arterial systolic pressure(PASP)and anti-U1-RNP with decreased forced vital capacity(FVC).Conclusions:Anti-topoisomerase-I was the commonest autoantibody in patients with systemic sclerosis in Bangladesh.Anti-RNA polymerase III antibody had significant association with raised PASP and anti-U1-RNP with decreased FVC.
