As one of the largest global emitters of sulfur dioxide(SO_(2)),China faces increasing pressure to achieve sustainable economic and social development.Using panel data of 58 prefecture-level cities in North China betw...As one of the largest global emitters of sulfur dioxide(SO_(2)),China faces increasing pressure to achieve sustainable economic and social development.Using panel data of 58 prefecture-level cities in North China between 2003 and 2017,this paper considers the dynamic spatio-temporal characteristics of industrial SO_(2) emissions in the"2+26"in North China and extended cities in North China and decomposes the determinants of industrial SO_(2) emissions into eight effects using the Generalized Divisia Index Model(GDIM).The contributions of each effect on changes in emissions are assessed on regional,provincial,and prefectural levels,as well as according to various stages.The results indicate the following.First,industrial SO2 emissions in the"2+26"cities in North China and extended cities in North China exhibit spatial autocorrelation and agglomeration effects.Cities with high-high(HH)and low-low(LL)agglomeration patterns were concentrated in Shanxi and Henan provinces,respectively.Second,industrialization,energy consumption,and economic development were the main factors that increased industrial SO2 emissions,while technology,energy sulfur intensity,and economic sulfur intensity were the key factors that reduced them.Third,13 cities,induding Tangshan,were the most important regions where further emissions regulations need to be implemented.These cities were divided into three types and different corresponding measures for reducing their emissions are suggested.Based on the conclusions of this study,this paper puts forward some targeted policy recommendations for reducing industrial SO_(2) emissions according to different categories of cities.展开更多
The ozone occurs naturally in the atmosphere and presents a filter of protection, absorbing the radiations wavelengths lower than 310 nm. The industrial generation of ozone is the classical application of the non-equi...The ozone occurs naturally in the atmosphere and presents a filter of protection, absorbing the radiations wavelengths lower than 310 nm. The industrial generation of ozone is the classical application of the non-equilibrium air plasmas at the atmospheric pressure. A low temperature is needed because the ozone quickly decays at the high temperature. This study is based on a temporal kinetic model for the production of ozone. The chemical kinetics take into account 96 reactions with 19 species atomic and molecular created in the discharge. In this work, the model allows to calculate the temporal evolution of neutral, ionized and excited species concentrations in plasma. The results show the influence of the kinetic on the ozone production yield and on the gas heating by Joule effect.展开更多
In this study,we examined plant health prediction through the Normalized Difference Vegetation Index(NDVI)calculated from satellite image derived reflectance values in the near-infrared and red spectra.The problem is ...In this study,we examined plant health prediction through the Normalized Difference Vegetation Index(NDVI)calculated from satellite image derived reflectance values in the near-infrared and red spectra.The problem is formulated as a temporal data prediction problem.Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset,we designed and implemented Reservoir Computing(RC)models and transformer-based models including pretrained language model,and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression,Decision Tree,Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM)network,and DLinear.It is observed that the DLinear/LSTM model showed exceptional predictive accuracy,while the pretrained RC model significantly enhanced traditional RC model forecasts.Additionally,Frozen Pretrained Transformer(FPT),a pretrained language model,showed superior performance in predicting specific NDVI values(most often peak or lowest NDVI),suggesting its effectiveness in precise temporal predictions.Furthermore,transformer-based models,specifically PatchTST and FPT,demonstrated substantial mean squared error reductions,particularly in limited data scenarios(1%,5%,15%and 50%sample sizes),indicating their robustness in precise NDVI temporal predictions when data is limited.The findings in this study demonstrated the effectiveness of emerging machine learning techniques such as reservoir computing and pretrained language model for remote sensing and their contributions in precision agriculture.展开更多
The temporal evolution feature of a microscopic phase field model is utilized to study the antisite defects of L1 2-Ni 3 Al;this is quite different from other physicist’ interests.There are mainly two points in brief...The temporal evolution feature of a microscopic phase field model is utilized to study the antisite defects of L1 2-Ni 3 Al;this is quite different from other physicist’ interests.There are mainly two points in brief.Firstly,antisite defects Ni Al and Al Ni ,which are caused by the deviation from the stoichiometric Ni 3 Al,coexist in the Ni 3 Al phase.The surplus Ni atom in the Ni-rich side is prone to substitute Al thus producing the antisite defect Ni Al that maintains the stability of the L1 2 structure.In other case,the surplus Al atom in the Al-rich side is accommodated by a Ni sublattice consequently giving rise to antisite defect Al Ni .The calculated equilibrium occupancy probability of Ni Al is much higher than that of Al Ni .This point is generally in line with other theoretical and experimental works.Additionally,both Ni Al and Al Ni have a strong negative correlation to time step during the disorder-order transformation.Since the initial value of Ni Al and Al Ni on each site of the matrix is right at the concentration that we set,we can observe the decrease process of Ni Al and Al Ni from the initial disordered high anti-structure state to their respective equilibrium state,i.e.to the result of the ordering process further coarsening.展开更多
基金the financial support from the National Natural Science Foundation of China[Grant number.72074183,Grant number.71403120]the Humanities and Social Science Foundation of Chinese Ministry of Education[Grant number.20YJC630104]+1 种基金the National Social Science Foundation of China[Grant number.18ZDA052]the Fundamental Research Funds for the Central Universities[Grant number.JBK2007186].
文摘As one of the largest global emitters of sulfur dioxide(SO_(2)),China faces increasing pressure to achieve sustainable economic and social development.Using panel data of 58 prefecture-level cities in North China between 2003 and 2017,this paper considers the dynamic spatio-temporal characteristics of industrial SO_(2) emissions in the"2+26"in North China and extended cities in North China and decomposes the determinants of industrial SO_(2) emissions into eight effects using the Generalized Divisia Index Model(GDIM).The contributions of each effect on changes in emissions are assessed on regional,provincial,and prefectural levels,as well as according to various stages.The results indicate the following.First,industrial SO2 emissions in the"2+26"cities in North China and extended cities in North China exhibit spatial autocorrelation and agglomeration effects.Cities with high-high(HH)and low-low(LL)agglomeration patterns were concentrated in Shanxi and Henan provinces,respectively.Second,industrialization,energy consumption,and economic development were the main factors that increased industrial SO2 emissions,while technology,energy sulfur intensity,and economic sulfur intensity were the key factors that reduced them.Third,13 cities,induding Tangshan,were the most important regions where further emissions regulations need to be implemented.These cities were divided into three types and different corresponding measures for reducing their emissions are suggested.Based on the conclusions of this study,this paper puts forward some targeted policy recommendations for reducing industrial SO_(2) emissions according to different categories of cities.
文摘The ozone occurs naturally in the atmosphere and presents a filter of protection, absorbing the radiations wavelengths lower than 310 nm. The industrial generation of ozone is the classical application of the non-equilibrium air plasmas at the atmospheric pressure. A low temperature is needed because the ozone quickly decays at the high temperature. This study is based on a temporal kinetic model for the production of ozone. The chemical kinetics take into account 96 reactions with 19 species atomic and molecular created in the discharge. In this work, the model allows to calculate the temporal evolution of neutral, ionized and excited species concentrations in plasma. The results show the influence of the kinetic on the ozone production yield and on the gas heating by Joule effect.
基金supported by USDA/NIFA Award 2022-38821-37338(Project No.TEXXQRD9908).
文摘In this study,we examined plant health prediction through the Normalized Difference Vegetation Index(NDVI)calculated from satellite image derived reflectance values in the near-infrared and red spectra.The problem is formulated as a temporal data prediction problem.Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset,we designed and implemented Reservoir Computing(RC)models and transformer-based models including pretrained language model,and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression,Decision Tree,Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM)network,and DLinear.It is observed that the DLinear/LSTM model showed exceptional predictive accuracy,while the pretrained RC model significantly enhanced traditional RC model forecasts.Additionally,Frozen Pretrained Transformer(FPT),a pretrained language model,showed superior performance in predicting specific NDVI values(most often peak or lowest NDVI),suggesting its effectiveness in precise temporal predictions.Furthermore,transformer-based models,specifically PatchTST and FPT,demonstrated substantial mean squared error reductions,particularly in limited data scenarios(1%,5%,15%and 50%sample sizes),indicating their robustness in precise NDVI temporal predictions when data is limited.The findings in this study demonstrated the effectiveness of emerging machine learning techniques such as reservoir computing and pretrained language model for remote sensing and their contributions in precision agriculture.
基金financially supported by the National Natural Science Foundation of China (Grant Nos.50875217,10902086,and 50941020)the Doctorate Foundation of Northwestern Polytechnical University of China (Grant No.CX200806)the Natural Science Foundation of Shaanxi Province (Grant Nos.SJ08-ZT05 and SJ08-B14)
文摘The temporal evolution feature of a microscopic phase field model is utilized to study the antisite defects of L1 2-Ni 3 Al;this is quite different from other physicist’ interests.There are mainly two points in brief.Firstly,antisite defects Ni Al and Al Ni ,which are caused by the deviation from the stoichiometric Ni 3 Al,coexist in the Ni 3 Al phase.The surplus Ni atom in the Ni-rich side is prone to substitute Al thus producing the antisite defect Ni Al that maintains the stability of the L1 2 structure.In other case,the surplus Al atom in the Al-rich side is accommodated by a Ni sublattice consequently giving rise to antisite defect Al Ni .The calculated equilibrium occupancy probability of Ni Al is much higher than that of Al Ni .This point is generally in line with other theoretical and experimental works.Additionally,both Ni Al and Al Ni have a strong negative correlation to time step during the disorder-order transformation.Since the initial value of Ni Al and Al Ni on each site of the matrix is right at the concentration that we set,we can observe the decrease process of Ni Al and Al Ni from the initial disordered high anti-structure state to their respective equilibrium state,i.e.to the result of the ordering process further coarsening.
