GRACE-based estimates for groundwater storage(GWS)changes in North America substantially depend upon correction of glacialisostatic adjustment(GIA)effects,which are usually removed with GIA models.In this study,GIA ef...GRACE-based estimates for groundwater storage(GWS)changes in North America substantially depend upon correction of glacialisostatic adjustment(GIA)effects,which are usually removed with GIA models.In this study,GIA effects are eliminated by employing an independent separation approach with the aid of Global Navigation Satellite System(GNSS)vertical velocity data.Our goal is to provide an independent estimate for monthly GWS changes within North America in 1-degree-grids and their trends over the whole GRACE mission lifetime from April 2002 to June 2017.This estimate is derived from the release-6 version of GRACE monthly level-2 data,GNSS data,land surface models for soil moisture and snow wa-ter equivalent,and satellite altimetric lake leveldata.We find a GWS anomaly in form of an increasing trend in Saskatchewan,which affects the Saskatchewan Province and the states of Montana,North Dakota and Minnesota,and 4 GWS anomalies with declining trends in Nevada,California,Arizona and Texas,respec-tively.The monthly changes of these GWS anomalies,except for the one in Nevada,are validated by well level data.We provide results for average monthly GWS changes and the trends for the 5 anomalies but also in separate form for the 13 affected states or provinces.The increasing trends of the Saskatchewan GWS anomaly and the affected 3 states are related to increasing precipitation and can be elucidated by the decreasing drought intensity level.On the contrary,the declining trends in GWS can be explained by weakening precipitation and are mostly supported by the increasing drought intensity level in the other 4 anomalies and the affected states,which are Nevada,California,Arizona,New Mexico,Texas,Oklahoma,Kansas,and Colorado.Ourestimates of monthly GWS changes and their trendscan serveas alternativeand beneficial input for the sustainable management of groundwater resources in North America.展开更多
With the rapid increase in urban gas consumption,the frequency of maintenance and repair of gas pipelines has escalated,leading to a rise in safety accidents during these processes.The traditional manual supervision m...With the rapid increase in urban gas consumption,the frequency of maintenance and repair of gas pipelines has escalated,leading to a rise in safety accidents during these processes.The traditional manual supervision model presents challenges such as inaccurate monitoring results,incomplete risk factor analysis,and a lack of quantitative risk assessment.This research focuses on developing a dynamic risk assessment technology for gas emergency repair operations by integrating the monitoring outcomes of artificial olfactory for gas leakage information and video object recognition for visual safety factor monitoring data.To quantitatively evaluate the risk of the operation process,a three-dimensional risk assessment model combining gas leakage with riskcorrelated sensitivity was established as well as a separate three-dimensional risk assessment model integrating visual risk factors with predictable risk disposition.Furthermore,a visual risk quantification expression mode based on the risk matrix-radar map method was introduced.Additionally,a risk quantification model based on the fusion of visual and olfactory results was formulated.The verification results of simulation scenarios based on field data indicate that the visual-olfactory fusion risk assessment method can more accurately reflect the dynamic risk level of the operation process compared to simple visual safety factor monitoring.The outcomes of this research can contribute to the identification of safety status and early warning of risks related to personnel,equipment,and environmental factors in emergency repair operations.Moreover,these results can be extended to other operational scenarios,such as oil and gas production stations and long-distance pipeline operations.展开更多
Ice velocity constitutes a key parameter for quantifying ice-sheet discharge rates and is thus crucial for improving the coupled models of the Antarctic ice sheet towards accurately predict its contribution to future ...Ice velocity constitutes a key parameter for quantifying ice-sheet discharge rates and is thus crucial for improving the coupled models of the Antarctic ice sheet towards accurately predict its contribution to future global sea-level rise.However,in Antarctica,high-resolution and continuous ice velocity estimates remain elusive,which is key to unravel Antarctica’s present-day ice mass balance processes.Here,we present a suite of newly estimated Antarctic-wide,annually-sampled ice velocity products at 105-m grid-spacing observed by Landsat 8 optical images data.We first describe a procedure that can automatically calibrate and integrate ice displacement maps to generate Antarcticwide seamless ice velocity products.The annual ice velocity mosaics are assembled using a total of 250,000 displacement maps inferred from more than 80,000 Landsat 8 images acquired between December 2013 and April 2019.The new annual Antarctic ice velocity data product exhibits an improved quantification of near-decadal Antarctic-wide ice flow,and an opportunity to investigate ice dynamics at a higher spatial resolution and annual sampling,as compared to existing data products.Validation studies confirmed improved accuracy and consistency of this new data product,when compared with independently estimated optical and radar ice velocity data products,as well as in situ data.展开更多
Although the Sichuan basin is a stable block with low historical seismicity,the Suining M5.0 earthquake on January31,2010 occurred near the center of the basin,causing casualty and substantial damage.Previous studies ...Although the Sichuan basin is a stable block with low historical seismicity,the Suining M5.0 earthquake on January31,2010 occurred near the center of the basin,causing casualty and substantial damage.Previous studies have shown that the earthquake is very shallow and may occur in the sedimentary cover rocks,but its causative fault has not been identified.Based on local broadband seismic waveform data as well as a pair of ALOS PALSAR ascending orbit data,we explore the seismogenic mechanism via further constraining the source depth and the ruptured fault.The earthquake caused ground uplift in the southeast of the epicenter area,with a maximum line of sight displacement of about 13.6 cm,much larger than the displacement caused by a M5 earthquake at a typical depth of 10 km,which indicates that the earthquake is very shallow.Through joint inversion of seismic waveform and InSAR data,we obtain the moment magnitude of Suining earthquake as MW4.5,with the strike,dip,and rake of its fault plane as 17°,66° and 90°,respectively,and the centroid depth less than 1 km,supporting that the earthquake occurred at the shallow part of a high angle thrust fault dipping to the southeast.It is further confirmed that the earthquake may be triggered by the diffusion of high-pressure fluid migrating from the underside gas reservoir.展开更多
A scalable wideband equivalent circuit model of silicon-based on-chip transmission lines is presented in this paper along with an efficient analytical parameter extraction method based on improved characteristic funct...A scalable wideband equivalent circuit model of silicon-based on-chip transmission lines is presented in this paper along with an efficient analytical parameter extraction method based on improved characteristic function approach,including a relevant equation to reduce the deviation caused by approximation.The model consists of both series and shunt lumped elements and accounts for high-order parasitic effects.The equivalent circuit model is derived and verified to recover the frequency-dependent parameters at a range from direct current to 50 GHz accurately.The scalability of the model is proved by comparing simulated and measured scattering parameters with the method of cascade,attaining excellent results based on samples made from CMOS 0.13 and 0.18 μm process.展开更多
An improved single-π equivalent circuit model for on-chip inductors in the GaAs process is presented in this paper. Considering high order parasites, the model is established by comprising an improved skin effect bra...An improved single-π equivalent circuit model for on-chip inductors in the GaAs process is presented in this paper. Considering high order parasites, the model is established by comprising an improved skin effect branch and a substrate lateral coupling branch. The parameter extraction is based on an improved characteristic function approach and vector fitting method. The model has better simulation than the previous work over the measured data of 2.5r and 4.5r on-chip inductors in the GaAs process.展开更多
The rapid emergence of massive datasets in various fields poses a serious challenge to tra-ditional statistical methods.Meanwhile,it provides opportunities for researchers to develop novel algorithms.Inspired by the i...The rapid emergence of massive datasets in various fields poses a serious challenge to tra-ditional statistical methods.Meanwhile,it provides opportunities for researchers to develop novel algorithms.Inspired by the idea of divide-and-conquer,various distributed frameworks for statistical estimation and inference have been proposed.They were developed to deal with large-scale statistical optimization problems.This paper aims to provide a comprehensive review for related literature.It includes parametric models,nonparametric models,and other frequently used models.Their key ideas and theoretical properties are summarized.The trade-off between communication cost and estimate precision together with other concerns is discussed.展开更多
One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models hav...One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.展开更多
We thank the editor,Professor Jun Shao,for orga-nizing this stimulating discussion.We are grateful to all discussants for their insightful comments on our review article on the distributed statistical inference.Due to...We thank the editor,Professor Jun Shao,for orga-nizing this stimulating discussion.We are grateful to all discussants for their insightful comments on our review article on the distributed statistical inference.Due to the urgent need to process the datasets with massive sizes,various distributed computing methods have been proposed for the large-scale statistical prob-lems.Meanwhile,some important theoretical results were established.While we want to give a relatively comprehensive overview on this hot topic,there are still some important works that have been missed in our review written over a year ago.However,we are glad to see the discussants provide reviews of some new works and references.We hope that these discussions and our review would serve as a stimulus for further studies in this rapidly developing area.展开更多
WHAT,WHERE,AND WHEN In the early morning of February 6th,2023,an M7.8 earthquake occurred in southeastern T€urkiye near the northern border of Syria.The event initiated a complex sequence of aftershocks,including an M...WHAT,WHERE,AND WHEN In the early morning of February 6th,2023,an M7.8 earthquake occurred in southeastern T€urkiye near the northern border of Syria.The event initiated a complex sequence of aftershocks,including an M7.6 earthquake about 9 h later and 90 km to the north(Figures 1A and 1B).The earthquake sequence is also referred to as a strong doublet earthquake sequence.Aftershocks of the two strong earthquakes occurred along two separate branches of the East Anatolia Fault,with lengths of up to 300 km,and some aftershocks occurred in Syria(NEIC/USGS,2023).展开更多
Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are ...Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes(NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational efficiency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance.A real data example is also analyzed for illustration purpose.展开更多
基金This work is funded by the National Key R&D Program of China(2017YFA0603103)the National Natural Science Foundation of China(41974009 and 42004007)+1 种基金Key Research Program of Frontier Sciences,CAS(QYZDB-SSW-DQC042 and QYZDJ-SSW-DQC027)Alberta Innovates(the Groundwater Recharge in the Prairies project)。
文摘GRACE-based estimates for groundwater storage(GWS)changes in North America substantially depend upon correction of glacialisostatic adjustment(GIA)effects,which are usually removed with GIA models.In this study,GIA effects are eliminated by employing an independent separation approach with the aid of Global Navigation Satellite System(GNSS)vertical velocity data.Our goal is to provide an independent estimate for monthly GWS changes within North America in 1-degree-grids and their trends over the whole GRACE mission lifetime from April 2002 to June 2017.This estimate is derived from the release-6 version of GRACE monthly level-2 data,GNSS data,land surface models for soil moisture and snow wa-ter equivalent,and satellite altimetric lake leveldata.We find a GWS anomaly in form of an increasing trend in Saskatchewan,which affects the Saskatchewan Province and the states of Montana,North Dakota and Minnesota,and 4 GWS anomalies with declining trends in Nevada,California,Arizona and Texas,respec-tively.The monthly changes of these GWS anomalies,except for the one in Nevada,are validated by well level data.We provide results for average monthly GWS changes and the trends for the 5 anomalies but also in separate form for the 13 affected states or provinces.The increasing trends of the Saskatchewan GWS anomaly and the affected 3 states are related to increasing precipitation and can be elucidated by the decreasing drought intensity level.On the contrary,the declining trends in GWS can be explained by weakening precipitation and are mostly supported by the increasing drought intensity level in the other 4 anomalies and the affected states,which are Nevada,California,Arizona,New Mexico,Texas,Oklahoma,Kansas,and Colorado.Ourestimates of monthly GWS changes and their trendscan serveas alternativeand beneficial input for the sustainable management of groundwater resources in North America.
基金This work was supported the Key Research and Development Program of Shaanxi Province[2023-YBSF-266]the China Postdoctoral Science Foundation[2021T140544].
文摘With the rapid increase in urban gas consumption,the frequency of maintenance and repair of gas pipelines has escalated,leading to a rise in safety accidents during these processes.The traditional manual supervision model presents challenges such as inaccurate monitoring results,incomplete risk factor analysis,and a lack of quantitative risk assessment.This research focuses on developing a dynamic risk assessment technology for gas emergency repair operations by integrating the monitoring outcomes of artificial olfactory for gas leakage information and video object recognition for visual safety factor monitoring data.To quantitatively evaluate the risk of the operation process,a three-dimensional risk assessment model combining gas leakage with riskcorrelated sensitivity was established as well as a separate three-dimensional risk assessment model integrating visual risk factors with predictable risk disposition.Furthermore,a visual risk quantification expression mode based on the risk matrix-radar map method was introduced.Additionally,a risk quantification model based on the fusion of visual and olfactory results was formulated.The verification results of simulation scenarios based on field data indicate that the visual-olfactory fusion risk assessment method can more accurately reflect the dynamic risk level of the operation process compared to simple visual safety factor monitoring.The outcomes of this research can contribute to the identification of safety status and early warning of risks related to personnel,equipment,and environmental factors in emergency repair operations.Moreover,these results can be extended to other operational scenarios,such as oil and gas production stations and long-distance pipeline operations.
基金supported by National Key R&D Program of China:[grant number 2017YFA0603103]the Key Research Program of Frontier Sciences,Chinese Academy of Sciences(CAS):[grant number QYZDBSSWDQC027 and QYZDJ-SSW-DQC042]+1 种基金the National Natural Science Foundation of China:[grant number 41974009,41590854 and 41974040]Strategic Priority Research Program,CAS:[grant number XDA19070302 and XDA19070104].
文摘Ice velocity constitutes a key parameter for quantifying ice-sheet discharge rates and is thus crucial for improving the coupled models of the Antarctic ice sheet towards accurately predict its contribution to future global sea-level rise.However,in Antarctica,high-resolution and continuous ice velocity estimates remain elusive,which is key to unravel Antarctica’s present-day ice mass balance processes.Here,we present a suite of newly estimated Antarctic-wide,annually-sampled ice velocity products at 105-m grid-spacing observed by Landsat 8 optical images data.We first describe a procedure that can automatically calibrate and integrate ice displacement maps to generate Antarcticwide seamless ice velocity products.The annual ice velocity mosaics are assembled using a total of 250,000 displacement maps inferred from more than 80,000 Landsat 8 images acquired between December 2013 and April 2019.The new annual Antarctic ice velocity data product exhibits an improved quantification of near-decadal Antarctic-wide ice flow,and an opportunity to investigate ice dynamics at a higher spatial resolution and annual sampling,as compared to existing data products.Validation studies confirmed improved accuracy and consistency of this new data product,when compared with independently estimated optical and radar ice velocity data products,as well as in situ data.
基金supported by the National Natural Science Foundation of China (Grant Nos. 42030311, 41874068, 41974009)。
文摘Although the Sichuan basin is a stable block with low historical seismicity,the Suining M5.0 earthquake on January31,2010 occurred near the center of the basin,causing casualty and substantial damage.Previous studies have shown that the earthquake is very shallow and may occur in the sedimentary cover rocks,but its causative fault has not been identified.Based on local broadband seismic waveform data as well as a pair of ALOS PALSAR ascending orbit data,we explore the seismogenic mechanism via further constraining the source depth and the ruptured fault.The earthquake caused ground uplift in the southeast of the epicenter area,with a maximum line of sight displacement of about 13.6 cm,much larger than the displacement caused by a M5 earthquake at a typical depth of 10 km,which indicates that the earthquake is very shallow.Through joint inversion of seismic waveform and InSAR data,we obtain the moment magnitude of Suining earthquake as MW4.5,with the strike,dip,and rake of its fault plane as 17°,66° and 90°,respectively,and the centroid depth less than 1 km,supporting that the earthquake occurred at the shallow part of a high angle thrust fault dipping to the southeast.It is further confirmed that the earthquake may be triggered by the diffusion of high-pressure fluid migrating from the underside gas reservoir.
基金supported by National Natural Science Foundation of China(No.61674036)
文摘A scalable wideband equivalent circuit model of silicon-based on-chip transmission lines is presented in this paper along with an efficient analytical parameter extraction method based on improved characteristic function approach,including a relevant equation to reduce the deviation caused by approximation.The model consists of both series and shunt lumped elements and accounts for high-order parasitic effects.The equivalent circuit model is derived and verified to recover the frequency-dependent parameters at a range from direct current to 50 GHz accurately.The scalability of the model is proved by comparing simulated and measured scattering parameters with the method of cascade,attaining excellent results based on samples made from CMOS 0.13 and 0.18 μm process.
基金Project supported by the National Natural Science Foundation of China(No.61674036)
文摘An improved single-π equivalent circuit model for on-chip inductors in the GaAs process is presented in this paper. Considering high order parasites, the model is established by comprising an improved skin effect branch and a substrate lateral coupling branch. The parameter extraction is based on an improved characteristic function approach and vector fitting method. The model has better simulation than the previous work over the measured data of 2.5r and 4.5r on-chip inductors in the GaAs process.
基金This work is supported by National Natural Science Foun-dation of China(No.11971171)the 111 Project(B14019)and Project of National Social Science Fund of China(15BTJ027)+3 种基金Weidong Liu’s research is supported by National Program on Key Basic Research Project(973 Program,2018AAA0100704)National Natural Science Foundation of China(No.11825104,11690013)Youth Talent Sup-port Program,and a grant from Australian Research Council.Hansheng Wang’s research is partially supported by National Natural Science Foundation of China(No.11831008,11525101,71532001)It is also supported in part by China’s National Key Research Special Program(No.2016YFC0207704).
文摘The rapid emergence of massive datasets in various fields poses a serious challenge to tra-ditional statistical methods.Meanwhile,it provides opportunities for researchers to develop novel algorithms.Inspired by the idea of divide-and-conquer,various distributed frameworks for statistical estimation and inference have been proposed.They were developed to deal with large-scale statistical optimization problems.This paper aims to provide a comprehensive review for related literature.It includes parametric models,nonparametric models,and other frequently used models.Their key ideas and theoretical properties are summarized.The trade-off between communication cost and estimate precision together with other concerns is discussed.
基金supported by the Major Program of the National Natural Science Foundation of China (Grant No. 11731101)National Natural Science Foundation of China (Grant No. 11671349)+6 种基金supported by National Natural Science Foundation of China (Grant No. 72171226)the Beijing Municipal Social Science Foundation (Grant No. 19GLC052)the National Statistical Science Research Project (Grant No. 2020LZ38)supported by National Natural Science Foundation of China (Grant Nos. 71532001, 11931014, 12171395 and 71991472)the Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economicssupported by National Natural Science Foundation of China (Grant No. 11831008)the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science (Grant No. Klatasds-Moe-EcnuKlatasds2101)
文摘One of the key research problems in financial markets is the investigation of inter-stock dependence.A good understanding in this regard is crucial for portfolio optimization.To this end,various econometric models have been proposed.Most of them assume that the random noise associated with each subject is independent.However,dependence might still exist within this random noise.Ignoring this valuable information might lead to biased estimations and inaccurate predictions.In this article,we study a spatial autoregressive moving average model with exogenous covariates.Spatial dependence from both response and random noise is considered simultaneously.A quasi-maximum likelihood estimator is developed,and the estimated parameters are shown to be consistent and asymptotically normal.We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.
文摘We thank the editor,Professor Jun Shao,for orga-nizing this stimulating discussion.We are grateful to all discussants for their insightful comments on our review article on the distributed statistical inference.Due to the urgent need to process the datasets with massive sizes,various distributed computing methods have been proposed for the large-scale statistical prob-lems.Meanwhile,some important theoretical results were established.While we want to give a relatively comprehensive overview on this hot topic,there are still some important works that have been missed in our review written over a year ago.However,we are glad to see the discussants provide reviews of some new works and references.We hope that these discussions and our review would serve as a stimulus for further studies in this rapidly developing area.
基金funded by National Natural Science Foundation of China(Grant No.42030311,S.N.)US Dept.of Energy Grant DESC0019759(D.Y.)US National Science Foundation Grant EAR-1918126(D.Y.).
文摘WHAT,WHERE,AND WHEN In the early morning of February 6th,2023,an M7.8 earthquake occurred in southeastern T€urkiye near the northern border of Syria.The event initiated a complex sequence of aftershocks,including an M7.6 earthquake about 9 h later and 90 km to the north(Figures 1A and 1B).The earthquake sequence is also referred to as a strong doublet earthquake sequence.Aftershocks of the two strong earthquakes occurred along two separate branches of the East Anatolia Fault,with lengths of up to 300 km,and some aftershocks occurred in Syria(NEIC/USGS,2023).
基金supported by National Natural Science Foundation of China (Grant Nos. 11701560, 11501093, 11631003, 11690012, 71532001 and 11525101)the Fundamental Research Funds for the Central Universities+5 种基金the Fundamental Research Funds for the Central Universities (Grant Nos. 130028613, 130028729 and 2412017FZ030)the Research Funds of Renmin University of China (Grant No. 16XNLF01)the Beijing Municipal Social Science Foundation (Grant No. 17GLC051)Fund for Building World-Class Universities (Disciplines) of Renmin University of ChinaChina’s National Key Research Special Program (Grant No. 2016YFC0207700)Center for Statistical Science at Peking University
文摘Naive Bayes(NB) is one of the most popular classification methods. It is particularly useful when the dimension of the predictor is high and data are generated independently. In the meanwhile, social network data are becoming increasingly accessible, due to the fast development of various social network services and websites. By contrast, data generated by a social network are most likely to be dependent. The dependency is mainly determined by their social network relationships. Then, how to extend the classical NB method to social network data becomes a problem of great interest. To this end, we propose here a network-based naive Bayes(NNB) method, which generalizes the classical NB model to social network data. The key advantage of the NNB method is that it takes the network relationships into consideration. The computational efficiency makes the NNB method even feasible in large scale social networks. The statistical properties of the NNB model are theoretically investigated. Simulation studies have been conducted to demonstrate its finite sample performance.A real data example is also analyzed for illustration purpose.
