Currently,the extraction of coseismic offset signals primarily relies on earthquake catalog data to determine the occurrence time of earthquakes.This is followed by the process of differencing the average GPS coordina...Currently,the extraction of coseismic offset signals primarily relies on earthquake catalog data to determine the occurrence time of earthquakes.This is followed by the process of differencing the average GPS coordinate time series data,with a time interval of 3 to 5 days before and after the earthquake.In the face of the huge amount of GPS coordinate time series data today,the conventional approach of relying on earthquake catalog data to assist in obtaining coseismic offset signals has become increasingly burdensome.To address this problem,we propose a new method for automatically detecting coseismic offset signals in GPS coordinate time series without an extra earthquake catalog for reference.Firstly,we pre-process the GPS coordinate time series data for filtering out stations with significant observations missing and detecting and removing outliers.Secondly,we eliminate other signals and errors in the GPS coordinate time series,such as trend and seasonal signals,leaving the coseismic offset signals as the primary signal.The resulting coordinate time series is then modeled using the first-order difference and data stacking method.The modeling method enables automatic detection of the coseismic offset signals in the GPS coordinate time series.The aforementioned method is applied to automatically detect coseismic offset signals using simulated data and the Searles Valley GPS data in California,USA.The results demonstrate the efficacy of our proposed method,successfully detecting coseismic offsets from vast amounts of GPS coordinate time series data.展开更多
The reliable operation of flexible display devices poses a significant engineering challenge regarding the metrology of high barriers against water vapor.No reliable results have been reported in the range of 10–6 g∙...The reliable operation of flexible display devices poses a significant engineering challenge regarding the metrology of high barriers against water vapor.No reliable results have been reported in the range of 10–6 g∙(m^(2)∙d)1,and there is no standard ultra-barrier for calibration.To detect trace amount of water vapor permeation through an ultra-barrier with extremely high sensitivity and a greatly reduced test period,a predictive instrument was developed by integrating permeation models into high-sensitivity mass spectrometry measurement based on dynamic accumulation,detection,and evacuation of the permeant.Detection reliability was ensured by means of calibration using a standard polymer sample.After calibration,the lower detection limit for water vapor permeation is in the range of 10–7 g∙(m^(2)∙d)1,which satisfies the ultra-barrier requirement.Predictive permeation models were developed and evaluated using experimental data so that the steady-state permeation rate can be forecasted from non-steady-state results,thus enabling effective measurement of ultra-barrier permeation within a significantly shorter test period.展开更多
The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. Fi...The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5(ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are-0.41/1.15 hpa and-0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.展开更多
Dear Editor, First discovered during 1947 in Uganda from febrile rhesus macaques, Zika virus （ZIKV） is a mosquito-borne, reemerging flavivirus historically known to be present in much of Africa and Asia, occasionall...Dear Editor, First discovered during 1947 in Uganda from febrile rhesus macaques, Zika virus （ZIKV） is a mosquito-borne, reemerging flavivirus historically known to be present in much of Africa and Asia, occasionally causing outbreaks amongst the local populace （Haddow et al., 2012）. ZIKV infections in humans are mostly asymptomatic, but a small percentage of patients may show clinical symptoms such as a fever and rash, which resolve within a week or less.展开更多
In this paper,we study the distributionally robust joint chance-constrained Markov decision process.Utilizing the logarithmic transformation technique,we derive its deterministic reformulation with bi-convex terms und...In this paper,we study the distributionally robust joint chance-constrained Markov decision process.Utilizing the logarithmic transformation technique,we derive its deterministic reformulation with bi-convex terms under the moment-based uncertainty set.To cope with the non-convexity and improve the robustness of the solution,we propose a dynamical neural network approach to solve the reformulated optimization problem.Numerical results on a machine replacement problem demonstrate the efficiency of the proposed dynamical neural network approach when compared with the sequential convex approximation approach.展开更多
Optimization stands as a foundational research discipline,permeating various domains such as engineering,and management,and beyond,where many problems inherently entail optimization.The development of algorithms tailo...Optimization stands as a foundational research discipline,permeating various domains such as engineering,and management,and beyond,where many problems inherently entail optimization.The development of algorithms tailored to solve optimization problems not only holds significant theoretical implications but also promises substantial practical applications.展开更多
基金supported by the National Natural Science Foundation of China(No.42104008,42204006,41904031)the Jiangxi Provincial Natural Science Foundation(20232BAB213075)+1 种基金the Key Laboratory for Digital Land and Resources of Jiangxi Province,East China University of Technology(DLLJ202016)Open Fund of Hubei Luojia Laboratory(No.230100020,230100019)。
文摘Currently,the extraction of coseismic offset signals primarily relies on earthquake catalog data to determine the occurrence time of earthquakes.This is followed by the process of differencing the average GPS coordinate time series data,with a time interval of 3 to 5 days before and after the earthquake.In the face of the huge amount of GPS coordinate time series data today,the conventional approach of relying on earthquake catalog data to assist in obtaining coseismic offset signals has become increasingly burdensome.To address this problem,we propose a new method for automatically detecting coseismic offset signals in GPS coordinate time series without an extra earthquake catalog for reference.Firstly,we pre-process the GPS coordinate time series data for filtering out stations with significant observations missing and detecting and removing outliers.Secondly,we eliminate other signals and errors in the GPS coordinate time series,such as trend and seasonal signals,leaving the coseismic offset signals as the primary signal.The resulting coordinate time series is then modeled using the first-order difference and data stacking method.The modeling method enables automatic detection of the coseismic offset signals in the GPS coordinate time series.The aforementioned method is applied to automatically detect coseismic offset signals using simulated data and the Searles Valley GPS data in California,USA.The results demonstrate the efficacy of our proposed method,successfully detecting coseismic offsets from vast amounts of GPS coordinate time series data.
基金This work was supported by the National Natural Science Foundation of China(51835005 and 51911540476)the Hubei Provincial Natural Science Foundation of China(2019CFB527)+2 种基金the Hubei Provincial Natural Science Foundation of China for innovative research groups(2020CFA030)the Independent Research and Development Fund of Huazhong University of Science and Technology(HUST)(2019kfyXMBZ025)the State Key Lab of Digital Manufacturing Equipment&Technology(0225100102).
文摘The reliable operation of flexible display devices poses a significant engineering challenge regarding the metrology of high barriers against water vapor.No reliable results have been reported in the range of 10–6 g∙(m^(2)∙d)1,and there is no standard ultra-barrier for calibration.To detect trace amount of water vapor permeation through an ultra-barrier with extremely high sensitivity and a greatly reduced test period,a predictive instrument was developed by integrating permeation models into high-sensitivity mass spectrometry measurement based on dynamic accumulation,detection,and evacuation of the permeant.Detection reliability was ensured by means of calibration using a standard polymer sample.After calibration,the lower detection limit for water vapor permeation is in the range of 10–7 g∙(m^(2)∙d)1,which satisfies the ultra-barrier requirement.Predictive permeation models were developed and evaluated using experimental data so that the steady-state permeation rate can be forecasted from non-steady-state results,thus enabling effective measurement of ultra-barrier permeation within a significantly shorter test period.
基金supported by Open Fund of Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources (Grant MEMI-2021-2022-27)funded by the National Natural Science Foundation of China (Grants 41904031,42374040,42061077)+2 种基金the Jiangxi Provincial Natural Science Foundation (Grants 20202BABL213033)the State Key Laboratory of Geodesy and Earth's Dynamics (Grants SKLGED2021-2-2)the Graduate Innovation Foundation of East China University of Technology (Grants YC2022-s604,YC2022-s609)。
文摘The pressure and temperature significantly influence precipitable water vapor(PWV) retrieval. Global Navigation Satellite System(GNSS) PWV retrieval is limited because the GNSS stations lack meteorological sensors. First, this article evaluated the accuracy of pressure and temperature in 68 radiosonde stations in China based on ERA5 Reanalysis data from 2015 to 2019 and compared them with GPT3model. Then, the accuracy of pressure and temperature calculated by ERA5 were estimated in 5 representative IGS stations in China. And the PWV calculated by these meteorological parameters from ERA5(ERA5-PWV) were analyzed. Finally, the relation between ERA5-PWV and precipitation was deeply explored using wavelet coherence analysis in IGS stations. These results indicate that the accuracy of pressure and temperature of ERA5 is better than the GPT3 model. In radiosonde stations, the mean BIAS and MAE of pressure and temperature in ERA5 are-0.41/1.15 hpa and-0.97/2.12 K. And the mean RMSEs are 1.35 hpa and 2.87 K, which improve 74.77% and 40.58% compared with GPT3 model. The errors of pressure and temperature of ERA5 are smaller than the GPT3 model in bjfs, hksl and wuh2, and the accuracy of ERA5-PWV is improved by 18.77% compared with the GPT3 model. In addition, there is a significant positive correlation between ERA5-PWV and precipitation. And precipitation is always associated with the sharp rise of ERA5-PWV, which provides important references for rainfall prediction.
文摘Dear Editor, First discovered during 1947 in Uganda from febrile rhesus macaques, Zika virus （ZIKV） is a mosquito-borne, reemerging flavivirus historically known to be present in much of Africa and Asia, occasionally causing outbreaks amongst the local populace （Haddow et al., 2012）. ZIKV infections in humans are mostly asymptomatic, but a small percentage of patients may show clinical symptoms such as a fever and rash, which resolve within a week or less.
基金supported by National Natural Science Foundation of China(Grant Nos.11991023 and 12371324)National Key R&D Program of China(Grant No.2022YFA1004000)。
文摘In this paper,we study the distributionally robust joint chance-constrained Markov decision process.Utilizing the logarithmic transformation technique,we derive its deterministic reformulation with bi-convex terms under the moment-based uncertainty set.To cope with the non-convexity and improve the robustness of the solution,we propose a dynamical neural network approach to solve the reformulated optimization problem.Numerical results on a machine replacement problem demonstrate the efficiency of the proposed dynamical neural network approach when compared with the sequential convex approximation approach.
文摘Optimization stands as a foundational research discipline,permeating various domains such as engineering,and management,and beyond,where many problems inherently entail optimization.The development of algorithms tailored to solve optimization problems not only holds significant theoretical implications but also promises substantial practical applications.
