Electron density differences resulting from atom displacement patterns aligned with phonon modes in MgB2 have been calculated using density functional theory (DFT). The extent of phonon anomalies, identified as indica...Electron density differences resulting from atom displacement patterns aligned with phonon modes in MgB2 have been calculated using density functional theory (DFT). The extent of phonon anomalies, identified as indicators of the superconducting transition temperature, Tc, under a range of conditions in AlB2-type structures, reduce as boron atoms are displaced from their equilibrium positions along E2g mode directions. The Fermi energy for displacements along the directions of the E2g phonon mode accounts for changes in the covalent B-B bond electronic charge density. We applied differential atom displacements to show that the shifted σ band structure associated with the light effective mass became tangential to the Fermi level and that the Fermi surface undergoes a topological transition at a critical relative displacement of ~0.6% of the boron atoms from equilibrium. The difference in Fermi energies at this critical displacement and at the equilibrium position correspond to the superconducting energy gap. The net volume between tubular σ surfaces in reciprocal space correlated with the depth of the phonon anomaly and, by inference, it is a key to an understanding of superconductivity. This ab initioapproach offers a phenomenological understanding of the factors that determine Tc based on knowledge of the crystal structure.展开更多
With the improvements in the density and quality of satellite altimetry data,a high-precision and high-resolution mean sea surface model containing abundant information regarding a marine gravity field can be calculat...With the improvements in the density and quality of satellite altimetry data,a high-precision and high-resolution mean sea surface model containing abundant information regarding a marine gravity field can be calculated from long-time series multi-satellite altimeter data.Therefore,in this study,a method was proposed for determining marine gravity anomalies from a mean sea surface model.Taking the Gulf of Mexico(15°–32°N,80°–100°W)as the study area and using a removal-recovery method,the residual gridded deflections of the vertical(DOVs)are calculated by combining the mean sea surface,mean dynamic topography,and XGM2019e_2159 geoid,and then using the inverse Vening-Meinesz method to determine the residual marine gravity anomalies from the residual gridded DOVs.Finally,residual gravity anomalies are added to the XGM2019e_2159 gravity anomalies to derive marine gravity anomaly models.In this study,the marine gravity anomalies were estimated with mean sea surface models CNES_CLS15MSS,DTU21MSS,and SDUST2020MSS and the mean dynamic topography models CNES_CLS18MDT and DTU22MDT.The accuracy of the marine gravity anomalies derived by the mean sea surface model was assessed based on ship-borne gravity data.The results show that the difference between the gravity anomalies derived by DTU21MSS and CNES_CLS18MDT and those of the ship-borne gravity data is optimal.With an increase in the distance from the coast,the difference between the gravity anomalies derived by mean sea surface models and ship-borne gravity data gradually decreases.The accuracy of the difference between the gravity anomalies derived by mean sea surface models and those from ship-borne gravity data are optimal at a depth of 3–4 km.The accuracy of the gravity anomalies derived by the mean sea surface model is high.展开更多
The authors studied the seismic activity, precursory anomalies and abnormal animal behavior before the April 14, 2010 Ms 7.1 Yushu earthquake. Analysis showed that anomalies were not rich before the Ms 7.1 Yushu earth...The authors studied the seismic activity, precursory anomalies and abnormal animal behavior before the April 14, 2010 Ms 7.1 Yushu earthquake. Analysis showed that anomalies were not rich before the Ms 7.1 Yushu earthquake, but prominent anomalies were observed, such as the long and mid-term trend anomaly characterized by the seismic quiescence of Ms6. 0, MsS. 0 and Ms4.0 earthquakes, and the anomalies in precursor observation of surface water temperature in Yushu and Delingha and electromagnetic measurement in Ping＇an. There were a large number of animal behavior anomalies appearing one week before the earthquake. An M4.7 earthquake occurred 130 minutes before the main shock. In this paper, we studied the dynamic process of the Yushu earthquake preparation using the earthquake focal mechanism solutions on the Bayan Har block boundary since 1996. The results show that the Kalakunlun M7.1 earthquake in 1996, the Mani M7.5 earthquake and the Yushu Ms7.1 earthquake have the same dynamic process. Long and mid-term trend anomalies may be related to the dynamics of evolution of different earthquakes. This paper also discusses the recurrence interval of strong earthquakes, foreshock identification and precursor observation of the Yushu Ms7. 1 earthquake.展开更多
During winter of 2021/2022,the temperature in China is characterized by a warm-to-cold transition,and the average temperature anomaly in February 2022 is−1.6℃,the coldest February in 2013-2022.We revealed the circula...During winter of 2021/2022,the temperature in China is characterized by a warm-to-cold transition,and the average temperature anomaly in February 2022 is−1.6℃,the coldest February in 2013-2022.We revealed the circulation regimes and physical mechanisms associated with this reversal event and demonstrated the advantage of a regional model downscaling over the use of the global model alone in predicting.In early winter,the warm anomalies are mainly related to an anomalous anticyclonic system downstream of a PNA-like(Pacific-North America)Rossby-wave train induced by La Niña.In late winter,due to the circulation response to the central Pacific warming and negative tropical Indian Ocean Dipole(TIOD),two‘−+−’Rossby-wave trains from high latitudes and the tropical Indian Ocean jointly lead to an anomalous cyclonic system in China.Meanwhile,an anticyclonic blocking system on the northern side of Baikal brings strong and cold air to China.These two systems together cause a significant drop in surface air temperature anomaly in China during the late winter.The Beijing Climate Center climate system model(BCC_CSM1.1 m)can essentially predict this temperature reversal in China about five months in advance.However,the reversal amplitude is weaker due to warm deviations over the tropical Pacific Ocean and equatorial Indian Ocean.Using dynamic downscaling,a regional Climate-Weather Research and Forecasting(CWRF)model correctly predicts the cold SAT anomalies in late winter 2021/2022.The regional model depicts more realistic circulation patterns in East Asia;the anomalous cyclonic system in Inner Mongolia accompanied by the northerly anomalies contribute to a lower-than-normal SAT over China.This study reveals the cooperative effect of wave trains from high latitudes and the tropics on the subseasonal temperature reversal and demonstrates a possible solution to improve the forecast skill by dynamic downscaling according to precise characterization of local surface information.展开更多
现有的基于深度学习模型的词嵌入方法用于Web异常检测时,通常将语料库中没有出现的未知词汇(Out of Vocabulary,OOV)设置为unknown,并赋予零或随机向量输入到模型中进行训练,未考虑未知词汇在Web请求语句中的上下文关系。同时,在Web系...现有的基于深度学习模型的词嵌入方法用于Web异常检测时,通常将语料库中没有出现的未知词汇(Out of Vocabulary,OOV)设置为unknown,并赋予零或随机向量输入到模型中进行训练,未考虑未知词汇在Web请求语句中的上下文关系。同时,在Web系统代码开发过程中,基于个人习惯并为了增加代码的可读性,程序员设计的请求路径代码往往存在一定的模式。因此,考虑到Web请求的模式和单词语义间的相关性,研究基于Word2vec的动态未知词表示方法DUWe(Dynamic Unknown Word Embedding),该方法通过分析Web请求路径中单词上下文的关系来赋予未知词向量的表示内容。在CSIC-2010和WAF Dataset数据集上的实验评估表明,增加未知词表示方法比仅用Word2vec静态特征提取方法具有更好的性能,同时在准确性、精准率、召回率和F1-Score方面均有提高,在训练时间上最大降低1.14倍。展开更多
Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficient...Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.展开更多
文摘Electron density differences resulting from atom displacement patterns aligned with phonon modes in MgB2 have been calculated using density functional theory (DFT). The extent of phonon anomalies, identified as indicators of the superconducting transition temperature, Tc, under a range of conditions in AlB2-type structures, reduce as boron atoms are displaced from their equilibrium positions along E2g mode directions. The Fermi energy for displacements along the directions of the E2g phonon mode accounts for changes in the covalent B-B bond electronic charge density. We applied differential atom displacements to show that the shifted σ band structure associated with the light effective mass became tangential to the Fermi level and that the Fermi surface undergoes a topological transition at a critical relative displacement of ~0.6% of the boron atoms from equilibrium. The difference in Fermi energies at this critical displacement and at the equilibrium position correspond to the superconducting energy gap. The net volume between tubular σ surfaces in reciprocal space correlated with the depth of the phonon anomaly and, by inference, it is a key to an understanding of superconductivity. This ab initioapproach offers a phenomenological understanding of the factors that determine Tc based on knowledge of the crystal structure.
基金The National Natural Science Foundation of China under contract Nos 42274006,42174041,41774001the Research Fund of University of Science and Technology under contract No.2014TDJH101.
文摘With the improvements in the density and quality of satellite altimetry data,a high-precision and high-resolution mean sea surface model containing abundant information regarding a marine gravity field can be calculated from long-time series multi-satellite altimeter data.Therefore,in this study,a method was proposed for determining marine gravity anomalies from a mean sea surface model.Taking the Gulf of Mexico(15°–32°N,80°–100°W)as the study area and using a removal-recovery method,the residual gridded deflections of the vertical(DOVs)are calculated by combining the mean sea surface,mean dynamic topography,and XGM2019e_2159 geoid,and then using the inverse Vening-Meinesz method to determine the residual marine gravity anomalies from the residual gridded DOVs.Finally,residual gravity anomalies are added to the XGM2019e_2159 gravity anomalies to derive marine gravity anomaly models.In this study,the marine gravity anomalies were estimated with mean sea surface models CNES_CLS15MSS,DTU21MSS,and SDUST2020MSS and the mean dynamic topography models CNES_CLS18MDT and DTU22MDT.The accuracy of the marine gravity anomalies derived by the mean sea surface model was assessed based on ship-borne gravity data.The results show that the difference between the gravity anomalies derived by DTU21MSS and CNES_CLS18MDT and those of the ship-borne gravity data is optimal.With an increase in the distance from the coast,the difference between the gravity anomalies derived by mean sea surface models and ship-borne gravity data gradually decreases.The accuracy of the difference between the gravity anomalies derived by mean sea surface models and those from ship-borne gravity data are optimal at a depth of 3–4 km.The accuracy of the gravity anomalies derived by the mean sea surface model is high.
基金funded by Earthquake Tendency Tracing of 2011 of Department of Monitoring and Prediction of CEA under the"Earthquake Short and Imminent Prediction Climb Program of2020"(2011016301)
文摘The authors studied the seismic activity, precursory anomalies and abnormal animal behavior before the April 14, 2010 Ms 7.1 Yushu earthquake. Analysis showed that anomalies were not rich before the Ms 7.1 Yushu earthquake, but prominent anomalies were observed, such as the long and mid-term trend anomaly characterized by the seismic quiescence of Ms6. 0, MsS. 0 and Ms4.0 earthquakes, and the anomalies in precursor observation of surface water temperature in Yushu and Delingha and electromagnetic measurement in Ping＇an. There were a large number of animal behavior anomalies appearing one week before the earthquake. An M4.7 earthquake occurred 130 minutes before the main shock. In this paper, we studied the dynamic process of the Yushu earthquake preparation using the earthquake focal mechanism solutions on the Bayan Har block boundary since 1996. The results show that the Kalakunlun M7.1 earthquake in 1996, the Mani M7.5 earthquake and the Yushu Ms7.1 earthquake have the same dynamic process. Long and mid-term trend anomalies may be related to the dynamics of evolution of different earthquakes. This paper also discusses the recurrence interval of strong earthquakes, foreshock identification and precursor observation of the Yushu Ms7. 1 earthquake.
基金supported by the National Natural Science Foundation of China(U2242207)the National Key Research and Development Program of China(2022YFE0136000)+1 种基金the National Natural Science Foundation of China(41790471,42105037,41965005)the Innovative Development Special Project of China Meteorological Administration(CXFZ2023J003,CXFZ2023P025).
文摘During winter of 2021/2022,the temperature in China is characterized by a warm-to-cold transition,and the average temperature anomaly in February 2022 is−1.6℃,the coldest February in 2013-2022.We revealed the circulation regimes and physical mechanisms associated with this reversal event and demonstrated the advantage of a regional model downscaling over the use of the global model alone in predicting.In early winter,the warm anomalies are mainly related to an anomalous anticyclonic system downstream of a PNA-like(Pacific-North America)Rossby-wave train induced by La Niña.In late winter,due to the circulation response to the central Pacific warming and negative tropical Indian Ocean Dipole(TIOD),two‘−+−’Rossby-wave trains from high latitudes and the tropical Indian Ocean jointly lead to an anomalous cyclonic system in China.Meanwhile,an anticyclonic blocking system on the northern side of Baikal brings strong and cold air to China.These two systems together cause a significant drop in surface air temperature anomaly in China during the late winter.The Beijing Climate Center climate system model(BCC_CSM1.1 m)can essentially predict this temperature reversal in China about five months in advance.However,the reversal amplitude is weaker due to warm deviations over the tropical Pacific Ocean and equatorial Indian Ocean.Using dynamic downscaling,a regional Climate-Weather Research and Forecasting(CWRF)model correctly predicts the cold SAT anomalies in late winter 2021/2022.The regional model depicts more realistic circulation patterns in East Asia;the anomalous cyclonic system in Inner Mongolia accompanied by the northerly anomalies contribute to a lower-than-normal SAT over China.This study reveals the cooperative effect of wave trains from high latitudes and the tropics on the subseasonal temperature reversal and demonstrates a possible solution to improve the forecast skill by dynamic downscaling according to precise characterization of local surface information.
文摘现有的基于深度学习模型的词嵌入方法用于Web异常检测时,通常将语料库中没有出现的未知词汇(Out of Vocabulary,OOV)设置为unknown,并赋予零或随机向量输入到模型中进行训练,未考虑未知词汇在Web请求语句中的上下文关系。同时,在Web系统代码开发过程中,基于个人习惯并为了增加代码的可读性,程序员设计的请求路径代码往往存在一定的模式。因此,考虑到Web请求的模式和单词语义间的相关性,研究基于Word2vec的动态未知词表示方法DUWe(Dynamic Unknown Word Embedding),该方法通过分析Web请求路径中单词上下文的关系来赋予未知词向量的表示内容。在CSIC-2010和WAF Dataset数据集上的实验评估表明,增加未知词表示方法比仅用Word2vec静态特征提取方法具有更好的性能,同时在准确性、精准率、召回率和F1-Score方面均有提高,在训练时间上最大降低1.14倍。
基金supported by the Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology(Grant No.202202H)the National Key R&D Program of China(Grant No.2019YFB1600702)the National Natural Science Foundation of China(Grant Nos.51978600&51808336).
文摘Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.
