前几天,我在20多年前教过的一位学生来看我。我们谈话时,他随手拿起我书桌上的一本英语口语教材翻看,他说:“呀,老师,教材里怎么有错句?”我拿起教材一看,是“Long time no see”。我笑了。“老师,我清楚地记得,我们在中学时...前几天,我在20多年前教过的一位学生来看我。我们谈话时,他随手拿起我书桌上的一本英语口语教材翻看,他说:“呀,老师,教材里怎么有错句?”我拿起教材一看,是“Long time no see”。我笑了。“老师,我清楚地记得,我们在中学时,您不止一次地在黑板上纠正过类似的错句,怎么20年后它又成对的了?”我说:“你问得好。”接着我对此作了解释。展开更多
"Long time no see" is a very interesting English expression used as a greeting by people who have not seen each other for a while. The essay shows how Chinese people and native English speaker think about &q..."Long time no see" is a very interesting English expression used as a greeting by people who have not seen each other for a while. The essay shows how Chinese people and native English speaker think about "Long time no see". Meanwhile, it does research upon the historical appearances of the phrase.展开更多
In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciat...In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.展开更多
Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep lear...Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.展开更多
Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial featur...Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.展开更多
In this article we extend ours framework of long time convergence for numeracal approximations of semilinear parabolic equations prorided in “Wu Haijun and Li Ronghua, Northeast. Math. J., 16(1)(2000), 1—28”, to t...In this article we extend ours framework of long time convergence for numeracal approximations of semilinear parabolic equations prorided in “Wu Haijun and Li Ronghua, Northeast. Math. J., 16(1)(2000), 1—28”, to the Gauss Ledendre full discretization. When apply the result to the Crank Nicholson finiteelement full discretization of the Navier Stokes equations, we can remore the grid ratio restriction of “Heywood, J. G. and Rannacher, R., SIAM J. Numer. Anal., 27(1990), 353—384”, and weaken the stability condition on the continuous solution.展开更多
In this paper,a nonparametric multivariate regression model with long memory covariates and long memory errors is considered.We approximate the nonparametric multivariate regression function by the weighted additive o...In this paper,a nonparametric multivariate regression model with long memory covariates and long memory errors is considered.We approximate the nonparametric multivariate regression function by the weighted additive one-dimensional functions.The local linear smoothing and least squares method are proposed for the one-dimensional regression estimation and the weight parameters estimation,respectively.The asymptotic behaviors of the proposed estimators are investigated.展开更多
Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each appl...Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.展开更多
We present a numerical study of the long time behavior of approxima- tion solution to the Extended Fisher-Kolmogorov equation with periodic boundary conditions. The unique solvability of numerical solution is shown. I...We present a numerical study of the long time behavior of approxima- tion solution to the Extended Fisher-Kolmogorov equation with periodic boundary conditions. The unique solvability of numerical solution is shown. It is proved that there exists a global attractor of the discrete dynamical system. Furthermore, we obtain the long-time stability and convergence of the difference scheme and the upper semicontinuity d(Ah,τ, .A) → O. Our results show that the difference scheme can effectively simulate the infinite dimensional dynamical systems.展开更多
The non-stationary buffeting response of long span suspension bridge in time domain under strong wind loading is computed. Modeling method for generating non-stationary fluctuating winds with probabilistic model for n...The non-stationary buffeting response of long span suspension bridge in time domain under strong wind loading is computed. Modeling method for generating non-stationary fluctuating winds with probabilistic model for non-stationary strong wind fields is first presented. Non-stationary wind forces induced by strong winds on bridge deck and tower are then given a brief introduction. Finally,Non-stationary buffeting response of Pulite Bridge in China,a long span suspension bridge,is computed by using ANSYS software under four working conditions with different combination of time-varying mean wind and time-varying variance. The case study further confirms that it is necessity of considering non-stationary buffeting response for long span suspension bridge under strong wind loading,rather than only stationary buffeting response.展开更多
A large earthquake (Mw=7.6) occurred in Jiji (Chi-Chi), Taiwan, China on September 20, 1999, and was followed by many moderate-size shocks in the following days. Two of the largest aftershocks with the magnitudes of M...A large earthquake (Mw=7.6) occurred in Jiji (Chi-Chi), Taiwan, China on September 20, 1999, and was followed by many moderate-size shocks in the following days. Two of the largest aftershocks with the magnitudes of Mw=6.1 and Mw=6.2, respectively, were used as empirical Green's functions (EGFs) to obtain the source time functions (STFs) of the main shock from long-period waveform data of the Global Digital Seismograph Network (GDSN) including IRIS, GEOSCOPE and CDSN. For the Mw=6.1 aftershock of September 22, there were 97 pairs of phases clear enough from 78 recordings of 26 stations; for the Mw=6.2 aftershock of September 25, there were 81 pairs of phases clear enough from 72 recordings of 24 stations. For each station, 2 types of STFs were retrieved, which are called P-STF and S-STF due to being from P and S phases, respectively. Totally, 178 STF individuals were obtained for source-process analysis of the main shock. It was noticed that, in general, STFs from most of the stations had similarities except that those in special azimuths looked different or odd due to the mechanism difference between the main shock and the aftershocks; and in detail, the shapes of the STFs varied with azimuth. Both of them reflected the stability and reliability of the retrieved STFs. The comprehensive analysis of those STFs suggested that this event consisted of two sub-events, the total duration time was about 26 s, and on the average, the second event was about 7 s later than the first one, and the moment-rate amplitude of the first event was about 15% larger than that of the second one.展开更多
The numerical approximations of the dynamical systems governed by semilinear parabolic equations are considered. An abstract framework for long time error estimates is established. When applied to reaction diffusion...The numerical approximations of the dynamical systems governed by semilinear parabolic equations are considered. An abstract framework for long time error estimates is established. When applied to reaction diffusion equation, Navier Stokes equations and Chan Hilliard equation, approximated by Galerkin and nonlinear Galerkin methods in space and by Runge Kutta method in time, our framework yields error estimates uniform in time.展开更多
Long non-coding RNAs(lncRNAs) play a key role in craniocerebral disease, although their expression profiles in human traumatic brain injury are still unclear. In this regard, in this study, we examined brain injury ti...Long non-coding RNAs(lncRNAs) play a key role in craniocerebral disease, although their expression profiles in human traumatic brain injury are still unclear. In this regard, in this study, we examined brain injury tissue from three patients of the 101 st Hospital of the People's Liberation Army, China(specifically, a 36-year-old male, a 52-year-old female, and a 49-year-old female), who were diagnosed with traumatic brain injury and underwent brain contusion removal surgery. Tissue surrounding the brain contusion in the three patients was used as control tissue to observe expression characteristics of lncRNAs and mRNAs in human traumatic brain injury tissue. Volcano plot filtering identified 99 lncRNAs and 63 mRNAs differentially expressed in frontotemporal tissue of the two groups(P < 0.05, fold change > 1.2). Microarray analysis showed that 43 lncRNAs were up-regulated and 56 lncRNAs were down-regulated. Meanwhile, 59 mRNAs were up-regulated and 4 mRNAs were down-regulated. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses revealed 27 signaling pathways associated with target genes and, in particular, legionellosis and influenza A signaling pathways. Subsequently, a lncRNA-gene network was generated, which showed an absolute correlation coefficient value > 0.99 for 12 lncRNA-mRNA pairs. Finally, quantitative real-time polymerase chain reaction confirmed different expression of the five most up-regulated mRNAs within the two groups, which was consistent with the microarray results. In summary, our results show that expression profiles of mRNAs and lncRNAs are significantly different between human traumatic brain injury tissue and surrounding tissue, providing novel insight regarding lncRNAs' involvement in human traumatic brain injury. All participants provided informed consent. This research was registered in the Chinese Clinical Trial Registry(registration number: ChiCTR-TCC-13004002) and the protocol version number is 1.0.展开更多
It is helpful to improve the seismic design theory of long-span continuous bridges for studying the seismic performance of each cantilever construction state.Taking the Bridge 1 in the north of Changbai-Mountain inter...It is helpful to improve the seismic design theory of long-span continuous bridges for studying the seismic performance of each cantilever construction state.Taking the Bridge 1 in the north of Changbai-Mountain international tourism resort as an example,the authors studied it in shutdown phase and the cantilever construction process,established the simulation model by using Midas / civil,and analyzed time-history of each construction stage for the bridge.The study shows that long-span bridge cantilever construction in northeastern China can be divided into two-year tasks for construction(suspending in winter).It is needed to think about seismic stability of the cantilever position in shut-down phase of winter.The effect of longitudinal vibration is the most disadvantageous influence to bridge,and its calculation results can provide reference for seismic design of similar bridges in the future.展开更多
Based on the method of torsional creep, the creep laws of ananhydrite specimen are studied in this paper. When a shearing stressapplied to the specimen is less than a value, only the primary stagetakes place. How- eve...Based on the method of torsional creep, the creep laws of ananhydrite specimen are studied in this paper. When a shearing stressapplied to the specimen is less than a value, only the primary stagetakes place. How- ever, when the shearing stress is more than anothervalue, all the three stages of a creep curve, i. e. primary, steady-state and accelerated are exhibited.展开更多
We study the long-time limit behavior of the solution to an atom's master equation. For the first time we derive that the probability of the atom being in the α-th (α = j + 1 -jz, j is the angular momentum quantu...We study the long-time limit behavior of the solution to an atom's master equation. For the first time we derive that the probability of the atom being in the α-th (α = j + 1 -jz, j is the angular momentum quantum number, jz is the z-component of angular momentum) state is {(1 - K/G)/[1 - (K/G)2j+1]}(K/G)^α-1 as t → +∞, which coincides with the fact that when K/G 〉 1, the larger the a is, the larger the probability of the atom being in the α-th state (the lower excited state) is. We also consider the case for some possible generaizations of the atomic master equation.展开更多
In this paper, we first provide a generalized difference method for the 2-dimensional Navier-Stokes equations by combing the ideas of staggered scheme m and generalized upwind scheme in space, and by backward Euler ti...In this paper, we first provide a generalized difference method for the 2-dimensional Navier-Stokes equations by combing the ideas of staggered scheme m and generalized upwind scheme in space, and by backward Euler time-stepping. Then we apply the abstract framework of to prove its long-time convergence. Finally, a numerical example for solving driven cavity flows is given.展开更多
The earthquakes with Ms≥6.0 are often gathered into belts or clusters and are roughly consistent with tectonic structure trends in the Sichuan-Yunnan (Chuan-Dian) region. The middle south part(98°-106°E, 21...The earthquakes with Ms≥6.0 are often gathered into belts or clusters and are roughly consistent with tectonic structure trends in the Sichuan-Yunnan (Chuan-Dian) region. The middle south part(98°-106°E, 21°-34°N) of South-North Seismic Zone can be zoned into seven small areas. There all were strong quakes with M_s≥7.0 historically in each small area. Ten earthquakes with M_s≥7.0 have occurred in this region since 1970 and they appeared in five small areas respectively. The relationships between occurrence-time and cumulative frequencies of strong quakes in these five areas are shown to be an exponential distribution or power function. By examining the inner coincidence it is indicated that these relationships are of definite significance to mid-long term macroseismic prediction of each area.展开更多
文摘前几天,我在20多年前教过的一位学生来看我。我们谈话时,他随手拿起我书桌上的一本英语口语教材翻看,他说:“呀,老师,教材里怎么有错句?”我拿起教材一看,是“Long time no see”。我笑了。“老师,我清楚地记得,我们在中学时,您不止一次地在黑板上纠正过类似的错句,怎么20年后它又成对的了?”我说:“你问得好。”接着我对此作了解释。
文摘"Long time no see" is a very interesting English expression used as a greeting by people who have not seen each other for a while. The essay shows how Chinese people and native English speaker think about "Long time no see". Meanwhile, it does research upon the historical appearances of the phrase.
基金supported in part by the Gansu Province Higher Education Institutions Industrial Support Program:Security Situational Awareness with Artificial Intelligence and Blockchain Technology.Project Number(2020C-29).
文摘In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.
基金funded by the Natural Science Foundation of Fujian Province,China (Grant No.2022J05291)Xiamen Scientific Research Funding for Overseas Chinese Scholars.
文摘Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.
基金This work is supported by the National Nature Science Foundation of China(NSFC)under Grant Nos.61571106,61501169,41706103the Fundamental Research Funds for the Central Universities under Grant No.2242013K30010.
文摘Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.
文摘In this article we extend ours framework of long time convergence for numeracal approximations of semilinear parabolic equations prorided in “Wu Haijun and Li Ronghua, Northeast. Math. J., 16(1)(2000), 1—28”, to the Gauss Ledendre full discretization. When apply the result to the Crank Nicholson finiteelement full discretization of the Navier Stokes equations, we can remore the grid ratio restriction of “Heywood, J. G. and Rannacher, R., SIAM J. Numer. Anal., 27(1990), 353—384”, and weaken the stability condition on the continuous solution.
基金Supported by the National Natural Science Foundation of China(No.11671194 and No.11501287)
文摘In this paper,a nonparametric multivariate regression model with long memory covariates and long memory errors is considered.We approximate the nonparametric multivariate regression function by the weighted additive one-dimensional functions.The local linear smoothing and least squares method are proposed for the one-dimensional regression estimation and the weight parameters estimation,respectively.The asymptotic behaviors of the proposed estimators are investigated.
基金the Beijing Chaoyang District Collaborative Innovation Project(No.CYXT2013)the subject support of Beijing Municipal Science and Technology Key R&D Program-Capital Blue Sky Action Cultivation Project(Z19110900910000)+1 种基金“Research and Demonstration ofHigh Emission Vehicle Monitoring Equipment System Based on Sensor Integration Technology”(Z19110000911003)This work was supported by the Academic Research Projects of Beijing Union University(No.ZK80202103).
文摘Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.
基金The NSF (10871055) of Chinathe Fundamental Research Funds (HEUCFL20111102)for the Central Universities
文摘We present a numerical study of the long time behavior of approxima- tion solution to the Extended Fisher-Kolmogorov equation with periodic boundary conditions. The unique solvability of numerical solution is shown. It is proved that there exists a global attractor of the discrete dynamical system. Furthermore, we obtain the long-time stability and convergence of the difference scheme and the upper semicontinuity d(Ah,τ, .A) → O. Our results show that the difference scheme can effectively simulate the infinite dimensional dynamical systems.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51408174)Anhui Provincial Natural Science Foundation(Grant No.1408085QE95)+1 种基金China Postdoctoral Science Foundation(Grant No.2013M540511 and 2015T80652)Key University Science Research Project of Anhui Province(Grant No.KJ2016A294)
文摘The non-stationary buffeting response of long span suspension bridge in time domain under strong wind loading is computed. Modeling method for generating non-stationary fluctuating winds with probabilistic model for non-stationary strong wind fields is first presented. Non-stationary wind forces induced by strong winds on bridge deck and tower are then given a brief introduction. Finally,Non-stationary buffeting response of Pulite Bridge in China,a long span suspension bridge,is computed by using ANSYS software under four working conditions with different combination of time-varying mean wind and time-varying variance. The case study further confirms that it is necessity of considering non-stationary buffeting response for long span suspension bridge under strong wind loading,rather than only stationary buffeting response.
基金State Natural Science Foundation of China (49904004) and IPGP of France.Contribution No. 02FE2007, Institute of Geophysics, Ch
文摘A large earthquake (Mw=7.6) occurred in Jiji (Chi-Chi), Taiwan, China on September 20, 1999, and was followed by many moderate-size shocks in the following days. Two of the largest aftershocks with the magnitudes of Mw=6.1 and Mw=6.2, respectively, were used as empirical Green's functions (EGFs) to obtain the source time functions (STFs) of the main shock from long-period waveform data of the Global Digital Seismograph Network (GDSN) including IRIS, GEOSCOPE and CDSN. For the Mw=6.1 aftershock of September 22, there were 97 pairs of phases clear enough from 78 recordings of 26 stations; for the Mw=6.2 aftershock of September 25, there were 81 pairs of phases clear enough from 72 recordings of 24 stations. For each station, 2 types of STFs were retrieved, which are called P-STF and S-STF due to being from P and S phases, respectively. Totally, 178 STF individuals were obtained for source-process analysis of the main shock. It was noticed that, in general, STFs from most of the stations had similarities except that those in special azimuths looked different or odd due to the mechanism difference between the main shock and the aftershocks; and in detail, the shapes of the STFs varied with azimuth. Both of them reflected the stability and reliability of the retrieved STFs. The comprehensive analysis of those STFs suggested that this event consisted of two sub-events, the total duration time was about 26 s, and on the average, the second event was about 7 s later than the first one, and the moment-rate amplitude of the first event was about 15% larger than that of the second one.
文摘The numerical approximations of the dynamical systems governed by semilinear parabolic equations are considered. An abstract framework for long time error estimates is established. When applied to reaction diffusion equation, Navier Stokes equations and Chan Hilliard equation, approximated by Galerkin and nonlinear Galerkin methods in space and by Runge Kutta method in time, our framework yields error estimates uniform in time.
基金supported by the National Natural Science Foundation of China,No.81571939(to KX),81601719(to JZ)and 81772134(to KX)Key Research and Development Program of Hunan Province of China,No.2018SK2091(to KX)+1 种基金Wu Jie-Ping Medical Foundation of the Minister of Health of China,No.320.6750.14118(to KX)Teacher Research Foundation of Central South University of China,No.2014JSJJ026(to KX)
文摘Long non-coding RNAs(lncRNAs) play a key role in craniocerebral disease, although their expression profiles in human traumatic brain injury are still unclear. In this regard, in this study, we examined brain injury tissue from three patients of the 101 st Hospital of the People's Liberation Army, China(specifically, a 36-year-old male, a 52-year-old female, and a 49-year-old female), who were diagnosed with traumatic brain injury and underwent brain contusion removal surgery. Tissue surrounding the brain contusion in the three patients was used as control tissue to observe expression characteristics of lncRNAs and mRNAs in human traumatic brain injury tissue. Volcano plot filtering identified 99 lncRNAs and 63 mRNAs differentially expressed in frontotemporal tissue of the two groups(P < 0.05, fold change > 1.2). Microarray analysis showed that 43 lncRNAs were up-regulated and 56 lncRNAs were down-regulated. Meanwhile, 59 mRNAs were up-regulated and 4 mRNAs were down-regulated. Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) analyses revealed 27 signaling pathways associated with target genes and, in particular, legionellosis and influenza A signaling pathways. Subsequently, a lncRNA-gene network was generated, which showed an absolute correlation coefficient value > 0.99 for 12 lncRNA-mRNA pairs. Finally, quantitative real-time polymerase chain reaction confirmed different expression of the five most up-regulated mRNAs within the two groups, which was consistent with the microarray results. In summary, our results show that expression profiles of mRNAs and lncRNAs are significantly different between human traumatic brain injury tissue and surrounding tissue, providing novel insight regarding lncRNAs' involvement in human traumatic brain injury. All participants provided informed consent. This research was registered in the Chinese Clinical Trial Registry(registration number: ChiCTR-TCC-13004002) and the protocol version number is 1.0.
文摘It is helpful to improve the seismic design theory of long-span continuous bridges for studying the seismic performance of each cantilever construction state.Taking the Bridge 1 in the north of Changbai-Mountain international tourism resort as an example,the authors studied it in shutdown phase and the cantilever construction process,established the simulation model by using Midas / civil,and analyzed time-history of each construction stage for the bridge.The study shows that long-span bridge cantilever construction in northeastern China can be divided into two-year tasks for construction(suspending in winter).It is needed to think about seismic stability of the cantilever position in shut-down phase of winter.The effect of longitudinal vibration is the most disadvantageous influence to bridge,and its calculation results can provide reference for seismic design of similar bridges in the future.
文摘Based on the method of torsional creep, the creep laws of ananhydrite specimen are studied in this paper. When a shearing stressapplied to the specimen is less than a value, only the primary stagetakes place. How- ever, when the shearing stress is more than anothervalue, all the three stages of a creep curve, i. e. primary, steady-state and accelerated are exhibited.
基金Project supported by the National Natural Science Foundation of China (Grant No. 11105133)
文摘We study the long-time limit behavior of the solution to an atom's master equation. For the first time we derive that the probability of the atom being in the α-th (α = j + 1 -jz, j is the angular momentum quantum number, jz is the z-component of angular momentum) state is {(1 - K/G)/[1 - (K/G)2j+1]}(K/G)^α-1 as t → +∞, which coincides with the fact that when K/G 〉 1, the larger the a is, the larger the probability of the atom being in the α-th state (the lower excited state) is. We also consider the case for some possible generaizations of the atomic master equation.
基金The project supported by Laboratory of Computational Physics,Institute of Applied Physics & Computational Mathematics,T.O.Box 80 0 9,Beijing 1 0 0 0 88
文摘In this paper, we first provide a generalized difference method for the 2-dimensional Navier-Stokes equations by combing the ideas of staggered scheme m and generalized upwind scheme in space, and by backward Euler time-stepping. Then we apply the abstract framework of to prove its long-time convergence. Finally, a numerical example for solving driven cavity flows is given.
文摘The earthquakes with Ms≥6.0 are often gathered into belts or clusters and are roughly consistent with tectonic structure trends in the Sichuan-Yunnan (Chuan-Dian) region. The middle south part(98°-106°E, 21°-34°N) of South-North Seismic Zone can be zoned into seven small areas. There all were strong quakes with M_s≥7.0 historically in each small area. Ten earthquakes with M_s≥7.0 have occurred in this region since 1970 and they appeared in five small areas respectively. The relationships between occurrence-time and cumulative frequencies of strong quakes in these five areas are shown to be an exponential distribution or power function. By examining the inner coincidence it is indicated that these relationships are of definite significance to mid-long term macroseismic prediction of each area.