Through the statistical analysis of earthquake distribution along 51 strike-sli p active fault segments on the Chinese continent, we found that strong earthquak e distribution along the seismogenic fault segments is i...Through the statistical analysis of earthquake distribution along 51 strike-sli p active fault segments on the Chinese continent, we found that strong earthquak e distribution along the seismogenic fault segments is inhomogeneous and the dis tribution probability density p(K) can be stated as p(K)=1.1206e -3.947K in which K=S/(L/2), S refers to the distance from earthquake epicenter to the center of a fault segment, L is the length of the fault segment. The above model can be utilized to modify the probability density of earthquake occurrence of t he maximum magnitude interval in a potential earthquake source. Nevertheless, it is only suitable for those potential earthquake sources delineated along a sing le seismogenic fault. This inhomogeneous model has certain effects on seismic risk assessment, especia ll y for those potential earthquake sources with higher earthquake reoccurrence rat es of the maximum magnitude interval. In general, higher reoccurrence rate of th e maximum magnitude interval and lower exceeding probability level may bring lar ger difference of the results in seismic risk analysis by adopting the inhomogen eous model, the PGA values increase inner the potential earthquake source, but r educe near the vicinity and out of the potential earthquake source. Taking the T angyin potential earthquake source as an example, with exceeding probability of 10% and 2% in 50 years, the difference of the PGA values between inhomogeneous m odel and homogenous models can reach 12%.展开更多
In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology consi...In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.展开更多
This study investigates calendar anomalies： day-of-the-week effect and seasonal effect in the Value-at-Risk （VaR） analysis of stock returns for AAPL during the period of 1995 through 2015. The statistical propertie...This study investigates calendar anomalies： day-of-the-week effect and seasonal effect in the Value-at-Risk （VaR） analysis of stock returns for AAPL during the period of 1995 through 2015. The statistical properties are examined and a comprehensive set of diagnostic checks are made on the two decades of AAPL daily stock returns. Combing the Extreme Value Approach together with a statistical analysis, it is learnt that the lowest VaR occurs on Fridays and Mondays typically. Moreover, high Q4 and Q3 VaR are observed during the test period. These results are valuable for anyone who needs evaluation and forecasts of the risk situation in AAPL. Moreover, this methodology, which is applicable to any other stocks or portfolios, is more realistic and comprehensive than the standard normal distribution based VaR model that is commonly used.展开更多
A two-dimensioual stress analysis was developed to evaluate the failure of composite joints using characteristic length method. In this study, the accuracy of characteristic length method on the prediction of failure ...A two-dimensioual stress analysis was developed to evaluate the failure of composite joints using characteristic length method. In this study, the accuracy of characteristic length method on the prediction of failure strength and failure mode using different failure criteria was investigated. The stresses required for evaluating the joints were computed from stress functions obtained from displacement expressions that satisfy boundary conditions of the hole. The available experimental data for joint strength in literature were compared with the predicted failure loads and modes of failure for different composite pinned joints. No single failure criterion utilized to evaluate the failure gave a universally best fit across the three joints evaluated. However, the accuracy of characterizing the joints failure varies with joint laminate and choice of failure criterions.展开更多
文摘Through the statistical analysis of earthquake distribution along 51 strike-sli p active fault segments on the Chinese continent, we found that strong earthquak e distribution along the seismogenic fault segments is inhomogeneous and the dis tribution probability density p(K) can be stated as p(K)=1.1206e -3.947K in which K=S/(L/2), S refers to the distance from earthquake epicenter to the center of a fault segment, L is the length of the fault segment. The above model can be utilized to modify the probability density of earthquake occurrence of t he maximum magnitude interval in a potential earthquake source. Nevertheless, it is only suitable for those potential earthquake sources delineated along a sing le seismogenic fault. This inhomogeneous model has certain effects on seismic risk assessment, especia ll y for those potential earthquake sources with higher earthquake reoccurrence rat es of the maximum magnitude interval. In general, higher reoccurrence rate of th e maximum magnitude interval and lower exceeding probability level may bring lar ger difference of the results in seismic risk analysis by adopting the inhomogen eous model, the PGA values increase inner the potential earthquake source, but r educe near the vicinity and out of the potential earthquake source. Taking the T angyin potential earthquake source as an example, with exceeding probability of 10% and 2% in 50 years, the difference of the PGA values between inhomogeneous m odel and homogenous models can reach 12%.
文摘In this work a neural network model for climate forecasting is presented. The model is built by training a neural network with available reanalysis data. In order to assess the model, the development methodology considers the use of data reduction strategies that eliminate data redundancy thus reducing the complexity of the models. The results presented in this paper considered the use of Rough Sets Theory principles in extracting relevant information from the available data to achieve the reduction of redundancy among the variables used for forecasting purposes. The paper presents results of climate prediction made with the use of the neural network based model. The results obtained in the conducted experiments show the effectiveness of the methodology, presenting estimates similar to observations.
文摘This study investigates calendar anomalies： day-of-the-week effect and seasonal effect in the Value-at-Risk （VaR） analysis of stock returns for AAPL during the period of 1995 through 2015. The statistical properties are examined and a comprehensive set of diagnostic checks are made on the two decades of AAPL daily stock returns. Combing the Extreme Value Approach together with a statistical analysis, it is learnt that the lowest VaR occurs on Fridays and Mondays typically. Moreover, high Q4 and Q3 VaR are observed during the test period. These results are valuable for anyone who needs evaluation and forecasts of the risk situation in AAPL. Moreover, this methodology, which is applicable to any other stocks or portfolios, is more realistic and comprehensive than the standard normal distribution based VaR model that is commonly used.
文摘A two-dimensioual stress analysis was developed to evaluate the failure of composite joints using characteristic length method. In this study, the accuracy of characteristic length method on the prediction of failure strength and failure mode using different failure criteria was investigated. The stresses required for evaluating the joints were computed from stress functions obtained from displacement expressions that satisfy boundary conditions of the hole. The available experimental data for joint strength in literature were compared with the predicted failure loads and modes of failure for different composite pinned joints. No single failure criterion utilized to evaluate the failure gave a universally best fit across the three joints evaluated. However, the accuracy of characterizing the joints failure varies with joint laminate and choice of failure criterions.
