Purpose:Corneal topograph-guided laser subepithelial keratomileusis (LASEK) can effectively correct decentered ablation occurring post laser in situ keratomileusis (LASIK) and to enhance our understanding and diagnosi...Purpose:Corneal topograph-guided laser subepithelial keratomileusis (LASEK) can effectively correct decentered ablation occurring post laser in situ keratomileusis (LASIK) and to enhance our understanding and diagnosis of decentered ablation following LASIK. Methods:Previous studies in the relevant literature are reviewed, and a case report is provided. Results:A patient with high myopia undergoing LASIK in both eyes presented with distorted vision in the left eye, which interfered with the vision in the right eye and caused blurred vision in both eyes. The patient was unable to see objects with both eyes. After receiving corneal topography-guided LASEK,the signs of distorted vision in the left eye and bilateral blurred vision were significantly alleviated,and the patient could see objects with both eyes simultaneously. Conclusion: Clinical ophthalmologists should be aware of the occurrence of decentered ablation after LASIK. Corneal topography-guided LASEK is an efficacious tool for correcting decentered ablation.展开更多
The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and interpretation. In this paper...The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and interpretation. In this paper, we propose a flexible Bayesian Lasso and adaptive Lasso quantile regression by introducing a hierarchical model framework approach to enable exact inference and shrinkage of an unimportant coefficient to zero. The error distribution is assumed to be an infinite mixture of Gaussian densities. We have theoretically investigated and numerically compared our proposed methods with Flexible Bayesian quantile regression (FBQR), Lasso quantile regression (LQR) and quantile regression (QR) methods. Simulations and real data studies are conducted under different settings to assess the performance of the proposed methods. The proposed methods perform well in comparison to the other methods in terms of median mean squared error, mean and variance of the absolute correlation criterions. We believe that the proposed methods are useful practically.展开更多
The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on...The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze.In order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning.Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level features.eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features,as well as predict haze.Establish PM2.5 concentration pollution grade classification index,and grade the forecast data.The expert experience knowledge is utilized to assist the optimization of the pre-warning results.The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.展开更多
文摘Purpose:Corneal topograph-guided laser subepithelial keratomileusis (LASEK) can effectively correct decentered ablation occurring post laser in situ keratomileusis (LASIK) and to enhance our understanding and diagnosis of decentered ablation following LASIK. Methods:Previous studies in the relevant literature are reviewed, and a case report is provided. Results:A patient with high myopia undergoing LASIK in both eyes presented with distorted vision in the left eye, which interfered with the vision in the right eye and caused blurred vision in both eyes. The patient was unable to see objects with both eyes. After receiving corneal topography-guided LASEK,the signs of distorted vision in the left eye and bilateral blurred vision were significantly alleviated,and the patient could see objects with both eyes simultaneously. Conclusion: Clinical ophthalmologists should be aware of the occurrence of decentered ablation after LASIK. Corneal topography-guided LASEK is an efficacious tool for correcting decentered ablation.
文摘The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and interpretation. In this paper, we propose a flexible Bayesian Lasso and adaptive Lasso quantile regression by introducing a hierarchical model framework approach to enable exact inference and shrinkage of an unimportant coefficient to zero. The error distribution is assumed to be an infinite mixture of Gaussian densities. We have theoretically investigated and numerically compared our proposed methods with Flexible Bayesian quantile regression (FBQR), Lasso quantile regression (LQR) and quantile regression (QR) methods. Simulations and real data studies are conducted under different settings to assess the performance of the proposed methods. The proposed methods perform well in comparison to the other methods in terms of median mean squared error, mean and variance of the absolute correlation criterions. We believe that the proposed methods are useful practically.
基金The work was financially supported by National Natural Science Fund of China,specific grant numbers were 61371143 and 61662033initials of authors who received the grants were respectively Z.YM,H.L,and the URLs to sponsors’websites was http://www.nsfc.gov.cn/.This paper was supported by National Natural Science Fund of China(Grant Nos.61371143,61662033).
文摘The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze.In order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning.Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level features.eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features,as well as predict haze.Establish PM2.5 concentration pollution grade classification index,and grade the forecast data.The expert experience knowledge is utilized to assist the optimization of the pre-warning results.The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.
