Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide...Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.展开更多
There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR）...There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.展开更多
Flow and heat transfer characteristic of supercritical carbon dioxide(SCO_(2))are numerically investigated in the horizontal and vertical tubes.TWL turbulent Prandtl number model could well describe the behavior of SC...Flow and heat transfer characteristic of supercritical carbon dioxide(SCO_(2))are numerically investigated in the horizontal and vertical tubes.TWL turbulent Prandtl number model could well describe the behavior of SCO_(2) affected by the buoyancy.Under the cooling condition,the heat transfer performance of SCO_(2) along the upward direction is best and that along the downward direction is worst when bulk fluid temperatures are below the pseudocritical temperature.Reducing the ratio of heat flux to mass flux could decrease the difference of convective heat transfer coefficient in three flow directions.Under the heating condition,heat transfer deterioration only occurs in vertical upward and horizontal flow directions.Heat transfer deterioration of SCO_(2) could be delayed by increasing the mass flux and the deterioration degree is weakened in the second half of tube along the vertical upward flow direction.Compared with the straight tube,the corrugated tube shows better comprehensive thermal performance.展开更多
基金supported by the National Natural Science Foundation of China(U1204608,61472370,61672469,61822701)
文摘Ribonucleic acid(RNA)hybridization is widely used in popular RNA simulation software in bioinformatics.However limited by the exponential computational complexity of combin atorial problems,it is challenging to decide,within an acceptable time,whether a specific RNA hybridization is effective.We hereby introduce a machine learning based technique to address this problem.Sample machine learning(ML)models tested in the training phase include algorithms based on the boosted tree(BT)random forest(RF),decision tree(DT)and logistic regression(LR),and the corresponding models are obtained.Given the RNA molecular coding training and testing sets,the trained machine learning models are applied to predict the classification of RNA hybridization results.The experiment results show that the op timal predictive accuracies are 96.2%,96.6%,96.0%and 69.8%for the RF,BT,DT and LR-based approaches,respectively,un der the strong constraint condition,compared with traditiona representative methods.Furthermore,the average computation efficiency of the RF,BT,DT and LR-based approaches are208679,269756,184333 and 187458 times higher than that o existing approach,respectively.Given an RNA design,the BT based approach demonstrates high computational efficiency and better predictive accuracy in determining the biological effective ness of molecular hybridization.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2014 ZX03001027)
文摘There are various heterogeneous networks for terminals to deliver a better quality of service. Signal system recognition and classification contribute a lot to the process. However, in low signal to noise ratio（SNR） circumstances or under time-varying multipath channels, the majority of the existing algorithms for signal recognition are already facing limitations. In this series, we present a robust signal recognition method based upon the original and latest updated version of the extreme learning machine（ELM） to help users to switch between networks. The ELM utilizes signal characteristics to distinguish systems. The superiority of this algorithm lies in the random choices of hidden nodes and in the fact that it determines the output weights analytically, which result in lower complexity. Theoretically, the algorithm tends to offer a good generalization performance at an extremely fast speed of learning. Moreover, we implement the GSM/WCDMA/LTE models in the Matlab environment by using the Simulink tools. The simulations reveal that the signals can be recognized successfully to achieve a 95% accuracy in a low SNR（0 dB） environment in the time-varying multipath Rayleigh fading channel.
文摘Flow and heat transfer characteristic of supercritical carbon dioxide(SCO_(2))are numerically investigated in the horizontal and vertical tubes.TWL turbulent Prandtl number model could well describe the behavior of SCO_(2) affected by the buoyancy.Under the cooling condition,the heat transfer performance of SCO_(2) along the upward direction is best and that along the downward direction is worst when bulk fluid temperatures are below the pseudocritical temperature.Reducing the ratio of heat flux to mass flux could decrease the difference of convective heat transfer coefficient in three flow directions.Under the heating condition,heat transfer deterioration only occurs in vertical upward and horizontal flow directions.Heat transfer deterioration of SCO_(2) could be delayed by increasing the mass flux and the deterioration degree is weakened in the second half of tube along the vertical upward flow direction.Compared with the straight tube,the corrugated tube shows better comprehensive thermal performance.
