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Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to reduce the sulfur and olefin and the loss of octane number so as to promote the clean production of gasoline, an octane number loss prediction model is established based on data accumulated by the S Zorb device.First, the Lasso is used to screen out the modeling variables, then the index factor contributions are calculated by the BP neural network, based on which 15 main variables are screened out to build the model.Second, four modeling approaches are compared and analyzed, which shows that the BP neural network has better prediction accuracy thus is more suitable to model the octane number loss.The ten-fold cross-validation produces the average MSE value of 0.027 193 and the average R2 value of 0.904 87, verifying the reliability of the model.Furthermore, the main variables are optimized and adjusted by multiple linear regression under the premise that the sulfur content is not greater than 5 μg/g.The results show that multiple variables need to be adjusted simultaneously to reduce the octane number loss by more than 30%.The multiple linear regression model has good prediction accuracy and can adjust main variables positively or negatively according to a certain proportion.The trajectories of octane number and sulfur content are also visualized in the paper. [ABSTRACT FROM AUTHOR]

he process of screening and developing new drugs through experiments is very slow and requires a lot of manpower and material resources, and the use of computer-aided prediction of the molecular properties of drugs can greatly save time and cost of drug development. Therefore, in order to enable anti-breast cancer candidate drugs to have good biological activity and ADMET properties for inhibiting ERα,the random forest classifier was first used for the collected 1 974 compounds to screen the top 20 molecular descriptors with the most significant effects on biological activity. Then a QSAR model was established using this and pIC50 value as characteristic data. The biological activity values of 50 new compounds were predicted via the PSO optimized BP neural network, with the model fit of 0. 833 7 and the root mean square error of 0. 731 5,which were more consistent with the actual values than the predicted results of the BP neural network. Subsequently, in order to improve the success rate of drug development, the ADMET classification prediction model was constructed using PSO to optimize the SVM based on the existing ADMET property data. The algorithm cross-validation CV accuracy rate reached 94. 076 7%,and the prediction accuracy rates of the five index models were all above 79%. The results show that the proposed model has better prediction performance than the benchmark model, and the adopted prediction strategy is effective, which can provide reference for the discovery and development of anti-breast cancer drugs. [ABSTRACT FROM AUTHOR]

Copyright of Fudan University Journal of Medical Sciences is the property of Fudan University Journal of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Fudan University Journal of Medical Sciences is the property of Fudan University Journal of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Agriculturae Zhejiangensis is the property of Acta Agriculturae Zhejiangensis Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)