The design operating conditions of rubber dams were analyzed,and it is found that the operating conditions are similar to the actual operating conditions of changes in the internal pressure ratio of a specific rubber ...The design operating conditions of rubber dams were analyzed,and it is found that the operating conditions are similar to the actual operating conditions of changes in the internal pressure ratio of a specific rubber dam bag in the process of filling and draining. Based on this,the linear relationship curve between the internal pressure head H0 and the real-time dam height H and its approximate analytical formula can be obtained,which can be used as a supplement and correction method for the measurement method of real-time dam height during rubber dam operation,and provides reference for rubber dam project managers.展开更多
Several chromatography systems with ionic liquids and a mixture of water with the modifier as mobile phase were characterized via the linear solvation energy relationships（LSER） model. The effects of the ionic liqui...Several chromatography systems with ionic liquids and a mixture of water with the modifier as mobile phase were characterized via the linear solvation energy relationships（LSER） model. The effects of the ionic liquids and modifier（methanol） concentrations on the retention of 10 solutes（caffeine, pyridine, aniline, phenol, methylparaben, acetopenone, m-cresol, p-cresol, o-cresol, and benzene） were discussed. The LSER model demonstrated high potential to predict retention factors with high squared correlation coefficients（r^2〉 0.97）. A comparison of predictable and experimental retention factors revealed that LSER can adequately reproduce the experimental retention factors of the solutes under different investigated experimental conditions. This model is a helpful tool to evaluate the retention characteristics of ionic liquid systems and to understand the interactions of solutes and ionic liquids.展开更多
The correlation relationships of apparent extraction equilibrium constant (1gK(ex)) with the electronic effect parameter( Sigma sigma(Phi)) and the steric effect parameter ( Sigma upsilon ) of the substituents in extr...The correlation relationships of apparent extraction equilibrium constant (1gK(ex)) with the electronic effect parameter( Sigma sigma(Phi)) and the steric effect parameter ( Sigma upsilon ) of the substituents in extractant molecules are investigated by linear regression analysis in the extraction of rare earths by various classes and structures of monoacidic organophosphorus extractants. The results indicate that in Linear free energy relationship formula 1gK(ex) = rho Sigma sigma(Phi) + psi Sigma upsilon + h generally follows for this kind of extraction systems. Accordingly, the quantitative structure-behaviour relationships of extractants are discussed. These relationships can be preliminarily applied to predict the 1gK(ex) values of rare earth extraction with definite structures of this class of extractants, and thus can provide some directions for the design of new RE extractants.展开更多
Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph ...Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.展开更多
The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR...The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.展开更多
In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of ...In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.展开更多
文摘The design operating conditions of rubber dams were analyzed,and it is found that the operating conditions are similar to the actual operating conditions of changes in the internal pressure ratio of a specific rubber dam bag in the process of filling and draining. Based on this,the linear relationship curve between the internal pressure head H0 and the real-time dam height H and its approximate analytical formula can be obtained,which can be used as a supplement and correction method for the measurement method of real-time dam height during rubber dam operation,and provides reference for rubber dam project managers.
基金Supported by the Center for Advanced Bioseparation Technology, Inha University, Korea
文摘Several chromatography systems with ionic liquids and a mixture of water with the modifier as mobile phase were characterized via the linear solvation energy relationships（LSER） model. The effects of the ionic liquids and modifier（methanol） concentrations on the retention of 10 solutes（caffeine, pyridine, aniline, phenol, methylparaben, acetopenone, m-cresol, p-cresol, o-cresol, and benzene） were discussed. The LSER model demonstrated high potential to predict retention factors with high squared correlation coefficients（r^2〉 0.97）. A comparison of predictable and experimental retention factors revealed that LSER can adequately reproduce the experimental retention factors of the solutes under different investigated experimental conditions. This model is a helpful tool to evaluate the retention characteristics of ionic liquid systems and to understand the interactions of solutes and ionic liquids.
文摘The correlation relationships of apparent extraction equilibrium constant (1gK(ex)) with the electronic effect parameter( Sigma sigma(Phi)) and the steric effect parameter ( Sigma upsilon ) of the substituents in extractant molecules are investigated by linear regression analysis in the extraction of rare earths by various classes and structures of monoacidic organophosphorus extractants. The results indicate that in Linear free energy relationship formula 1gK(ex) = rho Sigma sigma(Phi) + psi Sigma upsilon + h generally follows for this kind of extraction systems. Accordingly, the quantitative structure-behaviour relationships of extractants are discussed. These relationships can be preliminarily applied to predict the 1gK(ex) values of rare earth extraction with definite structures of this class of extractants, and thus can provide some directions for the design of new RE extractants.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 62273272,62303375 and 61873277in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-243+2 种基金in part by the Natural Science Foundation of Shaanxi Province under Grants 2022JQ-606 and 2020-JQ758in part by the Research Plan of Department of Education of Shaanxi Province under Grant 21JK0752in part by the Youth Innovation Team of Shaanxi Universities.
文摘Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.
文摘The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.
基金Supported by the Ministry of Environmental Protection of China(No.2011467037)
文摘In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.
