The in-medium quark condensate is studied with an equivalent-mass approach in which one does not need to make assumptions on the derivatives of model parameters with respect to the quark current mass.It is shown that ...The in-medium quark condensate is studied with an equivalent-mass approach in which one does not need to make assumptions on the derivatives of model parameters with respect to the quark current mass.It is shown that the condensate is generally a decreasing function of both the density and temperature with the decreasing speed depending on the confinement parameter.Specially,at given density,the condensate decreases on increasing temperature.The decreasing speed is comparatively small at lower temperature,and becomes very fast at higher temperature.展开更多
Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy o...Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.展开更多
In this article, we calculate the contributions of the vacuum condensates up to dimension-10 in the operator product expansion, and study the C γμ- Cγνtype scalar, axial-vector and tensor tetraquark states in deta...In this article, we calculate the contributions of the vacuum condensates up to dimension-10 in the operator product expansion, and study the C γμ- Cγνtype scalar, axial-vector and tensor tetraquark states in details with the QCD sum rules. In calculations, we use the formula μ = √M^2X/ Y /Z-（2Mc）^2 to determine the energy scales of the QCD spectral densities. The predictions MJ =2=（4.02-0.09^＋0.09） GeV, MJ =1=（4.02-0.08^＋0.07） GeV favor assigning the Zc（4020） and Zc（4025） as the J^PC= 1^＋-or 2^＋＋diquark-antidiquark type tetraquark states, while the prediction MJ =0=（3.85-0.09^＋0.15） GeV disfavors assigning the Z（4050） and Z（4250） as the J^P C= 0^＋＋ diquark-antidiquark type tetraquark states. Furthermore, we discuss the strong decays of the 0^＋＋, 1^＋-, 2^＋＋diquark-antidiquark type tetraquark states in details.展开更多
基金Supported by National Natural Science Foundation of China under Grant Nos.11045006 and 11135011the Key Project from Chinese Academy of Sciences(12A0A0012)the President Foundation by the Graduate University of Chinese Academy of Sciences
文摘The in-medium quark condensate is studied with an equivalent-mass approach in which one does not need to make assumptions on the derivatives of model parameters with respect to the quark current mass.It is shown that the condensate is generally a decreasing function of both the density and temperature with the decreasing speed depending on the confinement parameter.Specially,at given density,the condensate decreases on increasing temperature.The decreasing speed is comparatively small at lower temperature,and becomes very fast at higher temperature.
基金Supported by the National Natural Science Foundation of China (61074153, 61104131)the Fundamental Research Fundsfor Central Universities of China (ZY1111, JD1104)
文摘Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.
基金Supported by National Natural Science Foundation of China under Grant No.11375063Natural Science Foundation of Hebei Province under Grant No.A2014502017
文摘In this article, we calculate the contributions of the vacuum condensates up to dimension-10 in the operator product expansion, and study the C γμ- Cγνtype scalar, axial-vector and tensor tetraquark states in details with the QCD sum rules. In calculations, we use the formula μ = √M^2X/ Y /Z-（2Mc）^2 to determine the energy scales of the QCD spectral densities. The predictions MJ =2=（4.02-0.09^＋0.09） GeV, MJ =1=（4.02-0.08^＋0.07） GeV favor assigning the Zc（4020） and Zc（4025） as the J^PC= 1^＋-or 2^＋＋diquark-antidiquark type tetraquark states, while the prediction MJ =0=（3.85-0.09^＋0.15） GeV disfavors assigning the Z（4050） and Z（4250） as the J^P C= 0^＋＋ diquark-antidiquark type tetraquark states. Furthermore, we discuss the strong decays of the 0^＋＋, 1^＋-, 2^＋＋diquark-antidiquark type tetraquark states in details.
