超核的相对论平均场计算

利用相对论平均场方法计算了奇异、粲和底超核的部分性质,包括各种不同味的超核的单粒子能量以及其他一些静态性质,并对结果进行了比较.对各种味的重子的势阱深度和耦合常数做了估计,得到丰富的结果.建议寻找质量数大于10?0的粲超核.同时探讨了不同重子杂质对原子核芯性质的可能影响.     

单极巨共振的相对论平均场计算

利用最近完成的相对论平均场理论的计算程序,研究了原子核单极巨共振.计算了¹⁶O,⁴⁰Ca和²⁰⁸Pb的同位旋标量和同位旋矢量单极巨共振.得到的结果与实验符合较好.     

Fission Barrier for ^240Pu in the Quadrupole Constrained Relativistic Mean Field Approach

The fission barrier for ^240pu is investigated beyond the second saddle point in the potentiM energy surface by the constrained relativistic mean field method with the newly proposed parameter set PK1. The microscopic correction for the centre-of-mass motion is essentiM to provide the correct potential energy surface. The shell effects that stabilize the nuclei against the fission is also investigated by the Strutinsky method. The shapes for the ground state, fission isomer and saddle-points, etc, are studied in detail.     

N－N关联和有限核相对论平均场方法

密度有关的核子－介子相互作用耦合常数是在相对论平均场近似下用核物质的相对论Ｂｒｕｅｃｋｎｅｒ－Ｈａｒｔｒｅｅ－Ｆｏｃｋ近似计算的自能参数化得到的．这种密度有关的相互作用考虑了介质中Ｎ－Ｎ关联效应，用这种密度有关的相互作用来研究有限核的基态性质，如单粒子能级，平均结合能，电荷均方根半径，与实验值较好地符合，同时还与其它模型的结果进行了比较。     

人工神经网络对VOCs的自动识别

利用人工神经网络(ANN)对严重混叠的傅里叶变换红外光谱图进行了定性和定量解析。通过大量模拟数据训练神经网络后,引用了新的评价标准——逼近度来选择最优网络模型。利用此优化网络对两类光谱图进行了解析,考察了网络的泛化能力。结果表明：该网络不仅能够对两组分同时存在时的样本进行准确解析,而且对于未知单组分光谱图,也能够进行准确鉴别和定量分析。可见, 该研究为人工神经网络在单组分和多组分未知物的定性和定量分析方面提供了一种新思路。     

Modeling of Thermal Conductivity of Concrete with Vermiculite by Using Artificial Neural Networks Approaches

In this article, the thermal conductivity of concrete with vermiculite is determined and also predicted by using artificial neural networks approaches, namely the radial basis neural network and multi-layer perceptron. In these models, 20 datasets were used. For the training set, 12 datasets (60%) were randomly selected, and the residual datasets (8 datasets, 40%) were selected as the test set. The root mean square error, the mean absolute error, and determination coefficient statistics are used as evaluation criteria of the models, and the experimental results are compared with these models. It is found that the radial basis neural network model is superior to the other models.     

A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks

Measuring the predictability and complexity of time series using entropy is essential tool designing and controlling a nonlinear system. However, the existing methods have some drawbacks related to the strong dependence of entropy on the parameters of the methods. To overcome these difficulties, this study proposes a new method for estimating the entropy of a time series using the LogNNet neural network model. The LogNNet reservoir matrix is filled with time series elements according to our algorithm. The accuracy of the classification of images from the MNIST-10 database is considered as the entropy measure and denoted by NNetEn. The novelty of entropy calculation is that the time series is involved in mixing the input information in the reservoir. Greater complexity in the time series leads to a higher classification accuracy and higher NNetEn values. We introduce a new time series characteristic called time series learning inertia that determines the learning rate of the neural network. The robustness and efficiency of the method is verified on chaotic, periodic, random, binary, and constant time series. The comparison of NNetEn with other methods of entropy estimation demonstrates that our method is more robust and accurate and can be widely used in practice.     

Boundary Conditions of Wigner-Seitz Cell in Inner Crust of Neutron Stars with Relativistic Mean Field Approach

The microscopic structure of the Wigner-Seitz （W-S） cell in the inner crust of neutron stars is investigated with the relativistic mean field （RMF） approach. The W-S cell is composed of a cluster of neutrons and protons localized in a region around the centre and surrounded by a neutron gas of approximately uniform density. In order to generate the density of the W-S cell, appropriate boundary conditions in the calculation of the single-particle wavefunctions are necessary. We emphasize on the choice of the boundary conditions in the RMF approach. Three kinds of boundary conditions are suggested. The properties of the W-S cell with the three kinds of boundary conditions are investigated. The neutron density distributions in the RMF and Hartree-Fock-Bogoliubov （HFB） models are compared. It is found that the neutron gas densities of the W-S cell in the RMF model is higher than those obtained in the HFB model.     

Relativistic Mean Field Study on Halo Structures of Mirror Nuclei

Halo structures of some light mirror nuclei are investigated with the relativistic mean field （RMF） theory. The calculations show that the dispersion of the valence proton is larger than that of the valence neutron in its mirror nucleus, the difference between the root-mean-square （rms） radius of the valence nucleon in each pair of mirror nuclei becomes smaller with the increase of the mass number A, and all the ratios of the rms radius of the valence nucleon to that of the matter in each pair of mirror nuclei decrease almost linearly with the increase of the mass number A.     

Predicting kinetic parameters for coal devolatilization by means of Artificial Neural Networks

The chemical percolation devolatilization (CPD) model has been shown to represent the devolatilization process of different coals and heating conditions with good accuracy. However, its use in computational fluid dynamics is limited because of its relatively high computational cost. Here, an Artificial Neural Network (ANN) based model for predicting coal devolatilization kinetics is developed based on a database constructed with the CPD model for a wide range of coals and heating rates. The heating rates and the information of ultimate and proximate analysis are chosen as inputs of the ANN model to consider the effects of coal types and heating conditions on coal devolatilization; the outputs are the kinetic parameters for the two-step kinetic model. The learning, validation, and application results show that the proposed ANN model has a competitive prediction capability on both the total volatile release and release rates when compared with the CPD model, but has obvious computational efficiency advantages. Furthermore, the relative impact of the coal type and heating rate on each kinetic parameter for coal devolatilization is quantitatively evaluated through the Garson equation. It is found that the heating rate has the strongest effect on the pre-exponential factor, while the coal types show significant influence on the activation energy and final yield of the two reactions in the two-step model.     

Relativistic Mean Field Study of the Z=117 Isotopic Chain

The properties of the Z = 117 isotopic chain are studied within the framework of the axially deformed relativistic mean field theory （RMFT） in the blocked BCS approximation. The ground-state properties, such as hinging energies, deformations as well as the possible α decay energies and lifetimes are calculated with the parameter set of NL-Z2 and compared with results from the finite range droplet model. The analysis by RMFT shows that the isotopes in the range of mass number A =291 ~ 300 exhibit higher stability, which suggests that they may be promising nuclei to be hopefully synthesized in the lab among the nuclei Z = 117.     

Stability of Strutinsky Shell Correction Energy in Relativistic Mean Field Theory

The single-particle spectrum obtained from the relativistic mean field （RMF） theory is used to extract the shell correction energy with the Strutinsky method. Considering the delicate balance between the plateau condition in the Strutinsky smoothing procedure and the convergence for the total binding energy, the proper space sizes used in solving the RMF equations are investigated in detail by taking ^208 Pb as an example. With the proper space sizes, almost the same shell correction energies are obtained by solving the RMF equations either on basis space or in coordinate space.     

O同位素的相对论平均场计算中对能隙参数的研究

在相对论平均场理论框架下, 研究了各种对能隙参数对O同位素偶偶核的适用性. 通过对原子核结合能和四极形变的系统计算表明, 在O同位素区域, 对能隙参数Λ_n和Λ_p均可取为0.5, 这样既简化了计算, 又可得到比较满意的结果.     

RMF理论框架下用粒子数守恒方法处理对关联

在相对论平均场理论框架下,利用粒子数守恒方法处理对关联,给出了具体的理论公式和数值细节;并以24Ne为例,用该方法研究了它的基态和低激发态性质. Particle-number conserving method is adopted to treat the pairing correlations in the relativistic mean-field theory. The formalism and numerical techniques are presented. As an example, the ground state properties and low-lying excited states in~(24)Ne are studied.     

Majorana Representation and Mean Field Approach for Interacting-Boson System

The Majorana representation, which represents a quantum state by stars on the Bloch sphere, provides us an intuitive tool to study the quantum evolution in high dimensional Hilbert space. In this work, we investigate the second quantized model and the mean-field model for the interacting-boson system in the Majorana representation. It is shown that the motions of states in the two models are same in the linear case. Furthermore, the contribution of the nonlinear interaction to the star motions in the second quantized model can be expressed by a single star part which is equal to the nonlinear part of the equation for the star in mean-field model under large boson number limit and an extra part caused by the correlation between stars. These differences and relations can not only be reflected by the population differences between the two boson modes in the two models, but also lie with the differences between the continuous changes of the second quantized evolution with the nonlinear interacting strength and the critical behavior of the mean-field evolution which related to the self-trapping effect. The reason of the difference between the two models is also discussed by an effective Hamiltonian.     

Mean Field Limit of Interacting Filaments and Vector Valued Non-linear PDEs

Families of N interacting curves are considered, with long range, mean field type, interaction. They generalize models based on classical interacting point particles to models based on curves. In this new set-up, a mean field result is proven, as \(N\rightarrow \infty \). The limit PDE is vector valued and, in the limit, each curve interacts with a mean field solution of the PDE. This target is reached by a careful formulation of curves and weak solutions of the PDE which makes use of 1-currents and their topologies. The main results are based on the analysis of a nonlinear Lagrangian-type flow equation. Most of the results are deterministic; as a by-product, when the initial conditions are given by families of independent random curves, we prove a propagation of chaos result. The results are local in time for general interaction kernel, global in time under some additional restriction. Our main motivation is the approximation of 3D-inviscid flow dynamics by the interacting dynamics of a large number of vortex filaments, as observed in certain turbulent fluids; in this respect, the present paper is restricted to smoothed interaction kernels, instead of the true Biot–Savart kernel.     

Determining Nuclear Form factor for Detection of Dark Matter in Relativistic Mean Field Theory

In this work, we derive the nuclear form factor for the spin-independent collision between the WIMPs and nucleus in terms of the relativistic mean field (RMF) theory. Comparison with the traditional form factors which are commonly used in literature is given and it is found that our results are slightly above that of the 2PF model by 4% to 8%, but deviate from the Helm form factor by 15% to 25% for the whole recoil energy spectrum of 0 ∽ 100keV. Moreover, taking Xe and Ge as examples, we show the dependenceof the form factor on the recoil energy.     

Mean Field Evolution of Fermions with Coulomb Interaction

We study the many body Schrödinger evolution of weakly coupled fermions interacting through a Coulomb potential. We are interested in a joint mean field and semiclassical scaling, that emerges naturally for initially confined particles. For initial data describing approximate Slater determinants, we prove convergence of the many-body evolution towards Hartree–Fock dynamics. Our result holds under a condition on the solution of the Hartree–Fock equation, that we can only show in a very special situation (translation invariant data, whose Hartree–Fock evolution is trivial), but that we expect to hold more generally.