社会主义协商民主制度的途径完善研究——基于系统熵理论的研究视角

社会主义协商民主是我国人民民主的重要形式,从其本身作为一个系统的角度来考虑,社会主义协商民主的过程实质上也是系统熵变的过程,其每个层次存在的状态及活动状况的调节,就体现了对这个系统熵变化的控制。因此,基于系统熵理论可从以下几方面完善社会主义协商民主制度:一是努力提高协商民主制度参与主体的公共理性和协商能力,调节负熵流;二是促进公民精神的形成发展,减少无效耗能,促进系统正熵的输出;三是进一步完善协商民主制度信息公开机制,强化管理监督,规范系统正熵的输出方式;四是扩大社会主义协商民主制度对外开放,促进系统熵降。

Integrating unascertained measurement and information entropy theory to assess blastability of rock mass

Due to the complex features of rock mass blastability assessment systems, an evaluation model of rock mass blastability was established on the basis of the unascertained measurement （UM） theory and the actual characteristics of the project. Considering a comprehensive range of intact rock properties and discontinuous structures of rock mass, twelve main factors influencing the evaluation blastability of rock mass were taken into account in the UM model, and the blastability evaluation index system of rock mass was constructed. The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively. Then, the UM function of each evaluation index was obtained based on the initial data for the analysis of the blastability of six rock mass at a highway improvement cutting site in North Wales. The index weights of the factors were calculated by entropy theory, and credible degree identification （CDI） criteria were established according to the UM theory. The results of rock mass blastability evaluation were obtained by the CDI criteria. The results show that the UM model assessment results agree well with the actual records, and are consistent with those of the fuzzy sets evaluation method. Meanwhile, the unascertained superiority degree of rock mass blastability of samples S1-$6 which can be calculated by scoring criteria are 3.428 5, 3.453 3, 4.058 7, 3.675 9, 3.516 7 and 3.289 7, respectively. Furthermore, the proposed method can take into account large amount of uncertain information in blastability evaluation, which can provide an effective, credible and feasible way for estimating the blastability of rock mass. Engineering practices show that it can complete the blastability assessment systematically and scientifically without any assumption by the proposed model, which can be applied to practical engineering.

Risk analysis for a cascade reservoir system using the brittle risk entropy method

The principal means of conserving water and utilizing hydropower in China is to exploit the use of a series of reservoirs in a cascade. This method and its inherent engineering safety problems are receiving increasing attention nowadays. In the field of engineering safety analysis, much work has focused on single reservoir projects in the past few years, but there is little research available on the safety risk analysis of cascade reservoirs, either within China or internationally. Therefore, a framework for risk analysis on the cascade reservoir system based on the theory of system engineering is constructed in this article. A cascading failure model is established and the connection degree factor discussed. In addition, the importance degree of the subsystem, which can be calculated by combining the analytical hierarchy process and the entropy weight method, is explained. According to brittleness theory of a complex system, brittle risk entropy is proposed as a performance index for measuring the collapse uncertainty of the cascade reservoir system. In addition, the brittle risk of the cascade reservoir system is predicted, which provides a reference for safety analysis in water conservation and hydropower construction projects in China.

Attribute reduction in interval-valued information systems based on information entropies

Interval-valued data appear as a way to represent the uncertainty affecting the observed values. Dealing with interval-valued information systems is helpful to generalize the applications of rough set theory. Attribute reduction is a key issue in analysis of interval-valued data. Existing attribute reduction methods for single-valued data are unsuitable for interval-valued data. So far, there have been few studies on attribute reduction methods for interval-valued data. In this paper, we propose a framework for attribute reduction in interval-valued data from the viewpoint of information theory. Some information theory concepts, including entropy, conditional entropy, and joint entropy, are given in interval-valued information systems. Based on these concepts, we provide an information theory view for attribute reduction in interval-valued information systems. Consequently, attribute reduction algorithms are proposed. Experiments show that the proposed framework is effective for attribute reduction in interval-valued information systems.

Information theory in nonlinear error growth dynamics and its application to predictability:Taking the Lorenz system as an example

In nonlinear error growth dynamics,the initial error cannot be accurately determined,and the forecast error,which is also uncertain,can be considered to be a random variable.Entropy in information theory is a natural measure of the uncertainty of a random variable associated with a probability distribution.This paper effectively combines statistical information theory and nonlinear error growth dynamics,and introduces some fundamental concepts of entropy in information theory for nonlinear error growth dynamics.Entropy based on nonlinear error can be divided into time entropy and space entropy,which are used to estimate the predictabilities of the whole dynamical system and each of its variables.This is not only applicable for investigating the dependence between any two variables of a multivariable system,but also for measuring the influence of each variable on the predictability of the whole system.Taking the Lorenz system as an example,the entropy of nonlinear error is applied to estimate predictability.The time and space entropies are used to investigate the spatial distribution of predictability of the whole Lorenz system.The results show that when moving around two chaotic attractors or near the edge of system space,a Lorenz system with lower sensitivity to the initial field behaves with higher predictability and a longer predictability limit.The example analysis of predictability of the Lorenz system demonstrates that the predictability estimated by the entropy of nonlinear error is feasible and effective,especially for estimation of predictability of the whole system.This provides a theoretical foundation for further work in estimating real atmospheric multivariable joint predictability.

Relativization of dynamical properties

In the past twenty years,great achievements have been made by many researchers in the studies of chaotic behavior and local entropy theory of dynamical systems.Most of the results have been generalized to the relative case in the sense of a given factor map.In this survey we offer an overview of these developments.

Quantum field entropy of the system with interaction between moving Bell-state atoms and coherent light field

The field entropy of the system with two moving atoms interacting with the coherent state is investigated by means of the full quantum theory. Under the different initial states with two atoms, the influences of the light field intensity and the atomic motion on the field entropy are discussed. The results indicate that the motion of the atoms leads to strict periodicity in the field entropy evolution. When the two atoms are in the Bell state ｜β11〉 initially, the system is in a completely disentangled state. For the atoms initially at other Bell states, the field periodically entangles with the atoms.