突出矿井瓦斯抽放限制影响因素的AHP-熵权法评价模型

为提高煤与瓦斯突出矿井瓦斯抽放效果,建立了3个一级指标、14个二级指标的突出矿井瓦斯抽放限制影响因素评价指标体系,利用AHP和熵权法分别确定指标因子主、客观权重.通过实地调研分析和反馈验证了AHP-熵权法的可行性和正确性,利用加权平均法确定评价模型的综合权重.研究表明:封孔方式、钻孔半径、抽放时间、煤体裂隙发育程度和抽放负压是目前影响煤矿瓦斯抽放效果的主控因素.     

Localization and quantification of radical production in cavitating flows with luminol chemiluminescent reactions

Hydrodynamic cavitation experiments in microfluidic systems have been performed with an aqueous solution of luminol as the working fluid. In order to identify where and how much reactive radical species are formed by the violent bubble collapse, the resulting chemiluminescent oxidation reaction of luminol was scrutinized downstream of a constriction in the microchannel. An original method was developed in order to map the intensity of chemiluminescence emitted from the micro-flow, allowing us to localize the region where radicals are produced. Time averaged void fraction measurements performed by laser induced fluorescence experiments were also used to determine the cavitation cloud position. The combination void fraction and chemiluminescence two-dimensional mapping demonstrated that the maximum chemiluminescent intensity area was found just downstream of the cavitation clouds. Furthermore, the radical yield can be obtained with our single photon counting technique. The maximum radical production rates of 1.2*10⁷ OH/s and radical production per processed liquid volume of 2.15*10¹⁰ HO/l were observed. The proposed technique allows for two-dimensional characterisation of radical production in the microfluidic flow and could be a quick, non-intrusive way to optimise hydrodynamic cavitation reactor design and operating parameters, leading to enhancements in wastewater treatments and other process intensifications.     

A Structural Entropy Measurement Principle of Propositional Formulas in Conjunctive Normal Form

The satisfiability (SAT) problem is a core problem in computer science. Existing studies have shown that most industrial SAT instances can be effectively solved by modern SAT solvers while random SAT instances cannot. It is believed that the structural characteristics of different SAT formula classes are the reasons behind this difference. In this paper, we study the structural properties of propositional formulas in conjunctive normal form (CNF) by the principle of structural entropy of formulas. First, we used structural entropy to measure the complex structure of a formula and found that the difficulty solving the formula is related to the structural entropy of the formula. The smaller the compressing information of a formula, the more difficult it is to solve the formula. Secondly, we proposed a λ-approximation strategy to approximate the structural entropy of large formulas. The experimental results showed that the proposed strategy can effectively approximate the structural entropy of the original formula and that the approximation ratio is more than 92%. Finally, we analyzed the structural properties of a formula in the solution process and found that a local search solver tends to select variables in different communities to perform the next round of searches during a search and that the structural entropy of a variable affects the probability of the variable being flipped. By using these conclusions, we also proposed an initial candidate solution generation strategy for a local search for SAT, and the experimental results showed that this strategy effectively improves the performance of the solvers CCAsat and Sparrow2011 when incorporated into these two solvers.     

熵的一种通俗教法

熵如力、能量和动量一样是物理学中一个重要概念,若能用一种通俗易懂的方法设计熵的教学,对文科物理的教学有重要意义.为此本文提出了一种通俗的熵的教法,这一教法不需要学生学习热力学第二定律也可以建立熵的概念.具体教学设计如下:通过日常生活例子引入熵的概念(也就是玻尔兹曼熵),设计两个例子让学生会计算熵,通过具体问题的讨论让学生充分理解熵的意义,通过一个实例由玻尔兹曼熵引入克劳修斯熵公式,设计一个演示实验强化教学效果,将熵与环境保护联系起来融入人文情怀,最后还强调了熵计算的不同层次.教学设计完全采用基于问题学习(PBL)的教学模式.     

基于串联两阶段网络DEA模型的供应链规模收益分析

数据包络分析(DEA)是评价供应链系统(Supply chain system)间相对有效性的一种重要的工具,但是传统的DEA不考虑供应链的内部结构,对系统效率评价偏高;而本文所研究两阶段串联供应链系统,考虑把部分中间产品作为最终产品输出,增加额外中间投入的情形.基于所提出的供应链系统结构,本文建立相应的串联结构下的网络DEA模型,并针对所建立模型进行相关理论的研究,给出了串联结构下的生产可能集和规模收益情况判定方法.最后,进行数值实验,以验证我们提出的结论.     

An inequality connecting entropy distance,Fisher Information and large deviations

In this paper we introduce a new generalisation of the relative Fisher Information for Markov jump processes on a finite or countable state space, and prove an inequality which connects this object with the relative entropy and a large deviation rate functional. In addition to possessing various favourable properties, we show that this generalised Fisher Information converges to the classical Fisher Information in an appropriate limit. We then use this generalised Fisher Information and the aforementioned inequality to qualitatively study coarse-graining problems for jump processes on discrete spaces.     

Small activation entropy bestows high-stability of nanoconfined D-mannitol

It has been a long-standing puzzling problem that some glasses exhibit higher glass transition temperatures(denoting high stability) but lower activation energy for relaxations(denoting low stability). In this paper, the relaxation kinetics of the nanoconfined D-mannitol(DM) glass was studied systematically using a high-precision and high-rate nanocalorimeter.The nanoconfined DM exhibits enhanced thermal stability compared to the free DM. For example, the critical cooling rate for glass formation decreases from 200 K/s to below 1 K/s; the Tg increases by about 20 K–50 K. The relaxation kinetics is analyzed based on the absolute reaction rate theory. It is found that, even though the activation energy E~*decreases,the activation entropy S~*decreases much more for the nanoconfined glass that yields a large activation free energy G~*and higher thermal stability. These results suggest that the activation entropy may provide new insights in understanding the abnormal kinetics of nanoconfined glassy systems.     

An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.