现代中西方文艺理论学科基点研究

文艺理论的学科基点 ,是用严密的逻辑理论或学理思路组织起来的。现代西方文艺理论学科基点的研究 ,经历了一个由依傍型到偏激独立型 ,再到成熟独立型的过程。隐伏于其后的逻辑理论分别是黑格尔的“对立面的综合”、克罗齐的“度的理论”以及洛特曼的新思路。中国当代文艺理论学科基点的研究 ,目前正处在由依傍型到独立型转化的时期 ;应当注意避免偏激独立型的发展阶段。

Numerical Simulation of Independent Advance of Ore Breaking in the Non-pillar Sublevel Caving Method

The mechanism of stress generation and propagation by detonation loading in five separate independent advance of ore breaking patterns is discussed in the paper. An elastic numerical model was developed using AN- SYS/LS-DYNA 3D Nonlinear Dynamic Finite Element Software. In this package ANSYS is the preprocessor and LS-DYNA is the postprocessor. Numerical models in the paper to actual were l：10 and the element mesh was dissected in scanning mode utilizing the symmetry characteristics of the numerical model. Five different advance rates were studied. Parameters, such as the time required to maximum stress, the action time of the available stress, the maximum velocity of the nodes, the stress penetration time, the magnitude of the stress peak and the time duration for high stress were numerically simulated. The 2.2 m advance appeared optimum from an analysis of the simulation results. The results from numerical simulation have been validated by tests with physical models.

Theoretical generalization of Markov chain random field from potential function perspective

The inner relationship between Markov random field(MRF) and Markov chain random field(MCRF) is discussed. MCRF is a special MRF for dealing with high-order interactions of sparse data. It consists of a single spatial Markov chain(SMC) that can move in the whole space. Generally, the theoretical backbone of MCRF is conditional independence assumption, which is a way around the problem of knowing joint probabilities of multi-points. This so-called Naive Bayes assumption should not be taken lightly and should be checked whenever possible because it is mathematically difficult to prove. Rather than trap in this independence proving, an appropriate potential function in MRF theory is chosen instead. The MCRF formulas are well deduced and the joint probability of MRF is presented by localization approach, so that the complicated parameter estimation algorithm and iteration process can be avoided. The MCRF model is then applied to the lithofacies identification of a region and compared with triplex Markov chain(TMC) simulation. Analyses show that the MCRF model will not cause underestimation problem and can better reflect the geological sedimentation process.

Hyperferritinemia is a risk factor for steatosis in chronic liver disease

AIM: To investigate the relationship between ferritin and steatosis in patients with chronically abnormal liver function tests (LFTs) and high ferritin level. METHODS: One hundred and twenty-four consecutive patients with hyperferritinemia (male > 300 ng/mL, female > 200 ng/mL) were evaluated; clinical, biochemical and serological data, iron status parameters, HFE gene mutations and homeostasis model assessment score were obtained. Steatosis was graded by ultrasound as absent or present. Histology was available in 53 patients only. RESULTS: Mean level of ferritin was 881 ± 77 ng/mL in men and 549 ± 82 ng/mL in women. The diagnosis was chronic hepatitis C in 53 (42.7%), non-alcoholic fatty liver disease/non-alcoholic steatohepatitis in 57 (45.9%), and cryptogenic liver damage in 14 (11.3%). None was diagnosed as hereditary hemochromatosis (HH). Hepatic siderosis on liver biopsy was present in 17 of 54 (32%) patients; grade 1 in eight and grade 2 in nine. Overall, 92 patients (74.2%) had steatosis. By logistic regression, ferritin and γ-glutamyltransferase were independent predictors of steatosis. Ferritin levels were signifi cantly related to low platelet count, steatosis and hepatitis C virus infection. CONCLUSION: In a non-obese cohort of non-alcoholic patients with chronically abnormal LFTs without HH, high serum ferritin level is a risk factor for steatosis.

Algebraic Independence Measures for the Values of Mahler Type Functions

In this paper, we give the algebraic independence measures for the values ofMahler type functions in complex number field and p-adic number field, respectively.

Ranking Independent Variables in Linear Models

In this article, the true model of sizep is considered. The importance of the independent variables will be studied. The model by dropping independent variable one at a time is called a reduced model, and the size of the model is p-1. The non-centrality quantity is employed to measure the strength of influence on the true model for the independent variable dropped. The larger influence is defined to be more important in the true model. In this article, the goal is ranking the importance of the independent variables to study the structure of the models. It is important in the practical application. The confidence interval approach is used to rank the degrees of the influence of the independent variables in linear models and achieve the goal. An illustrative example is given, and the modeling procedure is studied to check the assumptions step by step in this example to make sure the assumptions satisfied in the true model. As a result, the proposed method can be used efficiently.

Blind source separation based on generalized gaussian model

Since in most blind source separation(BSS)algorithms the estimations of probability density function(pdf)of sources are fixed or can only switch between one sup-Gaussian and other sub-Gaussian model,they may not be efficient to separate sources with different distributions.So to solve the problem of pdf mismatch and the separation of hybrid mixture in BSS,the generalized Gaussian model(GGM)is introduced to model the pdf of the sources since it can provide a general structure of univariate distributions.Its great advantage is that only one parameter needs to be determined in modeling the pdf of different sources,so it is less complex than Gaussian mixture model.By using maximum likelihood(ML)approach,the convergence of the proposed algorithm is improved.The computer simulations show that it is more efficient and valid than conventional methods with fixed pdf estimation.

Study on Solitary Waves of a General Boussinesq Model

In this paper, we employ the bifurcation method of dynamical systems to study the solitary waves and periodic waves of a generalized Boussinesq equations. All possible phase portraits in the parameter plane for the travelling wave systems are obtained. The possible solitary wave solutions, periodic wave solutions and cusp waves for the general Boussinesq type fluid model are also investigated.

A Study on sEMG Simulation Modeling and Its Decomposition Methods

In this paper, we establish a surface electromyography(sEMG) signal model and study the signal decomposition method from noisy background. Firstly, single fiber action potential (SFAP), motor unit action potential (MUAP) and motor unit action potential train(MUAPT) are simulated based on the tripolar signal source model, and then the sEMG is obtained; secondly, the simulated sEMG signal is extracted from the mixed signals that consists of white noises, power frequency interference signal and electrocardio signal by independent component analysis (ICA) algorithms; lastly, the spikes corresponding to each motor unit action potential from the simulated sEMG signals were detected by applying the wavelet transform (WT) method. Simulation results showed that sEMG model could describe the physiological process of sEMG, ICA and WT methods could extract the sEMG signal and its features, which will lay a foundation for further classifying the MUAP.

图书馆阅读推广活动中读者活动忠诚的认知过程分析

在图书馆开展阅读推广活动的情境下,探究"关联型"激励、"独立型"激励和活动环境激励对读者活动忠诚的认知过程的影响机理,对图书馆制定科学、有效的阅读推广活动方案及塑造阅读推广活动品牌具有积极意义。在整合社会认知理论、自我建构理论以及动机理论等理论的基础上,从"关联型"激励、"独立型"激励和活动环境激励的多维视角出发,提出研究假设并构建了理论模型。实证研究部分,以参与过图书馆阅读推广活动的读者为研究对象,采用问卷调研的方式获取613份研究数据对理论模型进行检验。结果显示,"关联型"激励和"独立型"激励对活动环境激励具有积极影响作用,同时"关联型"激励、"独立型"激励和活动环境激励均对活动忠诚有积极影响作用。

Model-free conditional independence feature screening for ultrahigh dimensional data

Feature screening plays an important role in ultrahigh dimensional data analysis.This paper is concerned with conditional feature screening when one is interested in detecting the association between the response and ultrahigh dimensional predictors(e.g.,genetic makers)given a low-dimensional exposure variable(such as clinical variables or environmental variables).To this end,we first propose a new index to measure conditional independence,and further develop a conditional screening procedure based on the newly proposed index.We systematically study the theoretical property of the proposed procedure and establish the sure screening and ranking consistency properties under some very mild conditions.The newly proposed screening procedure enjoys some appealing properties.(a)It is model-free in that its implementation does not require a specification on the model structure;(b)it is robust to heavy-tailed distributions or outliers in both directions of response and predictors;and(c)it can deal with both feature screening and the conditional screening in a unified way.We study the finite sample performance of the proposed procedure by Monte Carlo simulations and further illustrate the proposed method through two real data examples.

Asymptotic Distribution of the Jump Change-Point Estimator

The asymptotic distribution of the change-point estimator in a jump change- point model is considered. For the jump change-point model Xi - α ＋ θ{[nτ0] 〈 i ≤ n} ＋ εi, where εi （i = 1,…. , n） are independent identically distributed random variables with Eεi -= 0 and Var（εi） 〈 ∞, with the help of the slip window method, the asymptotic distribution of the jump change-point estimator τ is studied under the condition of the local alternative hypothesis.

Segregation between the parietal memory network and the default mode network： effects of spatial smoothing and model order in ICA

Abstract A brain network consisting of two key parietal nodes, the precuneus and the posterior cingulate cortex, has emerged from recent fMRI studies. Though it is anatomically adjacent to and spatially overlaps with the default mode network （DMN）, its function has been associated with memory processing, and it has been referred to as the parietal memory network （PMN）. Independent component analysis （ICA） is the most common data-driven method used to extract PMN and DMN simultaneously. However, the effects of data preprocessing and parameter determi- nation in ICA on PMN-DMN segregation are completely unknown. Here, we employ three typical algorithms of group ICA to assess how spatial smoothing and model order influence the degree of PMN-DMN segregation. Our findings indicate that PMN and DMN can only be stably separated using a combination of low-level spatial smoothing and high model order across the three ICA algorithms. We thus argue for more considerations on parametric settings for interpreting DMN data.

A strong approximation of self-normalized sums

Let {X,Xn,n1} be a sequence of independent identically distributed random variables with EX=0 and assume that EX2I(|X|≤x) is slowly varying as x→∞,i.e.,X is in the domain of attraction of the normal law.In this paper a Strassen-type strong approximation is established for self-normalized sums of such random variables.

Continuous-Time Independent Edge-Markovian Random Graph Process

In this paper, the continuous-time independent edge-Markovian random graph process model is constructed. The authors also define the interval isolated nodes of the random graph process, study the distribution sequence of the number of isolated nodes and the probability of having no isolated nodes when the initial distribution of the random graph process is stationary distribution, derive the lower limit of the probability in which two arbitrary nodes are connected and the random graph is also connected, and prove that the random graph is almost everywhere connected when the number of nodes is sufficiently large.