民族互嵌社区精准治理的理论向度与实践路径——以贵州省兴义市S街道社区为例

民族互嵌社区是中华民族多元一体格局空间场域延展的实践形态,是对“各民族相互嵌入式的社会结构和社区环境”这一时代命题的现实阐释。现代城市社区功能的集聚和细化为民族互嵌的再造及重释提供了场域空间,由于民族互嵌社区具有公共性、聚合性和互动性的本质特征,从而形成了立体化嵌套结构,催生出精准互嵌理论和实践的“双重愿景”:理论层面,要深刻把握民族互嵌社区精准治理的目标向度、方法向度、内容向度;实践层面,要精准识别民族互嵌社区的场域情境,以智能技术描绘民族互嵌过程的“全景图”和“晴雨表”,通过社区制度体系的精准供给,提升民族互嵌社区精准治理的效能。     

Moderate Deviations for Bayes Posteriors

Let ( X_k ) k be a sequence of i.i.d. random variables taking values in a set , and consider the problem of estimating the law of X₁ in a Bayesian framework. We prove, under mild conditions on the prior, that the sequence of posterior distributions satisfies a moderate deviation principle.     

深水钻孔灌注桩基础施工技术

针对济阳黄河公路大桥钻孔灌注桩桩身长、孔壁易失稳垮塌、单桩混凝土灌注量大等施工难题，通过合理机械选型和控制护壁泥浆配比，取得了良好效果。以4″主墩为例介绍了施工条件及钻孔灌注桩施工技术措施，总结了施工效果。     

救赎：寻求民族文化的出路——解读莫里森的《柏油孩子》

高速铁路具有高度集成、高精度的技术特点，运营过程中经受列车质量、速度、密度等多种因素影响，地理位置因素往往是影响铁路设备状态演变的决定性因素。采用网格化管理技术可将空间上连续分布的管理对象划分成较小的单元网格，有利于从空间位置角度研究管理对象状态的变化规律。随着信息系统技术、大数据技术的迅猛发展，基于位置而不是基于专业更符合高速铁路的管理需求，网格化管理技术给高速铁路管理带来了新的视角。     

Conditional entropy,entropy density,and strong law of large numbers for generalized controlled tree-indexed Markov chains

In this paper, we first introduces a tree model without degree boundedness restriction namely generalized controlled tree T, which is an extension of some known tree models, such as homogeneous tree model, uniformly bounded degree tree model, controlled tree model, etc. Then some limit properties including strong law of large numbers for generalized controlled tree-indexed non homogeneous Markov chain are obtained. Finally, we establish some entropy density properties, monotonicity of conditional entropy, and entropy properties for generalized controlled tree-indexed Markov chains.     

The almost sure convergence rate of the estimator of optimized certainty equivalent risk measure under α-mixing sequences

Conditional value-at-risk (CVaR) model is a kind of financial risk measure that is extensively supported and accepted by international financial community. Its optimized form can be regarded as an optimized certainty equivalent (OCE) risk measurement. In this paper, we mainly discuss and analyze the strong laws of large numbers and the convergence rate of OCE's estimator under α-mixing sequences. The result shows that the almost sure convergence rate of CVaR estimator is given by the results of OCE estimator. Its convergence rate is inversely proportional to the square root of the sample size under certain conditions. Its effectiveness is verified by simulation experiments for two classical α-mixing sequences.     

Precise large deviations for the difference of two sums of END random variables with heavy tails

Assume that there are two types of insurance contracts in an insurance company, and the ith related claims are denoted by {X_ij, j ? 1}, i = 1, 2. In this article, the asymptotic behaviors of precise large deviations for non random difference ∑^n₁(t)_{j = 1}X_1j ? ∑^n₂(t)_{j = 1}X_2j and random difference ∑^N₁(t)_{j = 1}X_1j ? ∑^N₂(t)_{j = 1}X_2j are investigated, and under several assumptions, some corresponding asymptotic formulas are obtained.     

Hypothesis Testing in a Rayleigh Diffusion Model

Applying the large and moderate deviations for the log-likelihood ratio of the Rayleigh diffusion model, we give the negative regions in testing Rayleigh diffusion model and obtain the decay rates of the error probabilities.     

Complete convergence and the strong laws of large numbers for pairwise NQD random variables

The authors study the strong convergence for sequences of pairwise negatively quadrant dependent (NQD) random variables under some wide conditions, and present some new theorems on the complete convergence and the strong laws of large numbers. The obtained results extend and improve some theorems in existing literature.     

Strong law of large numbers and complete convergence for non-identically distributed WOD random variables

In this paper, we establish the strong law of large numbers and complete convergence for non-identically distributed WOD random variables. We derive some new inequalities of Fuk–Nagaev type for the sums of non-identically distributed WD random variables. All these results further extend and refine previous ones.