Two-Stage Variables Acceptance Sampling Plans Using Process Loss Functions

In this article, a variable two-stage acceptance sampling plan is developed when the quality characteristic is evaluated through a process loss function. The plan parameters of the proposed plan are determined by using the two-point approach and tabulated according to various quality levels. Two cases are discussed when the process mean lies at the target value and when it does not, respectively. Extensive tables are provided for both cases and the results are explained with examples. The advantage of the proposed plan is compared with the existing variable single acceptance sampling plan using the process loss function.     

An augmented data scoring algorithm for maximum likelihood

The expectation-maximization (EM) method facilitates computation of max¬imum likelihood (ML) and maximum penalized likelihood (MPL) solutions. The procedure requires specification of unobservabie complete data which augment the measured or incomplete data. This specification defines a conditional expectation of the complete data log-likelihood function which is computed in the E-stcp. The EM algorithm is most effective when maximizing the iunction Q{0) denned in the F-stnp is easier than maximizing the likelihood function.

The Monte Carlo EM (MCEM) algorithm of Wei & Tanner (1990) was introduced for problems where computation of Q is difficult or intractable. However Monte Carlo can he computationally expensive, e.g. in signal processing applications involving large numbers of parameters. We provide another approach: a modification of thc standard EM algorithm avoiding computation of conditional expectations.     

Multiplicative Algorithms for Maximum Penalized Likelihood Inversion with Non Negative Constraints and Generalized Error Distributions

In many linear inverse problems the unknown function f (or its discrete approximation Θ _p×1), which needs to be reconstructed, is subject to the non negative constraint(s); we call these problems the non negative linear inverse problems (NNLIPs). This article considers NNLIPs. However, the error distribution is not confined to the traditional Gaussian or Poisson distributions. We adopt the exponential family of distributions where Gaussian and Poisson are special cases. We search for the non negative maximum penalized likelihood (NNMPL) estimate of Θ. The size of Θ often prohibits direct implementation of the traditional methods for constrained optimization. Given that the measurements and point-spread-function (PSF) values are all non negative, we propose a simple multiplicative iterative algorithm. We show that if there is no penalty, then this algorithm is almost sure to converge; otherwise a relaxation or line search is necessitated to assure its convergence.     

Developing a variables repetitive group sampling plan based on process capability index C pk with unknown mean and variance

In this paper, variables repetitive group sampling plans are developed based on the process capability index C _pk when the quality characteristic follows a normal distribution with unknown mean and variance. The sampling plan parameters such as the sample size and the acceptance constant are determined to minimize the average sample number. Symmetric and asymmetric cases, in percent defectives due to two specification limits, are dealt with for specified combinations of acceptable quality level and limiting quality level. Tables are provided and examples are given to use proposed plans in practice.     

中国产业链供应链现代化水平的区域差异及收敛性研究

提升产业链供应链现代化水平是推动经济体系优化升级的重点任务。论文首先界定了产业链供应链现代化的内涵,并根据其内涵构建综合评价指标体系。然后,研究2011—2020年产业链供应链现代化水平的分布动态、区域差异与空间收敛性。结果表明：我国产业链供应链现代化水平在空间上呈现出“东高、中西低”的阶梯状空间分布特征;区域产业链供应链现代化水平差异主要来源于区域间差异,而区域间差异主要来源于东部-中部和东部-西部的地区差异;全国及各区域产业链供应链现代化水平在空间上无明显σ收敛特征,全国及各区域存在显著的绝对β收敛和条件β收敛,表明各区域朝着自身稳定状态发展。最后根据研究结果,提出关于产业链供应链现代化水平提升的对策建议。     

“三位一体”综合合作何以可能：金融赋能农业产业发展的逻辑

在金融赋能农业产业发展的探索中形成了以农业产业组织为主导力量,金融全方位支持的生产合作、供销合作、信用合作“三位一体”综合合作,“三位一体”综合合作是社会系统结构与行动者的认知和实践相互作用的结果。外在的法律规则、政策制度等制度性要素和经济诱因、社会网络、模仿创新等内在的技术性要求,共同形塑了“三位一体”综合合作的生成路径、演化空间与组织边界,以及实践发展需要克服的困境。在新的时代背景下,发展“三位一体”综合合作需要营造良好的外部环境、构建不同农业经营服务主体紧密的联结机制、提升农业经营服务主体能力水平、推动为农服务资源聚集,更好地引领小农生产与现代农业发展有机衔接,更好地服务于农业强国建设。     

隐喻指称及其语用功能

隐喻指称是一种特殊的指称照应方式。以汉语中的隐喻指称为例,对隐喻指称的分类、构式和语用功能进行了探讨。研究显示：作为喻体的指称形式既可用于回指,也可用于下指;根据喻体和本体的相似程度,隐喻指称又可分为常规隐喻指称和新鲜隐喻指称,前者的使用频率远高于后者。隐喻指称的主要构式有P+NP、NP+NP、S(s)+NP、NP+P和NP+S(s),其核心功能是表达生动精炼,也具有增补信息、评价和委婉功能。     

基于三维框架的省级教育信息化政策文本量化分析

构建适切的省级教育信息化政策体系,是国家教育信息化建设取得成功的重要保障。基于政策工具的视角,以2015年以来省级政府公开的教育信息化政策为研究对象,从政策工具、政策主题和政策要素三个维度对政策文本进行挖掘分析。研究发现：省级教育信息化“政策林”已基本形成,政策文本形式以解释类文本为主,但发文主体较为单一;政策工具使用存在结构性失调,表现为对权威工具高度依赖,对激励工具和象征与劝诫工具存在一般性依赖,对能力建设与系统变革工具存在弱依赖;区域教育信息化政策内容相似度高,但在教育信息化“建”与“用”的政策主题上存在明显不同偏好,且在政策要素方面的政策供给存在“差序化格局”。最后,从丰富政策形式、优化政策工具结构、完善政策内容三个方面提出政策优化的建议。     

毕摩特依·毕摩·毕——基于卡塔西斯谱系的辨识

毕摩特依是毕摩沿袭“残缺”传统的“书写”作品,具有诗性的特质,其所指是通过“毕”这个“说唱艺术”的能指实现的。因此,毕摩特依、毕摩、毕三者只有共在,才能构成一个完整的模仿或叙事方式的“说唱”场域,各异的毕摩、毕摩特依、毕的组合凭借各自的结构力量占据空间、决定运作逻辑。“卡塔西斯”（Catharsis）,作为亚里士多德《诗学》的叙事功能,依之可以理解与解释毕摩特依、毕摩、毕共在中聚焦于伦理、心理、宗教等视域的“净化”“陶冶”“疏泄”——即“卡塔西斯”的场和力。     

Modular-transform based clustering

Spectral clustering uses eigenvectors of the Laplacian of the similarity matrix. It is convenient to solve binary clustering problems. When applied to multi-way clustering, either the binary spectral clustering is recursively applied or an embedding to spectral space is done and some other methods, such as K-means clustering, are used to cluster the points. Here we propose and study a K-way clustering algorithm – spectral modular transformation, based on the fact that the graph Laplacian has an equivalent representation, which has a diagonal modular structure. The method first transforms the original similarity matrix into a new one, which is nearly disconnected and reveals a cluster structure clearly, then we apply linearized cluster assignment algorithm to split the clusters. In this way, we can find some samples for each cluster recursively using the divide and conquer method. To get the overall clustering results, we apply the cluster assignment obtained in the previous step as the initialization of multiplicative update method for spectral clustering. Examples show that our method outperforms spectral clustering using other initializations.