健美操、韵律操与体育锻炼

本文论述健美操和韵律操在学校体育教学中的作用及开展健美操、韵律操体育活动的建议。     

论刘禹锡诗歌的语言风格

刘禹锡作为中唐著名诗人,"无体不备.蔚为大家",其诗语言能合气骨、情致、韵律于一体,熔豪健、含蓄、明快于一炉.既重雕琢之功,豪宕而不俚俗;又不遗斧凿痕迹,明快而又含蕴.     

Parameter estimation procedures for exponential-family random graph models on count-valued networks: A comparative simulation study

The exponential-family random graph models (ERGMs) have emerged as an important framework for modeling social networks for a wide variety of relational types. ERGMs for valued networks are less well-developed than their unvalued counterparts, and pose particular computational challenges. Network data with edge values on the non-negative integers (count-valued networks) is an important such case, with examples ranging from the magnitude of migration and trade flows between places to the frequency of interactions and encounters between individuals. Here, we propose an efficient parallelizable subsampled maximum pseudo-likelihood estimation (MPLE) scheme for count-valued ERGMs, and compare its performance with existing Contrastive Divergence (CD) and Monte Carlo Maximum Likelihood Estimation (MCMLE) approaches via a simulation study based on migration flow networks in two U.S. states. Our results suggest that edge value variance is a key factor in method performance, while network size mainly influences their relative merits in computational time. For small-variance networks, all methods perform well in point estimations while CD greatly overestimates uncertainties, and MPLE underestimates them for dependence terms; all methods have fast estimation for small networks, but CD and subsampled multi-core MPLE provides speed advantages as network size increases. For large-variance networks, both MPLE and MCMLE offer high-quality estimates of coefficients and their uncertainty, but MPLE is significantly faster than MCMLE; MPLE is also a better seeding method for MCMLE than CD, as the latter makes MCMLE more prone to convergence failure. The study suggests that MCMLE and MPLE should be the default approach to estimate ERGMs for small-variance and large-variance valued networks, respectively. We also offer further suggestions regarding choice of computational method for valued ERGMs based on data structure, available computational resources and analytical goals.     

Longitudinal network models and permutation-uniform Markov chains

Consider longitudinal networks whose edges turn on and off according to a discrete-time Markov chain with exponential-family transition probabilities. We characterize when their joint distributions are also exponential families with the same parameter, improving data reduction. Further we show that the permutation-uniform subclass of these chains permit interpretation as an independent, identically distributed sequence on the same state space. We then apply these ideas to temporal exponential random graph models, for which permutation uniformity is well suited, and discuss mean-parameter convergence, dyadic independence, and exchangeability. Our framework facilitates our introducing a new network model; simplifies analysis of some network and autoregressive models from the literature, including by permitting closed-form expressions for maximum likelihood estimates for some models; and facilitates applying standard tools to longitudinal-network Markov chains from either asymptotics or single-observation exponential random graph models.     

A Structural Model of Dense Network Formation

This paper proposes an empirical model of network formation, combining strategic and random networks features. Payoffs depend on direct links, but also link externalities. Players meet sequentially at random, myopically updating their links. Under mild assumptions, the network formation process is a potential game and converges to an exponential random graph model (ERGM), generating directed dense networks. I provide new identification results for ERGMs in large networks: if link externalities are nonnegative, the ERGM is asymptotically indistinguishable from an Erdős–Rényi model with independent links. We can identify the parameters only when at least one of the externalities is negative and sufficiently large. However, the standard estimation methods for ERGMs can have exponentially slow convergence, even when the model has asymptotically independent links. I thus estimate parameters using a Bayesian MCMC method. When the parameters are identifiable, I show evidence that the estimation algorithm converges in almost quadratic time.     

板式塔塔板结构的新设计法──负荷性能图法

提出由负荷性能图中各性能曲线计算式确定塔板主要结构尺寸。通过浮阀塔负荷性能图中各性能曲线与塔板各主要结构尺寸的简要分析，阐述了设计步骤。     

图形的逻辑力量：因果图的概念及其应用

本文旨在系统性地引介因果图方法,一种社会科学领域新近发展起来的探究因果推断的非参数估计工具。首先对因果图的基本概念和构型进行介绍,讨论变量之间不同“通路”对应的开启和阻断规则及因果推断中的三种偏差来源,即混淆偏差、过度控制偏差、内生性选择偏差。在此基础上,本文将因果图框架与现有定量社会科学研究中基于回归模型的因果推断方法思路进行融合,结合实际案例使用因果图阐释包括遗漏变量、样本选择、自选择及联立性在内的四种内生性问题,并对多元回归与匹配、代理变量、实验、工具变量、面板模型等因果推断方法的运行机制进行了图形化。最后,本文使用因果图厘清一些关于因果推断的不准确理解。     

民族地区农村经济研究的演进——基于1981-2022年CNKI期刊数据的可视化分析

本文选择CNKI数据库中1981年1月至2022年3月共计1070篇相关期刊论文为分析对象，采用CiteSpace5.5生成可视化知识图谱，对民族地区农村经济相关研究的现状、热点和前沿进行时空特征与关键词分析。民族地区农村经济研究在时间分布上呈现出明显的趋热性与阶段性，在空间上分布不均衡且欠缺跨区域交流合作；关键词分析显示研究发展进程可分为五个阶段，研究内容相对集中，但在深度与广度上还有所欠缺。随着乡村振兴战略已上升为国家级战略，以及铸牢中华民族共同体意识上升为新时代民族工作的主线，民族地区农村经济研究或将开启新的篇章，值得广大学者对其展开深入的探讨与研究。