Analyzing Binomial Data in a Split-Plot Design: Classical Approach or Modern Techniques?

Modeling data that are non-normally distributed with random effects is the major challenge in analyzing binomial data in split-plot designs. Seven methods for analyzing such data using mixed, generalized linear, or generalized linear mixed models are compared for the size and power of the tests. This study shows that analyzing random effects properly is more important than adjusting the analysis for non-normality. Methods based on mixed and generalized linear mixed models hold Type I error rates better than generalized linear models. Mixed model methods tend to have higher power than generalized linear mixed models when the sample size is small.     

Empirical bayes tests in a positive exponential family with partial information on priors

We investigate an empirical Bayes testing problem in a positive exponential family having pdf f{x/θ)=c(θ)u(x) exp(?x/θ), x>0, θ>0. It is assumed that θ is in some known compact interval [C₁, C₂]. The value C₁ is used in the construction of the proposed empirical Bayes test δ^* _n. The asymptotic optimality and rate of convergence of its associated Bayes risk is studied. It is shown that under the assumption that θ is in [C₁, C₂] δ^* _n is asymptotically optimal at a rate of convergence of order O(n^?1/n n). Also, δ^* _n is robust in the sense that δ^* _n still possesses the asymptotic optimality even the assumption that "C₁≦θ≦C₂ may not hold.     

论唐前小说中的祈雨文化

祈雨属于祈禳术之一,与民间信仰、巫术、宗教等息息相关。不论受何种因素影响而产生,其文化内涵有共通之处,都寄寓着农耕时代的人们对生命的珍视,对风调雨顺的祈请与诉求。不少史料文献对祈雨皆有相关记载,唐前小说则以自己的方式对此加以呈现：祈雨的主体不受身份限制,普通民众、仕人、术士皆可参与,但主要为仕人与术士;祈雨仪式不拘一格,民间自发举行的较为简单、随意,重在书写施法者、法术的神异、祭祀主体的德行、祷祝的最终结果,受远古遗留的“万物有灵”“巫术”等思想影响较深。祈祷的神灵有山川树木等自然神、神话传说中的神仙、龙等,杂糅了儒释道等多种思想,有一定的地域色彩。官府发起的程式严整规范,且繁冗、复杂,意在区分等级,彰显对天神的虔诚与敬畏,以及心系苍生的民本思想、农业兴邦的治国理念。唐前小说对祈雨主体、仪式,以及祝祷神灵的书写,反映了当时人们真实的生存状况,有着深厚的文化蕴涵。     

德勒兹式仿像:真哲人与真智者的柏拉图式二元对待

本文对柏拉图真哲人与真智者的二元对待进行德勒兹式阐释.柏拉图以真理为根基评判对真理知识的种种宣称.真哲人居于宣称者等级的顶端而智者被示为作伪.真哲人以绝对真理为知识模型,由于对真理的分有度最高,其知识为绝对真理的优秀摹本;而智者仅为他的模型真哲人的仿像,其对相对真理知识的宣称因此被指斥为没有根基,为仿像.德勒兹则恢复智者差异的权利,赋予仿像永久、切近、临在的在场地位.柏拉图式二元对待也就此被揭示为德勒兹式仿像.     

《全宋诗》及其“订补”失收诗人七家补遗

明初编纂的大型中国古代诗词总集《诗渊》被称为“古诗,尤其是宋诗辑佚之渊薮”,在《全宋诗》的编纂及其订补过程中虽然得到了相当广泛的应用,然而尚多有遗珠.考订该书所录宋诗,《全宋诗》及其“订补”所失收者尚多有之,今择其要者凡诗人7家12题31首,公诸同好,并就教于方家.     

混合式教学情境下生成性资源的进化与促进策略

随着教育信息化的发展，网络教学与传统课堂教学优势互补的混合式教学模式被广泛应用，混合式教学将成为未来教学的主要方式。由于混合式教学情境既不同于传统课堂教学，又与在线教学有所区别，因此生成性资源在混合式教学情境中的进化方式也呈现出新的特点。通过分析、对比传统课堂教学情境与在线教学情境生成性资源的特点，归纳总结出混合式教学情境下生成性资源的特征，并根据混合式教学活动的实施过程，构建出生成性资源的进化模型，进而提出生成性资源进化的促进策略。     

词语考源疏解八则

本文对八条词语语源作了考证或疏解。“兰家女”诸多旧解有误,“兰家”义指大户人家,富贵人家。“翠峰如簇”之“簇”是屏障义。“略迹原情”,“原情”应解为推本情理。“抚尘”的朋友之义源自《汉书·王吉传》之典故。“膜拜”为梵汉结合词,“膜”出自梵语南元之音译语略,“膜拜”即口称南元而礼拜。“下担”的“担”取义于檐子,“下担”义相当于“下轿”。作者纠正《汉语大词典》的说法,指出“战越”之“越”不是取义“殒越”义,而是“　”的音借。“不拣”明其本字,补充了唐人用例。     

工程训练课程体系的研究与实践

在分析国内外工程实践教学特点和中国工程实践教学现状的基础上,结合中国工程实践教育的特点,提出了工程训练的培养方针、教学定位、教学理念和改革与建设的基本原则.构建并实践了工程训练的新课程体系:一种分层次、分阶段、递进式的教学体系,在每一个阶段覆盖机、电、控技术内容,具有综合性的特点;层次化的、不间断的实践过程又体现了工程实践教学实践性的特点.通过开放性实践教学平台的建设,在教学方法上实现了开放性和个性化的教学思想.在多年的实践教学中,取得了满意的效果.     

An approximation algorithm for maximum weight budgeted connected set cover

This paper studies approximation algorithm for the maximum weight budgeted connected set cover (MWBCSC) problem. Given an element set \(X\), a collection of sets \({\mathcal {S}}\subseteq 2^X\), a weight function \(w\) on \(X\), a cost function \(c\) on \({\mathcal {S}}\), a connected graph \(G_{\mathcal {S}}\) (called communication graph) on vertex set \({\mathcal {S}}\), and a budget \(L\), the MWBCSC problem is to select a subcollection \({\mathcal {S'}}\subseteq {\mathcal {S}}\) such that the cost \(c({\mathcal {S'}})=\sum _{S\in {\mathcal {S'}}}c(S)\le L\), the subgraph of \(G_{\mathcal {S}}\) induced by \({\mathcal {S'}}\) is connected, and the total weight of elements covered by \({\mathcal {S'}}\) (that is \(\sum _{x\in \bigcup _{S\in {\mathcal {S'}}}S}w(x)\)) is maximized. We present a polynomial time algorithm for this problem with a natural communication graph that has performance ratio \(O((\delta +1)\log n)\), where \(\delta \) is the maximum degree of graph \(G_{\mathcal {S}}\) and \(n\) is the number of sets in \({\mathcal {S}}\). In particular, if every set has cost at most \(L/2\), the performance ratio can be improved to \(O(\log n)\).