“两个大局”思想的辩证思考及其现实意义

“两个大局”思想是贯穿邓小平理论的一个重要战略思想。它的提出既针对当时中国的现实国情,也考虑到将来发展中遇到的问题。“两个大局”蕴舍丰富的辩证思想,体现了现实利益与长远利益、效率与公平、分工与协作、权利与义务的统一。在改革开放已进入关键阶段的今天,“两个大局”思想仍具有重要的现实意义。     

金融危机视角的农地制度安排:四村庄个案

由于地域差异明显,各地农地制度的安排与实践也有明显的区别,因而形成了不同的应对金融危机的实践形态。对这些实践形态进行比较分析发现,农地承包关系的灵活抑或刚性、农地产权的集体所有抑或私有并不是一个非此即彼的关系。从公平与效率两者结合的角度出发,构造一种将农村耕地划分为相对刚性的口粮田以保障农户的基本生存和相对灵活的责任田以保障农户的农业生产,并随人口变动而实现农户个体内部的口粮田与责任田自行补充的＂新两田制＂,有利于农村社会和整个国家应对危机,并为其处理危机赢得了相对宽松的时间与空间。     

通信企业"三网融合"方案设计研究

随着经济和技术的发展,传统的业务服务已经不能满足人们的需求,"三网融合"已经成为网络业务发展的必然趋势.通过对"三网融合"国内外现状的研究,分析了当今"三网融合"面临的问题,结合通信企业的实际情况,提出采用分布式流媒体技术进行通信企业"三网融合"方案设计,并针对不同规模通信企业,提出了紧凑型组网、城域网本地组网、多级分布式组网三种组网方案.该"三网融合"设计方案可以有效地节省骨干网带宽、保证QoS,提高用户满意度,对各级通信企业搭建多媒体数据中心具有一定推广作用.     

加涅思想对教育技术学发展的影响及启示

罗伯特·加涅是美国当代著名教育心理学家,后半生将主要精力集中于学习理论、教学设计乃至教育技术学基础理论的研究和构建中,成为心理学和教育技术学这两个研究领域公认的大师级人物,被认为是“当代心理科学与学校教育相结合的典范”。加涅思想的核心是学习论与教学论,其对我国教育技术学研究与发展的启示主要体现在以下几方面:第一,以兼容并包的学术态度积极开展教育技术研究,促进我国教育技术学科的繁荣发展;第二,树立教育技术理论研究的实践观,注重理论研究与教育实践的结合;第三,树立现代教学设计观,注重培养学生“学会认知”的能力;第四,树立现代人才观,培养以教学设计能力为中心的教育技术学专业人才。     

论现代报纸版面设计艺术

现代报纸版面设计利用电子排版技术,使版面更富有艺术性。报纸版面设计的艺术性表现在:模块版式、多层色彩、 图文并重、厚题薄文、直线分割几个方面。     

Adaptive designs for IVPT data with mixed scaled average bioequivalence

In vitro permeation tests (IVPT) offer accurate and cost-effective development pathways for locally acting drugs, such as topical dermatological products. For assessment of bioequivalence, the FDA draft guidance on generic acyclovir 5% cream introduces a new experimental design, namely the single-dose, multiple-replicate per treatment group design, as IVPT pivotal study design. We examine the statistical properties of its hypothesis testing method—namely the mixed scaled average bioequivalence (MSABE). Meanwhile, some adaptive design features in clinical trials can help researchers make a decision earlier with fewer subjects or boost power, saving resources, while controlling the impact on family-wise error rate. Therefore, we incorporate MSABE in an adaptive design combining the group sequential design and sample size re-estimation. Simulation studies are conducted to study the passing rates of the proposed methods—both within and outside the average bioequivalence limits. We further consider modifications to the adaptive designs applied for IVPT BE trials, such as Bonferroni's adjustment and conditional power function. Finally, a case study with real data demonstrates the advantages of such adaptive methods.     

Remove unwanted variation retrieves unknown experimental designs

Remove unwanted variation (RUV) is an estimation and normalization system in which the underlying correlation structure of a multivariate dataset is estimated from negative control measurements, typically gene expression values, which are assumed to stay constant across experimental conditions. In this paper we derive the weight matrix which is estimated and incorporated into the generalized least squares estimates of RUV-inverse, and show that this weight matrix estimates the average covariance matrix across negative control measurements. RUV-inverse can thus be viewed as an estimation method adjusting for an unknown experimental design. We show that for a balanced incomplete block design (BIBD), RUV-inverse recovers intra- and interblock estimates of the relevant parameters and combines them as a weighted sum just like the best linear unbiased estimator (BLUE), except that the weights are globally estimated from the negative control measurements instead of being individually optimized to each measurement as in the classical, single measurement BIBD BLUE.     

Web based resource for Statistical Consultants in the Pharmaceutical Industry

The intention of this article is to highlight sources of web-based reference material and software that will aid consulting statisticians when designing clinical trials. The article includes websites that provide links to explanation of statistical concepts for non-statisticians, regulatory guidelines, and free statistical study design software.     

Generalized phase I-II designs to increase long term therapeutic success rate

Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short-term outcomes will maximize the agent's long-term therapeutic success rate. In many clinical settings, this assumption is not true. A dose selected in an early phase oncology trial may give suboptimal progression-free survival or overall survival time, often due to a high rate of relapse following response. To address this problem, a new family of Bayesian generalized phase I-II designs is proposed. First, a conventional phase I-II design based on short-term outcomes is used to identify a set of candidate doses, rather than selecting one dose. Additional patients then are randomized among the candidates, patients are followed for a predefined longer time period, and a final dose is selected to maximize the long-term therapeutic success rate, defined in terms of duration of response. Dose-specific sample sizes in the randomization are determined adaptively to obtain a desired level of selection reliability. The design was motivated by a phase I-II trial to find an optimal dose of natural killer cells as targeted immunotherapy for recurrent or treatment-resistant B-cell hematologic malignancies. A simulation study shows that, under a range of scenarios in the context of this trial, the proposed design has much better performance than two conventional phase I-II designs.     

How Large Are Your G-Values? Try Gosset’s Guinnessometrics When a Little “p” Is Not Enough

Abstract

A crisis of validity has emerged from three related crises of science, that is, the crises of statistical significance and complete randomization, of replication, and of reproducibility. Guinnessometrics takes commonplace assumptions and methods of statistical science and stands them on their head, from little p-values to unstructured Big Data. Guinnessometrics focuses instead on the substantive significance which emerges from a small series of independent and economical yet balanced and repeated experiments. Originally developed and market-tested by William S. Gosset aka “Student” in his job as Head Experimental Brewer at the Guinness Brewery in Dublin, Gosset’s economic and common sense approach to statistical inference and scientific method has been unwisely neglected. In many areas of science and life, the 10 principles of Guinnessometrics or G-values outlined here can help. Other things equal, the larger the G-values, the better the science and judgment. By now a colleague, neighbor, or YouTube junkie has probably shown you one of those wacky psychology experiments in a video involving a gorilla, and testing the limits of human cognition. In one video, a person wearing a gorilla suit suddenly appears on the scene among humans, who are themselves engaged in some ordinary, mundane activity such as passing a basketball. The funny thing is, prankster researchers have discovered, when observers are asked to think about the mundane activity (such as by counting the number of observed passes of a basketball), the unexpected gorilla is frequently unseen (for discussion see Kahneman 2011 Kahneman, D. (2011), Thinking Fast and Slow, New York: Farrar, Straus and Giroux. [Google Scholar]). The gorilla is invisible. People don’t see it.