A variable-neighbourhood search algorithm for finding optimal run orders in the presence of serial correlation

The responses obtained from response surface designs that are run sequentially often exhibit serial correlation or time trends. The order in which the runs of the design are performed then has an impact on the precision of the parameter estimators. This article proposes the use of a variable-neighbourhood search algorithm to compute run orders that guarantee a precise estimation of the effects of the experimental factors. The importance of using good run orders is demonstrated by seeking D-optimal run orders for a central composite design in the presence of an AR(1) autocorrelation pattern.     

论马克思哲学“现实个人”理论及其当代审视

现实个人是马克思哲学的理论核心,依据社会形态理论其生成史可以阐发为三种形态：群体形态的个人,独立形态的个人,自由形态的个人,三种形态的历史演绎构成了现实个人发展的内在逻辑。当代中国正处于市场经济条件下,人表现为以物为基础的独立形态的个人,人的物化状态不可避免。物化状态的积极作用在于它是对群体形态个人的扬弃,对自由形态个人的基奠;其消极作用在于人以“物”而非“人”作为价值尺度,物质利益驱动使人对有限资源展开竞争而形成人际矛盾,而这一矛盾是引发社会种种不和谐现象的主要原因。因此,解决人际矛盾、实现入际和谐是构建和谐社会的根本着力点,其实现的根本途径在于民主、法治、道德的实现。     

今文《尚书》双宾语句型和双宾语动词的选择

汉语双宾语如何生成演变,双宾语动词有无选择性,学术界向少措意。对今文《尚书》的双宾语句进行穷尽性的分析,归纳出其中的两种主要句型七种主要句式,并通过与金甲文以及后代文献语言的纵横对比,演绎其语法特征和变化规律,对汉语双宾语句的研究具有语料价值和理论价值。     

略论语文心育的内在规定性

建国以来几十年语文教育的经验告诉我们,在语文教学中,育心是育人的根本所在.从语文的教学、心能目标、操作模式及实施效应等三个方面探讨语文心育的内在规律性,可以为语文心育提供合理的方式和方法.     

新世纪学报工作的发展研究

形势的发展,要求学报进行创新式的工作,将学报的发展与市场经济建设相联系;由此,大推进式的建构学报工作的新路径。     

早期中国话剧研究的学术定位及其理论意义

外倾社会心态制约着近代以来的中国戏剧变革最终走向了移植西方话剧的道路。人们对话剧的热情有时并不在话剧本身 ,而在于赋予话剧之外的那层心理色彩 ,在于人们对话剧的热情中把自己的社会心理倾向表达出来了。早期中国话剧研究的成果 ,对认识当代中国文艺的诸种现实多有裨益。话剧传入中国的文化史价值 ,主要不在于它传达了近代中国的意识形态 ,而在于它改变了传统中国戏剧的格局。早期话剧在文化史上不可取代的历史地位 ,恰恰在于它的艺术特征。     

Can the minimum living standard guarantee scheme enable the poor to escape the poverty trap in rural China?

The rural Minimum Living Standard Guarantee (Dibao) Scheme is the most important social assistance programme in rural China. However, how the rural Dibao programme affects household expenditures and whether it can enable the poor to escape the poverty trap are questions that remain largely unexplored. This study used data from the 2012 Rural Household Survey in China to investigate the impact of the rural Dibao programme on household expenditures. We found that the programme significantly improved the well‐being of low‐income households. Particularly, the programme significantly increased household expenditures on housing, education and health; furthermore, the impact was greater for households with educated household heads. However, the programme did not significantly affect household expenditures on food, transportation or farming inputs. The results imply that participation in the rural Dibao programme induces household investment in human capital, which could help to break the inter‐generational transmission of poverty and raise long‐run welfare.     

Southern knowledge online? Climate change research discoverability and communication practices

The networked age promises global digital cultures with flattened power relations, given the affordances of information and communication technologies to collapse distance, enable easier cross-country collaborations and create new opportunities for knowledge production and sharing. In the academic domains, indications are that knowledge patterns continue to reflect physically based geopolitical realities – where knowledge from the South is still peripheral while knowledge from the North still dominates in terms of all the conventional metrics. This study explores the potential role of digital affordances to challenge structural Northern bias and generates questions about knowledge production and dissemination in the climate change knowledge domain. It is framed by the field of scholarly communication within an African setting and by the emergent field of climate change which is fraught with debates and contestations, particularly regarding mitigation and adaptation. It draws on Southern theory which interrogates the global dynamics of knowledge production and dissemination. It explores the intersection of the discoverability and visibility of local climate change research methodologically from the outside in, through an experiment of searches for ‘climate change/South Africa’ and from the inside out by reviewing the online presence of one climate change group in a top-ranked African university.     

Generalized minimum aberration mixed-level orthogonal arrays: A general approach based on sequential integer quadratically constrained quadratic programming

Orthogonal fractional factorial designs and in particular orthogonal arrays (OAs) are frequently used in many fields of application, including medicine, engineering, and agriculture. In this article, we present a methodology and an algorithm to find an OA, of given size and strength, which satisfies the generalized minimum aberration criterion. The methodology is based on the joint use of polynomial counting functions, complex coding of levels, and algorithms for quadratic optimization and puts no restriction on the number of levels of each factor.     

Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.