Estimation of Generalized DINA Model with Order Restrictions

Cognitive diagnostic models provide valuable information on whether a student has mastered each of the attributes a test intends to evaluate. Despite its generality, the generalized DINA model allows for the possibility of lower correct rates for students who master more attributes than those who know less. This paper considers the use of order-constrained parameter space of the G-DINA model to avoid such a counter-intuitive phenomenon and proposes two algorithms, the upward and downward methods, for parameter estimation. Through simulation studies, we compare the accuracy in parameter estimation and in classification of attribute patterns obtained from the proposed two algorithms and the current approach when the restricted parameter space is true. Our results show that the upward method performs the best among the three, and therefore it is recommended for estimation, regardless of the distribution of respondents’ attribute patterns, types of test items, and the sample size of the data.     

Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data

Traditionally latent class (LC) analysis is used by applied researchers as a tool for identifying substantively meaningful clusters. More recently, LC models have also been used as a density estimation tool for categorical variables. We introduce a divisive LC (DLC) model as a density estimation tool that may offer several advantages in comparison to a standard LC model. When using an LC model for density estimation, a considerable number of increasingly large LC models may have to be estimated before sufficient model-fit is achieved. A DLC model consists of a sequence of small LC models. Therefore, a DLC model can be estimated much faster and can easily utilize multiple processor cores, meaning that this model is more widely applicable and practical. In this study we describe the algorithm of fitting a DLC model, and discuss the various settings that indirectly influence the precision of a DLC model as a density estimation tool. These settings are illustrated using a synthetic data example, and the best performing algorithm is applied to a real-data example. The generated data example showed that, using specific decision rules, a DLC model is able to correctly model complex associations amongst categorical variables.     

Improved Classification for Compositional Data Using the <Emphasis Type="Italic">α</Emphasis>-transformation

In compositional data analysis, an observation is a vector containing nonnegative values, only the relative sizes of which are considered to be of interest. Without loss of generality, a compositional vector can be taken to be a vector of proportions that sum to one. Data of this type arise in many areas including geology, archaeology, biology, economics and political science. In this paper we investigate methods for classification of compositional data. Our approach centers on the idea of using the α-transformation to transform the data and then to classify the transformed data via regularized discriminant analysis and the k-nearest neighbors algorithm. Using the α-transformation generalizes two rival approaches in compositional data analysis, one (when α=1) that treats the data as though they were Euclidean, ignoring the compositional constraint, and another (when α = 0) that employs Aitchison’s centered log-ratio transformation. A numerical study with several real datasets shows that whether using α = 1 or α = 0 gives better classification performance depends on the dataset, and moreover that using an intermediate value of α can sometimes give better performance than using either 1 or 0.     

Civic Meaningfulness ‘Revisited’: A Final Reply to Honohan and Dekker

This final reply responds to Honohan’s invitation to articulate the Arendtian tone of the key-note paper. It spells out the philosophical intuition that the political life of citizens, at least potentially, is capable of making visible what makes human life worthwhile and fully meaningful, and the philosophical curiosity to see whether traces of this deep political awareness can be retrieved in dialogues with volunteers. In response to Dekker’s critical doubts, this final reply clarifies the central stakes of Claes’s paper. The core argument was not to show that the biographical model of meaningfulness is the prevailing approach of meaning in/of volunteering, but to assess the potentials and limits of the model’s interpretive power. Moreover, the paper argues for an alternative, existential model of meaningfulness. This approach refers to deep experiences of meaning that emerge from the practice of volunteering and that shift into powerful political experiences of hope, and a lived sense of equality.     

Reasons of Love: Response to Wolf

According to Wolf’s fitting fulfillment view, meaningfulness depends on the person’s subjective attraction to an activity being grounded in ‘reasons of love’ that concern the objective value of those activities. In this short comment, I argue that ‘reasons of love’—and thus reasons for regarding as meaningful—are not limited to those having to do with the objective value of activities and relationships, but include also what I call ‘reasons for the initiated’ and ‘reasons for me’.     

微燃机沼气发电与ORC组成的联合循环系统研究

基于"温度、品位对口,能量梯级利用"的供能原则,讨论了微燃机沼气发电与有机朗肯循环组成的联合循环系统,并给出了该联合循环系统的流程布置。在此基础上,将国外某30kW级微型燃气轮机与使用R245fa为工质的有机朗肯循环组成联合循环,进行模拟计算和数据分析。结果表明,联合循环比单纯的微燃机沼气发电提升了37.46%的输出功,循环效率也提升了37.44%,同时ORC工质流量、燃烧室出口温度和环境温度等因素的变化均会对系统效率产生一定的影响。     

麦麸米糠混合发酵生产红曲色素条件的研究

以廉价农产品废弃物麦麸和米糠为原料,通过单因素试验,正交试验和附加碳、氮源及微量元素试验,研究了接种量、发酵温度和发酵时间对红曲SM01色素产量的影响。确定了红曲SM01的最佳发酵条件为接种量13%,发酵温度30℃,发酵时间10 d;最佳附加方案为葡萄糖+硝酸铵+甘油+ KH2PO4+ MgSO4。 在上述培养条件下,以低成本的麦麸、米糠、葡萄糖、硝酸铵、甘油及微量元素混合物来代替大米培养红曲霉,色价高达3 789 U/g,降低了约30%的生产成本。     

新西兰海底地名管理工作初探

作为开展海底地名工作相对较晚的国家,新西兰通过制定技术标准、缔结多边协议等一系列措施,为海底地理实体的命名、更名等确立了原则和工作程序,确保海底地名工作既符合国际惯例,又体现本国特色。这些措施有效促进了新西兰海底地名工作的进步。     

一种改进的网络突发话题检测算法

引进文本相关度这一影响因子,提出了一种基于蚁群聚类算法的突发话题检测算法,该算法结合蚁群聚类算法的优势,综合考虑文本聚类和文本相关度的影响,得到对网络突发话题检测的最优聚类效果,并对近年来网络突发话题进行实验,达到了很好的聚类速度和聚类效果,验证了算法对突发话题检测的准确性和即时性.     

用于镧系元素理化性质相关性研究的新的键参数拓扑指数

根据镧系元素的原子序数Z、价电子数N、离子半径r提出了一个新的键参数拓扑指数HZ,并将HZ、镧系元素相对分子质量Mr共同与其11种理化性质进行关联,其结果显示了满意的线性相关性.