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’.     

Electro-deoxidation of V<Subscript>2</Subscript>O<Subscript>3</Subscript> in molten CaCl<Subscript>2</Subscript>-NaCl-CaO

The electro-deoxidation of V₂O₃ precursors was studied. Experiments were carried out with a two-terminal electrochemical cell, which was comprised of a molten electrolyte of CaCl₂ and NaCl with additions of CaO, a cathode of compact V₂O₃, and a graphite anode under the potential of 3.0 V at 1173 K. The phase constitution and composition as well as the morphology of the samples were studied by X-ray diffraction (XRD) and scanning electron microscopy (SEM). 3 g of V₂O₃ could be converted to vanadium metal powder within the processing time of 8 h. The kinetic pathway was investigated by analyzing the product phase in samples prepared at different reduction stages. CaO added in the reduction path of V₂O₃ formed the intermediate product CaV₂O₄.     

Electrochemical properties of LiNi<Subscript>0.5</Subscript>Mn<Subscript>1.3</Subscript>Ti<Subscript>0.2</Subscript>O<Subscript>4</Subscript>/Li<Subscript>4</Subscript>Ti<Subscript>5</Subscript>O<Subscript>12</Subscript> cells

Spinel compounds LiNi_0.5Mn_1.3Ti_0.2O₄ (LNMTO) and Li₄Ti₅O₁₂ (LTO) were synthesized by different methods. The particle sizes of LNMTO and LTO are 0.5–2 and 0.5–0.8 μm, respectively. The LNMTO/LTO cell exhibits better electrochemical properties at both a low current rate of 0.2C and a high current rate of 1C. When the specific capacity was determined based on the mass of the LNMTO cathode, the LNMTO/LTO cell delivered 137 mA·h·g⁻¹ at 0.2C and 118.2 mA·h·g⁻¹ at 1C, and the corresponding capacity retentions after 30 cycles are 88.5% and 92.4%, respectively.     

Test research on sticking mechanism during hot rolling of SUS 430 ferritic stainless steel

The sticking phenomenon during hot rolling of SUS 430 ferritic stainless steel was investigated by means of a two-disc type high-temperature wear tester. The test results indicate that sticking particles on the surfaces of high chromium steel (HiCr) and high-speed steel (HSS) rolls undergo nucleation, growth, and saturation stages. Grooves on the roll surface generated by grinding provide nucleation sites for sticking particles. The number of sticking particles on the HiCr roll surface is greater than that on the HSS roll surface. The average surface roughnesses (R_a) of HiCr and HSS rolls change from 0.502 and 0.493 μm at the initial stage to 0.837 and 0.530 μm at the saturation stage, respectively. The test further proves that the sticking behavior is strongly dependent on roll materials, and the HSS roll is more beneficial to prevent particles sticking compared with the HiCr roll under the same hot-rolling conditions.     

Precipitation and stability of reversed austenite in 9Ni steel

A new method was used to analyze the factors affecting the precipitation of reversed austenite during tempering. The samples were kept at various tempering temperatures for 10 min and their length changes were recorded. Then, the precipitation of reversed austenite which led to the length reduction was shown by thermal expansion curves. The results show that the effects of process parameters on the precipitation of reversed austenite can be determined more accurately by this method than by X-ray diffraction. When the quenching and tempering process is adopted, both the lower quenching temperature and higher tempering temperature can promote the precipitation of reversed austenite during tempering; and when the quenching, lamellarizing, and tempering process is used, intercritical quenching is considered beneficial to the precipitation of reversed austenite in the subsequent tempering because of Ni segregation during holding at the intercritical temperature.     

Influence of isothermal aging on σ precipitation in super duplex stainless steel

The time-temperature-transformation (TTT) curve of the 00Cr25Ni7Mo4N duplex stainless steel was obtained with a Formastor-digital thermal dilatometer, and the influence of isothermal aging on σ precipitation was studied by metallographic observation, X-ray diffraction (XRD), and transmission electron microscopy (TEM). The results show that the decomposition of ferrite phase is accompanied by the formation of σ phase at 750–1000℃, especially in the range of 800–900℃. The longer the aging time, the higher the amount of σ precipitation. The area fraction of various phases remains at a certain value upon the completion of ferrite deformation. The temperature of 850℃ is the most sensitive transaction temperature, the incubation time for the formation of σ precipitation is less than 1 min, and aging for 20 min leads to the complete transformation of ferrite. The σ phase is formed preferentially at the α/α/γ junction, and then grows along the α/α boundary in the matrix.