Indicators of buildability of 3D-printable mixture: modeling and experimental research

Представлены результаты экспериментальных исследований реологического поведения цементно-песчаных смесей для 3D-печати. Для изучения реологического поведения, в частности оценки формоустойчивости показателя пластичности, использованы сдавливающие тесты с постоянной скоростью деформирования. Найдены значения структурной и пластической прочности, пластических деформаций цементных систем, характеризующие их способность сохранять форму при действии возрастающих сжимающих напряжений в процессе печати. Охарактеризованы особенности реологического поведения, получены количественные данные о влиянии содержания песка заданной гранулометрии на пластичность и формоустойчивость цементных смесей. Определено рациональное содержание песка в составе смесей, при котором обеспечиваются критериальные для процессов 3D-печати значения показателей пластичности, структурной прочности и деформативности. На основании результатов моделирования структуры показано, что регулирование вязко-пластических свойств цементных систем при изменении содержания заполнителя определяется толщиной прослойки цементного теста как условно однородной дисперсионной среды. Эту величину предложено рассматривать в качестве критериальной структурной характеристики, определяющей тип реологического поведения смесей для 3D-печати.

Finite Mixture Modeling for Program Evaluation: Resampling and Pre-processing Approaches

Background Finite mixture models cluster individuals into latent subgroups based on observed traits. However, inaccurate enumeration of clusters can have lasting implications on policy decisions and allocations of resources. Applied and methodological researchers accept no obvious best model fit statistic, and different measures could suggest different numbers of latent clusters. Objectives The purpose of this article is to evaluate and compare different cluster enumeration techniques. Research Design Study I demonstrates how recently proposed resampling methods result in no precise number of clusters on which all fit statistics agree. We recommend the pre-processing method in Study II as an alternative. Both studies used nationally representative data on working memory, cognitive flexibility, and inhibitory control. Conclusions The data plus priors method shows promise to address inconsistencies among fit measures and help applied researchers using finite mixture models in the future.

Profiles of competence development in mathematics and reading in early secondary education

This article examines the development of reading and mathematical competence in early secondary education and aims at identifying distinct profiles of competence development. Since reading and mathematical competences are highly correlated both cross-sectionally and longitudinally, we expected to find a generalized profile of competence development with students developing parallel in reading and mathematical competences. Moreover, previous research confirmed individuals’ specific focus on one of the two domains, for example, in their interest, self-concept, or motivation. Also, differences in competence levels between both domains were found in cross-sectional studies. Therefore, we hypothesized that additional to the generalized profile, there are specialized profiles of competence development with students developing distinctively faster in one of the two domains. To identify both types of profiles, latent growth mixture modeling was used on a sample of 5,301 students entering secondary education from the German National Educational Panel Study. To demonstrate the robustness of the results, these analyses were repeated using different model specifications and subgroups with higher homogeneity (with students belonging to the highest track, i.e., “Gymnasium”). The results indicate only small to non-existent specialized profiles of competence development in all conditions. This finding of roughly parallel development of reading and mathematical competences throughout early secondary education indicates that potential specializations are less important at this point in students’ educational careers.

Supplemental Material: Detrital zircon provenance of the Cretaceous–Neogene East Coast Basin reveals changing tectonic conditions and drainage reorganization along the Pacific margin of Zealandia

<div>Table S1: Data sources for composite basement terranes. Table S2: Relative proportions of age fractions for composite basement terranes. Table S3: U-Th-Pb isotopic composition of detrital zircon analyzed at the University of Arizona LaserChron Center. Table S4: U-Th-Pb isotopic composition of detrital zircon analyzed at the University California, Santa Cruz. Table S5: Relative proportions of age fractions for Cenozoic East Coast Basin cover stratigraphy. Table S6: Relative proportions of age fractions for Cretaceous East Coast Basin cover stratigraphy. Table S7: Mixture modeling results for detrital zircon samples. Figure S1: Map of all samples from the basement terrane and cover stratigraphy with detrital zircon U-Pb ages. File S1: Systematic analysis of mixture modeling results. <br></div>

Supplemental Material: Detrital zircon provenance of the Cretaceous–Neogene East Coast Basin reveals changing tectonic conditions and drainage reorganization along the Pacific margin of Zealandia

<div>Table S1: Data sources for composite basement terranes. Table S2: Relative proportions of age fractions for composite basement terranes. Table S3: U-Th-Pb isotopic composition of detrital zircon analyzed at the University of Arizona LaserChron Center. Table S4: U-Th-Pb isotopic composition of detrital zircon analyzed at the University California, Santa Cruz. Table S5: Relative proportions of age fractions for Cenozoic East Coast Basin cover stratigraphy. Table S6: Relative proportions of age fractions for Cretaceous East Coast Basin cover stratigraphy. Table S7: Mixture modeling results for detrital zircon samples. Figure S1: Map of all samples from the basement terrane and cover stratigraphy with detrital zircon U-Pb ages. File S1: Systematic analysis of mixture modeling results. <br></div>