Compositional Effects on Indentation Mechanical Properties of Chemically Strengthened TiO2-Doped Soda Lime Silicate Glasses

TiO2 is an important oxide for property modifications in the conventional soda lime silicate glass family. It offers interesting optical and mechanical properties, for instance, by substituting heavy metals such as lead in consumer glasses. The compositional effects on the hardness, reduced elastic modulus and crack resistance as determined by indentation of chemically strengthened (CS) TiO2-doped soda lime silicate glass was studied in the current paper. The CS, which was performed by a K+ for Na+ ion exchange in a molten KNO3 salt bath at 450 °C for 15 h, yielded significant changes in the indentation mechanical properties. The hardness of the glass samples increased, and this was notably dependent on the SiO2, CaO and TiO2 content. The reduced elastic modulus was less affected by the CS but showed decrease for most samples. The crack resistance, an important property in many applications where glasses are subjected to contact damage, showed very different behaviors among the series. Only one of the series did significantly improve the crack resistance where low CaO content, high TiO2 content, high molar volume and increased elastic deformation favored an increased crack resistance.

Identification of Preferences in Forced-Choice Conjoint Experiments: Reassessing the Quantity of Interest

Forced-choice conjoint experiments have become a standard component of the experimental toolbox in political science and sociology. Yet the literature has largely overlooked the fact that conjoint experiments can be used for two distinct purposes: to uncover respondents’ multidimensional preferences, and to estimate the causal effects of some attributes on a profile’s selection probability in a multidimensional choice setting. This paper makes the argument that this distinction is both analytically and practically relevant, because the quantity of interest is contingent on the purpose of the study. The vast majority of social scientists relying on conjoint analyses, including most scholars interested in studying preferences, have adopted the average marginal component effect (AMCE) as their main quantity of interest. The paper shows that the AMCE is neither conceptually nor practically suited to explore respondents’ preferences. Not only is it essentially a causal quantity conceptually at odds with the goal of describing patterns of preferences, but it also does generally not identify preferences, mixing them with compositional effects unrelated to preferences. This paper proposes a novel estimand—the average component preference—designed to explore patterns of preferences, and it presents a method for estimating it.

The accuracy of absolute differential abundance analysis from relative count data

Concerns have been raised about the use of relative abundance data derived from next generation sequencing as a proxy for absolute abundances. In the differential abundance setting compositional effects are hypothesized to contribute to increased rates of spurious differences (false positives). However in practice, partial reconstruction of total abundance can be imputed through renormalization of observed per-sample abundance. Given the renormalized data differential abundance need not be called on relative counts themselves but on estimates of absolute counts. We use simulated data to explore the consistency of differential abundance calls made on these adjusted relative abundances and find that while overall rates of false positive calls are low substantial error is possible. Conditions consistent with microbial community profiling are the most at risk of error induced by compositional effects. Increasing complexity of composition (i.e. increasing feature number) is generally protective against this effect. In real data sets drawn from 16S metabarcoding, expression array, bulk RNA-seq, and single-cell RNA-seq experiments, results are similar: though median accuracy is high, microbial community profiling and single-cell transcriptomic data sets can have poor outcomes. However, we show that problematic data sets can often be identified by summary characteristics of their relative abundances alone, giving researchers a means of anticipating problems and adjusting analysis strategies where appropriate.

Trends in short sleep duration in the National Health and Interview Survey 2004-2018: A Decomposition Analysis

Since the early 2000s, it is increasingly common that people have short sleep durations (<=6 per 24-hour), making this a high-prevalence public health issue, especially among Black individuals. We investigate how trends in short sleep duration are influenced by changes in population aging, socioeconomic factors (e.g., education, labor force participation, marital status), and health conditions and behaviors (e.g., pain, smoking, drinking, obesity, psychological distress). We use the pooled cross-sectional data from the 2004-2018 National Health and Interview Survey, a large and nationally representative study. We partition overall trends in short sleep duration into (a) compositional effects due to distributional changes in age structure, socioeconomic and health factors and (b) unexplained rate effects. Subsequently, we compare the relative contribution of each factor to the total compositional effects among Black and White participants. Results demonstrate that greater education is associated with greater prevalence of short sleep duration among Black Americans, but reduced prevalence among White Americans. For both racial groups, population aging contributes relatively little to temporal patterns in short sleep duration; by contrast, higher distress and lower labor force participation in recent years are associated with the increasing prevalence of short sleep duration.