A TEM study on the microstructure of spark plasma sintered ZrB_2-based composite with nano-sized SiC dopant

Sintering behavior of ZrB_2 ceramic with nano-sized SiC dopant was studied. ZrB_2-25 vol% nano-sized SiC was selected as the starting mixture to fabricate the composite. The manufacturing process was accomplished at 1800℃ for 5 min under 25 MPa via spark plasma sintering(SPS). The as-sintered sample reached a relative density of 99%. Besides the initial phases, namely ZrB_2 and SiC, the high-resolution X-ray diffraction(HRXRD) was used to study the formation of an in-situ ZrC phase. The possible chemical interactions during the ZrC phase formation were scrutinized. The microstructure of the composite was studied by the field emission scanning electron microscopy(FESEM) and transmission electron microscopy(TEM). Elemental analysis through FESEM evaluations revealed the formation of amorphous phases, rich in Zr, C, Si, B, and O elements, which was in harmony with the thermodynamical assessments. TEM studies endorsed the formation of such phases, containing a glassy bed of Si–B–O with ZrC and C islands dispersed therein.     

Little-known and phylogenetically obscure South African estuarine microgastropods (Mollusca: Truncatelloidea) as living animals

Areas of the Knysna estuarine bay in the Western Cape are dominated by three endemic South African truncatelloid microgastropods, temporarily known as ‘Hydrobia’ knysnaensis (Krauss), ‘Assiminea’ capensis (Sowerby) and ‘Assiminea’ globulus Connolly. Although first described 80–170 years ago and present in abundance (up to 100,000 m^?2), they remain surrounded by confusion and still await taxonomic assignment, largely because they appear most atypical members of their groups by virtue of anatomy and/or biogeography and/or habitat. This study contributes in-life perspectives to morphological and phylogenetic analyses known to be on-going. At Knysna, they are syntopic: at least two occurring in >85% and all three in >40% of individual 0.0026 m² samples from their region of dominance. Nevertheless, they tend to greater abundance in divergent microhabitats; ‘A.’ globulus dominating higher tidal levels, and ‘A.’ capensis and ‘Hydrobia’ lower ones; the former especially unvegetated sediment, the latter, if anything, seagrass. Interspecific feeding interactions appear unlikely to be responsible for these patterns, other evidence suggesting that all are maintained below carrying capacity. Field biology of ‘H.’ knysnaensis generally appears equivalent to that of northern-hemisphere intertidal hydrobiids and that of ‘A.’ globulus is typically assimineid, albeit at atypically low shore height. Unlike assimineids, however, ‘A.’ capensis is truly aquatic. The success of these truncatelloids in unusual circumstances may be consequent on the absence from South Africa of other microgastropod groups that fill their niches elsewhere in the southern hemisphere.     

Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.