Comparison of the surface roughness of gypsum models constructed using various impression materials and gypsum products

AbstractBackground/purposeThis study compared the surface roughness of gypsum models constructed using various impression materials, gypsum products, and storage times before repouring.Materials and methodsThree alginate impression materials, four commercial silicone impression materials, and three types of gypsum product (MG crystal rock, Super hard stone, and MS plaster) were used. Impression materials were mixed and poured into five plastic rings (20 mm in diameter and 2 mm high) for each group, and the surfaces of the set gypsum product models of 63 groups, which were poured immediately, and 1 hour and 24 hours later, were assessed using a surface roughness tester. One-way ANOVA and Bonferroni's comparison tests were used for the statistical analyses.ResultsThe surface roughness: (1) was greater for most specimens constructed from alginate impression material (2.72 ± 0.45–7.42 ± 0.66 μm) than from silicone impression materials (1.86 ± 0.19–2.75 ± 0.44 μm); (2) differed with the type of gypsum product when using alginate impression materials (surface roughness of Super hard stone > MG crystal rock > MS plaster), but differed little for silicone impression materials; and (3) differed very little with the storage time before repouring.ConclusionThe surface roughness of stone models was mainly determined by the type of alginate impression material, and was less affected by the type of silicone rubber impression material or gypsum product, or the storage time before repouring

Hard Sample Aware Network for Contrastive Deep Graph Clustering

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via contrastive mechanisms, is a challenging research spot. Among the recent works, hard sample mining-based algorithms have achieved great attention for their promising performance. However, we find that the existing hard sample mining methods have two problems as follows. 1) In the hardness measurement, the important structural information is overlooked for similarity calculation, degrading the representativeness of the selected hard negative samples. 2) Previous works merely focus on the hard negative sample pairs while neglecting the hard positive sample pairs. Nevertheless, samples within the same cluster but with low similarity should also be carefully learned. To solve the problems, we propose a novel contrastive deep graph clustering method dubbed Hard Sample Aware Network (HSAN) by introducing a comprehensive similarity measure criterion and a general dynamic sample weighing strategy. Concretely, in our algorithm, the similarities between samples are calculated by considering both the attribute embeddings and the structure embeddings, better revealing sample relationships and assisting hardness measurement. Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones. In this way, our method can mine not only the hard negative samples but also the hard positive sample, thus improving the discriminative capability of the samples further. Extensive experiments and analyses demonstrate the superiority and effectiveness of our proposed method.Comment: 9 pages, 6 figure

Population aging in the People's Republic of China

This paper provides a factual assessment of China's population aging and its social and economic consequences. It is projected that China will have a substantially older population in the middle of the 21st century. Major policy implications concerning old age support and health care have been examined.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26458/1/0000546.pd

Future change in extreme precipitation in East Asian spring and Mei-yu seasons in two high-resolution AGCMs

Precipitation in the spring and Mei-yu seasons, the main planting and growing period in East Asia, is crucial to water resource management. Changes in spring and Mei-yu extreme precipitation under global warming are evaluated based on two sets of high-resolution simulations with various warming pattern of sea surface temperature (SST'spa). In the spring season, extreme precipitation exhibits larger enhancements over the northern flank of the present-day prevailing rainy region and a tendency of increased occurrence and enhanced intensity in the probability distribution. These changes imply a northward extension of future spring rainband. Although the mean precipitation shows minor change, enhanced precipitation intensity, less total rainfall occurrence, and prolonged consecutive dry days suggest a more challenging water resource management in the warmer climate. The projected enhancement in precipitation intensity is robust compared with the internal variability related to initial conditions (σˆint) and the uncertainty caused by SST'spa (σˆΔSST). In the Mei-yu season, extreme precipitation strengthens and becomes more frequent over the present-day prevailing rainband region. The thermodynamic component of moisture flux predominantly contributes to the changes in the spring season. In the Mei-yu season, both the thermodynamic and dynamic components of moisture flux enhance the moisture transport and intensify the extreme precipitation from southern China to northeast Asia. Compared with spring season, projecting future Mei-yu precipitation is more challenging because of its higher uncertainty associated with 1) the σˆint and σˆΔSST embedded in the projections and 2) the model characteristics of present-day climatology that determines the spatial distribution of precipitation enhancement.publishedVersio