The thermal and mechanical properties of b-Yb 2 Si 2 O 7 were investigated using a combination of first-principles calculations and experimental investigations. Theoretically, anisotropic chemical bonding and elastic properties, weak interatomic (010) and (001) planes in the crystal structure, damage tolerance, and low thermal conductivity are predicted. Experimentally, preferred orientation, superior mechanical properties, and damage tolerant behavior for hot-pressed bulk b-Yb 2 Si 2 O 7 are approved. Slipping along the weakly bonded {010}, {001}, or {100} planes, grain delamination, buckling, and kinking of nanolaminated grains are identified as main mechanisms for damage tolerance. The anisotropic linear thermal expansion coefficients (CTEs) are: a a = (3.57 AE 0.18) 3 10 À6 K À1

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Two new MAX compounds, (Cr2/3Ti1/3)3AlC2 and (Cr5/8Ti3/8)4AlC3, were successfully synthesized by hot‐pressing elemental powders at 1500°C for 1 h under 30 MPa in a flowing argon atmosphere. Their crystal structures were indentified and characterized by X‐ray diffraction and transmission electron microscopy analysis. (Cr2/3Ti1/3)3AlC2 and (Cr5/8Ti3/8)4AlC3 have the same crystal structures with the well‐characterized Ti3AlC2 and Ti4AlN3, respectively.

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A new‐layered compound, (Ti0.5Nb0.5)5AlC4, which belongs to MAX phases was discovered. The new‐layered carbide was synthesized by reactive hot pressing of Ti, Nb, Al, and C powders. The crystal structure was determined by the combination of X‐ray diffraction and transmission electron microscopy analyses, which consists of a 5(Ti, Nb)–C bond chain linked by an Al layer. The lattice parameters are a=3.100 Å, c=28.89 Å, with Ti(Nb)1 at (0, 0, 0), Ti(Nb)2 at (1/3, 2/3, 0.0877), Ti(Nb)3 at (2/3, 1/3, 0.1723), Al at (0, 0, 0.25), C1 at (2/3, 1/3, 0.0463), and C2 at (0, 0, 0.1566).

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Dengue fever (DF) is one of the most common and rapidly spreading mosquito-borne viral diseases in tropical and subtropical regions. In recent years, this imported disease has posed a serious threat to public health in China, especially in the Pearl River Delta (PRD). Although the severity of DF outbreaks in the PRD is generally associated with known risk factors, fine scale assessments of areas at high risk for DF outbreaks are limited. We built five ecological niche models to identify such areas including a variety of climatic, environmental, and socioeconomic variables, as well as, in some models, extracted principal components. All the models we tested accurately identified the risk of DF, the area under the receiver operating characteristic curve (AUC) were greater than 0.8, but the model using all original variables was the most accurate (AUC = 0.906). Socioeconomic variables had a greater impact on this model (total contribution 55.27%) than climatic and environmental variables (total contribution 44.93%). We found the highest risk of DF outbreaks on the border of Guangzhou and Foshan (in the central PRD), and in northern Zhongshan (in the southern PRD). Our fine-scale results may help health agencies to focus epidemic monitoring tightly on the areas at highest risk of DF outbreaks.

Dengue fever (DF) is a common and rapidly spreading vector-borne viral disease in tropical and subtropical regions. In recent years, this imported disease has posed an increasing threat to public health in China, especially in many southern cities. Although the severity of DF outbreaks in these cities is generally associated with known risk factors at various administrative levels, spatial heterogeneities of these associations remain little understood on a finer scale. In this study, the neighboring Guangzhou and Foshan (GF) cities were considered as a joint area for characterizing the spatial variations in the 2014 DF epidemic at various grid levels from 1 × 1 km2 to 6 × 6 km2. On an appropriate scale, geographically weighted regression (GWR) models were employed to interpret the influences of socioeconomic and environmental factors on this epidemic across the GF area. DF transmissions in Guangzhou and Foshan cities presented synchronous temporal changes and spatial expansions during the main epidemic months. Across the GF area, this epidemic was obviously spatially featured at various grid levels, especially on the 2 × 2 km2 scale. Its spatial variations were relatively sufficiently explained by population size, road density, and economic status integrated in the GWR model with the lowest Akaike Information Criterion (AICc = 5227.97) and highest adjusted R square (0.732) values. These results indicated that these three socioeconomic factors acted as geographical determinants of spatial variability of the 2014 DF epidemic across the joint GF area, although some other potential factors should be added to improve the explaining the spatial variations in the central zones. This work improves our understanding of the effects of socioeconomic conditions on the spatial variations in this epidemic and helps local hygienic authorities to make targeted joint interventions for preventing and controlling this epidemic across the GF area.