Asphalt mixture is a kind of granular material, which consists of aggregates with different particle sizes. Extracting the particle distribution around the failure location of asphalt mixture is important for revealing the failure mechanism. In this paper, the digital image correlation (DIC) method is used to obtain the full-field strain of asphalt mixture, and then the high strain zone of asphalt mortar is extracted by digital image processing technology. For getting the aggregate distribution around the failure position, aggregate extraction method is proposed based on the image dilation algorithm. For AC-16, SMA-16 and OGFC-16 asphalt mixtures, under different temperature and loading speed conditions, according to the analysis of the aggregates distribution around the high strain area, it can be seen that the proportion of the main skeleton aggregates and the disruption aggregates around the high strain area is constantly changing with the loading process. It shows that the proportion of the main skeleton aggregates around the high strain area is decreasing, while the proportion of the disruption aggregates is increasing. This phenomenon shows that the damage evolution of asphalt mixture is expanding from the surrounding area of main skeleton aggregates to that of disruption aggregates.

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As the concentration of acetone (C₃H₆O) in exhaled gas of diabetics is significantly higher than that of healthy people. Here, the sensing performance of X doped MoSe₂ (X–MoSe₂, X = Ti, Ni and Cu) for acetone is studied theoretically. It is found that Ti–MoSe₂ shows absolute advantages in both adsorption energy and charge transfer. Additionally, the changes of bandgap for C₃H₆O/Ti–MoSe₂ in the adsorption process are the largest in all adsorption systems, indicating it will produce the largest electrical signal that can be detected. Besides, the co-adsorption system (consisting of C₃H₆O, H₂O, CO₂, N₂ and O₂) will be more stable after O₂ is removed and the adsorption site occupied by acetone restricts the contact of other disturbing gases with Ti–MoSe₂. Importantly, comparing with the reported acetone sensing materials ((110) face of SnO₂, (MgO)₁₂-graphene and oxygen-plasma-treated ZnO (ZnO–O)), Ti–MoSe₂ demonstrates its superiority in terms of the absolute value of charge transfer (0.37 e) and adsorption energy (2.42 eV). All these results show that Ti–MoSe₂ is expected to become the reliable sensing material for acetone and has enormous potential for the application in noninvasive and rapid detection of type-1 diabetes.

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Rare-earth zirconates (RE₂Zr₂O₇, RE = rare earth elements) are considered as promising thermal barrier coating (TBCs) materials. However, their thermal expansion coefficient (TEC) is relatively low comparing with that of either the state-of-the-art TBCs material Yttria-stabilized Zirconia (YSZ), or the metallic bond-coat. In present research, Cerium was introduced as the substitution of Zirconium to improve the TEC of rare-earth zirconates. A series of Yb₂(Zr_1−xCe_x)₂O₇ solid solutions were synthesized and investigated. Experimental results revealed that their TEC is remarkably increased with the Cerium substitution content. Meanwhile, their thermal conductivity shows no obvious increase compared with that of Yb₂Zr₂O₇due to the phonon scattering effect.

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Extensive studies have concluded that the GNSS observations are heteroscedastic and physically correlated. Typically, the observation precisions are elevation dependent and between-frequency cross-correlations and time correlations exist. The influence of these stochastic characteristics on the GNSS positioning has been numerically well understood. However, their influence on the statistic tests of reliability has been rarely studied. We will systematically study the influence of GNSS stochastic characteristics on the statistic tests involved in reliability. With BeiDou as an example, the realistic elevation-dependent model, cross-correlations and time correlations are estimated. Then their impacts on the reliability are numerically analyzed by comparing with the empirical stochastic model where the stochastic characteristics, i.e., elevation-dependent precisions, cross-correlations and time correlations, are not adequately specified. Besides the overall test and w-test, the minimal detectable bias (MDB) and the separability of two w-test statistics are examined. The results show that the realistic elevation-dependent model will reduce probabilities of both false alarm and wrong detection for both overall test and w-test. Introducing the cross-correlations and time correlations properly can obtain the realistic MDBs together with reasonable separability measures, which all are helpful for users to make objective decisions in quality control of real GNSS applications.@en