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ObjectivesThis work aims to examining the latest early childhood caries situation in children aged 3 and 5 and its related risk factors in Zhejiang Province during the COVID-19 pandemic.MethodThere are 3,537 children and their main caregivers participate in this study. We used chi square test or U-test to analyze whether there were differences in the prevalence of dental caries under different variables. The risk factors on ECC were determined by multivariate logistic analysis.ResultsThe ECC rate of children in this study was 57.51%. The mean decayed missing filled teeth (dmft) scores were 3.01. The result of multivariate logistic analysis showed higher ECC prevalence was found in children as age increasing, with lower district economic level, with high frequency of confectionary consumption, having oral medical treatment behavior and bad evaluation of children's oral health by parents.ConclusionIn general, the prevalence rate of ECC in this study was lower than five years ago, but still higher than those developed countries. And it was associated with age, district economic level, frequency of confectionary consumption, oral medical treatment behavior and evaluation of children's oral health by parents.

In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life prediction. To extract the features with time-series information to predict the remaining cutter life more effectively, this article proposes a new deep neural network, which is named the multi-scale cyclic convolutional neural network. In the multi-scale cyclic convolutional neural network, a multi-scale cyclic convolutional layer is constructed to memorize the degradation state at different moments and to mine the timing characteristics of multiple sensor data. Multi-scale features are extracted through multi-scale convolution, and the convergence of parameters is improved by layer-by-layer training and fine-tuning. Finally, the remaining cutter life is predicted based on the features. The comparison with the published prediction methods of convolutional neural network and recurrent neural network models proves that our method (multi-scale cyclic convolutional neural network) is superior in improving the precision and accuracy of remaining cutter life prediction. This method breaks through the limitations of the convolutional neural network prediction model in this field and provides a theoretical basis for evaluating the remaining service life of the cutter.

A novel ceramic Gas Electron Multiplier (GEM) has been developed to meet the demand of high counting rate for the neutron detection which is an alternative to 3He-based detector at China Spallation Neutron Source (CSNS). An experiment was performed to measure the neutron transmittance of ceramic-GEM and FR4-GEM at the small angle neutron scattering (SANS) instrument. The result showed the ceramic-GEM has higher transmittance and less self-scattering especially for cold neutrons. One single ceramic GEM could give a gain of 102-104 in the mixture gas of Ar and CO2 (90%:10%) and its energy resolution was about 27.7% by using 55Fe X ray of 5.9 keV. A prototype has been developed in order to investigate the performances of the ceramic GEM-based neutron detector. Several neutron beam tests, including detection efficiency, spatial resolution, two-dimensional imaging, and wavelength spectrum, were carried out at CSNS and China Mianyang Research Reactor (CMRR). The results show that the ceramic GEM-based neutron detector is a good candidate to measure the high intensity neutrons.

Design of shape memory alloy based structure in high–frequency or dynamic applications needs to build on a luminous comprehension for high speed deformation mechanisms of the corresponding material. In present work, dynamic responses and bulk texture evolutions are studied in martensitic NiTi through multi–scale characterization efforts. The objective is to quantitatively decompose the macroscopic strain according to deformation mechanisms and construct a phenomenological constitutive model. This study uses quasi–static in–situ and dynamic ex–situ neutron diffraction techniques to follow the evolution of bulk texture, twin volume fraction, and active twinning modes under various strain rates. It is demonstrated that the mechanical responses are strongly rate sensitive. The observation is attributed to the variation in slip activity. A distinct twinning mode change during compression is captured through in–situ neutron diffraction, and the shear induced reorientation is well described. Based on experimental observations, a constitutive model that includes effects of elasticity, reorientation of accommodation twinning, and plasticity is constructed and validated in a wide strain rate range. The overall results provide insights into strain rate dependent mechanical response of martensitic NiTi in both experiment and model aspects. This work will guide the design and application of shape memory alloys, especially in dynamic condition.

Helium status is the primary effect of material properties under radiation. 10B-doped aluminum samples were prepared via arc melting technique and rapidly cooled with liquid nitrogen to increase the boron concentration during the formation of compounds. An accumulated helium concentration of ~6.2 × 1025 m−3 was obtained via reactor neutron irradiation with the reaction of 10B(n, α)7Li. Temperature-stimulated helium evolution was observed via small-angle X-ray scattering (SAXS) and was confirmed via transmission electron microscopy (TEM). The SAXS results show that the volume fraction of helium bubbles significantly increased with temperature. The amount of helium bubbles reached its maximum at 600°C, and the most probable diameter of the helium bubbles increased with temperature until 14.6 nm at 700°C. A similar size distribution of helium bubbles was obtained via TEM after in situ SAXS measurement at 700°C, except that the most probable diameter was 3.9 nm smaller.

Helium status is the primary effect of material properties under radiation. 10B-doped aluminum samples were prepared via arc melting technique and rapidly cooled with liquid nitrogen to increase the boron concentration during the formation of compounds. An accumulated helium concentration of ∼6.2 × 1025 m−3 was obtained via reactor neutron irradiation with the reaction of 10B(n, α)7Li. Temperature-stimulated helium evolution was observed via small-angle X-ray scattering (SAXS) and was confirmed via transmission electron microscopy (TEM).The SAXS results show that the volume fraction of helium bubbles significantly increased with temperature. The amount of helium bubbles reached its maximum at 600°C, and the most probable diameter of the helium bubbles increased with temperature until 14.6 nm at 700°C. A similar size distribution of helium bubbles was obtained via TEM after in situ SAXS measurement at 700°C, except that the most probable diameter was 3.9nm smaller. [ABSTRACT FROM AUTHOR]