An aeroelastic model is developed by using multivariable solid-shell elements. Geometrical nonlinearity and the elimination of corresponding locking phenomena are considered accurately in the modeling. Unsteady aerodynamic forces are addressed by solving Euler flow dynamic equations. The computational-structural-dynamics solver is loosely coupled to the computational-fluid-dynamics solver, and the full match of the computational structural dynamics and computational fluid dynamics interfaces is achieved by three-dimensional modeling. An interpolation method using finite-element shape functions is developed to transform forces from aerodynamic grids to structural grids. The aerodynamic mesh deformation is obtained by using radial basis functions combined with the transfinite interpolation method. The aeroelastic model is validated through the study of the flutter and the limit cycle oscillation of a cropped delta wing in transonic flow. Moreover, the establishment of the aeroelastic model using multivariable solid shell elements is compared with other modeling methods. Improved numerical results show that the multivariable finite-element method is an efficient and capable tool for simulating geometrical nonlinearity, and the developed three-dimensional data-exchange method also plays a key role in the nonlinear aeroelastic modeling.
Based on the polarization division multiplexing and the polarization modulation incorporating a DCF, a novel single-pass-band microwave photonic filter is demonstrated. Experimental results verify the theoretical analysis with a tunability ranging from 0- to 20-GHz.
Abstract Background : We performed an updated meta-analysis to clarify the relationship between the CEBPE rs2239633 polymorphism and the CALL susceptibility. Methods: All the case-control studies updated on July 31, 2019 through Web of Science, Pubmed, Cochrane Library, Embase, China Nationa Knowledge Infrastructure (CNKI) electronic database. The heterogeneity in the study was tested by the Q-test and I 2 , and then the random ratio or fixed effect was utilized to merge the odds ratios (OR) and 95% confidence interval (CI). To estimate the impact of individual studies on aggregate estimates, we performed sensitivity analysis. Using funnel plot and Begger’s regression test investigated the publication bias. All data Statistical analyses were performed using Stata 12.0. Results: A total of 23442 participants (7014 patients; 16428 controls) were included in twenty case-control studies selected. There was no association of CEBPE rs2239633 polymorphism with CALL (CC vs CT + TT: OR = 1.08, 95% CI = 0.94 –1.26; CC + CT vs TT: OR = 1.10, 95% CI = 0.94–1.30; C vs T: OR =1.02, 95% CI = 0.92–1.13). In the subgroup analysis by ethnicity, no significant association of this polymorphism and CALL risks among Asia and Caucasian populations for the comparison of CC vs CT + TT, CC + CT vs TT and C vs T genetic models . Conclusion: This meta-analysis did not find the CEBPE rs2239633 polymorphism can increase and decrease the risk of susceptibility to CALL.
Abstract In this work, the sintering kinetics of pure UO 2 and 0.5 wt.%MnO‐doped UO 2 was studied by a high‐temperature dilatometer heated up to 1500°C. In addition, the sintering behavior of pure UO 2 and 0.5 wt.%MnO‐doped UO 2 was studied by pressureless sintering technique. The results showed that MnO doping enhanced the grain boundary diffusion of UO 2 , which can effectively decrease the densification temperature and promote grain growth. The sintering temperature of UO 2 was significantly reduced by about 200°C with the addition of 0.5 wt.%MnO. The microscopic morphology studies showed that there were still fine particles agglomerated, forming sintered spheres in the matrix even if no severe agglomeration and bimodal size distribution were observed in raw UO 2 powder. The microstructure evolution of the sintered sphere and UO 2 matrix during the densification process was studied by isothermal sintering. Finally, the present analyses indicated that the densification of UO 2 matrix can be accelerated by adding MnO or increasing the sintering temperature, thus improving the densification inhomogeneity of UO 2 matrix.
The majority work on speech recognition is based on voiced sounds and has already achieved great successes. However, in several unique scenarios, the voice might be unavailable. Recently, Gaddy and Klein (2020) presented an initial study of silent speech analysis, aiming to voice the silent speech from facial electromyography (EMG). In this work, we present the first study of neural silent speech recognition in Chinese, which goes one step further to convert the silent facial EMG signals into text directly. We build a benchmark dataset and then introduce a neural end-to-end model to the task. The model is further enhanced with two auxiliary tasks for better feature learning. Besides, we suggest several simple data augmentation techniques to enhance the model. Experimental results show that our final best model can achieve a character error rate of 38.0% on the silent speech recognition. In-depth analyses are also offered to comprehensively understand our task and the proposed various models. Apparently, our final performance is far from the ideal level which still needs great future attention.
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