The propagation model of orbital angular momentum (OAM) modes carried by the perfect vortex (pv) beam through anisotropic oceanic turbulence links is established and the factors influencing the OAM propagation are discussed. The findings show that the self-focusing property of pv beams is beneficial to the propagation of OAM modes: a smaller topological charge, a smaller initial radius, and an optimized half-ring width can alleviate degrading effects of turbulence on the pv beam. Additionally, the pv beam with a longer wavelength is more resistant to turbulent interference. The oceanic conditions with a higher dissipation rate of kinetic energy per unit mass of seawater, larger values of anisotropy and inner-scale factor, a smaller temperature–salinity contribution ratio, or a lower mean-squared temperature dissipation rate can improve the signal mode detection probability. The results are expected to further optimize the design of OAM-based underwater wireless communication systems.

We present what is, to the best of our knowledge, the first pure-three-level Yb:CaGdO(BO) (Yb:GdCOB) laser emitting at 976 nm based on the F2-F2 transition, generally used for a quasi-three-level emission at 1032 nm. A maximum power of 782 mW at 976 nm has been achieved in continuous-wave (CW) operation pumped by a quasi-three-level Nd:SrLaAlO laser emitting at 902 nm. Moreover, a self-frequency-doubling CW blue laser has also been demonstrated with a maximum power of 133 mW at 488 nm.

Atherosclerosis is a cardiovascular disease, which is characterized by the interaction between carbohydrates, lipids, cells and various other molecules and genetic factors. Previous studies have demonstrated that resveratrol (RV) served protective roles in numerous types of human disease by regulating different signaling pathways. The aim of the present study was to investigate the therapeutic effects of RV and analyze the potential RV-mediated mechanism in umbilical vein endothelial cells (UVECS) in atherosclerosis model mice. Reverse transcription-quantitative PCR, western blotting and immunohistochemistry were used to analyze the therapeutic effects of RV both in vitro and in vivo. The results demonstrated that total cholesterol, triglycerides, low-density lipoprotein cholesterin and high-density lipoprotein cholesterin levels were significantly decreased in the RV group compared with the control group. RV demonstrated significant anti-atherosclerotic activity, which was determined through the atherogenic index, 3-hydroxy-3-methyl-glutaryl-Coa (HMG-CoA) reductase activity and marker enzymes, such as lactate dehydrogenase, creatine phosphokinase, aspartate transaminase, alanine transaminase and alkaline phosphatase. It was also observed that RV treatment significantly decreased the area of the arteriosclerotic lesion in the RV group compared with the control, as well as significantly decreasing the expression levels of matrix metalloproteinase-9 and CD40 ligand (CD40L) in arterial lesion tissue compared with the control group. Serum expression levels of tumor necrosis factor-α and C-reactive protein were also significantly decreased by RV treatment compared with the control group. Furthermore, RV treatment significantly decreased the expression levels of PI3K, AKT and mTOR in UVECS in vitro. In conclusion, these results suggested that the anti-atherosclerotic activity of RV may be due to its modulatory activity over the PI3K/AKT/mTOR signaling pathway. These findings suggested a potential novel treatment option for patients with atherosclerosis.

Deep learning method was applied to rapidly and nondestructively predict the moisture content in withered leaves. In this study, a withering moisture detection method based on confidence of convolution neural network (CNN) was proposed. The method used data augmentation to preprocess the original image. The prediction results obtained by the CNN model were compared with the results of traditional partial least squares (PLS) and support vector machine regression (SVR) models. The results clarified that the quantitative prediction model of the moisture content in withering leaves based on the confidence of convolutional neural network has the best prediction performance. The performance parameters of the optimal prediction model: correlation coefficient (R p ), root-mean-square error of external verification set (RMSEP) and relative standard deviation (RPD) are 0.9957, 0.0059, and 9.5781, respectively. Compared with traditional linear PLS and nonlinear SVR algorithms, deep learning method can better characterize the correlation between images and moisture.The moisture-related information in the image can be extracted to a greater degree by the convolution kernel of the convolutional neural network. The model has better generalization, which can rapidly and nondestructively predict the moisture content in withered leaves. Practical applicationsCNN is increasingly used in food technology. This study solves the problem that the withered leaves moisture content cannot be quantitatively predicted based on the confidence of the proposed CNN. Compared with traditional machine vision methods, our proposed CNN model can retain more original information in addition to the color and texture features of withered leaves. And it can quickly and accurately judge the moisture content without destroying the tissue components of the withered leaves. This study is of great significance to the intelligence of black tea processing equipment. Simultaneously, the proposed model based on deep learning provides a new idea for the intelligent detection of black tea withering process. | INTRODUCTIONWithering is the basic process in the processing of black tea, which directly affects the quality of black tea. With the deepening of withering degree, the fresh tea leaves fade gradually, the leaf color changes from bright to dark and the green grass flavor is gradually disappearing (Mylott, Kutschera, & Widenhorn, 2014). The moisture content of the withering leaves is used as an indicator for the excessive wilting. When the moisture content of fresh leaves reaches 58-62%, it is considered as a moderate withering (Liang et al., 2018). In the Ting An and Huan Yu contributed equally to this work.