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The main production area of Camellia vietnamensis (C. vietnamensis) is in the low mountain and hilly areas of southern China. The low survival rate of seedlings caused by drought is one of the main obstacles restricting the development of the C. vietnamensis industry. An exploration of the key adaptation mechanism of C. vietnamensis to drought stress is important in order to improve its drought resistance. We conducted a study on the morphological, physiological, biochemical, and drought resistance-related genes of five C. vietnamensis cultivars grown in Hainan province under varying degrees of drought stress. The results indicate that drought stress can lead to a decrease in the relative water content and photosynthetic capacity of C. vietnamensis leaves. Compared with the control, the drought damage index, malondialdehyde, relative electrical conductivity, soluble protein, soluble sugar and proline contents of the five C. vietnamensis cultivars increased with drought-stress duration and degree. With increasing drought-stress intensity, the activity of antioxidant enzymes and the content of related metabolites (total polyphenols, total flavonoids, tea saponins) gradually increased, and the expression levels of phenylpropanoid pathway-related genes (Cv4CL1, CvCAD1, CvCAD2, CvPOX1, CvPOX2, CvPOX3) were upregulated. Based on the results of the drought tolerance coefficients, principal component analysis, and hierarchical cluster analysis, we classified five C. vietnamensis cultivars into drought-tolerant cultivars ('Haida 1'); moderately drought-tolerant cultivars ('Haida 4' and 'Wanhai 4'); and drought-sensitive cultivars ('Wanhai 3' and 'Wanhai 1'). The results of this study provide a theoretical basis for the promotion and cultivation of C. vietnamensis and the selection of drought-resistant cultivars. [ABSTRACT FROM AUTHOR]

Accurate arrival picking for microseismic signal is the basis for techniques such as source location.Most of the traditional methods for picking-up the first arrival of the events depend on manual intervention and require high signal to noise ratio(SNR) for the acquired data.In order to improve the picking accuracy of the weak signal in low signal to noise ratio, a pick-up method based on S-Transform and deep belief neural network(DBN) was proposed.The network model training was divided into two steps.First, the original data processed by S-Transformation was subjected to unsupervised pre-training by using a restricted Boltzmann machine(RBM), and the initial values of the network model parameters were obtained.Network parameters adjusted by error backpropagation were used to build the final DBN model.Then, the trained network was used to pick up the data.Compared with pick-up results of the STA/LTA method, the method we used has higher antinoise performance.

It is essential to pick P -wave and S -wave arrival times rapidly and accurately for the microseismic monitoring systems. Meanwhile, it is not easy to identify the arrivals at a true phase automatically using traditional picking method. This is one of the reasons that many researchers are trying to introduce deep neural networks to solve these problems. Convolutional neural networks (CNNs) are very attractive for designing automatic phase pickers especially after introducing the fundamental network structure from semantic segmentation field, which can give the probability outputs for every labelled phase at every sample in the recordings. The typical segmentation architecture consists of two main parts: (1) an encoder part trained to extracting coarse semantic features; (2) a decoder part responsible not only for recovering the input resolution at the output but also for obtaining sparse representation of the objects. The fundamental segmentation structure performs well; however, the influence of the parameters in the structure on the pickers has not been investigated. It means that the structure design just depends on experience and tests. In this paper, we solve two main questions to give some guidance on network design. First, we show what sparse features will learn from the three-component microseismic recordings using CNNs. Second, the influence of two key parameters in the network on pickers, namely, the depth of decoder and activation functions, is analysed. Increasing the number of levels for a certain layer in the decoder will increase the burden of demand on trainable parameters, but it is beneficial to the accuracy of the model. Reasonable depth of the decoder can balance prediction accuracy and the demand of labelled data, which is important for microseismic systems because manual labelling process will decrease the real-time performance in monitoring tasks. Standard rectified linear unit (ReLU) and leaky rectified linear unit (Leaky ReLU) with different negative slopes are compared for the analysis. Leaky ReLU with a small negative slope can improve the performance of a given model than ReLU activation function by keeping some information about the negative parts. [ABSTRACT FROM AUTHOR]

Non-fullerene acceptors have shown great promise for organic solar cells (OSCs). However, challenges in achieving high efficiency molecular system with conformational unicity and effective molecular stacking remain. In this study, we present a new design of non-fused tetrathiophene acceptor R4T-1 via employing the encapsulation of tetrathiophene with macrocyclic ring. The single crystal structure analysis reveals that cyclic alkyl side chains can perfectly encapsulate the central part of molecule and generate a conformational stable and planar molecular backbone. Whereas, the control 4T-5 without the encapsulation restriction displays cis- and twisted conformation. As a result, R4T-1 based OSCs achieved an outstanding power conversion efficiency (PCE) exceeding 15.10 % with a high short-circuit current density (J sc ) of 25.48 mA/cm 2 , which is significantly improved by ≈30 % in relative to that of the control. Our findings demonstrate that the macrocyclic encapsulation strategy could assist fully non-fused electron acceptors (FNEAs) to achieve a high photovoltaic performance and pave a new way for FNEAs design., (© 2023 Wiley-VCH GmbH.)