Additional file of Genome-wide identification and expression profiling of auxin response factor (ARF) gene family in maize
Abstract During the construction and operation period, an offshore wind farm is exposed to a variety of meteorological threats, which could lead to severe damages and massive financial loss. In this work, we analyse meteorological risks of offshore wind farms in the coastal areas of the south-east Jiangsu Province. Firstly, we identified the risk factors through the Delphi Method and analysis of historical data. The five major risks include typhoon, lightning, extreme temperatures, salt frog, and oceanic disasters caused directly by meteorological disasters. Then, using the Analytic Hierarchy Process (AHP), we established index module of each layer and calculated the risk value of each influence factor. After that, we established the judgement matrix and the weight of each factor. Based on that, we got the comprehensive risk value and considered it as a medium risk. According to the weighting module, we also came to the conclusion that typhoon contributes most to the meteorological disaster of offshore wind farms, with a weight of 0.3937.
The distribution of elliptical aggregates in concrete with wall effect is investigated by computer simulation.The simulation results show that,as the number of simulations increases,the aggregate distribution density at any point in concrete approaches to a fixed value for a given aggregate area fraction.After the validity of the proposed simulation method is verified with experimental results,the effects of the aggregate gradation,aspect ratio,minimum aggregate diameter,and maximum aggregate diameter on the boundary layer thickness are discussed.Based on the numerical results,it is found that the boundary layer thickness increases with an increase in aspect ratio,minimum aggregate diameter,and maximum aggregate diameter.It is also found that the aggregate gradation has a significant influence on the boundary layer thickness.
Thunderstorm detection based on the Atmospheric Electric Field (AEF) has evolved from time-domain models to space-domain models. It is especially important to evaluate and determine the particularly Weather Attribute (WA), which is directly related to the detection reliability and authenticity. In this paper, a strategy is proposed to integrate three currently competitive WA's evaluation methods. First, a conventional evaluation method based on AEF statistical indicators is selected. Subsequent evaluation approaches include competing AEF-based predicted value intervals, and AEF classification based on fuzzy c-means. Different AEF attributes contribute to a more accurate AEF classification to different degrees. The resulting dynamic weighting applied to these attributes improves the classification accuracy. Each evaluation method is applied to evaluate the WA of a particular AEF, to obtain the corresponding evaluation score. The integration in the proposed strategy takes the form of a score accumulation. Different cumulative score levels correspond to different final WA results. Thunderstorm imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm attributes. Empirical results confirm that the proposed strategy effectively and reliably images thunderstorms, with a 100% accuracy of WA evaluation. This is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation, which provides promising solutions for a more reliable and flexible thunderstorm detection.
A method of utilizing active resistance network to calibrate the temperature channel of data-acquisition unit is proposed in this paper. This method changes gate-source voltage by micro-controller so as to simulate the resistance. Compares resistance value measured by data-acquisition unit with the simulated resistances to calibrate the performance of the data-acquisition unit. Take DT50 data-acquisition unit in CAWS600 AWS for an example. By software commands, output current and A/D sample duration time of the data-acquisition unit temperature channel are changed to resolve the problem of parameters mismatch, then this data-acquisition unit is calibrated effectively. The experiment result shows this approach provide a reliable means to calibrate AWS data-acquisition unit.
In view of the problem that ZnO varistors are often subjected to thermal breakdown and deterioration due to lightning strikes in low-voltage power distribution systems, this article used a 8/20 µs multi-pulse surge current with a pulse time interval of 50 ms to perform shock experiments on ZnO varistors. SEM scanning electron microscope and an XRD diffractometer were used to analyze the structure of the grain boundary layer and the change of the crystalline phase material of ZnO varistor under the action of a multi-pulse current. The damage mechanism of ZnO varistor under the multi-pulse current was studied at the micro level. The results show that the average impact life of different types of ZnO varistor is significantly different. It was found that the types of trace elements and grain size in the grain boundary layer will affect the ability of ZnO varistor to withstand multi-pulse current. As the number of impulses increases, the grain structure of the ZnO varistor continues to degenerate. The unevenness of internal ion migration and the nonuniformity of the micro-grain boundary layer cause the local energy density to be too large and cause the local temperature rise to be too high, which eventually causes the internal grain boundary to melt through, and the local high temperature may cause the Bi element in the ZnO varistor to change in different crystal phases.
In the paper, to solve the problem that some existing methods of separating the weak signals from mixed chaotic signals have to use certain priori knowledge of chaotic signals such as the inherent properties, a FastICA method based on the negentropy is employed to separate the weak signals from the unknown mixed chaotic signals blindly. According to the maximum nongaussianity which is one of the basic ICA estimation principles, the algorithm uses negentropy as the measure. Then, the independence and high-order statistics information of every source of mixed chaotic signals are fully utilized, and a better separation performance can be obtained. The simulation results indicate that the weak signals can be separated fast and effectively and the error is relative less, even when the simulation is under the low SNR as -87.6 dB.
In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.
Marine sensors are highly vulnerable to illegal access network attacks. Moreover, the nation’s meteorological and hydrological information is at ever-increasing risk, which calls for a prompt and in depth analysis of the network behavior and traffic to detect network attacks. Network attacks are becoming more diverse, with a large number of rare and even unknown types of attacks appearing. This results in traditional-machine-learning (ML)-based network intrusion detection (NID) methods performing weakly due to the lack of training samples. This paper proposes an NID method combining the log-cosh conditional variational autoencoder (LCVAE) with convolutional the bi-directional long short-term memory neural network (LCVAE-CBiLSTM) based on deep learning (DL). It can generate virtual samples with specific labels and extract more significant attack features from the monitored traffic data. A reconstructed loss term based on the log-cosh model is introduced into the conditional autoencoder. From it, the virtual samples are able to inherit the discrete attack data and enhance the potential features of the imbalance attack type. Then, a hybrid feature extraction model is proposed by combining the CNN and BiLSTM to tackle the attack’s spatial and temporal features. The following experiments evaluated the proposed method’s performance on the NSL-KDD dataset. The results demonstrated that the LCVAE-CBiLSTM obtained better results than state-of-the-art works, where the accuracy, F1-score, recall, and FAR were 87.30%, 87.89%, 80.89%, and 4.36%. The LCVAE-CBiLSTM effectively improves the detection rate of a few classes of samples and enhances the NID performance.
Auxin signaling is vital for plant growth and development, and plays important role in apical dominance, tropic response, lateral root formation, vascular differentiation, embryo patterning and shoot elongation. Auxin Response Factors (ARFs) are the transcription factors that regulate the expression of auxin responsive genes. The ARF genes are represented by a large multigene family in plants. The first draft of full maize genome assembly has recently been released, however, to our knowledge, the ARF gene family from maize (ZmARF genes) has not been characterized in detail.In this study, 31 maize (Zea mays L.) genes that encode ARF proteins were identified in maize genome. It was shown that maize ARF genes fall into related sister pairs and chromosomal mapping revealed that duplication of ZmARFs was associated with the chromosomal block duplications. As expected, duplication of some ZmARFs showed a conserved intron/exon structure, whereas some others were more divergent, suggesting the possibility of functional diversification for these genes. Out of these 31 ZmARF genes, 14 possess auxin-responsive element in their promoter region, among which 7 appear to show small or negligible response to exogenous auxin. The 18 ZmARF genes were predicted to be the potential targets of small RNAs. Transgenic analysis revealed that increased miR167 level could cause degradation of transcripts of six potential targets (ZmARF3, 9, 16, 18, 22 and 30). The expressions of maize ARF genes are responsive to exogenous auxin treatment. Dynamic expression patterns of ZmARF genes were observed in different stages of embryo development.Maize ARF gene family is expanded (31 genes) as compared to Arabidopsis (23 genes) and rice (25 genes). The expression of these genes in maize is regulated by auxin and small RNAs. Dynamic expression patterns of ZmARF genes in embryo at different stages were detected which suggest that maize ARF genes may be involved in seed development and germination.
