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• Symbiotic nodulation is initiated in the roots of legumes in response to low nitrogen and rhizobial signal molecules and is dynamically regulated by a complex regulatory network that coordinates rhizobial infection and nodule organogenesis. • It has been shown that the miR156-SPL module mediates nodulation in legumes; however, conclusive evidence of how this module exerts its function during nodulation remains elusive. • Here, we report that the miR156b-GmSPL9d module regulates symbiotic nodulation by targeting multiple key regulatory genes in the nodulation signalling pathway of soybean. miR156 family members are differentially expressed during nodulation, and miR156b negatively regulates nodulation by mainly targeting soybean SQUAMOSA promoter-binding protein-like 9d (GmSPL9d), a positive regulator of soybean nodulation. GmSPL9d directly binds to the miR172c promoter and activates its expression, suggesting a conserved role of GmSPL9d. Furthermore, GmSPL9d was coexpressed with the soybean nodulation marker genes nodule inception a (GmNINa) and GmENOD40-1 during nodule formation and development. Intriguingly, GmSPL9d can bind to the promoters of GmNINa and GmENOD40-1 and regulate their expression. • Our data demonstrate that the miR156b-GmSPL9d module acts as an upstream master regulator of soybean nodulation, which coordinates multiple marker genes involved in soybean nodulation.

The method of acoustic radiation signal detection not only enables contactless measurement but also provides comprehensive state information during equipment operation. This paper proposes an enhanced feature extraction network (EFEN) for fault diagnosis of rolling bearings based on acoustic signal feature learning. The EFEN network comprises four main components: the data preprocessing module, the information feature selection module (IFSM), the channel attention mechanism module (CAMM), and the convolutional neural network module (CNNM). Firstly, the one-dimensional acoustic signal is transformed into a two-dimensional grayscale image. Then, IFSM utilizes three different-sized convolution filters to process input image data and fuse and assign weights to feature information that can attenuate noise while highlighting effective fault information. Next, a channel attention mechanism module is introduced to assign weights to each channel. Finally, the convolutional neural network (CNN) fault diagnosis module is employed for accurate classification of rolling bearing faults. Experimental results demonstrate that the EFEN network achieves high accuracy in fault diagnosis and effectively detects rolling bearing faults based on acoustic signals. The proposed method achieves an accuracy of 98.52%, surpassing other methods in terms of performance. In comparative analysis of antinoise experiments, the average accuracy remains remarkably high at 96.62%, accompanied by a significantly reduced average iteration time of only 0.25 s. Furthermore, comparative analysis confirms that the proposed algorithm exhibits excellent accuracy and resistance against noise.

Amid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural network based on multimodal feature fusion. Specifically, the proposed method employs an enhanced Residual Network Attention Module (RNAM) network to capture deep semantic features and utilizes CIELAB color (LAB) histograms to extract color distribution characteristics, achieving a complementary integration of multimodal information. An adaptive K-nearest neighbor algorithm is utilized to construct the dynamic graph structure, while the incorporation of a multi-head attention layer within the graph neural network facilitates the efficient aggregation of both local and global features. This design significantly enhances the model’s ability to discriminate among various garbage categories. Experimental evaluations reveal that on our self-curated KRHO dataset, all performance metrics approach 1.00, and the overall classification accuracy reaches an impressive 99.33%, surpassing existing mainstream models. Moreover, on the public TrashNet dataset, the proposed method demonstrates equally outstanding classification performance and robustness, achieving an overall accuracy of 99.49%. Additionally, hyperparameter studies indicate that the model attains optimal performance with a learning rate of 2 × 10−4, a dropout rate of 0.3, an initial neighbor count of 20, and 8 attention heads.

Low-frequency noise absorbers often require large structural dimensions, constraining their development in practical applications. In order to improve space utilization, an acoustic metamaterial with a spatial double helix, called a spatial double helix resonator (SDHR), is proposed in this paper. An analytical model of the spatial double-helix resonator is established and verified by numerical simulations and impedance tube experiments. By comparing the acoustic absorption coefficients of the spatial double-helix resonator, it is shown that the results of the analytical model, the numerical model, and the experiments are in good agreement, proving the accuracy of the theoretical model. The effects of different structural parameters on the peak sound absorption coefficient and resonance frequency are quantitatively revealed. The impedance variation law of the model is obtained, and the resistance and reactance distributions at the resonance frequency are analyzed. In the optimization model, the Back Propagation (BP) network is used to construct the mapping between the structural parameters and the resonance frequency and sound absorption coefficient, and this is used as the constraints of the equation, which is combined with Wild Horse Optimization (WHO) to establish the BP-WHO optimization model to minimize the volume of the spatial double helix resonator. The results show that, for a given noise frequency, the optimized structural parameters enhance the space utilization without affecting the performance of the space double helix resonator.

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings.

In order to actualize moral and collaborative education, the reform of ideological and political aspects within professional courses becomes imperative. This paper explicates the necessity of instigating ideological and political education due to the singular nature of traditional teaching in the ‘Industrial Robot Programming and Operation’ course and its lack of emphasis on ideological and political elements. It extensively explores the embedding of ideological and political components within the course. By amplifying teachers’ awareness of ideological and political principles, refining the course’s training objectives, diversifying teaching methodologies, and enhancing the course evaluation mechanism, we implement specific practices that integrate ideology and politics into the course. The aim is to cultivate highly proficient technical and skilled individuals.

The examination of ideology and political aspects within the realm of Industrial Robot Technology’s development through the lens of the “Trinity” perspective has been a primary focus. Commencing with the challenges evident in establishing ideological and political frameworks within contemporary higher education, this discourse delves into strategies for seamlessly incorporating ideological and political education into the domain of professional and technical instruction. This aims at attaining the overarching teaching objective of the “trinity” and fostering the convergence of nurturing individuals and cultivating skillsets. This approach offers instructive direction for civic education embedded within technical disciplines like Industrial Robot Technology. Furthermore, it presents both theoretical underpinnings and practical insights to guide educational transformations within higher education’s domain.

To extract valuable characteristic information from the acoustic radiation signal of rolling bearings, a novel mathematical morphological network (MMNet) is proposed. First, a mathematical morphological network layer is constructed by leveraging the advantages of a multi-scale enhanced top-hat morphological operator (MEAVGH) that can extract positive and negative pulses, which are then integrated into the deep learning network. Second, the input signal undergoes processing with different scale structural elements (SEs) to obtain multi-branch data. This is followed by channel attention and spatial attention mechanism-based weighting of the generated multi-branch data. Finally, the fused information is fed to the neural network to yield the final result. The experimental results demonstrate the efficacy of the proposed method in extracting fault feature information, achieving a fault classification accuracy of 98.56%. Furthermore, the algorithm exhibits robustness and high training efficiency. Comparative analysis reveals that the proposed method outperforms other approaches regarding cluster analysis, accuracy, recall rate, and computational efficiency. These findings further highlight the advantages of MMNet in acoustic signal-based fault diagnosis for rolling bearings.

Early fault diagnosis of rolling element bearing is still a difficult problem. Firstly, in order to effectively extract the fault impulse signal of the bearing, a new enhanced morphological difference operator (EMDO) is constructed by combining two optimal feature extraction-type operators. Next, in the process of processing the test signal, in order to reduce the interference problem caused by strong background noise, the probabilistic principal component analysis (PPCA) method is introduced. In the traditional PPCA method, two important system parameters (decomposition principal component k and original variable n) are usually set artificially; this will greatly reduce the noise reduction performance of PPCA. To solve this problem, a parameter adaptive PPCA method based on grasshopper optimization algorithm (GOA) is proposed. Finally, combining the advantages of the above algorithms, a comprehensive rolling bearing fault diagnosis method named APPCA-EMDF is proposed in this paper. Experimental comparison results show that the proposed method can effectively diagnose the vibration signals of rolling element bearing.

The extraction of the vibration impulse signal plays a crucial role in the fault diagnosis of rolling element bearing. However, the detection of weak fault signals generally suffers the strong background noise. To solve this problem, a new adaptive multiscale enhanced combination gradient morphological filter (MECGMF) is proposed for the fault diagnosis of rolling element bearing. In this method, according to the filtering ability of four basic morphological filter operators, an enhanced combination gradient morphological operation (ECGMF) is first proposed. This design enhances the ability of MECGMF to extract impulse signals from strong background noise. And accordingly, a new adaptive selection strategy named kurtosis fault feature ratio (KFFR) is proposed to select an optimal structuring element (SE) scale. Subsequently, the optimal SE scale is the largest measure of multiscale morphological filtering for extracting bearing fault information. In the meanwhile, the effectiveness of the proposed method is verified by simulation and experiment. Finally, the experimental results demonstrate that MECGMF can effectively restrain the noise interference and extract fault characteristic signals of rolling element bearing from strong background noise. Moreover, comparative tests show that the proposed method is more effective in detecting wind turbine bearing failures.