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Nowadays, acoustic and vibration signals have been widely used to detect crack and its degree on compressor blades for non-destructive evaluation and structural health monitoring. Due to complex working conditions and interference of strong noise, single signal-based methods reach unsatisfactory accuracy. To improve the reliability and accuracy of crack detection, the two-level fusion method based on Mahalanobis distance (MD) and self-attention mechanism is proposed based on multi-acoustic, vibration, and acoustic emission information. First, the MD is calculated to measure the similarity between raw samples and group, which can fuse the same type of samples in data level. Then, the processed raw and data-level fusion samples are inputted to one-dimensional convolutional neural network, and the preliminary decisions are obtained. Finally, the decision-level fusion based on self-attention mechanism and credit assignment is proposed to modify and correct the preliminary decisions. The compressor experiments are implemented to test the proposed method under single and mixed working conditions with strong noise intensity. Further, the results illustrate that the proposed method can accurately detect crack for compressor blades. By comparing with other data fusion approaches, the advantage of the proposed method is validated under complex conditions.

For centrifugal fans, the emergence of amplitude modulation (AM) components is a pivotal feature for blade crack fault. However, AM features extraction are challenging under strong interferences. A novel method is proposed to solve this problem using variational mode decomposition (VMD) and time-delayed feedback nonlinear tri-stable stochastic resonance (TFNTSR). Firstly, an improved degree of cyclostationarity (IDCS) index is constructed for parameters optimization. Then, intrinsic mode functions (IMFs) are obtained using the VMD method, and one of them which has the maximum IDCS value is selected. Finally, the selected IMF is further processed by the TFNTSR method, thereby completing the blade crack detection. Verified by simulations and experiments, VMD-TFNTSR method has a better performance on blade crack detection.

To alleviate the difficulty of extracting weak fault features of hoisting machinery, a progressive decomposing and double screening strategy of variational mode decomposition (VMD) is presented in this paper. Firstly, the feasibility and effectiveness of extracting fault modes using progressive decomposition strategy is validated through numerical simulation, and it solves the problem of determining the mode number [Formula: see text] in traditional VMD. Secondly, a new index named energy fluctuation factor (EFF) is proposed. Specifically, EFF is more effective in detecting the signal periodicity compared with the kurtosis and the Shannon entropy (SE), and it is used to optimize the balance parameter [Formula: see text] of VMD. Thirdly, the criterion of double screening based on the kurtosis and the EFF is given to accurately localize and reconstruct the fault modes, and then Hilbert transform is utilized to demodulate the reconstructed mode. Finally, the numerical simulation and experimental and practical engineering applications verify that the proposed method can accurately extract the modes of weak fault and well solve the problem of determining the key parameters (i.e., [Formula: see text] and [Formula: see text] ) of VMD. Furthermore, the superiority of the proposed method is validated by comparing with other fault diagnosis methods.

H+-ATPases, including the phosphorylated intermediate–type (P-type) and vacuolar-type (V-type) H+-ATPases, are important ATP-driven proton pumps that generate membrane potential and provide proton motive force for secondary active transport. P- and V-type H+-ATPases have distinct structures and subcellular localizations and play various roles in growth and stress responses. A P-type H+-ATPase is mainly regulated at the posttranslational level by phosphorylation and dephosphorylation of residues in its autoinhibitory C terminus. The expression and activity of both P- and V-type H+-ATPases are highly regulated by hormones and environmental cues. In this review, we summarize the recent advances in understanding of the evolution, regulation, and physiological roles of P- and V-type H+-ATPases, which coordinate and are involved in plant growth and stress adaptation. Understanding the different roles and the regulatory mechanisms of P- and V-type H+-ATPases provides a new perspective for improving plant growth and stress tolerance by modulating the activity of H+-ATPases, which will mitigate the increasing environmental stress conditions associated with ongoing global climate change.

The centrifugal compressor is widely used in modern industry, and the blades are prone to crack due to complex loads and working conditions. Owing to the fault features caused by initial blade cracks are week and easily interfered by strong noise, it is difficult to recognize accurately by traditional methods. In this study, the SNR estimation and adaptive stochastic resonance (SEASR) method is proposed for fault feature recognition of centrifugal compressor with cracked blade. Based on the relationship between SNR and stochastic resonance (SR) parameters, the subtract noise by empirical mode decomposition (SNEMD) method is established with second-order and fourth-order moment (M2M4), hence solving the problem of adaptive SR. The effectiveness of SEASR is tested and analyzed by simulation signals and experimental data. The results demonstrate that the proposed method can accurately recognize fault features of cracked blades and compound faults, where the output SNR is superior to that of the previous methods. It is a new method to realize fault diagnosis for centrifugal compressor with cracked blade and other rotating machinery.

Nowadays, acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) for compressor blades. However, traditional AE features and feature selection methods are generally difficult to identify the cracked blades of compressor due to its complex structure and background noise. To solve this problem, the crack damage monitoring method based on novel feature and hybridized feature selection is proposed to identify crack of compressor blades, which is aimed at improving the crack identification accuracy with optimal features. First, the novel feature of spectral centroid with energy shift (SCES) is established. Besides, the hybridized feature selection method is proposed based on Laplacian random forest scores (LRFS), which can evaluate and select features adaptively. By fusing information of selected features from AE sensors, the long short-term memory (LSTM) network is used to classify cracked blades. The proposed method is applied experimentally to identify cracks at different speeds and locations of AE sensors, which has the average accuracy of 98.93%. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM for compressor blades under different working conditions.

Moderate soil drying (MSD) is a promising agricultural technique that can reduce water consumption and enhance rhizosheath formation promoting drought resistance in plants. The endophytic fungus Piriformospora indica (P. indica) with high auxin production may be beneficial for rhizosheath formation. However, the integrated role of P. indica with native soil microbiome in rhizosheath formation is unclear. Here, we investigated the roles of P. indica and native bacteria on rice rhizosheath formation under MSD using high-throughput sequencing and rice mutants. Under MSD, rice rhizosheath formation was significantly increased by around 30% with P. indica inoculation. Auxins in rice roots and P. indica were responsible for the rhizosheath formation under MSD. Next, the abundance of the genus Bacillus, known as plant growth-promoting rhizobacteria, was enriched in the rice rhizosheath and root endosphere with P. indica inoculation under MSD. Moreover, the abundance of Bacillus cereus (B. cereus) with high auxin production was further increased by P. indica inoculation. After inoculation with both P. indica and B. cereus, rhizosheath formation in wild-type or auxin efflux carrier OsPIN2 complemented line rice was higher than that of the ospin2 mutant. Together, our results suggest that the interaction of the endophytic fungus P. indica with the native soil bacterium B. cereus favors rice rhizosheath formation by auxins modulation in rice and microbes under MSD. This finding reveals a cooperative contribution of P. indica and native microbiota in rice rhizosheath formation under moderate soil drying, which is important for improving water use in agriculture.

In the traditional quantitative detection model for blade cracks in centrifugal fan, it is assumed that the data distribution is fixed or stable. However, the new data brought by the crack propagation would break the stable distribution, thereby disturbing the old data, and resulting in a decrease in the detection performance of the model. To overcome catastrophic forgetting and reduce the extra computational cost of retaining intact old data, a quantitative detection method based on incremental learning bidirectional long short-term memory (BiLSTM) with dynamic proportional adjustment mechanism and experience replay for blade crack propagation is proposed. First, a basic BiLSTM model is constructed by inputting the data of cracks with a length of 0–5 mm. Second, the fully connected layer features in the model are selected for t-distributed stochastic neighbor embedding (t-SNE) dimensional reduction, and the Kullback–Leibler divergence is used as an indicator of feature distribution evaluating the representative old data. Third, a dynamic proportional adjustment mechanism for the old data retention proportion is constructed according to the feature distribution index and the model detection accuracy. Finally, the data of the crack with a length of 6–10 mm are gradually input to proceed with the incremental learning of the model. Verified by the measured data of the centrifugal fan, the model can adjust the retained number of old crack length data dynamically, and import new crack length data for incremental learning, making it characterized by high detection accuracy, stability, and plasticity for the quantitative detection of crack length propagation in blades.

Heterotrimeric G protein is involved in plant growth and development, while the role of rice (Oryza sativa) G protein γ subunit qPE9-1 in response to low-phosphorus (LP) conditions remains unclear. The gene expression of qPE9-1 was significantly induced in rice roots under LP conditions. Rice varieties carrying the qPE9-1 allele showed a stronger primary root response to LP than the varieties carrying the qpe9-1 allele (mutant of the qPE9-1 allele). Transgenic rice plants with the qPE9-1 allele had longer primary roots and higher P concentrations than those with the qpe9-1 allele under LP conditions. The plasma membrane (PM) H+ -ATPase was important for the qPE9-1-mediated response to LP. Furthermore, OsGF14b, a 14-3-3 protein that acts as a key component in activating PM H+ -ATPase for root elongation, is also involved in the qPE9-1 mediation. Moreover, the overexpression of OsGF14b in WYJ8 (carrying the qpe9-1 allele) partially increased primary root length under LP conditions. Experiments using R18 peptide (a 14-3-3 protein inhibitor) showed that qPE9-1 is important for primary root elongation and H+ efflux under LP conditions by involving the 14-3-3 protein. In addition, rhizosheath weight, total P content, and the rhizosheath soil Olsen-P concentration of qPE9-1 lines were higher than those of qpe9-1 lines under soil drying and LP conditions. These results suggest that the G protein γ subunit qPE9-1 in rice plants modulates root elongation for phosphorus uptake by involving the 14-3-3 protein OsGF14b and PM H+ -ATPase, which is required for rice P use.

Acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) of hoisting machinery recently. Kernel entropy component analysis (KECA) is generally applied to extract the AE features based on its excellent nonlinear ability. However, traditional KECA specifically requires a considerable number of components (e.g. eigenvalues and eigenvectors) to excellently describe the original data, which leads to a reduction in the effect of approximate dimensionality reduction of high-dimensional data, thus causing readily unacceptable condition monitoring result. To overcome this weakness, a novel method named moving window-improved kernel entropy component analysis (MW-IKECA) is proposed in this study for structural condition monitoring of hoisting machinery, which is aimed at extracting more AE feature information and improving the condition identification accuracy. Firstly, a twiddle factor is introduced in the KECA model for the purpose of breaking the restriction that the projection axes originate only from the feature vectors and maximizing the independence between the components. Meanwhile, the moving window local strategy is incorporated into the proposed IKECA to extract more rich and effectiveness AE feature information at different scales. Finally, the Cauchy–Schwarz (CS) statistic is utilized to calculate the similarity between probability density functions and maintain the angular structure of the MW-IKECA feature space for the task of improving the monitoring accuracy and shortening the monitoring time-delay of MW-IKECA. Results of the experimental and practical engineering application validate the effectiveness and superiority of the proposed method in AE-based crane SHM under different working conditions compared with the traditional KECA and some combinatorial methods.

Nowadays, the localization and identification of acoustic emission (AE) source is widely utilized to structural health monitoring (SHM) of complex metallic structures. However, traditional AE source localization methods are generally difficult to localize and characterize AE sources in plate-like structure that has complex geometric features. To alleviate the problem, a novel AE source localization method based on all-phase fast Fourier transform and multiple cross-correlation analysis is proposed in this article. Moreover, least squares and Geiger iteration algorithm are applied to determine the coordinates of AE sources. In addition, an improved Bayesian information criterion (BIC) version named autoregressive BIC (i.e., AR-BIC) is presented to increase the accuracy of source localization. To validate the performance of the proposed approach, the classical pencil lead break tests are carried out on a 316 L stainless steel with 10 laser cladding layers. Experimental waveforms are generated from AE sources near laser cladding layers, the surface of the structure, and on its edges. Additionally, to evaluate the performance of the proposed approach in three-dimensional AE source localization, an industrial storage tank is used to acquire three-dimensional AE sources through manually striking. Finally, to further verify the effectiveness of the proposed approach, comparisons with conventional AE source location methods (i.e., PAC or SAMOS AE acquisition system, Newton’s method, and multiple cross-correlation based on Geiger algorithm) and two representative approaches (i.e., deep learning and Bayesian methodology) for localizing AE sources generated by complex metallic structures are conducted. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM of complex metallic structures.

Cluster roots of white lupin are induced by low phosphorus (LP) to efficiently access unavailable P, but how soilborne microbes are associated with cluster root formation (CRF) is unclear. We investigated the roles of soilborne bacteria in CRF response to LP by high-throughput sequencing and root-bacteria interactions. Cluster root number was significantly decreased in plants grown in sterilized soil compared with nonsterilized soil. Proteobacteria was enriched in CR, as shown by microbiome analysis of soil (bulk, rhizosphere, and rhizosheath) and roots (main, lateral, and CR). Large-scale gene expression level implicated ethylene mediation in CRF. Klebsiella pneumoniae (P7), a soilborne bacterium belonging to Proteobacteria, was isolated from CR. Among 11 isolated strains, P7 exhibited the highest 1-aminocyclopropane-1-carboxylate deaminase (ACCD) activity; this enzyme inhibits the biosynthesis of ethylene in plants by the cleavage of the ethylene precursor 1-aminocyclopropane-1-carboxylic acid and promotes CRF under LP. We constructed an ACCD-deficit mutant accd in the P7 genetic background. The loss-of-function mutation failed to promote CRF under LP conditions. Also, auxin responses may be involved in K. pneumoniae-ethylene-mediated CRF. Overall, we propose that the soilborne bacterium K. pneumoniae promotes CRF of white lupin in response to LP by ethylene mediation.

The metallic panel acoustic emission signal with strong non-stationary properties is normally composed of multiple components (e.g. impulses, background noise, and other external signal), where impulses relevant to metallic panel are easily contaminated by background noise and other external signal, making it difficult to excavate the inherent acoustic emission signal features. To address this issue and achieve the damage monitoring of metallic panels based on acoustic emission technology, a new scheme based on adaptive improvement variational mode decomposition–wavelet packet transform is developed for extracting acoustic emission signal features of metallic panels. Specifically, three different dimensions of Q235B steel plates are utilized to collect acoustic emission signal during three-point bending experiments, to evaluate the effectiveness of the proposed approach and to investigate the influence of size effect on the acoustic emission signal characteristics. In addition, the transient process and centroid frequency distribution of each damage stage are investigated, and the internal structure variations in the bending damage process are detected by scanning electron microscopy inspection. Moreover, the generalization of the proposed damage monitoring method is evaluated for plate-like structures that have complex geometric features, such as welds. The results demonstrate the effectiveness of the proposed method for acoustic emission–based structural health monitoring of metallic plate-like structures.

On the condition of variable speeds, the blade crack signal generated by the single-mode sensor is an inherent risk of being misled by noise. Besides, it is challenging to reach the required integrity of information through feature extraction in single analysis domain, leading to the loss of detection accuracy for blade cracks. Therefore, a blade crack detection method based on domain adaptation and autoencoder (AE) of multidimensional vibro-acoustic feature fusion is proposed to extract more complete fault information from multi-sensor and multi-analysis domains. This method extracts one-dimensional (1D) and two-dimensional (2D) features from the frequency domain and smoothed pseudo-wigner-ville distribution (SPWVD) spectrum of the vibro-acoustic signal. Firstly, the 1D and 2D features are extracted without supervision through Stacked Denoising Autoencoder (SDAE) and Convolution Denoising Autoencoder (CDAE), respectively. Then, the extracted multidimensional features are fused and classified through the fully connected network. Finally, for insufficient diagnosis accuracy and data imbalance under certain working condition models with variable speed and combined with inductive transfer mechanism and domain adaptation, the stable diagnosis model under specific speed is applied for transferring and learning the unsupervised networks of other models compensating the weak generalization ability and robustness of the model. The experimental results show that this method is characterized by higher accuracy and robustness compared with the diagnosis method of single-domain feature for the single-mode sensor. Moreover, the proposed migration mechanism can well solve the data imbalance under different working conditions and improve the generalization ability of the few-shot model.

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Laser cladding technology has become a central issue in industrial research and application recently. Due to the complexity of the processing and the difficulty of quantitative analysis, it is particularly important to effectively and reliably detect the forming quality of the cladding layer. Therefore, the laser cladding processing condition of metallic panels is monitored and identified based on acoustic emission technology in this article. Consequently, laser cladding acoustic emission signal of 316L stainless steel plate is first collected, and a multi-domain acoustic emission signal feature extraction method based on time–frequency domain and waveform parameters is constructed by integrating time–frequency, empirical, and inter-relationship diagraph analysis. Moreover, a feature optimization approach based on t-distributed stochastic neighbor embedding algorithm is proposed, which combines with correlation analysis to realize the de-redundancy and optimization of acoustic emission signal features. In addition, on the basis of optimizing the characteristics of acoustic emission signal, laser cladding acoustic emission signals under three technological parameters are collected. Second, a laser cladding condition identification method, which is a least square support vector machine algorithm based on niche particle swarm optimization, is proposed. The optimal parameter combination of niche particle swarm optimization algorithm is mainly selected to improve the accuracy of identification and classification. The results demonstrate the proposed t-distributed stochastic neighbor embedding feature optimization method can effectively extract the sensitive information related to the processing condition in the feature space, and the optimization of least square support vector machine parameters through niche particle swarm optimization can significantly improve the identification rate of classification. Specifically, the feasibility of the proposed t-distributed stochastic neighbor embedding–niche particle swarm optimization–least square support vector machine model to analyze laser cladding acoustic emission signal characteristics is verified, and the effectiveness of acoustic emission–based structural condition monitoring and identification of laser cladding plate-like structures is also demonstrated.

Potassium (K+) has been reported to alleviate ammonium (NH4+) toxicity in rice through some underlying mechanisms, but it still not clear. In addition, K+ is an important cation for activation of plasma membrane (PM) H+-ATPase activity. Here, we hypothesized that K+ alleviated NH4+ toxicity by mediating PM H+-ATPase function in rice root. In this study, rice plants were cultivated in hydroponic solution with various concentrations of K+ and NH4+. By concurrently supplying K+ with NH4+ or re-supplying K+ after NH4+ toxicity, we found that high K+ concentration reduced the NH4+ uptake rate, enhanced the H+ extrusion rate by the roots, and alleviated rice NH4+ toxicity. The gene expression levels of PM H+-ATPase members (OsA1, 3, 7, 8, and 9) were upregulated by application of increasing concentrations of K+ under NH4+ toxicity. The PM H+-ATPase activity and protein expression in rice roots were also enhanced. Furthermore, the enhancement of PM H+-ATPase activity by a specific stimulator (fusicoccin) rescued rice seedlings from NH4+ toxicity. Taken together, these results indicate that K+ can alleviate NH4+ toxicity, possibly by activating PM H+-ATPase to extrude more H+ and inhibit NH4+ uptake by root. Our results may enhance understanding of the strategy of applying K+ fertilizer to mitigate crop NH4+ toxicity in agriculture.

Background Triptonide (TN) was recently proved to have anti-tumor effects. The current study explored whether TN inhibited thyroid cancer and the possible underlying mechanism. Methods MDA-T68 and BCPAP cells were treated by TN. Cell viability, migration and invasion rate were detected by MTT and Transwell. Protein expressions were determined by Western blot and mRNA expressions were detected by Real-time Quantitative PCR (qPCR). Results TN at the concentration higher than 50 nmol/L inhibited cell viability, migration and invasion of MDA-T68 and BCPAP cells, and astrocyte elevated gene (AEG-1) expression, was decreased by TN at the concentration higher than 50 nmol/L. Furthermore, AEG-1 overexpression inhibited cell viability, migration and invasion capacity of MDA-T68 and BCPAP cells, while TN reduced AEG-1 expression, and weaken the effect of AEG-1 overexpression on cell viability, migration and invasion capacities. Moreover, TN depressed the increase of matrix metalloproteinase (MMP) 2, MMP9 and N-cadherin expressions caused by AEG-1 overexpression. Meanwhile, E-cadherin expression reduced by AEG-1 overexpression was increased by TN. Conclusions TN could inhibit the metastasis potential of thyroid cancer cells through inhibiting the expression of AEG-1. Our findings reveal the mechanism of TN in the treatment of thyroid cancer, which should be further explored in the study of thyroid cancer. Keywords Triptonide; metastasis; thyroid cancer; regulation; drug monomer