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• Plant hormones can adjust the physiology and development of plants to enhance their adaptation to biotic and abiotic challenges. Jasmonic acid (JA), one of the immunity hormones in plants, triggers genome-wide transcriptional changes in response to insect attack and wounding. Although JA is known to affect the development of trichomes, epidermal appendages that form a protective barrier against various stresses, it remains unclear how JA interacts with developmental programs that regulate trichome development. • In this study, we show that JA affects trichome length in tomato by releasing the transcriptional repression mediated by Jasmonate ZIM (JAZ) proteins. We identified SlJAZ4, a negative regulator preferentially expressed in trichomes, as the critical component in JA signaling in tomato trichomes. • We also identified a homeodomain-leucine zipper gene, SlHD8, as the downstream regulator of JA signaling that promotes trichome elongation. SlHD8 is also highly expressed in trichomes and physically interacts with SlJAZ4. Loss-of-function mutations in SlHD8 caused shorter trichomes, a phenotype that was only partially rescued by methyl jasmonate treatment. • Our dual-luciferase and chromatin immunoprecipitation–quantitative PCR assays revealed that SlHD8 regulates trichome elongation by directly binding to the promoters of a set of cell-wall-loosening protein genes and activating their transcription. Together, our findings define SlHD8-SlJAZ4 as a key module mediating JA-induced trichome elongation in tomato.

Since artificial intelligence (AI) was introduced into engineering fields, it has made many breakthroughs. Machine learning (ML) algorithms have been very commonly used in structural health monitoring (SHM) systems in the last decade. In this study, a vibration-based early stage of bolt loosening detection and identification technique is proposed using ML algorithms, for a motor fastened with four bolts (M8 × 1.5) to a stationary support. First, several cases with fastened and loosened bolts were established, and the motor was operated in three different types of working condition (800 rpm, 1000 rpm, and 1200 rpm), in order to obtain enough vibration data. Second, for feature extraction of the dataset, the short-time Fourier transform (STFT) method was performed. Third, different types of classifier of ML were trained, and a new test dataset was applied to evaluate the performance of the classifiers. Finally, the classifier with the greatest accuracy was identified. The test results showed that the capability of the classifier was satisfactory for detecting bolt loosening and identifying which bolt or bolts started to lose their preload in each working condition. The identified classifier will be implemented for online monitoring of the early stage of bolt loosening of a multi-bolt structure in future works.

With the wide application of bolt connection, the application environment of the bolt is more and more complicated. The problem of bolt loosening caused by high temperatures cannot be ignored. Based on the research status of domestic and foreign experts, this paper analyzes the causes of high-temperature bolt loosening and introduces theoretical research on this problem. The conclusion is drawn that the factors affecting bolt loosening under high-temperature conditions and the anti-loosening measures are put forward.

Bolts are subject to complex loads and prone to loosening failure in transverse random service environments. To establish a loosening life evaluation method applicable to different types of bolts, numerous bolt clamping-force recession curves were obtained by bolt-loosening tests, and the relationship between the transverse displacement external load and loosening life ( D – N curve) was established. Subsequently, an equivalent model of the bolt external load and screw load was established based on Castigliano’s theorem, and an equivalent model of the screw load and screw-tooth root stress was established based on the deformation coordination equation of elastic mechanics. Based on the above bolt-load equivalent models, the screw-tooth root stresses were equivalently characterized by the transverse displacement of the bolted connection, and then the screw-tooth root equivalent stress–loosening life curve ( S u – N curve) was established to normalize the D – N curves with different bolt diameters. Finally, a bolt-loosening bench test of an antenna bracket under transverse random vibration was performed, and the loosening life of the bolts was evaluated using the S u – N curve. The maximum relative error between the test and theoretically predicted lives was only 6.26%, which verified the accuracy of the bolt-loosening life evaluation method based on the bolt-load equivalence and provided a certain reference for the bolt-loosening life evaluation and anti-loosening design in a random vibration environment.

This paper discusses the bolt-loosening response subject to transverse cyclic loading and its analogy to metal fatigue behavior. A quantitative model accounting for the response was proposed, and the loosening life characteristics were examined. Junker tests were performed on two types of bolts, M6 and M8, along with supplementary FE analysis. Displacement amplitudes ranging from 0.18 to 0.65 mm were applied to each bolt type, and clamping forces with respect to the number of cycles were measured. From the result, a few distinctive stages were found to occur in the bolt-loosening process, and all the normalized clamping force curves were represented by a single mathematical equation, that is, a phenomenological model. In addition, the loosening lives were successfully modeled using a power law relation, as employed in the S–N curves. Consequently, the similarities between bolt-loosening and fatigue processes were verified, providing a valuable reference for future reliability evaluations of bolted structures.

Bolted connections are crucial in joining mechanical assemblies and ensuring the integrity and reliability of structural components. This study proposes a method for estimating bolt loosening life by practically applying material fatigue life prediction methods. First, the study employed the linear cumulative damage rule to predict the loosening life of single bolts under two-block loading conditions. Second, a test device with two bolt attachment points on a single structure was fabricated to model the multi-bolted structure, and tests were conducted. Finite element analysis (FEA) analysis was employed to identify vulnerable points. The loosening life of single bolts predicted using the linear cumulative damage rule exhibited enhanced accuracy within a ±1.2× error band compared with the experimental data despite variations in bolt types and test conditions. The FEA results for the multi-bolt structure demonstrated that the loosening life could be predicted by identifying vulnerable points and estimating the displacements. This study effectively predicts the bolt loosening life, offering valuable data for the reliability assessment of bolted structures.

A high-strength bolt connection is the key component of large-scale steel structures. Bolt loosening and preload loss during operation can reduce the load-carrying capacity, safety, and durability of the structures. In order to detect loosening damage in multi-bolt connections of large-scale civil engineering structures, we proposed a multi-bolt loosening identification method based on time-frequency diagrams and a convolutional neural network (CNN) using vi-bro-acoustic modulation (VAM) signals. Continuous wavelet transform was employed to obtain the time-frequency diagrams of VAM signals as the features. Afterward, the CNN model was trained to identify the multi-bolt loosening conditions from the raw time-frequency diagrams intelligently. It helps to get rid of the dependence on traditional manual selection of simplex and ineffective damage index and to eliminate the influence of operational noise of structures on the identification accuracy. A laboratory test was carried out on bolted connection specimens with four high-strength bolts of different degrees of loosening. The effects of different excitations, CNN models, and dataset sizes were investigated. We found that the ResNet-50 CNN model taking time-frequency diagrams of the hammer excited VAM signals, as the input had better performance in identifying the loosened bolts with various degrees of loosening at different positions. The results indicate that the proposed multi-bolt loosening identification method based on VAM and ResNet-50 CNN can identify bolt loosening with a reasonable accuracy, computational efficiency, and robustness.

Loosening of threaded fasteners is a key failure mode, which is mainly caused by the slippage and friction behaviors on the thread and bearing surfaces, and will affect the integrity and reliability of products. Numerous scholars have conducted research on the loosening of threaded fasteners; however, comprehensive reviews on the loosening of threaded fasteners have been scarce. In this review article, we define loosening as a loss of preload and divide it into non-rotational and rotational loosening. The causes and mechanisms of non-rotational and rotational loosening are summarised. Some essential topics regarding loosening under transverse vibration have also attracted significant attention and have been investigated widely, including the loosening curve, critical condition of loosening, and influencing factors of loosening. The research carried out on these three topics is also summarised in this review. It is believed that our work will not only help new researchers quickly understand the state-of-the-art research on loosening, but also increase the knowledge of engineers on this critical subject. In the future, it is important to conduct more quantitative research on local slippage accumulation, and the relationship between local slippage accumulation and rotational loosening, which will have the potential to comprehensively unravel the loosening mechanism, and effectively guide the anti-loosening design of threaded fasteners.

Threaded fasteners are widely used in mechanical structures primarily owing to their easy disassembly for maintenance and low cost. However, the loosening mechanism of threaded fasteners due to dynamic loading has remained unclear for the past six decades. Current researches on complex structures comprising three or more components are insufficient. The two most common failure modes of threaded fasteners subjected to dynamic loading are fatigue and vibration-induced loosening. This study focuses on the failure of threaded fasteners by vibration-induced loosening due to dynamic shear loads. This study comprises experimental analysis and numerical analysis. The loosening mechanism of threaded fasteners for complex structures is analytically and experimentally identified. This work provides the equations and assessment method for the loosening, and the criteria of primary and secondary loosening are established. To verify the proposed loosening mechanism, tightening and loosening experiments are conducted for three types of bolted joints. The primary and secondary loosening forces of each bolt are thus obtained, and the proposed loosening mechanism can be verified for complex structures. In numerical analysis, a three-dimensional finite element (FE) model for tightening and loosening analysis is proposed. A FE model is used to study the loosening process which is characterized by a decline of the preload and moving distance for predicting loosening states. The model seems to be well agreement in comparison with theoretical and experimental results. As a result, the assessment method shows good performance in predicting loosening state. It is expected to verify the safety of bolted structures at the design stage. The FE model is expected to be used for the effective and safe design for joint components in various industrial fields such as wheel assemblies and other mechanical components under dynamic vibration.

Bolts are widely used in transmission lines and are key connecting parts of transmission lines. Transmission lines have been affected by the natural environment for a long time, and bolts are prone to frequent vibration leading to loosening or falling off. Bolt loosening endangers the safe and stable operation of transmission lines. This paper analyses the loosening mechanism of the transmission line screw connection, carries out the transverse vibration contrast test of the common bolt anti-loosening measures, and develops the integrated self-locking double nut for the hidden danger of the transmission line bolt loosening, which has good anti-loosening performance, convenient installation, and the conditions for engineering popularization and application.