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Nonreciprocal transmission of waves is highly desirable for the transport and redistribution of energy. However, building an asymmetric system to break time-reversal symmetry is relatively difficult because it tends to work under stringent guidelines, narrow bandwidth, or external impetus, particularly in a three-port system. Without breaking reciprocity, realizing “one-way” transmission of elastic waves by a linear and passive structure in a higher-dimensional asymmetric system, such as a three-port circulator, poses quite a challenge. Here, based on the wave-vector modulation mechanism, we propose an elastic-wave circulator that achieves this without breaking reciprocity, enabling perfect mode transition and wave trapping simultaneously. Requiring neither activated media nor relying on the nonlinearity of nonreciprocal devices, the circulator routes elastic waves purely in a clockwise direction, offering superior performance in broad bandwidth, robust behavior, and simple configuration. Our study provides a feasible platform for asymmetric wave transport in a three-port system, which can be useful in the routing, isolation, and harvesting of energy and can also be extended to other fields, such as electromagnetic and acoustic waves. Nonreciprocal transmission of waves is crucial for transport and redistribution of energy, yet the architecture to break time-reversal symmetry is hard to realise. Here, the authors proposed elastic-wave circulator that could achieve this without breaking reciprocity, enabling mode transition and wave trapping concurrently.

To solve the problems of low detection accuracy, large model size and slow reasoning speed of existing potato quality detection models, this paper proposes LPD-YOLOv7-Tiny, a lightweight potato sprout and spoilage detection model based on YOLOv7-Tiny. The proposed model introduces MobileNetV3 small, BiFormer, SimAM, and the Focal-EIOU loss function. MobileNetV3 small greatly reduces the number of parameters and computational complexity of the model, BiFormer enhances the multi-scale feature fusion capability of the model, and the SimAM module effectively suppresses irrelevant information and strengthens local features. The Focal-EIOU loss function improves the model’s attention to difficult classification samples and enhances its bounding box regression capability. LPD-YOLOv7-Tiny achieves excellent detection performance on potatoes under complex background conditions: mAP is increased to 90.3%, the number of parameters is reduced to 5.8 MB, the number of computations is reduced to 10.1 G, and the inference speed is increased to 142.5 fps. Compared with mainstream detection models such as the YOLO Basic series, SSD and speed-RCNN, LPD-YOLOv7-Tiny achieves significantly improved performance in terms of detection accuracy, positioning capability and computational efficiency, indicating it has wide application potential in resource-constrained and high-precision scenarios.

The vibration signal of gearboxes contains abundant fault information, which can be used for condition monitoring. However, vibration signal is ineffective for some non-structural failures. In order to resolve this dilemma, infrared thermal images are introduced to combine with vibration signals via fusion domain-adaptation convolutional neural network (FDACNN), which can diagnose both structural and non-structural failures under various working conditions. First, the measured raw signals are converted into frequency and squared envelope spectrum to characterize the health states of the gearbox. Second, the sequences of the frequency and squared envelope spectrum are arranged into two-dimensional format, which are combined with infrared thermal images to form fusion data. Finally, the adversarial network is introduced to realize the state recognition of structural and non-structural faults in the unlabeled target domain. An experiment of gearbox test rigs was used for effectiveness validation by measuring both vibration and infrared thermal images. The results suggest that the proposed FDACNN method performs best in cross-domain fault diagnosis of gearboxes via multi-source heterogeneous data compared with the other four methods.

Structural Heath Monitoring (SHM) can be considered one of the most prominent emerging components of modern engineering applications [...].Structural Heath Monitoring (SHM) can be considered one of the most prominent emerging components of modern engineering applications [...].

During shield tunneling, interactions with existing structures, such as pile foundations and bridges, are frequently encountered, leading to a growing implementation of shield cutting techniques. This study utilizes a metro tunnel project crossing under a ring expressway bridge in a city in Northeast China to develop a refined three-dimensional finite difference method (FDM) model. The 3D FDM model investigates the mechanical responses of existing pile foundations and the bridge structure due to shield cutting through residual pile foundations. The proposed numerical model is validated against field monitoring data. The simulation results show that the primary mechanical response of the existing pile foundations to shield tunneling is horizontal deformation, which decreases with increasing distance from the tunnel axis. In zones containing residual piles, tunnel crown settlement exhibits a wavy pattern, with lower settlement values compared to areas without residual piles. In contrast, bridge structure deformation remains minimal, with final deformation values below standard values. To comprehensively evaluate the effects of shield cutting through residual piles, the study quantitatively examines stratum disturbances using the ground loss ratio as an indicator. Results reveal that ground loss ratios surrounding residual pile foundations exceed 0.5%, reflecting a substantial influence. A parametric analysis underscores the critical role of ground loss ratios in influencing both bridge structure deformation and tunnel settlement.

Health monitoring and advanced fault diagnosis are now critical features in modern engineering systems, the increasing complexity and new frameworks of which elevated the pressure for dependable reliability and safety measures [...]

Fault diagnosis and health condition monitoring have always been critical issues in the engineering research community [...].Fault diagnosis and health condition monitoring have always been critical issues in the engineering research community [...].

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance.

Adjacent vertebral fracture (AVF) is a serious complication of percutaneous vertebroplasty (PVP) or kyphoplasty (PKP) for osteoporotic vertebral compression fracture (OVCF). This study aimed to explore the incidence and risk factors of AVF following PVP or PKP in postmenopausal women. The incidence of AVF was determined by spinal radiographic examinations. The potential risk factors of AVF were identified by univariate analysis, followed by multivariate logistic regression analyses to determine the independent risk factors. In total, 674 postmenopausal women who were treated with PVP or PKP from December 2019 to February 2022 were enrolled in the study. Among them, 58 (8.61%) women experienced an AVF following PVP or PKP. After adjusting for confounding factors, BMI (OR [95% CI] 0.863 [0.781–0.952]; p = 0.003), previous history of OVCF (OR [95% CI] 1.931 [1.044–3.571]; p = 0.036), and Hounsfield unit (HU) value (OR [95% CI] 0.979 [0.967–0.990]; p < 0.001) were found to be independent risk factors of AVF following PVP or PKP in postmenopausal women. The ROC analysis revealed that the BMI and HU thresholds were 21.43 and 65.15, respectively. In conclusion, the incidence of AVF was 8.61%. BMI, previous history of OVCF and HU value were independent risk factors of AVF following PVP or PKP in postmenopausal women.

As an important machine component, the gearbox is widely used in industry for power transmission. Condition monitoring (CM) of a gearbox is critical to provide timely information for undertaking necessary maintenance actions. Massive research efforts have been made in the last two decades to develop vibration-based techniques. However, vibration-based methods usually include several inherent shortages including contact measurement, localized information, noise contamination, and high computation costs, making it difficult to be a cost-effective CM technique. In this paper, infrared thermal (IRT) images, which can contain information covering a large area and acquired remotely, are based on developing a cost-effective CM method. Moreover, a convolutional neural network (CNN) is employed to automatically process the raw IRT images for attaining more comprehensive feature parameters, which avoids the deficiency of incomplete information caused by various feature-extraction methods in vibration analysis. Thus, an IRT–CNN method is developed to achieve online remote monitoring of a gearbox. The performance evaluation based on a bevel gearbox shows that the proposed method can achieve nearly 100% correctness in identifying several common gear faults such as tooth pitting, cracks, and breakages and their compounds. It is also especially robust to ambient temperature changes. In addition, IRT also significantly outperforms its vibration-based counterparts.