Search Results (524)

The stress distribution after excavation becomes highly complex in large-span bifurcated tunnel sections commonly found in urban underground interchanges. This study investigates the stress evolution induced by the excavation of large-span and bifurcated tunnel, focusing on the 32.17 m maximum-span section of the Shenzhen Baopeng–Shahe Underground Interchange. The results show that stress concentration near the tunnel walls of large-span sections is greater than that in sections with bifurcated tunnels. Adjusting the burial depth of the large-span tunnel, the influence of stiff layer thickness on the redistribution of surrounding rock stress was analyzed. When the tunnel is buried at a shallow depth and the stiff layer thickness is small, the maximum tangential stress of the surrounding rock occurs at the stiff layer boundary, and the surrounding rock remains entirely elastic. In large-span tunnels, as the thickness of the stiff layer increases from 5 m to 20 m, the stress relaxation zone grows from 0 m to 8 m, and the stress-bearing zone expands from 10 m to 27 m. As the burial depth increases and the stiff layer thickness grows, the maximum tangential stress shifts to within the stiff layer. In this case, the tangential stress distribution at the stiff layer boundary becomes non-smooth. Therefore, an appropriate stiff layer thickness must be selected to prevent the surrounding rock from entering a plastic state. The findings provide theoretical guidance and technical support for the design of large-scale underground interchange bifurcated tunnels, advancing the intelligent and scientific development of urban underground transportation facilities and offering significant practical and social benefits. Full article

The grid-forming static var generator (SVG) is a key device that supports the stable operation of power grids with a high penetration of renewable energy. The cooling efficiency of its forced water-cooling system directly determines the reliability of the entire unit. However, existing wired monitoring methods suffer from complex cabling and limited capacity to provide a full perception of the water-cooling condition. To address these limitations, this study develops a wireless monitoring system based on multi-source information fusion for real-time evaluation of cooling efficiency and early fault warning. A heterogeneous wireless sensor network was designed and implemented by deploying liquid-level, vibration, sound, and infrared sensors at critical locations of the SVG water-cooling system. These nodes work collaboratively to collect multi-physical field data—thermal, acoustic, vibrational, and visual information—in an integrated manner. The system adopts a hybrid Wireless Fidelity/Bluetooth (Wi-Fi/Bluetooth) networking scheme with electromagnetic interference-resistant design to ensure reliable data transmission in the complex environment of converter valve halls. To achieve precise and robust diagnosis, a three-layer hierarchical weighted fusion framework was established, consisting of individual sensor feature extraction and preliminary analysis, feature-level weighted fusion, and final fault classification. Experimental validation indicates that the proposed system achieves highly reliable data transmission with a packet loss rate below 1.5%. Compared with single-sensor monitoring, the multi-source fusion approach improves the diagnostic accuracy for pump bearing wear, pipeline micro-leakage, and radiator blockage to 98.2% and effectively distinguishes fault causes and degradation tendencies of cooling efficiency. Overall, the developed wireless monitoring system overcomes the limitations of traditional wired approaches and, by leveraging multi-source fusion technology, enables a comprehensive assessment of cooling efficiency and intelligent fault diagnosis. This advancement significantly enhances the precision and reliability of SVG operation and maintenance, providing an effective solution to ensure the safe and stable operation of both grid-forming SVG units and the broader power grid. Full article

Prosocial behavior is an important manifestation of socialization in young children. As the primary setting for socialization of young children, the family bears the significant responsibility of fostering prosocial behavior in young children. Drawing on family systems theory and Goleman’s emotional intelligence theory, the purpose of this study was to investigate the relationship between parenting styles and children’s prosocial behavior and the mediating role of children’s emotional intelligence in it. In this study, an online questionnaire was distributed to 869 young children’s parents using the Parenting Style Questionnaire, Children’s Prosocial Behavior Questionnaire, and Children’s Emotional Intelligence Questionnaire. The results indicated that democratic parenting style positively influenced children’s prosocial behavior, while indulgent parenting style, permissive parenting style and inconsistent parenting style negatively impacted it. Authoritarian parenting style had no significant effect on children’s prosocial behavior. Children’s emotional intelligence mediated the relationship between parenting styles and prosocial behavior. This study explored factors influencing children’s prosocial behavior from both external family systems and internal individual perspectives and revealed their underlying mechanisms, providing theoretical support for research and educational practice on children’s prosocial behavior. Full article

At present, fruit picking mainly relies on manual operation. Taking the litchi (litchi chinensis Sonn.)-picking robot as an example, visual perception is often affected by illumination variations, low recognition accuracy, complex maturity judgment, and occlusion, which lead to inaccurate fruit localization. This study aims to establish an embodied perception mechanism based on “perception-reasoning-execution” to enhance the visual perception and decision-making capability of the robot in complex orchard environments. First, a Y-LitchiC instance segmentation method is proposed to achieve high-precision segmentation of litchi clusters. Second, a generative artificial intelligence model is introduced to intelligently assess fruit maturity and occlusion, providing auxiliary support for automatic picking. Based on the auxiliary judgments provided by the generative AI model, two types of dynamic harvesting decisions are formulated for subsequent operations. For unoccluded main fruit-bearing branches, a skeleton thinning algorithm is applied within the segmented region to extract the skeleton line, and the midpoint of the skeleton is used to perform the first type of localization and harvesting decision. In contrast, for main fruit-bearing branches occluded by leaves, threshold-based segmentation combined with maximum connected component extraction is employed to obtain the target region, followed by skeleton thinning, thereby completing the second type of dynamic picking decision. Experimental results show that the Y-LitchiC model improves the mean average precision (mAP) by 1.6% compared with the YOLOv11s-seg model, achieving higher accuracy in litchi cluster segmentation and recognition. The generative artificial intelligence model provides higher-level reasoning and decision-making capabilities for automatic picking. Overall, the proposed embodied perception mechanism and dynamic picking strategies effectively enhance the autonomous perception and decision-making of the picking robot in complex orchard environments, providing a reliable theoretical basis and technical support for accurate fruit localization and precision picking. Full article

In the context of bearing anomaly detection, challenges such as imbalanced sample distribution and complex operational conditions present significant difficulties for data-driven deep learning models. These issues often result in overfitting and high false positive rates in complex real-world scenarios. This paper proposes a strategy that leverages multimodal fusion and Self-Adversarial Training (SAT) to construct and train a deep learning model. First, the one-dimensional bearing vibration time-series data are converted into Gramian Angular Difference Field (GADF) images, and multimodal feature fusion is performed with the original time-series data to capture richer spatiotemporal correlation features. Second, a composite data augmentation strategy combining time-domain and image-domain transformations is employed to effectively expand the anomaly samples, mitigating data scarcity and class imbalance. Finally, the SAT mechanism is introduced, where adversarial samples are generated within the fused feature space to compel the model to learn more generalized and robust feature representations, thereby significantly enhancing its performance in realistic and noisy environments. Experimental results demonstrate that the proposed method outperforms traditional baseline models across key metrics such as accuracy, precision, recall, and F1-score in abnormal bearing anomaly detection. It exhibits exceptional robustness against rail-specific interferences, offering a specialized solution strictly tailored for the unique, high-noise operational environments of intelligent railway maintenance. Full article

Reliable operation of rolling bearings is essential for mechanical systems. Acoustic emission (AE) offers a promising approach for bearing fault detection because of its high-frequency response and strong noise-suppression capability. This study proposes an intelligent diagnostic method that combines an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture. The method first applies wavelet denoising to AE signals, then uses ICEEMDAN decomposition followed by kurtosis-based screening to extract key fault components and construct feature vectors. Subsequently, a CNN automatically learns deep time–frequency features, and a BiLSTM captures temporal dependencies among these features, enabling end-to-end fault identification. Experiments were conducted on a bearing acoustic emission dataset comprising 15 operating conditions, five fault types, and three rotational speeds; comparative model tests were also performed. Results indicate that ICEEMDAN effectively suppresses mode mixing (average mixing rate 6.08%), and the proposed model attained an average test-set recognition accuracy of 98.00%, significantly outperforming comparative models. Moreover, the model maintained 96.67% accuracy on an independent validation set, demonstrating strong generalization and practical application potential. Full article

Monitoring the technical condition of conveyor belts is essential for the reliable and safe operation of pipe belt conveyors. Integrating passive UHF RFID tags directly into the belt structure enables continuous monitoring of belt circulation, elongation, and splice condition without interrupting operation. This study aimed to verify the technical feasibility of such an approach, optimize the RFID system architecture, and experimentally evaluate the impact resistance of tags vulcanized into a rubber–textile conveyor belt. A multicriteria decision-making approach (AHP and TOPSIS) was used to select a suitable UHF antenna and mounting system for the experimental pipe conveyor TMEL, resulting in the choice of a circularly polarized Alien ALR-8698 patch antenna and a fully adjustable portal-type holder. Impact tests on an S 250/2 RA belt with integrated RFID tags showed that all tags remained functional up to complete mechanical failure of the specimens, even under direct impact, with maximum impact forces of 6–12 kN depending on specimen width. The integration of RFID tags did not introduce a critical weakening of the load-bearing belt structure, confirming that RFID is a robust and suitable complement for intelligent condition monitoring of pipe conveyors. Full article

To address the challenge of bearing load monitoring in elastically supported marine shafting systems, a multi-data fusion-based prediction model is constructed. In view of the small-sample nature of measured bearing load data, transfer learning is adopted to migrate the physical relationships embedded in finite element simulations to the measurement domain. A limited number of actual samples are used to correct the simulation data, forming a high-fidelity hybrid training set. The system—supported by air-spring isolators mounted on the raft—is divided into multiple sub-regions according to their spatial layout, establishing local mappings from air-spring pressure variations to bearing load increments to reduce model complexity. On this basis, a Stacking ensemble learning framework is further incorporated into the prediction model to integrate multi-source information such as air-spring pressure and raft strain, thereby enriching the model’s information acquisition and improving prediction accuracy. Experimental results show that the proposed transfer learning-based multi-sub-region bearing load prediction model performs significantly better than the full-parameter input model. Furthermore, the strain-enhanced Stacking-based multi-data fusion bearing load prediction model improves the characterization of shafting features and reduces the maximum prediction error. The proposed multi-data fusion modeling strategy offers a viable approach for condition monitoring and intelligent maintenance of marine shafting systems. Full article

Rolling bearing fault diagnosis is critical for the reliable operation of rotating machinery. However, many existing deep learning-based methods rely on complex signal preprocessing and lack interpretability. This paper proposes an adversarial autoencoder (AAE)-based framework that integrates adaptive, data-driven signal decomposition directly into a neural network. A convolutional autoencoder is employed to extract latent representations while preserving temporal resolution, enabling encoder channels to be interpreted as nonlinear signal components. A channel attention mechanism adaptively reweights these components, and a classifier acts as a discriminator to enhance class separability. The model is trained in an end-to-end manner by jointly optimizing reconstruction and classification objectives. Experiments on three benchmark datasets demonstrate that the proposed method achieves high diagnostic accuracy (99.64 ± 0.29%) without additional signal preprocessing and outperforms several representative deep learning-based methods. Moreover, the learned representations exhibit interpretable characteristics analogous to classical envelope demodulation, confirming the effectiveness and interpretability of the proposed approach. Full article

South Korea’s 11th Long-term Plan for Transmission and Substation Equipment (LPTSE, 2024–2038) invests KRW 72.8 trillion (USD 52.3 billion) to integrate 91.9 GW renewables while securing supply for semiconductor/artificial intelligence demand concentrated in the Seoul Metropolitan Area. This study aims to quantify LPTSE’s national economic effects and spatial equity implications using input–output (IO) analysis. A demand-side IO model—calibrated to 2022 national tables with a novel transmission and substation investment sector—disaggregates investments across five key sectors and estimates production, value-added, wage, and employment multipliers, complemented by multiregional spatial analysis of high-voltage direct or alternating current corridors. The results project KRW 128.2 trillion (USD 92.2 billion) total production, KRW 54.1 trillion (USD 38.9 billion) value-added, KRW 30.9 trillion (USD 22.2 billion) wages, and 578,000 jobs over 2025–2038, with coastal generation regions bearing infrastructure burdens while benefits accrue nationally. The findings demonstrate transmission investments as macroeconomic catalysts, highlighting the need for regionally differentiated compensation addressing land-use conflicts along export or transit corridors. Full article

Charcot neuro-osteoarthropathy (CNO) is a devastating complication of peripheral neuropathy, characterized by progressive bone and joint destruction that leads to severe foot deformity, ulceration, and a high risk of amputation. The management of CNO is predicated on an accurate understanding of its biomechanical instability, yet conventional imaging modalities like non-weight-bearing computed tomography (CT) and magnetic resonance imaging (MRI) fail to capture the true, load-dependent nature of the deformity. This review elucidates the paradigm shift facilitated by weight-bearing computed tomography (WBCT) in the diagnosis and management of CNO. A comprehensive narrative review of the literature was conducted to synthesize the pathophysiology of CNO, the limitations of conventional imaging, and the technological principles, clinical applications, and future directions of WBCT in CNO management. The review integrates findings on CNO pathophysiology, radiological assessment, and the debate surrounding weight-bearing protocols in conservative management. WBCT provides a three-dimensional, functional assessment of the Charcot foot under true physiological load, overcoming the critical limitations of non-weight-bearing imaging. It reveals the full extent of osseous collapse, unmasking hidden instabilities and enabling the use of novel quantitative 3D metrics for deformity characterization and risk stratification. Clinically, WBCT enhances the entire management pathway, from improving early diagnostic accuracy and informing surgical strategy with patient-specific instrumentation to enabling objective postoperative evaluation of reconstructive outcomes. WBCT is a promising technology that redefines the assessment of CNO from a static, morphological description to a dynamic, quantitative biomechanical analysis. Its integration into clinical practice offers the potential to improve diagnostic precision, optimize surgical planning, and ultimately enhance patient outcomes. The future synergy of WBCT with artificial intelligence holds promise for further advancing patient care, moving towards a predictive and prescriptive model for managing this complex condition. Full article

The working conditions of bearings, as a key component in electromechanical systems, are becoming increasingly complex with the rapid development of current intelligent manufacturing technology. Therefore, it is difficult to accurately identify the abnormal operating state of the bearing through a single signal. In addition, data-based bearing fault diagnosis methods insufficiently utilize bearing prior knowledge under complex working conditions. To address the above issues, this paper proposes a bearing fault diagnosis method based on multi-source information fusion with physical prior knowledge (MSIF-PPK). An information fusion module and a physical embedding module are designed: the former module fuses frequency-domain, time–frequency-domain, and working condition information through an attention mechanism, while the latter one embeds physical working condition data and features. The feasibility and the effectiveness of the modules are verified through comparative experiments and ablation experiments using the Southeast University (SEU) Bearing Dataset, the Mehran University of Engineering and Technology (MUET) Induction Motor Bearing Vibration Dataset, and the Harbin Institute of Technology (HIT) Aeroengine Bearing Dataset. Experimental results show that this method is feasible, reliable, and interpretable for bearing fault diagnosis under complex working conditions. Full article

Accurate reservoir identification from well-logging data is crucial for hydrocarbon exploration, yet challenges persist due to a series of factors, including limitations such as low efficiency and subjectivity of manual processing for massive datasets, as well as class imbalance and its impact on machine learning model training. This study develops an intelligent identification model using a Convolutional Neural Network (CNN) enhanced with Focal Loss, applied to real well-logging data from the Chang 8 Member of the Yanchang Formation in the Jiyuan Oilfield, Ordos Basin. A well-based data partitioning strategy is adopted to ensure the model’s generalization ability to new wells, avoiding the overoptimistic performance associated with random sample splitting. Experimental results demonstrate that the proposed model achieves an Accuracy of 84% and a Recall of 83% for oil-bearing layers. In comparison, the Random Forest model achieves a lower Recall of 56% for oil-bearing layers, and the CNN-LSTM model achieves 77%. The key influential well-logging parameters identified are bulk density (DEN), spontaneous potential (SP), true resistivity (RT), and natural gamma ray (GR). The findings confirm that the Focal Loss-enhanced CNN effectively mitigates class imbalance issues and provides a reliable, automated method for reservoir identification, offering significant practical value for the secondary interpretation of well logs in similar tight sandstone reservoirs. Full article

This study presents a machine learning framework for predicting the axial compressive strength of circular concrete-filled steel tube (CFST) columns subjected to concentric and eccentrically applied axial loads. A harmonized database of 1287 test specimens was compiled, encompassing diverse material strengths, geometric configurations, and eccentricity levels. Among the trained models, the CatBoost (CatB) algorithm exhibited the highest predictive performance. A 300-run Monte Carlo simulation yielded a mean R² of 0.966 (Min: 0.804; Max: 0.996), with a mean RMSE of 588.8 kN and MAPE of 8.36%, demonstrating accuracy and robustness across repeated randomized splits. Comparative benchmarking against current design equations revealed that CatBoost substantially reduced prediction scatter, improving the mean ratio and reducing the COV from 70–75% (ACI/AIJ/Wang) to 5.43%, while maintaining a nearly unbiased mean prediction ratio of 1.00. In addition, inverse prediction models based on CatBoost achieved test-set R² values of 0.908 for compressive strength and 0.945, 0.900, and 0.816 for key design parameters (D, t, L), indicating promising capability for supporting preliminary sizing and parameter selection. The outcomes of this study highlight the potential of data-driven modelling to complement existing design provisions and assist engineers in early-stage decision-making for axially loaded circular CFST columns. Full article

Earthquake-induced soil liquefaction poses significant geotechnical hazards, including sand boiling, loss of foundation bearing capacity, lateral spreading, pipeline flotation, uneven settlement, and slope instability. While cyclic liquefaction constitutive models can effectively simulate and predict site liquefaction behavior, their reliability hinges on the accurate calibration of constitutive parameters. Traditional calibration methods often fail to capture the comprehensive material response, are labor-intensive, time-consuming, and susceptible to subjective judgment. To overcome these limitations, this study develops an intelligent calibration framework for a cyclic liquefaction constitutive model by integrating a numerical solver for unit tests with the genetic algorithm (GA)-based optimization framework. The proposed method is rigorously evaluated in terms of calibration accuracy, convergence, repeatability, uncertainty, and computational efficiency. Validation via a series of laboratory unit tests on materials from an extremely high earth-rock dam project confirms the method’s effectiveness. Results demonstrate that the intelligent calibration approach achieves a high accuracy of 91.84%, offering a reliable, efficient, and robust solution for parameter determination. Full article