Magnetic particle imaging technology, a novel medical imaging technology, possesses rapid imaging, high penetration depth, and is free from ionizing radiation. However, the system point spread function causes imaging blurring, which can be further exacerbated by external environmental interferences. Although hardware improvements and system optimization can mitigate blurring, these approaches are often expensive and time-consuming, particularly for low-field imaging in large-scale systems. This article proposes a Fast Context-aware Saliency-enhanced Deblurring Network, FCS-edNET, to solve the challenging issue by deblurring the reconstructed images. The network introduces the Multi-scale Global module to enhance the multi-scale feature perception ability. The Multi-scale Denoising Prior algorithm, which employs a low-frequency filter operator to restrict image noise and offers priors for each layer of subnetworks, is designed to improve the model robustness. Finally, proposing a Multi-level Joint loss optimizes model parameters to promote model convergence speed and space distribution simulation capability. Extensive experiments on multiple public and private datasets demonstrate that FCS-edNET outperforms the state-of-the-art methods in MPI image deblurring efficiently, suggesting its potential to support future research toward clinical imaging applications. The code is available at https://github.com/ydz1118/FCS-edNET.

In industrial processes, accurate quality prediction is essential for optimizing operations and ensuring product consistency. However, the inherent complexity of industrial processes typically involves a large number of monitoring variables, accompanied by significant challenges such as high nonlinearity and data redundancy. To address this issue, this article proposes the broad learning system combined with feature-weighted stacked target-related Laplacian autoencoder (BLS-FW-STLapAE). The proposed model innovatively incorporates hidden-layer features extracted by the stacked target-related Laplacian autoencoder (STLapAE) into the feature nodes of the broad learning system (BLS). This approach addresses the issue of insufficient feature extraction caused by random mapping in BLS. This integration enables the model to capture both local geometric structures and key target-related features, significantly enhancing predictive performance in complex industrial environments. In addition, mutual information (MI) is introduced in STLapAE to evaluate the correlation between features and the target variable, further improving the model’s ability to capture relevant information about the target variable and ensuring that all relevant information is effectively utilized. Finally, the feasibility and practical value of the proposed algorithm were validated on benchmark public datasets and two real-world industrial applications: penicillin fermentation and solid propellant manufacturing. The superior predictive performance demonstrated across these tests enables operators to proactively adjust process parameters and prevent quality deviations, thereby significantly improving production safety and reducing operational costs in such high-risk chemical engineering domains.

Path planning for multi-degree-of-freedom robot manipulators is the key to realizing automated assembly tasks. However, traditional path search algorithms often fail to converge rapidly to feasible solutions in complex environments. To address this limitation, we propose an environment-adaptive RRT-Connect algorithm (EA-RRT-Connect). The method employs a node safety environment margin assessment to quantify local environmental risk, based on which it dynamically selects parent nodes and adjusts the guidance strategy (including the expansion direction, step size, and node growth constraints), achieving a balance between exploration efficiency and obstacle avoidance reliability. Comparative simulation experiments with RRT, RRT-Connect, and Improved-RRT were conducted in three distinct scenarios. The results indicate that EA-RRT-Connect outperforms baseline methods in planning time, path length, and robustness against local minima.

Traditional online monitoring systems often rely on high‐quality data samples or external expert knowledge, and their model accuracy degrades significantly when equipment states deviate from expected conditions over time. In the preresearch stage of mechanical equipment, researchers face not only data scarcity and severe class imbalance but also performance degradation caused by nonstationary processes and varying operational conditions. To address these challenges, this paper proposes a novel fusion‐enhanced model based on local neighborhood standardization and recursive quadratic kernel entropy component analysis. The proposed LNS‐RQKECA framework constructs a data‐driven vibration signal analysis model for early fault detection in mechanical equipment under multiple load conditions, relying solely on historical data from normal operation without requiring external experience or prior knowledge. The local neighborhood standardization technique transforms the multiload problem into a single‐modal problem, enabling effective load‐case fusion. To counteract the feature dilution effect induced by sample fusion, the proposed quadratic kernel entropy component analysis employs advanced kernel‐based entropy optimization to enhance feature discriminability through deeper data mining. Additionally, a recursive updating mechanism ensures adaptive model adjustment, allowing the system to handle nonstationary load conditions dynamically. This proposed model is validated using both public datasets and real‐world operational data. Experimental results demonstrate that under multiload conditions, the proposed model achieves strong generalization capability and nonstationary process adaptability with minimal training data. Compared with the traditional method, the false missing rate and false alarm rate are reduced by 12% and 15%, respectively.

Sequential tightening redistributes load among bolts and produces a residual preload field that depends on the complete operation history. This study presents a physics-informed graph-learning framework for a 60-bolt circular flange. The model receives initial preloads and tightening order and predicts the residual preload field on a complete causal directed graph. All 1770 admissible later-to-earlier edges are retained. Among them, 240 local edges carry non-zero Elastic Interaction Coefficient Method (EICM) attributes, while 1530 distant edges remain available to learned message passing. The multi-fidelity dataset combines online EICM responses, prescribed nonlinear-discrepancy labels, and repeated ultrasonic preload measurements. On the augmented development split, TransformerConv-L3 achieved an MAE of 138.3 N, an RMSE of 170.9 N, an NLL of 8.839, and 93.9% coverage for the nominal 95% prediction interval. Its RMSE was 56.9% lower than that of the strongest recurrent baseline evaluated under the same protocol. These results demonstrate the value of combining causal topology with mechanics-informed edge attributes for residual preload prediction.