This study investigates the optimization of ultrasonic-assisted electroless plating to address the issue of high internal porosity in Ni-P Alloy coatings which deposited on X-ray mirror molds. By refining ultrasonic processing parameters, we successfully produced electroless plating layers that meet stringent performance requirements. Experimental results demonstrate that employing an ultrasonic frequency of 40 kHz and an acoustic intensity of 350 W/m² significantly reduces porosity, with internal voids accounting for only 0.03% of the total coating volume—a marked improvement over conventional electroless plating. Additionally, the maximum observed pore radius was limited to 9.8 μm, satisfying the specifications for Ni-P alloy coatings on small-scale X-ray mirror molds.

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This paper proposes a CNN-Transformer hybrid model for ink formulation prediction, named CTNet. The model leverages Convolutional Neural Networks (CNN) to extract local features from the spectral reflectance of sample surfaces and incorporates the self-attention mechanism of the Transformer to achieve efficient mapping between color and formulation. In addition, Bayesian optimization is introduced for hyperparameter tuning, further enhancing model performance. Experimental results demonstrate that CTNet outperforms CNN, RNN, LSTM, and the standard Transformer model in terms of Mean Absolute Error (MAE), achieving higher prediction accuracy. This provides an effective solution for high-precision and automated ink color matching, showing promising potential for industrial applications.

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This study thoroughly investigates how civil engineering management can effectively control project costs. Through analyzing key cost control strategies within civil engineering management, the research reveals the significance of economic principles and rational resource utilization. Using a case study approach, diagnostic analysis of actual engineering projects was conducted, validating the effectiveness of measures such as detailed budgeting, strengthened cost accounting, and enhanced cost management. The findings suggest that implementing scientifically sound civil engineering management can significantly reduce project costs and enhance project efficiency. Additionally, the study recommends reforming traditional management approaches and integrating modern management concepts and methodologies, further optimizing cost control in civil engineering projects. The results offer essential reference value for improving cost management, increasing project benefits, and promoting long-term civil engineering development.

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To address the challenges in detecting abrasion-resistant color fastness samples – including limited sample instances, non-uniform shapes, and insufficiently distinct texture variations that compromise localization accuracy – this paper optimizes the detection framework through the integration of three key strategies: Global Attention Mechanism (GAM), Dynamic Sampling (DySample), and Adaptively Spatial Feature Fusion (ASFF), thereby enhancing detection accuracy and efficiency. Initially, Mosaic data augmentation is implemented to enrich dataset diversity and improve model robustness. Subsequently, the GAM attention mechanism is embedded into the backbone network to enhance target feature extraction capabilities. DySample replaces conventional upsampling methods in the neck network to achieve more effective feature reconstruction. Finally, the ASFF module is integrated into the Detect module within the head network to enable adaptively spatial weight learning for multi-scale feature map fusion. Compared with baseline algorithms, the improved framework demonstrates performance gains of 1.2% in Precision, 3.0% in Recall, 1.2% in mAP@0.5, and 13.5% in mAP@0.5:0.95. Experimental results validate the effectiveness of the proposed method, which maintains satisfactory performance across additional datasets, demonstrating strong robustness and superior generalization capability.

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