Shunde Polytechnic

This paper investigates the impact of rotation speed on the internal stresses of prestressed concrete (PC) girder bridges constructed using swivel method. A numerical model is established to analyze the bridge’s structural response under varying rotary speeds. The results reveal a positive correlation between the escalation of angular velocity and the magnification of concrete stresses, which are most pronounced at the top slab of the PC box girder near the pier. The stress of the PC girder at the selected cross-sections increases by 527% when the angular velocity goes from 0.02 rad/min to 0.05 rad/min. During the rotation acceleration phase, heightened stress concentrations are observed in the main girder and the underlying pier. Comparatively small stresses are achieved across the bridge under an angular velocity of 0.02 rad/min and rotational acceleration of 2.00 × 10−3 rad/min2. The analytical framework employed in this study, which integrates stress monitoring data from the PC girder with advanced numerical simulations, confirms that the concrete stresses encountered during the rotation construction process remain within acceptable limits. The successful use of the rotation construction technique demonstrates its feasibility and effectiveness as a construction method for large-span asymmetric bridges overpass multi-line railways.

With the increasing maturity of adaptive signal processing technology, signal interference caused during electronic communication signal processing has become increasingly prominent. Therefore, this paper proposes to use the analysis of signal periodic characteristics and interference characteristics as variables, and modify the update relationship of step length in traditional algorithms. This enables the improved adaptive filtering algorithm to maintain its fast adaptive characteristics and excellent adaptive interference performance even under low signal-to-noise ratio conditions, achieving good experimental results. It can effectively extract changes in the periodic characteristics of communication signals. The application of the variable step-size least-squares algorithm for tracking time-varying signals is explored, specifically by introducing changes in the adaptive forgetting factor during the update process. This allows the forgetting factor value to be more flexible and have a more appropriate numerical range, thereby further enhancing its interference suppression capability and numerical stability. Under the same signal-to-noise ratio conditions (5 dB), the convergence speed of the improved variable step-size least-squares algorithm increased by 67%, and the steady-state value decreased by 2.7 dB. Similarly, in the application scenario of improving the time-varying tracking of the recursive least squares algorithm, when the interference signal-to-noise ratio is 20 dB, the convergence time is only 17.8 ms. This paper analyzes how to effectively improve the signal processing capability and interference resistance performance in the electronic communication signal processing process, thereby enhancing signal processing quality and effectiveness.

Rice leaf diseases pose a significant and escalating threat to global food security. Timely and accurate detection, particularly in the critical early stages characterized by subtle lesions, is paramount for effective disease management. However, existing solutions often struggle with the complexities of real-world field environments (e.g., variable lighting, occlusions, complex backgrounds), computational constraints on edge devices, and limited generalizability across diverse disease types and plant species. To address these challenges, this study proposes a novel lightweight deep learning framework specifically designed for robust rice leaf disease detection. Our key innovations include: (1) A Multi-branch Large-kernel Fusion Depthwise (MLFD) module enhancing multi-scale contextual feature extraction critical for identifying subtle early lesions; (2) A Multi-scale Dilated Transformer Attention (MDTA) module integrating spatial and channel attention mechanisms to improve feature representation under complex conditions; (3) A Lightweight Detection Head (Lo-Head) optimized with grouped and depthwise convolutions, drastically reducing model complexity without sacrificing accuracy. Crucially, extensive experiments demonstrate the framework's superior performance. On a dedicated rice leaf disease dataset, it achieves a mean Average Precision mAP@0.5:0.95 of 62.62%, outperforming state-of-the-art lightweight detectors including YOLOv5n (56.73%), YOLOv8n (57.41%), YOLOv10n (56.14%), and the baseline YOLOv11n (60.85%), while maintaining low computational demands (6.3 GFLOPs, 2.66M parameters). Significantly, rigorous generalization experiments validate the model's exceptional transferability. Evaluated on independent datasets encompassing potato and tomato leaf diseases, the proposed framework consistently surpasses comparable models in mAP@0.5:0.95, demonstrating its robust capability to detect diseases across different plant species. This combination of high accuracy, computational efficiency, and remarkable cross-crop generalizability positions our framework as a highly promising tool for practical deployment on resource-limited edge devices (e.g., drones, field sensors) in smart agriculture systems, enabling proactive disease surveillance and precision control strategies across diverse crops.

Concrete, as an important construction material in modern construction projects, is widely used in various fields of engineering and plays a great role in it, and different types of concrete have different impacts on project costs. The study adopts Box-Behnken experimental design method in response surface methodology to establish a response surface test model with water-cement ratio, recycled brick aggregate substitution rate, and polypropylene fiber volume fraction as factors. Meanwhile, an objective function considering economic cost and carbon emission is constructed, and an improved non-dominated sequential genetic algorithm (I-NSGA-II) is proposed for the optimal design method of concrete proportion. The results show that the testing model is well fitted with high prediction accuracy; for flexural strength, split tensile strength and compressive strength, the corresponding optimal BF admixtures are 0.2%, 0.1% and 0.2%, respectively. After testing, the improved algorithm significantly improves the speed of convergence and the uniformity of the distribution of optimal solutions. Finally, the multi-objective optimization model proposed in this paper is applied to concrete cost analysis, and 10 sets of optimal mixes are found among 200 sets of mixes by Topsis method. The objective function range is used as a constraint to establish a constrained multi-objective optimization model to find 10 groups of optimal fit ratios by the same method, and comparing the two types of optimal fit ratios, the multi-objective optimization model in this paper can obtain a higher Topsis score.