Pine wilt disease (PWD), also known as pine wilt nematode disease, is a severe forest disease caused by the pine wood nematode ( Bursaphelenchus xylophilus ), which spreads rapidly and causes severe ecological damage in China. However, the existing detection methods have low accuracy in identifying early‐stage infected trees and are prone to missing detections. To address these issues, this study proposes YOLOv11‐OC, a detection method of pine wilt disease based on improved YOLOv11 with UAV remote sensing images. The omni‐dimensional dynamic convolution (ODConv) is employed to optimize the C3K2 module, thereby enhancing the feature extraction ability for small targets and improving the accuracy of identifying early‐stage disease areas. Meanwhile, the context anchor attention (CAA) mechanism is introduced to improve the C2PSA module, enhancing the model's context awareness, effectively reducing missed detections, and improving detection performance in complex backgrounds. Experimental results demonstrate that the proposed YOLOv11‐OC algorithm outperformed the original YOLOv11, achieving a precision of 94.2%, a recall of 83.1%, a F1‐score of 88.3%, and a mean average precision (mAP) of 88.9%. Compared to the original algorithm, the improved version shows increases of 2.8% in precision, 1.0% in F1‐score, and 1.4% in mAP. Furthermore, the algorithm also demonstrates good generalization ability on the public PlantDoc dataset.

To address the complex challenge of recognizing mixed handwritten text in practical scenarios such as examination papers and to overcome the limitations of existing methods that typically focus on a single category, this paper proposes MHTR, a Multimodal Handwritten Text Adaptive Recognition algorithm. The framework comprises two key components, a Handwritten Character Classification Module and a Handwritten Text Adaptive Recognition Module, which work in conjunction. The classification module performs fine-grained analysis of the input image, identifying different types of handwritten content such as Chinese characters, digits, and mathematical formula. Based on these results, the recognition module dynamically selects specialized sub-networks tailored to each category, thereby enhancing recognition accuracy. To further reduce errors caused by similar character shapes and diverse handwriting styles, a Context-aware Recognition Optimization Module is introduced. This module captures local semantic and structural information, improving the model’s understanding of character sequences and boosting recognition performance. Recognizing the limitations of existing public handwriting datasets, particularly their lack of diversity in character categories and writing styles, this study constructs a heterogeneous, integrated handwritten text dataset. The dataset combines samples from multiple sources, including Chinese characters, numerals, and mathematical symbols, and features high structural complexity and stylistic variation to better reflect real-world application needs. Experimental results show that MHTR achieves a recognition accuracy of 86.63% on the constructed dataset, significantly outperforming existing methods. Furthermore, the context-aware optimization module demonstrates strong adaptive correction capabilities in various misrecognition scenarios, confirming the effectiveness and practicality of the proposed approach for complex, multi-category handwritten text recognition tasks.

Pine wilt disease, a highly contagious forest disease caused by the pine wood nematode and primarily transmitted via its insect vector, the pine sawyer beetle (Monochamus spp.), poses a significant threat to forest ecosystems. Accurate detection of infected trees is vital for effective prevention and control. This study pioneers the detection of pine wilt disease-infected trees in the China’s Qinba Mountain region, where the complex terrain and uneven forest distribution thinder feature extraction of diseased trees. To address data collection challenge, this paper proposes a novel feature recognition and detection method for pine wilt disease-infected trees based on an FLMP-YOLOv8 algorithm. The enhanced features include: first, integrating FasterBlock module into the backbone and neck networks of YOLOv8 to, boost the model’s feature extraction capability and reduce complexity, thereby achieving a balance between detection efficiency and accuracy. Second, a Large Separable Kernel Attention (LSKA) mechanism is incorporated into the Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv8, improving the model’s ability to perceive fine details of diseased trees and reducing interference from other elements in the forest. Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. This study provides a more reliable and cost-effective method for detecting trees infected with pine wilt disease.

Pine Wilt Disease (PWD) is a highly infectious and lethal disease that severely threatens global pine forest ecosystems and forestry economies. Early and accurate detection of infected trees is crucial to prevent large-scale outbreaks and support timely forest management. However, existing remote sensing-based detection models often struggle with performance degradation in complex environments, as well as a trade-off between detection accuracy and real-time efficiency. To address these challenges, we propose an improved object detection model, YOLOv8-MFD, designed for accurate and efficient detection of PWD-infected trees from UAV imagery. The model incorporates a MobileViT-based backbone that fuses convolutional neural networks with Transformer-based global modeling to enhance feature representation under complex forest backgrounds. To further improve robustness and precision, we integrate a Focal Modulation mechanism to suppress environmental interference and adopt a Dynamic Head to strengthen multi-scale object perception and adaptive feature fusion. Experimental results on a UAV-based forest dataset demonstrate that YOLOv8-MFD achieves a precision of 92.5%, a recall of 84.7%, an F1-score of 88.4%, and a mAP@0.5 of 88.2%. Compared to baseline models such as YOLOv8 and YOLOv10, our method achieves higher accuracy while maintaining acceptable computational cost (11.8 GFLOPs) and a compact model size (10.2 MB). Its inference speed is moderate and still suitable for real-time deployment. Overall, the proposed method offers a reliable solution for early-stage PWD monitoring across large forested areas, enabling more timely disease intervention and resource protection. Furthermore, its generalizable architecture holds promise for broader applications in forest health monitoring and agricultural disease detection.

When a lithium-ion cell’s series resistance is underestimated, the pre-update terminal-voltage innovation contains the first-order term $e_k\approx -I_k{R}_{0,k}\delta b$. This term breaks conditional sign symmetry and creates an odd response under current reversal. We test that mechanism before using it as an activation rule. The operational null is a near-zero conditional innovation centre with weak innovation–current coupling; declared falsifiers are comparable coupling under the nominal model, the wrong correlation sign under positive resistance error, failure of charge/discharge polarity reversal, or negative-control activation approaching ohmic-mismatch activation. A persistence-confirmed gate combines normalised-innovation-squared exceedances, innovation–current compatibility, a positive local resistance correction, and five consecutive qualifying windows. Sixty settings were ranked on 10 calibration seeds and frozen before disjoint holdouts. From 1.0× to 2.0× R₀, the sign-imbalance index increased from 0.0040 to 0.0786, and |$corr\left(e,I\right)$| increased from 0.0658 to 0.7777. The 30-seed static holdout produced 0/30 nominal activations, 27/30 detections at 1.5×, and 30/30 detections at 2.0–3.0×. A disjoint linear-drift audit yielded 0/30 pre-ramp activations and 30/30 detections at a median 1.71× multiplier, reducing late-drift SOC RMSE from 3.129% to 0.895%. A signed-current audit confirmed the predicted polarity reversal. An estimator-unseen audit gave 30/30 ohmic detections but retained 2/30 current-linked non-ohmic and 1/30 current-bias triggers, so the rule is not a unique fault classifier. NASA and LG records remain diagnostics of fixed versus always-on adaptation; they do not validate the gate or independent absolute SOC. The contribution is a falsifiable residual-symmetry mechanism with an explicit evidence boundary, rather than a post hoc symmetry label or hardware claim.