Ningbo Entry-Exit Inspection And Quarantine Bureau

The pinewood nematode (PWN), Bursaphelenchus xylophilus, is a pathogenic organism that causes pine wilt disease (PWD). To date, several molecular diagnostic methods have been developed; however, rapid, convenient, and inexpensive field diagnostic tools for detecting PWN are still limited. In this study, an enzyme-mediated duplex exponential amplification (EmDEA) method for detecting PWN from extracted nematodes or pinewood sawdust was developed and tested. This method comprised an EmDEA molecular test kit, which consisted of freeze-dried enzyme pellets that can be stored at room temperature (approximately 20–25 °C) for one year, a dry block heater, and a portable isothermal fluorescence amplification instrument. The whole procedure was completed within 30 min. The EmDEA assay could detect a single PWN at all life stages from a mixture of other nematode species or from pinewood sawdust. The detection limit was 10 copies (plasmid weight 32.66 ag) or 1/500 of that of a single adult PWN per reaction. Therefore, the EmDEA assay has potential applications in PWN detection in the field, as well as quarantine inspection in international trade. Moreover, modification of primers and probes will allow the rapid identification of other nematode species.

Plant-parasiticnematodes represent a significant biosecurity threat in cross-border plant quarantine, necessitating precise identification for effective border control. While DL models have demonstrated success in nematode image classification based on morphological features, the limited availability of high-quality samples and the species-specific nature of nematodes result in insufficient training data, which constrains model performance. Although generative models have shown promise in data augmentation, they often struggle to balance morphological fidelity and phenotypic diversity. This paper proposes a novel few-shot data augmentation framework based on a morphology-constrained latent diffusion model, which, for the first time, integrates morphological constraints into the latent diffusion process. By geometrically parameterizing nematode morphology, the proposed approach enhances topological fidelity in the generated images and addresses key limitations of traditional generative models in controlling biological shapes. This framework is designed to augment nematode image datasets and improve classification performance under limited data conditions. The framework consists of three key components: First, we incorporate a fine-tuning strategy that preserves the generalization capability of model in few-shot settings. Second, we extract morphological constraints from nematode images using edge detection and a moving least squares method, capturing key structural details. Finally, we embed these constraints into the latent space of the diffusion model, ensuring generated images maintain both fidelity and diversity. Experimental results demonstrate that our approach significantly enhances classification accuracy. For imbalanced datasets, the Top-1 accuracy of multiple classification models improved by 7.34–14.66% compared to models trained without augmentation, and by 2.0–5.67% compared to models using traditional data augmentation. Additionally, when replacing up to 25% of real images with generated ones in a balanced dataset, model performance remained nearly unchanged, indicating the robustness and effectiveness of the method. Ablation experiments demonstrate that the morphology-guided strategy achieves superior image quality compared to both unconstrained and edge-based constraint methods, with a Fréchet Inception Distance of 12.95 and an Inception Score of 1.21 ± 0.057. These results indicate that the proposed method effectively balances morphological fidelity and phenotypic diversity in image generation.

9%Ni steel process good mechanical properties, welding ability and low-temperature toughness, and has been widely used in LNG low-temperature storage tanks. Due to the existence of residual weld height in LNG storage tank and the different ray absorption of weld and base material,which brings great difficulties to radiography detection. The digital radiography technique and conventional film radiography technique is adopted in this paper which carried out in exposure chamber. The test results show that the imaging quality of digital radiography technique is better than that of film radiography technique. This paper proposes the digital radiography technique to detect the butt welds of 9% Ni steel, replacing the traditional film radiography technique. A set of automated inspection equipment for digital radiography of large LNG storage tanks has been developed, the inspection problem of butt welds of 9% Ni steel in large LNG storage tanks is successfully solved. Finally, the digital radiography technique is applied in the LNG installation engineering project.

With the continuous growth of maritime activities and the shipping trade, the application of maritime target detection in remote sensing images has become increasingly important. However, existing detection methods face numerous challenges, such as small target localization, recognition of targets with large aspect ratios, and high computational demands. In this paper, we propose an improved target detection model, named YOLOv5-ASC, to address the challenges in maritime target detection. The proposed YOLOv5-ASC integrates three core components: an Attention-based Receptive Field Enhancement Module (ARFEM), an optimized SIoU loss function, and a Deformable Convolution Module (C3DCN). These components work together to enhance the model’s performance in detecting complex maritime targets by improving its ability to capture multi-scale features, optimize the localization process, and adapt to the large aspect ratios typical of maritime objects. Experimental results show that, compared to the original YOLOv5 model, YOLOv5-ASC achieves a 4.36 percentage point increase in mAP@0.5 and a 9.87 percentage point improvement in precision, while maintaining computational complexity within a reasonable range. The proposed method not only achieves significant performance improvements on the ShipRSImageNet dataset but also demonstrates strong potential for application in complex maritime remote sensing scenarios.