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Identifying the classes and locations of prohibited items is the target of security inspection. However, X-ray security inspection images with insufficient feature extraction, imbalance between easy and hard samples, and occlusion lead to poor detection accuracy. To address the above problems, an object-detection method based on YOLOv8 is proposed. Firstly, an ASFF (adaptive spatial feature fusion) and a weighted feature concatenation algorithm are introduced to fully extract the scale features from input images. In this way, the model can learn further details in training. Secondly, CoordAtt (coordinate attention module), which belongs to the hybrid attention mechanism, is embedded to enhance the learning of features of interest. Then, the slide loss function is introduced to balance the simple samples and the difficult samples. Finally, Soft-NMS (non-maximum suppression) is introduced to resist the conditions containing occlusion. The experimental result shows that mAP (mean average precision) achieves 90.2%, 90.5%, 79.1%, and 91.4% on the Easy, Hard, and Hidden sets of the PIDray and SIXray public test set, respectively. Contrasted with original model, the mAP of our proposed YOLOv8n model increased by 2.7%, 3.1%, 9.3%, and 2.4%, respectively. Furthermore, the parameter count of the modified YOLOv8n model is roughly only 3 million.

The thyroid nodule segmentation of ultrasound images is a critical step for the early diagnosis of thyroid cancers in clinics. Due to the weak edge of ultrasound images and the complexity of thyroid tissue structure, it is still challenging to accurately segment the delicate contour of thyroid nodules. A local and context-attention adaptive network (LCA-Net) for thyroid nodule segmentation is proposed to address these shortcomings, which leverages both local feature information from convolution neural networks and global context information from transformers. Firstly, since most existing thyroid nodule segmentation models are skilled at local detail features and lose some context information, we propose a transformers-based context-attention module to capture more global associative information for the network and perceive the edge information of the nodule contour. Secondly, a backbone module with 7×1, 1×7 convolutions and the activation function Mish is designed, which enlarges the receptive field and extracts more feature details. Furthermore, a nodule adaptive convolution (NAC) module is introduced to adaptively deal with thyroid nodules of different sizes and positions, thereby improving the generalization performance of the model. Simultaneously, an optimized loss function is proposed to solve the pixels class imbalance problem in segmentation. The proposed LCA-Net, validated on the public TN-SCUI2020 and TN3K datasets, achieves Dice scores of 90.26% and 82.08% and PA scores of 98.87% and 96.97%, respectively, which outperforms other state-of-the-art thyroid nodule segmentation models. This paper demonstrates the superiority of the proposed LCA-Net for thyroid nodule segmentation, which possesses strong generalization performance and promising segmentation accuracy. Consequently, the proposed model has wide application prospects for thyroid nodule diagnosis in clinics.

Nanocrystalline soft magnetic alloy powders are promising microwave absorbents since they can work at diverse frequencies and are stable in harsh environments. However, when the alloy powders are in austenite phase, they are out of the screen for microwave absorbents due to their paramagnetic nature. In this work, we reported a strategy to enable strong microwave absorption in nanocrystalline austenite FeCoCr powders by deformation-thermal co-induced ferromagnetism via attritor ball milling and subsequent heat treatment. Results showed that significant austenite-to-martensite transformation in the FeCoCr powders was achieved during ball milling, along with the increase in shape anisotropy from spherical to flaky. The saturation magnetization followed parabolic kinetics during ball milling and rose from 1.43 to 109.92 emu/g after milling for 4 h, while it exhibited a rapid increase to 181.58 emu/g after subsequent heat treatment at 500 °C. A considerable increase in complex permeability and hence magnetic loss capability was obtained. With appropriate modulation of complex permittivity, the resultant absorbents showed a reflection loss of below −6 dB over 8~18 GHz at thickness of 1 mm and superior stability at 300 °C. Our strategy can broaden the material selection for microwave absorbents by involving Fe-based austenite alloys and simply recover the ferromagnetism of industrial products made without proper control of the crystalline phase.