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Lung nodule semantic segmentation using deep learning has achieved good results. However, problems such as information loss on lesion edges, boundary segmentation blurring, lung nodule misdection, and low segmentation accuracy remain in lung CT (Computed Tomography) detection using deep learning due to the high degree of heterogeneity and a wide variety of nodule sizes, shapes, and locations, as well as the characteristics of convolutional localized feature extraction and the limitations of the continuous downsampling receptive field. So, a new model called EMC-UNet (Edge-aware _ Multiscale feature extraction residual _ 3D CA-Net attention module _ 3D U-Net), which integrates edge-awareness, 3D attention (3D CA-Net, Three-dimensional coordinate attention mechanism network), and multiscale techniques for segmenting lung nodules, is introduced. The model first uses an edge-aware module to accurately locate lesion edges, extract key edge features in the image, and increase the perception of lesion edge features by the model. Then, a 3D attention mechanism is added to focus the network on important lesion image features, emphasizing that the lesion features can improve segmentation performance. In conclusion, the 3D multiscale feature extraction module enhances the network’s perceptual range by processing information at various scales simultaneously, capturing features to offer a more comprehensive object context. This approach achieves notable results, with a Dice coefficient of 87.95% and an IoU value of 78.5% on the publicly available LIDC-IDRI(The Lung Image Database Consortium and Image Database Resource Initiative) dataset, outperforming existing lung nodule segmentation models.

Background: Computed tomography (CT)‐based positron emission tomography (PET) attenuation correction (AC) is a commonly used method in PET AC. However, the CT truncation caused by the subject's limbs outside the CT field‐of‐view (FOV) leads to errors in PET AC. Purpose: In order to enhance the quantitative accuracy of PET imaging in the all‐digital DigitMI 930 PET/CT system, we assessed the impact of FOV truncation on its image quality and investigated the effectiveness of geometric shape‐based FOV extension algorithms in this system. Methods: We implemented two geometric shape‐based FOV extension algorithms. By setting the data from different numbers of detector channels on either side of the sinogram to zero, we simulated various levels of truncation. Specific regions of interest (ROI) were selected, and the mean values of these ROIs were calculated to visually compare the differences between truncated CT, CT extended using the FOV extension algorithms, and the original CT. Furthermore, we conducted statistical analyses on the mean and standard deviation of residual maps between truncated/extended CT and the original CT at different levels of truncation. Subsequently, similar data processing was applied to PET images corrected using original CT and those corrected using simulated truncated and extended CT images. This allowed us to evaluate the influence of FOV truncation on the images produced by the DigitMI 930 PET/CT system and assess the effectiveness of the FOV extension algorithms. Results: Truncation caused bright artifacts at the CT FOV edge and a slight increase in pixel values within the FOV. When using truncated CT data for PET AC, the PET activity outside the CT FOV decreased, while the extension algorithm effectively reduced these effects. Patient data showed that the activity within the CT FOV decreased by 60% in the truncated image compared to the base image, but this number could be reduced to at least 17.3% after extension. Conclusion: The two geometric shape‐based algorithms effectively eliminate CT truncation artifacts and restore the true distribution of CT shape and PET emission data outside the FOV in the all‐digital DigitMI 930 PET/CT system. These two algorithms can be used as basic solutions for CT FOV extension in all‐digital PET/CT systems. [ABSTRACT FROM AUTHOR]

As a new synthetic metal-free material, BCN has aroused great interests in the field of photocatalyst materials due to its lower cost as well as higher reliability and better sustainability. In this work, we prepared BCN nanofibers by electrospinning of precursor solution containing polyacrylonitrile and ammonia borane, and then pyrolyzed the polymer fibers in NH3. Infrared spectroscopy (FT-IR), Scanning emission microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) were used to analyse the composition and structure of the samples. The results show that the BCN nanofibers have an average diameter of 50-100 nm and mainly compose of BN nanocrystals. C atoms are distributed at the BN grain boundary in terms of B-C and B-N bonds. The band gap of the fibers is between 2.1-2.7 eV and can be adjusted by changing the ratio of PAN and AB in the raw materials.

Stretchable electrodes are highly demanded in various wearable and flexible electronic devices, whereas the efficient fabrication approach is still a challenge. In this work, an efficient shrinking method to fabricate carbon nanotube (CNT)‐based stretchable electrodes is proposed. The electrode is a layer of anisotropic CNT wrinkling film coated on a latex balloon substrate (CNT@latex), whose resistivity remains stable after 25 000 stretching cycles of 0 to 50% tensile strain, and can survive up to 500% tensile train. The highly conductive electrode can be used as the current collector of a stretchable Zinc‐ion battery, maintaining an output voltage of 1.3 V during the stretching process of 0 to 100%. The applications of the electrode in flexible triboelectric nanogenerators and Joule heating devices are also demonstrated, further indicating their good prospects in the field of stretchable electronic devices. [ABSTRACT FROM AUTHOR]

Graphene‐based aerogels (GAs) have been extensively studied for pressure sensing applications due to their high compressibility and conductivity. But the usually‐adopted high‐temperature treatment generally damages the flexibility and stability. It is still challenging to prepare piezoresistive sensors with a wide linear range, and reliable cyclic performance. Herein, we demonstrate an excellent wide‐range piezoresistive sensor based on an aerogel of methylcellulose reinforced reduced graphene oxide (MC/GA). A steaming reduction at 120 °C is proposed to properly control the intermolecular forces and the chemical bonding between MC and GA. The finite element analysis indicates that wrinkled lamellae with hierarchical pores are beneficial to minimize the stress concentrations. As a result of the combined architecture of reinforced, oriented, and wrinkled lamellae with hierarchical pores, the MC/GA has a superior linearity in a much wide range (0–17 kPa, R2 > 0.997), a high sensitivity (9.38 kPa−1), a short response time (<40 ms) and a reliable cyclic performance (>99% retention). As demonstrations, the MC/GA sensor can monitor vital signs of the upper lip, throat or wrist, and can sense the large motion of the fingers, elbows, and feet. These advantages make the MC/GA sensor have a great potential in various health monitoring and motion‐detecting fields. [ABSTRACT FROM AUTHOR]

Two types of silicon carbide fibers (SiCf) were prepared employing different pyrolysis techniques. The relationship between the microstructure and the electrical resistivity of the fibers was investigated. The results indicated that the carbon layer present on the fiber surface acted as the main conductive phase in the SiCf obtained by direct pyrolysis, whereas a free carbon phase determined the conductivity of the SiCf prepared by the preheated pyrolysis method. A core-shell model and a general effective media ( GEM) theory were proposed to explain the conductivity of different types of SiCf. Quantitative analysis based on these models indicated an electrical resistivity of ~10−2 Ω·cm for the carbon layer on the surface of SiCf obtained by direct pyrolysis. The electrical resistivity and the percolation threshold of the free carbon in SiCf prepared by the preheated pyrolysis method were 10−1 Ω·cm and 11.3% respectively. [ABSTRACT FROM AUTHOR]