Nanobubbles represent a special colloidal system, as they have high stability and large specific surface areas. The preparation of nanobubbles is currently a hot research topic, as it crucial to investigate their characteristics and expand their applications. This article explains the mechanism of generating nanobubbles based on chemical and physical methods, introduces their basic composition’s structure and properties, summarizes the methods of preparing bulk nanobubbles (BNBs) and surface nanobubbles (SNBs), and clarifies the preparation principles and techniques. Seven practical applications of nanobubbles are cited in this paper, including their use as ultrasonic contrast agents in medical imaging, drug delivery systems in drug transportation, promoters of plant growth by affecting plant respiration and water absorption at the roots, tools to remove dirt from surfaces by generating energy during nanobubble bursting, producers of high-density negative ions and free radicals to react with pollutants in wastewater, tools to reduce the resistance of the fluid flow through channels by lowering the internal friction, and means of improving the mineral flotation recovery rate by enhancing the absorption capacity of bubbles to minerals. Finally, the future development of nanobubble preparation technology is discussed, including their roles in optimizing equipment and preparation methods; improving the quantity, efficiency, stability, controllability, and homogeneity of nanobubble generation; and promoting the industrial production of nanobubbles.

Considering the potential risk of X-ray to patients, denoising of low-dose X-ray medical images is imperative. Inspired by deep learning, a convolutional autoencoder method for X-ray breast image denoising is proposed in this paper. First, image symmetry and flip are used to increase the number of images in the public dataset; second, the number of samples is increased further by image cropping segmentation, adding simulated noise, and producing the dataset. Finally, a convolutional autoencoder neural network model is constructed, and clean and noisy images are fed into it to complete the training. The results show that this method effectively removes noise while retaining image details in X-ray breast images, yielding higher peak signal-to-noise ratio and structural similarity index values than classical and novel denoising methods.

The Stockwell Transform (ST) is a time‐frequency signal decomposition that is gaining in popularity, likely because of its direct relation with the Fourier Transform (FT). A discrete and non‐redundant version of the ST, denoted the Discrete Orthonormal Stockwell Transform (DOST), has made the use of the ST more feasible. However, the matrix multiplication required by the DOST can still be a formidable computation, especially for high‐dimensional data. Moreover, the symmetric property of the ST and FT is not present in the DOST. In this paper, we investigate a new Symmetric Discrete Orthonormal Stockwell Transform (SDOST) that still keeps the non‐redundant multiresolution features of the DOST, while maintaining a symmetry property similar to that of the FT. First, we give a brief introduction for the ST and the DOST. Then we analyze the DOST coefficients and modify the transform to get a symmetric version. A small experiment shows that the SDOST has kept the abilities of the DOST and demonstrates the advantage of symmetry when applying the SDOST.

As the content of a full text page usually focuses on a specific topic, a topic language model adaption method is proposed to improve the recognition performance of homologous offline handwritten Chinese text image. Firstly, the text images are recognized with a character based bi-gram language model. Secondly, the topic of the text image is matched adaptively. Finally, the text image is recognized again with the best matched topic language model. To obtain a tradeoff between the recognition performance and computational complexity, a restricted topic language model adaption method is further presented. The methods have been evaluated on 100 offline Chinese text images. Compared to the general language model, the topic language model adaption has reduced the relative error rate by 11.94%. The restricted topic language model has lessened the running time by 19.22% at the cost of losing 0.35% of the accuracy.

To address the features of strong noise, blurred boundaries, and poor imaging quality in breast ultrasound images, we propose a method for segmenting breast ultrasound images using adaptive region growing and variation level sets. First, this method builds a template layer from the difference between the marked image and the original image. Second, the Otsu algorithm is used to measure the target and background using the maximum class variance method to set the threshold. Finally, through the level set of the pixel neighborhood, the boundary points of the adaptive region growth are specified by the level set of the pixel neighborhood, and it is therefore possible to accurately determine the contour perimeter and area of the lesion region. The results demonstrate that the value of Jaccard and Dice for benign tumors is greater than 0.99. Therefore, the segmentation effect of breast images can be achieved by utilizing a breast ultrasound image segmentation approach that uses adaptive region growth and variation level sets.

Deep learning based fault diagnosis methods have been widely studied for rolling bearings. However, for the existing such methods, the spatial and temporal features cannot be fully extracted and not considered. In order to solve the problems, a multi-attention parallel convolutional neural network-gate recurrent unit (MAPCG) fault diagnosis model is proposed in this paper. Firstly, variational modal decomposition(VMD) is used to obtain the eigenvectors through the original data of rolling bearing, which is utilized as the input of the proposed model. CNN and GRU are used to extract the spatial and temporal features as two parallel branches respectively. In addition, each branch is embedded with a specific attention module to distinguish the importance of features. Finally, the spatial feature and the temporal features are combined for the fault diagnosis. A mechanical fault simulation experiment platform is built for the experimental verification of the proposed model. And the experimental results indicates that the proposed model has superior fault diagnosis accuracy compared with existing models.