In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

Three-dimensionally cross-linked polymer nanocomposite networks coated nano sand light-weight proppants (LWPs) were successfully prepared via ball-milling the macro sand and subsequently modifying the resultant nano sand with sequential polymer nanocomposite coating. The modified nano sand proppants had good sphericity and roundness. Thermal analyses showed that the samples can withstand up to 411 ℃. Moreover, the proppant samples’ specific gravity (S.G.) was 1.02–1.10 g/cm³ with excellent water dispersibility. Therefore, cross-linked polymer nanocomposite networks coated nano sand particles can act as potential candidates as water-carrying proppants for hydraulic fracturing operations.

At this stage, network technology is developing rapidly. The resources in the network are massive, and a large number of resources are distributed in a decentralized and heterogeneous manner. With the continuous expansion of the application scope of distributed technology, it can provide effective scheme guidance for resource application. Combined with the current situation of network teaching platform and relevant functional requirements, it is very necessary to apply distributed technology. Taking DFS technology as an example, this paper studies the shared resource management scheme of this technology in network storage, and studies the specific application effect and path of DFS technology in distributed network teaching platform.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

The public health emergency has changed the environment and conditions of art teaching. Based on the abnormal teaching background, we can use this as an opportunity to explore new teaching forms. Relying on the unique functions of the network platform, the Art Cloud Classroom explores a new style of home-based art learning that is vivid, autonomous and interactive, develops students' art skills, develops positive interests and emotions, and makes every life a better place in the nourishment of art.

Under the background of the development of the network information age, the current Internet industry has obtained more development opportunities, but it has also brought corresponding challenges in the process of wide application. In the development and construction of modernization, society pays more attention to the supervision and determination of the characteristics of online public opinion. From the perspective of the current characteristics of network public opinion, because social information is more extensive and involves many fields, network public opinion has a high degree of complexity and diffusion. Therefore, it is necessary to strengthen the analysis and application of relevant data mining systems in order to achieve efficient management of network public opinion. The key to the disadvantage of the traditional excavation of public opinion communication characteristics lies in the lag of the excavation process, and it is difficult to deal with malignant public opinion in a timely and effective manner. Therefore, in order to truly solve the lagging problem of public opinion data dissemination feature mining technology, it is necessary to strengthen the application of artificial intelligence technology in it.