Three-dimensional (3D) geological models are essential for geological analysis and mineral resource estimation. Although conventional on-site survey methods, such as boreholes, provide local engineering geological information for 3D geological modeling, accurately predicting strata in areas with sparse borehole data remains a challenge. This study proposes a 3D geological modeling method using the Transformer model under the conditions of sparse borehole data. First, a K-dimensional tree was used to identify boreholes adjacent to the target point, and a borehole context sequence was constructed using stratigraphic information from neighboring boreholes. Subsequently, the relationship between the target point and its adjacent borehole sequence was calculated using the multi-head attention mechanism of the Transformer model. Finally, trained Transformer encoders were used to predict the stratigraphic category of the target point, and the normalized information entropy was used to quantify uncertainty during the modeling process. Experimental results showed that the accuracy of the method was 0.86, outperforming the accuracy and uncertainty of a recurrent neural network. The root mean square error is smaller than the inverse distance weight and Kriging. Compared to other methods, the proposed method can more accurately describe the geometric shape and distribution of geological bodies and reveal the sedimentary laws of the study area.

The design and cracking of text-based CAPTCHAs are important topics in computer security. This study proposes a method for the style transfer of text-based CAPTCHAs using Generative Adversarial Networks (GANs). First, a curated dataset was used, combining a text-based CAPTCHA library and image collections from four artistic styles—Van Gogh, Monet, Cézanne, and Ukiyo-e—which were used to generate style-based text CAPTCHA samples. Subsequently, a universal style transfer model, along with trained CycleGAN models for both single- and double-style transfers, were employed to generate style-enhanced text-based CAPTCHAs. Traditional methods for evaluating the anti-recognition capability of text-based CAPTCHAs primarily focus on recognition success rates. This study introduces topological feature analysis as a new method for evaluating text-based CAPTCHAs. Initially, the recognition success rates of the three methods across four styles were evaluated using Muggle-OCR. Subsequently, the graph diameter was employed to quantify the differences between text-based CAPTCHA images before and after style transfer. The experimental results demonstrate that the recognition rates of style-enhanced text-based CAPTCHAs are consistently lower than those of the original CAPTCHA, suggesting that style transfer enhances anti-recognition capability. Topological feature analysis indicates that style transfer results in a more compact topological structure, further validating the effectiveness of the GAN-based twice-transfer method in enhancing CAPTCHA complexity and anti-recognition capability.

Three-dimensional (3D) geological modeling plays a crucial role in the development of urban underground spaces. Currently, the process of 3D geological modeling for urban underground spaces confronts challenges related to data sparsity and limited automation. In addition, the attribute interpolation method does not consider the influence of strata, which undermines the precision of the interpolation. This paper presents an automated process for creating 3D geological models using multisource data. Initially, diverse geological profiles, seismic records, and contour data from various sources were amalgamated into unified virtual borehole data, augmenting the actual borehole data in 3D geological modeling. Subsequently, a hierarchical approach was developed and implemented to improve the explicit modeling efficiency of automatic 3D geological model construction. Ultimately, 3D geological structures are categorized into various configurations, such as single layer, horizontal stratification, and average layering, tailored to meet specific requirements. Incorporating pertinent stratigraphic data during the interpolation phase enhances the accuracy of attribute model interpolation. This study offers technical support for the development and utilization of urban underground spaces in Shanghai.

Mobile user identity is an important part of mobile Internet security research. In this paper, it introduces the development status of user identification and authentication technology in "Internet +" era, and briefly describes their implementation plan and evolution trend. the security problem of each general authentication technology is analyzed. This paper describes the combination of mobile device identification, user behavior analysis and forecasting based on big data, and routine identification, and realizes mobile user identification to improve the accuracy of mobile user identification. 210