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Re-identification technology for pedestrians and vehicles has been successfully applied in the field of intelligence analysis. However, there is a lack of research on re-identification technology for ship targets. In this paper, we propose a double-feature fusion-based maritime defogging re-identification network for intelligence analysis and supervision of ship targets. To reduce the impact of negative samples on features, we adopt a perspective-assisted adaptive query expansion method and a similarity-based feature fusion method. Furthermore, a defogging branch is embedded in the shallow layer of the re-identification branch. This branch utilizes weight sharing technology to extract fog-free features. The defogged image is then reconstructed using upsampling technology and the pyramid model, enhancing the recognition ability of the re-identification network in low-visibility scenarios. Finally, a pseudo-IOU based non-maximum suppression method is proposed to enhance the detection accuracy of ship targets. This method modifies the confidence of the detection frame. Experimental results demonstrate that the proposed method outperforms existing methods, and each module contributes to the network’s performance.

In order to better improve the level and quality of rural family education, this paper adopts text mining technology to interpret the relevant data of rural family education under the background of deep learning. On the basis of deep learning algorithms, web crawler technology is used to obtain rural family education research data. It is found that there are null values and repetitive values in the acquired research data, so it is necessary to further clean the research data. After washing, the combined Bi-LSTM-CRF algorithm is used to process the data for Chinese word segmentation, and at the same time, the stop word list is improved in order to achieve the best word segmentation effect. The LDA thematic model was used to determine the influencing factors of rural family education, and then the linear regression algorithm was used to analyze rural family education empirically. Rural teachers with different education levels were not significantly different in Q1 (1.608), Q2 (0.117), Q3 (2.019), Q4 (1.733), and Q5 (2.042), according to the results. The adjusted R2 is 0.133, indicating a fair fit between the model obtained from the regression analysis and the actual data, and the regression coefficient of family education is 0.411 after eliminating the confounding interference of grade and political appearance. This study purposefully proposes a path for the innovative development of rural family education, which can provide a theoretical reference program for the development and construction of modern rural family education.

In contrast with its strong compressive performance and durability, the low tensile strength of concrete often leads to a range of issues during long-term application, including deformation, cracking, and even fracturing of the structure. The addition of polyvinyl alcohol (PVA) fibers to concrete can effectively improve the toughness of materials. However, currently, the majority of studies on polyvinyl alcohol-engineered cementitious composites (PVA-ECC) have focused primarily on the influence of a single factor, such as the PVA fiber content, on the properties of materials, while research that elucidates the interplay between the mechanical properties of PVA-ECC and various mix parameters, to our best knowledge, is lacking. In this study, based on the principle of orthogonal testing, 25 groups of specimens with different mix ratios were designed for experiments, and the effects of the water–binder ratio, sand–binder ratio, fly ash content, and PVA fiber content on the properties of the materials were investigated. On the premise that the fluidity of the material can meet the basic working performance requirements, it was suggested that the content of fly ash should be 35%–52.5%, the water–binder ratio should be 0.375, the sand–binder ratio should be 0.375, and the PVA fiber content should be 1.5%. Under this mix ratio, the strength of the material could reach that of C40 concrete, the compressive toughness could reach more than 10 times that of ordinary concrete, and the ultimate tensile strain under uniaxial tension was close to 3%. These research results can provide a systematic and comprehensive reference for the mix design of PVA-ECC in engineering practice. [ABSTRACT FROM AUTHOR]