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Background Nonsuicidal self-injury (NSSI) behaviors—an important factor that profoundly affects the physical and mental health of young people—are induced by complex and diverse factors, while showing significant differences at the gender level. We examined mediating behaviors among parenting styles, students’ coping styles, and endogenous and exogenous influencing variables of adolescents’ NSSI behaviors. Methods In this cross-sectional study, Secondary school students in Ningbo, Zhejiang Province, China (n = 2,689; F/M:1532/1157) were surveyed for basic attributes, parenting styles, coping styles, and NSSI behaviors. After the initial screening of the sample data, several external derivatives were screened based on the single factor analysis method. On this basis, the construction of path analysis models under multivariate multiple elicitations was carried out. Results The overall prevalence of NSSI was 15.16%, and the incidence of NSSI in boys was lower than that in girls (OR = 0.334, 95% CI [0.235–0.474]). The path analysis model data fit well; the indicators of female and male part are: CFI = 0.913/0.923, GFI = 0.964/0.977, SRMR = 0.055/0.047, RMSEA = 0.097/0.069 with 90% confidence interval (CI) [0.084–0.111]/[0.054–0.084]. For female, when negative coping style and extreme education affect NSSI respectively, the standardized path coefficient values are 0.478 (z = 20.636, P = 0.000 < 0.01) and 0.151 (z = 6.524, P = 0.000 < 0.01) respectively, while for male, the corresponding values become 0.225 (z = 7.057, P < 0.001) and 0.104 (z = 3.262, P < 0.001). Conclusion In particular, we investigated the mediating effects of gender-specific NSSI influences and found that NSSI behaviors were strongly associated with environmental variables and individual factors, especially family parenting style and adolescent coping style, which influenced NSSI in a gender-specific manner. The results showed that males were the target of both positive and negative parenting styles, whereas females were more likely to choose negative coping styles directed towards emotions in response to external stimuli, and instead showed a more significant predisposition towards NSSI behaviors. This phenomenon seems to be influenced by multilevel factors such as sociocultural, individual value identity, and physiological structure differences. In the path analysis model with the introduction of mediating effects, the influence of gender differences on NSSI behavior becomes more pronounced under the interaction of multiple factors: women seem to be more significantly influenced by the external derivatives in the internal derivatives than male subjects, and are more likely to trigger NSSI behavior under the interaction of multiple factors. These findings effectively reveal the significant role of different end-influencing factors in NSSI behaviors at the level of gender differences, which can provide effective theoretical support to prevent and treat NSSI behaviors in adolescents.

During the process of ship coating, various defects will occur due to the improper operation by the workers, environmental changes, etc. The special characteristics of ship coating limit the amount of data and result in the problem of class imbalance, which is not conducive to ensuring the effectiveness of deep learning-based models. Therefore, a novel hybrid intelligent image generation algorithm called the IGASEN-EMWGAN model for ship painting defect images is proposed to tackle the aforementioned limitations in this paper. First, based on a subset of imbalanced ship painting defect image samples obtained by a bootstrap sampling algorithm, a batch of different base discriminators was trained independently with the algorithm parameter and sample perturbation method. Then, an improved genetic algorithm based on the simulated annealing algorithm is used to search for the optimal subset of base discriminators. Further, the IGASEN-EMWGAN model was constructed by fusing the base discriminators in this subset through a weighted integration strategy. Finally, the trained IGASEN-EMWGAN model is used to generate new defect images of the minority classes to obtain a balanced dataset of ship painting defects. The extensive experimental results are conducted on a real unbalanced ship coating defect database and show that, compared with the baselines, the values of the ID and FID scores are significantly improved by 4.92% and decreased by 7.29%, respectively, which prove the superior effectiveness of the proposed model in this paper.

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