Although cross-cultural research suggests that the development and functionality of secondary coping strategies are substantially influenced by the broader cultural context, research findings are not completely conclusive. Some studies indicate that secondary coping is more adaptive for Asian participants compared with Westerners, while others emphasize the adaptability of the coping style in Asian, but not Western, contexts. The main objective of the present study was to systematically test for ethnic and contextual differences in the effectiveness of secondary strategies (measured in the form of acceptance and positive reinterpretation) in reducing the negative effects of acculturative stress on somatic symptoms with samples of international students. A 3-month longitudinal study was conducted in two different contexts: (a) Asian and Western international students in China (Asians: n = 53, Westerners: n = 51) and (b) similar groups in New Zealand (Asians: n = 61, Westerners: n = 65). The data were subjected to a hierarchical regression analysis with changes in psychological symptoms functioning as the dependent variable. Findings indicated that stress, secondary coping, and cultural context significantly interacted in predicting changes in somatic symptoms. Specifically, secondary coping exacerbated the negative effects of acculturative stress on psychological adjustment in New Zealand. In contrast, secondary coping functioned as a buffer in China, such that it was effective at reducing the negative impact of stress over time. Findings indicated that ethnicity did not significantly moderate the stress–coping–adjustment relationship. Our results show that the effectiveness of secondary coping varies as a function of the cultural context.

The present study examined the relationship between difficulty in re-entry adjustment and job embeddedness, considering the mediating role of sense of professional identity. The online data on demographic characteristics, difficulty on re-entry adjustment, sense of professional identity, and job embeddedness were collected from 178 Indonesian returnees from multiple organizations. The results showed that difficulty in re-entry adjustment was a significant predictor of a sense of professional identity; a sense of professional identity was a significant predictor of job embeddedness. Furthermore, sense of professional identity is an effective mediating variable, bridging the relationship between post-return conditions to the home country and work atmosphere. Finally, the key finding of this study was that sense of professional identity mediated the effect of difficulty in re-entry adjustment on job embeddedness. The theoretical and practical implications, study limitations, and future research needs of our findings are noted.

Wafer surface defect detection plays an important role in controlling product quality in semiconductor manufacturing, which has become a research hotspot in computer vision. However, the induction and summary of wafer defect detection methods in the existing review literature are not thorough enough and lack an objective analysis and evaluation of the advantages and disadvantages of various techniques, which is not conducive to the development of this research field. This paper systematically analyzes the research progress of domestic and foreign scholars in the field of wafer surface defect detection in recent years. Firstly, we introduce the classification of wafer surface defect patterns and their causes. According to the different methods of feature extraction, the current mainstream methods are divided into three categories: the methods based on image signal processing, the methods based on machine learning, and the methods based on deep learning. Moreover, the core ideas of representative algorithms are briefly introduced. Then, the innovations of each method are compared and analyzed, and their limitations are discussed. Finally, we summarize the problems and challenges in the current wafer surface defect detection task, the future research trends in this field, and the new research ideas.

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In recent years, air pollutants have become an important issue in meteorological research and an indispensable part of air quality forecasting. To improve the accuracy of the Chinese Unified Atmospheric Chemistry Environment (CUACE) model’s air pollutant forecasts, this paper proposes a solution based on ensemble learning. Firstly, the forecast results of the CUACE model and the corresponding monitoring data are extracted. Then, using feature analysis, we screen the correction factors that affect air quality. The random forest algorithm, XGBoost algorithm, and GBDT algorithm are employed to correct the prediction results of PM2.5, PM10, and O3. To further optimize the model, we introduce the grid search method. Finally, we compare and analyze the correction effect and determine the best correction model for the three air pollutants. This approach enhances the precision of the CUACE model’s forecast and improves our understanding of the factors that affect air quality. The experimental results show that the model has a better prediction error correction effect than the traditional machine learning statistical model. After the algorithm correction, the prediction accuracy of PM2.5 and PM10 is increased by 60%, and the prediction accuracy of O3 is increased by 70%.