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Introduction There is relatively little research on the association between personality traits and sustainable behaviors. So, this research was designed to differentiate associations between the six personality traits and sustainable behaviors perceived by individuals. Methods A total of 1420 residents in a community of Nanjing participated in this survey. With the help of HEXACO‐60 and SBPI‐9, participants’ personality traits and the performance of sustainable behaviors perceived by individuals were measured. Subsequently, assisted by regression analysis, the quantitive relationship between HEXACO and sustainable behaviors perceived by individuals was explored. Results Honesty–humility (H–H), extraversion (X), consciousness (C), and openness to experience (O) are positively associated with sustainable behaviors perceived by individuals, whereas emotionality (E) and agreeableness (A) are negatively associated with it. Conclusions HEXACO have a significant association with sustainable behaviors perceived by individuals. Additionally, H–H, E, X, A, C, and O could explain 44.2% of changes in sustainable behaviors perceived by individuals.

IntroductionThere is relatively little research on the association between personality traits and sustainable behaviors. So, this research was designed to differentiate associations between the six personality traits and sustainable behaviors perceived by individuals.MethodsA total of 1420 residents in a community of Nanjing participated in this survey. With the help of HEXACO-60 and SBPI-9, participants’ personality traits and the performance of sustainable behaviors perceived by individuals were measured. Subsequently, assisted by regression analysis, the quantitive relationship between HEXACO and sustainable behaviors perceived by individuals was explored.ResultsHonesty–humility (H–H), extraversion (X), consciousness (C), and openness to experience (O) are positively associated with sustainable behaviors perceived by individuals, whereas emotionality (E) and agreeableness (A) are negatively associated with it.ConclusionsHEXACO have a significant association with sustainable behaviors perceived by individuals. Additionally, H–H, E, X, A, C, and O could explain 44.2% of changes in sustainable behaviors perceived by individuals.

This study investigates the quantitative relationship between the four dimensions of emotional intelligence and the two types of contemporary filial piety, academic achievement in a Chinese university setting. Based on a sample of 240 Chinese undergraduates, the regression analysis was employed to examine how academic achievement and the two types of contemporary filial piety, namely Pragmatic Obligation (PO) and Compassionate Reverence (CR), relate to four dimensions of emotional intelligence—Self-Emotional Monitoring (SEM), Emotional Utilization (EU), Social Competence (SC), and Others’ Emotional Appraisal (OEA). Results revealed that CR, PO, and Grade Point Average (GPA) predicted emotional intelligence positively and significantly. Notably, PO was the strongest predictor of emotional intelligence compared to CR and GPA. These findings advance theoretical understanding in two aspects. Firstly, they challenge the traditional dichotomy of filial piety by demonstrating that both CR and PO serve as cultural resources enhancing emotional competencies. Afterwards, the study bridges collectivistic values by filial piety with emotional intelligence, offering a culturally nuanced framework for interpreting academic success in Confucian societies.

In optical remote sensing images, the presence of clouds affects the completeness of the ground observation and further affects the accuracy and efficiency of remote sensing applications. Especially in quantitative analysis, the impact of cloud cover on the reliability of analysis results cannot be ignored. Therefore, high-precision cloud detection is an important step in the preprocessing of optical remote sensing images. In the past decade, with the continuous progress of artificial intelligence, algorithms based on deep learning have become one of the main methods for cloud detection. The rapid development of deep learning technology, especially the introduction of self-attention Transformer models, has greatly improved the accuracy of cloud detection tasks while achieving efficient processing of large-scale remote sensing images. This review provides a comprehensive overview of cloud detection algorithms based on deep learning from the perspective of semantic segmentation, and elaborates on the research progress, advantages, and limitations of different categories in this field. In addition, this paper introduces the publicly available datasets and accuracy evaluation indicators for cloud detection, compares the accuracy of mainstream deep learning models in cloud detection, and briefly summarizes the subsequent processing steps of cloud shadow detection and removal. Finally, this paper analyzes the current challenges faced by existing deep learning-based cloud detection algorithms and the future development direction of the field.

Wood plastic composites (WPCs) are low-cost biomass composite materials with good mechanical stability and good weather resistance that are mainly used in the areas with low stress levels. Aimed at improving the mechanical properties of WPCs, this paper proposes a new WPC reinforced with aluminum. The WPC and aluminum were hot pressed to form an aluminum reinforced wood plastic composites (A-WPC). The axial tensile properties, stress–strain relationship, and failure mechanism of the composite were studied experimentally. The results show that the ultimate stress and strain, elastic modulus, and other mechanical parameters of A-WPCs are much higher than those of WPCs. The elongation at break is 10.13 times that of WPCs, which greatly improves the ductility. Based on the equivalent stiffness theory, two calculation models were proposed to predict the tensile stress–strain relationship of A-WPCs. The tensile rebound process of A-WPCs was analyzed in depth, and then the calculation formula of the residual curvature was deduced to compare with the test results. The experimental results are in good agreement with the calculation results.

Uniaxial tensile tests of recycled waste wood plastic composites were conducted at 20, 40, and 60 °C. High density polyethylene (HDPE, 30%) was reinforced with poplar wood (50%), and calcium carbonate (15%), with 5% additives. The load values for three stress levels of 15%, 30%, and 45% were determined at each temperature. Subsequently, 24-h short-term creep tests of WPC were conducted under nine operating conditions. Both the ultimate strength and elastic modulus of the material was found to decrease with increasing temperature. The modulus and ultimate strength decreased from 3890 and 15.0 MPa at 20 °C to 1970 and 7.1 MPa at 60 ℃, respectively. Furthermore, the stress-strain curves of WPC specimens exhibit plastic behavior when the temperature exceeded 40 °C. The creep deformation of WPC was positively correlated with temperature and stress level. The Findley model exhibited distortion in fitting the creep performance of WPC only under the condition of 60 °C and 15% stress level. Conversely, the fractional-order model demonstrated a better fitting effect on the steady-state creep characteristics of WPC under this working condition.

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In recent years, deformation disasters in mine shafts have occurred frequently, posing a threat to mine safety. The nonlinear coupling relationship between shaft surrounding rock deformation and rock mass mechanical parameters is a key criterion for surrounding rock stability. However, existing machine learning prediction methods are rarely applied to shaft deformation, and issues such as poor accuracy and generalization of single models remain. To address this, the study proposes a feature-weighted Stacking ensemble model, which considers 15 feature variables; using RMSE, MAE, R2, and inter-model MAPE correlation as evaluation metrics, GBDT, XGBoost, KNN, and MLP are selected as base learners, with Lasso linear regression as the meta-learner. Prediction errors are corrected by weighting the outputs of base learners based on prediction accuracy. Experiments show that, using MAPE as the evaluation metric, the improved model reduces the error by 2.59% compared with the best base learner KNN, by 6.83% compared with XGBoost, and by 0.18% more than the traditional Stacking algorithm, making it suitable for predicting weak surrounding rock shaft deformation under multi-feature conditions.

Pear psylla (Cacopsylla chinensis), a major pear tree pest widely distributed in China, is increasingly affecting the productivity of orchards. This species exhibits seasonal polyphenism with two distinct forms, namely, a summer form and a winter form. Through topically applying Beauveria bassiana conidial suspensions to the abdominal cuticle of C. chinensis, we demonstrated that the entomopathogenic fungus B. bassiana exhibits significant yet phenotypically divergent virulence against these two forms. Using PacBio SMRT sequencing and Illumina RNA-seq, we analyzed transcriptomic changes post-infection, revealing form-specific immune responses, with 18,232 and 5027 differentially expressed genes identified in summer- and winter-form pear psylla, respectively, and a total of 3715 DEGs shared between the two seasonal phenotypes. In summer-form individuals, B. bassiana infection disrupted oxidative phosphorylation and downregulated immune recognition genes, cellular immune-related genes, and signaling genes, along with the upregulation of the immune inhibitor serpin, indicating immunosuppression. Conversely, in winter-form individuals, immune-related genes and glycolytic rate-limiting enzymes were upregulated after infection, suggesting that the winter-form immune system normally responds to B. bassiana infection and supports efficient defense through metabolic reprogramming to fuel energy-demanding defenses. These findings advance our understanding of C. chinensis/B. bassiana interactions, providing a basis for elucidating immune regulation in seasonally polymorphic insects. The results also inform strategies to optimize B. bassiana-based biocontrol, contributing to sustainable pear psylla management.

As data science advances, automated machine learning (AutoML) gains attention for lowering barriers, saving time, and enhancing efficiency. However, with increasing data dimensionality, AutoML struggles with large-scale feature sets. Effective feature selection is crucial for efficient AutoML in multi-task applications. This study proposes an efficient modeling framework combining a multi-stage feature selection (MSFS) algorithm and AutoSklearn, a robust and efficient AutoML framework, to address high-dimensional data challenges. The MSFS algorithm includes three stages: mutual information gain (MIG), recursive feature elimination with cross-validation (RFECV), and a voting aggregation mechanism, ensuring comprehensive consideration of feature correlation, importance, and stability. Based on multi-source and time series remote sensing data, this study pioneers the application of AutoSklearn for forest fire risk prediction. Using this case study, we compare MSFS with five other feature selection (FS) algorithms, including three single FS algorithms and two hybrid FS algorithms. Results show that MSFS selects half of the original features (12/24), effectively handling collinearity (eliminating 11 out of 13 collinear feature groups) and increasing AutoSklearn’s success rate by 15%, outperforming two FS algorithms with the same number of features by 7% and 5%. Among the six FS algorithms and non-FS, MSFS demonstrates the highest prediction performance and stability with minimal variance (0.09%) across five evaluation metrics. MSFS efficiently filters redundant features, enhancing AutoSklearn’s operational efficiency and generalization ability in high-dimensional tasks. The MSFS–AutoSklearn framework significantly improves AutoML’s production efficiency and prediction accuracy, facilitating the efficient implementation of various real-world tasks and the wider application of AutoML.