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Abstract 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.

Fire drills may reinforce the survival skills of participants on the one hand, and on the other hand, they might bring a certain amount of psychological discomfort. To identify elements associated with the psychological discomfort, a designed questionnaire was distributed to postgraduate students who had experienced fire drills in Islington (London) and 1640 valid responses were obtained. With the help of regression analysis, this research found that participants' awareness of precaution, individual initiative of participation, the personal judgement of simplified fire drills (SFDs), participation of SFDs, evaluation of SFDs' function in practical applications, and satisfaction with SFDs' performance are positively associated with participants' psychological discomfort, while the procedural arrangement of SFDs, time interval of joining in the last SFD, and the frequency of simplified fire drills experienced are negatively associated with participants' psychological discomfort. Moreover, personal awareness of precaution, individual initiative of participation, individual satisfaction with SFDs' performance, the time interval of joining in the last SFD, procedural arrangement of SFDs, and the frequency of simplified fire drills experienced could explain 30.02% of the variance in participants' psychological discomfort.

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. [ABSTRACT FROM AUTHOR]

Using a two‐step molding process, wood plastic composite (WPC) were prepared under temperatures ranging from 160 to 180°C. Uniaxial compression tests of WPC were conducted at temperatures of −20°C, 0°C, 20°C, and 40°C, determining stress levels of 30%, 45%, and 60% based on the test results. Subsequently, 24‐hour compression creep tests were performed for WPC under 12 different conditions. The Findley power law model was employed to fit the experimental results, indicating that creep deformation increased with both stress level and environmental temperature. Notably, specimens at 0°C failed after sustaining a load for 13.6 hours at 60% stress, while those at 20°C failed after only 0.72 hours under the same conditions. Overall, the Findley model effectively predicted the creep behavior of WPC under all temperature conditions and stress levels. Highlights: The influence of temperature variation on the strength of WPC.Short term creep of WPC under different stress levels.Creep of WPC at different temperatures.Prediction of creep performance under different operating conditions. [ABSTRACT FROM AUTHOR]

The timely and accurate mapping of sugarcane cultivation is significant to ensure the sustainability of the sugarcane industry, including sugarcane production, rural society, sugar futures, and crop insurance. Synthetic aperture radar (SAR), due to its all-weather and all-time imaging capability, plays an important role in mapping sugarcane cultivation in cloudy areas. However, the inherent speckle noise of SAR data worsens the "salt and pepper" effect in the sugarcane map. Therefore, in previous studies, an additional land cover map or optical image was still required. This study proposes a new application paradigm of time series SAR data for sugarcane mapping to tackle this limitation. First, the locally estimated scatterplot smoothing (LOESS) smoothing technique was exploited to reconstruct time series SAR data and reduce SAR noise in the time domain. Second, temporal importance was evaluated using RF MDA ranking, and basic parcel units were obtained only based on multi-temporal SAR images with high importance values. Lastly, the parcel-based classification method, combining time series smoothing SAR data, RF classifier, and basic parcel units, was used to generate a sugarcane extent map without unreasonable sugarcane spots. The proposed paradigm was applied to map sugarcane cultivation in Suixi County, China. Results showed that the proposed paradigm was able to produce an accurate sugarcane cultivation map with an overall accuracy of 96.09% and a Kappa coefficient of 0.91. Compared with the pixel-based classification result with original time series SAR data, the new paradigm performed much better in reducing the "salt and pepper" spots and improving the completeness of the sugarcane plots. In particular, the unreasonable non-vegetation spots in the sugarcane map were eliminated. The results demonstrated the efficacy of the new paradigm for mapping sugarcane cultivation. Unlike traditional methods that rely on optical remote sensing data, the new paradigm offers a high level of practicality for mapping sugarcane in large regions. This is particularly beneficial in cloudy areas where optical remote sensing data is frequently unavailable. [ABSTRACT FROM AUTHOR]

In this paper, a remote sensing image segmentation based on probabilistic fuzzy local information clustering algorithm is proposed. First, assuming that the spectral measure within the same ground object follows the same probability distribution. The dissimilarity between pixel and object is modeled by the negative logarithm of the Gaussian probability density function. It can improve the noise sensitive problem caused by the Euclidean distance which can only describe the data with isotropic distribution. Then, in order to consider the effect of local spatial constraint, on the one hand, the probability measure is used to modify the local fuzzy factor to establish the dissimilarity measure with spatial constraints. On the other hand, the hidden Markov random field is used to model the prior probability model of pixel membership. Next, the entropy regularization term of the objective function is built by combining the Kullback-Leibler(KL) maximum entropy model to further improve the robustness and noise resistance. The qualitative and quantitative analysis of simulated image and different types of real remote sensing images show that the proposed algorithm can effectively overcome the above problems and further improve the accuracy of image segmentation to over 95%. [ABSTRACT FROM AUTHOR]

This paper has been retracted. Chang, K., & Zhao, L. (2024). Retraction: The HEXACO model of personality is associated with loneliness in a university setting. Social Behavior and Personality: An international journal, 52(6), e14330. https://doi.org/10.2224/sbp.14330. The problem of loneliness among college students has attracted extensive attention from researchers. As a fundamental psychological framework, personality traits are associated with loneliness. Thus, we conducted a survey of 688 college students in Mainland China to examine the relationship between the HEXACO personality traits and loneliness. Regression analysis revealed a negative association between loneliness and the personality traits of agreeableness, conscientiousness, and openness to experience, and a positive association between loneliness and the personality traits of honesty–humility, emotionality, and extraversion. This model explained 33.78% of the variance in loneliness. Implications of the findings are discussed. [ABSTRACT FROM AUTHOR]

Locust plagues inflict severe agricultural damage. Climate change-induced extreme events like rainfall and droughts have expanded locust habitats. These new areas, often beyond routine monitoring, could become potential high-risk locust areas (PHRLA). Quantitatively understanding the outbreak mechanism driving drought and flood dynamics is crucial for identifying PHRLA, but such studies are scarce. To address this gap, we conducted a case study on locust outbreaks in Xiashan Reservoir, the largest reservoir in Shandong Province, China, in 2017 and 2018. Using time series satellite imagery and meteorological products, we quantitatively analyzed how drought–flood dynamics and temperature affect locust habitats, reproduction, and aggregation. Employing an object-oriented random forest classifier, we generated locust habitat classification maps with 93.77% average overall accuracy and Kappa coefficient of 0.90. Combined with meteorological analysis, we found that three consecutive drought years from 2014 to 2016 reduced the water surface area by 75%, expanding suitable habitats (primarily reeds and weeds) to cover 60% of the reservoir. Warm winters and high temperatures during locust key growth periods, coupled with expanding suitable habitats, promoted multi-generational locust reproduction. However, substantial flooding events in 2017 and 2018, driven by plentiful rainfall during key growth periods, reduced suitable habitats by approximately 54% and 29%, respectively. This compression led to high locust density, causing the locust plague and high-density spots of locusts (HDSL). Our study elucidates locust plague outbreak mechanisms under dynamic drought and flood environments. Based on this, we propose an approach to identify PHRLA by monitoring changes in drought and flood patterns around water bodies and variations in suitable habitat size and distribution, as well as surrounding topography. These findings hold significant implications for enhancing locust monitoring and early warning capabilities, reducing pesticide usage, and ensuring food and ecological security and sustainable agriculture. [ABSTRACT FROM AUTHOR]