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Academic procrastination, a common phenomenon among college students, is particularly prominent in the field of higher vocational education in China and has attracted much attention in the field of educational research because of its large influence on student academic achievements. In recent years, an increasing number of studies have held that academic procrastination results from student self-regulation failure, and there is an important internal relationship between self-regulation failure and relative deprivation. To examine the relationship mechanism between relative deprivation and academic procrastination in higher vocational college students, this study investigated 15,998 higher vocational college students in Chongqing, Sichuan, Guizhou and Yunnan by using the "Relative Deprivation Questionnaire," "Adolescent Academic Emotion Questionnaire," "Connor-Davidson Resilience Scale" ("CD-RISC") and "Middle School Student Academic Procrastination Scale." The results show the following. (a) Relative deprivation had a significant negative predictive effect on academic procrastination. (b) Negative academic emotions mediated the relationship between relative deprivation and academic procrastination. (c) Resilience moderated the relationship between negative academic emotions and academic procrastination.

Insulation materials can effectively reduce heat loss by conduction, thereby conserving energy in buildings. Traditional insulation materials usually consist of organic compounds, which are flammable. Despite the application of inorganic materials, such as fiberglass (GF), for their flame-retardant properties, their poor insulating performance and difficulties in dispersion and molding limit their use in construction. Therefore, creating a composite that effectively combines the high thermal insulation properties of organic materials with the high flame resistance of inorganic materials holds significant importance, yet poses substantial challenges. Herein, an Anisotropic Noncombustible Nanocomposite based on Fiberglass-Chitosan (ANNFC) has been prepared. GF was uniformly dispersed into the chitosan (CS) solution though hydrogen bonding. The ANNFC with anisotropic layered structures was prepared through a freeze-orientation process. The as-prepared ANNFC reached a high limiting oxygen index (LOI) of 99.48%, exhibited a low heat release rate (peak at 26 kW m–2), a low smoke production rate (peak at 0.00143 m2 s–1), and a high compressive modulus (3.65 MPa). As a conceptual demonstration, a mini house was constructed using ANNFC. After being baked at a high temperature of 50 °C for 1 h, the interior of the ANNFC house maintained a relatively steady temperature of 37 °C. Therefore, the successfully constructed ANNFC provided a feasible path for fabricating thermal insulation materials for construction. [ABSTRACT FROM AUTHOR]

During the operation of automatic navigation rice transplanter, the accuracy of path tracking is influenced by whether the transplanter can enter the stable state of linear path tracking quickly, thus affecting the operation quality and efficiency. To reduce the time to enter the path tracking stable state and improve the tracking accuracy and stability for the rice transplanter, path tracking control method based on variable universe fuzzy control (VUFC) and improved beetle antenna search (BAS) is proposed in this paper. VUFC is applied to achieve adaptive adjustment of the fuzzy universe by dynamically adjusting the quantization and scaling factors according to the variations of errors by the contraction–expansion factor. To solve the problem of setting the contraction–expansion factor in VUFC and real-time performance, an offline parameter optimization method is presented to calculate the optimal contraction–expansion factor by an iterative optimization algorithm in a path tracking simulation model, where the iterative optimization algorithm is the BAS algorithm improved by the isolated niching technique and adaptive step size strategy in this paper. To verify the effectiveness of the proposed path tracking control method, simulation and field linear path tracking experiments were carried out. Experimental results indicate that the proposed method reduces the time of entering the stable state of linear path tracking and improves the accuracy and stability of path tracking compared with the pure pursuit control method. [ABSTRACT FROM AUTHOR]

Aiming at the non-uniform blurring of image caused by optical system defects or external interference factors, such as camera shake, out-of-focus, and fast movement of object, a multi-scale cyclic image deblurring model based on a parallel void convolution-Resnet (PVC-Resnet) is proposed in this paper, in which a multi-scale recurrent network architecture and a coarse-to-fine strategy are used to restore blurred images. The backbone network is built based on Unet codec architecture, where a PVC-Resnet module designed by combinations of parallel dilated convolution and residual network is constructed in the encoder of the backbone network. The convolution receptive field is expanded with parallel dilated convolution to extract richer global features. Besides, a multi-scale feature extraction module is designed to extract the shallow features of different scale targets in blurred images, and then the extracted features are sent to the backbone network for feature refinement. The SSIM loss function and the L1 loss function are combined to construct the SSIM-L1 joint loss function for the optimization of the overall network to ensure that the image restoration at different stages can be optimized. The experimental results show that the average peak signal-to-noise ratio (PSNR) of the proposed model on different data sets is as high as 32.84 dB, and the structural similarity (SSIM) reaches 0.9235. and statistical structural similarity (Stat-SSIM) of 0.9249 on different datasets. Compared with other methods, the deblurred images generated by this method are superior to the methods proposed by Nah et al., Kupyn et al. and Cho S J et al., especially on the calibration board data set. The model proposed in this paper applies parallel dilated convolution and SSIM-L1 joint loss function to improve the performance of the network so that the edge and texture details of the restored image are clearer. [ABSTRACT FROM AUTHOR]

Unloading curve analysis is a popular method in nan-indentation testing process,while the physical signification of the power-law exponent is obscure. Based on the Sneedon’s solution to elastic indentation of plane half infinite space,a new model was presented to characterize the plastic unloading behavior of Berkovich tip. The residual stress among elastic-plastic stain regions was taken into accounted. The plastic unloading behavior of Berkovich tip was characterized approximately using parabolic tip and conical tip. Experimental results indicated that parabolic tip can characterize the plastic unloading behavior more accurate and the rationality of the new model was validated.

Accurate wucai seedling segmentation is of great significance for growth detection, seedling location, and phenotype detection. To segment wucai seedlings accurately in a natural environment, this paper presents a lightweight segmentation model of wucai seedlings, where U-Net is used as the backbone network. Specifically, to improve the feature extraction ability of the model for wucai seedlings of different sizes, a multi-branch convolution block based on inception structure is proposed and used to design the encoder. In addition, the expectation "maximizationexpectation" maximization attention module is added to enhance the attention of the model to the segmentation object. In addition, because of the problem that a large number of parameters easily increase the difficulty of network training and computational cost, the depth-wise separable convolution is applied to replace the original convolution in the decoding stage to lighten the model. The experimental results show that the precision, recall, MIOU, and F1-score of the proposed model on the self-built wucai seedling dataset are 0.992, 0.973, 0.961, and 0.982, respectively, and the average recognition time of single frame image is 0.0066 s. Compared with several state-of-the-art models, the proposed model achieves better segmentation performance and has the characteristics of smaller-parameter scale and higher real-time performance. Therefore, the proposed model can achieve good segmentation effect for wucai seedlings in natural environment, which can provide important basis for target spraying, growth recognition, and other applications. [ABSTRACT FROM AUTHOR]