In China, more than 5 million students enter higher vocational colleges each year, and the positive emotions of their students merits much attention.
This study aimed to explore the effect of teacher ...support on positive emotions among higher vocational students by further investigating the mediating role of procrastination behavior and interpersonal assistance.
A questionnaire survey was conducted with 676 higher vocational students from Zhejiang Province, China. We used SPSS 26.0 for data analysis, which included correlation analysis, regression analysis and bootstrap-mediated effects tests.
Teacher support positively predicted positive emotions (
= 0.302,
< 0.001) and interpersonal assistance (
= 0.170,
< 0.001), while procrastination behavior negatively predicted interpersonal assistance (
= -0.161,
< 0.001) and positive emotions (
= -0.088,
< 0.01). Interpersonal assistance positively predicted positive emotions (
= 0.279,
< 0.001). This study found that teacher support positively predicted positive emotions; either procrastination behavior or interpersonal assistance independently mediated the relationship between teacher support and positive emotions. These two variables (procrastination and interpersonal assistance) also acted as a chain mediator between teacher support and positive emotions. The direct effect of teacher support and the mediating role of interpersonal assistance had a greater effect.
The study deeply explored the effects of teacher support on positive emotions among Chinese higher vocational students and found that teacher support plays an important role in positive emotion management. At the same time, we found the key roles played by procrastination behavior and interpersonal assistance between teacher support and positive emotion, which could provide data support and decision-making reference for enhancing higher education students' well-being and positive emotions. This study can be regarded as a case study of social support theory and demonstrates the applicability of the theory in the field of positive emotions of higher vocational students.
In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with ...backlighting, occlusion, overlap, sun, cloud, or rain). First, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computational complexity while improving the detection speed. Second, a new feature branch is added to the backbone network and the feature fusion layer in the neck network is reconstructed to effectively combine the lower- and higher-level features, which improves the accuracy of the model while maintaining its lightweight nature. Finally, a knowledge distillation method is used to transfer knowledge from the more capable teacher model to the less capable student model, significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than that of the original YOLOv5s model. Furthermore, the model size is 7.14 MB, half that of the original model, and its real-time detection frame rate is 11.25 FPS when implemented on the Jetson Nano. The proposed model is found to outperform state-of-the-art models in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruit in complex orchard scenes, offering valuable technical support for the development of orchard picking robots and greatly improving the intelligence level of orchards.
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Floating photovoltaics (PVs) are progressively constructed in the ocean sea; therefore, the effect that freak waves have on their structural design needs to be considered. This paper developed a ...dedicated numerical model coupling the floating PV platform and mooring line structures to investigate their dynamic responses under freak waves. A feasible superposition approach is presented to generate freak wave sequences via the combination of transient waves and random waves. A large floating PV platform moored by twenty lines for a water depth of 45 m was designed in detail according to the actually measured ocean environmental and geological conditions. The global time domain analyses of the floating PV mooring structures were implemented to obtain dynamic responses, including PV platform motions and the mooring line configuration and tension under freak waves. A comparison of the response results with those caused by random waves was conducted to illustrate the intuitive evidence of the freak wave effects, which offer a significant reference for the preliminary design of the floating PV platform and mooring line structures.
•A lightweight tomato target detection algorithm based on YOLOv5 is proposed.•The improved model has a significantly higher detection speed on the CPU platform.•The model is deployed on smartphones ...using model quantization and an APP is developed to achieve local real-time detection of tomatoes.•The 16-bit quantized model performs the best and the detection speed meets the needs of real-time.
The current deep-learning-based tomato target detection algorithm has many parameters; it has drawbacks of large computation, long time consumption, and reliance on high-computing-power devices such as graphics processing units (GPU). In this study, we propose a lightweight improved YOLOv5 (You Only Look Once) based algorithm to achieve real-time localization and ripeness detection of tomato fruits. Initially, this algorithm used a down-sampling convolutional layer instead of the original focus layer, reconstructing the backbone network of YOLOv5 using the bneck module of MobileNetV3. Then, it performs channel pruning for the neck layer to further reduce the model size and uses a genetic algorithm for hyperparameter optimization to improve detection accuracy. We evaluate the improved algorithm using a homemade tomato dataset. The experimental results demonstrated that the improved model number of parameters and floating point operations per second (FLOPs) were compressed by 78% and 84.15% compared to the original YOLOv5s, while the mAP reached 0.969. Meanwhile, the detection speed on the central processing unit (CPU) platform was 42.5 ms, which was 64.88% better. This study further utilized the Nihui convolutional neural network (NCNN) framework to quantize the improved model and developed an Android-based real-time tomato monitoring application (app). Experimental results demonstrated that the 16-bit quantized model achieved an average detection frame rate of 26.5 frames per second (fps) on the mobile side with lower arithmetic power, which was 268% better than the original YOLOv5s, and the model size was reduced by 51.1% while achieving a 93% true detection rate.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The flavor and market value of loquats are substantially affected by their soluble solids content (SSC). This study explored the ability of hyperspectral imaging combined with multiple regression ...models to detect SSC of loquats using small sample size. In this study, one hundred and fifteen loquats were collected as samples, and regions of interest in the hyperspectral images of the samples were extracted. The spectral data processed through direct orthogonal signal correction (DOSC), the best among the five processing methods examined, was selected for the feature spectrum process. Successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), improved uninformative variable elimination algorithm (imUVE), and imUVE-SPA algorithms were used to extract feature spectra and establish partial least squares regression (PLSR), support vector machine regression (SVR), back-propagation neural network (BPNN), and customized convolutional neural network (CNN) models, respectively. The results demonstrated that the CNN model based on the 18 feature spectra extracted by CARS was the best (correlation coefficient of validation = 0.904, root mean square error of validation = 0.336 %, and residual predictive deviation = 2.339). Consequently, hyperspectral images with small sample size combined with appropriate regression models can be used for nondestructive detection of loquat brix. Furthermore, the wavelength feature extraction algorithm effectively improves the performance of CNN models with small sample sizes. This study can serve as a valuable reference for the nondestructive detection of fruit brix in other limited sample situations.
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•Hyperspectral imaging can predict the SSC of loquat from small sample sizes.•The performance of customized CNN was the best in SSC prediction.•Feature selection algorithms could improve the performance of the CNN models.•Deep learning can also perform well in the regression problem.
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The Mycogone perniciosa disease of Agaricus bisporus is highly contagious, with insignificant early symptoms and a long infestation period. Currently, there is a lack of a convenient and rapid means ...to detect the disease for early control. By using the transmission route of the disease, this paper focuses on M. perniciosa chlamydospore detection in contaminated soil. Microscopic hyperspectral images of M. perniciosa chlamydospore in contaminated soil were obtained to establish a detection model. Given a small target and a complex background, we proposed an improved spore detection model based on a faster regional convolutional neural network (Faster R-CNN). Furthermore, we combined the residual network Resnet50 and feature pyramid network (FPN) to extract thick spore target features at multiple scales. Meanwhile, we optimized the region proposal network (RPN) region proposal generation by adding two small scales to improve the performance of the detection model. The first three principal components (with 95% or more information) and RGB images were selected as model inputs, respectively, and the final average precision (AP) was 94.68% and 92.35%, respectively. This PC-based model also was compared to the VGG16 and Resnet50-based feature extraction networks of Faster R-CNN and Darknet53-based feature extraction network of the YOLOv3 model, and the AP was found to improve by 5.41%, 4.78%, and 6.34%, respectively. The results showed that micro-hyperspectral imaging combined with deep learning methods could accurately detect the chlamydospore in the soil, providing new methods and ideas for the early prevention and detection of M. perniciosa disease of A. bisporus.
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•A new means of early warning of disease infection in A. bisporus cultivation.•Micro-hyperspectral imaging were combined with deep learning for target detection.•An improved spore detection model based on Faster-RCNN was proposed.•PCA images got from micro-hyperspectral images provide richer feature information.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
There is no standard for rock-socketed pile foundation of offshore wind farm at China and abroad. According to the practical geological and environmental conditions of offshore wind power ...plant, response of rock-socketed pile under horizontal load was analyzed by using a finite element software, ABAQUS. Then, the parameters of rock-socketed pile, i.e. depth, pile diameter, ratio of pile diameter to thickness, and grouting thickness were investigated by orthogonal analysis and parametric analysis. It is found that the thickness of grouting has little influence on the displacement of pile head. The diameter and the ration of diameter to thickness of pile, rock-socketed depth have significant influence. The displacement of pile tip tends to stable and the bending moment at bottom of pile become zero when the rock-socketed depth reaches 3d
p
. The maximum Tresca stress of grouting body and 1st principal stress of rock mass decrease with an increase of rock-socketed depth.
The capacity of steel circular hollow section (CHS) T-joints reinforced with external stiffeners under axial compression is studied by finite element (FE) modeling and theoretical analysis in this ...paper. The FE modeling approach is first validated by results from axial compressive experiments on nine T-joints with and without stiffeners. Then 256 T-joint FE models are established and analyzed to investigate the influence of the size of external stiffeners and joint geometry on the improvement of ultimate capacity of the joints under axial compression. The results from parametric analysis show that the increment in ultimate capacity decreased when the diameter ratio between the brace and the chord β increased, whereas it remained almost proportional to the factor of stiffener length to the brace diameter η. In addition, for such T-joints, when the thickness of external stiffener was no less than that of the chord wall, the effect of the factor of stiffener thickness to the chord thickness λ and the diameter to thickness ratio of the chord 2γ on the improvement of ultimate capacity was not obvious. On the basis of the yield line model, a theoretical formula is further derived to predict the ultimate strength enhancement of reinforced T-joints with external stiffeners.
•Verified modeling of steel CHS T-joints with and without external stiffeners.•Comprehensive parametric study completed for joint strength enhancement effect.•Theoretic formulation on strength enhancement of CHS T-joints is derived.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
This paper studies circular hollow section (CHS) T-joints by conducting experiments on the comparative axial compressive strength of unreinforced and reinforced T-joints. Three pairs of unreinforced ...and reinforced T-joints, a total of six specimens with the ratio of brace to chord diameter equal to 0.26, 0.51, and 0.74 respectively, were tested to determine their compressive load capacity. The specimen parameters, experimental setup, and test results are presented. The failure modes of the unreinforced and reinforced T-joints were compared. The unreinforced joints failed due to chord plastification, while the reinforced joints failed due to chord bending. The experiment indicated that the external stiffening ring significantly increased the axial compressive strength of the T-joints. Every reinforced joint behaved like a beam when loaded by concentrated force at the brace end; the strength of the reinforced joint can be predicted by calculating the plastic bending moment at the crown section.
•A new reinforcement method that uses an external stiffening ring welded to a circular hollow section (CHS) T-joint is investigated.•An axial compressive experiment involving six T-joints (three unreinforced and three reinforced) is conducted to verify the reinforcement effect.•The failure modes of the unreinforced and reinforced T-joins are compared.•The external stiffening ring significantly increases the joint strength, and the ultimate capacity of the joint can be calculated according to the plastic bending of the crown section.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Experimental and numerical investigations on three large diameter concrete-filled high strength steel tubular (CFHSST) stub columns under axial compression were performed. The three large size steel ...tubes had the same dimension in the test. The outer diameter was approximately 550mm, the length was approximately 1000mm, and the thickness was approximately 16mm. The steel tubes were made of high strength steel Q550 and filled with C30 concrete. A 40,000kN press machine was adopted to apply the required axial compression to the three specimens. It was found that the load-displacement curves of the three concrete-filled high strength circular steel tubular stub columns were notably close to each other, and the ultimate capacities were approximately 30,000kN. Finite element analysis (FEA) was performed to analyze the stub columns, and the FEA results are consistent with the experimental results. The formulas from three types of design codes were used to calculate the column loading capacity, and the calculation results were compared with the experimental results. The results were close to the experimental results. The EC4 design code gives the most accurate estimations, with discrepancy less than 4% being observed.
•Three concrete filled high strength steel tubular columns are compressed by test.•A finite element model is established to simulate the experiments and verified.•Three design codes are used to calculate the strength of the columns and evaluated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
