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The study of teacher development and teaching interaction in physical classrooms has presented research problems, but with the development of classroom observation and video analysis, quantitative and visual analysis of the teaching process has been realized. In order to better understand the teaching models of teachers, this study is devoted to mining classroom observation data. First, based on content analysis, a Dynamic Network Analysis of Classroom Teaching Elements (CTE-DNA) framework has been developed in which classroom observation data are divided into three dimensions, teaching behavior, instructional media, and technological pedagogical content knowledge (TPACK). Second, using the method of social network analysis, teacher's teaching models are analyzed by measuring adjacency matrix and relative centrality. Last, key nodes of the network as well as teaching models are visualized. Based on CTE-DNA, classroom observation data can be used for evaluating teacher's teaching behaviors and performance, and can be beneficial to teacher's professional development.

This paper developed a radish disease detection system based on a hybrid attention mechanism, significantly enhancing the precision and real-time performance in identifying disease characteristics. By integrating spatial and channel attentions, this system demonstrated superior performance across numerous metrics, particularly achieving 93% precision and 91% accuracy in detecting radish virus disease, outperforming existing technologies. Additionally, the introduction of the hybrid attention mechanism proved its superiority in ablation experiments, showing higher performance compared to standard self-attention and the convolutional block attention module. The study also introduced a hybrid loss function that combines cross-entropy loss and Dice loss, effectively addressing the issue of class imbalance and further enhancing the detection capability for rare diseases. These experimental results not only validate the effectiveness of the proposed method, but also provide robust technical support for the rapid and accurate detection of radish diseases, demonstrating its vast potential in agricultural applications. Future research will continue to optimize the model structure and computational efficiency to accommodate a broader range of agricultural disease detection needs. [ABSTRACT FROM AUTHOR]

Recently, Virtual Reality (VR) has developed rapidly. Some studies propose that VR has a great deal of potential as an instructional media for learning, while other studies indicate that VR may be ineffective or even negatively influence learners' learning experience. Few studies provide reference to educators or teachers based on empirical data. This study investigates 55 10th-grade learners' perceptions and presences in a VR-based blended learning environment. A research framework based on social presence theory and the Community of Inquiry model (CoI) was adopted in order to study social presence, teaching presence, and cognitive presence of learners in a VR-based practical, blended learning environment. The research findings are as follows: (1) VR enhanced learners' perception of the teaching equipment and the classroom, social presence, teaching presence. (2) VR-based blended learning can have a positive effect on the interest, attention, and interaction of learners. (3) For transferring well-structured knowledge, there is no significant difference between traditional instructional media and VR. But for transferring ill-structured knowledge, VR is more effective at promoting learning than traditional instructional media. In general, VR-based learning environments can positively affect students' perceptions and presence.

This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. The system significantly enhances the accuracy and efficiency of disease detection through an innovative graph attention mechanism and optimized loss functions. Experimental results demonstrate that this system significantly outperforms traditional methods across key metrics such as precision, recall, and accuracy, with the graph attention mechanism excelling in all aspects, particularly achieving a precision of 0.94, a recall of 0.92, and an accuracy of 0.93. Furthermore, comparative experiments with various loss functions further validate the effectiveness of the graph attention loss mechanism in enhancing model performance. This research not only advances the application of deep learning in agricultural disease detection theoretically but also provides robust technological tools for disease management and decision support in actual agricultural production, showcasing broad application prospects and profound practical value. [ABSTRACT FROM AUTHOR]

In this study, a novel approach integrating multimodal data processing and attention aggregation techniques is proposed for pear tree disease detection. The focus of the research is to enhance the accuracy and efficiency of disease detection by fusing data from diverse sources, including images and environmental sensors. The experimental results demonstrate that the proposed method outperforms in key performance metrics such as precision, recall, accuracy, and F1-Score. Specifically, the model was tested on the Kaggle dataset and compared with existing advanced models such as RetinaNet, EfficientDet, Detection Transformer (DETR), and the You Only Look Once (YOLO) series. The experimental outcomes indicate that the proposed model achieves a precision of 0.93, a recall of 0.90, an accuracy of 0.92, and an F1-Score of 0.91, surpassing those of the comparative models. Additionally, detailed ablation experiments were conducted on the multimodal weighting module and the dynamic regression loss function to verify their specific contributions to the model performance. These experiments not only validated the effectiveness of the proposed method but also demonstrate its potential application in pear tree disease detection. Through this research, an effective technological solution is provided for the agricultural disease detection domain, offering substantial practical value and broad application prospects. [ABSTRACT FROM AUTHOR]

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model's capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results demonstrated significant improvements in precision, recall, accuracy, and mean average precision (mAP). Specifically, precision was 0.92 , recall was 0.89 , accuracy was 0.90 , and mAP was 0.91 , surpassing traditional models such as YOLOv5, YOLOv8, RetinaNet, EfficientDet, and DEtection TRansformer (DETR). Furthermore, through ablation studies, the critical roles of ASLVN and the spatial state attention mechanism in enhancing detection performance were validated. These experiments not only showcased the contributions of each component for improving model performance but also highlighted the method's capability to address the challenges of apricot tree disease detection in complex environments. Eight types of apricot tree diseases were detected, including Powdery Mildew and Brown Rot, representing a technological breakthrough. The findings provide robust technical support for disease management in actual agricultural production and offer broad application prospects. [ABSTRACT FROM AUTHOR]

The newly revised GB 13057-2014 'The strength of the seats and their anchorages of passenger vehicles' replaces the static test with a dynamic test, which puts forward higher requirements on seat safety. Considering safety and lightweight design, the LFT (Long glass Fiber reinforced Thermoplastic) is used to replace the original steel seat back frame. Combined with energy absorption model and topology optimisation, the force transmission path and detail structure design under both the forward loading and rearward loading conditions are carried out. In order to analyse the occupant protection performance, numerical models of composite seat and HybridIII dummy are established, and the safety and lightweight effect of LFT seat back frame are optimised and verified. [ABSTRACT FROM AUTHOR]

The newly revised GB 13057-2014 put forward high requirements for the dynamic occupant protection of passenger car seats. Due to the high density of steel, there is currently not much space for lightweight through structure improvement. In this paper, the quasi-static and dynamic mechanical properties of CFRP and corresponding simulations are used to study the mechanical behaviour of CFRP and verify the accuracy of simulation. It is now applied to the composite seat backplane structure design, and combined with the CFRP moulding process and the energy absorption characteristics of the car seat backplane to initially design the CFRP seat backplane material and structure. At the same time, considering the designability of the material itself, the ply optimisation technology under multi-condition is used to design the material and structure of the CFRP passenger car seat backplane. The new CFRP passenger car seat achieves significant weight reduction while meeting regulatory requirements. [ABSTRACT FROM AUTHOR]