Background: Enhancing the academic performance (AP) of college students can contribute to the overall scientific literacy among this population, thereby fostering societal progress.Objective: The study investigates the correlation between college students’ AP and the socio-educational environment (SEE, including family, roommates, and teachers), study motivation (SM, including self-efficacy and study behaviors). Based on the research findings, recommendations are offered to students, educators, and school administrators.Settings: Utilizing a stratified sampling approach, data was collected by selecting a sample of 330 first-year computer science students from a specific local university in Hebei Province, China.Methods: Data will be collected through a hierarchical sampling method. Using correlation analysis, difference analysis, and structural equation modeling (SEM) as data analysis methods. The data passed reliability and validity analysis (Cronbach’s Alpha = 0.88, KMO = 0.88, χ²/df = 1.49RMSEA = 0.04).Results: The independent sample T-test results showed that female students had higher academic performance than male students (p < 0.001), and there was no significant difference in academic performance between students from single parent or orphan families and students from normal families (p = 0.14), from non urban areas and from urban areas (p = 0.67). The results of the mediating effect analysis indicate that SM exerts complete mediation in the association between SEE and AP, with a mediating effect value of 0.18.Conclusion: The educational disparity between urban and rural areas in China is gradually narrowing. Support policies for students from impoverished families in higher education institutions are showing initial effectiveness. The conducive learning environment and educational atmosphere for students can indirectly influence their psychological state, thus impacting their academic performance during their university years.

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive characteristics, which are an important way of virtual learning and are also one of the important ways in which to promote the development of smart education. Based on the above background, this article proposes an intelligent analysis system for Teaching and Learning Cognitive engagement in an IVR environment based on computer vision. By automatically analyzing the cognitive investment of students in the IVR environment, it is possible to better understand their learning status, provide personalized guidance to improve learning quality, and thereby promote the development of smart education. This system uses Vue (developed by Evan You, located in Wuxi, China) and ECharts (Developed by Baidu, located in Beijing, China) for visual display, and the algorithm uses the Pytorch framework (Developed by Facebook, located in Silicon Valley, CA, USA), YOLOv5 (Developed by Ultralytics, located in Washington, DC, USA), and the CRNN model (Convolutional Recurrent Neural Network) to monitor and analyze the visual attention and behavioral actions of students. Through this system, a more accurate analysis of learners’ cognitive states and personalized teaching support can be provided for the education field, providing certain technical support for the development of smart education.

To address uneven airflow distribution and pesticide deposition coverage in orchard pesticide application, we developed a double-flexible duct spraying device. Utilizing FLUENT 2022 software for airflow field simulation, we analyzed various structural parameters to identify optimal configurations for the air duct type, diameter, and nozzle outlet diameter. The results indicated that the nozzle outlet diameter most significantly influences wind field uniformity, followed by the air duct diameter and type. The optimal settings were identified as follows: C-Type air duct, 100 mm duct diameter, and 50 mm nozzle outlet diameter. Validation tests confirmed these settings, with simulated and actual wind speed measurements, showing no more than a 10% relative error, affirming the simulation’s accuracy. Field tests demonstrated an average droplet density of 35.38 droplets/cm² within tree canopies, indicating strong penetration ability. Droplet distribution followed a lower > middle > upper pattern in the canopy’s vertical direction, fulfilling technical requirements for high spindle-shaped fruit trees and providing a foundation for achieving a uniform canopy coverage.

The rapid proliferation of generative artificial intelligence (GenAI) in higher education raises critical concerns regarding educational equity. However, t

Traditional classroom teaching evaluations often rely on subjective feedback and post hoc academic metrics, which limits their objectivity and timeliness. To address these limitations, this study proposes YOLOv11_BWC, a real-time object detection framework for student behavior detection, classroom attention assessment, and teaching quality evaluation in intelligent education. YOLOv11_BWC integrates a BiFormer module to improve occlusion robustness in complex classroom scenes, a Wise-IoU v3 loss to enhance multi-scale localization, and a lightweight Shuffle Attention mechanism to improve computational efficiency. Using real classroom videos from 257 students, three posture-related behaviors, namely head-up, head-down, and sprawled posture, were annotated, augmented from 840 to 1,326 images, and used for model training and evaluation. On the classroom behavior dataset, YOLOv11_BWC achieved 0.9672 mAP@0.5, 0.7594 mAP@0.5:0.95, 0.931 recall, and 136 FPS, outperforming YOLO26, YOLOv8, YOLO11, RT-DETR, and YOLOv12 in the overall balance between detection accuracy and real-time performance. Additional Jetson Orin Nano testing and POCO/SCB-Dataset3 validation further supported its embedded deployment efficiency and cross-domain robustness. Heatmap visualizations verified more focused feature responses, and temporal analysis revealed an exploratory classroom attention pattern of high initial attention, mid-class decline, and final resurgence. The framework provides objective, non-invasive, and fine-grained behavioral evidence for instructional feedback and dynamic classroom quality assessment.