Woosong University

With the wide application of Virtual Reality (VR) technology in university education, the design of an efficient Human-Computer Interaction (HCI) interface is crucial for enhancing the learning experience and teaching effectiveness. This study constructs an experimental teaching platform based on VR technology, designs and evaluates an interactive interface suitable for college students. Meanwhile, it incorporates healthy psychological factors into the interface design to stimulate students' learning interest and participation. Through the evaluation of the interface in actual user experience and experimental analysis, the effects of the interface's color style and function design are assessed. The results show that students prefer the "relaxed" and "refined" color styles the most (mean value = 4.1, 3.7), while the "serious" style is the least popular (M=2.1). Regarding function evaluation, the scores for operational relevance, convenience, and functional rationality are the highest (mean value = 3.92, 3.79, 3.73). Moreover, the evaluation of operational interest reaches 71%, indicating that a user-friendly and interesting design is key to the learning experience. In terms of learning performance, the score of the VR-HCI group increases by 12.1 points, and the task time is shortened by 4.1 min, significantly exceeding that of the control group. This shows that the platform has advantages in improving learning efficiency and effectiveness. This study innovatively combines healthy psychology with VR-HCI interface design. It provides interface design guidelines and expands the theoretical research on VR in educational applications. Thus, it offers a novel and efficient teaching model for university experimental teaching and lays a practical foundation for designing intelligent and personalized interactive interfaces in the future.

This paper evaluates and predicts green economic efficiency (GEE) across 248 Chinese cities from 2010 to 2021 using a three-stage network SBM model based on subsystems of economic production, social development, and environmental governance. To enhance accuracy in both assessment and forecasting, machine learning methods are incorporated, and the Dagum Gini coefficient is employed to analyze regional disparities. This study innovatively proposes a three-stage network SBM model to resolve the "black box" limitation of conventional DEA approaches, while a DEA-ML model is developed to achieve enhanced prediction accuracy. The results reveal that GEE in Chinese cities remains low, with the eastern region leading and the western region trailing. However, efficiency has improved since 2016, primarily driven by advancements in environmental governance. Regional disparities, largely attributed to interregional differences, are gradually decreasing. Among forecasting models, the backpropagation neural network (BPNN) delivers the highest accuracy, predicting sustained leadership in the east, strong growth in the northeast, and a reduction in national disparities. This study offers a comprehensive framework for evaluating and predicting GEE, providing valuable insights for sustainable development policies.

Precise and timely identification of cotton leaf diseases is essential for sustaining crop yield and quality, yet manual inspection remains time-consuming, labor-intensive, and prone to error. Existing automated approaches are limited by insufficient dataset diversity, inconsistent evaluation practices, limited use of explainable AI (XAI), and high computational cost. To address these challenges, we propose an attention-enhanced CNN ensemble, namely CottonLeafNet, which integrates lightweight convolutional neural networks for accurate cotton leaf disease classification across two publicly available datasets. CottonLeafNet achieves state-of-the-art performance, obtaining 98.33% accuracy, a macro F1-score of 0.9833, Cohen’s kappa of 0.9800, a mean PPV of 0.9838, and an NPV of 0.9967 on Dataset D1, with an inference time of 0.51 s per image. On Dataset D2, it reaches 99.43% accuracy, a macro F1-score of 0.9942, Cohen’s kappa of 0.9924, a mean PPV of 0.9943, and an NPV of 0.9981, with a 0.40 s inference time. Moreover, a unified eight-class dataset created by merging both datasets yields a test accuracy of 99.08%. Robustness analysis under artificially induced class imbalance further confirms the model’s stability, with consistently strong macro F1-scores. To evaluate the generalization capability of the proposed CottonLeafNet, we conducted cross-dataset experiments, and the results indicate that the model maintains moderate performance even when trained and tested on different datasets. Gradient-Weighted Class Activation Mapping (Grad-CAM) visualizations demonstrate that CottonLeafNet reliably attends to disease-relevant regions, enhancing interpretability. Finally, real-time feasibility is validated through a web-based deployment achieving $$\approx$$1 s inference per image. These results establish CottonLeafNet as an accurate, robust, interpretable, and computationally efficient solution for automated cotton leaf disease diagnosis.

As GenAI technologies become more pervasive in higher education (HE), scholars call for guidance on AI governance. To meet this need, a Delphi technique and collective writing was used in gathering expert perspectives from across 22 countries/locations and six continents. This resulted in the development of a HE GenAI policy/guidelines framework with eight core areas: (1) academic integrity, (2) ethical use and responsible use, (3) privacy and protection, (4) equitable access, (5) GenAI literacy, (6) integration strategy, (7) human oversight and accountability, and (8) institutional support and infrastructure. In addition, a six-part framework was developed to ensure that policies remain current and relevant: (1) creating a dedicated GenAI Committee, (2) conducting regularly scheduled policy reviews, (3) providing ongoing professional development and support, (4) communicating with all stakeholders, (5) evaluating the effectiveness and impact of GenAI, and 6) monitoring external developments. By providing a robust, eight-part framework for policy and guidelines, alongside a six-part mechanism for continued review, this study offers faculty, students, administrators, educational leaders, policymakers, and funders a responsible, adaptable, and consensus-driven blueprint for navigating the integration of GenAI in HE, ensuring that technological innovation serves pedagogical excellence.

The rapid and precise identification of apple leaf diseases is crucial for minimizing yield loss in precision agriculture. However, many existing deep learning methods struggle to be applicable in real-world settings, are not easily interpretable, and often lack sufficient statistical validation. To address these difficulties, we propose our solution approach LeafSightX. This dual-backbone architecture combines features from DenseNet201 and InceptionV3 using Multi-Head Self-Attention (MHSA) techniques, enhancing representational capability and spatial context reasoning. Our extensive procedure includes specialized preprocessing and limited data augmentation, improving model resilience in many scenarios. Furthermore, LeafSightX integrates explainable AI techniques with Grad-CAM visualizations to improve transparency. In assessments of a five-class apple leaf disease dataset featuring field and laboratory images, LeafSightX demonstrates exceptional performance, attaining a test accuracy of 99.64%, an F1-score of 0.9962, and AUC and PR-AUC scores of 1.000, far surpassing all baseline CNNs. Cross-validated Cohen's Kappa (mean = 0.9917, σ = 0.0020) and AUC (mean = 0.9998) indicate a significant level of predictive consistency. Despite its architectural complexity, the model offers real-time inference capabilities, ensuring per-sample latency suitable for edge device deployment. Additionally, the proposed LeafSightX framework was trained and evaluated on an additional independent apple leaf disease dataset, achieving a test accuracy of 99.69%, demonstrating its robustness and generalization. Our approach is a rigorously evaluated, clear, and highly accurate system for identifying plant diseases, providing a reproducible foundation for the actual application of AI in agriculture.