This paper reports the results of a study on the implementation of a sustainable teaching model based on the OBE (Outcome-Based Education) concept and the TSEM (Teach, Study, Evaluate, and Manage) framework in computer science and technology at NingboTech University, China. In the context of digital education, the OBE concept and the TSEM framework are integrated to explore sustainable teaching and learning models based on “artificial intelligence and education”. Based on the core concept of engineering professional education accreditation, the course is designed by using the PCCM (Professional Competency Correlation Matrix) method to build a model based on big data analysis, deepen the classroom teaching reform of “artificial intelligence and education”, and explore the integrated digital sustainable teaching mode of “teaching, learning, evaluation, and management”. The aim of this study is to explore the effectiveness of the teaching model based on OBE and the TSEM framework on students’ sustainable development. The results show that students deepen their learning in computer science while enhancing their own learning initiative, teamwork skills, innovation skills, and awareness of sustainable development. Research shows that our teaching model plays an important role in the development of student sustainable education, enhancing student engineering practice and innovation capabilities and cultivating applied innovative talents. The efficacy of the teaching model based on the OBE concept and the TSEM framework for improving students’ competence in sustainable education warrants further investigation.

The increasing demand for precise load forecasting for distribution networks has become a crucial requirement due to the continual surge in power consumption. Accurate forecasting of peak loads for distribution networks is paramount to ensure that power grids operate smoothly and to optimize their configuration. Many load forecasting methods do not meet the requirements for accurate data and trend fitting. To address these issues, this paper presents a novel forecasting model called Prophet-LSTM, which combines the strengths of the Prophet model’s high trend fitting and LSTM model’s high prediction accuracy, resulting in improved accuracy and effectiveness of peak load forecasting. The proposed algorithm models the distribution network peak load using the Prophet-LSTM algorithm. The researchers then analyzed the experimental data and model of the algorithm to evaluate its effectiveness. We found that the Prophet-LSTM algorithm outperformed the Prophet and LSTM models individually in peak load prediction. We evaluate the proposed model against commonly used forecasting models using MAE (mean absolute error) and RMSE (root mean square error) as evaluation metrics. The results indicate that the proposed model has better forecasting accuracy and stability. As a result, it can predict the peak load of distribution networks more accurately. In conclusion, this study offers a valuable contribution to load forecasting for distribution networks. The proposed Prophet-LSTM algorithm provides a more precise and stable prediction, making it a promising approach for future applications in distribution network load forecasting.

Unmanned aerial vehicle (UAV) remote sensing technology has been widely applied in pine wilt disease (PWD) detection. UAV-collected PWD images are characterized by complex backgrounds, small target sizes, and uneven spatial distribution. State-of-the-art generic detection methods typically use Transformers or Mamba-based backbones to mitigate background interference by enhancing the global receptive field. However, directly applying these generic detectors to detect PWD results in suboptimal performance due to insufficient utilization of local features. Although current PWD detection methods use the attention mechanisms to improve the recognition of infected targets, the limitations of Convolutional Neural Networks (CNNs) in capturing long-range dependencies hinder their ability to separate targets from the background. To overcome these challenges, a novel PWD detection method called feature interaction and dependency capturing-YOLO (FIDCYOLO) is proposed in this article. Firstly, to effectively extract the discriminative features of PWD targets, the shuffle efficient layer aggregation network (SELAN) is proposed to promote information interaction between features, improving the model's learning capability. Then, to address the interference of complex backgrounds, the bidirectional extension of Mamba (Hydra) is improved by employing the partial dependency capture (PDC) module, which captures both global and local dependencies for suppressing background information. Additionally, the feature aggregation-spatial pyramid pooling fast (FA-SPPF) module is introduced in front of PDC, which aggregates cross-level features to enhance detection performance. Finally, we use ADown as the downsampling module to avoid the loss of target information. Extensive experiments on our self-constructed PWD dataset and the public DIOR dataset demonstrate the excellent performance of FIDC-YOLO.

The continuing development of intelligent transportation terminals and the massive generated traffic data have placed tremendous pressure on traffic data acquisition. However, most of existing intelligent transportation systems and applications rely on well-defined data, while few studies focus on how to collect live traffic data from various vehicle terminals in a large number. To solve this problem, we propose an efficient and non-blocking data acquisition system in this paper, which can retrieve traffic data based on different priority or QoS requirements from a large number of various terminals in real-time, so that the application layer can easily access certain type of traffic data it needs. Extensive experiment and simulation results prove the efficiency, reliability, and scalability of our proposed system. Besides, two real applications based on the proposed system are introduced in the paper.