With the increasing proportion of renewable energy sources, the output characteristics of wind and solar power in power systems have become increasingly stochastic. This phenomenon not only affects clean energy utilization efficiency but also poses significant challenges to the safety and stability of traditional power grids. In these conditions, energy storage devices have become essential for reducing power fluctuations from renewable sources, and they are crucial for source-storage joint dispatching and dynamic control. Through literature review and comprehensive analysis, this study investigates characterization methods and quantification approaches for renewable energy uncertainty within source-storage coordination frameworks, explores optimization model design philosophies and related strategies, and evaluates current mainstream control technology trends. The uncertainty assessment system, established using parameters such as prediction deviations and fluctuation amplitudes, demonstrates greater scientific rigor than conventional coarse scenario segmentation methods. Multi-objective function-based collaborative planning approaches are gradually replacing single-objective modeling paradigms. Regarding real-time response performance improvement techniques, model predictive control (MPC) integrated with reinforcement learning algorithms has become a new research focus. This study aims to clarify academic frameworks in relevant fields and provide theoretical foundations and technical support for practical applications of source-storage coordination mechanisms in high-renewable-energy environments.

Wide bandgap semiconductors, particularly gallium nitride (GaN) and silicon carbide (SiC), are critical materials for next-generation electronic and high-frequency devices. However, material defects and interface states significantly degrade device performance and reliability. This study presents an integrated multiscale computational framework combining density functional theory, molecular dynamics simulations, and machine learning approaches to analyze defect formation and interface optimization in wide-bandgap semiconductor systems. Key findings include: (1) identification of dominant dislocation configurations in GaN heteroepitaxy and their impact on electron mobility; (2) development of a graph neural network model that predicts defect formation energies with an accuracy exceeding 85%; and (3) optimization of a SiC/SiO₂ interface passivation scheme that reduces interface state density by 60%. The proposed framework bridges atomic-scale defects to device-level performance, providing actionable insights for material synthesis and device fabrication.

With the rapid development of information technology, smart port and shipping has become an inevitable trend in the development of port and shipping field. This paper focuses on the innovation and development of logistics management under the background of smart port and shipping, and expounds its important position in economic development through an overview of port and shipping. In-depth analysis of the application and development of intelligent port and shipping information technology, including automated terminal technology, blockchain technology and big data technology in the port and shipping field, taking Yangshan Port as an example to discuss how these technologies can improve the efficiency of logistics management, reduce costs, enhance safety and transparency, and provide theoretical support and practical guidance for the reform of port and shipping logistics management.