Constructing a new power system is crucial for strengthening energy security and achieving the carbon peaking and carbon neutralization goals in China. Improving the security resilience is the core of the safe and stable development of the system, which requires the digital technology that could play a key enabling role. This study analyzes the implications and characteristics regarding the security resilience of new power systems, and sorts out the challenges faced by the improvement in security resilience, including frequent extreme events, complex system structure, and multi-energy coordination conflicts. This study expounds on the enabling effect of the digital technology on the improvement in security resilience of new power systems, summarizes the major problems existing in the enabling process, and further proposes a key technology system for improving the security resilience of new power systems through the digital technology, involving multimodal data fusion technology based on artificial intelligence, intelligent situation awareness and early-warning technology based on cloud-edge collaboration, multi-energy collaborative optimization and control technology based on big data analysis, and post-disaster emergency decision-making technology based on digital twins. To provide theoretical support for the development of new power systems, this study proposes the following suggestions: (1) emphasizing the top-level design of major projects regarding climate resilience, (2) strengthening the research and development of key digital and power technologies, (3) building data infrastructure while improving the quality assurance mechanism, and (4) optimizing the construction of a compound talent echelon in the power industry.

With the continuous breakthroughs in technologies such as artificial intelligence, advanced manufacturing, and new materials, China's humanoid robot industry is experiencing explosive growth. Technological innovation and supportive policies have fostered a diversified and competitive landscape. However, China's humanoid robot industry still faces significant challenges, including lagging core technologies, high difficulty in mass production, and obstacles to commercialization. This study explores the major subfields and technological frontiers of humanoid robotics, offering an in-depth analysis of global trends in policies, technologies, and industrial development. It examines the current state of humanoid robotics in China and identifies key opportunities and challenges. Furthermore, the study proposes strategic recommendations to address these challenges, focusing on technological innovation, pilot demonstration, improvement in laws and regulations, and policy support. Specifically, the research recommends encouraging breakthroughs in core technologies, strengthening industrial layout, building humanoid robot infrastructure, and implementing demonstrative projects. These efforts aim to help China's humanoid robot industry overcome technical challenges, improve its industrial ecosystem, and achieve large-scale production and commercialization, thereby enhancing the global competitiveness of the industry.

The operation and maintenance of power equipment is a crucial aspect of the construction of new power systems. The artificial intelligence large language model (AI-LLM) presents significant opportunities for the digital intelligence of traditional power equipment operation and maintenance. This study aims to explore the enabling role of multimodal AI-LLM in health assessment, operational state prediction, fault diagnosis, life prediction, and maintenance strategy recommendation, among other specific scenarios of power equipment operation and maintenance. Additionally, this study analyzes the challenges faced by multimodal AI-LLM in enabling power equipment operation and maintenance, including the varying quality of multimodal data, the "black box" nature of algorithms leading to low transparency in decision-making processes, and model performance deterioration induced by environmental changes. To address these challenges, this study combines knowledge graph retrieval-augmented generation, multimodal alignment, fine-tuning and continuous learning, and other big model application optimization techniques to construct an AI-LLM power equipment maintenance system. It then sorts out the implementation process of multimodal AI-LLM in the operation and maintenance of power equipment, covering four stages: demand analysis, model training, application deployment, and operational management. Furthermore, strategies for continuously optimizing model performance are proposed, including the continuous monitoring and optimization of data quality, use of continuous learning algorithms, and establishment of a feedback loop mechanism for model performance. Finally, this study explores the future directions for multimodal AI-LLM in the field of power equipment operation and maintenance and provides a series of implementation safeguards to promote the intelligent transformation of power equipment operation and maintenance and support the construction of new power systems.

High-performance storage chips are the core driving force underlying the robust expansion of global artificial intelligence. They are crucial for promoting the information technology industry, improving the performance of electronic devices, driving the evolution of servers and data centers, and fostering nascent technologies such as artificial intelligence, machine learning, the Internet of Things, virtual reality, and augmented reality. This study explores the essence of high-performance storage chips and sorts out their development requirements and international development trend. Moreover, it summarizes the development status of high-performance storage chips in China, delves into the problems and challenges encountered, and pinpoints the transformative opportunities. Furthermore, it proposes the following policy suggestions: (1) implementing a stratified approach to solidify the foundation while revolutionizing strategies to strive for breakthroughs; (2) stressing on both traditional and novel technologies and pursuing parallel development along multiple pathways; and (3) accelerating the establishment of a novel technological framework to progressively break the market monopoly.