Underwater optical imaging faces significant challenges due to wavelength-dependent light absorption, scattering, and color distortion, which degrade image quality and hinder marine exploration applications. Traditional enhancement methods often lack adaptability to diverse underwater conditions, while conventional deep learning approaches impose prohibitive computational demands for real-time deployment on resource-constrained platforms. This comprehensive review systematically examines the state-of-the-art in lightweight deep learning architectures specifically designed for real-time underwater image enhancement. We present a detailed taxonomy of efficiency-oriented designs including depth wise separable convolutions, attention mechanisms, neural architecture search, and model compression techniques. The paper critically analyzes implementation strategies, benchmark datasets, and evaluation metrics, both perceptual quality indicators and computational efficiency measures. Furthermore, we synthesize comparative performance analyses across multiple lightweight architectures and identify persistent challenges in domain generalization, temporal consistency, and hardware-software co-design. Emerging research directions including physics-informed networks, multimodal fusion, and ultra-low-power deployment paradigms are discussed. This review aims to consolidate current knowledge and guide future research toward robust, efficient vision systems for underwater autonomous platforms.

The field of Artificial Intelligence (AI) is at a critical inflection point, transitioning from narrow, task-specific models to Advanced Artificial Intelligence systems characterized by autonomy, agency, and complex goal-directed behavior. This comprehensive research paper provides an in-depth analysis of this paradigm shift, focusing on Agentic AI as the primary driver toward Artificial General Intelligence (AGI). We detail the foundational architectures, including the Transformer model and the critical role of Foundation Models (LLMs/VLMs), and explore the technical mechanisms of Agentic systems, such as the autonomous loop, advanced memory architectures like Retrieval-Augmented Generation (RAG) (Figure 4), and multi-agent collaboration (Figure 2). The paper further examines the transformative applications across enterprise, finance, and scientific discovery, and analyzes the profound challenges, including the hardware bottleneck (Section 6), the legal dilemma of accountability (Section 7), and the societal impact on the future of work (Section 8). By integrating five detailed diagrams and 37 scholarly references, this work provides a robust framework for understanding the current state, future trajectory, and responsible development of autonomous AI systems.

Zhu Guangqian's Psychology of Literature and Art is a key text in the construction of modern aesthetics in China. This book systematically analyzes the aesthetic experience and its manifestations in literary and artistic activities from the perspective of psychology, which embodies a distinct research orientation of "proceeding from empirical facts" in methodology, and theoretically completes the selective absorption of modern western aesthetics and the integration of China's traditional aesthetic experience. Because the text itself spans many fields of psychology, aesthetics and literature and art, its theoretical structure is not nonlinear, and beginners are prone to break their understanding and confuse their concepts during reading. This paper does not attempt to make a comprehensive review of Psychology of Literature and Art, but focuses on "how to learn this classic", sorting out a relatively clear and operable learning path from four aspects: academic context, theoretical core, research method and practice transformation, in order to provide reference for the systematic study of graduate students.

2025 marks a pivotal preparatory phase preceding the explosive growth of artificial intelligence(AI)technology.Its integration into education is driving systemic transformation from"standardized instruction"to"personalized enhancement".This paper examines key annual advancements in AI-powered education,focusing on three core domainsintelligent teaching agents, edge-based educational AI hardware,and AI governance frameworks.Research findings reveal a global educational AI ecosystem characterized by both competition and collaboration.The study concludes that engineering implementation and compliant applications remain current priorities.Future efforts should emphasize educational ethics and multimodal teaching integration,propelling AI evolution from educational auxiliary tools to"human-machine collaborative enhanced educational partners".

With the rapid development of artificial intelligence, large language models (LL Ms) are increasingly applied in education. Ideological and political theory courses in universities, which are crucial for moral education, face challenges such as outdated cases, weak interaction, and uniform content. This study explores the intelligent generation of cases and interactive teaching empowered by LL Ms, proposing a “triad” paradigm: intelligent generation, multi-dimensional interaction, and value guidance. An ELM-based case-generation system and an interactive platform were constructed to realize real-time content updates, precise case matching, enhanced classroom interaction, and data-driven assessment. Results show that LL Ms significantly improve the attractiveness and pertinence of ideological education, providing technical support for the goal of “educating students in accordance with the times and trends”. Finally, strategies for addressing ethical, security, and teacher-role issues are discussed.