This article delves into the profound impact of computational design and digital fabrication on the architectural landscape, presenting a comprehensive overview of their theoretical foundations, technological advancements, and environmental implications. It explores the transition from traditional design methodologies to algorithmic and generative approaches, highlighting how these technologies facilitate the creation of innovative, efficient, and sustainable architectural solutions. Through the lens of pioneering case studies, the analysis demonstrates significant efficiency gains and the potential for reducing construction waste and energy consumption. The integration of computational design with digital fabrication heralds a new era of architecture that not only challenges conventional construction practices but also aligns with the urgent need for sustainability in the built environment. The article further investigates the role of material innovation, robotic automation, and software development in pushing the boundaries of what can be achieved, ultimately underscoring the environmental benefits of these integrated technologies.

This article delves into the importance of applying machine learning and deep reinforcement learning techniques in cloud resource management and virtual machine migration optimization, highlighting the role of these advanced technologies in dealing with the dynamic changes and complexities of cloud computing environments. Through environment modeling, policy learning, and adaptive enhancement, machine learning methods, especially deep reinforcement learning, provide effective solutions for dynamic resource allocation and virtual intelligence migration. These technologies can help cloud service providers improve resource utilization, reduce energy consumption, and improve service reliability and performance. Effective strategies include simplifying state space and action space, reward shaping, model lightweight and acceleration, and accelerating the learning process through transfer learning and meta-learning techniques. With the continuous progress of machine learning and deep reinforcement learning technologies, combined with the rapid development of cloud computing technology, it is expected that the application of these technologies in cloud resource management and virtual machine migration optimization will be more extensive and in-depth. Researchers will continue to explore more efficient algorithms and models to further improve the accuracy and efficiency of decision making. In addition, with the integration of edge computing, Internet of Things and other technologies, cloud computing resource management will face more new challenges and opportunities, and the application scope and depth of machine learning and deep reinforcement learning technology will also expand, opening new possibilities for building a more intelligent, efficient and reliable cloud computing service system.

In recent years, large language models (LLMs) have revolutionized natural language processing (NLP) with their transformative architectures and sophisticated training techniques. This paper provides a comprehensive overview of LLMs, focusing on their architecture, training methodologies, and diverse applications. We delve into the transformer architecture, attention mechanisms, and parameter tuning strategies that underpin LLMs' capabilities. Furthermore, we explore training techniques such as self-supervised learning, transfer learning, and curriculum learning, highlighting their roles in empowering LLMs with linguistic proficiency. Additionally, we discuss the wide-ranging applications of LLMs, including text generation, sentiment analysis, and question answering, showcasing their versatility and impact across various domains. Through this comprehensive examination, we aim to elucidate the advancements and potentials of LLMs in shaping the future of natural language understanding and generation.

The integration of Computer-Aided Design (CAD), Building Information Modeling (BIM), and Internet of Things (IoT) with Artificial Intelligence (AI) represents a transformative shift in the architectural and engineering practices for large-span structures. This paper delves into the quantitative and qualitative enhancements brought about by these technologies, focusing on their pivotal roles in design precision, structural analysis, project management, and maintenance strategies. CAD's evolution from simple drafting to complex 3D modeling has significantly reduced design time and errors, while its integration with structural analysis software has improved load distribution accuracy and material cost efficiency. BIM's contribution to design, construction, and sustainability emphasizes optimized resource allocation and energy-efficient practices. Furthermore, IoT and AI's role in real-time monitoring and predictive maintenance underpins a proactive approach to structural health, ensuring longevity and safety. Through statistical analysis, mathematical modeling, and case studies, this paper highlights the synergistic impact of these technologies on reducing project timelines, enhancing collaboration, and fostering innovation in large-span structure engineering. The findings advocate for a multidisciplinary approach, combining engineering expertise with advanced computational tools to achieve superior outcomes in the construction and maintenance of large-span structures.