Edge computing has emerged as a vital paradigm for processing data near its source, significantly reducing latency and improving data privacy. Simultaneously, large language models (LLMs) such as GPT-4 and BERT have showcased impressive capabilities in data analysis, natural language processing, and decision-making. This survey explores the intersection of these two domains, specifically focusing on the adaptation and optimization of LLMs for data analysis tasks in edge computing environments. We examine the challenges faced by resource-constrained edge devices, including limited computational power, energy efficiency, and network reliability. Additionally, we discuss how recent advancements in model compression, distributed learning, and edge-friendly architectures are addressing these challenges. Through a comprehensive review of the current research, we analyze the applications, challenges, and future directions of deploying LLMs in edge computing. This analysis aims to facilitate intelligent data analysis across various industries, including healthcare, smart cities, and the internet of things.

Fuel cladding tubes are devices used in reactors to encapsulate fuel clots and transmit heat to coolants. However, zirconium alloy materials which are widely used in the fuel cladding pipe of pressurized water reactors have noticeable safety risks in resisting design basis accidents. Therefore, it is very important to improve the corrosion resistance of fuel envelope tubes to high-temperature water vapor oxidation. High-entropy alloys are considered to be a potential protective coating material for cladding tubes. In this study, AlCrNbSiTi high-entropy alloy (HEA) coatings were prepared by magnetron sputtering at different bias voltages. The effect of bias on coating morphologies, structure, mechanical properties, and resistance to high-temperature water vapor corrosion were studied. Experimental results showed that the bias significantly affects the coating surface roughness. In terms of mechanical properties, the sample at 50 V bias exhibited maximum hardness and elastic modulus of 18.2 GPa and 232.4 GPa, respectively. The highest adhesive force of the coating to the substrate of 36 N was obtained at 100 V bias. The optimum water vapor corrosion resistance of the AlCrNbSiTi HEA coating was achieved at 50 V bias, in which sample-point corrosion was the main corrosion failure mechanism.