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概述了人工智能（artificial intelligence，AI）大模型研究的焦点、发展趋势及其技术本质，分析了国家层面的人工智能发展战略、国防领域的紧迫需求以及船舶领域应用的基础。从智能绿色船舶的发展、防务装备体系的革新、管控体系的建设以及知识密集产业的转型等方面，探讨了将人工智能大模型技术应用于船舶领域的广阔前景。人工智能大模型技术与“平行系统”“知识工厂”和“数字员工”等理念相结合，能够催生“AI设计”+“数字工厂”+“平行验证”等新型设计、研发和验证手段。此外，人工智能大模型技术可以从船体设计、船舶建造、航运管理、节能减排等方面为船舶行业注入智能和绿色元素，优化船舶各项功能，提高企业运营的效率，提升经济性和环保性。与新能源、新材料、先进制造和电子信息等战略新兴产业结合后，人工智能大模型技术能够基于新理念和新形态塑造未来海洋防务装备体系。同时，人工智能大模型技术能够赋能船舶管控体系的建设，优化规划计划、助力科技创新、提高管理效率和改善用装质效等。特别是随着船舶领域知识工厂的建立、数字人员工的培训、工业机器人的推广以及对深远海领域的拓展，人工智能大模型技术将能够推动船舶领域“自然人”“机器人”和“数字人”的有机结合和密切协同，加速船舶行业向知识密集型和智能密集型升级，使产业生态和价值创造模式向高端化、智能化和绿色化转变，实现更注重质量和效率的发展方式。

建立超高效液相色谱-串联质谱法（UPLC-MS/MS）测定植物油中4种杂环胺(MeIQx、4，8-DiMeIQx、7,8-DiMeIQx、PhIP)的分析方法。样品经含1%氨水乙腈提取、乙腈饱和的正己烷脱脂后，利用PCX固相萃取柱进行净化。净化后的样品采用乙腈和10 mmol/L甲酸铵溶液作为流动相进行梯度洗脱，在正离子模式下以电喷雾电离串联质谱对杂环胺的定量离子和定性离子进行监测，并利用内标法进行定量。结果表明：4种杂环胺在0.5~100.0 ng/mL范围内线性关系良好，相关系数均大于0.99，方法检出限为0.01~0.04 μg/kg，定量限为0.03~0.13 μg/kg；在1.0、5.0、10.0 μg/kg的加标水平下，杂环胺的平均回收率在64.5%~96.5%之间，相对标准偏差在2.83%~8.52%之间。本方法快速、准确、灵敏，可应用于植物油中4种杂环胺的同时检测。 A method for determination of four heterocyclic aromatic amines(MeIQx, PhIP, 4,8-DiMeIQx and 7,8-DiMeIQx) in vegetables oils by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was established. The samples were extracted by acetonitrile containing 1% ammonium hydroxide, defatted by n-hexane saturated by acetonitrile, followed by the purification with PCX solid phase extraction column. Then the heterocyclic aromatic amines in the extracts were separated by a gradient elution of acetonitrile and 10 mmol/L ammonium formate aqueous solution, then quantitative and qualitative ion of heterocyclic aromatic amines were monitored by electrospray ionization tandem mass spectrometry in positive ion mode, and quantified by internal standard method. The results showed that all of the analytes showed good linearity in the range of 05-1000 ng/mL and the correlation coefficients (r2) were all above 0.99. The limits of detection for heterocyclic aromatic amines were 0.01-0.04 μg/kg, and the limits of quantification were 0.03-0.13 μg/kg. The average recoveries of heterocyclic aromatic amines at spiked levels of 1.0, 5.0, 10.0 μg/kg ranged from 64.5% to 96.5%, with the relative standard deviations of 2.83%-8.52%. The method is rapid, accurate and sensitive, and is suitable for the determination of four heterocyclic aromatic amines in vegetable oils.

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In the traditional centralized data trading system, sensitive data such as electric power data are easily abused due to duplicability. To address this, the blockchain-based power data's capitalization and trading system was proposed. Considering the trust in blockchain technology, a multi-party trusty network can be established between multiple parties in distributed ledger with cryptographic algorithms, multi-centralized architecture and distributed multi-party consensus protocol. The system was developed with Hyperledger's consortium blockchain framework Fabric, which made power data trading safe, transparent, traceable and immutable by means of the technical characteristics of the blockchain. In addition, a supervision mechanism was introduced. The system also provided Web service as application layer for ease of use. Finally, the system was tested by simulating multi-node deployment on a single device. And the results show that the system runs well in integrated test, meets the design requirements, and has low maintenance costs and enhances security. Compared with the traditional data trading system, the blockchain-based power data's capitalization and trading system demonstrates comprehensive functionality and security advantages. [ABSTRACT FROM AUTHOR]

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