两院外籍院士是：Arokia Nathan（阿洛基亚·那桑），现任山东大学讲席教授，主要从事纳米光电 材料及器件、新型光电显示技术和新型TFT薄膜晶体管的应用研究，当选中国科学院外籍院士；Adi Shamir（阿迪·沙米尔），山东大学名誉博士，主要研究方向为密码系统构建与密码分析，当选中国科 学院外籍院士；张友明，现任山东大学讲席教授、微生物改造技术全国重点实验室主任，主要研究方向 为基因组编辑技术开发和应用，当选中国工程院外籍院士。（记者 刘一颖 王亚楠） 11月21日，中国科学院、中国工程院公布2025年院士增选结果，我省5名专家当选两院院士，3名专 家当选两院外籍院士，均为历史最好水平。新当选院士中，中国科学院院士5名，来自山东大学和崂山 实验室；中国工程院院士3名，分别来自山东大学、海军潜艇学院和中国科学院海洋研究所。 两院院士是：梁作堂，现任山东大学讲席教授、粒子物理与粒子辐照教育部重点实验室主任，主要 研究方向为原子核物理理论，当选中国科学院数学物理学部院士；刘建亚，现任山东大学讲席教授，主 要研究方向为数论，当选中国科学院数学物理学部院士；唐波，现任崂山实验室研究员，主要研究方向 为化学传感与成 ...

Core Insights - The article emphasizes the importance of ensuring food security and sustainable agricultural development in the face of global population growth, climate change, and decreasing arable land resources [1] Group 1: Technological Innovations in Crop Improvement - A review paper published in Nature discusses the integration of multi-omics, genome editing, protein design, high-throughput phenotyping, and artificial intelligence (AI) in crop genetic improvement [2][3] - Modern omics technologies, such as genomics and metabolomics, provide unprecedented capabilities to analyze crop genetic information, revealing new loci for precise trait improvement [4] - High-throughput phenotyping (HTP) technologies utilize drones and sensors for rapid and accurate assessment of crop traits, effectively linking genotype to phenotype [4] Group 2: Genome Editing and Protein Design - Genome editing technologies, exemplified by CRISPR, enable efficient and precise modifications of crop genomes, significantly shortening breeding cycles and rapidly creating desirable traits [4] - AI-driven protein design technologies are emerging, allowing the creation of novel proteins with specific functions, which can lead to breakthroughs in disease resistance and environmental monitoring [4] Group 3: AI-Assisted Crop Design Framework - The review introduces an "AI-assisted crop design" model that integrates and analyzes multimodal big data from genomics, phenomics, environment, and management practices [19] - Breeders can set specific improvement goals, such as yield enhancement or stress resistance, while AI generates optimized breeding plans through deep learning and knowledge reasoning [19] Group 4: Challenges and Future Directions - The article discusses the challenges and future directions for the application of new technologies, highlighting the need for high-quality, standardized data for training AI models [21] - Regulatory policies for genome-edited crops are evolving towards more scientific and simplified frameworks, creating favorable conditions for the widespread application of new technologies [21]