University of Cambridge（剑桥大学）

From physical surfaces to human-centric heat stress: LST and UTCI heat mapping reveals nonlinear effects of urban morphology

超越地表温度：可解释的空间机器学习揭示城市形态对以人类为中心的热压力的影响

Yuan Wang, Shengao Yi, Xiaojiang Li, Pengyuan Liu, Zhiwei Yang, Ronita Bardhan, Rudi Stouffs

机构 * Department of Architecture, National University of Singapore, Singapore 117566, Singapore ； Cambridge Centre for Advanced Research ； Sustainable Design Group, Department of Architecture, University of Cambridge, Cambridge, United Kingdom ； Department of City ； Regional Planning, University of Pennsylvania, Philadelphia, PA 19104, USA ； Urban Analytics Subject Group, Urban Studies \& Social Policy Division, University of Glasgow ； Laboratory for Earth Surface Processes, Ministry of Education, College of Urban ； Environmental Sciences, Peking University, Beijing 100871, China 纠错

AI总结 本文通过比较地表温度与通用热气候指数，揭示城市形态对人类热压力的影响，采用可解释的机器学习方法分析两者在空间分布和机制上的差异。

Comments Accepted manuscript. The final published version is available at https://doi.org/10.1016/j.scs.2026.107659

A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction

用于脂质纳米颗粒转染效率预测的机器学习基准框架

Asal Mehradfar, Mohammad Shahab Sepehri, Jose Miguel Hernandez-Lobato, Glen S. Kwon, Mahdi Soltanolkotabi, Salman Avestimehr, Morteza Rasoulianboroujeni

机构 * Department of Electrical and Computer Engineering, University of Southern California（电气与计算机工程系，南加州大学） ； University of Cambridge（剑桥大学） ； School of Pharmacy, University of Wisconsin-Madison（威斯康星大学麦迪逊分校药学院） ； University of Wisconsin-Madison（威斯康星大学麦迪逊分校） ； East Tennessee State University（东田纳西州立大学） 纠错

AI总结 研究针对脂质纳米颗粒转染效率预测，提出机器学习基准框架，系统测试不同分子表示与机器学习架构，用特定数据集评估模型，显示利用显式分子子结构编码的模型准确性最高，为相关预测模型发展建立基线。

Comments Published in Communications AI & Computing (Nature Portfolio), 2026

Journal ref Commun. AI Comput. 1, 2 (2026)