Results 171 to 180 of about 112,373 (246)

Pattern‐Aware Intelligence Enables Nondestructive, Rapid Quantification of High‐Aspect‐Ratio Silicon Etching

Advanced Science, EarlyView.
Pattern‐dependent etching is converted into a physical prior for intelligent reconstruction of high‐aspect‐ratio silicon structures. Combining YOLO‐Pose feature extraction with a topography network, the framework retrieves depth, sidewall angle, and scallop texture from minimal destructive observations, enabling accurate cross‐scale metrology and near ...
Shuyan He, Lang Chen, Bo Wen, Yufeng Jin, Wei Wang   +4 more
wiley   +1 more source

Inversion of kiwifruit canopy nitrogen using UAV multispectral technology and ensemble learning. [PDF]

Front Plant Sci
Zhou B, Wang Y, Zhang J, Bai X, Tian M, Guo R.   +5 more
europepmc   +1 more source

A Panel of Circulating Exosomal sncRNAs Associated With Lung Cancer Risk up to 10 Years in Advance

Advanced Science, EarlyView.
Lung cancer is often diagnosed too late, and current screening overlooks many people at risk. In a long‐term study of smokers, a panel of small non‐coding RNAs carried in blood exosomes signals elevated lung cancer risk up to ten years before diagnosis, pointing toward a blood‐based tool for earlier risk detection.
Zhuokun Feng, Masaki Nasu, Ayman Abdul‐Ghani, Yu Chen, Shaoqiu Chen, Isam Mohd Ibrahim, Hanqiu Zhang, Lauren Higa, Zitong Gao, Hua Yang, Raphael Bueno, Loïc Le Marchand, Youping Deng   +12 more
wiley   +1 more source

MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning. [PDF]

Brief Bioinform
Xian L, Zou Q, Qi R, Niu M, Wang Y.
europepmc   +1 more source

Unveiling Mechanisms of SEI Formation and Sodium Loss in Sodium Batteries via Interface Reactor Sampling

Advanced Science, EarlyView.
An “Interface Reactor” strategy boosts simulation stability by 2–3 orders of magnitude, enabling stable 100 ns molecular dynamics of electrode‐electrolyte interfaces. Distinct SEI formation mechanisms are revealed: mixed co‐formation in carbonates versus surface‐energy‐controlled NaF crystallization in ethers. Metadynamics simulations further elucidate
Zhoulin Liu, Ziliang Wang, Zherui Chen, Jianchun Sha, Fengzijun Pan, Pingyang Zhang, Yinghe Zhang   +6 more
wiley   +1 more source

Improved prediction of childhood anemia using hybrid ensemble learning and dual-level explainability. [PDF]

J Public Health Res
Kukkar A, Loibor JM, Cifci MA, Barman MP, Hussain S, Gaftandzhieva S, Gaftandzhieva S, Doneva R.   +7 more
europepmc   +1 more source

Prognostic Power of Ensemble Learning in Colorectal Cancer with Peritoneal Metastasis: A Multi-Institutional Analysis. [PDF]

Bioengineering (Basel)
Bamba Y, Itabashi M, Kobayashi H, Kotake K, Kawasaki M, Kanemitsu Y, Kinugasa Y, Ueno H, Maeda K, Suto T, Funahashi K, Ozawa H, Koyama F, Noura S, Ishida H, Ohue M, Kiyomatsu T, Ishihara S, Koda K, Baba H, Kawada K, Hashiguchi Y, Goi T, Toiyama Y, Tomita N, Sunami E, Akagi Y, Watanabe J, Hakamada K, Nakayama G, Sugihara K, Ajioka Y.   +31 more
europepmc   +1 more source

Trace Li Pre‐Doping of Activated Carbon Cathode Enabling Accelerated Li‐Ion Transport for High‐Power Lithium‐Ion Capacitors

Advanced Science, EarlyView.
Trace‐level Li pre‐doping on activated carbon cathodes is demonstrated to significantly enhance Li‐ion transport and high‐rate performance in lithium‐ion capacitors. An ultrathin Li2CO3 interfacial layer, formed through controlled thermal conversion, facilitates rapid ion transport without pore blockage.
Kyungmin Do, Jihyeon Ryu, Dong Gyun Im, Seong‐Yong Kim, Tae‐Hyun Kwon, Byeong Guk Kim, Seojin Jeon, Woosun Jang, Myoung Joon Hwang, Ki‐Hun Nam, Seung Yol Jeong, Ick‐Jun Kim, Seungwook Eom, Jaeik Hyun, Sunhye Yang   +14 more
wiley   +1 more source

Ensemble Learning to Categorize the Bethesda System of Reporting of Cervical Cytology. [PDF]

J Cytol
Dey P.
europepmc   +1 more source

Biochemically Constrained Multi‐Omics Integration Reveals Protein–Metabolite Dependencies Across Diseases

Advanced Science, EarlyView.
ProMetNet introduces a biologically constrained deep learning framework for proteo‐metabolomic integration by embedding Reactome‐derived pathway topology into neural networks. It captures non‐linear molecular dependencies and pathway‐level metabolic reorganization, enabling interpretable discrimination.
Minghui Zhao, Na Zhou, Ruotong Liu, Xiaofei Li, Jian Li, Fuzhong Xue, Qingzhen Hou   +6 more
wiley   +1 more source