Results 111 to 120 of about 171,040 (210)

Forecasting Root Rot Disease through Predictive Microbial Functional Profiling

Advanced Science, EarlyView.
Predicting soil‐borne disease moves beyond observation with a framework that elevates microbial functional genes into reliable forecasting biomarkers. By coupling targeted qPCR assays for core stress‐response genes with machine learning, this method detects root rot risks in pre‐symptomatic soils with over 80% accuracy.
Chuan You, Peng Ren, Ying Guan, Kai Gong, Zhen Hua, Wuxian Zhou, Xinlan Mei, Yikui Wang, Xiaofang Wang, Yangchun Xu, Qirong Shen, Zhong Wei   +11 more
wiley   +1 more source

Design and Optimization of Full‐Stokes Hyperspectro‐Polarimetric Encoding Metasurfaces Based on Conditional Multi‐Task Deep Learning

Advanced Science, EarlyView.
A conditional multi‐task deep learning framework is developed for designing and optimizing Full‐Stokes Hyperspectro‐Polarimetric Encoding Metasurfaces (FHPEMs). This framework achieves joint spectro‐polarimetric learning and unified forward–inverse design.
Chenjie Gong, Haodong Shi, Qi Wang, Yingchao Li, Hongyu Sun, Jiayu Wang, Guanlin Li, Jian Zhang, Qiang Fu, Huilin Jiang   +9 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Machine‐Learning‐Guided Design of Incommensurate Antiferroelectrics via Field‐Driven Phase Engineering

Advanced Science, EarlyView.
The key to enhancing the energy storage performance of antiferroelectrics lies in regulating the phase transition and reverse phase transition. A phase‐field‐machine learning framework is employed to predict the energy storage performance of Pb‐based incommensurate antiferroelectrics with multi‐scale regulation strategy, thereby revealing the dynamic ...
Ke Xu, Xiaoming Shi, Zhaochen Xi, Shouzhe Dong, Changqing Guo, Rongzhen Gao, Letao Yang, Jing Wang, Di Zhou, Houbing Huang   +9 more
wiley   +1 more source

Prompt Engineering Accelerates the Data‐Driven Discovery of Photocatalysts via an LLM‐Based Model Ensemble Strategy

Advanced Science, EarlyView.
This work establishes a pipeline that transforms fragmented literature into a structured database for graphitic carbon nitride photocatalyst discovery. A prompt‐engineered, cross‐model large language model ensemble automates high‐fidelity extraction, enabling interpretable machine learning to identify dominant performance descriptors. These data‐driven
Dianyuan Li, Xichen Sun, Shaohua Sun, Runzhou Wang, Miaomiao Zhang, Meng Xiao, Yue Wang, Yuezhou Zhang   +7 more
wiley   +1 more source

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring

Advanced Science, EarlyView.
This study presents a semantic representation framework for clinically interpretable cardiac monitoring from contactless radio signals. It formulates radio semantic learning as an information‐bottleneck problem and approximates the objective via intra‐modal compression and cross‐modal alignment, structuring radio measurements into meaningful semantic ...
Jinbo Chen, Haoyu Wang, Guixin Xu, Yuqin Yuan, Xinmeng Cai, Zhengwu Wei, Dongheng Zhang, Qibin Sun, Tianwei Zhang, Cuntai Guan, Yan Chen   +10 more
wiley   +1 more source

Hyperparameter optimization to enhance the performance of deep learning models for the early detection of invasive turtles in Korea. [PDF]

Sci Rep
Baek JW, Kim JI, Mun MH, Kim CB.
europepmc   +1 more source

Recent Advances in Sustainable Single‐Atom Catalysts from Biomass and Solid Waste: Design, Synthesis and Applications

Advanced Science, EarlyView.
Biomass‐ and solid waste‐derived sustainable single‐atom catalysts (Sus‐SACs) provide a cost‐effective and renewable approach to catalyst design. This review summarizes precursor selection, including AI‐assisted screening, synthesis strategies with emphasis on ultrafast methods, and advanced characterization techniques.
Hongzhe He, Yuhang Qiu, Ruoqun Zhang, Sasha Yang, Jingwei Wang, Jianglong Yu, Lian Zhang, Ning Chen, Baiqian Dai   +8 more
wiley   +1 more source

Evolving spiking neural networks: the role of neuron models and encoding schemes in neuromorphic learning. [PDF]

Front Neurosci
Loyola-Jara B, Fernández-Rodríguez G, Baladron J.   +2 more
europepmc   +1 more source

Bioinspired Cold‐Laminated Ultrathin Hydrogels as a Broadly Adaptive Platform for Physiological Monitoring

Advanced Science, EarlyView.
A cold‐lamination strategy is introduced to fabricate ultrathin, nanomesh‐reinforced hydrogel bioelectronics with controlled thickness, tunable mechanics, and reversible adhesion. By mechanically interlocking a TPU nanomesh within a temperature‐responsive hydrogel, the platform enables robust epidermal and implantable cardiac interfaces, supporting ...
Hui Chen, Jian Zhou, Jianfei Xie, Lin Shi, Lu Wang, Yuanfan Yang, Yihao Guo, Jianhui Cao, Qingyun Fu, Yongqing Fu, Huigao Duan   +10 more
wiley   +1 more source