Results 141 to 150 of about 3,160,703 (293)

Underground Signals: The Ecological Power of Root Volatiles

Advanced Science, EarlyView.
Root volatiles mediate rhizosphere chemical communication, shaping interactions among plants, microbes, herbivores, and natural enemies. Their production, transport, and perception underpin ecological adaptation, multitrophic interactions, and plant resilience, offering opportunities for sustainable crop protection, resilient agroecosystems, and ...
Xueyao Cao, Nanli Song, François Verheggen, Meritxell Pérez‐Hedo, Andrea Clavijo McCormick, Antonino Cusumano, Arthur de Fouchier, Bing Wang, Chen‐Zhu Wang, Emmanuelle Jacquin‐Joly, Gary W. Felton, Geoff M. Gurr, Hao Guo, Jiancai Li, Jin Zhang, Li Chen, Manqun Wang, Michele Ricupero, Peng Han, Peng He, Qi Yan, Qingbo Tang, Qingsong Liu, Shiheng An, Xiangbo Kong, Xiaoling Sun, Xueming Ren, Yuanyuan Song, Yang Liu, Yanhui Guo, Yunhe Li, Xiao Sun   +31 more
wiley   +1 more source

Arabic text preprocessing for dynamic searchable symmetric encryption: An empirical evaluation and preprocessor selection framework. [PDF]

PLoS One
Al-Saleh KS.
europepmc   +1 more source

Pre‐Regenerative Endothelial Cells Empowered by Antler Blood Exosomes Orchestrate Peripheral Nerve Repair

Advanced Science, EarlyView.
The pre‐regenerative vascular niche (PVN) is essential for nerve repair, yet its endothelial blueprint remains unclear. We identified angiogenic ECs (AECs) as the dominant pre‐regenerative subset and found that antler blood–derived exosomes (AB‐EXO) promote repair via IMP3.
Jinsheng Huang, Qiang Chen, Aihua Jin, Xiaoqi Yang, Hui Zhou, Weiquan Gong, Jin Cheng, Jun Dai, Minjin Wang, Meihua Piao, Yuanyi Wang, Nan Zhou   +11 more
wiley   +1 more source

Enhancing student data privacy in virtual learning with blockchain and advanced encryption. [PDF]

PLoS One
Fan Y.
europepmc   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

BloomSec: Scalable and privacy-preserving searchable encryption for cloud environments. [PDF]

PLoS One
Khan AN, Naveed A, Mehmood A, Arora D, Khan AUR, Ali J.   +5 more
europepmc   +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

SENSH: a blockchain-based searchable encrypted data sharing scheme in smart healthcare. [PDF]

PeerJ Comput Sci
Luo S, Tan L, Hu T, Hu M.
europepmc   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source