Results 161 to 170 of about 2,814,666 (297)

Thought Confidence: Consumer Emotions and the Confidence Premise Hypothesis [PDF]

Management Studies, 2016
openaire   +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

ANKS1B in the Nucleus Accumbens Controls Escalated Cocaine Self‐Administration via Regulating CBP‐FoxO3 Complex

Advanced Science, EarlyView.
ANKS1B in the nucleus accumbens plays a critical role in the transition from controlled to escalated cocaine intake. Mechanistically, ANKS1B interacts with CBP to epigenetically suppress FoxO3 through H3K27 acetylation. The ANKS1B‐CBP‐FoxO3 signaling cascade presents a novel theraputic target for the treatment of cocaine addiction.
Liping Yang, Xiaoxuan Wu, Xuan Chen, Chao Peng, Zihang Li, Shumin Gao, Shiqiu Meng, Jing Dong, Dong Wu, Liying Lv, Ying Han, Yanxue Xue, Lin Lu, Jie Shi, Jianfeng Liu, Yan Sun   +15 more
wiley   +1 more source

Human‐Centered Innovation: Precision Nutrition and the Future of Food

Advanced Science, EarlyView.
ABSTRACT Nutritional science is moving beyond one‐size‐fits‐all recommendations toward more precise and personalized approaches, yet its implementation pathway remains unclear. This paper proposes a human‐centered precision nutrition framework that highlights the central roles of artificial intelligence (AI), food innovation, and human agency.
Shanshan Zhang, Yonggan Sun, Jiaqi Jiang, Shaoping Nie   +3 more
wiley   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +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

Integrating Machine Learning With Constant‐Potential Simulation to Unravel Charge‐Transfer Mechanisms in Electrochemical Nitrogen Fixation

Advanced Science, EarlyView.
Integrating interpretable machine learning with the fixed‐potential method reveals a novel mechanism: the catalytic activity of the electrochemical nitrogen reduction reaction is governed by partial charge transfer, induced by variations in the intermediate potential of zero charge under constant potential.
Yufei Xue, Dushuo Feng, Yuefei Zhang, Yang Zhang, Yalong Jiao, Aijun Du, Guoping Gao   +6 more
wiley   +1 more source

US Consumer Confidence Responses to Shocks: COVID19 Scenarios and Recovery.Under review: REHO Special Issue

, 2020
Elmassah S, A.Hassanein E.
europepmc   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source