Results 201 to 210 of about 15,438 (311)

Foundations of Constraint Satisfaction

, 1993
Constraint satisfaction has become an important field in computer science. This technology is embedded in millions of pounds of software used by major companies.
Edward Tsang
core  

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

Advanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su, Yanda Zhu, Hao Peng, Manman Li, Xuzihan Zhang, Meixiao Wu, Siyuan Zhang, Yuhan Zeng, Jiwen Liao, Ming Luo, Hetaishan Huang, Yutong Wang, Sean Li, Wenxian Li   +13 more
wiley   +1 more source

A Context for Constraint Satisfaction Problem Formulation Selection

, 2008
: Much research effort has been applied to finding effective ways for solving constraint satisfaction problems. However, the most fundamental aspect of constraint satisfaction problem solving, problem formulation, has received much less attention.
James E. Borrett (contact, Colchester Co Sq, Edward P. K. Tsang   +2 more
core  

Exceptional CO2 Hydrogenation to Gasoline Enabled by GaZrOx/Ga‐ZSM‐5 Tandem Catalyst

Advanced Science, EarlyView.
A GaZrOx/Ga‐ZSM‐5 tandem catalyst achieves 17.2% CO2 conversion with 83.2% C5+ selectivity and >400 h stability relies on optimized O‐vacancies and Ga‐H coverage for CO2 hydrogenation. ABSTRACT CO2 hydrogenation to gasoline offers a promising route for addressing energy challenges and enabling the effective utilization of carbon resources. However, the
Wenhui Li, Yujing Sun, Bingyu Liu, Yaoyang Ni, Hong Yang, Xinwen Guo   +5 more
wiley   +1 more source

Joint optimization of task offloading and energy trading in edge-enabled smart grids using deep reinforcement learning. [PDF]

PLoS One
Xue R, Li B, He W, Wang Y.
europepmc   +1 more source

A Phase‐Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease‐Associated Protein Aggregation and Guides Suppressor Design

Advanced Science, EarlyView.
SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio, Wei Xuan Wilson Loo, Yan Shan Loo, Wen Xin Tan, Hui Xuan Lim, Huitao Liu, Chen Seng Ng   +6 more
wiley   +1 more source

SPADE: A Deep Learning Framework for Spatial Mapping and Quantitative Cell–Cell Interaction Inference

Advanced Science, EarlyView.
SPADE integrates spatial transcriptomics with single‐cell RNA sequencing by using cell–cell communications (CCC) as a guide for spatial mapping. It improves cell‐type localization, enhances sparse gene‐expression signals, and reveals CCC programs at single‐spot resolution.
Xinyi Li, Ning Zhang, Zijie Jin
wiley   +1 more source

A similarity-based predictive scheduling method for dynamic electric vehicle charging load management. [PDF]

Sci Rep
Firouzjah KG, Ghasemi J.
europepmc   +1 more source

Physics‐Informed Neural Network‐Enabled Forward Prediction and Inverse Design of Ring Origami

Advanced Science, EarlyView.
This work presents a KRT‐PINN framework that integrates Kirchhoff rod theory with physics‐informed neural networks for the forward prediction and inverse design of ring origami consisting of closed‐loop rods. The framework predicts stable states of segmented rings with prescribed natural‐curvature profiles and determines the natural‐curvature profiles ...
Luyuan Ning, Lu Lu, Sophie Leanza, Ruike Renee Zhao   +3 more
wiley   +1 more source

It Is What It Isn't: Introducing a Constraint-Based Approach to Structure Learning. [PDF]

Entropy (Basel)
Lundbak Olesen C, Mikuš N, Hansen M, Legrand N, Thestrup Waade P, Mathys C.   +5 more
europepmc   +1 more source