Results 231 to 240 of about 6,410,351 (366)

Multi-UAV forest area inspection path planning based on concave polygon region decomposition. [PDF]

Sci Rep
Kang B, Wang C, Su Y, Zeng J.
europepmc   +1 more source

A Continuous Energy Ising Machine Leveraging Difference-of-Convex Programming [PDF]

Dhrubojyoti Banerjee, Santanu Mahapatra, Kunal N. Chaudhury   +2 more
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

APDCA: An accurate and effective method for predicting associations between RBPs and AS-events during epithelial-mesenchymal transition. [PDF]

PLoS Comput Biol
He Y, Bai ZJ, Ching WK, Zou Q, Qiu Y.
europepmc   +1 more source

Linear Rate Convergence of the Alternating Direction Method of Multipliers for Convex Composite Programming

Mathematics of Operations Research, 2017
Deren Han, Defeng Sun, Liwei Zhang
semanticscholar   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

A Two-Step SD/SOCP-GTRS Method for Improved RSS-Based Localization in Wireless Sensor Networks. [PDF]

Sensors (Basel)
Chang S, Li L.
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Enhanced twin delayed DDPG with prioritized experience replay and Noisy Nets for regional economic dispatch. [PDF]

Sci Rep
Xu C, Hayashi N, Inuiguchi M, Raymond WJK, Mokhlis H, Illias HA.   +5 more
europepmc   +1 more source