Results 181 to 190 of about 5,189 (258)

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

ASAS-NANP SYMPOSIUM: mathematical modeling in animal nutrition: the importance of animal science flight simulators to enhance the competitiveness and sustainability of livestock production. [PDF]

J Anim Sci
Menendez Iii HM, Chang Y, Palmer E, La Manna F, Moreno ERV, Parsons I, Turner BL, Moradi Rekabdarkolaee H, Husmann AL, Tedeschi LO.   +9 more
europepmc   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Artificial Intelligence-Based Decision Support System for UAV Control in a Simulated Environment. [PDF]

Sensors (Basel)
Sujecki P, Frąszczak D.
europepmc   +1 more source

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai, Yefeng Yang, Tao Huang, Boyang Li   +3 more
wiley   +1 more source

Simulation of aircraft flights with turbulence and icing conditions. [PDF]

Sci Data
Dampeyrou C, Ghienne M, Tschannen V, Dion JL.   +3 more
europepmc   +1 more source

Magnetic Field Control Using an Electromagnetic Actuation System with Combined Air‐Core and Metal‐Core Coils

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
An electromagnetic manipulation system enhances magnetic field strength in the Z‐direction for 3D control of microrobots and nanoparticles. Featuring eight metal‐core coils and two air‐core coils arranged hemispherically, it ensures unimpeded workspace access and integrates imaging tools.
Nader Latifi Gharamaleki, Dong‐in Kim, Gyudong Lee, Jin‐Young Kim, Hongsoo Choi   +4 more
wiley   +1 more source

To assure aviation safety: the pilot fatigue detection based on short-term multimodal physiological signals. [PDF]

Front Hum Neurosci
Chen K, Liu J, Zhu J, Xu Y, Zhang L, Gao Z.   +5 more
europepmc   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Multimodal feedback enhances human belief updating performance and reduces cognitive load in urban drone operation. [PDF]

Front Robot AI
Sun B, Wu J, You H, Du J.
europepmc   +1 more source