Results 211 to 220 of about 4,537,864 (317)
Optimization of traction power conservation and energy efficiency in agricultural mobile robots using the TECS algorithm. [PDF]
Koca YB, Gökçe B, Aslan Y.
europepmc +1 more source
Bistable Mechanisms 3D Printing for Mechanically Programmable Vibration Control
This work introduces a 3D‐printed bistable mechanism integrated into tuned mass dampers (TMDs) for mechanically adaptive passive vibration suppression. Through optimized geometry, the bistable design provides adaptable vibration reduction across a broad range of scenarios, achieving effective vibration mitigation without complex controls or external ...
Ali Zolfagharian+4 more
wiley +1 more source
An Increase in Dietary Net Energy Concentration Affects Nutrient Digestibility and Noxious Gas Emissions and Reveals a Better Growth Rate in Growing-Finishing Pigs. [PDF]
Kolawole UK, Kim IH.
europepmc +1 more source
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley +1 more source
Multireference diffusion Monte Carlo reaches 2D materials. [PDF]
Spanedda N+3 more
europepmc +1 more source
This study demonstrates a novel, additive manufacturing approach to produce complex, porous tungsten carbide structures using water‐based direct ink writing/robocasting. Leveraging a modified commercial printer and heat treatment, the process yields lightweight, electrically conductive 3D architectures capable of supporting a mechanical load.
James Bentley Bevis+3 more
wiley +1 more source
Low carbon optimization for wind integrated power systems with carbon capture and energy storage under carbon pricing. [PDF]
Meng Q+4 more
europepmc +1 more source
Liveweight and Milk-Energy Yield at Various Feeding Intensities
S.D. Musgrave, W.L. Gaines
openalex +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani+4 more
wiley +1 more source