Results 191 to 200 of about 2,907,251 (335)
Applications of visualization technology in the structural sciences. [PDF]
Eng ET, Valdez NR.
europepmc +1 more source
The unrolling of the peltate leaves in Syngonium podophyllum is analyzed and quantified (left‐hand side to center). These measurements serve to verify a mathematical model for leaf unrolling based on the model used in Schmidt (2007). An additional formula for obtaining a layer mismatch from a prescribed radius is derived.
Michelle Modert +4 more
wiley +1 more source
Optical tweezers with light aligned along the particle's trajectory enable playing tennis with light rackets. [PDF]
Droby A +5 more
europepmc +1 more source
Conveying Principles of Finite Strain with Standard Graphics Software
Marcia Bjørnerud
openalex +1 more source
ErB4 and NdB4 nanostructured powders are produced by mechanochemical synthesis. 5 h mechanical alloying and 4 M HCl acid leaching are used in the production. ErB4 and NdB4 powders exhibit maximum magnetization of 0.4726 emu g−1 accompanied with an antiferromagnetic‐to‐paramagnetic phase transition at about TN = 18 K and 0.132 emu g−1 with a maximum at ...
Burçak Boztemur +5 more
wiley +1 more source
Research on Multi-Sensor Data Fusion Based Real-Scene 3D Reconstruction and Digital Twin Visualization Methodology for Coal Mine Tunnels. [PDF]
Zhu H, Jin J, Zhao S.
europepmc +1 more source
Hydrostatic bearings excel in high‐precision applications, but their performance hinges on a continuous external supply. This study evaluates various material combinations for sliding surfaces to mitigate damage during supply failures or misalignment and to discover the most effective materials identified for enhancing the reliability and efficiency of
Michal Michalec +6 more
wiley +1 more source
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
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi +4 more
wiley +1 more source

