Results 191 to 200 of about 3,889,100 (366)

Projective differential geometry of correspondences between two spaces. VI [PDF]

, 1952
Eduard Čech
openalex   +1 more source

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing – Part I: Surface, Flowability, and Optical Properties of SiC, Si, and Si3N4 Coated Metal Powders

Advanced Engineering Materials, EarlyView.
Herein, silicon‐based nanoparticle coatings on X2CrNiMo17‐12‐2 metal powder are presented. The coating process scale, process parameters, nanoparticle size (65–200 nm) as well as the coating amount are discussed regarding powder properties. The surface roughness affects the flowability, while reflectance depends on the coating material and surface ...
Arne Lüddecke, Nick Hantke, Harald Zetzener, Jan T. Sehrt, Arno Kwade   +4 more
wiley   +1 more source

Multiscale Differential Geometry Learning for Protein Flexibility Analysis. [PDF]

J Comput Chem
Feng H, Zhao JY, Wei GW.
europepmc   +1 more source

Projective differential geometry of correspondences between two spaces. VII [PDF]

, 1953
Eduard Čech
openalex   +1 more source

Enhanced Fog Water Harvesting on Superhydrophobic Steel Meshes

Advanced Engineering Materials, EarlyView.
Fog harvesting using mesh designs offers a sustainable solution to water scarcity. This study highlights key considerations for fog harvesting research and develops a methodology for a standardized protocol reflecting fog characteristics and environmental conditions.
Pegah Sartipizadeh, Mohammad Reza Mohammadizadeh, Carlo Antonini, Raziyeh Akbari   +3 more
wiley   +1 more source

Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data. [PDF]

Comput Biol Med
Feng H, Cottrell S, Hozumi Y, Wei GW.
europepmc   +1 more source

Book Review: Differential line geometry [PDF]

, 1955
Alice T. Schafer
openalex   +1 more source

Functional-differential geometry [PDF]

Transactions of the American Mathematical Society, 1912
openaire   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
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, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

On the differential geometry of frame bundles [PDF]

, 1966
Tanjiro Okubo
openalex   +1 more source