Results 181 to 190 of about 520,977 (330)

Designing Memristive Materials for Artificial Dynamic Intelligence

Advanced Intelligent Discovery, EarlyView.
Key characteristics required of memristors for realizing next‐generation computing, along with modeling approaches employed to analyze their underlying mechanisms. These modeling techniques span from the atomic scale to the array scale and cover temporal scales ranging from picoseconds to microseconds. Hardware architectures inspired by neural networks
Youngmin Kim, Ho Won Jang
wiley   +1 more source

A Cohomology Theory for Planar Trivalent Graphs with Perfect Matchings [PDF]

, 2018
Scott Baldridge
openalex   +1 more source

Classification of LRS Bianchi-I spacetime in context of f(T) gravity via its self-similar symmetry. [PDF]

PLoS One
Sidaoui R, Alfedeel AHA, Khan J, Alsharafi M, Hassan EI, Aldwoah K.   +5 more
europepmc   +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

Regularity of the edge ideals of perfect [ν,h]-ary trees and some unicyclic graphs

Fatima Tul Zahra, Muhammad Ishaq, Sarah Aljohani   +2 more
openalex   +1 more source

GFSeeker: a splicing-graph-based approach for accurate gene fusion detection from long-read RNA sequencing data. [PDF]

Brief Bioinform
Wang B, Hu H, Gao R, Wang G, Jiang T.
europepmc   +1 more source

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

Domination perfect graphs

, 1990
Vadim Zverovich
openalex   +1 more source

SynKit: A Graph-Based Python Framework for Rule-Based Reaction Modeling and Analysis. [PDF]

J Chem Inf Model
Phan TL, González Laffitte ME, Weinbauer K, Merkle D, Andersen JL, Fagerberg R, Gatter T, Stadler PF.   +7 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source