Results 161 to 170 of about 81,104 (237)

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

Advanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz, Gisya Abdi, Andrzej Sławek, Marlena Gryl, Konrad Szaciłowski   +4 more
wiley   +1 more source

Research on characteristic recognition and quantification of internal powder residue in LPBF porous structure based on image processing. [PDF]

Sci Rep
Shi W, Cao S, Hou Q, Zhang X, Li J, Usman HM.   +5 more
europepmc   +1 more source

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

Advanced Intelligent Discovery, EarlyView.
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri, Leala Carbonneau, Eric M. Roberts, Alison W. Roberts, Candace H. Haigler, Alexey V. Gulyuk, Yaroslava G. Yingling   +6 more
wiley   +1 more source

Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Advanced Intelligent Discovery, EarlyView.
In this work, the Doubao large language model (LLM) is involved in the formula derivation processes for Hubbard U determination regarding the second‐order perturbations of the chemical potential. The core ML tool is optimized for physical domain knowledge, which is not limited to parameter prediction but rather serves as an interactive physical theory ...
Mingzi Sun, Tong Wu, Baian Chen, Zikang Li, Qiuyang Lu, Lu Lu, Hon Ho Wong, Cheuk Hei Chan, Bolong Huang   +8 more
wiley   +1 more source

Matrix Quantum Mechanics and Entanglement Entropy: A Review. [PDF]

Entropy (Basel)
Fliss JR, Frenkel A.
europepmc   +1 more source

On submanifolds of a Riemannian manifold with a semi-symmetric non-metric connection

, 2010
Under investigation were the submanifolds of a Riemannian manifold with a semi-symmetric non-metric connection. We have proved that the induced connection is also a semi-symmetric non-metric connection. The totaly geodesicness and the minimality of a submanifold of a Riemannian manifold with a semi-symmetric non-metric connection were also considered ...
openaire   +2 more sources

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

SegClarity: An Attribution-Based XAI Workflow for Evaluating Historical Document Layout Models. [PDF]

J Imaging
Brini I, Rahal N, Mehri M, Ingold R, Essoukri Ben Amara N.   +4 more
europepmc   +1 more source

On A Semi-symmetric Non-metric Connection In An Indefinite Para Sasakian Manifold

Journal of Mathematics and Computer Science, 2014
S.k. Pandey, G. Pandey, K. Tiwari, R.n. Singh   +3 more
openaire   +2 more sources

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source