Results 61 to 70 of about 30,777 (218)

Quantum‐Like Dynamics in Whole‐Brain Models of the Human Connectome

Advanced Science, EarlyView.
Quantum‐like dynamics in the human brain. The level of quantum‐like behavior in a non‐quantum system of coupled oscillators is regulated by the spectral gap of the coupling graph. Whole‐brain modelling using QL fits the empirical data significantly better and has a lower model‐derived energy cost than the non‐QL model.
Gustavo Deco, Yonatan Sanz Perl, Natasha Greenstein, Shamil Chandaria, Gregory Scholes, Morten L. Kringelbach   +5 more
wiley   +1 more source

A New Fixed Point Result of Perov Type and Its Application to a Semilinear Operator System

Mathematics, 2019
In this paper, we present a new generalization of the Perov fixed point theorem on vector-valued metric space. Moreover, to show the significance of our result, we present both a nontrivial comparative example and an application to a kind of semilinear ...
Ishak Altun, Nawab Hussain, Muhammad Qasim, Hamed H. Al-Sulami   +3 more
doaj   +1 more source

Exceptional Antimodes in Multi‐Drive Cavity Magnonics

Advanced Electronic Materials, EarlyView.
Driven‐dissipative cavity‐magnonics provides a flexible platform for engineering non‐Hermitian physics such as exceptional points. Here, using a four‐port, three‐mode system with controllable microwave interference, antimodes and coherent perfect extinction (CPE) are realized, enabling active tuning to antimode exceptional points.
Mawgan A. Smith, Ryan D. McKenzie, Alban Joseph, Robert L. Stamps, Rair Macêdo   +4 more
wiley   +1 more source

Electric Field‐Induced Hole‐ and Electron‐Type Flat Bands in Twisted Double Bilayer Graphene

Advanced Electronic Materials, EarlyView.
The electronic structure of twisted double bilayer graphene is visualized using angle‐resolved photoemission spectroscopy with micrometer spatial resolution at twists of 3.1∘$^\circ$ and 6.0∘$^\circ$ as a function of gate voltage. Tunable hybridization effects and flat band formation occurs between valence and conduction band states due to a finite ...
Zhihao Jiang, Dongkyu Lee, Alfred J. H. Jones, Youngju Park, Paulina Majchrzak, Kimberly Hsieh, Chakradhar Sahoo, Thomas S. Nielsen, Kenji Watanabe, Takashi Taniguchi, Philip Hofmann, Yong P. Chen, Jeil Jung, Søren Ulstrup   +13 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

A regression and comparative study of United States and South African yield curves using principal component analysis

South African Journal of Economic and Management Sciences, 2018
Volatile markets and economic environments can significantly distort the shape and smoothness of yield curve movements. This study explores the influence of movements in United States interest rates on South African interest rates.
Kavir Patel, Ashfaaq Mohamed, Gary W. van Vuuren   +2 more
doaj   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Advanced Intelligent Discovery, EarlyView.
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah, Muhammad Zulqarnain Zeb, Muhammad Husnain Zeb, Babar Shabbir   +3 more
wiley   +1 more source

New approach to finding eigenvectors for repeated eigenvalues of a matrix

Sistemnì Doslìdženâ ta Informacìjnì Tehnologìï
An efficient method of calculating eigenvectors for multiple eigenvalues of a matrix is proposed. This method is based on a formalized transformation of the problem of solving degenerate systems of equations into a regular problem by “repairing” their ...
Anatolii Petrenko
doaj   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source