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Physical aspects of f(R,G_(μν)T^(μν))modified gravity theories

Chinese Physics C 2023年第10期47卷 237-248页

作者： Mihai Marciu dana maria ioan Faculty of Physics University of Bucharest405 AtomistilorPOB MG-11RO-077125Bucharest-MagureleRomania

The paper extends basic Einstein–Hilbert action by incorporating an invariant derived from a specific contraction between the Einstein tensor and energy momentum *** represents a non–minimal coupling between the space–time geometry and matter *** fundamental Einstein–Hilbert action is extended by considering a generic function f(R,GμνTμν).This function is subsequently dissected into its primary components:a geometric part reliant on the scalar curvature,and another segment encapsulating the interaction between geometry and matter *** cosmological models are formulated at the backdrop of these dynamics,rooted in specific associations between the matter energy-momentum tensor and Einstein *** deriving the consequent field equations,the cosmological model's physical implications are explored through a dynamical system analysis applied to diverse coupling *** exploration reveals that the current model aligns with various phases in the Universe's evolution,potentially elucidating several late-time historical epochs.

关键词： dark energy cosmology modified gravity theories

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Physical aspects of f(R,GμvTμv) modified gravity theories

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Chinese Physics C 2023年第10期47卷 241-252页

作者： Mihai Marciu dana maria ioan Faculty of Physics University of Bucharest

The paper extends basic Einstein-Hilbert action by incorporating an invariant derived from a specific contraction between the Einstein tensor and energy momentum *** represents a non-minimal coupling between the space-time geometry and matter *** fundamental Einstein-Hilbert action is extended by considering a generic function f(R,GμvTμv).This function is subsequently dissected into its primary components:a geometric part reliant on the scalar curvature,and another segment encapsulating the interaction between geometry and matter *** cosmological models are formulated at the backdrop of these dynamics,rooted in specific associations between the matter energy-momentum tensor and Einstein *** deriving the consequent field equations,the cosmological model’s physical implications are explored through a dynamical system analysis applied to diverse coupling *** exploration reveals that the current model aligns with various phases in the Universe’s evolution,potentially elucidating several late-time historical epochs.

关键词： dark energy cosmology modified gravity theories

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Deep Feature Extraction and Feature Fusion for Bi-Temporal Satellite Image Classification

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Computers, Materials & Continua 2021年第1期66卷 373-388页

作者： Anju Asokan Bogdan Patrut dana Danciulescu D.Jude Hemanth Department of ECE Karunya Institute of Technology and SciencesCoimbatoreIndia Department of Computer Science Alexandru Ioan Cuza University of la?ila?iRomania Department of Computer Science University of CraiovoCraiovoRomania

Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change *** feature extraction is important for multispectral image classification and is evolving as an interesting research area in change ***,many deep learning framework based approaches do not consider both spatial and textural details into *** order to handle this issue,a Convolutional Neural Network(CNN)based multi-feature extraction and fusion is introduced which considers both spatial and textural *** method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural *** the fused image is classified into change and unchanged *** presence of mixed pixels in the bitemporal satellite images affect the classification accuracy due to the misclassification *** proposed method was compared with six state-of-theart change detection methods and *** main highlight of this method is that by taking into account the spatio-spectral and textural information in the input channels,the mixed pixel problem is *** indicate the effectiveness of this method and demonstrate that it possesses low misclassification errors,higher overall accuracy and kappa coefficient.

关键词： Remote sensing convolutional neural network deep learning multispectral bitemporal change detection

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