Patterns of Ring Currents in Conjugated Molecules:  A Few-Electron Model Based on Orbital Contributions

2001 ◽  
Vol 105 (41) ◽  
pp. 9553-9562 ◽  
Author(s):  
E. Steiner ◽  
P. W. Fowler
Keyword(s):  
Electron Model ◽  
Conjugated Molecules ◽  
Model Based ◽  
Ring Currents

scholarly journals Inelastic O+H collisions and the O I 777 nm solar centre-to-limb variation

2018 ◽  
Vol 616 ◽  
pp. A89 ◽  
Author(s):  
A. M. Amarsi ◽  
P. S. Barklem ◽  
M. Asplund ◽  
R. Collet ◽  
O. Zatsarinny
Keyword(s):  
Impulse Approximation ◽  
Rate Coefficients ◽  
Line Formation ◽  
Linear Combinations ◽  
Electron Model ◽  
Atomic Orbitals ◽  
Model Based ◽  
Free Electron Model ◽  
Collisional Rate Coefficients ◽  
Collisional Rate

The O I 777 nm triplet is a key diagnostic of oxygen abundances in the atmospheres of FGK-type stars; however, it is sensitive to departures from local thermodynamic equilibrium (LTE). The accuracy of non-LTE line formation calculations has hitherto been limited by errors in the inelastic O+H collisional rate coefficients; several recent studies have used the Drawin recipe, albeit with a correction factor SH that is calibrated to the solar centre-to-limb variation of the triplet. We present a new model oxygen atom that incorporates inelastic O+H collisional rate coefficients using an asymptotic two-electron model based on linear combinations of atomic orbitals, combined with a free electron model based on the impulse approximation. Using a 3D hydrodynamic STAGGER model solar atmosphere and 3D non-LTE line formation calculations, we demonstrate that this physically motivated approach is able to reproduce the solar centre-to-limb variation of the triplet to 0.02 dex, without any calibration of the inelastic collisional rate coefficients or other free parameters. We infer log ϵO = 8.69 ± 0.03 from the triplet alone, strengthening the case for a low solar oxygen abundance.

Molecular orbital perturbation theory II. Charge displacement and stabilization in conjugated molecules

1955 ◽  
Vol 233 (1193) ◽  
pp. 233-241 ◽  
Keyword(s):  
Wave Function ◽  
Molecular Orbital ◽  
Excited States ◽  
Electron Interaction ◽  
Unperturbed System ◽  
Electron Model ◽  
Conjugated Molecules ◽  
Orbital Perturbation ◽  
Self Consistent ◽  
Charge Displacement

This paper develops a method of investigating changes in the electronic structure of a molecule caused by a perturbation such as a substituent or the field of another particle. The method is based on a Hamiltonian including electron interaction. The total wave function for the perturbed molecule is written as a linear combination of approximate self-consistent molecular orbital functions for the ground and excited states of the unperturbed system . First- and second-order expressions for changes in energy and charge distribution are given which are generalizations of results previously obtained using an independent-electron model.

Molecular orbital perturbation theory II. Charge displacement and stabilization in conjugated molecules

1955 ◽  
Vol 233 (1193) ◽  
pp. 241-247 ◽  
Keyword(s):  
Perturbation Theory ◽  
Charge Distribution ◽  
Lone Pair ◽  
Electron Interaction ◽  
Electron Model ◽  
Conjugated Molecules ◽  
Orbital Perturbation ◽  
Self Consistent ◽  
Charge Displacement ◽  
Lone Pair Electrons

The general perturbation method outlined in part I is used to study energy and charge distribution changes caused by inductive and electromeric-type perturbations in conjugated molecules. Self-consistent orbitals for the unperturbed systems are found by approximating electron interaction integrals and are used as a basis for the perturbed systems. It is found that the change of charge distribution caused by an inductive substituent in benzene is similar to that predicted by the independent-electron model. Second-order perturbation theory is used to discuss electromeric displacements in which lone pair electrons take part in conjugation.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Model-Based Software Testing and Analysis with C#

2007 ◽  
Author(s):  
Jonathan Jacky ◽  
Margus Veanes ◽  
Colin Campbell ◽  
Wolfram Schulte
Keyword(s):  
Software Testing ◽  
Model Based
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