Erratum: Topological constraints on the charge distributions for the Thomson problem [Phys. Rev. B74, 052102 (2006)]

2007 ◽  
Vol 75 (9) ◽  
Author(s):  
Alfredo Iorio ◽  
Siddhartha Sen
Keyword(s):  
Topological Constraints ◽  
Charge Distributions ◽  
Thomson Problem

201. Electrostatic Charge Distributions on Airborne Microorganisms

2000 ◽  
Author(s):  
G. Mainelis ◽  
K. Willeke ◽  
S. Grinshpun ◽  
T. Reponen ◽  
S. Trakumas ◽  
...  
Keyword(s):  
Electrostatic Charge ◽  
Airborne Microorganisms ◽  
Charge Distributions

Theoretical Computations on the Pyrolysis of Alkyl (dithio)acetates

2020 ◽  
Vol 17 (3) ◽  
pp. 224-233
Author(s):  
Xun Zhu ◽  
Chen Jian ◽  
Xiuqin Zhou ◽  
Abdullah M. Asiri ◽  
Khalid A. Alamry ◽  
...  
Keyword(s):  
Transition States ◽  
Membered Ring ◽  
Pyrolysis Process ◽  
Alkyl Esters ◽  
Charge Distributions ◽  
Theoretical Analyses

The pyrolysis of methyl alkyl esters I to III and dithioesters IV to VI were theoretically calculated. All possible pyrolysis paths were considered. Both esters and dithioesters presented three potential paths via six-, four- and five-membered ring transition states, respectively. The calculation processes were calculated using MP2/6-31G(d) set. In-depth theoretical analyses were also presented, including NBO related analyses, synchronicities, and charge distributions, to reveal the detailed pyrolysis process.

Comparison of non-empirical and semiempirical SCF MO calculations of non-substituted dihydropyridines

1981 ◽  
Vol 46 (9) ◽  
pp. 2068-2075 ◽  
Author(s):  
Stanislav Böhm ◽  
Josef Kuthan
Keyword(s):  
Ab Initio ◽  
Mo Calculations ◽  
Charge Distributions ◽  
Characteristic Features

Results of ab initio MO calculations of the dihydropyridine molecules I-V are confronted with analogous CNDO/2 and MINDO/3 calculations. The molecular energies calculated by means of the 4-31 G base predict the 6pi-electron isomers I and II to be the most stable dihydropyridine forms in contrast to the STO-3G and CNDO/2 data preferring the 4pi-electron isomers III-V. The charge distributions calculated non-empirically and semiempirically show different characteristic features.

scholarly journals ModularBoost: an efficient network inference algorithm based on module decomposition

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinyu Li ◽  
Wei Zhang ◽  
Jianming Zhang ◽  
Guang Li
Keyword(s):  
Network Inference ◽  
Detection Methods ◽  
Inference Problem ◽  
Topological Constraints ◽  
Inference Algorithms ◽  
Module Detection ◽  
Series Expression ◽  
Gene Modules ◽  
Inference Methods ◽  
Complicated Task

Abstract Background Given expression data, gene regulatory network(GRN) inference approaches try to determine regulatory relations. However, current inference methods ignore the inherent topological characters of GRN to some extent, leading to structures that lack clear biological explanation. To increase the biophysical meanings of inferred networks, this study performed data-driven module detection before network inference. Gene modules were identified by decomposition-based methods. Results ICA-decomposition based module detection methods have been used to detect functional modules directly from transcriptomic data. Experiments about time-series expression, curated and scRNA-seq datasets suggested that the advantages of the proposed ModularBoost method over established methods, especially in the efficiency and accuracy. For scRNA-seq datasets, the ModularBoost method outperformed other candidate inference algorithms. Conclusions As a complicated task, GRN inference can be decomposed into several tasks of reduced complexity. Using identified gene modules as topological constraints, the initial inference problem can be accomplished by inferring intra-modular and inter-modular interactions respectively. Experimental outcomes suggest that the proposed ModularBoost method can improve the accuracy and efficiency of inference algorithms by introducing topological constraints.

scholarly journals Topological constraints in the reconnection of vortex braids

2021 ◽  
Vol 33 (5) ◽  
pp. 056101
Author(s):  
S. Candelaresi ◽  
G. Hornig ◽  
B. Podger ◽  
D. I. Pontin
Keyword(s):  
Topological Constraints
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