Valency-Based Topological Descriptors and Structural Property of the Generalized Sierpiński Networks

In the fields of chemical graph theory, topological index is a type of a molecular descriptor that is calculated based on the graph of a chemical compound. Topological indices help us collect information about algebraic graphs and give us mathematical approach to understand the properties of algebraic structures. With the help of topological indices, we can guess the properties of chemical compounds without performing experiments in wet lab. ere are more than 148 topological indices in the literature, but none of them completely give all properties of under study compounds. Together, they do it to some extent; hence, there is always room to introduce new indices. In this paper, we present first and second reserve Zagreb indices and first reverse hyper-Zagreb indices, reverse GA index, and reverse atomic bond connectivity index for the crystallographic structure of molecules. We also present first and second reverse Zagreb polynomials and first and second reverse hyper-Zagreb polynomials for the crystallographic structure of molecules.

Section: Introductionmentioning

confidence: 99%

Reverse Zagreb and Reverse Hyper-Zagreb Indices for Crystallographic Structure of Molecules

Wang¹,

Chaudhry

Naseem

et al. 2020

“…where R represents the pixel comprehensive reflectivity, ω a represents the upward scattering rate corresponding to the anthropogenic aerosol, ω b represents the upward scattering rate of the background aerosol, ω m represents the upward scattering rate of the atmospheric molecule, ω bd represents the downward scattering rate corresponding to the background aerosol, and ω ad represents the downward scattering rate corresponding to the anthropogenic aerosol. It can be deemed that the background aerosol is horizontally uniform within a scale of tens of miles, and the factors of season and geographical location will affect the type of background aerosol [14][15][16][17]. Based on the above analysis, the function of aerosol turbidity β 1 + β 2 and β 3 , as well as the pixel comprehensive reflectivity R and the ground reflectivity, is obtained:…”

Section: Quantitative Remote Sensing Monitoring Algorithm Of Air Pollmentioning

confidence: 99%

Research on Quantitative Remote Sensing Monitoring Algorithm of Air Pollution Based on Artificial Intelligence

Liu

Jing

2020

When the current algorithm is used for quantitative remote sensing monitoring of air pollution, it takes a long time to monitor the air pollution data, and the obtained range coefficient is small. The error between the monitoring result and the actual result is large, and the monitoring efficiency is low, the monitoring range is small, and the monitoring accuracy rate is low. An artificial intelligence-based quantitative monitoring algorithm for air pollution is proposed. The basic theory of atmospheric radiation transmission is analyzed by atmospheric radiation transfer equation, Beer–Bouguer–Lambert law, parallel plane atmospheric radiation theory, atmospheric radiation transmission model, and electromagnetic radiation transmission model. Quantitative remote sensing monitoring of air pollution provides relevant information. The simultaneous equations are constructed on the basis of multiband satellite remote sensing data through pixel information, and the aerosol turbidity of the atmosphere is calculated by the equation decomposition of the pixel information. The quantitative remote sensing monitoring of air pollution is realized according to the calculated aerosol turbidity. The experimental results show that the proposed algorithm has high monitoring efficiency, wide monitoring range, and high monitoring accuracy.

“…Using MM-PBSA, the free binding energies can be calculated after the solute molecules are removed in the regular dynamic simulation to obtain the structures that change with the passage of time [31,32]. e calculation was defined in an attempt to obtain the contribution of each energy so as to have a better analysis of the interaction between renin and the saponins [33]. e equation is as follows:…”

Section: E Calculation Of Free Binding Energies Of the Complexesmentioning

confidence: 99%

Molecular Interactions of Renin with Chikusetsusaponin IV and Momordin IIc

Zhang

Zhu

Chen

2019

The paper dealt with the molecular mechanism for the binding sites and driving forces of renin with chikusetsusaponin IV and momordin IIc by means of molecular docking and free energy calculation based on the crystal structure. The result showed that renin and the saponins fit well. As shown by LigPlot + software analyzing the hydrogen bonding and hydrophobic effect between renin and the saponins, the amino acid residues such as Ser230, Tyr85, and Tyr201 form the hydrogen bonds, with S3sp, S3, and S2′ being the active pockets. In addition, there are relatively strong hydrophobic interactions of renin with saponins in S3sp, S3, S2, S1, S1′, and S2′, with Gly228, Val36, Ala229, Gln19, Met303, Gln135, Ser41, Ile137, Asp38, Arg82, and Tyr83 being the key amino acids. The dynamics reached equilibration after about 1000 ps simulation with average root-mean-square deviations of 0.222 nm and 0.217 nm. The molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) yielded −1.10812 kcal/mol and −39.0587 kcal/mol total binding energy for the two complexes, respectively, which were primarily contributed by electrostatic and van der Waals interaction energies, and the binding was strongly unfavored by polar solvation energy, a further confirmation that momordin IIc has stronger hydrogen bonding and hydrophobic effect in the inhibition of renin than the chikusetsusaponin IV.