This paper investigates the uncertain maximum flow of a network whose capacities are random fuzzy variables. We have developed the expected value model (EVM) and the chance‐constrained model (CCM) for maximum flow problem (MFP) under random fuzzy environment and formulated their crisp equivalent models. To solve these models, we have proposed a varying population genetic algorithm with indeterminate crossover (VPGAwIC). In VPGAwIC, selection of a chromosome depends on its lifetime. An improved lifetime allocation strategy (iLAS) has also been proposed to determine the lifetime of the chromosome. The ages of the chromosomes are defined linguistically as Young, Middle, and Old, which follow some uncertainty distributions. The crossover probability is indeterminate, and it depends on the ages of the parents, which is defined by an uncertain rule base. The number of offspring, generated from a population of parents, is determined by the reproduction ratio. The population is updated in 2 ways: (i) All the chromosomes with ages greater than their lifetimes are discarded from the population, and (ii) the offspring are combined with their parents for the next generation. The proposed VPGAwIC is compared with the genetic algorithm developed by Gen, Cheng, and Lin (2008) for maximum flow problem. Wilcoxon signed‐rank test has been performed to show the superiority of the proposed VPGAwIC.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which emerged in late 2019, causes COVID-19, a disease that has been spreading rapidly worldwide. In human lung epithelial cells and monocytes, RLF-100 (aviptadil) has been found to inhibit the RNA replication machinery of SARS-CoV-2, which includes several non-structural proteins (nsp) that play essential roles in synthesizing and replicating viral RNA. This virus is unique in requiring nsp10 and nsp16 for methyltransferase (MTase) activity. This enzyme is essential for RNA stability, protein translation, and viral ability to escape the host's immune recognition. Therefore, we aimed to use bioinformatics tools to analyze aviptadil's inhibitory effect on the SARS-CoV-2 nsp10/nsp16 complex. We present a comprehensive, in silico-generated picture showing how aviptadil may interact with the nsp complex. Specifically, our model predicts how the initial binding of aviptadil to nsp10 and nsp16 may occur. This knowledge can assist drug development efforts against SARS-CoV-2 by providing more target information against nsp16.
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