COVID-19 and Its Impact on Diverse Aspects of Women's Lives

The objective of this article is to study the impact of COVID-19 on the lives of women by exploring different aspects like their daily work patterns, hygiene practices, psychological effects and nutritional status during the pandemic. 510 women participated in the online survey. The majority of the respondents belonged to the age group of 20-29 years and were either graduates or above. 37.3% of the working respondents reported increased professional responsibilities during the pandemic. Cooking and cleaning occupied most of the time during the lockdown. Anxiety, lack of concentration and frequent arguments with the family members were reported by the respondents. Many of the respondents took up physical activities to maintain their fitness. They also believed that usage of masks would prevent them from catching the infection. 75.2% of women included vitamin-rich sources in their diet. This level of consciousness might be linked to the educational profile of the respondents.

Computational Drug Designing, Synthesis, Characterization and Anti-bacterial Activity Evaluation of Some Mixed Ligand–Metal Complexes of 2-hydroxybenzaldehydethiosemicarbazone as Primary Ligand

This paper reports the mixed ligand–metal complexes of CuSO4·5H2O and ZnSO4·7H2O with salicylaldehyde thiosemicarbazone (2-hydroxybenzaldehyde thiosemicarbazone) as primary ligand and imidazole (im), pyridine (py) and triphenylphosphine (PPh3) as secondary ligands through a general preparatory route. The ligand and complexes were characterized by FTIR, UV, 1H-NMR and molar conductance techniques. Computational studies to know the physicochemical parameters, bioactivity scores, absorption, distribution, metabolism, excretion and toxicity (ADMET) properties were carried out through Molinspiration, SwissADME and admetSAR softwares. Molecular docking was perfomed with Mproof SARS-CoV-2 (PDB i.d.6LU7), Aspartate Kinase (PDB i.d.5YEI) and Transforming Growth Factor β (PDB i.d. 3KFD) using PyRx automated docking software. The antibacterial activity was tested using Agar well method. Computational findings revealed that almost all the complexes had clogP values less than 5 indicating their bioavailability. The bioactivity scores of the complexes were between moderate to good. The mixed ligand complexes having imidazole as secondary ligand displayed relatively high FCsp3, indicating their potential as lead candidates. [Zn(C8H9N3OS)(PPh3)2(SO4)] and [Cu(C8H9N3OS)(im)2(SO4)] exhibited appreciable binding affinity against the selected proteins. Furthermore, the molecular simulation findings with the ligated [Cu(C8H9N3OS)(im)2(SO4)] and aspartate kinase showed compact folding, less deviations and significant stability. The stability of the ligand was further confirmed by the frontier molecular orbitals (FMOs) gap. The energy gap (− 0.423 eV) indicated molecular stability. The ligand was active against L. monocytogenes, S. aureus and E.coli having zone of inhibition of 11, 11 and 10 mm respectively. Among the complexes, [Cu(C8H9N3OS)(im)2(SO4)] had the minimum inhibitory concentrations (MIC) ranging between 32 and 128 µg/mL against the selecetd bacterial strains. Graphical : [Figure not available: see fulltext.] © 2023, The Tunisian Chemical Society and Springer Nature Switzerland AG.

Modeling of Computer Virus Propagation with Fuzzy Parameters

Typically, a computer has infectivity as soon as it is infected. It is a reality that no antivirus programming can identify and eliminate all kinds of viruses, suggesting that infections would persevere on the Internet. To understand the dynamics of the virus propagation in a better way, a computer virus spread model with fuzzy parameters is presented in this work. It is assumed that all infected computers do not have the same contribution to the virus transmission process and each computer has a different degree of infectivity, which depends on the quantity of virus. Considering this, the parameters beta and gamma being functions of the computer virus load, are considered fuzzy numbers. Using fuzzy theory helps us understand the spread of computer viruses more realistically as these parameters have fixed values in classical models. The essential features of the model, like reproduction number and equilibrium analysis, are discussed in fuzzy senses. Moreover, with fuzziness, two numerical methods, the forward Euler technique, and a nonstandard finite difference (NSFD) scheme, respectively, are developed and analyzed. In the evidence of the numerical simulations, the proposed NSFD method preserves the main features of the dynamic system. It can be considered a reliable tool to predict such types of solutions.

Optimization of Coronavirus Pandemic Model Through Artificial Intelligence

Artificial intelligence is demonstrated by machines, unlike the natural intelligence displayed by animals, including humans. Artificial intelligence research has been defined as the field of study of intelligent agents,which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The techniques of intelligent computing solve many applications of mathematical modeling. The researchworkwas designed via a particularmethod of artificial neural networks to solve the mathematical model of coronavirus. The representation of the mathematical model is made via systems of nonlinear ordinary differential equations. These differential equations are established by collecting the susceptible, the exposed, the symptomatic, super spreaders, infection with asymptomatic, hospitalized, recovery, and fatality classes. The generation of the coronavirus model's dataset is exploited by the strength of the explicit Runge Kutta method for different countries like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, validation, and testing processes for each cyclic update in Bayesian Regularization Backpropagation for the numerical treatment of the dynamics of the desired model. The performance and effectiveness of the designed methodology are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis. © 2023 Tech Science Press. All rights reserved.