A Study on the Impact of Mobility on the Performance of Routing Protocols in MANET-Based Online Learning Platforms

A rapid development in online learning systems to maintain the continuity of student education during the COVID-19 pandemic has been observed. The telecommunication technologies based on Mobile Ad hoc Network (MANET) are very useful and widely used to connect online learners staying remotely in jungles, forests and hilly areas where it is difficult to establish permanent infrastructure for communication. Routing protocols play a vital role in the background, which provide an uninterrupted learning session. In this context, by considering the mobile gadgets and other electronic devices as the nodes of the MANET system, it becomes quite imperative on the part of the researcher to further analyze and understand the impact of node mobility on the performance on MANET-based online learning systems in rural Indian terrain. A change in mobility can be achieved by changing the online learner's node speed during its movement or by changing the pause-time at its halt points during its random movement. Change in mobility causes link breaks between neighbor learners nodes;hence, a large number of packets drop in the network, which in other terms reduces the network performance and, simultaneously, the reliability of the online learning system. The performance of the MANET-based online platform is analyzed by taking parameters like packet delivery fraction, normalized routing load and average link break. This paper thoroughly discusses the performance variation due to change in mobility. © 2023 selection and editorial matter, Zdzislaw Polkowski, Samarjeet Borah, Sambit Kumar Mishra, and Darshana Desai;individual chapters, the contributors.

Synthesis, structural investigations, XRD, DFT, anticancer and molecular docking study of a series of thiazole based Schiff base metal complexes

A novel Schiff base (SB) ligand, abbreviated as HDMPM, resulted from the condensation of 2-amino-4 -phenyl-5-methyl thiazole and 4-(diethylamino)salicyaldehyde, and its metal complexes with [Co(II), Cu(II), Ni(II), and Zn(II)] ions in high yield were formed. The physico-chemical techniques such as elemental analysis, molar conductance, IR, 1 H and 13 C NMR, mass spectroscopy, and electronic absorption studies were utilized to characterize the synthesized compounds. The studied compounds were examined for their possible anticancer activity against a number of human cancerous cell lines, including A549 lung carcinoma, HepG2 liver cancer, HCT116 colorectal cancer, and MCF-7 breast cancer cell lines, with dox-orubicin serving as the standard. The study revealed that Zn(II) complex showed significant activity to inhibit growth of HepG2, MCF7, A549, and HCT116 cell lines by a factor of 88, 70, 75, and 70, respec-tively, when compared to untreated. In addition, the reported compounds were optimized by employing Gaussian16 program package with B3LYP functional incorporating dispersion with two different basis sets (LanL2DZ and 6-31G(d,p)). Moreover, Autodock Vina software was used to assess the biological effective-ness of the studied compounds against SARS-CoV-2 Omicron variant (PDB ID: 7T9K).(c) 2022 Elsevier B.V. All rights reserved.

Effect of Natural Compounds to Inhibit Human Respiratory Syncytial Virus

Current COVID-19 effects are forcing us to think about other deadly viral diseases. Respiratory syncytial virus (RSV) is one of them. Every year thousands of children lost their lives due to respiratory diseases which are occurred by this RSV. Nowadays, bioactive compounds show an enormous effect on many deadly diseases and show excellent therapeutic effects. In this study, we have identified five bioactive compounds from the plant which will be used in the treatment of RSV. Molecular docking on the protein was done by Autodock. Hydrogen was added and routable bonds were fixed in the preparation time of protein for docking. All those compounds show their non-toxic nature which is evaluated by Lipinski's Rule of Five. Molecular docking on RSV matrix protein and surface glycoprotein with those bioactive compounds shows very promising results. Between all those compounds Baicalein appears as a lead compound. It shows -8.1 Kcal/mol in the case of matrix protein and -7.9 kcal/mol in the case of the surface glycoprotein of RSV. Due to its availability and non-toxic nature, it can be used in the treatment of RSV. AS it is derived from plants, it also has very fewer side effects than chemical drugs.

In silico comparative structural and compositional analysis of glycoproteins of RSV to study the nature of stability and transmissibility of RSV A

The current scenario of COVID-19 makes us to think about the devastating diseases that kill so many people every year. Analysis of viral proteins contributes many things that are utterly useful in the evolution of therapeutic drugs and vaccines. In this study, sequence and structure of fusion glycoproteins and major surface glycoproteins of respiratory syncytial virus (RSV) were analysed to reveal the stability and transmission rate. RSV A has the highest abundance of aromatic residues. The Kyte–Doolittle scale indicates the hydrophilic nature of RSV A protein which leads to the higher transmission rate of this virus. Intra-protein interactions such as carbonyl interactions, cation–pi, and salt bridges were shown to be greater in RSV A compared to RSV B, which might lead to improved stability. This study discovered the presence of a network aromatic–sulphur interaction in viral proteins. Analysis of ligand binding pocket of RSV proteins indicated that drugs are performing better on RSV B than RSV A. It was also shown that increasing the number of tunnels in RSV A proteins boosts catalytic activity. This study will be helpful in drug discovery and vaccine development. © 2022, Jiangnan University.

Computational Intelligence in Identification of Some FDA Approved Drug Compounds for Treatment of COVID-19

Due to unavailability of FDA approved drug for COVID-19, pursuing of available drugs are highlighted to stop COVID-19. Insilico investigation by molecular docking and molecular dynamics simulation help to identify some FDA pre-approved drugs which have a therapeutic effect on SARS-CoV-2. In this study, four drug compounds have been identified by descriptor properties, molecular docking, and molecular dynamics simulation. Between them, Darunavir appeared as the best drug molecule to inhibit the 3C like main protease of SARS-CoV-2. It showed −9.1 kcal/mol binding energy in molecular docking with 3C like main protease of SARS-CoV-2. This study also enlightens on the theory “one molecule, multiple targets”. Multiple target protein was docked by every single drug compound, to check their high therapeutic effect. Molecular dynamics simulations indicate the stable binding of drugs with the target protein. Until the approval of any drug for COVID-19, Darunavir might use as an anti-covid drug. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Negative and positive environmental perspective of COVID-19: air, water, wastewater, forest, and noise quality

The ongoing coronavirus disease 2019 (COVID-19) pandemic driven by severe acute respiratory syndrome coronavirus–2 (SARS-CoV-2) has become the most critical universal health disaster of this century. Millions of people are staying at home obeying lockdown to halt the spread of this novel virus. The spread of the virus has forced people to use the mask, gloves, hand sanitizer, etc. daily, and healthcare workers to use personal protection equipment following the WHO guidelines, resulting in huge amounts of medical waste. This pandemic has led to a slowdown of economic activities significantly, and consequently, stock markets have nosedived beyond speculation. Although the deadly coronavirus has taken away millions of precious lives and the livelihood of many sections of people worldwide, it has brought several positive changes in the world. Furthermore, it has led to a massive restoration of the environment and improved air and water quality. Pandemic showed the resilient nature of the environment, including air and water, when human activities are paused. In addition, we also discussed how this pandemic affects human lifestyle behavior. © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Covid-19 Face Mask Prediction Using Machine Learning Techniques

Entire world including India is going through a pandemic that has arisen due to the outbreak of COVID-19. Medicines and Vaccine for Covid-19 are still under developmental stage. Wearing a Face Mask is the best viable option for humans to prevent the spread of infection due to Corona virus. As a result, controlling government agencies may want to know the percentage of people wearing masks during a period as well as which group of people are most likely to wear masks when they go outside. To help answer these questions, this paper introduces a model that can classify faces among masked faces and unmasked faces using Python 3.0 Language. In the present face detecting model, Vietnam based mask classifier dataset, CelebA dataset, WiderFace dataset and MAFA datasets are used for achieving better results. Single Stage Headless Face Detector (SSH) is successfully implemented to segregate human faces with or without mask. Experimental results with the Mask Classifier model show that it can achieve about 96.5% accuracy during testing stage. Selected on road going people video is tested successfully where the present model clearly segregated human faces with and without mask. The present model is useful to safeguard people from spread of Covid-19 virus in public places. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.