Monitoring, Tracking and Fighting Pandemics using Drone-based Artificial Intelligence in IoT

The world has altered since the World Health Organization (WHO) designated (COVID-19) a worldwide epidemic. Everything in society, from professions to routines, has shifted to accommodate the new reality. The World Health Organization warns that future pandemics of infectious diseases are likely and that people should be ready for the worst. Therefore, this study presents a framework for tracking and monitoring COVID-19 using a Deep Learning (DL) perfect. The suggested framework utilises UAVs (such as a quadcopter or drone) equipped with artificial intelligence (AI) and the Internet of Things (IoT) to keep an eye on and combat the spread of COVID-19. AI/IoT for COVID-19 nursing and a drone-based IoT scheme for sterilisation make up the bulk of the infrastructure. The proposed solution is based on the use of a current camera installed in a face-shield or helmet for use in emergency situations like pandemics. The developed AI algorithm processes the thermal images that have been detected using multi-scale similar convolution blocks (MPCs) and Res blocks that are trained using residual learning. When infected cases are detected, the helmet's embedded Internet of Things system can trigger the drone system to intervene. The infected population is eradicated with the help of the drone's sterilisation process. The developed system undergoes experimental evaluation, and the findings are presented. The developed outline delivers a novel and well-organized arrangement for monitoring and combating COVID-19 and additional future epidemics, as evidenced by the results. © 2023 IEEE.

General Adversarial Networks: A Tool to Detect the Novel Coronavirus from CT Scans

Detection of the novel Corona virus in the early stages is crucial, since no known vaccines exist. Artificial Intelligence- aided prognosis using CT scans can be used as an effective method to identify symptoms of the virus and can thus significantly reduce the workload on the radiologists, who have to perform this task using their eyes. Among the most widely used deep learning convolutional neural networks, research shows that the Xception, Inception and the ResNet50 provide the best accuracy in detecting Covid-19. This paper proposes that using General Adversarial Network (GAN) as a data augmentation technique, in combination with these models will significantly improve the accuracy and thereby increase the chances of detecting the same. The paper also compares and contrasts how each of the three GANs namely DCGAN, LSGAN, CoGAN, perform in association with the aforementioned models. The main aim of this paper is to determine the most credible GAN network to carry out the task of data augmentation as well to prove that involving GANs would improve the existing accuracy of our model, paving way for an effective approach to train the model. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Mutational landscape and in silico structure models of SARS-CoV-2 spike receptor binding domain reveal key molecular determinants for virus-host interaction.

BACKGROUND: SARS-CoV-2, the causative agent of COVID-19 pandemic is a RNA virus prone to mutations. Formation of a stable binding interface between the Receptor Binding Domain (RBD) of SARS-CoV-2 Spike (S) protein and Angiotensin-Converting Enzyme 2 (ACE2) of host is pivotal for viral entry. RBD has been shown to mutate frequently during pandemic. Although, a few mutations in RBD exhibit enhanced transmission rates leading to rise of new variants of concern, most RBD mutations show sustained ACE2 binding and virus infectivity. Yet, how all these mutations make the binding interface constantly favourable for virus remain enigmatic. This study aims to delineate molecular rearrangements in the binding interface of SARS-CoV-2 RBD mutants. RESULTS: Here, we have generated a mutational and structural landscape of SARS-CoV-2 RBD in first six months of the pandemic. We analyzed 31,403 SARS-CoV-2 genomes randomly across the globe, and identified 444 non-synonymous mutations in RBD that cause 49 distinct amino acid substitutions in contact and non-contact amino acid residues. Molecular phylogenetic analysis suggested independent emergence of RBD mutants. Structural mapping of these mutations on the SARS-CoV-2 Wuhan reference strain RBD and structural comparison with RBDs from bat-CoV, SARS-CoV, and pangolin-CoV, all bound to human or mouse ACE2, revealed several changes in the interfacial interactions in all three binding clusters. Interestingly, interactions mediated via N487 residue in cluster-I and Y449, G496, T500, G502 residues in cluster-III remained largely unchanged in all RBD mutants. Further analysis showed that these interactions are evolutionarily conserved in sarbecoviruses which use ACE2 for entry. Importantly, despite extensive changes in the interface, RBD-ACE2 stability and binding affinities were maintained in all the analyzed mutants. Taken together, these findings reveal how SARS-CoV-2 uses its RBD residues to constantly remodel the binding interface. CONCLUSION: Our study broadly signifies understanding virus-host binding interfaces and their alterations during pandemic. Our findings propose a possible interface remodelling mechanism used by SARS-CoV-2 to escape deleterious mutations. Future investigations will focus on functional validation of in-silico findings and on investigating interface remodelling mechanisms across sarbecoviruses. Thus, in long run, this study may provide novel clues to therapeutically target RBD-ACE2 interface for pan-sarbecovirus infections.

A network science-based performance improvement model for the airline industry using networkx

Background: COVID 19 created a challenging situation for many of the industries across the globe. Our primary focus is on the most affected airline industry. In this paper, the connectivity and profits of an airline company were analyzed with the theoretical approach and by proposing a novel model to increase the performance of the above parameters. In our previous work, two airlines were investigated, and it was observed that adding trips to a non-profit airline concerning profit Airlines is one of the optimal techniques to improve the performance. In this paper, multi-airlines have been considered. Methods: In the first step, identify the appropriate data sets for three airline companies and the collected data set in image format, to convert them into graph format consisting of nodes and edges. In the next step, an analysis has been conducted on data set graphs by considering the parameters like diameter, density, average degree, clustering coefficient and the shortest path generated to identify the profitable airlines. The proposed algorithms will apply either trimming or adding operations on low-profit airline operators with respective profitable airlines. In the last step, the proposed algorithm will generate an output with better connectivity and profits. Results: In this research, other interesting findings, which are relatively contrasted to the previous findings, were observed. In the present research findings, trimming of trips to non-profit airlines concerning the profit airlines can also be an optimal solution for better performance. Discussion: In this research, complex multigraph airlines were analyzed by using the graph analytics technique for the optimum solution. Standard parameters like edges, nodes, degree, clustering, and shortest path on indigo, spice jet, and AirAsia airline systems were also compared. Conclusion: The proposed algorithm analyzes the connectivity of airline systems and applies either trimming or enhancing techniques. Indigo airlines have the best-connected network as compared to the other two models. Trimming operations will be performed on Indigo, whereas on Air Asia and spice jet, both cutting and enhancing will be served. © 2021 Bentham Science Publishers.

High dose vitamin-C in COVID-19 induced neuro-cognitive dysfunction

BACKGROUND: The current pandemic Covid-19 causes several clinical manifestations from asymptomatic presentation to more serious Severe Acute Respiratory Syndrome (SARS). There is no specific antiviral treatment for Covid-19 and the management is mainly symptomatic. The symptoms mainly include many neurological as well as cognitive impairment in majority of patients. AIM: The study aims to find out the covid induced neurological symptoms and cognitive dysfunction and the role of high-dose of oral liposomal vitamin C in patients admitted in Government Medical College & ESI Hospital, Coimbatore, India. MATERIAL: All the patients admitted with positive covid19 confirmed by RTPCR are randomly selected and included in study age between 18 years to-60 years only are included, male and female total number 150 are taken for study after getting informed consent. RESULTS: More than one Neurological Symptoms are experienced by 12-88% of patients and Cognitive Impairment by 82% of people. By Administrating Symptomatic treatments and supplements with 2 gms of liposomal Vitamin -C in a palatable juice form, 76% of them improved within 5 days rest of them in 10 days. CONCLUSION: Vit-c is most under estimated vitamin. It is cheap, easily available, more efficient and helps the body to fight against covid -19 too. High dose intravenous as well as oral liposomal vitamin-C is more effective in protection and recovery from covid -19 induced neuro-cognitive dysfunction.

Study of outcome of high dose oral liposomal vitamin-C in COVID-19 patients

Background: The current pandemic Covid-19 causes several clinical manifestations from asymptomatic presentation to more serious Severe Acute Respiratory Syndrome (SARS) on humans. There is no specific antiviral treatment for Covid-19 and the management is mainly symptomatic. Aim: To describe the role and outcome of high-dose of oral liposomal vitamin C treatment in a drink form for Covid-19 patients admitted in Government Medical College & ESI Hospital, Coimbatore, India. Materials and methods : Patients who admitted to the with the diagnosis of COVID-19 by RTPCR in government medical college and esi hospital Coimbatore during 15 th march 2020 to april 30,2020 are taken for study is one of the earliest cases studies of Covid-19 in the Indian subcontinent.

Prone position ventilation in COVID-19 ARDS patients- a prospective study

Introduction: SARS-CoV-2 virus causes a pneumonia that was identified through fever, dyspnoea, and acute respiratory symptoms and named COVID-19. This disease exacerbates in a number of patients and causes pulmonary edema, multi-organ failure, and acute respiratory distress syndrome (ARDS). Prevalence of ARDS among COVID-19 patients has been reported to be up to 17%. In the absence of effective targeted therapies for COVID-19, optimisation of supportive care is essential. micro vascular thrombi, Lung injury with features of acute respiratory distress syndrome (ARDS) appears to be the principal characteristic of severe acute respiratory syndrome corona virus 2 infection Among treatment methods for management of ARDS patients, prone position can be used as an adjuvant therapy for improving ventilation in these patients Aim : To find out the beneficial effects of prone position ventilation in COVID-19 patients ARDS. Presently, no published trials investigate the effectiveness of prone positioning in awake patients with typical ARDS Material and methods: Patients admitted in various tertiary care COVID-19 hospitals in coimbatore are taken for study. totally 300 patients on ventilators, invasive and non invasive ventilation are taken for study. Study period 1 st March 2020 to 28 th February 2021.

Techno-economic review on short-term anthropogenic emissions of air pollutants and particulate matter

It is well known that pandemics not only change people's social habits but have also changed most activities related to energy consumption, especially industry and transport. Over the past year, a plethora of case studies have been published mapping the environmental impacts in specific locations in terms of changes in wastewater composition, noise, solar radiation and more. However, policymakers are demanding a global perspective and are looking for a synthesis of all these reports that will indicate whether, or to what extent, these changes interact with global climate change. The most urgent question is whether artificially inducing such a pandemic could be justified, given the loss of human life and economic losses. Robust analysis on air pollutants such as PM2.5, PM10, NOx, SO2, CO, O3 and NH3 confirmed significant improvement in air quality indicators especially in India and China. The study indicates that key hypotheses can be confirmed or refuted, but further measurements are needed.