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1.
Mausam ; 73(1):115-128, 2022.
Article in English | English Web of Science | ID: covidwho-1880647

ABSTRACT

This paper presents the comparative results of surface and satellite measurements made during the Phase 1 (25 March to 14 April), Phase 2 (15 April to 3 May) and Phase 3 (3 May to 17 May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O-3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase 1, Phase 2 and Phase 3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O-3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by-8.29% during 2nd Phase of lockdown period.

2.
6th International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering, IC4ME2 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1874265

ABSTRACT

Social distancing, isolation, and quarantining are very familiar words since the outbreak of the coronavirus (COVID-19). COVID - 19 is a highly contagious pathogenic viral infection. It is very risky to get close contact with people who have COVID-19 symptoms or COVID-19 positive;nevertheless, covid patient monitoring is also significant for saving his/her life. To solve the Covid-19 pandemic situation accentuates a focus on remote patient monitoring. A small smart healthcare support system is built to monitor COVID-19 patients' health status and the patient emergency abet. This system can also trace the patient location;thus, aid can be provided to the patient promptly. This system uses a respiration sensor, oxygen saturation sensor, temperature sensor, heart rate sensor, GPS. All the sensors, as well as GPS, are connected with Arduino-Uno. By processing sensor data, the smart system can discern the patient's critical condition and forward this information to the doctor/nurse or hospital in charge and patient relative's smartphone as a text message. This paper aims to develop a system to support COVID-19 patients and develop a remote healthcare platform for monitoring pandemic situations and providing emergency aid promptly as a text to the smartphone. © 2021 IEEE.

3.
3rd International Conference on Sustainable Technologies for Industry 4.0, STI 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1788774

ABSTRACT

Corona viruses are a type of virus with a large family which can cause several terrible and devastating infectious diseases like middle east respiratory syndrome and severe acute respiratory syndrome. The first task of the authority is to screen as many people as possible to detect COVID-19 patients which arises the challenge of rapid screening. Although polymerase chain reaction(PCR) tests are primarily used for the COVID-19 test but because of it's high false negative results and need of experts leading to an alternative diagnostic system based on radiological images like chest X-ray. Moreover, computer aided diagnosis systems from radiography images has significantly been advanced during the last decade with promising efficiency which can overcome the need of both time and experts. In this case, machine learning(ML) and deep learning(DL) based screening techniques can provide automated, fast and reliable results. Therefore, many researchers have proposed several deep neural network(DNN) models for rapid screening of COVID-19 using chest X-ray images. Nevertheless, the vulnerability issue DNN models are overlooked or poorly evaluated in the COVID-19 screening. DNN models are remarkably vulnerable to perturbation which is addressed universal adversarial perturbation (UAP). UAP can falsely influence a DNN model and can eventually lead to going wrong in most of the classification problems. Here, we experimented and evaluated the performance of several DNN based automated COVID-19 diagnostic models, and investigated the robustness of these models against two types of adversarial attack:non targeted and targeted. We showed that DNN based COVID-19 detection models are highly vulnerable to adversarial attack and it is substantially important to be aware of the risk factors of DNN models before deploying for real life applications. © 2021 IEEE.

4.
Mymensingh Medical Journal: MMJ ; 31(2):466-476, 2022.
Article in English | MEDLINE | ID: covidwho-1776948

ABSTRACT

The study was aimed to assess the psychological aspects and relevant factors of the health-care workers (HCWs) working in COVID 19 pandemic condition in Bangladesh. This online cross-sectional survey was conducted from different tertiary, secondary and primary hospitals in Bangladesh. Eligible 638 HCWs who were directly involved in the caring of confirmed or suspected COVID-19 patients were recruited in this study. The mental health was assessed by the Patient Health Questionnare-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7) and Athens Insomnia Scale (AIS). High frequency of depression 536(84.0%), anxiety 386(60.5%) and insomnia 302(47.3%) was found among the HCWs, which were significantly higher in physicians (p<0.001) than nurses. Moderate to severe depression was significantly higher in female, whereas minimal to mild depression was significant in male HCWs (p=0.014). Symptoms of depression (p<0.001), anxiety (p<0.001) and insomnia (p=0.004) were significantly higher among the HCWs of primary and secondary compared to the tertiary level. The HCWs developed psychological trauma due to family health (45.3%) and contagious disease property (66.6%). After adjusting confounders, multivariable logistic regression analysis showed that physicians and HCWs of secondary hospital had significant symptoms of severe depression (OR=2.95, 95% CI=0.50-17.24;p<0.001), anxiety (OR=2.64, 95% CI=0.80-8.72;p<0.001) and insomnia (OR=2.67, 95% CI=1.23-5.84;p=0.018);whereas female HCWs had more risk of developing symptoms of severe insomnia (OR= 1.84;95% CI=1.23-2.75;p=0.003). High rate of depression, anxiety and insomnia was found among HCWs working in the COVID-19 pandemic condition in this survey.

5.
Frontiers in Nanotechnology ; 3, 2022.
Article in English | Scopus | ID: covidwho-1715020

ABSTRACT

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a contagious virus that spreads exponentially across the world, resulting in serious viral pneumonia. Several companies and researchers have put their tremendous effort into developing novel vaccines or drugs for the complete eradication of COVID-19 caused by SARS-CoV-2. Bionanotechnology plays a vital role in designing functionalized biocompatible nanoparticulate systems with higher antiviral capabilities. Thus, several nanocarriers have been explored in designing and delivering drugs and vaccines. This problem can be overcome with the intervention of biomaterials or bionanoparticles. The present review describes the comparative analysis of SARS infection and its associated etiological agents. This review also highlighted some nanoparticles that have been explored in the treatment of COVID-19. However, these carriers elicit several problems once they come in contact with biological systems. Often, the body’s immune system treats these nanocarriers as foreign particles and antigens. In contrast, some bionanoparticles are highlighted here with their potential application in SARS-CoV-2. However, bionanoparticles have demonstrated some drawbacks discussed here with the possible outcomes. The scope of bioinspired nanoparticles is also discussed in detail to explore the new era of research. It is highly essential for the effective delivery of these nanoparticles to the target site. For effective management of SARS-CoV-2, different delivery patterns are also discussed here. Copyright © 2022 Debnath and Srivastava.

6.
Studies in Systems, Decision and Control ; 366:929-955, 2022.
Article in English | Scopus | ID: covidwho-1516838

ABSTRACT

The spreading and Development of COVID-19 have analyzed which was first officially reported in Wuhan City, in December 2019. Firstly the data have explored in terms of information and quality and after that, the data have cleaned and gone through with feature engineering. Analyzed different types of machine learning-based prediction methods, namely Linear Regression, ARIMA, and SARIMA on the spread of COVID-19 in different regions all over the world. In the end, It has been concluded with the best machine learning model among them for COVID-19 spread forecasting based on theoretical and results in analysis. And also we have discussed that how deep learning can be considered with data limit problem in order to improve the result more dynamically with combination and comparisons of state-of-art approaches for time series problems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

7.
J. Phys. Conf. Ser. ; 1797, 2021.
Article in English | Scopus | ID: covidwho-1139940

ABSTRACT

The COVID-19, the most deadly challenge human being could have imagined, has become a reality now and the whole world is passing through the worst pandemic situation. From the challenges of invention of life saving vaccine or medicine to keeping economy at right track are the most talked about hurdles in front of all of us. But if we look at the other side of the coin, the blessings that came in form of disguise could be realised. The paper emphasizes on those aspects during lockdown that was imposed in India for three weeks initially that is 24th March to 14th April 2020 and later on till 3rd of May 2020. As a result of this forced restrictions, pollution level in whole India, specially in metro or mega cities where large population and pollution is a deadly combination, drastically changed. For analysing air quality, the metro-cities like Delhi, Kolkata, Chennai and Mumbai has been investigated here. The pollutant parameters PM 2.5, SO2, NO2, along with CO, O3 and overall AQI (Air Quality index) has been collected and comparative study was done before and after lockdown and even for the year 2018 and 2019 too. Reduction of level of pollutants in significant amount of percentage is observed for all the metro cities where variation of reduction level from city to city is also significant. The effectiveness of lockdown over different metro cities are also very significant, pointing towards alertness of local people and population concentration. Finally, the study can be used in future as case study for controlling pollution with controlled lockdown and this can be practiced in future once or twice yearly to save our motherland. © 2021 Institute of Physics Publishing. All rights reserved.

8.
J. Phys. Conf. Ser. ; 1797, 2021.
Article in English | Scopus | ID: covidwho-1139917

ABSTRACT

We have developed a “Self Sanitizing Voice Controlled Intelligent Wheelchair.” The wheelchair will be totally voice controlled. There will be sanitizing system for both the wheelchair and the surrounding area. In Covid-19situation, maintaining social distance is very important. Keep this in mind we will add the alert system in the wheelchair. When any person will come very close to the wheelchair it will announce a voice alert. Also this wheelchair will climb the stair without anyone’s help. This wheelchair will be developed for the disabled person as well as for the Corona patient. The assistant of the disabled person or the disabled person can be Corona affected. As the wheelchair is voice controlled, it will not need someone to push the wheelchair. It will also helpful to carry the Corona patient, where the nurse or assistant can move the wheelchair by giving voice command from a safe distance. The wheelchair will have voice controlled sanitizing system where it will be able to sanitize itself and its surrounding area. After carrying the Corona patient this wheelchair can be sanitized without going near to it. Also this wheelchair will help to sanitize the patient’s room. So, before entering the patient’s room, by giving voice command the room can be sanitized. © 2021 Institute of Physics Publishing. All rights reserved.

9.
J. Phys. Conf. Ser. ; 1797, 2021.
Article in English | Scopus | ID: covidwho-1139908

ABSTRACT

Pandemic relates to a situation where any disease starts spreading geographically and affects a entire country or the whole world. So when an epidemic becomes pandemic, it really a question of our survival. COVID -19 has become a pandemic as we all know and needs real and underneath research on that. The procession of death is uncountable still now. It can cause significant economic, social, and political disruption. So it’s very necessary to know the impact of it on originating venue so that we can analyze its potential and rate of spreads. So to do this we have applied here some Machine learning algorithm and concepts of regression for prediction. In this present work we have made prediction model of confirmed cases, Recovered and death cases using K-Nearest Neighbour regressor and Gradient Boosting Regressor. The model performance is very good in predicting all the cases. The R squared value is very near to 1. © 2021 Institute of Physics Publishing. All rights reserved.

10.
Eurasian Journal of Medicine and Oncology ; 4(4):336-348, 2020.
Article in English | Web of Science | ID: covidwho-1034381

ABSTRACT

Objectives: The outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) remains a serious global threat. At the time of writing, there are no specific therapeutic agents or vaccines to combat this disease. This study was designed to identify the SARS-CoV-2 main protease inhibitors using drug molecule information retrieved from DrugBank 5.0 (Wishart et al.) Methods: A set of common pharmacophores were generated from a series of 22 known SARS-CoV inhibitors. The best pharmacophore used for virtual screening (VS) of DrugBank using the Phase module followed by structure-based virtual screening (VS) using Glide (Release 2020-1;Schrodinger LLC, New York, NY, USA) with SARS-CoV-2 main protease and 50 ns molecular dynamics (MD) simulation studies. Results: Six hits were selected based on the fitness score, extra-precision Glide score, and binding affinity with the main protease (Mpro). The predicted inhibitor constant (Ki) values of the 3 best hits, DB03777, DB06834, and DB07456, were 0.8176, 0.2148, and 0.1006 mu M, respectively. An MD simulation of DB07456 and DB13592 with the Mpro demonstrated stable protein-ligand complexes. Conclusion: The selected inhibitors displayed a similar type of binding interaction with co-ligands and remdesivir, and the predicted Ki values of 2 inhibitors were found to be superior to remdesivir. These selected hits may be used for further in vitro and in vivo studies against the SARS- CoV-2 Mpro.

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