A novel technique for the detection of Covid-19 patients with the applications of three-way decisions using variance-based criterion.

Everyone is making constant efforts to establish an effective diagnostic approach, therapy and control of the spread of the pandemic. Due to a flexible formulation, the parameters prior to the normal distributions and explicitly formulate assumptions on the transition probabilities between these categories over time. The spread of the COVID-19 pandemic represents a serious threat for scientists and academics, health professionals and even governments today. The Hospital wards are classified into Intensive Care Unit (ICU), Regular Wards (RW) with Recovered (R) and Deceased (D).. The formulation may be truncated to include particular hypotheses with an epidemiological interpretation. The principles of Three-Way Decision Theory could be used to anticipate and diagnose COVID-19 patients were classified into one of three zones based on their symptoms: Positive, Negative, or Boundary, and treatment are recommended if necessary. The thresholds that distinguish the three zones are determined using a variance-based criterion. Examine the impact of nonpharmaceutical interventions and the findings from data gathered during the second wave of the pandemic in Trivandrum, India.The Three-Way Decision Theory model has a good fit and gives good predictive performance, especially for RW and ICU patients, according to suitable discrepancy metrics that were created to assess and compare models. 95 percent accuracy increased and calculated values for 10 days to demonstrate the temporal aspects of the expected daily reproduction number R.

Research article A study on immediate and training effect of Bhramari pranayama on heart rate variability in healthy adolescents

Introduction and Aim: Bhramari pranayama (Humming bee breath) is the most common slow pranayama practice involving inhalation through both nostrils followed by exhalation which produces sound of humming bee. Practicing pranayama reduces the effects of stress on different systems by increasing the vagal tone. With the ongoing COVID-19 pandemic, there are heightened feelings of stress, anxiety and depression and pranayama can be an effective way to improve our mental and emotional well-being. This study has been chosen to assess the effect of Bhramari pranayama on autonomic functions and to compare the immediate and training effects of Bhramari pranayama in the study group. Materials and Methods: An observational study was conducted in a medical college among 110 students aged between 18-22 years for a period of 2 months. The participants were instructed to do 9 rounds of Bhramari pranayama every day for 3 weeks. The immediate and training effect of pranayama practice on HRV (Heart Rate Variability) was assessed. The parameters of HRV (time and frequency domains) were assessed by PHYSIOPAC-PP4 (MEDICAID SYSTEMS, Chandigarh). Results: After training of Bhramari pranayama for 3 weeks, there was a decrease in Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP) and PNN50 and a decrease in frequency domain parameters, LFnu and LF/HF ratio were observed significantly. Conclusion: The study shows the possibility that Bhramari pranayama has a beneficial effect on HRV in medical students, as the autonomic balance produces the improvement of parasympathetic tone.

Social distancing detection by using deep neural network

This article helps in presenting a clear view of detecting social distance in order to evaluate the distance covered by the people from each other. It helps in providing alarming notification to the people in making safety during this pandemic period by the use of video feed. The frame of the video clipping is used as an input and is implemented based on the hybrid computer vision. The Deep neural network dependent algorithm named YOLOv3 has been used in alarming the detection of distance between the people. It is used along with the mapping technique known as Inverse Perspective Mapping (IPM) which is one of the tracking mechanisms that helps in monitoring the social distance. It is tested against MS COCO and image data sets that has been obtained from Google. The precision was found to be 97% that helps to design the outlook of the place where the public involvement is too high. It could help in controlling the violations of trespassers who does not obey the rules of social distancing and also servers to be precaution to control the disease prone zones.

Impact of lockdown during COVID-19 pandemic on the learning status of undergraduate and postgraduate students of Bangalore

Background: The COVID 19 pandemic has created various impacts on every human's life. COVID 19 lockdown has provoked enormous changes in the education sector which in turn influences the student's life in many aspects. The scope of this study is to understand the impact in both undergraduate and postgraduate students. Aim: This study aims at incisively analyzing the impact of lockdown imposed due to the COVID-19 pandemic on graduate students of Bangalore. Method: It is an online survey that encompasses a structural questionnaire with open-ended questions created using Google Forms, which were sent across the students through social media platforms. Results: A total of 115 students from both undergraduate and postgraduate programs have participated in this survey. Simple percentage distribution was estimated to evaluate the pedagogy, opinion on educational decisions, modes of learning, socio-economic conditions, and problems pertaining to academia because of this pandemic. As per this analysis, 80.9% of students faced difficulty due to lockdown. 67% of students thought that their family's income will be affected by this pandemic. 68.7% of students felt stressed, depressed and 52.3% of students could not find a suitable environment in their home to study during this lockdown. When we see this pandemic in an optimistic light, it has created various opportunities such as Digital learning and adoption of new health habits. © 2021. RIGEO. All Rights Reserved.

On modelling growing menace of household emissions under COVID-19 in Indian metros.

While local anthropogenic emission sources contribute largely to deteriorate metro air quality, long range transport can also play a significant role in influencing levels of pollutants, particularly carbon monoxide (CO) that has a relatively long life span. A nationwide lockdown of two months imposed across India amid COVID-19 led to a dramatic decline in major sources of emissions except for household, mainly from cooking. This initially led to declined levels of CO in two of the largest megacities of India, Delhi and Mumbai under stable weather conditions, followed by a distinctly different variability under the influence of prevailing mesoscale circulation. We hereby trace the sources of CO from local emissions to transport pathways and interpret the observed variability in CO using the interactive WRF-Chem model and back trajectory analysis. For this purpose, COVID-19 emission inventory of CO has been estimated. Model results indicate a significant contribution from externally generated CO in Delhi from surrounding regions and an unusual peak on 17th May amid lockdown due to long range transport from the source region of biofuel emissions in central India. However, the oceanic winds played a larger role in keeping CO levels in check in a coastal megacity Mumbai which otherwise has high CO emissions from household sources due to a larger share of urban slums. Keeping track of evolving carbon-intensive pathways can help inform government responses to the COVID-19 pandemic to prioritize controls of emissions sources.

COVID-19 lockdown and air quality of SAFAR-India metro cities

A drastic decline in the sources of emissions of pollutants under COVID-19 induced lockdown resulted in an unprecedented trends in most hazardous pollutants PM2.5, PM10 and NO2 in India. To realize the impact of lockdown in the concentrations of PM2.5, PM10 and NO2, we compared the trend of lockdown period (20nd March to 15th April) with several (3–7) years of past data in four Indian mega cities (Delhi, Pune, Mumbai, and Ahmedabad) of different micro-climate and geography. The significant reduction in the concentrations of NO2 in the ranges of ~60–65% is noticed in four megacities within the lockdown period when compared with the averaged data of past years. However, relatively low reduction in PM2.5 (~25–50%) and PM10 (~36–50%) is observed and city to city variation is found to be significant. The prevailing secondary aerosol formation and enhancement of any natural source of emissions could be some factors preventing PM2.5 levels to go down significantly. Under near negligible fossil fuel emission, contrary to the expectation, an increase in the ratio as compared to normal scenario is observed in Delhi on some days whereas on some selected days, PM2.5/PM10 ratio is found to decline significantly.

Towards baseline air pollution under COVID-19: implication for chronic health and policy research for Delhi, India

The Megacity of Delhi, home to 19 million inhabitants, is infamous for its poor air quality mainly due to anthropogenic emissions. While the COVID-19 pandemic is a health emergency, lockdown due to it saw an unprecedented decline in emission sources of pollutants by ~85%-90% in Delhi, resulting in sharp decline in the concentration of majority of pollutants. Here we report the experimental estimate of baseline level that is defined as the minimum level reached after lockdown under consistent fair weather condi-tion of major criteria pollutants. This may be consi-dered as an indicator of the background levels to which the population is chronically exposed. The con-sequences of such chronic air pollution exposure are excess respiratory and cardiovascular morbidity and mortality which are reported to be more serious than severe pollution episodes by epidemiologists. As the lockdown which was imposed on 24 March 2020, was extended during April and May, we present the pre-vailing ambient pollution levels and compare them with the baseline levels. Results are based on India’s largest monitoring network of 34 stations in Delhi. The findings are critical for policymakers to fine-tune ambient air quality standards and regulations leading to the development of effective risk management poli-cies and control strategies. © 2020. All rights reserved.

COVID-19 and environmental -weather markers: Unfolding baseline levels and veracity of linkages in tropical India.

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe due to its contagion nature. We hereby report the baseline permanent levels of two most toxic air pollutants in top ranked mega cities of India. This could be made possible for the first time due to the unprecedented COVID-19 lockdown emission scenario. The study also unfolds the association of COVID-19 with different environmental and weather markers. Although there are numerous confounding factors for the pandemic, we find a strong association of COVID-19 mortality with baseline PM2.5 levels (80% correlation) to which the population is chronically exposed and may be considered as one of the critical factors. The COVID-19 morbidity is found to be moderately anti-correlated with maximum temperature during the pandemic period (-56%). Findings although preliminary but provide a first line of information for epidemiologists and may be useful for the development of effective health risk management policies.