Computational fluid dynamic analysis of corona virus patients breathing in an airplane

Effective ventilation systems are essential to control the transmission of airborne aerosol particles, such as the SARS-CoV-2 virus in aircraft cabins, which is a significant concern for people commuting by airplane. Validated computational fluid dynamic models are frequently and effectively used to investigate air distribution and pollutant transport. In this study, the effectiveness of different ventilation systems with varying outlet vent locations were computationally compared to determine the best ventilation system for minimizing the risk of airborne transmission. The cabin air conditioning system was optimized to determine how design variables (air inlet temperature, outlet valve width and location, and mass flow rate) affect output parameters, including particle residence time, age of air, and thermal comfort conditions. Inlet mass flow rate was observed to be an influential variable impacting all output parameters, especially on age of air, where it was the most influential. In contrast, the least effective variable was width of the outlet valve, which only affected the particle residence time. Also, Predicted Mean Vote and Predicted Percentage Dissatisfied indices were the most affected by air inlet temperature, which had an inverse relation, while the outlet valve location had the greatest effect on particle residence time. © 2023 Author(s).

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity.

As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.

Impact of Demographic Variables and effectiveness of Rewards and recognition on Employee Engagement of Select Private Sector Banks in Telangana during pandemic

This covid-19 pandemic has made the mankind to think & rethink due to a lot of adversities, insecurities, etc & this pandemic has affected all businesses across the world, a quite similar to the one like the recession hit in 2008 and led to helplessness among people. Most of the businesses across have suffered due to this pandemic situation, especially service sector. Partial solution to this critical challenge is to have engaged employees who are the strong pillars because the entire working of the organisation depends on them. Employee engagement is the extent to which employees put their discretionary efforts into their work, mental ability, passion and energy. Engaged employee is always self-motivated and full of enthusiasm. Fully engaged employees can provide higher productivity, greater deliverables, higher self-motivation, reliability, loyalty towards organization, reduced employee turnover and lower absenteeism (Baumruk et al, 2004). The present study focuses on how the demographics variables(age, gender, work experience) and rewards and recognition has an effect on the employee engagement levels of select private sector banks in Telangana state during this pandemic period. Employee engagement helps to build good relationship with the customers. Primary data was collected using structured questionnaire. Selection of samples was done by using random sampling technique and the data was analyzed using appropriate statistical tools. It was observed that Age, work experience, rewards and recognition had influence on employee engagement. While gender had no influence on employee engagement in the select private sector banks for the study. © 2022. Specialusis Ugdymas. All Rights Reserved.

Deep Learning Based Intelligent Screening Mechanism

Facial recognition is widely used for identification of people as one of the biometric authentications. Biometric authentication consists of two types physiological and behavioral features. In physiological biometrics, faces, iris, and fingerprints are used for identifying the person. In behavioral biometrics, their characteristic features namely voice, DNA and hand writing is used. While using facial recognition, an individual can be identified using the previously trained model using deep learning based on the Haar cascade algorithm. Biometric authentication has been generally used for surveillance purposes. However, due to the COVID 19 pandemic, people of each nation are in need to wear face masks for their safety. Our project uses deep learning and open cv to recognize the person and to identify whether he wears a face mask or not by using transfer learning techniques and convolution neural network. One large dataset of people with mask and people without a mask was used as a training model. Our project was able to achieve an accuracy of 96.8% during the training and testing phase. © 2022 IEEE.

A Diagnostic Study on Prediction of Covid-19 by Symptoms Using Machine Learning

Effective screening helps for quick and accurate detection of COVID-19 and it also decreases the burden on the healthcare system. Prediction models with numerous criteria have been developed to estimate the probability of infection. These are designed to assist medical workers across the world in triaging victi ms, especially in places with limited medical resources. For predicting the COVID-19 using symptoms, the dataset is taken from the website of the Israeli Ministry of Health. The dataset contains 9 attributes and 2, 78, 848 samples. The raw dataset is cleaned using pre-processing techniques. The Machine learning algorithms like Random Forest, K Nearest Neighbor, Decision Tree, and hybrid Random Forest, K Nearest Neighbor, and Decision Tree are applied on the 1, 95, 194 samples to identify the model. The predicted model is tested on 83, 654 samples to ensure the quality of the designed model. The performance metrics like ROC [Receiver Operating Characteristic] curve, True Positive and Negative Rate, False Positive and Negative Rate, Positive and Negative Predictive Value, and Accuracy are applied to check the model. From the evaluation result, the proposed hybrid model gives high accuracy of 98.97%. The proposed technique might be utilized to priorities COVID-19 screening when testing capabilities are constrained., among several other things. © 2022 IEEE.

Prevalence of stress and depression among work from home professionals during COVID-19 lockdown

The software industry is a capital-intensive industry for humans. A study was conducted on the prevalence of stress and depression among work from home professionals. The objectives were (1) To evaluate the prevalence of stress among work from home professionals using a perceived stress scale. (2) To analyze the prevalence of depression in work from home professionals using the Hamilton depression rating scale. Data was Collected from 377 IT professionals of either genders. Almost 33.42% of the study participants were scored positive results in stress and in that 33.42% of study participants, 10% were severely depressed.

Environmental Effects of Covid-19 in India: A Qualitative Analysis

The Corona Virus Disease 2019 (COVID-19) is an acute virus creating respiratory disease and gastro intestine disease in humans. The outbreak of novel corona virus (COVID-19) has brought serious impact on all counties around the world. Spread of COVID-19 was controlled by countries through restricted movement, self-hygiene practices and social distancing. Despite all the efforts made by the governments, this pandemic brought serious effect on economy and environment. The impacts of COVID-19 on air, water and waste management were assessed and were observed that air and water quality has improved due to lockdown but the management of waste is a serious issue. This article describes the results of study performed on the environmental effects particularly in air and water by assessing the environmental conditions before and after the outbreak of pandemic COVID-19. The study results yields that the purity of air and water has been improved during the pandemic period when compared with the period before the outbreak of COVID-19 virus. Waste generated from self-quarantine houses, hospitals and self-hygiene practices followed by people has posed an enormous effect on waste management sector. Disposal of infectious waste along with municipal solid waste has created threat to people handling the waste and the environment. Based on the environmental analysis performed on air, water and waste management, solid guidelines has been provided in treating the waste management effectively. This article recommends the need for improving the waste treatment methodology and the significances of policy framework to face pandemic situation in future. This study improves the hope that, implementation of proposed guidelines will improve the purity level of environment and management of biomedical wastes effectively.

A Mechanistic Review on Plant-derived Natural Inhibitors of Human Coronaviruses with Emphasis on SARS-COV-1 and SARS-COV-2.

Coronaviruses have been receiving continuous attention worldwide as they have caused a serious threat to global public health. This group of viruses is named so as they exhibit characteristic crown-like spikes on their protein coat. SARS-CoV-2, a type of coronavirus that emerged in 2019, causes severe infection in the lower respiratory tract of humans and is often fatal in immunocompromised individuals. No medications have been approved so far for the direct treatment of SARS-CoV-2 infection, and the currently available treatment options rely on relieving the symptoms. The medicinal plants occurring in nature serve as a rich source of active ingredients that could be utilized for developing pharmacopeial and non-pharmacopeial/synthetic drugs with antiviral properties. Compounds obtained from certain plants have been used for directly and selectively inhibiting different coronaviruses, including SARS-CoV, MERS-CoV, and SARS-CoV-2. The present review discusses the potential natural inhibitors against the highly pathogenic human coronaviruses, with a systematic elaboration on the possible mechanisms of action of these natural compounds while acting in the different stages of the life cycle of coronaviruses. Moreover, through a comprehensive exploration of the existing literature in this regard, the importance of such compounds in the research and development of effective and safe antiviral agents is discussed. We focused on the mechanism of action of several natural compounds along with their target of action. In addition, the immunomodulatory effects of these active components in the context of human health are elucidated. Finally, it is suggested that the use of traditional medicinal plants is a novel and feasible remedial strategy against human coronaviruses.

Role of digital technologies to combat COVID-19 pandemic

Purpose: The unexpected epidemic of the latest coronavirus in 2019, known as COVID-19 by the Globe, a number of governments worldwide have been put in a vulnerable situation by the World Health Organization. The effect of the COVID-19 outbreak, previously experienced by China’s citizens alone, has now become more pronounced. For practically every nation in the world, this is a matter of grave concern. The lack of assets to withstand the infection of COVID-19, mixed with the perception of overwhelmed medical mechanisms, pressured a number of places in a state of partial or absolute lockdown. Design/methodology/approach: The medical photos such as computed tomography (CT) and X-ray playa key role in the worldwide battle against COVID-19, while artificial intelligence (AI) has recently appeared. The power of imaging is further increased by technology tools and support for medical specialists. In comparison to the related direct health effects because of the COVID-19 disaster, this research identifies its impacts on the overall society. Findings: This paper hereby examines the rapid answers in the medical imaging community toward COVID-19 (empowered by AI). For example, the acquisition of AI-empowered images will significantly assist automate the scanning process and reshape the procedure as well. AI, too, may improve the quality of the job by correctly delineating X-ray and CT image infections, promoting subsequent infections, quantification. In addition, computer-aided platforms support radiologists make medical choices, i.e. for illness tracking, diagnosis and prognosis. Originality/value: This research encompasses the whole medical imaging pipeline and methods for research related to COVID-19, include a collection of images, segmentation, diagnosis and monitoring. In drawing stuff to minimize the effects of the COVID-19 epidemic, this paper is investigating the use of technologies such as the internet of things, unmanned aerial vehicles, blockchain, AI, big data and 5G. © 2021, Emerald Publishing Limited.

Coronaviruses: the most dangerous pathogen of present era

Coronaviruses are the major group of viruses belonging to the Order Nidovirales. Four families- Coronoviridae, Arteriviridae, Mesoniviridae, and Roviviridae are included in this Order. All CoVs are pleomorphic RNA viruses characterized by club-like spikes that project from their surface. These viruses have unusually large RNA genome, with a unique replication strategy. CoVs were not considered as highly pathogenic for humans until the emergence of SARS-CoV in 2002-03 in China. The emergence of another highly pathogenic CoV, Middle East Respiratory syndrome (MERS-CoV) in the Middle East Countries confirmed the occurrence of highly pathogenic human viruses among the Coronaviruses. In this article we provide a brief introduction to coronaviruses, mode of action, pathogenesis, current situation, prevention and control of COVID-19.

The first COVID-19 incidence in India: A lesson of struggle and survival

The coronavirus disease 2019 (COVID-19) outbreak, which originated in Wuhan, China, has now spread to more than 200 countries and administrative regions infecting 3,09,04,45 individuals of all ages as of 3rd April, 2020. Though most of the infected individuals exhibit mild symptoms including fever, upper respiratory tract infections, shortness of breath and diarrhea or are asymptomatic altogether. Severe cases of infection can lead to pneumonia, multiple organ failure and death. Globally, at least 2, 07,973 deaths have been directly attributed to COVID-19 and this number is expected to rise with the ongoing epidemic. WHO declared the outbreak to be a public health Emergency of International concern on January 30, 2020. The same day, a laboratory confirmed case of COVID-19 was reported in Kerala. That was the first reported case of COVID-19 in India. Since then 498 disease cases were reported in Kerala, while in India this has gone up to 33,050 with 1,074 deaths. During the first phase of the COVID-19 outbreak in Kerala, the health authorities have responded in a stellar manner. Kerala has not only traced hundreds of contacts of the confirmed cases and notified them to the Integrated Disease Surveillance Programme (IDSP) for monitoring, but also used unique community-based isolation methods, innovated while dealing with the Nipah virus outbreaks of 2018 and 2019. The model of monitoring with the District Collector as the administrative unit has been shared as a best practice with all states. © 2020 Indian Society for Malaria and Communicable Diseases. All rights reserved.