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Since the COVID-19 pandemic is on the rise again with hazardous effects in China, it has become very crucial for global individuals and the authorities to avoid spreading of the virus. This research aims to identify algorithms with high accuracy and moderate computing complexity at the same time (although conventional machine learning works on low computation power, we have rather used CNN for our research work as the accuracy of CNN is drastically greater than the former), to identify the proper enforcement of face masks. In order to find the best Neural Network architecture we used many deep CNN Methodologies to solve classification problem in regards of masked and non masked image dataset. In this approach we applied different model architectures, like VGG16, Resnet50, Resnet101 and VGG19, on a large dataset to train on and compared the model on the basis of accuracy in which VGG16 came out to be the best. VGG16 was further tuned with different optimizers to determine the one best fit of the model. VGG16 gave an ideal accuracy of 99.37% with the best fit optimizer over a real life data set. © 2023 IEEE.
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The research is to see how E-banking affects the performance of a bank. As a measure of the electronic method of transactions, we used credit cards, debit cards, National Electronic Fund Transfer (NEFT), Real Time Gross Settlement (RTGS), and Point of Sale (POS), while Return on Assets (ROA) was used as a metric of profitability. The research focuses on India's top ten public sector banks, as determined by market capitalization. The findings reveal that digital payment instruments considerably influence return on assets, indicating that internet banking might help banks increase their profitability. Furthermore, the study shows that electronic banking has a significant favorable influence on bank profitability. Financial institutions were able to reduce their banking expenses after the advent of e-banking services. Furthermore, technical progress in the banking industry provides additional potential for banks to improve their interaction with customers, easier access to banking facilities by clients, and banks' market reach with e-banking.
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Background: COVID-19 has caused pandemic during 2019-2020 and has presented with illnesses ranging from the usual mild flu to serious respiratory problems/complications, even leading to considerable mortality. Recent literatures have suggested that the health (especially psychological) impacts of quarantine are substantial and can be long lasting. Aim: The purpose of this study was to assess the mental health status (psychological distress) of experienced quarantine and compliance to quarantine during the outbreak of COVID-19 in Nuh district. Methods: The study included 543 subjects (adults aged 18 years or more) who were sent for quarantine at home or state-run facilities and included "Flu corner " screened patient and health-care staff working in COVID-19 outpatient and wards. The psychological impact was assessed using the Kessler Psychological Distress Scale (K10). Categorical data were presented as percentages (%), and bivariable logistic regression was applied to find out the association, and it was considered significant if the P < 0.05. Results: The doctors and nursing staff were among two-fifth of the subjects (217/543, 40.1%), and only 11.6% of quarantined subjects (63/543) were compliant with all protective measures. The mean score obtained on Kessler Psychological Distress Scale (K10) subjects was 18.69 +/- 4.88, whereas out of 543 subjects, 152 (27.9%) had a score of 20 or more, and it has a significant association with the elderly age group, female gender, and workplace as exposure setting (P < 0.05). Conclusion: Given the developing situation with coronavirus pandemic, policymakers urgently need evidence synthesis to produce guidance for the public. Thus, the outcomes of this study will positively help authorities, administrators, and policymakers to apply quarantine measures in a better way.
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This research aims to check the chargeability of sodium hypochlorite and the efficacy evaluation of an air-assisted electrostatic disinfection device. Five different inanimate surfaces i.e., wood, glass, stainless steel, plastic and fabric were considered to examine the performance in terms of efficacy, survival time, off-target losses, spray coverage and the volume of disinfectant consumed. A significant charge-to-mass level of 2.43 mC/kg was achieved for sodium hypochlorite at an applied voltage of 2.0 kV, a liquid flowrate of 253 ml/min and applied air pressure of 4.0 bar. The experimental results found that 1000 mg/L of sodium hypochlorite concentration effectively eliminated Pseudomonas aeruginosa, Clostridium perfringens and Bacteriophage MS2 colonies. © 2023 Elsevier B.V.
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The European Centre of Disease Prevention & Control's analytical statistics show that the new corona virus (Covid-19) is rapidly spreading amongst millions of people & causing the deaths of thousands of them. Despite the daily increase in cases, there are still a finite quantity of Covid-19 test kits available. The use of an automatic recognition system is crucial for the diagnosis and control of Covid-19. Three important Inception-ResNetV2, InceptionV3, & ResNet50 models of convolutional neural networks are utilized to detect the Corona Virus in lung X-ray radiography. The ResNet50 version has the best result & accuracy rate of the present system. As compared to the current models, a novel procedures and ensuring on the CNN model delivers better specific, sensitivities, and precision. By using confusion matrix and ROC assessment, fivefold validation data is utilized to analyze the current models and compare them to the proposed system. © 2023 IEEE.
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Background: Cymbopogon martinii (palmrose essential oil, PEO) and Cymbopogon citratus (lemon grass essential oil, (LEO) are used as complementary and traditional medicine worldwide. PEO and LEO from Cymbopogon genus, contains a diversity of pharmacologically active compounds. Due to the complex nature of essential oils, their antifungal mechanism of action against aspergillosis and mucormycosis is still not completely understood. Purpose: Hence, the present study aimed at determining the chemical profile of each PEO and LEO and performing a molecular docking of two of their components geraniol and geranial against fungal enzymes involved in riboflavin synthesis pathway viz: riboflavin synthase (RS), riboflavin biosynthesis protein RibD domain-containing protein (RibD), and 3,4-dihydroxy-2-butanone 4-phosphate synthase (DBPS) as opposite sites for drug designing against aspergillosis and mucormycosis and in vitro confirmation. Study design and method: Chemical profile of PEO and LEO was performed by GC-FID analysis. For molecular docking, patch-dock tool was conducted. Ligand-enzyme 3-D interactions were also calculated. ADMET properties (absorption, distribution, metabolism, excretion and toxicity) were also calculated. Antifungal activity was evaluated agaist three test pathogens Aspergillus niger, Aspergillus oryzae and Mucor indicus using poisoned food technique. Results: GC-FID showed geraniol/geranial as the major components in PEO/LEO, thus, they were selected for docking analysis. Docking analysis specified active binding of geraniol and geranial to riboflavin synthase (RS), riboflavin biosynthesis protein RibD domain-containing protein (RibD), and 3,4-dihydroxy-2-butanone 4-phosphate synthase (DBPS) fungal enzymes. Wet-lab authentication was achieved by three fungal strains A. niger, A. oryzae and M. indicus. Docking studies revealed that the ligands geraniol/geranial exhibited interactions with RS, RibD, and DBPS fungal enzymes by H- bond and hydrophobic interactions. Geraniol and geranial obeyed LIPINSKY rule, and exhibited adequate bioactivity. Wet lab results indicated that PEO/LEO was able to inhibit fungal growth against test pathogens. Conclusions: These findings confirm the fungicidal properties PEO/LEO essential oils as possible alternatives to synthetic fungicides. © 2023 The Author(s)
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might have originated from the recombination of a Pangolin-CoV-like virus with a Bat-CoV-RaTG13-like virus and then transmitted to a human at Wuhan city of China. On February 11, 2020, the WHO announced a name for the new coronavirus disease as COVID-19. Finally, the WHO declared the novel coron-avirus outbreak a global pandemic on March 11, 2020. Within a few months, SARS-CoV-2 had spread across the world to 220 countries, areas or territories. The main objective of this work is to review the existing knowledge about COVID-19, its updated status, available treatment procedures and future challenges. The available literature based on the COVID-19 was thoroughly reviewed and concise, evidence-based information was explored for the public interest. Various authentic databases like PubMed, Scopus, and Google Scholar together with the official sites of some Govt. Organizations were carefully searched for all relevant information about the current status of COVID-19, including the published research on coronavirus. More than 68 million people are already infected, including around 20% severely ill, with almost 1.5 million casualties due to this virus which is expected to infect approximately 70% population worldwide. Currently, maximum confirmed cases and death are reported in the USA. The epicentre of the pandemic was initially shifted from China to Europe, then to the USA, Brazil and now India. In between, the understanding of pathogenesis and mode of transmission has been developed;repurposing drugs are being validated and the development of a new vaccine is underway. The study concludes that there is no established treatment available for COVID-19, although 26 clinical and 139 preclinical trials are un-derway to develop vaccines globally. Although three vaccines are at the advanced stage of develop-ment, their efficacy and adverse effects are yet to be validated and recorded. Recently, the Pfizer vaccine has been started for vaccination in emergency cases in England and Bahrain, and the United States of America will start it soon. Meanwhile, prevention, rigorous global containment and quarantine efforts are practiced worldwide to control its spread.Copyright © 2021 Bentham Science Publishers.
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Covid -19 second wave was considered a disaster in India as it was more havoc than the first one. Shortness of breath in patients leads to more demand for oxygen and hospitalization. So, there was a challenge for the hospitals to combat this disease. In the covid second wave, moderate to severe cases were treated at three hospital levels (CHC, Sub-district, and District hospital). This disease was not limited to bigger cities but spread to rural and hilly areas. We conducted quantitative research among government hospitals in five hilly districts of Uttarakhand at three levels of hospitals. Data were collected from a close-ended questionnaire using a judgmental sampling technique and analysed with the help of tables and bar charts. Questions were set based on the pilot study. The challenges explored through this study were divided into five main headings and eleven sub-headings. The main headings were Manpower, Surge capacity, logistics, coordination, and management of non-covid patients. Sub-headings were a shortage of medical staff, shortage of paramedical staff, shortage of sweepers, shortage of ambulance drivers, shortage of ICU beds, shortage of oxygen beds, shortage of covid drugs (Remdesivir and Steroids), oxygen cylinders, PPE kits, difficulty in coordination with staff and difficulty in managing non- covid patients.Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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At present supply chains are dynamic and interactive in nature which integrates suppliers, manufacturers, distributors, and consumers. An important objective of supply chain management is to ensure that each supply chain partner is in the coordination with others so that supply chain potential and enhanced surplus can be realized in sales. In general, this coordination breaks due to distrust, misinformation, poor logistics and transportation infrastructure;however, in specific cases like Covid-19, it arises due to uncertainties caused by various types of risks such as delays and disruptions. During pandemic Covid-19 global supply chains have been distorted badly due to multiple lockdowns and country specific decisions to contain the spread of coronavirus. For dealing with such pandemic situation in future, we have learned and proposed some of the strategies from literature and practice that a supply chain manager can think of to minimize supply chain disruptions during a pandemic. These supply chain strategies include Resilience, Outsourcing/Offshoring, Agility, and Digitalization. For helping in decision making to the practitioners, we have applied Best Worst Method (BWM) to evaluate these strategies during pandemic times and found that Digitalization strategy (0.574) has been most differentiating among the proposed four strategies in a pandemic scenario;whereas, Outsourcing/Offshoring strategy is most hampered/ineffective during such times. © 2022 Kharazmi University. All rights reserved.
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Health systems and their trends are continually evolving with advanced research on new tools and techniques. Since every health system has its limitations, there is a requirement for the integration of different medical systems to better serve mankind. In this direction, a practitioner of modern medicine should take into consideration traditional medicine practices, while the traditional medicine practitioner should also integrate the beneficial strategies of modern medicine. In spite of different approaches, the aim of all these medical systems is the same, which is to serve mankind by treating various health problems. Although traditional medicine has the potential to treat a variety of diseases, its acceptance by the global community is less than that of modern medicine due to inadequate scientific validation of its therapeutic benefits. In recent years, many new diseases have emerged, perhaps due to changes in geography, environment, weather conditions, and soil composition. A complete treatment of such diseases is a challenge for all medical practitioners, whether belonging to modern or traditional medicine. Therefore, an in-depth multidisciplinary research is needed to find an effective therapeutic strategy by connecting modern and traditional medical systems with biomedical sciences. In this direction, biotechnology can play an important role in developing a diagnostic method and treatment protocol. The present review provides an overview of the available Ayurvedic treatment options and future possibilities in which biotechnology may assist as a service provider. Copyright © 2023 Bentham Science Publishers.
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The demand for personal hygiene products has increased during COVID-19 pandemic outbreaks. It has resulted in the increasing production of hygiene products. During the coronavirus epidemic, globalised and uncertain demand for personal hygiene products creates complicated situations for the manufacturing firms. This article explores the fuzzy model of economic production for hygiene products with uncertainty in demand and production. Due to the rising demand for hygiene products, the demand rate has been set as a linear time function, while the production rate has been taken directly proportional to the rate of demand. Therefore, due to ambiguity or vagueness in demand, this proposed model has considered the triangular fuzzy number with an upper and lower split. The weighted sum method has turned the multi-objective problem into a single objective. The optimisation technique was used to minimise the producer's overall cost under the condition mentioned earlier, and the model is validated numerically. © 2022 Inderscience Enterprises Ltd.
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The present study contributes to the literature by investigating the impact of E-Payment System on Currency in circulation after facing three major reforms i.e., Demonetization of 500 and 1000 rupee note, implementation of GST and Current pandemic (Covid-19) situation. Results imply that with the increase in the volume of all the respective electronic payment systems the currency in circulation (in physical form) got minimized in the economy. Moreover, NEFT shows much higher influence on currency in circulation as compare to RTGS and IMPS but PPI’s shows the highest influence on currency in circulation from the selected E-Payment systems. Furthermore, Vector Autoregressive model suggests, RTGS, IMPS, NEFT, CARDS (POS), PPI’s, M-Banking are expected to increase whereas NACH is expected to observe a downfall in the near future. © 2022, School of Management Sciences. All rights reserved.
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This paper evaluates the need for corrugated boxes in the industry & the need for fast and efficient manufacturing of such boxes, even at a small scale. Nowadays, online shopping is increasing at a tremendous rate and with the advent of the corona pandemic, this rate has been increasing exponentially due to people's trust in packaged products. Corrugated boxes are lightweight, cost-effective, good shock absorbers, and help in keeping the product safe from contamination. With this project, we are manufacturing an automatic corrugated box- making line using PLC which will work at an efficient and fast pace, keeping the safety of people around at priority. The assembly line will consist of steps like pasting, punching and cutting using pneumatics, making all the steps work in a synchronized manner through the controlling feature of PLC. © 2022
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To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, relations and events) from scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence. All of the data, KGs, reports(1), resources, and shared services are publicly available(2).
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With an ongoing episode of Covid, the world health security and precaution need reformation and a new approach to be dealt with. The health concerns of the individual is a topic of utmost importance for every nation fighting the pandemic. With limited healthcare staff and the large public to look after, the assistance of Computer vision and AI is needed. Social distancing is a very effective way of containing the spread of a pandemic. Social distancing becomes difficult when dealing with a number of subjects like at gateways of offices, Airports, and many other sectors that have significant footfall in a day. In this paper we have tried to compare the different models for the recognition of mask on the face, for doing so we have used Real world masked face dataset (RMFD) (Iqbal et al, Renewable power for sustainable growth, Springer Nature, Berlin, LNEE, 2020) and Kaggle (Tomar et al, Machine learning, advances in computing, renewable energy and communication, vol 768. Springer Nature, Berlin, LNEE, 2020) dataset. At first we gather the images where face have actual mask on it and also augmented the image with editing the image of unmasked face with mask so that model can learn very details of the image and result will come more accurate and clean. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Across the globe, the havoc of the pandemic known to be a black swan event has brought miseries, deaths, uncertainty, loss of lives and jobs holding the humanity in a state of despair. The financial markets have been equally hit by the pandemic due to on-going uncertainty and hopelessness among the masses. The aim of this study is to examine the volatility contagion and dynamic conditional correlations between eight stock indices during the gloomy period to validate that there is a scope for revisiting the investment portfolio, create natural hedge in the investment portfolio by using exponential generalised autoregressive conditional heteroscedasticity (EGARCH) and dynamic conditional correlation generalised autoregressive conditional heteroscedasticity (DCC-GARCH) approach. We conducted an in-depth analysis of capturing volatility among stock indices ranging from tracking the volatility followed by estimating persistence and multivariate volatility contagion of major stock indices of developed and developing economies during turbulent times of the pandemic when the globe was reeling under the taxing consequences of the first and second wave of COVID-19. There are very few studies that have conducted an in-depth analysis of capturing volatility of stock indices ranging from tracking the asymmetric volatility followed by estimating persistence and multivariate volatility contagion of major stock indices of developed and developing economies during turbulent times of the pandemic when the globe was reeling under the taxing consequences of the first and second wave of COVID-19. © 2022 Management Development Institute.
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Looking at the massive spread of SARS CoV2(COVID-19), it not only requires medical solutions at this point but different alternatives must also be examined to prevent its contagious nature getting its hands on a large number of individuals. Getting some prior information before its actual cause can help us to prepare ourselves to fight this pandemic better. It can assist authorities and administration to make better decisions in relatively less time to figure out the most suitable solutions. Since it is difficult to devise a permanent solution to this kind of pandemic, such data analysis can be used to strategize ourselves to cope with it. This study focuses on the forecasting of the number of active cases using deep neural networks. The models used in this approach are Multilayer Perceptron(MLP), Convolution Neural Networks(CNN) and Long Short Term Memory(LSTM). The performance of all three models is analyzed and although all of them are reasonably well, the MLP model outperforms the other two. These models can be used to predict the number of cases on a given day and a potential future outbreak. © 2022 IEEE.
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This study examined the rates of depressive symptoms in active component U.S. service members prior to and during the COVID-19 pandemic and eval-uated whether SARS-CoV-2 test results (positive or negative) were associated with self-reported depressive symptoms. Depressive symptoms were mea-sured by the Patient Health Questionnaire-2 (PHQ-2) screening instrument and were defined as positive if the total score was 3 or greater. From 1 January 2019 through 31 July 2021, 2,313,825 PHQ-2s were completed with an increase in the positive rate from 4.0% to 6.5% (absolute % difference, +2.5%;relative % change, +67.1%) from the beginning to the end of the period. While there was a gradual increase of 19.8% in the months prior to the pandemic (1.4%/month average), this increase grew to 40.4% during the pandemic (2.5%/month average). However, no association was found between a positive or negative SARS-CoV-2 test result and the PHQ-2 screening instrument result. These findings suggest that the accelerated increase in depressive symptoms is likely a function of the environment of the COVID-19 pandemic instead of the SARS-CoV-2 infection itself. Further research to better understand specific factors of the pandemic leading to depressive symptoms will improve efficient allocation of military medical resources and safeguard military medical readiness. © 2022, Armed Forces Health Surveillance Center. All rights reserved.
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This study examined monthly prevalence of obesity and exercise in active component U.S. military members prior to and during the COVID-19 pan-demic. Information about obesity (BMI≥30) and self-reported vigorous exercise (≥150 minutes per week) were collected from Periodic Health Assessment (PHA) data. From 1 January 2018 through 31 July 2021, there was a gradual increase in obesity and an overall decrease in vigorous exercise. Comparing the mean monthly percentage of obesity during the 12-month period prior to the pandemic to the 12 months after its start showed an overall increase in obesity (0.43%);however, no obvious spike in the obesity trend was appar-ent following the onset of the pandemic. The prevalence of vigorous exercise showed an abrupt decrease following the onset of the COVID-19 pandemic, but this change did not coincide with an abrupt change in the obesity trend. These results suggest that the COVID-19 pandemic had a small effect on the trend of obesity in the active component U.S. military and that obesity prevalence continues to increase. © 2022, Armed Forces Health Surveillance Center. All rights reserved.
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Rationale: COVID-19 patients present with a number of clinical symptoms ranging from mild, moderate to severe, while only a subgroup of patients, who requires high-dependency critical care resources, accounts for most of the COVID-19 associated health care expenditure and death. A reliable prognostic tool is therefore required to identify patients at risk of developing severe COVID-19 pneumonia. To address this unmet need, we tested a wide range of potentially important peripheral blood biomarkers in a group of clinically risk-stratified COVID-19 patients in order to identify most relevant candidate biomarker(s) predictive of disease progression. Methods: Patients and healthy controls recruited to this study are summarised in Figure 1. Biomarkers levels were analysed using ANOVA across the severity groups. Spearman-correlation coefficients against pairs of average levels from each biomarker within severity-group and healthy controls were assembled into a 76x76 matrix and agglomerative hierarchical clustering was applied to generate the final heatmaps. Linear-discriminant analysis (LDA) was carried out on a reduced optimised set of biomarkers to explore the boundaries between the clinical severity groups.Results: Degree of lymphopaenia, neutrophil levels, TNF-α, INR-levels, and pro-inflammatory cytokines;IL6, IL8, CXCL9 and D-dimers were significantly increased in COVD-19 patients compared to healthy controls (p<0.05, 95% C.I.). C3a and C5 was significantly elevated in all categories of severity compared to healthy controls (p<0.05), C5a levels were significantly different between “moderate” and “severe” categories (p<0.01). sC5b-9 was significantly elevated in the “moderate” and “severe” category of patients compared to healthy controls (p<0.001).Heatmap analysis demonstrated distinct visual differences of biomarker profiles between the clinical severity groups. LDA on the deteriorators, non-deteriorators and healthy volunteers as a combined function of the predictor variables: C3, eosinophil-counts, granulocyte colony-stimulating factor (G-CSF), fractalkine, IL10, IL27, LTB4, lymphocyte count, MIG/CXCL9, M-CSF, platelet count and sC5b-9 showed clear separation between the groups based on biomarker/blood-count levels.Conclusions: Diagnostic and clinical assessments followed by robust statistical and machine learning approaches could identify peripheral blood biomarkers for prognostic stratification of patients in COVID-19. Our results would be helpful for clinicians and supports the use of point of care devices that can quantify multiple analytes. (Lui G, et al., Pointof- care detection of cytokines in cytokine storm management and beyond: Significance and challenges. VIEW. 2021;2: 1-20.). Such would allow for more efficient management and resource allocation. 1 (Figure Presented).