ABSTRACT
Objective: To study the mechanism of Runfei Ningshen Decoction in the treatment of insomnia caused by corona virus disease 2019(COVID-19) by using network pharmacology and molecular docking analysis. Methods: The chemical components and targets of Chinese medicinal materials of Runfei Ningshen Decoction in TCMSP, Batman, and CTD databases were searched. The relevant targets of novel coronavirus pneumonia and insomnia in Disgenet, GeneCards, CTD, and Malacards databases were searched. The component-target-disease network was established by using Cytoscape 3.2.1 software;The protein-protein intereation(PPI) network was constructed in string database. The common targets were enriched by using Cluster Profiler software package in R language software platform. The molecular docking of core targets related to insomnia caused by COVID-19 was carried out by using Discovery Studio 4.0 software. Results: 349 medicinal ingredients in Runfei Ningshen Decoction, 1 904 targets, 1 505 new coronavirus pneumonia-related targets, and 1 337 insomnia-related targets were collected. When the intersection of Venn diagrams were used, 404 common targets were obtained for the 2 diseases. 250 targets were intersected with the 2 diseases, and 33 core targets were screened out by the analysis of the interaction network between targets. Pathway enrichment analysis showed that Runfei Ningshen Decoction mainly acts on AKT1, INS, TP53, IL-6, key targets such as AKT1, INS, TP53, IL-6, JUN, CASP3, TNF, CAT, PTGS2 and CXCL8, which are involved in the important pathway processes such as human cytomegalovirus infection, fluid shear stress, and AGE-RAGE signaling pathways in complications of atherosclerosis and diabetes. The results of molecular docking showed that the core target has a high affinity with beta-sitosterol, 1-methoxy phaseolin, 3'-hydroxy-4'-O-methylglycyrrhizin, and anhydroicariin. The prescription treatment of insomnia caused by COVID-19 may be through the targets such as PTGS2, AR, PPARG, NOS2, HSP90 AA1 and so on. Conclusion: Runfei Ningshen Decoction can treat insomnia caused by COVID-19 by inhibiting IL-6 and TNF-a.
ABSTRACT
Spread prevention actions (SPAs) during Coronavirus pandemic period, such as increased hand-washing, temporary lock-downs, preventions in transportation, and the reduction of recreational and industrial activities may change the routines in social behaviors. Accordingly, SPAs can be effective on the quality and quantity of raw municipal wastewater. This research evaluates the aforementioned hypothesis and recommends solutions for the proper operation of wastewater treatment plants (WWTPs). Methods: For this purpose, the quantity and quality of sewage in 23 municipal WWTPs in Isfahan province, as the study area, were surveyed and compared from 2015 to 2020. SPSS software (version 23) was used for statistical analysis. Results: Results indicated that the annual growth rate of sewage discharged in the spring and summer of 2020 (period of SPAs) in the study area is 24% more than the average of annual growth rate in long-term (2015-2019). This increase is 45% in small WWTPs, while it is only 5% in large WWTPs. Results also revealed that the concentration of chemical oxidation demand (COD) of sewage was reduced 24% on average in this period. In addition, the biodegradability of wastewater is increased in large WWTPs mainly due to the decrease of industrial activities. Conclusion: Therefore, SPAs in the pandemic period of Coronavirus could increase the quantity of municipal wastewater and reduce its COD concentrations. These variations may provide more appropriate operational conditions for waste stabilization ponds rather than activated sludge units.
ABSTRACT
Forest products and timber harvesting businesses were severely affected by the COVID-19 pandemic. This article describes how forest products companies used Paycheck Protection Program (PPP) loans to keep over 487,000 workers in the forest industry on payroll through the pandemic. This article also summarizes the Pandemic Assistance for Timber Harvesters and Haulers (PATHH) program, payments provided to timber harvesting and hauling businesses that experienced losses in revenue in 2020. Timber harvesting and hauling companies that received a PATHH payment reported US$1.27 trillion in total revenue lost between 2019 and 2020 because of the COVID-19 pandemic. © 2022 Forest Products Society. All rights reserved.
ABSTRACT
Objective: To introduce the principle and method ofa-Sutte model, establish a a-Sutte model by using software R, compare the fitting and prediction effects of thea-Sutte model and multiple seasonal autoregressive integrated moving average model, SARIMA model and provides reference for the application of thea-Sutte model in epidemic prediction.
ABSTRACT
The current economic environment characterized by the implementation of new ICT technologies, globalization, and the pandemic period has determined the growth of online communication, the development of the e-commerce sector, and the change in online consumer behavior. The research aims to analyze online Romanian consumer behavior trends and perspectives. In order to observe the current position of Romanian online commerce, a comparison was made between the Romanian e-commerce market and three other e-commerce groups: the average for EU-27 countries, the group of four countries with the highest e-commerce values (called 4gc—Denmark, the Netherlands, Germany, and Norway) and the country with the lowest values in e-commerce. A comparison was made using mathematical simulation to predict the potential of e-commerce in the future and identify possible risks. Based on the simulation, the results show that the Romanian e-commerce market can continue to grow, becoming mature, and will provide opportunities for sustainable growth. In order to observe and analyze a possible future for 2021–2026, the regression function, correlation matrix, time series analysis, variable maximization, and QM for the Windows program have been implemented. The graphical representation indicates a positive and growing forecasted future trend for Romanian e-commerce.
ABSTRACT
The Covid-19 pandemic has shown that food supply chains are the most critical component of economic and human activities. It has also created a lot of interest among researchers, practitioners and policymakers to study the significant challenges of the food supply chain caused by the pandemic. Therefore this work wanted to investigate the critical supply chain challenges due to Covid-19 with the help of a systematic literature review of well-established articles published in interdisciplinary journals. The selection of thirty one papers was made through a research protocol that helped select and identify research papers which were coded with the help of qualitative software Atlasti 9.0 to study which supply chain challenge amongst disruption, forecasting and inventory was most prominently studied in the literature. Results of software coding revealed that disruption was coded 170 times whereas forecasting 10 times and inventory 37 times as challenges of food Supply Chain Management (SCM). Therefore, it was concluded that most of the researchers considered disruption as one of the significant food supply chain challenges. Further coding also revealed that lockdown and labour related issues were the primary reasons for food supply chain disruption.
ABSTRACT
Background: Patients with Coronavirus Disease (COVID-19) have a significant death rate due to comorbid diseases. As a result, identifying risk factors associated with poor outcomes in COVID-19 patients is important.
ABSTRACT
We are in the tech age and use social media in everyday life for variety of tasks. Communication methods have remarkably changed with launch of social media and it is changing every day with new software connecting people and bringing the world in a room. It wasn't a surprise when this mode of communication entered the education field. The current COVID 19 conditions are a big trigger in bringing out the maximum possible for effective communication through social media. The show must go on in education just like in other fields of life even with the disaster of CORONA-19. The students must be reached out and given their due education because education is a fundamental right of every individual. Social media proved to be a boon for education field right from primary to higher education and from general education to professional education. Nursing education couldn't be exception to this change. Although there was resistance initially, the nursing education via social media has got good momentum by now. The forgoing article details the experiences of nursing tutors as they use social media in nursing education.
ABSTRACT
Corona Virus or COVID-19 Disease is an infectious disease caused by newly discovered coronavirus. It caused a lot of damage to the Mankind around the world. It is also parallelly called as SARS-CoV-2 and was first reported in Wuhan City, China by the officials in December 2019. On 30 January 2020, the Director-General Tedros Adhanom of World Health Organization declared the outbreak of the coronavirus. Currently, many Pharma Companies aiming to develop vaccine to the COVID-19. To avoid being exposed to coronavirus, we must cover our nose and mouth with a mask, wash our hands frequently and avoid direct contact with the people who are already affected. This Paper aims to detect whether a person who is facing the camera is wearing a face mask or not in real time. Deep Learning is the best Technology present in the current Industry for its effectiveness in reorganization and classification using image processing. It proposes a retina face mask which a one stage detector and with further developments and modifications, this model can be used in Airports, Railway Stations and other highly crowed public areas.
ABSTRACT
Background: Educational system is affected because of COVID-19. Online classes were conducted by the management for the welfare of the students. Online classes are conducted over the internet and it is very convenient during lockdown. But technological challenges can make the students think back towards their blackboard class setup. Aim: To assess the perception and reception of online and offline education among adolescents in the Chennai population.
ABSTRACT
Mothers have high anxiety about the health of their child during the pandemic. They feel that visiting dental clinics will be more risky as it has the possibility of getting affected by viruses. The aim of the study was to assess the mother's knowledge, attitude and fear about dental visits during the pandemic. Materials and methods: The survey was taken from mothers of children visiting pediatric dental outpatients in a private dental college. A pre-validated questionnaire was prepared containing 14 questions in both English and vernacular language (Tamil) was distributed directly to them. The results were analysed statistically through SPSS software version 23.0. Descriptive statistics and inferential statistics were performed Results: In the present study 31.15% of mothers have the fear of waiting in the waiting room while their child is being treated. About 85.25% of mothers have the fear of spreading the virus from someone in the clinic. About 83.61% of mothers have had anxiety since the beginning of COVID-19 pandemic. About 59.02% of mothers had extreme fear during their visit to the dental clinic.
ABSTRACT
There has been a rise in the use of online food delivery applications, especially at this time of the COVID-19 pandemic. Research has shown a few important factors that influence the public in using online food delivery applications. This study aims to determine the three main determinants that influence the acceptance of online food delivery applications. Hence, to gather data, an online questionnaire has been distributed through an online platform. A non-linear regression analysis was performed to explain the determinants, along with a descriptive analysis to answer the objective of the study. A total of 291 participants responded to the survey and the outcomes showed that performance expectancy, social influence, and perceived trust have a significant relationship with consumers' acceptance of online food delivery applications. Overall, in Malaysia, the majority of the online food delivery application users prioritised both performance expectation and perceived trust more than social influence.
ABSTRACT
Background: The COVID-19 pandemic has been a time where social media allows increased conversations about it. These conversations have spread various conspiracies about vaccines against COVID-19. It is, therefore, necessary to develop communication strategies, led by official accounts, that offer accessible information on vaccination as a preventive public health strategy. The aim of this study was to analyze the role of public institutions on Twitter campaign #yomevacuno to deal with misinformation about vaccination against COVID-19.
ABSTRACT
The learning system is evolving all throughout the world. From traditional classroom systems to digitalization systems, the new trend is everywhere. Because they provide end users with a flexible, easy-to-use, secure, and cost-effective learning process, cloud-based Learning Management Systems (LMS) will propel the educational industry forward in the next years. The cloud-based LMS model is the most appropriate and trustworthy learning model in the global educational sector, especially when the economy is in a slump due to a global pandemic situation. It can be utilized in faraway locations through the web with several users on the same platform. As a result, the initial requirement is secure access to the LMS model. Having said all the perfect uses of LMS, the model is having challenges like security, user friendly and traffic. In this pandemic situation like covid 19, the whole world is depending on secured LMS model to get the confidence of student and teachers. In this paper it is taken into consideration and tried to describe one LMS model which can gives its user optimum security, user friendly environment and fast access.
ABSTRACT
In India, nearly 70% of the people live in rural villages. In rural villages farmer's main income source is agriculture. Now everyone has been listening the word COVID-19 through Television, News papers, word of mouth and Social Networking sites etc., however COVID-19 is the name given by the World Health Organization on 11th February, 2020 for the disease caused by the novel corona virus SARS-CoV-2(Severe Acute Respiratory Syndrome Corona Virus 2). It started in Wuhan, China in late 2019 and has since spread worldwide. COVID-19 is an acronym that stands for corona virus disease of 2019. The pandemic is impacting global as well as domestic food systems especially agriculture sector. The WHO declared the COVID-19 disease to be a global pandemic. Along with the Central, State Governments have announced several policy decisions to prevent and contain the spread of the virus in their respective states. The present study focused the impacts of covid -19 on agri farmers in drought affected districts of Andhra Pradesh. The main objectives of the study are;To evaluate the impacts of covid-19 on agriculture farmers in drought affected districts of Andhra Pradesh, to examine the causes for spreading of covid-19 pandemic virus, to examine what are the precautions taken for covid-19 by the government of Andhra Pradesh for to prevent and contain the spread of the virus. This research study uses primary as well as secondary data of research. For data analysis purpose this research study uses SPSS software. Finally, this research study exhibits that how much agri farmers lost by covid-19 pandemic and what are the precautions taken by the government of AP. Along with this, this research study explains what are the precautions to be follow to people during covid-19 pandemic. This research study gives huge scope for further studies.
ABSTRACT
This study aims to describe the role of women landowners, analyze their involvement in land management assistance from the Land Redistribution Program, and their contribution to achieving household welfare during the COVID-19 pandemic. The research method uses a qualitative approach. The research was conducted in Jarangan Village, Pasuruan Regency, East Java Province, Indonesia. Data were collected through structured interviews, in-depth interviews, participatory observation, checklists, and documentation. The technique of determining informants is using snowball sampling and Key Informants are determined by purposive sampling. Data analysis used Harvard Model gender analysis, qualitative analysis of Nvivo 12 QSR software, and contribution analysis. The results of the study show that there are three roles of women's landowner in land redistribution management, namely productive, reproductive, and social. The results of the QSR Nvivo 12 Word Frequency Query, the word 'Pond' is the word with the most frequency that appears, namely 12.50% of all research data sources on the involvement of men farmers in the program. Meanwhile, for women farmers, the word 'Processing' is the word with the highest frequency, which is 3.25%. The men farmer contributed Rp. 34,436,441,/year or 62%. Meanwhile, women farmers contributed to the household economic welfare of Rp. 21,522,775,-/year or 38%. However, women farmers can allocate their time to continue to carry out their reproductive roles in the household.
ABSTRACT
This study was conducted to develop and evaluate the impact of education through Google Classroom on nursing education such as critical thinking, problem-solving ability, self-regulated learning ability and academic major satisfaction with the face-to-face instructional closure due to COVID-19. The methodology used in this research is a quasi-experimental study of a group of pretest-posttest design, which was designed to check for differences before and after the intervention through Google Classroom to the treatment group. For a total of six weeks from March 16, 2020 to April 24, 2020, H University, located in J city, has taught eight nursing courses in the first, second, third and fourth grades through Google Classroom. The collected data were analyzed with descriptive statistics and paired t-test using the SPSS WIN 23.0 program. The effects of education with Google Classroom on critical thinking ability, problem-solving ability, self-regulated ability and academic major satisfaction were statistically significant as follows;critical thinking ability ((t =,-3.48 p =).000), self-regulating learning ability (t = -3.35, p = .001), problem-solving ability (t = -3.49, p = .001), and academic major satisfaction (t = -2.81, p = .016). The above findings confirmed that applying Google Classroom to nursing education can have a positive impact on critical thinking, problem-solving ability and self-regulated learning ability among nursing students, and increase academic major satisfaction.
ABSTRACT
AIM: To assess the impact of COVID outbreak on food lifestyle among the general population. BACKGROUND: Documentation of the changes in food habits and knowledge about healthy food options creates awareness about better ways of improving immunity during COVID-19 pandemic. MATERIALS AND METHODS: A cross sectional study was conducted among the general population. Random sampling is used to minimize the sampling bias. The standard questionnaire is used and circulated through online mode. SPSS software was used to tabulate and analyze the collected data. The Pearson chi-square test was used to correlate the gender and questions relating to healthy food habits. RESULTS: A total of 103 people were involved in the study. 65% of the participants were males and 35% were females. Majority(90.36%) of the population was aware of a healthy lifestyle and diet;only 9.64% weren't aware. 48.19% of participants agreed that they had reduced their intake of fat or oil related foods and 46.99% to a decrease in intake of fat or oil related foods. On the other hand 46.99% responded to an increase in intake of fat or oil related foods. CONCLUSION: The overall awareness of the study participants were good. It was found that intake of junk foods, stress was increased during covid pandemic which shows that covid pandemic had a negative effect on a healthy food lifestyle.
ABSTRACT
Exposure to noise can cause damage to structures in the inner ear, often resulting in a loss of hearing. Recent findings in noise-exposedanimals raise a new specter that even moderate noise exposures may result in damage specifically located in the synaptic region betweenthe sensory cells in the cochlea and primary auditory neurons. There is no way currently that scientists and clinicians can diagnose possibleauditory synaptic damage in humans, and diagnosis is critical for the development of innovative treatments. The objective of this project isto develop a statistical model that will accurately predict the likelihood of synaptopathy in humans who have had noise exposures in theirlives. The development of the statistical model will be supported by collecting non-invasive measurements in both humans and guinea pigs.Regulatory documents supporting human and animal testing have been approved both locally and through the relevant offices of theUSAMRMC. The animal laboratory was outfitted with a new sound booth for auditory testing, and data collection is on schedule to begin shortly. Research technicians have been hired to assist in both the human and animal facilities. However, overall progress has been significantly slowed by the seven months and ongoing international pandemic.
ABSTRACT
The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9-30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.