This study employs Molecular Electron Density Theory (MEDT) to explore the [3+2] cycloaddition mechanisms involving 1-methyl-4-(prop-1-en-2-yl)cyclohex-1-ene (2-R) and nitrile oxide (3-R). Density Functional Theory (DFT) calculations using the B3LYP/6-311(d,p) method were performed to determine reactivity indices, activation energies, and reaction energies. The conceptual DFT analysis indicates that 1-methyl-4-(prop-1-en-2-yl)cyclohex-1-ene 2-R acts as a nucleophile, while nitrile oxide 3-R functions as an electrophile. The reaction exhibits notable chemoselectivity and regioselectivity, supported by activation energies that align with experimental data. BET analysis suggests a one-step mechanism with asynchronous bond formation. Additionally, molecular docking studies of the reaction products against HIV-1 and COVID-19 reveal that the presence of oxygen and nitrogen atoms enhances the interaction energy with proteins, indicating potential therapeutic benefits.

It is argued that managers took advantage of Covid-19 pandemic lockdowns and remote auditing and used earnings management (EM) practices extensively. Furthermore, the Covid-19 pandemic created new unsearched crisis-related incentives. This study, therefore, tests whether Covid-19 created a new incentive for managers to manipulate earnings. It also examines the association between corporate social responsibility (CSR) and board independence and EM during Covid-19. A data set of 384 firm-year observations from 2018 to 2021 of non-financial firms listed on the Amman Stock Exchange (ASE) was investigated. Results indicate that Jordanian firms engaged in EM during Covid-19 considerably more than when compared to pre-Covid-19, suggesting that Covid-19 created a new incentive for managers to manipulate earnings. Furthermore, Jordanian firms used income-increasing EM much more when compared to income-decreasing EM. However, when taking Covid-19 into account, no significant association was found between board independence and EM. In addition, the ability of CSR to constrain EM decreased. This adds to the current debate in the literature that even well-established monitoring mechanisms like board independence and CSR are unable to constrain EM practices in a unique business environment caused by Covid-19.

The Refrigerated Capacitated Vehicle Routing Problem (RCVRP) considers a homogeneous fleet with a refrigerated system to decide the selection of routes to be performed according to customers' requirements. The aim is to keep the energy consumption of the routes as low as possible. We use a thermodynamic model to understand the unloading of products from trucks and the variables' efficiency, such as the temperature during the day influencing energy consumption. By considering various neighborhoods and a shaking procedure, this paper proposes a Granular Tabu Search scheme to solve the RCVRP. Computational tests using adapted benchmark instances from the literature demonstrate that the suggested method delivers high-quality solutions within short computing times, illustrating the refrigeration system's effect on routing decisions.

India had broken all the records and counts of confirmed COVID-19 cases per day and daily death toll reached over thousands. India is way far from other developed nations in the number of vaccine doses per 100 population. Although vaccination is an effective measure to be followed to overcome this grave situation, still certain misconceptions and rumors throughout the country have pulled a decent part of the population from being vaccinated. Another big challenge is production and supply of vaccines to meet the demand. COVID-19 pandemic will not end until the entire population gets vaccinated that would protect them from this deadly disease. Therefore, this paper aims at clearly identifying the factors and subsequently prioritizing them as barriers in effective immunization against COVID-19 in India following multi-criteria decision making (MCDM) technique. In this study, a fuzzy decision-making trail and evaluation laboratory (F-DEMATEL) approach is applied for understanding the contextual relationship among the barriers for effective immunization against COVID-19. The methodology is followed in a fuzzy environment to address the issue of uncertainty in the data gathered. The result suggests that the ‘Misinformation/ Misconceptions/ Lack of vaccine education in underserved communities’, ‘Lack of information regarding a vaccination center close to home’, ‘Difficulties in getting appointments’, ‘Supply chain issues in the distribution of vaccine’, and ‘Lack of access for marginalized communities’ are the important barriers in effective immunization against COVID-19. Recommendations have been made to overcome this situation and help to immunize the population and drag COVID-19 down to earth.

COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19.

In 2019, the COVID-19 epidemic swept throughout the globe. The virus was first identified in Wuhan, China. By the time several months had gone by, this virus had spread to numerous locations throughout the world. Consequently, this virus has become a worldwide pandemic. Multiple efforts have been made to limit the transmission of this virus. A possible course of action is to lock down the territory. Unfortunately, this strategy wrecked the economy, worsening the terrible situation. The world health organization (WHO) would breathe a sigh of relief if there were to be no new cases. However, the government should explore employing data from the future in addition to the data it already has. Prediction of time series may be utilized for this purpose. This study indicated that the Gaussian processes method outperformed the least median squared linear regression method (LMSLR). Applying a Pearson VII-based global kernel produces MAE and RMSE values of 23.12 and 53.43, respectively.

This study aims to determine an accurate forecasting model, especially an error rate of around 0, and to examine how the automatic rejection system reacts to stock price as a result of the pandemic. The statistical clustering method is used for the dataset in form of daily observations, while the sample covers the period of cases before and after COVID-19 pandemic from 02 January 2019 to 20 June 2020 at the Trinitan Minerals and Metal Company. Furthermore, the data used in the estimation are the opening and closing price of returns, which are later processed using SAS analysis tools. It is shown that the most appropriate decision-making processes are those proven to be most effective. Therefore, predicting future events based on a suitable time series model will help policymakers and strategists make decisions and develop appropriate strategic plans regarding the stock market. Meanwhile, 98% of the ARIMA (1,1,1) is a forecasting model which can be applied to predict stock prices. The new approach of this study is an integrated autoregressive moving average used as an attempt to accurately predict stock prices during a pandemic.

The motivation of this study is to provide empirical evidence of service companies’ performance regarding nine dimensions in a total quality management model during the COVID-19 pandemic. The nine dimensions highlight strategic company activities, and it allows a comparative analysis of the overall effect of having a QMS such as ISO 9001:2015 on Peruvian service companies. A total of 630 Peruvian service companies were surveyed for this study. The questionnaire included 35 Likert-scale items that were further classified into nine (9) dimensions. The Mann-Whitney U test was used to estimate any significant differences between the ISO 9001 certified and non-certified companies. Our findings showed that the performance of ISO 9001:2015 certified companies was significantly higher than that of non-certified companies in all dimensions. Moreover, our findings showed that managers in ISO 9001:2015 certified companies effectively implemented the nine dimensions of the model. The originality of this study lies in proving the positive effect of having a QMS in service companies in a context of slow economic growth and decline of consumer demand such as the COVID-19 pandemic. The findings might encourage service companies, especially those in developing countries, to allocate the necessary resources to obtain a QMS such as the ISO 9001:2015.

This study was conducted in order to analyze the effect of the nurses’ quality of work life based on fear about COVID-19 and examine the psychological well-being as a moderating variable in this relationship. The survey questionnaire was administered among nurses between 1 November 2020 and 14 November 2020. The self-report survey comprised the nurse information survey, Fear of COVID-19 Scale, work quality of life scale, and psychological well-being scale as data collection tools. Data were obtained from 339 nurses. The findings show that fear of COVID-19 negatively affects nurses’ quality of work life. It has been determined that PWB plays a moderating role in this relationship. While the fear of COVID-19 negatively affects the quality of work life in nurses with low psychological well-being, there is not any kind of significant effect on the quality of work life in nurses with high psychological well-being. This result shows that as the psychological well-being of nurses’ increases, fear of COVID-19 effect on quality of work life decreases. The results of the study show that responses designed to enhance psychological well-being can enhance nurses' working conditions that could reduce the negative effects of the fear of COVID-19. There is an urgent need for clinical and policy strategies to help increase nurses’ PWB in order to increase the quality of work life by reducing fear and also anxiety among nurses fighting on the front line during COVID-19.

The new Coronavirus disease (COVID-19) pandemic triggers panic buying behavior among consumers in several countries. Previous research on panic buying behavior is less likely to look at the phenomenon from the perspective of consumers (demand-side). Therefore, this study aims to examine the influence of media credibility and social contagion on panic buying behavior and the mediating effect of consumer anxiety in the relationship between media credibility, social contagion and panic buying behavior. Data was collected from young and adult consumers in Greater Jakarta, Indonesia using convenience sampling techniques. Three hundred and fifty responses were collected through an online survey. The hypotheses were tested using structural equation models that could simultaneously analyze the effects of variables in the complex model. The results indicate that media credibility does not have a significant effect on consumer anxiety and panic buying. On the other hand, social contagion has a direct and indirect influence on consumer anxiety. Likewise, consumer anxiety mediates the relationship between social contagion and panic buying behavior. The findings from this research can be used by manufacturers and retailers to maintain goods availability during the pandemic, and the government as a basis for economic decision making. This research also contributes to development of the academic literature related to consumer behavior during a pandemic.