A New Reliability Class-Test Statistic for Life Distributions under Convolution, Mixture and Homogeneous Shock Model: Characterizations and Applications in Engineering and Medical Fields

Over the past few decades, a new area of reliability known as classes of life distributions has developed as a result of the creation of metrics for evaluating the success or failure of reliability. This paper proposes a new reliability class-test statistic for life distributions. In some reliability processes, such as convolution, mixture, and homogeneous shock models, the closure characteristics of the proposed class-test statistic are investigated. To compare the proposed class-test against some competitive tests, the Weibull, linear failure rate (LFR), and Makeham distributions are evaluated. In addition, the relationship between sample size, level of confidence, and critical values is considered to assess the efficacy of the proposed class-test. Furthermore, a Monte Carlo null distribution critical points simulation and some applications of the censored and uncensored data are performed to demonstrate the validity of the proposed class-test in reliability analysis.

The role of traditional aspects, health consciousness and environmental concerns in Italian agri-food consumption during Covid-19

PurposeThe study investigates how consumers' food purchasing habits changed during the Covid-19 pandemic in Italy. The research aims to understand if traditional aspects, health consciousness and environmental concerns have influenced and changed the purchases of food products post-pandemic.Design/methodology/approachThe authors developed a theoretical model to understand whether health consciousness, traditional aspects and environmental concerns affect consumers' purchasing intention. The study collects secondary data to analyse state of the art and investigate consumer behaviour in the agri-food system after the pandemic. Thereafter, a survey was conducted via a convenience random sampling procedure. The data (n = 622) were analysed using the formulated research framework and tested through the structural equation modelling procedure.FindingsThe findings reveal that health consciousness and traditional aspects (culinary traditions, ingredients usage from one's territory of origin, products' origin attention) are among the main reasons for purchasing agri-food goods after the pandemic. Instead, environmental concerns negatively affect consumers' purchase intentions.Originality/valueThe study identifies which aspects influenced consumers' purchasing intentions after the Covid-19 pandemic. It also provides insights for food companies and policymakers on the factors to be improved to optimize the agri-food sector following a sustainable perspective and in order to develop effective business strategies.

Mexican Validation of the MOS Questionnaire on Perceived Social Support in the Context of the COVID-19 Pandemic

Introduction: The COVID-19 pandemic has had a very negative impact on people's overall mental health and psychosocial well-being, but the study of available social support to cope with such an adverse situation has received hardly any attention. Objective: To exa-mine the psychometric properties of the MOS Perceived Social Support Questionnaire among the Mexican population in the context of the COVID-19 pandemic. Method: Non-experimental cross-sectional study. A sociodemographic questionnaire and the Medical Outcomes Study were applied in a non-probabilistic sample. A total of 898 people from different regions in Mexico, 258 males and 640 females, participated in the study in the context of the COVID-19 pandemic. Results: The analysis yielded a bi-factor model with two factors, Emotional/informational support and Tangible support, with satisfactory goodness of fit indices. Reliability was adequate with a high hierarchical omega coefficient, as well as in the factors. Likewise, the H coefficient was adequate in the general factor and its dimensions. Conclusions: Results showed that the scale is a valid and reliable measure of perceived social support among the Mexican population. © 2022 Fundación Universitaria Konrad Lorenz.

Self-Service Technology Service Quality and Brand Loyalty in Zimbabwe's Banking Sector: A SEM Approach

Purpose– This article examined the relationship between self-service technology service quality and brand loyalty in Zimbabwe`s banking sector with customer satisfaction and behaviour intentions playing the mediating role. The main objective was to develop a path analysis model for the banking industry in Zimbabwe. Approach– The study followed a deductive approach with an online survey used to collect primary data from more than 110 bank customers. The PLS-SEM algorithm was used to empirically test the path analysis model. Findings– The construct measures were confirmed reliable and valid with structural model showing goodness of fit based on the R2, Q2, SRMR, and path significance. The results further confirmed hypothesis H1, H2, H3, H4,and H7 whilst rejecting H5and H6. Practical implications– Self-service technologies have proven to be a critical enhancer of brand loyalty in the banking sector. The ‘FinTech' industry has gone under a critical test due to COVID-19 pandemic that has seen global restrictions nearly paralyzing a number of sectors. Technology developers, policymakers, researchers, and regulators will have a better understanding of self-service technologies and their impact on brand loyalty in the service industry. Originality/value– Literature has shown some knowledge gaps in this field especially in Zimbabwe where the ‘FinTech' industry is still in its infancy stages.

Chinese pregnant women's knowledge, attitude, and practice of self-protection against coronavirus disease 2019 during the post-pandemic period: A structural equation modeling-based survey.

This study aimed to investigate the Chinese pregnant women's levels of knowledge, attitude, and practice (KAP) of self-protection against coronavirus disease 2019 (COVID-19) during the post-pandemic period, to aid the development of targeted health education. An online questionnaire was conducted for 2156 Chinese pregnant women from October 1, 2021, to December 31, 2021, to collect socio-demographic and KAP information. Structural equation modeling (SEM) was used to determine self-protection-related factors. The mean age of the participants was 30 ± 4.1 years. SEM indicated that pregnant women's level of knowledge can directly and indirectly affect the practice of self-protection (r = 0.23) through their belief, with a correlation coefficient of 0.56 and 0.46 between knowledge and belief and belief and practice, respectively. The "basic protection" and "hospital visits after infection" exerted the greatest impact on knowledge formation, with correlation coefficients of 0.85 and 0.89, respectively. Attitude had a direct effect on practice with a correlation coefficient of 0.46. "Awareness of prevention and control" and "family and social support" had the greatest impact on belief formation, with correlation coefficients of 0.77 and 0.73, respectively. Pregnant Chinese women were generally familiar with COVID-19 knowledge, and their levels of knowledge and beliefs particularly affect the practice of self-protection. Health education aimed at improving pregnant women's knowledge and belief toward self-protection against COVID-19 may be an effective way to guide them toward positive practices and promote their health and that of their babies.

A New Coronavirus Estimation Global Score for Predicting Mortality During Hospitalization in Patients with COVID-19.

Objective: Coronavirus disease 2019 (COVID-19) exists as a pandemic. Mortality during hospitalization is multifactorial, and there is urgent need for a risk stratification model to predict in-hospital death among COVID-19 patients. Here we aimed to construct a risk score system for early identification of COVID-19 patients at high probability of dying during in-hospital treatment. Methods: In this retrospective analysis, a total of 821 confirmed COVID-19 patients from 3 centers were assigned to developmental (n = 411, between January 14, 2020 and February 11, 2020) and validation (n = 410, between February 14, 2020 and March 13, 2020) groups. Based on demographic, symptomatic, and laboratory variables, a new Coronavirus estimation global (CORE-G) score for prediction of in-hospital death was established from the developmental group, and its performance was then evaluated in the validation group. Results: The CORE-G score consisted of 18 variables (5 demographics, 2 symptoms, and 11 laboratory measurements) with a sum of 69.5 points. Goodness-of-fit tests indicated that the model performed well in the developmental group (H = 3.210, P = 0.880), and it was well validated in the validation group (H = 6.948, P = 0.542). The areas under the receiver operating characteristic curves were 0.955 in the developmental group (sensitivity, 94.1%; specificity, 83.4%) and 0.937 in the validation group (sensitivity, 87.2%; specificity, 84.2%). The mortality rate was not significantly different between the developmental (n = 85,20.7%) and validation (n = 94, 22.9%, P = 0.608) groups. Conclusions: The CORE-G score provides an estimate of the risk of in-hospital death. This is the first step toward the clinical use of the CORE-G score for predicting outcome in COVID-19 patients.

A comparative study to choose the appropriate growth model to forecast COVID-19 cases in Iraq

COVID-19 infection cases forecasting is a process of estimating future values based on historical data which is playing an important role in health decision making in various fields. Daily infection cases of COVID-19 can be considered as a time series represent the growth of the number of infected persons in a population. Consequently, the growth models may be used to forecast any population growth such as population of infected people with the Covid-19 virus. The popular models of growth such as logistic, log-logistic, Gompertz, Weibull and Richards models are useful to describe the growth of many phenomena like an epidemic and the spread of the number of infected people. The main objective of this paper is to choose a successful growth model after comparing these models to make good use of the current data on COVID-19 in Iraq to better understand the spread of this disease and to forecast the future daily infection cases. AIC, BIC and other goodness of fit criteria and daily infection cases in Iraq for the period from 1st Jan. 2021 until 30th April 2021 were used to compare these models and choose the successful model. The results of fitting these model show that the appropriate models are Weibull type 1 and log-logistic with five parameters models, and the predicted numbers of infected cases are near the actual numbers of infected cases.

The Impact Factors of Industry 4.0 on ESG in the Energy Sector

Digital transformation refers to highly thought-out social, manufacturing, and organizational transitions driven by digital revolutions and emerging technologies. On the other hand, energy is a critical pillar of the economic growth of the country. Meanwhile, global interest in environmental, social, and governance (ESG) investment is growing. The conventional investment paradigm is being phased out in favor of investments that prioritize environmental, social, and corporate responsibility. The energy sector is one of the most significantly affected. Presently, the field of digital transformation is limited in its analysis about the sustainability factors and is still controversial, especially in the energy business. This paper identifies an in-corporation factor in Industry 4.0, taking into account the effect on ESG. The research papers and the World Economic Forum reports were investigated and identified the correlation factor using machine learning to analyze their contents. We spotlighted the documents relevant to the energy industry and sustainable development. To quantify the model, confirmatory factor analysis (CFA) is proposed to generate a valid model, followed by path analysis with latent variables to evaluate the structural equation modeling (SEM). The result provides the conceptual model with impact factors and their correlations. The goodness of fit value is acceptable for the agreed-upon condition, as well as a descriptive that incorporates Industry 4.0 and ESG in terms of business, industry, and ESG in relation to the energy sector’s key issues.

Generalized Rescaled Polya urn and its statistical application

We introduce the Generalized Resealed Polya (GRP) urn, that provides a generative model for a chi-squared test of goodness of fit for the long-term probabilities of clustered data, with independence between clusters and correlation, due to a reinforcement mechanism, inside each cluster. We apply the proposed test to a data set of Twitter posts about COVID-19 pandemic: in a few words, for a classical chi-squared test the data result strongly significant for the rejection of the null hypothesis (the daily long-run sentiment rate remains constant), but, taking into account the correlation among data, the introduced test leads to a different conclusion. Beside the statistical application, we point out that the GRP urn is a simple variant of the standard Eggenberger-Polya urn, that, with suitable choices of the parameters, shows "local" reinforcement, almost sure convergence of the empirical mean to a deterministic limit and different asymptotic behaviours of the predictive mean. Moreover, the study of this model provides the opportunity to analyze stochastic approximation dynamics, that are unusual in the related literature.

Biomechanical and Physiological Evaluation of Respiratory Protective Equipment Application.

Purpose: Respiratory protective equipment is widely used in healthcare settings to protect clinicians whilst treating patients with COVID-19. However, their generic designs do not accommodate the variability in face shape across genders and ethnicities. Accordingly, they are regularly overtightened to compensate for a poor fit. The present study aims at investigating the biomechanical and thermal loads during respirator application and the associated changes in local skin physiology at the skin-device interface. Materials and Methods: Sixteen healthy volunteers were recruited and reflected a range of gender, ethnicities and facial anthropometrics. Four single-use respirators were evaluated representing different geometries, size and material interfaces. Participants were asked to wear each respirator in a random order while a series of measurements were recorded, including interface pressure, temperature and relative humidity. Measures of transepidermal water loss and skin hydration were assessed pre- and post-respirator application, and after 20 minutes of recovery. Statistical analysis assessed differences between respirator designs and associations between demographics, interface conditions and parameters of skin health. Results: Results showed a statistically significant negative correlation (p < 0.05) between the alar width and interface pressures at the nasal bridge, for three of the respirator designs. The nasal bridge site also corresponded to the highest pressures for all respirator designs. Temperature and humidity significantly increased (p < 0.05) during each respirator application. Significant increases in transepidermal water loss values (p < 0.05) were observed after the application of the respirators in females, which were most apparent at the nasal bridge. Conclusion: The results revealed that specific facial features affected the distribution of interface pressures and depending on the respirator design and material, changes in skin barrier function were evident. The development of respirator designs that accommodate a diverse range of face shapes and protect the end users from skin damage are required to support the long-term use of these devices.

Percepción de dieta, hábitos de salud, agencia personal durante la COVID-19

Analyzing the effect of the variables Eating Habits, Emotional Condition and Physical Activity (PA) Agency on Diet Perception and PA Time, in Colombian university students under COVID-19 confinement conditions. Preliminary correlational research was conducted through a comparative survey with both exploratory and explanatory scope. It was applied to 389 students who voluntarily completed the instrument on a Google Form. The structural model explains respectively 38% and 53% of the variability of the students’ diet perception and PA time. The model shows both statistical (χ² = 84 [47 gl p = 0,09]) and practical (IBBAN = 96;IBBANN = 99;IAC = 0,99 and RMSEA = 0,02 [0,00, 0,04]) goodness of fit. Hence, it can be stated that this inclusive model has the same explanatory power as the saturated one, which relates all variables to each other. Eating habits and intention were found to have a direct effect on the university students’ diet perception. Just as well, eating habits, intention and diet perception were observed to increase PA time.Alternate :Analizar el efecto de hábitos alimentarios, situación emocional y agencia personal de la actividad física sobre la percepción de dieta y tiempo de actividad física, de universitarios colombianos en condiciones de confinamiento por la COVID-19. Investigación exploratoria de tipo correlacional con alcance explicativo en su nivel de conocimiento. Se aplicó de forma voluntaria un cuestionario en plataforma Google a 389 estudiantes. El modelo estructural explica el 38% de la variabilidad de la percepción de dieta de los estudiantes y 53% de la variabilidad del tiempo dedicado a la AF. Posee bondad de ajuste tanto estadístico, χ² = 84 (47 gl), p = 0,09, como práctico, IBBAN = 96, IBBANN = 99, IAC = 0,99 y RMSEA = 0,02 (0,00, 0,04), por lo que se puede afirmar que este modelo inclusivo tiene el mismo poder de explicación que el modelo saturado, que relaciona todas las variables entre sí. Se evidencia un efecto directo entre las variables hábitos alimenticios, intencionalidad y la percepción que los estudiantes universitarios tienen de su dieta. Así mismo, se observa que, las variables antes mencionadas contribuyen a que aumente el tiempo en minutos de AF.

The role of stress mindsets and coping in improving the personal growth, engagement, and health of small business owners

The COVID-19 pandemic has had a devastating impact on small businesses and nonprofit organizations worldwide, resulting in rising stress and worry for many small business owners. While stress is typically considered to be harmful to health and well-being, recent work suggests that improving one's mindset about the benefits of stress can help one to respond to stress more effectively. In the current study, we use a preintervention and postintervention design and latent change score analysis to examine the impact of changing one's stress mindset on changes in personal growth, engagement, and health among small business owners-via changes in coping behaviors. Further, we examine how the perceived likelihood of needing to permanently close one's business may strengthen the effects of changing one's stress mindset on changes in approach and avoidance coping, and subsequent outcomes. In doing so, we begin to uncover the theoretical mechanisms underlying how having a stress-is-enhancing mindset can bring about changes in personal growth, engagement, and health. We also incorporate qualitative data to better contextualize the stress and coping-related attitudes and behaviors of small business owners during the pandemic. This work has significant practical implications for small business owners and others experiencing work-related stressors.

Behavioral Intention to Resist the Consumption of Wild Animals in China: Netizen Survey

Since the beginning of 2020, China has banned the consumption of wild animals to combat the spread of zoonoses. Most existing studies focus on the intention and behavior of wildlife consumption and their causes;however, few have looked at public willingness to resist wildlife consumption, as well as the cause and effects of such actions. In this study, a framework for an extended theory of planned behavior was constructed. Based on a 7-point Likert scale, a sample of 1194 respondents from eight provinces across China was obtained through an online survey. Structural equation modeling was used to analyze netizen behavioral intention to resist consuming wild animals and their causes to provide a reference for the implementation and optimization of relevant policies. The study model passed the goodness-of-fit test, confirming the robustness of the results. The results showed that Chinese netizens’ intention to resist consuming wild animals was moderate, with 55.19% willing to participate in activities against it, i.e., it is important to resist eating wild animals as a standard. Attitude, subjective norm, perceived behavioral control, and past experience of the Chinese netizen had significant positive effects on resistance intention, i.e., (1) netizens’ current living area with severe outbreaks were more likely to resist wildlife consumption, (2) highly knowledge level netizens were more likely to resist wildlife consumption than less knowledgeable ones, and (3) lower income level had higher behavioral intentions of netizens. The findings suggest that the government must take a lead role in wildlife protection and strengthen its restrictions, laws, and regulations. The media should also be used to promote conservation and popularize a protective message in favor of wild animals. Public quality and assurance of wildlife protection should be culturally reinforced to effectively ban the illegal trade of wild animals and their products.

A Comparative Analysis of the Forecasted Mortality Rate under Normal Conditions and the COVID-19 Excess Mortality Rate in Malaysia

SARS-CoV-2, known as COVID-19, has affected the entire world, resulting in an unexpected death rate as compared to the death probability before the pandemic. Prior to the COVID-19 pandemic, death probability has been assessed in a normal context that is different from those anticipated during the pandemic, particularly for the older population cluster. However, there is no such evidence of excess mortality in Malaysia to date. Therefore, this study determines the excess mortality rate for specific age groups during the pandemic outbreak in Malaysia. Before determining the excess mortality rate, this study aims to establish the efficiency of various parametrized mortality models in reference to the data set before the pandemic. This study employs the hold-out, repeated hold-out, and leave-one-out cross-validation procedures to identify the optimal mortality law for fitting the mortality data. Based on the goodness-of-fit measures (mean absolute percentage error, mean absolute error, sum square error, and mean square error), the Heligman-Pollard model for men and Rogers Planck model for women are considered as the optimal models. In assessing the excess mortality, both models favour the hold-out technique. When the COVID-19 mortality data are incorporated to forecast the mortality rate for people aged 60 and above, there is an excess mortality rate. However, the men’s mortality rate appears to be delayed and more prolonged than the women’s mortality rate. Consequently, the government is recommended to amend the existing policy to reflect the post COVID-19 mortality forecast.

COVID-19 and medical tourism intentions for Iran - a test of the risk perception attitude framework (RPAF)

Purpose This paper aims to examine whether medical tourism can be a frontrunner in terms of post-pandemic recovery for the industry Design/methodology/approach A mixed-method analysis of 17 interviews and 210 questionnaires involving medical tourists to Iran was applied. Findings Medical tourists perceived the risks posed by COVID-19 as a temporal one, and attitudes toward post pandemic visitation intentions remained strong. In addition, these tourists can mostly be classified into responsive individuals, who demonstrate not only high risk but also high efficacy levels to negotiate the threats posed by the pandemic. No gender differences were located between male and female medical tourists in terms of post-COVID-19 travel intentions to Iran. Originality/value This research extends the application of the risk perception attitude framework to a medical tourism context. Furthermore, medical tourists are uncovered as another segment of crisis-resistant tourists.

Comparison between some estimation methods for an intuitionistic fuzzy semi-parametric logistic regression model with practical application about covid-19

In this paper, the intuitionistic fuzzy set and the triangular intuitionistic fuzzy number were displayed, as well as the intuitionistic fuzzy semi-parametric logistic regression model when the parameters and the dependent variable are fuzzy and the independent variables are crisp. Two methods were used to estimate the model on fuzzy data representing Coronavirus data, which are the suggested method and The Wang et al method, through the mean square error and the measure of goodness-of-fit, the suggested estimation method was the best.

Applying Benford's law to COVID-19 data: the case of the European Union.

BACKGROUND: Previous studies have used Benford's distribution to assess the accuracy of COVID-19 data. Data inaccuracies provide false information to the media, undermine global response and hinder the preventive measures taken by authorities. METHODS: Daily new cases and deaths from all the countries of the European Union were analyzed and the conformance to Benford's distribution was estimated. Two statistical tests and two measures of deviation were calculated to determine whether the reported statistics comply with the expected distribution. Four country-level developmental indexes were included, the GDP per capita, health expenditures, the Universal Health Coverage (UHC) Index and the full vaccination rate. Regression analysis was implemented to examine whether the deviation from Benford's distribution is affected by the aforementioned indexes. RESULTS: The findings indicate that Bulgaria, Croatia, Lithuania and Romania were in line with Benford's distribution. Regarding daily cases, Denmark, Ireland and Greece, showed the greatest deviation from Benford's distribution. Furthermore, it was found that the vaccination rate is positively associated with deviation from Benford's distribution. CONCLUSIONS: The findings suggest that overall, official data provided by authorities are not confirming Benford's law, yet this approach acts as a preliminary tool for data verification. More extensive studies should be made with a more thorough investigation of countries that showed the greatest deviation.

Covid-19 Future Predictions using Machine Learning Algorithms

The ongoing destructive pandemic of Coronavirus Disease (COVID-19) has been the biggest virus that affected more than 190 countries and territories across the world. It seems uncontrollable in many countries and some countries have taken and implemented proper safety measures to eradicate the virus and is under process. We have used machine learningbased prediction tools. As various machine learning algorithms have proved their importance for forecasting and making future decisions. This paper aims to study, analyze and visualize the spreading of the virus in India and the world considering confirmed cases, recovered cases, and fatalities and how in real-world situations we can use machine learning models. It helps to evaluate the spread and pattern of COVID-19 in India by performing Linear Regression, and Support Vector Machine and evaluating parameters using MAE & MSE score, which is the goodness of fit measure. In training a model, the selection of the best learning model is challenging as the data has anomalies because data is not standardized. Therefore, proper study and analysis of the data should be done so that it is easy to understand and act accordingly. Using datasets from Johns Hopkins University the data has been analyzed, obtained from January 22, 2020, till May 17, 2021, for the world. Using this analysis, we can predict the confirmed cases for the following 10 days. The result proves that Linear Regression is much more accurate than the Support Vector Machine.

Impact of COVID-19 on Engineering and Technology Course Outcomes

Student learning experience can be disrupted significantly if the plan of study changes suddenly like it did due to the COVID-19 global pandemic in March 2020. The purpose of this paper is to compare the outcomes of two courses at Indiana State University such as student grades, number of students dropping the course, available resources, etc. before (pre-) and during (post-) the pandemic. The compared two courses are from two separate departments where one course is Engineering Economics and the other course is DC Circuits and Design. The course DC Circuits and Design has both theory and laboratory components. The null hypothesis is that there exists no difference between the course grade outcomes of pre- and post- pandemic. The hypothesis has been tested using Chi-square goodness of fit test at p=0.1. Engineering Economics on-campus post-unplanned pandemic section in Spring 2020 is found to be significantly different from the pre-pandemic in Spring 2019. However, in the online section, there is no difference between the post- and pre- because the online section is planned for virtual mode. Similar finding is reached for DC Circuits and Design that the post-unplanned pandemic section in Spring 2020 is found to be significantly different from the pre-pandemic in Fall 2019;but the post-planned in Fall 2020 is found to be statistically same as the pre-pandemic. Practical implication of this study will be helpful in planning to teach courses for pandemics or other situations outside of our control. © American Society for Engineering Education, 2021

Economic Policy Uncertainty and Conditional Dependence between China and U.S. Stock Markets

In this paper, we investigate the impact of economic policy uncertainty (EPU) on the conditional dependence between China and U.S. stock markets by employing the Copula-mixed-data sampling (Copula-MIDAS) framework. In the case of EPU, we consider the global EPU (GEPU), the American EPU (AEPU), and the China EPU (CEPU). The empirical analysis based on the Shanghai Stock Exchange Composite (SSEC) index in China and the S&P 500 index in the U.S. shows that the tail dependence between China and U.S. stock markets is symmetrical, and the t Copula outperforms alternative Copulas in terms of in-sample goodness of fit. In particular, we find that the t Copula-MIDAS model with EPU dominates the traditional time-varying t Copula in terms of in-sample fitting. Moreover, we observe that both the GEPU and AEPU have a significantly positive impact on the conditional dependence between China and U.S. stock markets, whereas CEPU has no significant impact. The tail dependence between China and U.S. stock markets exhibits an increasing trend, particularly in the recent years.