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To study the spreading trend and risk of COVID-19, according to the characteristics of COVID-19, this paper proposes a new transmission dynamic model named SLIR(susceptible-low-risk-infected-recovered), based on the classic SIR model by considering government control and personal protection measures. The equilibria, stability and bifurcation of the model are analyzed to reveal the propagation mechanism of COVID-19. In order to improve the prediction accuracy of the model, the least square method is employed to estimate the model parameters based on the real data of COVID-19 in the United States. Finally, the model is used to predict and analyze COVID-19 in the United States. The simulation results show that compared with the traditional SIR model, this model can better predict the spreading trend of COVID-19 in the United States, and the actual official data has further verified its effectiveness. The proposed model can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures. Copyright ©2023 Control and Decision.
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Messaging platforms like WhatsApp are some of the largest contributors to the spread of Covid-19 health misinformation but they also play a critical role in disseminating credible information and reaching populations at scale. This study explores the relationships between verification behaviours and intention to share information to users that report high trust in their personal network and users that report high trust in authoritative sources. The study was conducted as a survey delivered through WhatsApp to users of the WHO HealthAlert chatbot service. An adapted theoretical model from news verification behaviours was used to determine the correlation between the constructs. Due to an excellent response, 5477 usable responses were obtained, so the adapted research model could be tested by means of a Structural Equation Model (SEM) using the partial least squares algorithm on SmartPLS4. The findings suggest significant correlations between the constructs and suggest that participants that have reported high levels of trust in authoritative sources are less likely to share information due to their increased behaviours to verify information. © 2023 IEEE.
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Academic fraud is a perennial problem, and the Covid-19 pandemic has exacerbated it with most universities moving to online learning. We conducted a survey with 259 students from three universities about their perceptions of academic fraud in online learning. This article examines whether individual factors drawing from the dark triad of personality and three situational factors: academic integrity culture, academic fraud ambiguity, and pressure, influence the intention to engage in academic fraud. Using partial least square-structural equation modeling, the results show that academic integrity culture, pressure, and the dark triad of personality significantly affect students' intention to engage in academic fraud. The implication of such findings is discussed. [ FROM AUTHOR] Copyright of Journal of Criminal Justice Education is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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Percentage of body fat, age, weight, height, and 14 circumference measurements (e.g., waist) are given for 184 women aged 18–25. Body fat, one measure of health, was accurately determined by an underwater weighing technique which requires special equipment and training of the individuals conducting the process. Modeling body fat percentage using multiple regression provides a convenient method of estimating body fat percentage using measures collected using only a measuring tape and a scale. This dataset can be used to show students the utility of multiple regression and to provide practice in model building.
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According to World Bank statistics in 2019, Indonesia ranked two in the average unemployment rate with 5.28% in South East Asia. Although the unemployment rate can be reduced by an equitable distribution of human resource empowerment and national development, the global pandemic COVID-19 made a major impact on increasing the rate of unemployment. This paper tests the spatial autocorrelation on the average unemployment in Indonesia using Ordinary Least Squares (OLS) and Moran's I. The OLS method was used to examine the effects that affect the unemployment rate using an independent variable. In contrast, the Moran's I used to prove the existence of spatial effect on the level of movement in Indonesia. From the experiment, there are four variables that influence the unemployment rate by using the OLS modeling method. The Moran's I test showed a p-value = 0.006 with α = 0.05. Therefore, there is a spatial autocorrelation between provinces in Indonesia. In addition, the model is tested using the Variance Inflation Factor. The model showed a VIF score ¡10, therefore there is no collinearity and the assumption is fulfilled. The model is also being tested using dwtest, bptest, and Lilliefors test. The result showed p-value = 0.6231 for dwtest, p-value = 0.932 for bptest, and p-value = 0.08438 for Lilliefors test.. © 2022 IEEE.
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The Dajia Mazu pilgrimage is one of the most well-known events in the world. It not only satisfies tourists' spiritual desires for religious beliefs but also drives the development of destination tourism. In recent years, the tourism industry has been severely impacted by COVID-19. However, tourists participating in the Dajia Mazu pilgrimage continue to do so without fear of the pandemic. Therefore, understanding the relationship between tourists' attraction to religious tourism, perception of happiness, and willingness to revisit can contribute to the sustainable development of religious tourism, especially in the context of COVID-19. Accordingly, this study explored the sustainable development of Taiwan's religious tourism from the perspectives of tourism attraction, experiential value, happiness, and revisit intention. The study conducted quantitative research to address the research issue. Three hundred and fifty valid questionnaires were collected through on-site questionnaire distribution, and the data were analyzed by descriptive statistics and the structural equation partial least squares method. According to the results, the tourism attraction of the Dajia Mazu pilgrimage and the experiential value of tourists significantly impact happiness and revisit intention. Happiness is part of the intermediary variables of tourism attraction, experiential value, and revisit intention. Notably, the attraction of the Dajia Mazu pilgrimage and the experiential value pursued by tourists have not diminished despite the pandemic. Instead, the attraction has become an opportunity for tourists to seek spiritual comfort and support sustainable religious tourism development. Accordingly, spiritual comfort and maintaining their health and safety can be considered strategies to promote the sustainability of religious tourism in Taiwan.
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This research examines the Modeling spatial relationships of the mortality of COVID-19 that were tested in 213 countries worldwide. The database used in the research included variables per 1000 population, as follows: the cumulative number of cases, hospital beds, and the unvaccinated population as health variables, the age population over 65 years, population number and population density as demographic variables for interpretation and prediction of mortality around the world. In total, it relied on 7 variables at the level of countries in the world based on the official COVID-19 data of the World Health Organization and World Bank indicators. Therefore, the aim of this research is to study whether the relationships between mortality rates and these variables differ spatially across different countries by means of applying modeling spatial relationships by Geographically Weighted Regression (GWR) and Ordinary Least Squares Rregression (OLS) available in statistical tools in a GIS environment. The results showed that there are spatially homogeneous relationships at the level of the countries to the variables of the cumulative number of cases, the number of the population over the age of 65 years, and the number of the unvaccinated population, which are statistically significant and collectively explained 97% of the variation in mortality of COVID-19. In conclusion, spatial information derived from this modeling provides valuable insights regarding the spatially varying relationship of COVID-19 mortality with these potential drivers to help establish preventive measures to reduce mortality around the world.
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PurposeThe COVID-19 pandemic outbreak has created disruptions across the supply chain that are beyond the resources of small and medium-sized enterprises (SMEs) to effectively deal with. This study aims to examine the idea that top managers' business and political ties can play direct roles in enhancing SCR in SMEs during COVID-19 by providing access to valuable resources. The study further investigates integrative capability as an underlying mechanism through which the effects of business and political ties can be transformed into enhanced SCR.Design/methodology/approachResponses from 217 SMEs in the country of Jordan were received via an online survey. The measurement and structural models were tested using the partial least squares structural equation modelling (PLS-SEM) technique.FindingsThe study found that business and political ties are positively related to SCR. However, integrative capability fully mediates the relationship between business ties and SCR, whereas it partially mediates the relationship between political ties and SCR.Research limitations/implicationsThe study examined only the direct and indirect impacts of business and political ties on SCR. It could be extended by exploring the conditions under which they influence SCR.Originality/valueThe study explicates the role of top managers' business and political ties on improving SCR in a developing country context. It further examines the mediating role of integrative capability in the relationships between business and political ties and SCR.
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The present paper investigates the location pattern of co-working spaces in Delhi which is absent in the existing body of knowledge. Delhi is a political, administrative, educational, scientific and innovation capital that accommodates many co-working spaces in India. We developed Ordinary least squares (OLS) and geographically weighted regression (GWR) models to understand the associations of co-working spaces of digital labourers with other urban socio-economic, services and lifestyle variables in Delhi using secondary data for 117 coworking locations in 280 municipal wards of NCT-Delhi. Model diagnostic suggested that the GWR model provides additional information regarding geographical distribution of coworking spaces, and density of bars, median house rent, fitness centres, metro train stations, restaurants, cinemas, cafés, and creative enterprises are statistically significant parameters to estimate them. The importance of coworking spaces has increased in the post-disaster period, so this study informs public policies to benefit people and companies who choose coworking routes, and recommends urban planners, developers, and real-estate professionals to consider the proximity of creative industries in planning and developing coworking spaces in the future. Also, in the post COVID-19 period, to increase local jobs and long-term place sustainability, a localised policy intervention for coworking spaces in Delhi is highly recommended.
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SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.
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There is evidence that it may be possible to detect viruses and viral infection optically using techniques such as Raman and infrared (IR) spectroscopy and hence open the possibility of rapid identification of infected patients. However, high-resolution Raman and IR spectroscopy instruments are laboratory-based and require skilled operators. The use of low-cost portable or field-deployable instruments employing similar optical approaches would be highly advantageous. In this work, we use chemometrics applied to low-resolution near-IR (NIR) reflectance/absorbance spectra to investigate the potential for simple low-cost virus detection suitable for widespread societal deployment. We present the combination of near-IR spectroscopy (NIRS) and chemometrics to distinguish two respiratory viruses, respiratory syncytial virus (RSV), the principal cause of severe lower respiratory tract infections in infants worldwide, and Sendai virus (SeV), a prototypic paramyxovirus. Using a low-cost and portable spectrometer, three sets of RSV and SeV spectra, dispersed in phosphate-buffered saline (PBS) medium or Dulbecco's modified eagle medium (DMEM), were collected in long- and short-term experiments. The spectra were preprocessed and analyzed by partial least-squares discriminant analysis (PLS-DA) for virus type and concentration classification. Moreover, the virus type/concentration separability was visualized in a low-dimensional space through data projection. The highest virus-type classification accuracy obtained in PBS and DMEM is 85.8% and 99.7%, respectively. The results demonstrate the feasibility of using portable NIR spectroscopy as a valuable tool for rapid, on- site, and low-cost virus prescreening for RSV and SeV with the further possibility of extending this to other respiratory viruses such as SARS-CoV-2.
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The COVID-19 pandemic has resulted in a rapid growth of online learning. While majority of the current research focus on different learning management systems, massive open online courses, or even specific softwares like Zoom and Microsoft Teams, the use of artificial-intelligence (AI) based voice assistants (VAs) for the purpose of online education is very rare. In this work we propose, validate, and test a research model that explains the continuance usage of VAs by students for learning purpose during their home quarantine period. We consider novel pandemic-specific psychological factors like loneliness and self-quarantine, together with anthropomorphic factors like voice attractiveness of the VAs for proposing the research model. The factors of satisfaction and continuance usage are borrowed from Expectation Confirmation Theory. Partial Least Squares Structural Equation Modelling is used for testing the proposed model. © 2023 IEEE.
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Macao is a tourist city. It is home to the Ruins of Saint Paul's, a unique 100-year-old landmark, which is still standing with manual maintenance, even after three fires and reconstruction events. Therefore, the continuous preservation of its culture, heritage education, and construction are important issues for Macao. With the development of digital technology in recent years, users can quickly search historical sites and save two-dimensional and three-dimensional images and videos through smartphones. These methods also enhance the communication power of culture. Virtual browsing on a smartphone requires computing power and storage space;yet, virtual reality devices are not widely used. Therefore, augmented reality and virtual reality are rarely used simultaneously for three-dimensional interactive guided tours and operation experiences on the same theme. However, by quickly creating virtual reality scenarios and preserving historical sites on mobile devices, 4DAGE's 4DKanKan technology can provide augmented reality and metaverse virtual reality experiences. 4DKanKan can also integrate mobile guides and navigation software to connect mobile devices and assist in cultural inheritance and conduct sustainable education. This research linked this technology to the web by incorporating augmented reality and virtual reality technology to make designs and discussed the influences among service design, behavioral intentions, and learning effects. We collated and analyzed relevant data and text materials through systematic testing, observation, operation processes, and semi-structured interviews. The PLS multigroup structural model was used to explore and analyze the degree of influence and explanatory power of system quality, information quality, behavioral intention, and learning effects among themselves. The results of this study show that most users accepted the proposed innovative mode of operation and found it to be interesting and fun. Augmented reality is not limited by space or time;however, virtual reality devices taking too long to operate, switching too frequently, and having too many functional interfaces can cause operational problems. This study identified and modified the influencing factors and problems of the proposed system, with the aim of continuing to expand the applications of 4DKanKan to other cultural attractions or museums in the future. In addition, the research results can provide a reference for the sustainable development of related cultural sites.
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PurposeThe purpose of this study is to empirically estimate the impact of a government microcredit program on the handloom weavers to promote small and medium enterprises (SMEs) in Bangladesh.Design/methodology/approachThe data were collected from 311 handloom weavers from the Sirajganj District of Bangladesh from July to December 2015 using a multistage sampling technique. The analysis was conducted using a two-stage least squares regression model incorporating instrumental variables to control for the probable endogeneity problem associated with the study.FindingsThis study finds that government microcredit had no significant impact on borrowers' investment in their business, whereas credit received from multiple sources other than government credit had a significant negative impact. Additionally, literacy level, household assets and the number of operational handloom units positively affected investment, while the number of non-operational handloom units and distance negatively affected the investment.Research limitations/implicationsThis study's findings are more specific for the selected case and may not be generalizable to all kinds of SMEs.Practical implicationsThe policy implications are targeted at increasing loan size based on the number of operational handloom units to improve the performance of government and other microcredit programs to facilitate the growth of SMEs in Bangladesh.Originality/valueThis study specifically focuses on estimating the financial performance of government microcredit programs for SME development within the handloom industry, which has not been sufficiently explored in the literature.
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PurposeThe main purpose of this paper is to examine the direct effects of knowledge sharing and systems thinking on creativity and organizational sustainability in the hotel industry in Malaysia. In addition, the study aims to examine the mediation effect of creativity between knowledge sharing, systems thinking and organizational sustainability.Design/methodology/approachA survey method based on a questionnaire was used to gather data from 407 middle managers in the hotel industry in Malaysia. The partial least squares technique was used to examine the hypotheses.FindingsThe study found support for the effects of systems thinking and knowledge sharing on organizational sustainability. It also found support for the impact of creativity on organizational sustainability. Besides, the mediating role of creativity between systems thinking and organizational sustainability, and between knowledge sharing and organizational sustainability was also supported by data.Originality/valueThis is a pioneer work that has combined various human resources (i.e. systems thinking, knowledge sharing, creativity) to examine their impacts on organizational sustainability. Moreover, this work has established comparatively new relationships, i.e. the impact of systems thinking and knowledge sharing on creativity and organizational sustainability. In addition, the mediation role of creativity between systems thinking, knowledge sharing and organizational sustainability is relatively new in the literature. Furthermore, this study has confirmed the validity and reliability of knowledge sharing and organizational sustainability at first and second orders in the hotel industry in non-Western context.
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Al-Shomrani et al. (2016) introduced a new family of distributions (TL-G) based on the Topp-Leone distribution (TL) by replacing the variable x by any cumulative distribution function G(t). With only one extra parameter which controls the skewness, this family is a good competitor to several generalized distributions used in statistical analysis. In this work, we consider the extended exponential as the baseline distribution G to obtain a new model called the Topp-Leone extended exponential distribution TL-EE. After studying mathematical and statistical properties of this model, we propose different estimation methods such as maximum likelihood estimation, method of ordinary and weighted least squares, method of percentile, method of maximum product of spacing, method of Cramer Von-Mises, modified least squares estimators and chi-square minimum method for estimating the unknown parameters. In addition to the classical criteria for model selection, we develop for this distribution a goodness-of-fit statistic test based on a modification of Pearson statistic. The performances of the methods used are demonstrated by an extensive simulation study. With applications to covid-19 data and waiting times for bank service, a comparison evaluation shows that the proposed model describes data better than several competing distributions.
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In this article, we investigate the effect of different energy variables on economic growth of several oil-importing EU member states. Three periods from 2000 to 2020 were investigated. Three different types of regression models were constructed via the gretl software. Namely, the OLS, FE, and SE approaches to panel data analysis were investigated. The FE approach was chosen as the final one. The results suggest the importance of the consumption of both oil and renewable energy on economic growth. Crises of certain periods also had a noteworthy effect as well.
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The event-driven model (EDM) is an emerging concept in human behavioural research, and understanding how EDMs can promote theory development remains a fundamental quest of predictive science. Traditionally, researchers have heavily depended upon theory confirmation and the inclusion of mediating constructs to clarify uncertainty associated with plausible events (e.g. political, socio-economic, technological, environmental). Though this approach has pushed the field forward, it has also steered mediation research towards largely ignoring the fundamental role of prediction as a key for better understanding future events represented by EDMs. Additionally, emerging research using partial least squares structural equation modelling to execute prediction-oriented analysis continues to overlook problematic endogeneity bias and plausible type IV errors due to omitted paths and neglect of indirect effect size estimation in mediation models that embrace the transmittal or segmentation mediation approaches. We aim to introduce prediction as a fundamental option for estimating EDMs and recommend that researchers employ the segmentation mediation approach when estimating EDMs. We further emphasize a novel direct and indirect (v) effect size measure, types of prediction and cases when they are useful. Best practices and practical implications are provided to foster a more useful interpretation of findings.
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In early 2020, the world was shocked by the outbreak of COVID-19. World Health Organization (WHO) urged people to stay indoors to avoid the risk of infection. Thus, more people started to shop online, significantly increasing the number of e-commerce users. After some time, users noticed that a few irresponsible online retailers misled customers by hiking product prices before and during the sale, then applying huge discounts. Unfortunately, the "discounted” prices were found to be similar or only slightly lower than standard pricing. This problem occurs because users were unable to monitor product pricing due to time restrictions. This study proposes a Web application named PriceCop to help customers' monitor product pricing. PriceCop is a significant application because it offers price prediction features to help users analyse product pricing within the next day;thus, it can help users to plan before making purchases. The price prediction model is developed by using Linear Regression (LR) technique. LR is commonly used to determine outcomes and used as predictors. Least Squares Support Vector Machine (LSSVM) and Artificial Bee Colony (ABC) are used as a comparison to evaluate the accuracy of the LR technique. LSSVM-ABC was initially proposed for stock market price predictions. The results show the accuracy of pricing prediction using LSSVM-ABC is 84%, while it is 62% when LR is employed. ABC is integrated into SVM to optimize the solution and is responsible for the best solution in every iteration. Even though LSSVM-ABC predicts product pricing more accurately than LR, this technique is best trained using at least a year's worth of product prices, and the data is limited for this purpose. In the future, the dataset can be collected daily and trained for accuracy.
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Mediation analysis draws increasing attention in many research areas such as economics, finance and social sciences. In this paper, we propose new statistical inference procedures for high dimensional mediation models, in which both the outcome model and the mediator model are linear with high dimensional mediators. Traditional procedures for mediation analysis cannot be used to make statistical inference for high dimensional linear mediation models due to high-dimensionality of the mediators. We propose an estimation procedure for the indirect effects of the models via a partially penalized least squares method, and further establish its theoretical properties. We further develop a partially penalized Wald test on the indirect effects, and prove that the proposed test has a χ 2 limiting null distribution. We also propose an F -type test for direct effects and show that the proposed test asymptotically follows a χ 2 -distribution under null hypothesis and a noncentral χ 2 -distribution under local alternatives. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed tests and compare their performance with existing ones. We further apply the newly proposed statistical inference procedures to study stock reaction to COVID-19 pandemic via an empirical analysis of studying the mediation effects of financial metrics that bridge company's sector and stock return.