The Impact of COVID-19 Lockdown on Ambient Air Quality in Shanghai, 2022

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.

Aligning the Goals Hybrid Model for the Diagnosis of Mental Health Quality

The social distancing imposed by the COVID-19 pandemic has been described as the "greatest psychological experiment in the world”. It has tested the human capacity to extract meaning from suffering and challenged individuals and society in Brazil and abroad to promote cohesion that cushions the impact of borderline experiences on mental life. In this context, a survey was conducted with teachers, administrative technicians, and outsourced employees at the Federal Institute of Piauí (IFPI). This educational institution offers professional and technological education in Piauí, Brazil. This study proposes a system for the early diagnosis of health quality during social distancing in the years 2020 and 2021, over the COVID-19 pandemic, combining multi-criteria decision support methodology, the Analytic Hierarchy Process (AHP) with machine learning algorithms (Random Forest, logistic regression, and Naïve Bayes). The hybrid approach of the machine learning algorithm with the AHP multi-criteria decision method with geometric mean accurately obtained a classification that stood out the most in the characteristics' performance concerning emotions and feelings. In 2020, the situation was reported as the SAME AS BEFORE, in which the hybrid AHP with Geographical Average with the machine learning Random Forest algorithm stands out, highlighting the atypical situation in the quality of life of the interviewees and the timely manner in which they realized that their mental health remained unchanged. After that, in 2021, the situation was reported as WORSE THAN BEFORE, in which the hybrid AHP with geometric mean with the machine learning Random Forest algorithm provided an absolute result. © 2023 by the authors.

Assessment of a pandemic emergency financing facility

The pandemic bond issued by the World Bank (WB) in 2017 is a financial innovation enabling the transfer of the pandemic risk from the underdeveloped/developing countries to the financial market. It covers perils of various diseases that could overwhelm the global health systems and adversely impact the world economy. If all the triggers are activated, the bond's principal and coupons are used to finance coordinated, swift and resilient medical response to safeguard the well-being of the populace. This product, however, is criticised for its onerous trigger requirements. We examine the WB's pandemic-bond pricing framework, which requires inputs that are only partially available. From a rather unstructured COVID-19 data set, an information database is created and customised for pandemic-bond valuation. A vector auto-regressive moving average model is utilised to jointly describe the triggers dynamics. Our modelling simulations of risk triggers reveal that the bond payout could be made in less than half of the WB's earliest opportunity of 85 days. © 2023 The Authors

Statistical properties and estimation of the three-parameter lindley distribution with application to COVID-19 Data

In a recent article by Shanker et al. (2017), the three-parameter Lindley distribution has been studied. The present paper is a continuation of the investigation of the properties of this distribution because of its high flexibility for modeling lifetime data. We studied some statistical properties of this distribution as central tendency measures, dispersion measures, and shape measures. In addition, the mode and the quantile function of the distribution were derived by the authors. The three parameters were estimated by the Maximum Product of Spacing Method (MPS) due to its fame in estimating parameters. A simulation study is carried out to examine the consistency of estimators using mean square error (MSE). The estimators showed that they have the property of consistency because MSEs decrease with increasing the size of the sample. On the practical side, the MPS estimates were used to obtain statistical properties, probability density function (p.d.f), cumulative distribution function (c.d.f), survival function, and hazard function for real data which represents COVID-19 Data in Iraq/Al-Anbar Province. We found the flexibility of the distribution in representing life data and the possibility of getting the patients probability of death and probability of survival for the time. © 2023 Penerbit Universiti Kebangsaan Malaysia. All rights reserved.

Managing Butterfly Career Attitudes: The Moderating Interplay of Organisational Career Management

A protean career attitude is the most attractive and coping career adjustment attitude nowadays. Based on the social exchange theory, this study empirically analyses the association between protean career attitude and affective organisational commitment for Malaysian hotel industry employees. It also examines the COVID-19 situation's retrospective repercussions and career uncertainty. The study also investigates the moderating role of organisational career management on the relationship between protean career attitudes and affective organisational commitment. During the pandemic, a cross-sectional survey was given to 403 hotel managers working in four- or five-star hotels. The data were analysed using structural equation modelling in Smart-PLS. The results showed that self-directed and value-driven protean career attitudes undermine affective organisational commitment. Organisational career management significantly moderated the relationship between a protean career attitude and affective organisational commitment. In light of this, organisational career management is essential when dealing with protean careers. Lastly, the person's practical implications are significant. People should have a protean career attitude to deal with unpredictability, such as the COVID-19 epidemic and remain invincible over the long run. © 2023 by the authors.

COVID-19 Lockdown Effects on a Highly Contaminated Coastal Site: The Mar Piccolo Basin of Taranto

The COVID-19 pandemic has had a dramatic socio-economic impact on mankind;however, the COVID-19 lockdown brought a drastic reduction of anthropic impacts on the environment worldwide, including the marine–coastal system. This study is concentrated on the Mar Piccolo basin of Taranto, a complex marine ecosystem model that is important in terms of ecological, social, and economic activities. Although many numerical studies have been conducted to investigate the features of the water fluxes in the Mar Piccolo basin, this is the first study conducted in order to link meteo-oceanographic conditions, water quality, and potential reduction of anthropic inputs. In particular, we used the model results in order to study the response of the Mar Piccolo basin to a drastic reduction in the leakage of heavy metal IPAs from industrial discharges during the two months of the mandated nationwide lockdown. The results show the different behavior of the two sub-basins of Mar Piccolo, showing the different times necessary for a reduction in the concentrations of heavy metals even after a total stop in the leakage of heavy metal IPAs. The results highlight the high sensitivity of the basin to environmental problems and the different times necessary for the renewal of the water in both sub-basins. © 2023 by the authors.

Keep it real: Assessing destination image congruence and its impact on tourist experience evaluations

Inaccurate promotional information about tourist destinations may result in tourists' negative evaluations. This study proposes a new approach to measure the congruence between projected and received images of a destination's attractions. Based on online textual data, this study investigates how image congruence influences tourists' evaluations of their destination experiences. Using promotional messages and reviews of attractions in Hainan, China obtained from a leading Chinese online travel agency (Ctrip) and a three-way fixed-effects regression model, this study demonstrates that image congruence positively affects tourists' appraisal of their destination experiences. External crises (e.g., the COVID-19 pandemic), the readability of promotional messages, and tourists' expertise moderate this relationship, reducing the positive impact of image congruence on tourist experience evaluation. This study bridges theoretical and empirical gaps in destination image (in)congruence research, informing tourism marketing agencies of effective promotional strategies in different contexts. © 2023

Achieving Personalized Precision Education Using the Catboost Model during the COVID-19 Lockdown Period in Pakistan

With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student's participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students' performance. © 2023 by the authors.

Exploring the Consumers' Purchase Intention on Online Community Group Buying Platform during Pandemic

One of the main methods of shopping for many consumers during the COVID-19 pandemic was through online community group-buying. This shopping method caters to the consumer demand of reducing contact and centralized procurement. However, some online community group-buying platforms could not attract many consumers, and consumer participation was low. Therefore, determining which factors affect consumers' willingness to use online community group buying is important. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and perceived risk theory, this research explores the effects of performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived risk on consumers' willingness to use online community group buying. In this research, a questionnaire survey was used, and the sample randomly collected from online consumers who had experience in online community group buying. A total of 280 respondents were collected. The collected data were analyzed by descriptive statistics, reliability, validity, correlation, and regression analysis. The results show that performance expectancy, effort expectancy, and social influence have a significant positive effect on the purchase intention of community group-buying consumers, while facilitating conditions and perceived risk have no significant positive effect. This research further enriched and improved the research on the use intention of an online community group-buying platform by integrating the UTAUT model and perceived risk theory. In practice, this research provides a new perspective and practical reference for how the online community group-buying platform can better attract consumers and maintain sustainable long-term customer relations. © 2023 by the authors.

Consumers' Continued Intention to Use Online-to-Offline (O2O) Services in Omnichannel Retail: Differences between To-Shop and To-Home Models

Online-to-offline (O2O) commerce is a specific form of omnichannel retailing, wherein consumers search and purchase online and then consume offline. There are many different O2O models, and new O2O businesses are emerging during the COVID-19 pandemic;they can be cate-gorized into two types of O2O services: to-shop and to-home. However, few studies have focused on consumer behavior in a comprehensive O2O scenario, and no study has attempted to compare the differences between to-shop and to-home consumers. Therefore, this study aimed to propose a universal model to predict consumers' continued intention to use O2O services and to compare the differences between to-shop and to-home O2O in terms of factors influencing consumer behavior. A cross-sectional survey was conducted, and the PLS-SEM was used for data analysis. The basic SEM results indicated that habit, performance expectancy, confirmation, and offline facilitating conditions are the main predictors. The multigroup analysis showed differences between to-shop and to-home consumers regarding hedonic motivation, price value, and perceived risk. The study suggests that marketers and designers in various O2O scenarios can use the framework to build their business plans and develop different marketing strategies or sub-platforms for to-shop and to-home consumers. © 2023 by the authors.

Intention of young travellers to travel during the COVID-19 pandemic

The global health pandemic (COVID-19) has led to a significant decline in tourism activities and challenged existing norms and practices of the tourism sector. As international travel is restricted, the tourism sector is trying to promote domestic tourism by following health guidelines. This study aims to measure the relationships among social media travel content, perceived social risk of travel, attitude toward travel, and intention to travel during the pandemic time. The data were collected from young travellers in Bangladesh. The structural equation modelling (SEM) technique was used to estimate the relationships among the constructs. The results show that both social media travel content and perceived social risk of travel are significantly related to attitude toward travel and intention to travel. The association between attitude toward travel and intention to travel is also found significant. Destination managers are recommended to implement social media activation programs (e.g., a persuasive advertising campaign) and promote safe travel on their social media platforms (e.g., Facebook) to reduce perceived social risk of travel and create a positive attitude of travellers toward travel domestically during the global pandemic. Copyright © 2022 Inderscience Enterprises Ltd.

Economic forecasting with an agent-based model

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national accounts, sector accounts, input–output tables, government statistics, and census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts (ESA 2010) and includes all economic sectors populated with millions of heterogeneous agents. In addition to being a competitive model framework for forecasts of aggregate variables, the detailed structure of the ABM allows for a breakdown into sector-level forecasts. Using this detailed structure, we demonstrate the ABM by forecasting the medium-run macroeconomic effects of lockdown measures taken in Austria to combat the COVID-19 pandemic. Potential applications of the model include stress-testing and predicting the effects of monetary or fiscal macroeconomic policies. © 2022 The Author(s)

Consumer adoption of autonomous delivery robots in cities: Implications on urban planning and design policies

Five hundred survey responses on consumer acceptance of autonomous delivery robots (ADRs) were collected because the pandemic has increased the emphasis on contactless deliveries, spurring some interest in ADRs to perform last-mile deliveries in urban cities. To examine consumers' intention to adopt ADRs, a comprehensive theoretical model grounded on the Health Belief Model and Task-Technology Fit Model was presented and structural equation modeling was applied to examine the survey data. The analysis revealed that the constructs from both theories have significant effects on outcome expectations and task-technology fit. Additionally, outcome expectations and task-technology fit are strong predictors of consumers' intention, as indicated by their direct and indirect effects. Thus, this study enriches existing research by interpreting consumers' intention to adopt ADRs through health and technology perspectives. It also provides practical implications and policy recommendations for urban planning and design. © 2022 Elsevier Ltd

Simulation of Evaporative Flow and Coupled Transport Behaviors of Aerosol Particles

Intense scientific interest in the mechanisms of aerosol transport has been aroused due to the global COVID-19 pandemic. In this study, a new droplet evaporation model considering solid components such as salt, has proposed to simulate the diffusion and evaporative flow behaviors of saliva-forming aerosols droplets, caused by human breathing, coughing and sneezing. The model considers the evaporation process on the surface of aerosols and couples the droplet kinetic equations, including the incorporation of influencing factors such as flow resistance, gravity, droplet-like size and initial velocity. Different ambient temperatures, relative humidity and wind speed have been simulated and the mechanisms of aerosols migration behaviors have been analyzed. For individual droplet, the results not only show that the larger the droplet size, the longer it remains suspended in airborne, but also the lower the humidity and the higher the temperature, the faster the evaporation rate. © 2022, Science Press. All right reserved.

STRUCTURAL EQUATION MODEL THE DEVELOPMENT OF THE COVID 19 COMMUNITY RESILIENCE IN PUJON KIDUL TOURISM VILLAGE

Pujon Kidul, Malang is a village tourism that offers agricultural potential as a tourist attraction to prosper the communities. Pandemic Covid-19 has caused instability in all sectors, including agricultural sector. The agricultural sector is the last line of defense, but that does not mean the pandemic has no impact on farming activities. On the contrary, the pandemic has slowed global economic growth and social growth, particularly in agriculture;as a result, social capital and local wisdom must be strengthened. As a result, this study was carried out to support Covid-19's Resilience Area in the tourism village of Pujon Kidul by investigating the role of social capital and resilience. Confirmatory Factor Analysis (CFA) and the Structural Equation Model (SEM) were used in this study. We employed structural equation model using AMOS program which the result of the study shows that the residents of Pujon Kidul Village Tourism already have favorable social capital circumstances, which are characterized by a high level of trust among residents and good social network. This trust and social network support the Covid-19 Resilience Village program's effectiveness. So far, the currently used model could explain the relationship between social capital and community resilience. © 2022 Editura Universitatii din Oradea. All rights reserved.

New Data of the 1755 Earthquake and Tsunami in Lisbon, Portugal

In spite of the significant number of studies focused on the 1755 earthquake and tsunami, there are still many unknowns regarding this event in Lisbon, Portugal. Thus, in this research the authors compiled historical documents, including some that had never been analyzed, complemented with a field survey and tsunami numerical modeling at the historical civil parish of Santo Estevão, Lisbon. It was possible to identify 13 buildings, including three religious buildings and five palaces. Furthermore, the new data showed that contradicting the general idea, the earthquake caused significant damage to the selected territory because the number of households decreased by 52%. The number of residents decreased to about 51%, and in 1756, 1041 residents were still living in 297 temporary shelters. There were more than 44 dead and 1122 residents were unaccounted for. The fire did not hit the area, and the tsunami numerical model results were validated by the historical accounts and cartography, which indicate that the coastal area of the studied area was not significantly inundated by the tsunami. The consultation of historical documents that had never been analyzed by contemporary researchers provides a breakthrough in the knowledge of the event since it allowed a very detailed analysis of the disaster impact.

Numerical Evaluation of a Novel Vertical Drop Airflow System to Mitigate Droplet Transmission in Trains

Owing to the outbreak of COVID-19, researchers are exploring methods to prevent contact and non-contact infections that occur via multiple transmission routes. However, studies on pre-venting infections caused by droplet transmission in public transportation are insufficient. To prevent the spread of infectious diseases, a new ventilation system in railway vehicles must be devel-oped. In this study, a novel vertical drop airflow (VDA) system is proposed to mitigate the effect of droplet transmission in a high-speed train cabin. The droplet transmission route and droplet fate are investigated using three-dimensional fluid dynamics simulations, performed employing the Eu-lerian–Lagrangian model. Additionally, a porous model is adopted to simulate the effect of close-fitting masks. The results indicate that 120 s after coughing, the decrease in the droplet number in the VDA system is 72.1% of that observed in the conventional system. Moreover, the VDA system effectively suppresses droplet transmission because the maximum droplet travel distances of the VDA systems are 49.9% to 67.0% of those of the conventional systems. Furthermore, the effect of reducing droplet transmission by wearing a close-fitting mask is confirmed in all systems. Thus, the decrease in both droplet number and droplet transmission area in train cabins validate that the proposed VDA system has an effective airflow design to prevent droplet infection. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Capacity-need gap in hospital resources for varying mitigation and containment strategies in India in the face of COVID-19 pandemic.

BACKGROUND: Due to uncertainties encompassing the transmission dynamics of COVID-19, mathematical models informing the trajectory of disease are being proposed throughout the world. Current pandemic is also characterized by surge in hospitalizations which has overwhelmed even the most resilient health systems. Therefore, it is imperative to assess health system preparedness in tandem with need projections for comprehensive outlook. OBJECTIVE: We attempted this study to forecast the need for hospital resources for one year period and correspondingly assessed capacity and tipping points of Indian health system to absorb surges in need due to COVID-19. METHODS: We employed age-structured deterministic SEIR model and modified it to allow for testing and isolation capacity to forecast the need under varying scenarios. Projections for documented cases were made for varying degree of containment and mitigation strategies. Correspondingly, data on health resources was collated from various government records. Further, we computed daily turnover of each of these resources which was then adjusted for proportion of cases requiring mild, severe and critical care to arrive at maximum number of COVID-19 cases manageable by health care system of India. FINDINGS: Our results revealed pervasive deficits in the capacity of public health system to absorb surge in need during peak of epidemic. Also, model suggests that continuing strict lockdown measures in India after mid-May 2020 would have been ineffective in suppressing total infections significantly. Augmenting testing to 1,500,000 tests per day during projected peak (mid-September) under social-distancing measures and current test to positive rate of 9.7% would lead to more documented cases (60, 000, 000 to 90, 000, 000) culminating to surge in demand for hospital resources. A minimum allocation of 13x, 70x and 37x times more beds for mild cases, ICU beds and mechanical ventilators respectively would be required to commensurate with need under that scenario. However, if testing capacity is limited to 9,000,000 tests per day (current situation as of 19th August 2020) under continued social-distancing measures, documented cases would plummet significantly, still requiring 5x, 31x and 16x times the current allocated resources (beds for mild cases, ICU beds and mechanical ventilators respectively) to meet unmet need for COVID-19 treatment in India.