AN AGENT-BASED MODELLING APPROACH FOR ASSESSING ENTREPRENEURIAL INTENTIONS AND DEVELOPMENT STRATEGIES IN THE RURAL HOSPITALITY INDUSTRY

Rural hospitality and tourism (RHT) play a key role in rural revitalization, especially due to the impact of COVID-19, with more citizens choosing to travel to the countryside for a staycation. Local SMEs, especially family-owned enterprises, make up the majority of the RHT sector, not only providing services and products to satisfy tourists, but also helping with local employment. However, entrepreneurs operating in rural areas face many challenges in terms of capital, skills and education. Hence, it is important to explore the entrepreneurial intention (EI) of local people and how policies can support or change their behaviours. Current research on the RHT industry, rarely study the EI of local people, and the literature on rural entrepreneurship concentrates on developed countries. This study therefore uses agent-based modelling to explore how locals' EI in Chongming island (China) respond to the current impact of COVID-19, and whether policies will bring about changes on the supply side of RHT sector. © SW 2023.All rights reserved

Projecting the Electricity Demand of the Residential Sector in Thailand During and After the COVID-19 Pandemic

The residential sector in Thailand has been a fast-growing energy consumption sector since 1995 at a rate of 6% per year. This sector makes a significant contribution to Thailand's rising electricity demand especially during the COVID-19 pandemic. This study projects Thailand's residential electricity consumption characteristics and the factors affecting the growth of electricity consumption using a system dynamics (SD) modeling approach to forecast long-term electricity consumption in Thailand. Furthermore, the COVID-19 pandemic and the lockdown can be seen as a forced social experiment, with the findings demonstrating how to use resources under particular circumstances. Four key factors affecting the electricity demand used in the SD model development include (1) work and study from home, (2) socio-demographic, (3) temperature changing, and (4) rise of GDP. Secondary and primary data, through questionnaire survey method, were used as data input for the model. The simulation results reveal that changing behavior on higher-wattage appliances has huge impacts on overall electricity consumption. The pressure to work and study at home contributes to rises of electricity consumption in the residential sector during and after COVID-19 pandemic. The government and related agencies may use the study results to plan for the electricity supply in the long term. © 2023 World Scientific Publishing Co.

Multizone Modelling of SARS-CoV-2 Airborne Transmission in Mechanically Ventilated Buildings

This study contributes to a better understanding of the airborne transmission risks in multizone, mechanically ventilated buildings and how to reduce infection risk. A novel modeling approach combining the Wells-Riley and the US National Institute of Standards and Technology (NIST) CONTAM models was applied to a multizone whole building to simulate exposure and assess the effectiveness of different mitigation measures. A case study for the US Department of Energy large office prototype building was conducted to illustrate the approach. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

What Do We Know? A Bibliometric Analysis of Current Literature on COVID-19 and its Implications

The COVID-19 pandemic has plunged the world into chaos by affecting people's lifestyles and imposing immense pressures on healthcare professionals. Since its outbreak in Wuhan, China, back in December 2019, researchers all across the globe have been working tirelessly to provide reliable insights to understand and combat the virus. As a result, the number of publications related to the novel coronavirus has been increasing rapidly. This study aims to quantify and summarize the progress of SARS-CoV-2 related research from November 2019 onwards to January 2021 by employing a bibliometric analysis and topic modelling approaches. A total of 33,159 research publications, downloaded from the Web of Science (WoS) core collection database, were analyzed. The key aspects of our study include identifying important publications, their distribution across countries and organizations, important journals and central authors who have made a significant contribution to the current literature. We have also delineated the major themes addressed in the academic community. © 2022 IEEE Computer Society. All rights reserved.

Understanding telemedicine service users' perceptions: A text mining analysis on social media discussion

Telemedicine has drawn noticeable attention due to the advancement of information technology, and it saw a surge in popularity during the COVID-19 pandemic. This study aims to understand telemedicine users' perceptions of their care services and identify the aspects of telemedicine that can be improved to enhance users' experience and satisfaction. Specifically, we utilized a topic modeling approach with Latent Dirichlet Allocation (LDA) to analyze telemedicine-related discussion posts on Reddit to discover the topics and themes that telemedicine service users are interested in, as well as the perceptions that users have of those topics and themes. 11 topics and 6 themes were discovered by the LDA algorithm. Lastly, we provide our suggestions and insights on how telemedicine services and practitioners can implement the themes, as well as directions for future study. © 2023 IEEE Computer Society. All rights reserved.

Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification

With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision. © 2022 Association for Computational Linguistics.

Towards Predicting Supplier Resilience: A Tree-Based Model Approach

With looming uncertainties and disruptions in today's global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions. © 2022 IEEE Computer Society. All rights reserved.

Measuring emotions in the COVID-19 real world worry dataset

The COVID-19 pandemic is having a dramatic impact on societies and economies around the world. With various measures of lockdowns and social distancing in place, it becomes important to understand emotional responses on a large scale. In this paper, we present the first ground truth dataset of emotional responses to COVID-19. We asked participants to indicate their emotions and express these in text. This resulted in the Real World Worry Dataset of 5,000 texts (2,500 short + 2,500 long texts). Our analyses suggest that emotional responses correlated with linguistic measures. Topic modeling further revealed that people in the UK worry about their family and the economic situation. Tweet-sized texts functioned as a call for solidarity, while longer texts shed light on worries and concerns. Using predictive modeling approaches, we were able to approximate the emotional responses of participants from text within 14% of their actual value. We encourage others to use the dataset and improve how we can use automated methods to learn about emotional responses and worries about an urgent problem. © ACL 2020.All right reserved.

Regional Maximum Hospital Capacity Estimation for COVID-19 Pandemic Patient Care in Surge Through Simulation

Estimating the capacity of a region to serve pandemic patients in need of hospital services is crucial to regional preparedness for pandemic surge conditions. This paper explores the use of techniques of stochastic discrete event simulation for estimating the maximum number of pandemic patients with intensive care and/or in-patient, isolation requirements that can be served by a consortium of hospitals in a region before requesting external resources. Estimates from the model provide an upper bound on the number of patients that can be treated if all hospital resources are re-allocated for pandemic care. The modeling approach is demonstrated on a system of five hospitals each replicating basic elements (e.g. number of beds) of the five hospitals in the Johns Hopkins Hospital System in the Baltimore-Washington, D.C. Metropolitan area under settings relevant to the COVID-19 pandemic. © 2022 IEEE.

Media Coverage and Public Perception of Distance Learning During the COVID-19 Pandemic: A Topic Modeling Approach Based on BERTopic

The current study proposes a topic modeling approach for analyzing press coverage and public perception of distance learning during the COVID-19 pandemic. We evaluate the applicability of a novel approach for neural topic modeling based on transformer-based language models. Our methodology is tested empirically on a large sample of news and discussions in various social and news media platforms to derive valuable insights on press coverage and public perception of COVID-19 impact on the education system in Bulgaria. The study outlines key advantages of using BERTopic in analyzing big data. Our work contributes to the body of literature devoted on value creation through big data and text analytics utilization in the public sector. © 2022 IEEE.

The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe

Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.

Determining Factors Affecting Customer Loyalty to Internet Service Provider during the COVID-19 Pandemic: A Structural Equation Modeling Approach

Internet Service Provider (ISP) has been a critical aspect during the COVID-19 pandemic especially in developing countries such as Indonesia. The purpose of this study was to determine factors affecting customer loyalty to ISP in Indonesia during the COVID-19 pandemic by utilizing a structural equation modeling (SEM) approach. There were 252 respondents who voluntarily answered an online questionnaire which consisted of 49 questions that covered several factors such as Customer Service Performance (CSP), Internet Quality (IQ), Router Quality (RQ), Payment Method (PM), Internet Package (IP), Security & Privacy (SP), Promotion (P), COVID-19 impact (CV), Customer Satisfaction (CS), and Customer Loyalty (CL). SEM showed that IQ was found to have the highest effect on CS which subsequently led to CL, followed by SP, CSP, RQ, and PM. This study is one of the first studies that explored customer loyalty to Internet Service Providers during the COVID-19 pandemic. The SEM construct can be applied and extended to enhance customer satisfaction on ISP particularly in developing countries. © 2022 IEEE.

Investigation of airborne exposure risk to infectious diseases during aircraft boarding process using agent-based modeling

The aircraft boarding process is characterized by great movement and close contact between passengers in a confined space, which is a situation of particular concern considering the risk of exposure to airborne infectious diseases such as the COVID-19. In order to evaluate the airborne exposure risk during a commercial aircraft boarding process, an agent-based simulation model approach is adopted in the present work. Since the elderly population is one of the most at risk groups, special features are included in the simulation model in order to evaluate how this group is affected in the process. Three aspects are considered: priority boarding (elders boarding order);boarding strategy;and social distancing measures. The main findings are that care must be taken when interpreting average exposure risks, since although the overall risk of exposure is low, there may be cases in which significant risk is presented. © 2022, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

Acceptance of Virtual Campus Tour Using the TAM Approach: a Case Study in UPN Veteran Jawa Timur, Indonesia

Various policies regarding the application of information and communication technology are one form of effort to realize efficient and effective information delivery, one of the institutions or agencies that implement information and communication technology policies is an educational institution. When technology is developing, especially communication technology along with the presence of the COVID-19 Pandemic. In this regard, to provide the best information is also felt by the National Development University 'Veteran' East Java in introducing the situation and condition of the campus by implementing a Virtual Campus Tour. The purpose of this study was to determine the factors that influence the acceptance of the virtual tour of UPN 'Veteran' Jawa Timur based on the Technology Acceptance Model (TAM) model. This study uses proportionate stratified random sampling with a total of 266 respondents who are active students at UPN 'Veteran' Jawa Timur. Based on the results of the analysis, it was found that the factors that influence user behavior in using the virtual tour of UPN 'Veteran' Jawa Timur on an ongoing basis are factors of perceived usefulness, perceived ease of use, attitude toward using, and usability with significant results on the intention to use the UPN 'Veteran' Jawa Timur virtual tour. While the factor that most influences the behavior of users to accept Virtual Tour of UPN 'Veteran' Jawa Timur is the usability factor, this is because usability has the largest path coefficient value, which is 0.714 compared to other variables. © 2021 IEEE.

Teachers' Burnout Risk During the COVID-19 Pandemic: Relationships With Socio-Contextual Stress-A Latent Profile Analysis.

The purpose of this cross-sectional study was to identify distinct burnout profiles of teachers and to examine their association with work-related stressors, such as workload, students' misbehavior, classroom resources, professional recognition needs and poor colleague relations, as well as socio-demographic variables. Survey data were collected from 330 kindergarten and primary school teachers (84 males, M age = 38.3, SD = 9.14). The latent profile analysis revealed four distinct profiles. The antecedents of teacher burnout (TB) profiles were the stress generated by workload, students' misbehavior, and low professional recognition. The socio-demographic variables, with the exception of gender, were covariates of the TB profiles. The findings implies that career opportunities prospects, classroom management and time-management programs may be useful in preventing teacher burnout.

Behavioral SIR models with incidence-based social-distancing

Most available behavioral epidemiology models have linked the behavioral responses of individuals to infection prevalence. However, this is a crude approximation of reality because prevalence is typically an unobserved quantity. This work considers a general endemic SIR epidemiological model where behavioral responses are incidence-based i.e., the agents perceptions of risks are based on available information on infection incidence. The differences of this modeling approach with respect to the standard ‘prevalence-based’ formulations are discussed and its dynamical implications are investigated. Both current and delayed behavioral responses are considered. We show that depending on the form of the ‘memory’ (i.e., in mathematical language, of the information delaying kernel), the endemic equilibrium can either be globally stable or destabilized via Hopf bifurcations yielding to stable recurrent oscillations. These oscillations can have a very long inter-epidemic periods and a very wide amplitude. Finally, a numerical investigation of the interplay between these behavior-related oscillations and seasonality of the contact rate reveals a strong synergic effect yielding to a dramatic amplification of oscillations. © 2021 Elsevier Ltd

Understanding Students' Behavioural Intentions To Use Social Media Learning In Education: A Utaut Model Approach

This paper seeks to examine students' behavioral intention to adopt social media learning in education through a unified theory of acceptance and use of technology (UTAUT) model based on data collected from 279 undergraduate students of different colleges of Delhi University, Delhi, India. The results revealed that students' behavioral intention to use social media learning in education is significantly affected by their perceptions about performance expectancy, effort expectancy, facilitating conditions, and Covid-19 induced social isolation but not by social factors. Out of four moderating variables, the impact of only one variable, i.e., area of residence is found to be most significant across all the relationships studied. Study results are important for the policymakers to incorporate social media tools as an essential part of their future policies for higher education in India, and by extension for educational levels as well. © 2021 IEEE.

Methods in Constrained Community Detection: An Integer Optimization Model and Heuristic Approach for Cohort Creation

As a result of the COVID-19 pandemic, many organizations and schools have switched to a virtual environ-ment. Recently, as vaccines have become more readily available, organizations and educational institutions have started shifting from virtual environments to physical office spaces and schools. For the highest level of safety and caution with respect to the containment of COVID-19, the shift to in-person interaction requires a thoughtful approach. With the help of an Integer Programming (IP) Optimization model, it is possible to formulate the objective function and constraints to determine a safe way of returning to the office through cohort development. In addition to our IP formulation, we developed a heuristic approximation method. Starting with an initial contact matrix, these methods aim to reduce additional contacts introduced by subgraphs representing the cohorts. These formulations can be generalized to other applications that benefit from constrained community detection. © 2021 IEEE.

Affectivity and Proxemic Distances: an experimental agent-based modeling approach

Many manifestations of interactive human behavior (social and with the environment) are conditioned by emotions, influencing reasoning and other rational decision making activities. The study of the interplay of emotional and non-emotional behaviors (spatial motion) is here faced through the modeling of affective agents where affective states are explicitly measured and represented thanks to the collection of data in a dedicated experiment with humans. During this experiment, we observed that subjects of different ages (focusing on elderly) react differently to particular spatial stimuli (proxemics distance calculation), manifesting a strict relation between distancing and emotional states. The agent-based modeling and simulation of this behavior here presented is a contribution to the comprehension of complex interplays between spatial distances and affective states, amplified by the recent experience of pandemic, where aware distancing become a mandatory and affecting factor of the life, especially for fragile and aged people. The presented modeling approach relies on data collected with an online experiment performed to understand what kind of personal, psychological and situational factors influenced people’s behavior while distancing from others, in particular during the COVID-19 pandemic. The focus of the experiment was in the comparison of different age reactions, involving 80 participants aged between 16 and 92 years. © 2020 Copyright for this paper by its authors.

A Tutorial on Participative Discrete Event Simulation in the Virtual Workshop Environment

Facilitated discrete event simulation offers an alternative mode of engagement with stakeholders (clients) in simulation projects. Pre-covid19 this was undertaken in face-to-face workshops but the new reality has meant that this is no longer possible for many of us around the globe. This tutorial explores PartiSim, short for Participative Simulation, as adapted to fit the new reality of holding virtual workshops with stakeholders. PartiSim is a participative and facilitated modelling approach developed to support simulation projects through a framework, stakeholder-oriented tools and manuals in facilitated workshops. We describe a typical PartiSim study consisting of six stages, four of which involve facilitated workshops and how it can be undertaken in a virtual workshop environment. We have developed games to provide those attending the tutorial with the experience of virtual facilitation. © 2021 IEEE.