Designing an Empathy Training for Depression Prevention Using Virtual Reality and a Preliminary Study

As virtual reality (VR) is labeled by many as 'an ultimate empathy machine,' immersive VR applications have the potential to assist in empathy training for mental healthcare such as depression [21]. In responding to the increasing numbers of diagnosed depression throughout COVID-19, a first-person VR adventure game called 'Schwer' was designed and prototyped by the authors' research team to provide a social support environment for depression treatment. To continue the study and assess the training effectiveness for an appropriate level of empathy, this current article includes a brief survey on data analytics models and features to accumulate evidence for the next phase of the study, an interactive game-level design for the 'Reconstruction' stage, and a preliminary study with data collection. The preliminary study was conducted with a post-game interview to evaluate the design of the levels and their effectiveness in empathy training. Results showed that the game was rated as immersive by all participants. Feedback on the avatar design indicated that two out of three of the non-player characters (NPCs) have made the intended effect. Participants showed mostly positive opinion towards their experienced empathy and provided feedback on innovative teleport mechanism and game interaction. The findings from the literature review and the results of the preliminary study will be used to further improve the existing system and add the data analytics model training. The long-term research goal is to contribute to the healthcare field by developing a dynamic AI-based biofeedback immersive VR system in assisting depression prevention. © 2023 IEEE.

Examining the effects of disinformation and trust on social media users' COVID-19 vaccine decision-making

Purpose: Disinformation on social media is a serious issue. This study examines the effects of disinformation on COVID-19 vaccination decision-making to understand how social media users make healthcare decisions when disinformation is presented in their social media feeds. It examines trust in post owners as a moderator on the relationship between information types (i.e. disinformation and factual information) and vaccination decision-making. Design/methodology/approach: This study conducts a scenario-based web survey experiment to collect extensive survey data from social media users. Findings: This study reveals that information types differently affect social media users' COVID-19 vaccination decision-making and finds a moderating effect of trust in post owners on the relationship between information types and vaccination decision-making. For those who have a high degree of trust in post owners, the effect of information types on vaccination decision-making becomes large. In contrast, information types do not affect the decision-making of those who have a very low degree of trust in post owners. Besides, identification and compliance are found to affect trust in post owners. Originality/value: This study contributes to the literature on online disinformation and individual healthcare decision-making by demonstrating the effect of disinformation on vaccination decision-making and providing empirical evidence on how trust in post owners impacts the effects of information types on vaccination decision-making. This study focuses on trust in post owners, unlike prior studies that focus on trust in information or social media platforms. © 2023, Emerald Publishing Limited.

Scenario-based incident response training: lessons learnt from conducting an experiential learning virtual incident response tabletop exercise

Purpose: This paper aims to discuss the experiences designing and conducting an experiential learning virtual incident response tabletop exercise (VIRTTX) to review a business's security posture as it adapts to remote working because of the Coronavirus 2019 (COVID-19). The pandemic forced businesses to move operations from offices to remote working. Given that this happened quickly for many, some firms had little time to factor in appropriate cyber-hygiene and incident prevention measures, thereby exposing themselves to vulnerabilities such as phishing and other scams. Design/methodology/approach: The exercise was designed and facilitated through Microsoft Teams. The approach used included a literature review and an experiential learning method that used scenario-based, active pedagogical strategies such as case studies, simulations, role-playing and discussion-focused techniques to develop and evaluate processes and procedures used in preventing, detecting, mitigating, responding and recovering from cyber incidents. Findings: The exercise highlighted the value of using scenario-based exercises in cyber security training. It elaborated that scenario-based incident response (IR) exercises are beneficial because well-crafted and well-executed exercises raise cyber security awareness among managers and IT professionals. Such activities with integrated operational and decision-making components enable businesses to evaluate IR and disaster recovery (DR) procedures, including communication flows, to improve decision-making at strategic levels and enhance the technical skills of cyber security personnel. Practical implications: It maintained that the primary implication for practice is that they enhance security awareness through practical experiential, hands-on exercises such as this VIRTTX. These exercises bring together staff from across a business to evaluate existing IR/DR processes to determine if they are fit for purpose, establish existing gaps and identify strategies to prevent future threats, including during challenging circumstances such as the COVID-19 outbreak. Furthermore, the use of TTXs or TTEs for scenario-based incident response exercises was extremely useful for cyber security practice because well-crafted and well-executed exercises have been found to serve as valuable and effective tools for raising cyber security awareness among senior leadership, managers and IT professionals (Ulmanová, 2020). Originality/value: This paper underlines the importance of practical, scenario-based cyber-IR training and reports on the experience of conducting a virtual IR/DR tabletop exercise within a large organisation. © 2023, Emerald Publishing Limited.

COVIBOT: A Smart Chatbot for Assistance and E-Awareness during COVID-19 Pandemic

The coronavirus pandemic has spread over the past two years in our highly connected and information-dense society. Nonetheless, disseminating accurate and up-to-date information on the spread of this pandemic remains a challenge. In this context, opting for a solution based on conversational artificial intelligence, also known under the name of the chatbot, is proving to be an unavoidable solution, especially since it has already shown its effectiveness in fighting the coronavirus crisis in several countries. This work proposes to design and implement a smart chatbot on the theme of COVID-19, called COVIBOT, which will be useful in the context of Saudi Arabia. COVIBOT is a generative-based contextual chatbot, which is built using machine learning APIs that are offered by the cloud-based Azure Cognitive Services. Two versions of COVIBOT are offered: English and Arabic versions. Use cases of COVIBOT are tested and validated using a scenario-based approach. © 2022 IEEE.

Design Suggestion of Epidemic Prevention System for Shared Car Based on Scenario and Data

Entering the post-epidemic era, the travel demand for shared cars is increasing day by day. In the normalized epidemic prevention and control, epidemic prevention in shared cars needs to be designed systematically. This paper analyzes the existing risk of COVID-19 propagation based on two perspectives: scenario and data, and discusses the existing means of protection. Then based on the existing measures, the design suggestions are given from two aspects: scenario-based and data-based. Based on the scenario, the layout design and disinfection is implemented in regard to various ways that COVID-19 is transmitted;based on data, travel data integration should be promoted to achieve macro-structural dynamic adjustment and integrated governance from the overall transportation system. In the context of the industries, the shared car industry should response to new trend immediately and implement innovative ideas to obtain a service that is better suited for individuals in the post-epidemic era. In the end, several major functions of design in terms of developing the urban transportation system are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Decrease of Taxi Ridership due to the Impact of COVID-19: A Case Study of New York City

The outbreak of COVID-19 has greatly impacted all industries of many countries in the world. As an important part of people's daily life, transportation is one of the most severely impacted industries. Taking New York City as an example, this paper explores the decline of taxi ridership due to the COVID-19. The decreased ratio of the actual taxi ridership to the taxi ridership predicted for the no COVID-19 scenario based on historical data is calculated as the dependent variable. The fractional response model is used to study the effect of built environment factors including demographic and socioeconomic, land use, and road-related on the decline of ridership. One model is constructed for each of the four periods, to explore the influence of influencing factors on the dependent variables in different periods. The model results show that the percentage of taxi trips decline is associated with the proportion of high-income people living in the area. The reason could be that these people have more flexible working hours and working places. They can choose to telecommute or travel by private cars to avoid contacting other people during transportation. The analysis of the other factors shows that industrial jobs are related to the low percentage of decline. The model results reveal to us the problem of equity exposed in New York City during the pandemic: limited by jobs(race/income), a portion of citizens are not fully free to choose their travel mode during the pandemic. According to the findings, this paper gives traffic management some policy suggestions. As a result, this study could provide an important reference for policymakers to develop appropriate measures to control the epidemic. © 2021 IEEE.

The Solution of Computer Vision for Combating Covid-19

Recently, the Covid-19 pandemic has become very complicated and seriously affecting the economy as well as society in every countries in the world. In this chapter, we explore the solution of Computer Vision for handling the Covid-19 pandemic situation. The given scenarios based on deep learning techniques are used to monitor the traffic of people and vehicles through the checkpoints to control the in-out movement in significant areas. In addition, we also need to pay attention to complying with the regulations on wearing masks and ensuring a safe social distance in public places. From there, the proposed system will effectively support organizations to deal with the Covid-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

System dynamics model for changing transportation demand during the pandemic in Japan

To realize a sustainable transportation system, it is necessary to estimate the environmental load caused by transportation. Here transportation demand affects carbon dioxide emissions directly. In general, traffic simulations or scenario-based evaluations have been used to predict transportation demand. However, the COVID-19 pandemic that began in late 2019 has changed transportation demand drastically, and such changes have not been considered in conventional simulation models. Therefore, it is important to quantify the impact of the pandemic on transportation demand and its magnitude. In this study, we developed a model focused on describing the changes in transportation demand caused by the COVID-19 pandemic in Japan. We developed a model using system dynamics because this method is effective in describing socio-technical systems such as transportation demand. Based on related studies, we categorized transportation demand by purpose and modeled it based on the cause-and-effect relationship between the amount of transportation and the prevalence of infectious diseases. To verify the developed model, we compared actual data of 2020 in Japan with the output of the model. We set scenarios with varying parameter values that contribute significantly to changes in transportation demand, such as individual awareness of the pandemic. As a result, the developed model was verified at the behavioral level. This model can be used in developing future transportation systems. © 2022 Elsevier B.V.. All rights reserved.

Knowledge management in turbulent times: time-based scenario analysis of vaccinations against COVID-19

Purpose>This paper aims to estimate the delay or timely effects of the national vaccination strategy for COVID-19 on Italian gross domestic product (GDP). By adopting a knowledge management lens, the study highlights the importance of “time” for Italian recovery. Indeed, recovering an adequate growth rate is crucial for the future of employment, well-being and management of Italian public debt.Design/methodology/approach>This study applies an epidemiological model of a universal access vaccination programme against COVID-19. The economic model is based on the time-shift of available quarterly projections deriving from the expected delay or acceleration of the national vaccination plan against COVID-19.Findings>The basic concept underlying the scenario analysis is that the sustainability of the expected recovery of the Italian economy due to the COVID-19 shock, and consequently the growth of the GDP, is time-dependent on the rollout of the national vaccination plan.Research limitations/implications>A delay in the vaccination campaign could have a twofold negative impact on the growth of the Italian gross product: it reduces the quarterly growth over the previous year in the short term and it delays the quarterly upwards trend over the next two years. Policymakers and practitioners are called to promptly face new dynamic scenarios due to public and economic policies to fight the COVID-19 crisis.Originality/value>To the best of the authors’ knowledge, this is the first attempt of research that focuses attention on the synchrony between the economic time necessary for recovery and the real-time necessary to achieve vaccination coverage for the restart of production activities.

Scenario-based preference modeling to examine the robustness of airport mega projects initiatives.

Airports are a high complex type of projects that are exposed to many disruptiveness. Proper management between airport expansion projects and airport operations is needed to ensure safety and efficiency of the project and the aviation. Scenario-based preference modeling is one of the robustness analysis techniques that describes the influence of disruptive scenarios on the project initiatives across different criteria. A scenario-based preference model was applied in this work to investigate the influence of different scenarios on Kuwait International Airport expansion. Scenario-based preference is a multi-criteria assessment, which allows the involvement of multiple stakeholders. The key target of the model was to illustrate the most and least robust initiatives, and the highest and least ranked initiatives over the scenarios. The analysis also ranked the scenarios based on their level of disruptiveness. The outcomes of this method can be used to mitigate the system and improve the project robustness by understanding each kind of risk impact. The results showed that the most and least disruptive scenarios were S3, economic crisis, and S5, compelling circumstance, and the most robust initiative was x1, completion of the main terminal building (T2). It is important to discuss the contribution and insights extracted from these analyses. For example, showing the most and least disruptive scenarios is not enough. It is important to mention what insights the readers can gain by knowing the importance of these scenarios. It is very important to highlight the significance of the results. The results showed that the most disruptive scenario was economic crisis. This indicates the fact that current Covid 19 pandemic had significantly affected the local economy, reduced country income that is based mainly on oil and consumed considerable budget on medical related activities. This had resulted in a lower expenditure on mega infrastructure projects as the airport, which caused considerable delay and interruption to major activities. Furthermore, the results showed that the most robust initiative was completion of the main terminal building (T2). This is mainly true due to the fact that the government is very keen to complete this major terminal in the airport as it will release the pressure on the old portion of the airport and increase the airport capacity. Based on these facts, it is clear that priorities in airport infrastructure activities and construction initiatives were affected considerably by the global and local circumstances and come first the global pandemic.

Designing a sustainable closed-loop pharmaceutical supply chain in a competitive market considering demand uncertainty, manufacturer's brand and waste management.

Pharmaceutical supply chain (PSC) is one of the most important healthcare supply chains and the recent pandemic (COVID-19) has completely proved it. Also, the environmental and social impacts of PSCs are undeniable due to the daily entrance of a large amount of pharmaceutical waste into the environment. However, studies on closed-loop PSCs (CLPSC) are rarely considered real-world requirements such as competition among diverse brands of manufacturers, the dependency of customers' demand on products' price and quality, and diverse reverse flows of end-of-life medicines. In this study, a scenario-based Multi-Objective Mixed-Integer Linear Programming model is developed to design a sustainable CLPSC, which investigates the reverse flows of expired medicines as three classes (must be disposed of, can be remanufactured and can be recycled). To study the competitive market and deal with demand uncertainty, a novel scenario-based game theory model is proposed. The demand function for each brand depends on the price and quality provided. Then, a hybrid solution approach is provided by combining the LP-metrics method with a heuristic algorithm. Furthermore, a real case study is investigated to evaluate the application of the model. Finally, sensitivity analysis and managerial insights are provided. The numerical results show that the proposed classification of reverse flows leads to proper waste management, making money, and reducing both disposal costs and raw material usage. Moreover, competition increases PSCs performance and improves the supply of products to pharmacies. Supplementary Information: The online version contains supplementary material available at 10.1007/s10479-021-03961-0.