Estimation of infection fields and pathways for SARS-CoV-2 and its variant based on the relationship among dose-response function parameter, quantum emission rate, and basic reproductive number

The theoretical model of the relationship among dose-response function parameters, quantum emission rate, and basic reproductive number for SARS-CoV-2 was constructed. Then, using this model, infection fields and pathways for SARS-CoV-2 and its variant were estimated. The parameters of the time activity, the number of contacts by the microenvironments and groups, and the COVID-19 risk from multiple pathways in near and far fields were used. Consequently, in lower transmissibility, droplet spray transmission in the near field was dominant, whereas in higher transmissibility, transmission from inhalation of smaller aerosols in the far field was dominant. Moreover, it was suggested that transmission from droplet spray, indirect contacts, and inhalation of smaller aerosols in the near field and inhalation of smaller aerosols in the far field was dominant for the wild-type strain, while transmission from inhalation of smaller aerosols in the far field were dominant for the Delta variant. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Assessment of respiratory disease infection risk and natural ventilation intervention countermeasures in teaching spaces: A campus case study

The risk of indoor respiratory disease transmission can be significantly reduced through interventions that target the built environment. Several studies have successfully developed theoretical models to calculate the effects of built environment parameters on infection rates. However, current studies have mainly focused on calculating infection rate values and comparing pre- and post-optimization values, lacking a discussion of safe baseline values for infection rates with risk class classification. The purpose of this paper is to explore the design of interventions in the built environment to improve the ability of buildings to prevent virus transmission, with a university campus as an example. The study integrates the Wells-Riley model and basic reproduction number to identify teaching spaces with high infection risk on campus and proposes targeted intervention countermeasures based on the analysis of critical parameters. The results showed that teaching buildings with a grid layout pattern had a higher potential risk of infection under natural ventilation. By a diversity of building environment interventions designed, the internal airflow field of classrooms can be effectively organized, and the indoor virus concentration can be reduced. We can find that after optimizing the building mentioned above and environment intervention countermeasures, the maximum indoor virus infection probability can be reduced by 22.88%, and the basic reproduction number can be reduced by 25.98%, finally reaching a safe level of less than 1.0. In this paper, we support university campuses' respiratory disease prevention and control programs by constructing theoretical models and developing parametric platforms. © 2023 Elsevier Ltd

Theory and Simulation of Multipurpose Antenna for Detection and Degrading of Viruses in Times of Pandemic

This paper present a theoretical model that aims to minimize the capabilities of viruses in public places through engineered electromagnetic fields. Thus, the modeling of antenna based at the infinitesimal dipole is used. In addition fields and directivity at the far field region are calculated. This proposal empathizes the fact that the radiated energy will affect the spike protein of viruses. In this manner the functionality of virus as to produce infection would be minimized. Simulations of the radiate electric field are presented. © 2022 IEEE.

Analysis on the Influencing Factors of College Students' Online Learning during the COVID-19 Epidemic

Novel coronavirus pneumonia (COVID-19) epidemic situation spread, colleges and universities can not start school normally. Under this background, the Ministry of education proposed a new measure to stop classes and stop learning, and encouraged all colleges and universities to carry out online teaching. College students have many problems during online learning, such as insufficient learning conditions, poor learning effect and so on. Based on this, this study aims to investigate the online learning status of college students during the epidemic, explore the influencing factors of their online learning effect, and put forward suggestions for better online teaching. Combined with literature, this paper determines three influencing factors of online learning effect, namely learning conditions, learning initiative and learning adaptability, and constructs a theoretical model of influencing factors of College Students' online learning effect. On this basis, a questionnaire was compiled according to the theoretical model, and a network questionnaire survey was conducted among 1330 students in the school of management of Wuhan University of technology. The reliability and validity of the questionnaire are analyzed by means of variable interpretation variance, factor load, Cronbach's alpha and correlation coefficient. The theoretical hypothesis is empirically tested by structural equation model method, and the influence degree of different factors is further explored by testing the goodness of fit index of the model. Through the research, it is found that students' learning conditions, learning initiative and learning adaptability have a significant positive impact on the effect of online learning (P value is significant at the level of 0.001). Based on this research, this paper puts forward the effect of environment oriented autonomous learning. Finally, based on the above research, this paper gives some suggestions to improve the effect of online learning, in order to provide guidance for College Online Teaching and college students' online learning in the future. © 2022 IEEE.

Safety and health at the workplace in the context of COVID-19: The case of a dental clinic

Governments of different countries and healthcare organisations working in various areas face enormous challenges when trying to combat the COVID-19 pandemic and protect employees, their families and communities. Workplaces can be high-risk environments in terms of the virus outbreak and transmission. This paper aims to disclose the ways for workplace safety improvement in dentistry in the context of COVID-19. The authors present the theoretical model of workplace safety improvement with regard to COVID-19 infection prevention and control measures. The expectations, fears and tasks of dental employees at their workplace in the context of the COVID-19 pandemic were investigated based on the systematic literature review and the qualitative empirical study conducted in Lithuania. The study disclosed that unmet employee expectations could lead to different kinds of fear;the most common sources of anxiety are linked to a higher risk of getting infected, a lack or misuse of protection measures and inadequately performed work. Occupational risks are closely related to the components of a workplace system. Therefore, it is important to apply a holistic approach to improve workplace safety, enhance work performance and minimise the negative effects on an employee, an organisation, a patient and a society. © 2022 R. Čiutienė et al.

Theoretical Models and Preliminary Results for Contact Tracing and Isolation

Efficient contact tracing and isolation is an effective strategy to control epidemics, as seen in the Ebola epidemic and COVID-19 pandemic. An important consideration in contact tracing is the budget on the number of individuals asked to quarantine-the budget is limited for socioeconomic reasons (e.g., having a limited number of contact tracers). Here, we present a Markov Decision Process (MDP) framework to formulate the problem of using contact tracing to reduce the size of an outbreak while limiting the number of people quarantined. We formulate each step of the MDP as a combinatorial problem, MinExposed, which we demonstrate is NP-Hard. Next, we develop two approximation algorithms, one based on rounding the solutions of a linear program and another (greedy algorithm) based on choosing nodes with a high (weighted) degree. A key feature of the greedy algorithm is that it does not need complete information of the underlying social contact network, making it implementable in practice. Using simulations over realistic networks, we show how the algorithms can help in bending the epidemic curve with a limited number of isolated individuals. © 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Online Collaborative Learning: Analysis of the Current State

The transition to online education in times of Covid-19 has led to the use of educational innovations in order to ensure relevant learning. In this new and challenging pandemic context, the objective of this study is to diagnose the current state of Collaborative Online Learning (ACL) in teachers and students at the university level. The methodology adopted in the study is descriptive, inferential and cross-sectional. A new theoretical model of online collaborative learning is shown, which will include the design, validation and application of a questionnaire in 90 students and 20 teachers from different universities in Lima (Peru). The applied instrument showed high internal consistency with a Cronbach's Alpha of 0, 943 for teachers and 0, 914 for students. The results obtained show pending challenges in digital skills and internal regulation of teams;potentialities related to the exchange of information, socio-emotional skills for collaboration and positive interdependence online. Thus, the implementation of innovative methodologies such as collaborative online learning should not be a spontaneous act, but rather reflective and conscious, based on a managerial-mediator role of the teacher and a leading role of the student, as indicators of pedagogical autonomy in these educational actors in the classroom context, in order to ensure its effectiveness in virtual environments and the achievement of relevant learning, as a basis for the formation of a global and ethical citizenship. © 2021 IEEE.

Study on the influencing factors of health information sharing behavior of the elderly under the background of normalization of pandemic situation

This study aims to solve the problem of unwise judgment, decisions, and correspondingly dangerous behaviors caused by error health information to the elderly. Based on the MOA model and self-determination theory, this paper constructs a health information sharing model for the elderly and analyzes it with Amos's structural equation model. The study finds that media richness, health information literacy, perceived benefits, and negative emotions of the coronavirus epidemic positively influence health information sharing behavior. In contrast, perceived risks have a significant negative impact on health information sharing behavior. At the same time, media richness positively affects health information literacy, perceived benefits, and negative emotions of the coronavirus epidemic but has no significant impact on perceived risks. Health literacy positively affects perceived benefits but does not significantly affect the perceived risks and negative emotions of the coronavirus epidemic. This study aims to assist government and online social platforms in taking relevant measures under the background of normalization of the pandemic situation, controlling the spread of error health information among the elderly, and guiding the elderly to share health information better. © 2021 International Consortium for Electronic Business. All rights reserved.

A Grounded Theory Analysis of COVID-19 Information and Resources Relayed Through University Webpages: Implications for a More Inclusive Community

This qualitative study investigates web pages documenting COVID-19 responses from 28 universities across the United States. Using grounded theory methodology, we inductively developed a model of universities' response to the pandemic. Four types of strategies were identified from the data and a theoretical model was developed describing (a) causal conditions that underlie the strategies for response to the pandemic, (b) the context that influenced the strategies adopted by the universities, (c) intervening conditions due to the pandemic that influenced strategy development, and (d) potential recommendations to make universities' responses more inclusive. This research has implications for improving the experience of the communities a university serves, including faculty development, especially for newer faculty who are joining the universities remotely and interacting with new colleagues only through the virtual mediums. Finally, this paper will be of use to engineering educators and administrators as they seek to improve inclusion and belonging within faculty at universities. © American Society for Engineering Education, 2021

DESIGNING AIRCRAFT TRAFFIC FLOWS USING DATA-DRIVEN QUEUING MODELS

This paper proposes a scientific and systematic method for designing future air traffic management systems by integrating data science, theoretical modeling, and simulation evaluation. Also, it presents a part of a case study focusing on the data-driven and theoretical modelings of arriving traffic flow in airports. A stochastic data analysis was conducted using actual radar tracks and flight plans before the impacts of COVID-19, where the queuing model parameters were estimated based on the conducted analysis. The proposed data-driven modeling approaches contribute to the analysis of the bottlenecks in air traffic and to their solutions. Overall, we believe that the outcomes of this study provide insights on future operational strategies and system designs, which can realize more efficient air traffic management systems. © 2021 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021. All rights reserved.

Together is Better: mRNA Co-Encapsulation in Lipoplexes is Required to Obtain Ratiometric Co-Delivery and Protein Expression on the Single Cell Level.

Liposomes can efficiently deliver messenger RNA (mRNA) into cells. When mRNA cocktails encoding different proteins are needed, a considerable challenge is to efficiently deliver all mRNAs into the cytosol of each individual cell. In this work, two methods are explored to co-deliver varying ratiometric doses of mRNA encoding red (R) or green (G) fluorescent proteins and it is found that packaging mRNAs into the same lipoplexes (mingle-lipoplexes) is crucial to efficiently deliver multiple mRNA types into the cytosol of individual cells according to the pre-defined ratio. A mixture of lipoplexes containing only one mRNA type (single-lipoplexes), however, seem to follow the "first come - first serve" principle, resulting in a large variation of R/G uptake and expression levels for individual cells leading to ratiometric dosing only on the population level, but rarely on the single-cell level. These experimental observations are quantitatively explained by a theoretical framework based on the stochasticity of mRNA uptake in cells and endosomal escape of mingle- and single-lipoplexes, respectively. Furthermore, the findings are confirmed in 3D retinal organoids and zebrafish embryos, where mingle-lipoplexes outperformed single-lipoplexes to reliably bring both mRNA types into single cells. This benefits applications that require a strict control of protein expression in individual cells.