Anomalies of O3, CO, C2H2, H2CO, and C2H6 detected with multiple ground-based Fourier-transform infrared spectrometers and assessed with model simulation in 2020: COVID-19 lockdowns versus natural variability

Anomalies of tropospheric columns of ozone (O3), carbon monoxide (CO), acetylene (C2H2), formaldehyde (H2CO), and ethane (C2H6) are quantified during the 2020 stringent COVID-19 world-wide lockdown using multiple ground-based Fourier-transform infrared spectrometers covering urban and remote conditions. We applied an exponential smoothing forecasting approach to the data sets to estimate business-as-usual values for 2020, which are then contrasted with actual observations. The Community Atmosphere Model with chemistry (CAM-chem) is used to simulate the same gases using lockdown-adjusted and business-as-usual emissions. The role of meteorology, or natural variability, is assessed with additional CAM-chem simulations. The tropospheric column of O3 declined between March and May 2020 for most sites with a mean decrease of 9.2% ± 4.7%. Simulations reproduce these anomalies, especially under background conditions where natural variability explains up to 80% of the decline for sites in the Northern Hemisphere. While urban sites show a reduction between 1% and 12% in tropospheric CO, the remote sites do not show a significant change. Overall, CAM-chem simulations capture the magnitude of the anomalies and in many cases natural variability and lockdowns have opposite effects. We further used the long-term record of the Measurements of Pollution in the Troposphere (MOPITT) satellite instrument to capture global anomalies of CO. Reductions of CO vary highly across regions but North America and Europe registered lower values in March 2020.The absence of CO reduction in April and May, concomitant with reductions of anthropogenic emissions, is explained by a negative anomaly in the hydroxyl radical (OH) found with CAM-chem.The implications of these findings are discussed for methane (CH4), which shows a positive lifetime anomaly during the COVID-19 lockdown period. The fossil fuel combustion by-product tracer C2H2 shows a mean drop of 13.6% ± 8.3% in urban Northern Hemisphere sites due to the reduction in emissions and in some sites exacerbated by natural variability. For some sites with anthropogenic influence there is a decrease in C2H6.The simulations capture the anomalies but the main cause may be related to natural variability. H2CO declined during the stringent 2020 lockdown in all urban sites explained by reductions in emissions of precursors. Copyright: © 2023 The Author(s).

Ultra High-Throughput Screening Assays for Inhibitors of TMPRSS2 and SPIKE Interaction

SARS-CoV-2, the coronavirus that causes the disease COVID- 19, was identified over three years ago, yet current small molecule therapies have limited usefulness and resistance to therapies and vaccines is inevitable. Ultra high-throughput screening (uHTS) assays for novel and repurposed inhibitors of a protein-protein interaction in the viral life cycle could be used to screen a vast number of compounds with a specific target of action. In particular, the interaction between viral SPIKE protein and human TMPRSS2 is an understudied, yet critical step in viral entry. Thus, we aim to create uHTS assays to rapidly and affordably identify inhibitors of the TMPRSS2 and SPIKE interaction for further biochemical studies and therapeutic development for SARS-CoV-2.We first sought to create a Time Resolved-Forster/Fluorescence Energy Transfer (TR-FRET) assay which uses lysates of cells with overexpressed SPIKE and TMPRSS2 and fluorescently labeled antibodies to detect interactions between these proteins. Initially, we developed and optimized this TR-FRET assay in a 384-well plate then miniaturized to a 1536-well plate. We conducted a pilot screen of compounds with known biological activity to test this assay's screening capabilities. To further narrow the hits from this TR-FRET screen, we developed an orthogonal uHTS Nanoluciferase Binary Technology (NanoBiT) assay to detect the interaction between tagged SPIKE and TMPRSS2 in live cells.With these two assays in hand, we expanded our TR-FRET screen to over 100 000 compounds and identified several that were also positive in the orthogonal NanoBiT assay. Four of these compounds were found to potentially interact with either SPIKE or TMPRSS2 from thermal shift experiments, providing support for their action as SPIKE and TMPRSS2 interaction inhibitors. Thus, we have developed TR-FRET and NanoBiT orthogonal uHTS assays which have allowed for the discovery of several possible repurposed and novel inhibitors of the SPIKE/ TMPRSS2 interaction. These uHTS assays can be employed as a model for future drug discovery efforts and the compounds identified may be used as exciting starting points for development of inhibitors of SARS-CoV-2. This research was supported in part by The Emory School of Medicine COVID Catalyst-I3 award, the NCI Emory Lung Cancer SPORE (SR, HF;P50CA217691) Career Enhancement Program (AI, P50CA217691), Emory initiative on Biological Discovery through Chemical Innovation (AI) and R01AI167356 (SS).Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.

A Bibliographical Analysis of Pragmatic Strategies Responding to the Pandemic Crisis in Aviation

The aviation industry has suffered from the COVID-19 pandemic since early 2020. Airlines, airports, and manufacturers reacted to fight against the disease to protect passengers as well as remain sustainable. The purpose of this study is to analyze existing archives and discover strategic plans implemented by essential actors of the commercial aviation system. Using inductive qualitative analysis in conjunction with VOSviewer bibliographical data visualization, this study unveils the practical strategies of resilience enacted by the airline industry, manufacturers, and commercial airports during the pandemic time. Based on the Crisis Response Matrix from Suk and Kim, airlines' survival strategies during COVID-19 include passenger protection, operational retrenchment, innovation, and long-term managerial plans. Manufacturers' main approaches are expanding business with maintenance, repair, and overhaul (MRO) on top of alternative fuel innovations for emission reduction. Remarkably, airports adopt policies and protocols to screen and protect passengers, share information about infected passengers, and create a contactless airport environment for the prevention and control of pandemic infectious diseases. Synthesis tables containing discoveries are provided for practitioners' future reference. © 2023, University Aviation Association. All rights reserved.

What is sustainable tourism in social media? Evidence from tweets

Social media is an essential tool and channel for tourism industry research. It has created significant value for tourism consumption, management, and trends. However, rare scientific achievements uncover sustainable tourism on Twitter, a famous social media worldwide. Therefore, this study aims to conceptualize sustainable tourism from the social media perspective. Based on 2,201 tweets, the study uses Nvivo12.0 to encode content thematically to explore the connotations and concepts of sustainable tourism. The study identified three crucial dimensions: tourism for sustainable development, sustainable tourism management, and sustainable tourism types. Tourism for sustainable development aims to promote the positive effects of sustainable practices in the tourism industry on various areas of life, economy, society, and environment. Moreover, its content serves for the better development in the future. Sustainable tourism management focuses on industries, leaders, experts, programs, and sectors. In addition, the study categorized sustainable tourism by entity, purpose, destination, and mode to determine the type of sustainable tourism. The paper helps to inspire stakeholders in the tourism industry to understand and pay attention to the content in the post-Covid-19 era and also provides recommendations for the tourism market, the tourism sector, and tourism consumers. © The author(s) 2023.

Demographic factors affecting Chinese tourists traveling to Thailand in the post-Covid-19 era

Chinese tourists contribute significantly to the development of the tourism industry in Thailand. However, only some studies have systematically discussed the role of demographic factors in developing Thai international tourism. The study aims to research the behavior of Chinese citizens traveling to Thailand in the post-Covid-19 era based on combining the behavioral dynamics, the push-pull theories and demographics. 432 Chinese travelers who have visited Thailand participated in the survey. The scale included four parts: international tourism development in Thailand (A1-A8);pushers (B1-B9);pullers (C1-C8);demographic variables: gender, age, occupation, income, education level, marital status, and location. The study used exploratory factor analysis, confirmatory factor analysis, correlation, and variance analyses with SPSS 26.0. Therefore, exploratory factor analysis identified for this study three factors: F1 (5 items), F2 (3 items), and F3 (4 items). The correlation between F1 and F2 is 0.8, between F1 and F2 is 0.87, between F2 and F3 is 0.79. The findings of the analysis of demographic variables indicate that: gender does not affect tourists' perceptions and changes;age has a significant impact on the three constructs;monthly income should be considered in the development of inbound tourism strategies;undergraduate and postgraduate visitors showed higher scores for research constructs;there is no need to consider the marital status of tourists. The study suggests that the Thai tourism department pay attention to the push and pull factors that motivate Chinese citizens to choose Thailand to expand international tourism. © Haiying Fu, Chonlavit Sutunyarak, 2023.

International currency markets and the COVID‐19 pandemic

We find that quantifying COVID‐19 pandemic shocks is critical to understanding international currency market returns. Scaled by population, shocks from between‐country differences in the number of weekly COVID‐19 deaths are informative in predicting exchange rate returns. Following Alfaro et al. (2020), we estimate the expected number of COVID‐19 deaths based on an exponential model and use it to construct two pandemic shocks that measure the unanticipated number of deaths on a weekly basis and the time‐varying correction of forecast provided new information from the previous week. We document negative impacts of COVID‐19 propagation on currency returns. In addition, we find that the government response, in particular fiscal and monetary stimulus packages, can help mitigate negative effects of COVID‐19 on currency returns. Our findings are robust to country‐specific pandemic measures, window sizes of the exponential model, and the choice of forecast model.

Knowledge Map of Chinese Tourists Travelling to Thailand: Based on the Bibliometric Method

Chinese tourists have essential economic and cultural exchange values for Thai tourism. The present research on Chinese tourists traveling to Thailand (CTTT) has made a significant breakthrough, but it rarely observes the research status quo, hot spots, and the future based on bibliometric methods and visualization technology. Based on 710 articles on the Web of Science (WoS), Cite Space visualization technology was used to analyze the publishing trend of CTTT, authors, institutions, countries, keywords, and categories, and build a knowledge map about CTTT. The study found that although the positive trend of published articles indicates excellent future development, the overall number of published articles is not ideal. Moreover, research on this topic has made significant progress and breakthroughs in the healthcare field, meaning that there is still much research space in the business and economic areas. Thai institutions and scientific research institutions and their scholars have significantly contributed to CTTT. They focus more on healthcare, disease, health, sexual safety, and equality in travel. Although there is an increasing focus on consumer behavior, habits, and models, However, the research on CTTT under the background of the digital economy, sharing economy, big data, artificial intelligence, and the COVID-19 pandemic is of great research value. Therefore, this study encourages different countries, institutions, and scholars to do more on this topic. Furthermore, interdisciplinary research is even more critical. © 2022.

Detection of Antibody versus Antigen, Optimal Option of Different Serological Assays Based Tests for COVID-19 Diagnosis: A Meta-Analysis

Background: In this study, the diagnostic efficacy of antigen test and antibody test were assessed. Additional-ly, the difference of sensitivity, specificity, and diagnostic odds ratio were compared concerning efficacy of antibody test versus antigen test for Corona Virus Disease 2019 (COVID-19) diagnosis. Methods: Online databases were searched for full-text publications and STATA software was used for data pooling and analysis before Sep 1st, 2022. Forrest plot was used to show the pooled sensitivity, specificity and diagnostic odds ratio. Combined receiver operating characteristic (ROC) curve was used to show the area of under curve of complex data. Results: Overall, 25 studies were included. The sensitivity (0.68, 95% CI: 0.53-0.80) and specificity (0.99, 95% CI: 0.98-0.99) in antibody or antigen was calculated. The time point of test lead to heterogeneity. The area under curve (AUC) was 0.98 (95% CI: 0.96-0.99), and the diagnostic odds ratio (DOR) was 299.54 (95% CI: 135.61-661.64). Subgroup analysis indicated antibody test with sensitivity (0.59, 95% CI: 0.44-0.73) and specificity (0.98, 95% CI: 0.95-0.99) and antigen test with sensitivity of 0.77 (95% CI: 0.53-0.91) and specificity of 0.99 (95% CI: 0.98-1.00). Higher AUC and DOR were proved in antigen test. Conclusion: The present study compared the efficacy of antibody test versus antigen test for COVID-19 di-agnosis. Better diagnostic efficacy, lower heterogeneity, and less publication bias of rapid antigen testing was suggested in this study. This study would help us to make better strategy about choosing rapid and reliable testing method in diagnosis of the COVID-19 disease. © 2023 Fu et al. Published by Tehran University of Medical Sciences.

Changes in operational risk and its determinants under Covid-19

This examines the direct impact of the Covid-19 pandemic on the operational risk of Chinese commercial banks and the moderating effect of bank size, business diversification and regulatory records. To address the lack of data in operational risk studies, we gather financial statements on operational risk to obtain empirical proxy variables. We conduct an empirical study using 639 financial statements from 20 listed commercial banks in China from 2011 Q4 to 2021 Q3 and find that the Covid-19 pandemic has increased the operational risk of commercial banks. Moreover, business diversification, bank size and poor regulatory record significantly increase the operational risk effects of the pandemic. Finally, we test the robustness of our results, supporting our conclusions and providing new insights into the interaction between the Covid-19 pandemic and banks’ operational risk. © 2022 Infopro Digital Risk (IP) Limited.

Toward a new, collaborative global financing architecture for fragile, low, and middle-income countries' data priorities

The COVID-19 pandemic has occurred against a sobering global backdrop: national data collection programs and the production of core economic statistics have long been under-funded (by national government as well as the international development community), and data gaps are still significant. The pandemic has highlighted the importance of NSOs and the urgent need to strengthen and modernize core data collection programs as the backbone of national data systems. As the severity of this problem and its damaging implications are becoming more salient, members of the international development and national statistics communities have joined forces in an effort to address it. A collective, high-level effort is now being mobilized by senior leadership of the World Bank and the United Nations, in close collaboration with the Global Partnership for Sustainable Development Data, to join forces to increase global investments in fragile, low-and-middle-income countries' data priorities and to better put data to work for green, resilient, inclusive development. Specifically, two new complementary funds have recently been launched by the World Bank and United Nations to support countries' data systems, data capital, and risk analytics in a coordinated way: the World Bank-hosted Global Data Facility and the UN-hosted Complex Risk Analytics Fund (CRAF'd). © 2022 - The authors.

On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study

Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation. Existing feature attribution algorithms treat all features in a dataset homogeneously, which may lead to misinterpretation of consequences of changing feature values. In this work, we consider partitioning features into controllable and uncontrollable parts and propose the Controllable fActor Feature Attribution (CAFA) approach to compute the relative importance of controllable features. We carried out experiments applying CAFA to two existing datasets and our own COVID-19 non-pharmaceutical control measures dataset. Experimental results show that with CAFA, we are able to exclude influences from uncontrollable features in our explanation while keeping the full dataset for prediction. © 2022 IEEE.

Xi'an network attention measurement in the post-epidemic era

Based on Baidu index data and the development timeline of COVID-19 in China, this study analyzes the spatial and temporal distribution pattern of network attention in Xi'an under epidemic prevention and control. The results show that: 1) In 2020, the network attention of Xi ' an affected by the epidemic is low. The trend of monthly network attention in the year is consistent with the time axis of domestic epidemic development, showing a ' double peak and double valley ' mode, and it is high in summer and autumn, and low in winter and spring. On the holidays, the attention increased before the festival, and the ' May 1 ' reached the peak one day before the festival, and the ' Eleventh ' reached the peak on the third day of the festival, showing a clear ' blowout ' trend. 2) The spatial distribution of Xi'an network attention is scattered, and shows the characteristics of high network attention in Henan, Sichuan and other surrounding provinces and Guangdong, Jiangsu, Zhejiang and other coastal economic developed areas. © COPYRIGHT SPIE.

Eye-tracking study of public acceptance of 5G base stations in the context of the COVID-19 pandemic

Purpose This study applied eye-tracking techniques and questionnaires within the framework of the Stimulus-Organism-Response Model (SOR) and Technology Acceptance Model (TAM), to investigate the influencing factors of the public acceptance of 5G base stations. Design/methodology/approach This study used a combination of eye-tracking experiments and questionnaires. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). Findings (1) The Technology Acceptance Model (TAM) could be used to explain the effects on public acceptance of 5G base stations in the context of the COVID-19 pandemic. The public's perceived usefulness and ease of use of 5G base stations positively affects public acceptance of 5G base stations. (2) The public's perceived risk of 5G base stations has a negative influence on the public acceptance of 5G base stations. (3) The public's visual attention to the different valence information about 5G base stations positively impacts the perceived ease of use while having negative impacts on perceived risk. (4) Visual attention to various valence information of 5G base stations can indirectly influence public acceptance through the perceived risk. Originality/value Applying the SOR and TAM to data obtained from eye-tracking experiments and questionnaires, this study analyzed the factors and mechanisms influencing public acceptance of 5G base stations in the context of the COVID-19 pandemic.

First contact: A series of zoom-based, virtual on call shifts

Aim: COVID-19 has resulted in reduced exposure to on-call shifts where medical students could increase confidence and proficiency in task prioritisation and decision making. Existing 'simulated on-calls' provide a substitute in a controlled environment, however in person teaching has also been limited by COVID-19. Our virtual on-call sessions use ZOOM to replicate the higher-level learning experiences normally conferred by live simulation. Method: We designed a series of virtual 'on-calls' for medical students. Participants were 'on-call', receiving 'bleeps' which were 'answered' by calling a facilitator via ZOOM. The facilitator would roleplay a scenario and the 'Electronic Patient Record' (EPR) on Google Forms contained patient notes and observations. Students needed to collect information from the facilitator and document a management plan into the EPR. Participants received 'bleeps' of varying complexity, urgency and relevance and were expected to prioritise and triage tasks accordingly. Evaluation was via a pre/post session quiz with separate feedback forms. Results: 23 students from 18 universities participated. Students reported increased confidence in managing on-call scenarios, and average scores improved in the post session quiz. Positive feedback was paid to the variety of scenarios, the EPR system and the feeling of realism elicited from the need to triage and prioritise jobs. Conclusions: Our framework uses readily accessible technology to provide interactive learning experience. Feedback suggested students engaged in higher order learning and thinking, achieving our stated aims. We aim to incorporate technologies such as automation software which will allow for a scalable, free, and accessible virtual on call.

Blockchain Meets COVID-19: A Framework for Contact Information Sharing and Risk Notification System

COVID-19 is a severe global epidemic in human history. Even though there are particular medications and vaccines to curb the epidemic, tracing and isolating the infection source is the best option to slow the virus spread and reduce infection and death rates. There are three disadvantages to the existing contact tracing system: 1. User data is stored in a centralized database that could be stolen and tampered with, 2. User's confidential personal identity may be revealed to a third party or organization, 3. Existing contact tracing systems [1] [2] only focus on information sharing from one dimension, such as location-based tracing, which significantly limits the effectiveness of such systems. We propose a global COVID-19 information sharing and risk notification system that utilizes the Blockchain, Smart Contract, and Bluetooth. To protect user privacy, we design a novel Blockchain-based platform that can share consistent and non-tampered contact tracing information from multiple dimensions, such as location-based for indirect contact and Bluetooth-based for direct contact. Hierarchical smart contract architecture is also designed to achieve global agreements from users about how to process and utilize user data, thereby enhancing the data usage transparency. Furthermore, we propose a mechanism to protect user identity privacy from multiple aspects. More importantly, our system can notify the users about the exposure risk via smart contracts. We implement a prototype system to conduct extensive measurements to demonstrate the feasibility and effectiveness of our system.

Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan. IEEE

Thermally Activated Charge Transfer in Dual-Emission Mn2+-Alloyed Perovskite Quantum Wells for Luminescent Thermometers

Owing to the pandemic of Coronavirus disease 2019 (COVID-19), the demands on ultracold-chain logistics have rapidly increased for the storage and transport of mRNA vaccines. Herein, we report a soluble luminescent thermometer based on thermally activated dual-emissions of Mn2+-alloyed 2D perovskite quantum wells (QWs). Owing to the Mn2+ alloying, the binding energy of perovskite QW exciton is reduced from 291 to 100 meV. It facilitates the dissociation of excitons into free charge carriers, which are then transferred and trapped on Mn2+. The temperature-dependent charge transfer efficiency can be tuned from 8.8% (-93 °C) to 30.6% (25 °C), leading to continuous ratiometrical modulation from exciton-dominated violet emission to Mn2+-dominated orange emission. The highest sensitivity (1.44% per K) is approximately twice that of the Mn2+-doped chalcogenide quantum dots. Taking advantage of highly reversible color switching, Mn2+-alloyed QWs provide an economical solution to monitor the ultracold-chain logistics of the COVID-19 vaccine. © 2022 American Chemical Society.

Estimating the number of undetected COVID-19 cases among travellers from mainland China

Background: As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries. Conclusions: Our analysis shows that a large number of COVID-19 cases remain undetected across the world. These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China. © 2021 Bhatia S et al.