Hashtag framing and stakeholder targeting: An affordance perspective on China's digital public diplomacy campaign during COVID-19

Social media platforms such as Twitter provide opportunities for governments to connect to foreign publics and influence global public opinion. In the current study, we used social and semantic network analysis to investigate China's digital public diplomacy campaign during COVID-19. Our results show that Chinese state-affiliated media and diplomatic accounts created hashtag frames and targeted stakeholders to challenge the United States or to cooperate with other countries and international organizations, especially the World Health Organization. Telling China's stories was the central theme of the digital campaign. From the perspective of social media platform affordance, we addressed the lack of attention paid to hashtag framing and stakeholder targeting in the public diplomacy literature. [ FROM AUTHOR] Copyright of Journal of Information Technology & Politics is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Studying the impact of COVID-19 on the European Air Transportation Network

The European Air Transportation Network was significantly impacted by the COVID-19 pandemic, resulting in an unprecedented loss of flight connections. Utilizing a combination of graph representation learning and time series analysis, this paper studies the evolution of both the global connectivity as well as the structure of the European Air Transportation Network from January 2020 to December 2022. Specifically, it finds strong differences in recovery rates for flights across six different market segments. In terms of network structure, the study finds that structural roles that are present in the pre-covid network have seen a loss in performance over the course of the pandemic, but have recovered to pre-covid levels. Using regional changes in structural roles, this study identifies Italy as the region with the strongest increase and the United Kingdom as the region with the strongest decrease in structural role, finding substantial differences in recovery rates per market segment. Lastly, this study pays special attention on the effect of the Russia-Ukrainian war on the European Air Transportation Network. © 2023 IEEE.

The Impact of Embodiment on Training Effectiveness

Due to the COVID-19 pandemic and compulsory social distancing, researchers in educational fields started to investigate alternatives to face-To-face (F2F) training methods with greater focus, such as video conferencing (VC) and virtual reality (VR) applications. This study investigated the differences between VC, VR and F2F training conditions by evaluating the level of body ownership and agency perceived by trainees. An electrical circuit repair task and multiple surveys were used to gather data from 106 participants in the form of four dependent variables: A circuit knowledge test, task completion rate, number of the subtasks completed by failing participants, and test phase duration. The study included two visits by each participant to measure knowledge retention while there were no training and surveys in Visit 2. Results showed significantly higher circuit learning and knowledge retention scores in F2F and VR conditions than in VC. Also, regarding the retention of knowledge, participants had significantly better knowledge retention in Visit 1 than Visit 2. The authors hope the results of this study enable training developers to enhance the learning process in computer mediated communications. © 2023 IEEE.

Presence of rare potential pathogenic variants in subjects under 60 years old with very severe or fatal COVID-19

Background/Objectives: Genetic variants affecting host defense against pathogens may help explain COVID-19 fatal outcomes. Our aim was to identify rare genetic variants related to COVID-19 severity in a selected group of patients under 60 years who required intubation or resulting in death. Method(s): Forty-four very severe COVID-19 patients were selected from the Spanish STOP-Coronavirus cohort, which comprises more than 3,500 COVID-19 patients. Genotype was performed by whole exome sequencing and variants were selected by using a gene panel of 867 candidate genes (immune response, primary immunodeficiencies or coagulation, among other). Variants were filtered, priorized and their potential pathogenicity was assessed following ACGM criteria. Result(s): We detected 44 different variants of interest, in 29 different patients (66%). Some of these variants were previously described as pathogenic (26%). Mostly, the candidate variants were located in genes related to immune response (38%), congenital disorders of glycosylation (14%) or damaged DNA binding genes (9%). A network analysis, showed three main components, consisting of 25 highly interconnected genes related to immune response and two additional networks enriched in carbohydrate metabolism and in DNA metabolism and repair processes. Conclusion(s): The variants identified affect different, but interrelated, functional pathways such as immune response and glycosylation. Further studies are needed for confirming the ultimate role of the new candidate genes described in the present study on COVID-19 severity.

Uncovering the Dynamics of Consumption-Based Household Water Footprint in Response to Pandemic: The Hysteresis Effect of Disruption

The socioeconomic disruption of COVID-19 has strong implications for water management. However, it remains unclear how water use related to urban and rural household consumption responded to the outbreak. Taking 15 provincial regions in China for a case study, we quantified the variations of consumption-based household water footprint induced by the first outbreak of the pandemic and tracked the responsive changes of interregional virtual water flows and control relationships. We found in many regions, the most drastic change occurred only a quarter after the major outbreak, when the average water footprint of urban and rural households decreased by 13% and 9%, respectively. This indicates the presence of a hysteretic effect of disruption to household expenditures. With the subsequent recovery of household consumption, the water footprints in many regions rebounded and even surpassed the historical values. Guangdong had a fast rebound in its net virtual water inflow related to urban households because of the fast recovery of its manufacturing and services activities. The pandemic-related water footprint dynamics suggest not only the necessity of timely managing supply chains to prevent shortage of water and water-intensive products, but also the importance of fostering consumption adjustments for conserving water in a post-pandemic era. © 2023 The Authors. Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union.

Analysis of the maritime networks: criticalities and resilience

Since the second half of the twentieth century, maritime transport has been characterized by specific dynamics. There has been an unprecedented growth of freight flows, in a context of decolonization, rapid globalization (second wave), and rise of emerging countries through production delocalization. Ports are key players in the global supply chain, as critical nodes at the interface between land and sea. However, disruptions of all kinds can have important repercussions on this supply chain. Maritime networks have reached a high level of optimality and concentration, but also of vulnerability, as global shipping circulations depend more and more on a handful of major hubs. The aim of this paper is to test the network impacts of the global financial crisis (2008-2009), which caused the collapse of the world demand for goods and significant decreases in GDP and maritime traffic volumes, in order to correlate them with those of the COVID-19 pandemic. Historical, daily data on containership movements allow us to trace such impacts with precision, in order to study port traffic dynamics, network topology, and geographical variations. Methods are borrowed from the field of graph theory and complex networks. This analysis contributes to the literature on network criticalities in general, while it also discusses the capacity of ports and maritime networks to recover from a major shock. © 2023 The Authors. Published by ELSEVIER B.V.

A social network analysis approach to estimate export disruption spread in the US during the Covid-19 pandemic: how policy response and industry ties relate

We examine how the Covid-19 pandemic led to the propagation of export disruptions on a state-by-state basis using a social network analysis model. We measure the impact of import disruptions, Covid-related hospitalizations, subsequent policy responses, and structural network effects on economic outcomes. In addition to examining contemporaneous effects, we include lagged policy response variables to determine their effect on disruption recovery trends. Findings suggest that disruptions cluster along shared industry connections. The results are consistent with previous work that shows that non-pharmaceutical policy interventions had limited contemporaneous and medium-term effects on trade flows. © 2023, The Author(s) under exclusive licence to Associazione Amici di Economia e Politica Industriale.

The Longitudinal Features of Depressive Symptoms During the COVID-19 Pandemic Among Chinese College Students: A Network Perspective.

There is substantial evidence that the Corona Virus Disease 2019 (COVID-19) pandemic increased the risk of depressive symptoms among college students, but the long-term features of depressive symptoms on a symptom level have been poorly described. The current study investigated interaction patterns between depressive symptoms via network analysis. In this longitudinal study, participants included 860 Chinese college students (65.8% female; Mage = 20.6, SDage = 1.8, range: 17-27) who completed a questionnaire at three-time points three months apart. Results demonstrated that fatigue was the most influential symptom, and the occurrence of fatigue could give rise to other depressive symptoms. In addition to predicting other symptoms, fatigue could be predicted by other symptoms in the measurement. The network structures were similar across time, suggesting that the overall interaction pattern of depressive symptoms was stable over the longitudinal course. These findings suggest that depressive symptoms during the COVID-19 period are associated with the presence of fatigue.

Network analysis of eating disorder and depression symptoms among university students in the late stage of COVID-19 pandemic in China.

Background: Eating disorders (EDs) and depression are common in university students, especially during the COVID-19 pandemic. The aim of this study was to elucidate characteristics of EDs and depression symptoms networks among Chinese university students in the later stage of the COVID-19 pandemic in China. Methods: A total of 929 university students completed the SCOFF questionnaire measuring EDs and Patient Health Questionnaire with 9 items (PHQ-9) measuring depression in Guangzhou, China. The network model was applied to identify central symptoms, bridge symptoms, and important connections between SCOFF and PHQ-9 using R studio. The subgroup analyses of both genders in medical and non-medical students were further explored. Results: In the networks of the whole sample, central symptoms included "Loss of control over eating" (EDs) and "Appetite changes" (depression). The bridge connections were between "Loss of control over eating" (EDs) and "Appetite changes" (depression), between "Deliberate vomiting" (EDs) and "Thoughts of death" (depression). "Appetite changes" (depression) and "Feeling of worthlessness" (depression) were central symptoms in both subgroups of medical and non-medical students. "Fatigue" (depression) was the central symptom in the female and medical students group. The edge between "Loss of control over eating" (EDs) and "Appetite changes" (depression) acted as a bridge in all subgroups. Conclusion: Social network approaches offered promising ways of further understanding the association between EDs and depression among university students during the pandemic of COVID-19 in China. Investigations targeting central and bridge symptoms would help to develop effective treatments for both EDs and depression for this population.

The relationship between anxiety and internet gaming disorder in children during COVID-19 lockdown: a network analysis.

Background: Internet gaming disorder (IGD) has become a social problem in children. Evidence from previous studies has proven that anxiety is associated with IGD. However, IGD was always assessed as a whole based on total scores, and the fine-grained relationship between anxiety and IGD was hidden. Objective: The present study aims to investigate the fine-grained relationship between anxiety and IGD in elementary school students during the COVID-19 lockdown, and to identify potential targets for psychological interventions. Methods: During the lockdown caused by the COVID-19 pandemic, 667 children from a primary school in China were investigated by the Spence Children's Anxiety Scale-Short Version and Internet Gaming Disorder Scale. R4.1.1 software was used to construct a network model, assess bridge centrality, and test the robustness of the network and conduct a network. Results: There were 23 cross-community edges (weight ranged from -0.03 to 0.12), and each node of anxiety was connected to different nodes of IGD. The nodes with the top 80th percentile bridge expected influence were A2 "social phobia" (0.20), A3 "panic disorder" (0.21) and IGD5 "escape" (0.22). The robustness of the network was acceptable. Conclusion: From the perspective of network analysis, the present study explored the correlation pathways between anxiety and IGD in children and identified social phobia and panic disorder as the best targets for intervention to reduce IGD.

A three-timepoint network analysis of Covid-19's impact on schizotypal traits, paranoia and mental health through loneliness.

The 2019 coronavirus (Covid-19) pandemic has impacted people's mental wellbeing. Studies to date have examined the prevalence of mental health symptoms (anxiety and depression), yet fewer longitudinal studies have compared across background factors and other psychological variables to identify vulnerable subgroups in the general population. This study tests to what extent higher levels of schizotypal traits and paranoia are associated with mental health variables 6- and 12-months since April 2020. Over 2300 adult volunteers (18-89 years, female = 74.9%) with access to the study link online were recruited from the UK, the USA, Greece and Italy. Self-reported levels of schizotypy, paranoia, anxiety, depression, aggression, loneliness and stress from three timepoints (17 April to 13 July 2020, N1 = 1599; 17 October to 31 January 2021, N2 = 774; and 17 April to 31 July 2021, N3 = 586) were mapped using network analysis and compared across time and background variables (sex, age, income, country). Schizotypal traits and paranoia were positively associated with poorer mental health through loneliness, with no effect of age, sex, income levels, countries and timepoints. Loneliness was the most influential variable across all networks, despite overall reductions in levels of loneliness, schizotypy, paranoia and aggression during the easing of lockdown (time 3). Individuals with higher levels of schizotypal traits/paranoia reported poorer mental health outcomes than individuals in the low-trait groups. Schizotypal traits and paranoia are associated with poor mental health outcomes through self-perceived feelings of loneliness, suggesting that increasing social/community cohesion may improve individuals' mental wellbeing in the long run.

Balancing Functional Tradeoffs between Protein Stability and ACE2 Binding in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB Lineages: Dynamics-Based Network Models Reveal Epistatic Effects Modulating Compensatory Dynamic and Energetic Changes.

Evolutionary and functional studies suggested that the emergence of the Omicron variants can be determined by multiple fitness trade-offs including the immune escape, binding affinity for ACE2, conformational plasticity, protein stability and allosteric modulation. In this study, we systematically characterize conformational dynamics, structural stability and binding affinities of the SARS-CoV-2 Spike Omicron complexes with the host receptor ACE2 for BA.2, BA.2.75, XBB.1 and XBB.1.5 variants. We combined multiscale molecular simulations and dynamic analysis of allosteric interactions together with the ensemble-based mutational scanning of the protein residues and network modeling of epistatic interactions. This multifaceted computational study characterized molecular mechanisms and identified energetic hotspots that can mediate the predicted increased stability and the enhanced binding affinity of the BA.2.75 and XBB.1.5 complexes. The results suggested a mechanism driven by the stability hotspots and a spatially localized group of the Omicron binding affinity centers, while allowing for functionally beneficial neutral Omicron mutations in other binding interface positions. A network-based community model for the analysis of epistatic contributions in the Omicron complexes is proposed revealing the key role of the binding hotspots R498 and Y501 in mediating community-based epistatic couplings with other Omicron sites and allowing for compensatory dynamics and binding energetic changes. The results also showed that mutations in the convergent evolutionary hotspot F486 can modulate not only local interactions but also rewire the global network of local communities in this region allowing the F486P mutation to restore both the stability and binding affinity of the XBB.1.5 variant which may explain the growth advantages over the XBB.1 variant. The results of this study are consistent with a broad range of functional studies rationalizing functional roles of the Omicron mutation sites that form a coordinated network of hotspots enabling a balance of multiple fitness tradeoffs and shaping up a complex functional landscape of virus transmissibility.

Exploration of the potential common pathogenic mechanisms in COVID-19 and silicosis by using bioinformatics and system biology.

Silicosis is an occupational lung disease that is common worldwide. In recent years, coronavirus disease 2019 (COVID-19) has provided daunting challenges to public healthcare systems globally. Although multiple studies have shown a close link between COVID-19 and other respiratory diseases, the inter-relational mechanisms between COVID-19 and silicosis remain unclear. This study aimed to explore the shared molecular mechanisms and drug targets of COVID-19 and silicosis. Gene expression profiling identified four modules that were most closely associated with both diseases. Furthermore, we performed functional analysis and constructed a protein-protein interaction network. Seven hub genes (budding uninhibited by benzimidazoles 1 [BUB1], protein regulator of cytokinesis 1 [PRC1], kinesin family member C1 [KIFC1], ribonucleotide reductase regulatory subunit M2 [RRM2], cyclin-dependent kinase inhibitor 3 [CDKN3], Cyclin B2 [CCNB2], and minichromosome maintenance complex component 6 [MCM6]) were involved in the interaction between COVID-19 and silicosis. We investigated how diverse microRNAs and transcription factors regulate these seven genes. Subsequently, the correlation between the hub genes and infiltrating immune cells was explored. Further in-depth analyses were performed based on single-cell transcriptomic data from COVID-19, and the expression of hub-shared genes was characterized and located in multiple cell clusters. Finally, molecular docking results reveal small molecular compounds that may improve COVID-19 and silicosis. The current study reveals the common pathogenesis of COVID-19 and silicosis, which may provide a novel reference for further research.

Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis.

BACKGROUND: Returning to social life after the lifting of COVID-19 lockdown may increase risk of social anxiety, which is highly co-morbid with depression. However, few studies have reported the association between them. AIMS: To explore the complex relationship between social anxiety and depression symptoms in left-behind children after the lifting of the COVID-19 lockdown. METHODS: A cross-sectional survey was conducted 6 months after the lockdown removal. A total of 3,107 left-behind children completed the survey with a mean age of 13.33 and a response rate of 87.77%. Depression and social anxiety severity were assessed by the DSM-5 Patient Health Questionnaire for Adolescents and the DSM-5 Social Anxiety Disorder Questionnaire, respectively. The symptom-level association between the two disorders was examined using network analysis. RESULTS: After the lifting of COVID-19 lockdown, the prevalence of depression and social anxiety in left-behind children was 19.57% and 12.36%, respectively, with a co-morbidity rate of 8.98%. Network analysis showed that "Social tension" and "Social avoidance" had the greatest expected influence; "Humiliation" and "Motor" were bridge symptom nodes in the network. The directed acyclic graph indicated that "Social fright" was at the upstream of all symptoms. CONCLUSION: Attention should be paid to social anxiety symptoms in left-behind children after the lifting of COVID-19 lockdown. Prevention and intervention measures should be taken promptly to reduce the comorbidity of social anxiety and depression symptoms in the left-behind children after the lifting of lockdown.

Understanding Changes in Social Cannabis Use among Young Adults During the COVID-19 Pandemic: A Social Network Analysis.

Introduction: As the COVID-19 pandemic has caused historic morbidity and mortality and disrupted young people's social relationships, little is known regarding change in young adults' social cannabis use following social distancing orders, or other factors associated with such changes before and during the pandemic. Methods: 108 young adult cannabis users in Los Angeles reported on their personal (egocentric) social network characteristics, cannabis use, and pandemic-related variables before (July 2019 - March 2020) and during the COVID-19 pandemic (August 2020 - August 2021). Multinomial logistic regression identified factors associated with increasing or maintaining the number of network members (alters) participants used cannabis with before and during the pandemic. Multilevel modeling identified ego- and alter-level factors associated with dyadic cannabis use between each ego and alter during the pandemic. Results: Most participants (61%) decreased the number of alters they used cannabis with, 14% maintained, and 25% increased. Larger networks were associated with a lower risk of increasing (vs. decreasing); more supportive cannabis-using alters was associated with a lower risk of maintaining (vs. decreasing); relationship duration was associated with a greater risk of maintaining and increasing (vs. decreasing). During the COVID-19 pandemic (August 2020 - August 2021), participants were more likely to use cannabis with alters they also used alcohol with and alters who were perceived to have more positive attitudes towards cannabis. Conclusions: The present study identifies significant factors associated with changes in young adults' social cannabis use following pandemic-related social distancing. These findings may inform social network interventions for young adults who use cannabis with their network members amid such social restrictions.

Identifying the Bridge Between Anxiety Response to COVID-19 and Depression Symptoms in General Population: A Network Analysis.

BACKGROUND: Previous studies have suggested links between anxiety response to the novel coronavirus (COVID-19) and depression symptoms in general population. However, a symptom-level investigation has not been reported. OBJECTIVE: This study aimed to use network analysis to identify central symptoms and bridge symptoms that link COVID-19 anxiety and depression. METHODS: Data from 1788 participants were analyzed. Coronavirus anxiety and depression symptoms were measured using the Stress and Anxiety to Viral Epidemics-6 Items Scale and the Patient Health Questionnaire-9, respectively. Network analysis was performed using R. RESULTS: The results revealed 'thoughts of suicide or self-harm' from Patient Health Questionnaire-9 and 'worry about others avoiding me' from Stress and Anxiety to Viral Epidemics-6 Items Scale as bridge symptoms. Findings suggest direct relationship between fear of social isolation and thoughts of suicide or self-harm. 'Feeling tired with little energy' and 'trouble concentrating' are strongly linked to 'thoughts of suicide or self-harm', suggesting these symptoms as risk factors for suicidal or self-injurious thoughts during the pandemic. CONCLUSIONS: The findings suggest fear of social isolation as a risk factor for developing thoughts of suicide or self-harm. These results should be taken into account during evaluation of risk of suicide or mental health interventions for the pandemic.

Economic Shock Transmission through Global Value Chains: An Assessment using Network Analysis

This paper examines the role of some key economies in the economic contagion across global value chains using input-output analysis and complex network statistics. The empirical research focuses on China, France, Germany, Italy, Japan, Korea, the United Kingdom, and the United States. A range of novel measures were used to measure the nature and extent of global value chain relationships. The empirical results reveal that, because of the high interdependence and interconnectedness in the global value chain networks, the transmission of an economic shock in China and the United States will be fast, wide, and in-depth in the global value chain networks. Sample countries are more exposed to an economic shock in China than a shock in the other four big economic partners, namely the United States, Germany, Japan, and Korea.

A survey on agents applications in healthcare: Opportunities, challenges and trends.

BACKGROUND AND OBJECTIVE: The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research. METHODS: We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis. RESULTS: Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics. CONCLUSIONS: Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.

FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease.

Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.

Prevalence, correlates, and network analysis of depression and its association with quality of life in survivors with myocardial infarction during the COVID-19 pandemic.

BACKGROUND: Depression is common among myocardial infarction (MI) survivors and is strongly associated with poor quality of life (QOL). The aim of this study was to examine the prevalence, correlates and the network structure of depression, and its association with QOL in MI survivors during the COVID-19 pandemic. METHODS: This cross-sectional study evaluated depression and QOL in MI survivors with the Chinese version of the nine-item Patient Health Questionnaire (PHQ-9) and the World Health Organization Quality of Life-BREF (WHOQOL-BREF), respectively. Univariable analyses, multivariable analyses, and network analyses were performed. RESULTS: The prevalence of depression (PHQ-9 total score ≥ 5) among 565 MI survivors during the COVID-19 pandemic was 38.1 % (95 % CI: 34.1-42.1 %), which was significantly associated with poor QOL. Patients with depression were less likely to consult a doctor regularly after discharge, and more likely to experience more severe anxiety symptoms and fatigue. Item PHQ4 "Fatigue" was the most central symptom in the network, followed by PHQ6 "Guilt" and PHQ2 "Sad mood". The flow network showed that PHQ4 "Fatigue" had the highest negative association with QOL. CONCLUSION: Depression was prevalent among MI survivors during the COVID-19 pandemic and was significantly associated with poor QOL. Those who failed to consult a doctor regularly after discharge or reported severe anxiety symptoms and fatigue should be screened for depression. Effective interventions for MI survivors targeting central symptoms, especially fatigue, are needed to reduce the negative impact of depression and improve QOL.