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Recently, the viral propagation of mis/disinformation has raised significant concerns from both academia and industry. This problem is particularly difficult because on the one hand, rapidly evolving technology makes it much cheaper and easier to manipulate and propagate social media information. On the other hand, the complexity of human psychology and sociology makes the understanding, prediction and prevention of users' involvement in mis/disinformation propagation very difficult. This themed series on "Multi-Disciplinary Dis/Misinformation Analysis and Countermeasures" aims to bring the attention and efforts from researchers in relevant disciplines together to tackle this challenging problem. In addition, on October 20th, 2021, and March 7th 2022, some of the guest editorial team members organized two panel discussions on "Social Media Disinformation and its Impact on Public Health During the COVID-19 Pandemic," and on "Dis/Misinformation Analysis and Countermeasures - A Computational Viewpoint." This article summarizes the key discussion items at these two panels and hopes to shed light on the future directions.
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SARS-CoV-2 has infected more than 600 million people worldwide and caused more than 6 million deaths. The emerging novel variants have made the epidemic rebound in many places. Meteorological factors can affect the epidemic spread by changing virus activity, transmission dynamic parameters and host susceptibility. This paper systematically analyzed the currently available laboratory and epidemiological studies on the association between the meteorological factors and COVID-19 incidence, in order to provide scientific evidence for future epidemic control and prevention, as well as developing early warning system.
Subject(s)
COVID-19 , SARS-CoV-2 , Humans , Meteorological Concepts , Laboratories , Epidemiologic StudiesABSTRACT
The paper provides an updated overview of the main achievements and ideas presented at the most recent PRES conferences and in the fields which have been covered by them. The conference history now reached a quarter of the century - from 1998 to 2022. The PRES conferences have become one of the main vehicles for spreading Process Integration (PI) into various research directions and fields of possible implementation. The PRES went successfully during the last period challenged by COVID-19 pandemics. Not all conferences managed to adapt well. However, PRES successfully implemented the hybrid mode and learned how to use it efficiently for enlarging the number of speakers as well as the audience while still keeping very intensive and beneficial cross-fertilisation and networking. Some experiences have been shared in this paper. The hybrid mode helped successfully intensify the research efforts on the research challenge, which remained as a consequence of the COVID-19 pandemics - an increased amount of waste and during the life and economy recovery also increasing environmental footprints. This short overview includes (i) Process Integration with Pinch Analysis (ii) Process Integration with another approach (iii) Development of heat exchanger systems for Process Integration (iv) Other extensions of Process Integration for wider Process Systems Engineering and recently (v) Circular economy (vi) Environmental footprints and nexuses and just (vii) COVID-19 pandemics energy and environmental consequences and recovery. This paper presents an attempt to demonstrate and make suggestions for the future growth of the Process Integration branching out during the next years. Copyright © 2022, AIDIC Servizi S.r.l.
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This study explores how firms sought to effectively match their internal competence with external resources from the supply chain network to improve operational resilience (OR) during the COVID-19 pandemic. Drawing upon matching theory, this study provides an internal-external matching perspective based on flexibility-stability features of OR to explain the operational mechanisms underlying the different matchings between internal flexibility (i.e., product diversity)/stability (i.e., operational efficiency) and external flexibility (i.e., structural holes)/stability (i.e., network centrality). We find that more heterogeneous matchings between internal (external) flexibility and external (internal) stability have a complementary effect that enhances OR, whereas more homogeneous matchings between internal flexibility (or stability) and external flexibility (or stability) have a substitutive effect that reduces OR. This study provides valuable contributions to research focusing on the supply chain, organizational resilience, and operations management.
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The coronavirus disease 2019 is a global pandemic that threatens lives of many people and poses a significant burden for healthcare systems worldwide. Computerized Tomography can detect lung infections, especially in asymptomatic cases, and the detection process can be aided by deep learning. Most of the recent research focused on the segmentation of the entire infected region in a lung. To automate a more fine-grained analysis, a generative adversarial network, comprising two convolutional neural networks, was developed for the segmentation of ground glass opacities and consolidations from tomographic images. The first convolutional neural network acts as a generator of segmented masks, and the second as a discriminator of real and artificially segmented objects, respectively. Experimental results demonstrate that the proposed network outperforms the baseline U-Net segmentation model on the benchmark data set of 929 publicly available images. The dice similarity coefficients of segmenting ground glass opacities and consolidations are 0.664 and 0.625, respectively.
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This paper reviews the Process Integration (PI) development related to the recent Conferences on Process Integration for Energy Saving and Pollution Reduction (PRES conferences) and makes suggestions for the future growth of PI branching out in the future. The conference history is now close to a quarter of the century-from 1998 to 2021, and has been flourishing despite the difficult COVID-19 period. The paper overviews the progresses in Process Integration with Pinch Analysis, heat exchangers and Process Integration, extensions of Process Integration for wider process system engineering, integration of renewable energy sources, Circular Economy, extended environmental footprints, extended water-energy nexus contribution to environmental assessment, COVID-19 pandemics environmental consequences, and ecosystem remediation and waste stream clean-up. Considerable progress in Process Integration has also been achieved thanks to PRES conferences. This overview is an attempt to demonstrate the contribution delivered and make suggestions for the future growth of the PI branching out during the next years. It has become apparent that further improvements of the PI technologies are necessary and possible for achieving sufficient reductions of resource demands and pollution so that available renewables and end-of-pipe cleaning can serve them, minimising the environmental impacts. The key methodology developments enabling this are multi-constraint Pinch Analysis and the joint use of several PI methods for delivering comprehensive macro-analyses. © 2021, AIDIC Servizi S.r.l.
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The COVID-19 pandemic has caused worldwide lockdowns and similar containment measures aiming to curb the spread of the virus. Lockdown measures have been implemented in cities amid the COVID-19 outbreak. After the pandemic is under control, cities will be gradually reopened. This study aims to investigate the variations in urban travel behavior during the lockdown and reopening phases. On the basis of long-term traffic congestion index data and subway ridership data in eight typical cities of China, this study carried out comparisons on urban travel behaviors with and without the pandemic. Changes in the multimodal travel behaviors in different times of day and days of week are analyzed during the lockdown and reopening phases. Multivariate and one-way analyses of variance are conducted to show the statistical significance of the changes. This study further investigates the relationship between the returned-to-work (RTW) rate and travel behaviors in the reopening phase. A stepwise multiple regression is conducted to quantify the impacts of influencing factors (i.e., population migration index, RTW rate, socioeconomic indices, and pandemic statistical indicators) on vehicular traffic after reopening. Results show that the lockdown measure has a significant impact on reducing the traffic congestion during the peak hours on workdays, and the subway ridership dropped to below 10% of the prepandemic level during the lockdown phase. Travel demands tended to switch from subways to private vehicular travel modes during the reopening phase, leading to a rapid recovery of vehicular traffic and a slow recovery of subway ridership. The recovery of vehicular traffic is proved to be related to the RTW rate, certain city characteristics, and new COVID-19 cases after city reopening. © 2021 American Society of Civil Engineers.
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The systemic risk induced by climate change represents one of the most prominent threats facing humanity and has attracted increasing attention since the outbreak of the COVID-19 pandemic at the end of 2019. The existing literature highlights the importance of systemic risk induced by climate change, but there are still deficiencies in understanding its dynamics and assessing the risk. Aiming to bridge this gap, this study develops a theoretical framework and employs two cases to illustrate the concept, origin, occurrence, propagation, evolution, and assessment framework of systemic risk induced by climate change. The key findings include: 1) systemic risk induced by climate change derives from the rapid growth of greenhouse gas emissions, increasingly complex connections among different socioeconomic systems, and continuous changes in exposure and vulnerability;2) systemic risk induced by climate change is a holistic risk generated by the interconnection, interaction, and dynamic evolution of different types of single risks, and its fundamental, defining feature is cascading effects. The extent of risk propagation and its duration depend on the characteristics of the various discrete risks that are connected to make up the systemic risk;3) impact domains, severity of impact, and probability of occurrences are three core indicators in systemic risk assessment, and the impact domains should include the economy, society, homeland security, human health, and living conditions. We propose to deepen systemic risk research from three aspects: to develop theories to understand the mechanism of systemic risk;to conduct empirical research to assess future risks;and to develop countermeasures to mitigate the risk. © 2021 The Authors
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Objective: To understand the viral genomic characteristics of a 2019-novel coronavirus (2019-nCoV) strain in the first COVID-19 patient found in Hangzhou, China. Methods: Viral RNA was extracted in throat swab and sputum sample of the patient and was performed real-time reverse transcription PCR detection and obtained viral genome by high-throughput sequencing method. Phylogenetic analysis was conducted using 29 2019-nCoV genomes and 30 ß-coronavirus genomes deposited in NCBI GenBank. Fifteen genomes from Wuhan were grouped by mutation sites and others were identified by Wuhan's or specific mutation sites. Results: A 29 833 bp length genome of the first 2019-nCoV strain in Hangzhou was obtained, covering full length of the coding regions of coronavirus. Phylogenetic analysis showed that the genome was closest to the genome of a bat SARS-like coronavirus strain RaTG13 with an identity of 96.11% (28 666/29 826). Among the genes between two genomes, E genes were highly conserved (99.56%), while S genes had lowest identity (92.87%). The genome sequence similarities among 29 strains from China (Hangzhou, Wuhan, and Shenzhen), Japan, USA, and Finland, were all more than 99.9%; however, some single nucleotide polymorphisms were identified in some strains. Conclusion: The genome of Hangzhou 2019-nCoV strain was very close to the genomes of strains from other cities in China and overseas collected at early epidemic phase. The 2019-nCoV genome sequencing method used in this paper provides an useful tool for monitoring variation of viral genes.