ABSTRACT
This study aims to conduct a framing analysis of Chinese and American media coverage of Chinese COVID-19 vaccine. Since 2021, China exports or donates vaccines to developing countries, arousing the attention of Chinese and American media. The competition between China and the USA over national image extends to the vaccine field. Media framing is a powerful tool to construct a national image. It is worth comparing how Chinese and American media use framings to construct national images on vaccine issues. This study conducts a framing analysis of news coverage in People's Daily (PD) and The New York Times (NYT) on Chinese COVID-19 vaccine from January to March 2021 to determine how the media use specific framings to construct national images. The results show PD favors official action and cooperation frames, constructing the discourse of "public goods” and "cooperation” and shaping a fair and cooperative China image;while NYT favors official action, conflict and skepticism frames, constructing the discourse of "diplomatic tool” and "competition” and shaping a selfish and competitive China image. A positive image is helpful for China to reposition itself in global public opinion and enhance its soft power, while a negative discourse deconstructs the positive China image and counterbalances China's vaccine diplomacy. © 2022 Asian Media Information and Communication Centre.
ABSTRACT
The COVID-19 pandemic, travel restrictions, and social distancing measures have made it difficult to observe, monitor, or manage urban life. To capture the experience of being in New York City during the first year of the COVID-19 pandemic, we used a novel method of remote ethnography to interview people who were walking the city. We developed the Walkie-Talkie Map to collect and present these interviews, enabling website visitors to see what the subject saw as they walked the route of their choice. Visitors can interactively scroll through the interview and have access to additional visualizations and imagery that contextualize the main narrative. Visitors are thus able to vicariously experience what it was like to be in New York City at the outset of the COVID-19 epidemic. This work provides a case study on how to perform observational research when geographic and bodily distance has become the norm. We discuss the advantages and limitations of our method and conclude with its contributions to the study of cities and for others looking to conduct remote observational research in different fields of knowledge. The Walkie-Talkie maps can be found on this url: https://www.socialdistancing.tech.cornell.edu/what-is-a-walike-talkie © The Author(s) 2022.
ABSTRACT
Sensing activities at the city scale using big data can enable applications to improve the quality of citizen life. While there are approaches to sense the urban heartbeat using sound, vision, radio frequency (RF), and other sensors, capturing changes at urban scale using such sensing modalities is challenging. Due to the enormous amount of data they produce and the associated annotation and processing requirement, such data can be of limited use. In this paper, we present a vision-to-language modeling approach to capture patterns and transitions that occur in New York City from March 2020 to August 2020. We use the model on ∼1 million street images captured by dashcams over 6 months. We then use the captions to train a language model based on Latent Dirichlet Allocation [4] and compare models from different periods using probabilistic distance measures. We observe distribution shifts in the model that correlate well with social distancing policies and are corroborated by different data sources, such as mobility traces. This language-based sensing introduces a new sensing modality to capture dynamics in the city with lower storage requirements and privacy concerns. © 2021 ACM.
ABSTRACT
Big data on the urban scale can enable many applications for improving city life and provide a more holistic understanding of urban life to researchers. While there are approaches to sense and model urban occupant behaviors using sound, radio frequency, and vision, how such behaviors are altered due to city governance and policies in response to emergencies such as a natural disaster or a public health crisis has been less explored. In this paper, we present a computer vision-based approach to capture patterns and interference in the urban life of New York City dwellers from March 2020 to August 2020. Using ∼1 million images gathered with cameras mounted on ride-sharing vehicles throughout the city, we approximated the social proximity of pedestrians to understand policy compliance on the street. Our analysis reveals a correlation between policy violation and virus transmission. We believe that such big data-driven city-scale citizen modeling can inform policy design and crisis management schemes for urban scale smart infrastructure. © 2021 ACM.
ABSTRACT
Classes involving physical making were severely disrupted by COVID-19. As workshops, makerspaces, and fab labs shut down in Spring 2020, instructors developed new models for teaching physical prototyping, electronics production, and digital fabrication at a distance. Instructors shipped materials and equipment directly to students, converted makerspaces to job-shops, and substituted low-tech construction methods and hobbyist equipment for industrial tools. The experiences of students and instructors during the pandemic highlighted new learning opportunities when making outside the makerspace. Simultaneously, the shutdown raised new questions on the limits of remote learning for digital fabrication, electronics, and manual craft. This panel brings together experts in making to discuss their experiences teaching physical production in art, design, and engineering during the pandemic. Panelists will discuss their teaching strategies, describe what worked and what did not, and argue for how we can best support students learning hands-on skills going forward. © 2021 Owner/Author.
ABSTRACT
More and more clinical evidence shows that patients with Coronavirus Disease 2019 (COVID-19) died due to severe complications such as acute respiratory distress syndrome and multiple organ failure due to the aggravation of the disease in the later period, and the main cause of the aggravation is "cytokine storm". There is no specific drug for the treatment of severe COVID-19 patients. Although western medicine can improve some symptoms, it leaves a large sequela, while traditional Chinese medicine plays an important role in this outbreak. In this paper, based on the clinical reported cytokines storm-related indicators, the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was used to mine and screen the traditional Chinese medicines acting on these cytokines based on the theory of "damp toxin invading the lung". It was found that 19 cytokines, including interleukin-6 (IL-6), tumor necrosis factor α (TNFα), granulocyte-macrophage colony stimulating factor (GM-CSF) and so on, were closely related to COVID-19, and 22 traditional Chinese medicines such as Ephedrae Herba, Glycyrrhizae Radix Et Rhizoma and Lonicerae Japonicae Flos acted on these cytokines, so as to provide certain reference for the reasonable choice of prescription and addition or modification of drugs for COVID-19 patients in the middle and late stage of Chinese medicine clinical treatment.