ABSTRACT
School lecture halls are often designed as confined spaces. During the period of COVID-19, indoor ventilation has played an even more important role. Considering the economic reasons and the immediacy of the effect, the natural ventilation mechanism becomes the primary issue to be evaluated. However, the commonly used CO2 tracer gas concentration decay method consumes a lot of time and cost. To evaluate the ventilation rate fast and effectively, we use the common methods of big data analysis - Principal Component Analysis (PCA), K-means and linear regression to analyze the basic information of the lecture hall to explore the relation between variables and air change rate. The analysis results show that the target 37 lecture halls are divided into two clusters, and the measured 11 lecture halls contributed 64.65%. When analyzing the two clusters separately, there is a linear relation between the opening area and air change rate (ACH), and the model error is between 6% and 12%, which proves the feasibility of the basic information of the lecture hall by calculating the air change rate. © 2023 Elsevier Ltd
ABSTRACT
The advance of digital technologies such as big data, cloud computing, and artificial intelligence ushers in the digital era for modern societies. Digital IT innovation plays an increasingly important role in helping supply chains recover from disruptions due to disastrous events like the COVID-19 outbreak. Nevertheless, there is a lack of systematic literature review on the phenomenon. As such an attempt, this paper explores the role of digital technology innovation in enhancing supply chain resilience and answers this question through a literature review and summarizes six dimensions of supply chain resilience, which provides some theoretical guidance for subsequent studies. © 2022 IEEE.
ABSTRACT
The outbreak of Covid-19 emerged from Wuhan, China during December 2019 and spread geo-spatially in more than 200 countries causing more than 182.969 million people of the global population infected and 3.963 million deaths ( as on 30 June 2021), which is still spreading in geo-spatiotemporal way with multiple peaks of Covid- 19 spectrum. This has seriously threatened the human health and life of the people posing the challenges to control the severity due to multiple peaks of Covid-19 spectrum observed during the pandemic period. The spatial spreading of covid-19 spectrum due to large-scale migration from Hubei province of China caused the outbreak in the Southeast Asian region covering the latitude between 38°N to 6°S. The Southeast Asian countries observed first and second wave of covid-19 spectrum with different spectrum envelope, which caused severe population mortality depending upon the spectrum pattern of the outbreak. This spreading of the spectrum caused marked variations in population mortality between different countries depending upon Covid-19 spectrum envelope characteristics with its spectrum peak height and width, existing healthcare infrastructure and its supply chain management of healthcare delivery systems of the country, which stressed the need for Covid-19 spectrum analysis of the first and second wave, and population mortality to develop predictive spectrum models of the third wave to determine the severity and population mortality. In this paper, big-data predictive spectrum models of mortality have been developed based on the analysis of Covid-19 spectrum of the Southeast Asian region using spectrum envelope characteristics and population mortality data from 15 April 2020 to 30 June 2021, for predicting severity of third wave of Covid-19 outbreak, for countries that lie at the latitude beyond 38°N, which can be used by decision makers to design the lockdown measures and geo-spatial supply chain management of healthcare delivery system. © ACRS 2021.All right reserved.
ABSTRACT
Along with the continuous outbreak of COVID-19, countries all over the world have taken measures to prevent and control the virus, moreover, the pandemic measures taken in the communities are the most strict ones in particular. Currently, the health data of residents is collected manually in most communities when the pandemic is stable to some extent. Then, the pandemic risks confronted by residents are assessed accordingly, however, only the minority of communities verify the health identities of residents by scanning their health code. In such a background, the completed and humanized platform systems have not yet been designed and promoted in the market for further pandemic prevention and control. This system distinguishes the administrator from the user, the user homepage mainly has the function of viewing the announcement, the problem feedback, the membership application, the volunteer service and so on. The administrator homepage has the functions of registered user management, isolation audit management, volunteer service management, access management and so on. In addition, based on the fundamental pandemic prevention measures, the system is also endowed with function as big data analysis, which can analyze the epidemic data of inland provinces in China at the present stage to further strengthen epidemic prevention and control. Prevent infected people from entering the community and form a large-scale infection. © 2021 IEEE.
ABSTRACT
This paper aims to understand complex social events that arise when communicating general concepts in the digital space. Today, we get informed through many different channels, at different times of the day, in different contexts, and on many different devices. In addition to that, more complexity is added by the bidirectional nature of the communication itself. People today react very quickly to specific topics through various means such as rating, sharing, commenting, tagging, icons, tweeting, etc. Such activities generate additional metadata to the information itself which become part of the original message. When planning proper communication we should consider all this. In such a complicated environment, the likelihood of a message’s real meaning being received in a distorted or confused way is very high. However, as we have seen recently during the Covid-19 pandemic, at times, there is the need to communicate something, somewhat complicated in nature, while we need to make sure citizens fully understand the actual terms and meaning of the communication. This was the case faced by many governments worldwide when informing their population on the rules of conduct during the various lockdown periods. We analyzed trends and structure of social network data generated as a reaction to those official communications in Italy. Our goal is to derive a model to estimate whether the communication intended by the government was properly understood by the large population. We discovered some regularities in social media generated data related to “poorly” communicated issues. We believe it is possible to derive a model to measure how well the recipients grasp a specific topic. And this can be used to trigger real-time alerts when the need for clarification arises. © 2022, Springer Nature Switzerland AG.