A comparative study of graph structures, traversability movement and exhibition strategy in museums during Covid-19

The global pandemic of COVID-19 has posed challenges in relation to how buildings re-open to use, particularly buildings attracting large numbers of visitors, such as museums and galleries. As these institutions started to reopen across the UK and internationally, a number of social distance measures were adopted in order to safely bring people into their premises and access their collections. Building on Bill Hillier's theorical model of spatial types and spatial structures (2019), we explore the spatial-curatorial changes implicated in the re-opening of five British museums (The National Gallery, The Tate Britain, Tate Modern, British Museum and The Wallace Collection in London) and one American museum (The MoMA, New York). Our purpose is not to provide practical solutions, but to set the search for spatial approaches to the reopening of museums within a theory of spatial structure in space syntax and inform the design future of public buildings. We present a model of a three-layered spatial system, interfacing the global and local structure of these buildings. We argue that the presence of intersecting cycles of movement in spatial layouts determines their capability for adapting to the one-way routes imposed by the pandemic. The spatial organisation of the display is a second factor influencing the reopening strategies, either limiting or optimising available spatial sequences to meet curatorial criteria. © 2022 Proceedings 13th International Space Syntax Symposium, SSS 2022. All rights reserved.

Analysis and Forecast of the COVID-19 Spreading Curve for the Resumption of In-person Classes

With the COVID-19 pandemic, maintaining social distancing is particularly important in daily life. In recently, indoor situations such as face-to-face teaching for university restart are tried to make feasible suggestions depend on the spread of the COVID-19. In this research, we analyze and forecast the COVID-19 spreading curve of the resumption of in-person classes at university by the graph structure with the spread weight of edges based on each student's relation. Our approach is based on the effectiveness of three distancing strategies designed to keep the curve flat and aid make the spread of the COVID-19 controllable. By detecting the possibility of student relation by three strategies, we can analyze the COVID-19 spreading curve by Graph Neural Network(GNN) and SIR model. The SIR model is a simple model that considers a population that belongs to one of the following states: Susceptible (S), Infected (I), and Recovered (R), and we calculate the contagion rate of the pathogen. In this article, we discuss two types of Open Group and Closed Group on university campuses and analyze face-to-face lectures, indoor social activities, and campus cafeterias. To verify the effectiveness of our two types of group, we simulated with the random infection curve by graph neural network model. The simulation analysis results show that our social distancing strategies can reduce the risk of COVID-19 transmission after school restarts. © 2022 IEEE.

Visualization Method for the Spreading Curve of COVID-19 in Universities using GNN

As the reopening of the university after the spread of COVID-19 on campus and we simulate and visualize the initial states spreading of COVID-19. In this research, we analyze and forecast the COVID-19 spreading curve of the resumption of in-person classes at university by the graph structure with the spread weight of edges based on each student's relation. Our approach is based on the effectiveness of three distancing strategies designed to keep the curve flat and aid make the spread of the COVID-19 controllable. By detecting the possibility of student relation based on three strategies, we can analyze the COVID-19 spreading curve by Graph Neural Network (GNN) and SIR model. In this article, we discuss two types of Open Group and Closed Group on university campuses and analyze face-to-face lectures, indoor social activities, and campus cafeterias. To verify the effectiveness of our two types of group, we simulated with the random infection curve by graph neural network model. At last, we visualized the COVID-19 spreading process and the results of diffusion prediction. © 2022 IEEE.

A Constraint Propagation Approach for Identifying Biological Pathways in COVID-19 Knowledge Graphs

Many mechanisms within biological systems can be modeled as pathways, chains of interactions between proteins, genes, chemicals, and other biological entities. These interactions can be represented using a graph structure, more specifically a knowledge graph representing known or inferred information about the entities in question. In this context, we propose a constraint propagation approach for identifying paths in a graph structure which represent potential biological pathways. We apply this approach to a knowledge graph dataset which was semantically extracted from literature on COVID-19. © 2021 IEEE.