PMC:7796058 / 18774-21138 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"152","span":{"begin":1787,"end":1793},"obj":"Species"},{"id":"153","span":{"begin":95,"end":103},"obj":"Disease"},{"id":"154","span":{"begin":268,"end":277},"obj":"Disease"},{"id":"155","span":{"begin":474,"end":483},"obj":"Disease"},{"id":"156","span":{"begin":754,"end":763},"obj":"Disease"},{"id":"157","span":{"begin":1373,"end":1381},"obj":"Disease"},{"id":"158","span":{"begin":1924,"end":1933},"obj":"Disease"},{"id":"159","span":{"begin":2002,"end":2010},"obj":"Disease"}],"attributes":[{"id":"A152","pred":"tao:has_database_id","subj":"152","obj":"Tax:9606"},{"id":"A153","pred":"tao:has_database_id","subj":"153","obj":"MESH:C000657245"},{"id":"A154","pred":"tao:has_database_id","subj":"154","obj":"MESH:D007239"},{"id":"A155","pred":"tao:has_database_id","subj":"155","obj":"MESH:D007239"},{"id":"A156","pred":"tao:has_database_id","subj":"156","obj":"MESH:D007239"},{"id":"A157","pred":"tao:has_database_id","subj":"157","obj":"MESH:C000657245"},{"id":"A158","pred":"tao:has_database_id","subj":"158","obj":"MESH:D007239"},{"id":"A159","pred":"tao:has_database_id","subj":"159","obj":"MESH:C000657245"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Aggregating different sensor observations for each room is essential for estimating the room’s COVID-19 risk and cleaning requirements. The visualization of Interior Space Risk State is another task which requires interior modelling. In order to represent the risk of infection and to identify which specific areas of a building require the most cleaning, the status of individual floors should be able to be viewed separately from other floors in the building. The risk of infection for various parts of each floor should be represented in a map with different ways of representing the data that is necessary for determining the risk in each area. Moreover, in order to visualize trajectories for contact tracing and to quickly identify the location of infection spreading behavior within an indoor space, the buildings should be visible as a 3D construct on the map. Therefore, in order to analyze the IoCT multi-sensors system and visualize it in indoor scenarios, an interoperable 3D building modelling standard, such as the “CityGML” Level of Detail 4 [45], “OGC IndoorGML” [46], building construction standards (e.g., “Building Information Modelling” (BIM), or “Industry Foundation Classes” (IFC) [47]), is necessary. The main concern for using those models is their fit and how often they are updated. Construction features of indoor spaces are not a major focus of COVID-19 workplace reopening scenarios. Instead, the aggregation of sensors in each room, and the connectivity between the rooms, is fundamental for risk assessment and tracing. Thus, the OGC IndoorGML is used for the IoCT indoor modelling. According to Ryoo et al. [43], the OGC IndoorGML can be used more effectively than CityGML or any other geometric representations of space for analyzing the trajectories of people inside buildings. This allows for more accurate appraisal of the types of intersection of trajectories, contact, and exposure for infection risk evaluation. Applications such as cleaning risk assessments for COVID-19 workplace reopenings that need to operate efficiently together with indoor scales, various sensors, and objects that are moving and changing over time would benefit from using the OGC IndoorGML. 3D geometry can be included in an IndoorGML document, and the overlap with other standards (e.g., OGC CityGML) can be addressed by adding external references."}

{"project":"LitCovid-sentences","denotations":[{"id":"T114","span":{"begin":0,"end":135},"obj":"Sentence"},{"id":"T115","span":{"begin":136,"end":233},"obj":"Sentence"},{"id":"T116","span":{"begin":234,"end":461},"obj":"Sentence"},{"id":"T117","span":{"begin":462,"end":648},"obj":"Sentence"},{"id":"T118","span":{"begin":649,"end":868},"obj":"Sentence"},{"id":"T119","span":{"begin":869,"end":1223},"obj":"Sentence"},{"id":"T120","span":{"begin":1224,"end":1308},"obj":"Sentence"},{"id":"T121","span":{"begin":1309,"end":1412},"obj":"Sentence"},{"id":"T122","span":{"begin":1413,"end":1550},"obj":"Sentence"},{"id":"T123","span":{"begin":1551,"end":1613},"obj":"Sentence"},{"id":"T124","span":{"begin":1614,"end":1811},"obj":"Sentence"},{"id":"T125","span":{"begin":1812,"end":1950},"obj":"Sentence"},{"id":"T126","span":{"begin":1951,"end":2205},"obj":"Sentence"},{"id":"T127","span":{"begin":2206,"end":2364},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Aggregating different sensor observations for each room is essential for estimating the room’s COVID-19 risk and cleaning requirements. The visualization of Interior Space Risk State is another task which requires interior modelling. In order to represent the risk of infection and to identify which specific areas of a building require the most cleaning, the status of individual floors should be able to be viewed separately from other floors in the building. The risk of infection for various parts of each floor should be represented in a map with different ways of representing the data that is necessary for determining the risk in each area. Moreover, in order to visualize trajectories for contact tracing and to quickly identify the location of infection spreading behavior within an indoor space, the buildings should be visible as a 3D construct on the map. Therefore, in order to analyze the IoCT multi-sensors system and visualize it in indoor scenarios, an interoperable 3D building modelling standard, such as the “CityGML” Level of Detail 4 [45], “OGC IndoorGML” [46], building construction standards (e.g., “Building Information Modelling” (BIM), or “Industry Foundation Classes” (IFC) [47]), is necessary. The main concern for using those models is their fit and how often they are updated. Construction features of indoor spaces are not a major focus of COVID-19 workplace reopening scenarios. Instead, the aggregation of sensors in each room, and the connectivity between the rooms, is fundamental for risk assessment and tracing. Thus, the OGC IndoorGML is used for the IoCT indoor modelling. According to Ryoo et al. [43], the OGC IndoorGML can be used more effectively than CityGML or any other geometric representations of space for analyzing the trajectories of people inside buildings. This allows for more accurate appraisal of the types of intersection of trajectories, contact, and exposure for infection risk evaluation. Applications such as cleaning risk assessments for COVID-19 workplace reopenings that need to operate efficiently together with indoor scales, various sensors, and objects that are moving and changing over time would benefit from using the OGC IndoorGML. 3D geometry can be included in an IndoorGML document, and the overlap with other standards (e.g., OGC CityGML) can be addressed by adding external references."}