Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China, in December 2019 and has subsequently spread worldwide, causing a pandemic of coronavirus disease 2019 (COVID-19). Recent studies have suggested that hypertension and type 2 diabetes mellitus (T2DM) were the most frequent comorbidities in infected patients and are also considered independent risk factors for disease severity and mortality. 1-4 However, hypertension and T2DM are commonly encountered together. Epidemiological

The construction industry is undergoing a digital revolution due to the emergence of new technologies. A significant trend is that construction projects have been transformed and upgraded to the digital and smart mode in the whole life cycle. As a critical technology for the construction industry’s innovative development, building information modeling (BIM) is widely adopted in building design, construction, and operation. BIM has gained much interest in the research field of smart buildings in recent years. However, the dimensions of BIM and smart building applications have not been explored thoroughly so far. With an in-depth review of related journal articles published from 1996 to July 2020 on the BIM applications for smart buildings, this paper provides a comprehensive understanding and critical thinking about the nexus of BIM and smart buildings. This paper proposes a framework with three dimensions for the nexus of BIM application in smart buildings, including BIM attributes, project phases, and smart attributes. According to the three dimensions, this paper elaborates on (1) the advantages of BIM for achieving various smartness; (2) applications of BIM in multiple phases of smart buildings; and (3) smart building functions that be achieved with BIM. Based on the analysis of the literature in three dimensions, this paper presents the cross-analysis of the nexus of BIM and smart buildings. Lastly, this paper proposes the critical insights and implications about the research gaps and research trends: (1) enhancing the interoperability of BIM software; (2) further exploring the role of BIM in the operation and refurbishment phase of smart buildings; (3) paying attention to BIM technology in the field of transportation infrastructure; (4) clarifying the economic benefits of BIM projects; and (5) integrating BIM and other technologies.

The pandemic of coronavirus Disease 2019 (COVID-19) caused enormous loss of life globally. 1-3 Case identification is critical. The reference method is using real-time reverse transcription PCR (rRT-PCR) assays, with limitations that may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that application of deep learning (DL) to the 3D CT images could help identify COVID-19 infections. Using the data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 patients. COVIDNet achieved an accuracy rate of 94.3% and an area under the curve (AUC) of 0.98. Application of DL to CT images may improve both the efficiency and capacity of case detection and long-term surveillance.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.