Digital Twin in construction and the built environment have started to attract the attention of researchers and practitioners in recent times. Its anticipated value proposition is focussed on its capability of generating new understanding and insights into an asset at all stages of its lifecycle, exploiting diverse data sets from a multitude of sources and professions, in real or near real-time. However, there is still a significant debate about the delineation (i.e. communalities and differences) between digital twin and other related concepts, particularly Building Information Modelling (BIM) and Cyber-Physical Systems (CPS). To date, this debate has been confined to social media discussions, insights blogs and position papers. This paper addresses this challenge using a systematic review. The aim is to investigate communalities and differences between the three concepts, Digital Twin, BIM and CPS. The results of this paper are expected to foster the discussion around this theme within construction and the built environment.
Site equipment represent a major cost element in construction projects. Measuring equipment productivity help to identify equipment inefficiencies and improve their productivity; however, measurement processes are time and resource intensive. Current literature has focused on automating equipment activity capture but still lack adequate approaches for measurement of equipment productivity rates. Our contribution is to present a methodology for automating equipment productivity measurement using kinematic and noise data collected through smartphone sensors from within equipment and deep learning algorithms for recognizing equipment states. The testing of the proposed method in a real world case study demonstrated very high accuracy of 99.78% in measuring productivity of an excavator.
Background: Sudden sensorineural hearing loss is an emergency condition requiring immediate diagnosis and treatment. There are several theories explaining pathogenesis of it. Some of them including vascular disease, viral infection, metabolic disease, autoimmunity, trauma and combinations of multiple factors are suggested to be the causes of it. Purpose: This study attempted to detect correlation between dyslipidemia and occurrence of Idiopathic sudden sensorineural hearing loss. Materials and methods: This study was a cross sectional that included patients attended to Audiology Unit in Hospital of Benha University in the period from January to November 2021 and diagnosed as having SSNHL within one week, radiological and laboratory studies were done to those patients. Results: One hundred and forty-seven patients diagnosed as having idiopathic sudden sensorineural hearing loss. Forty-one of them were excluded and the remaining 106 had laboratory studies (lipid profile) with the following results: 58 patients (54.7%) had increased level of total cholesterol, 61 patients (57.5%) with decreased high density lipoprotein, and 64 patients (60.3%) with elevated low density lipoprotein. Conclusions: The study concluded that dyslipidemia represents risk factor for occurrence of sudden sensorineural hearing loss.
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