Digital twins in manufacturing: systematic literature review for physical-digital layer categorization and future research directions

Modern technologies and recently developed digital solutions make their way into all aspects of lives of individuals and businesses, and manufacturing industry is no exception. In the era of digital revolution of industry, manufacturing processes can benefit from digitalization technologies immensely. Digital twin (DT) is a technology concept that aims to create a digital mirror of a physical system with a constant data flow between two components. This idea can be used for monitoring and optimization of the present system as well as forecasting and estimating future states of it. There have been theoretical and practical studies conducted on DT in manufacturing area. This systematic literature review (SLR) aims to summarize the current state of literature and shine a light on open areas for future research. Using a rigorous SLR method, 247 relevant studies from 2015 to 2020 are examined to answer a set of research questions. The current state of DT in manufacturing literature is analyzed and explained with an emphasis on where the future studies may go in this area

Use of oximetry as a screening tool for obstructive sleep apnea: a case study in Taiwan

[[abstract]]Obstructive sleep apnea (OSA) is a relatively common disease in the general population. Patients with OSA have a high risk of various comorbid medical diseases. Polysomnography (PSG) is the current gold standard for diagnosing OSA but is time consuming and expensive. This study aims to identify a sensitive screening parameter that can be used by clinicians to determine the time of referral for PSG examination in Taiwan. Eighty-seven patients, including 67 males and 20 females, were included in this study. We divided the patients into two groups: training data (n = 58) and testing group (n = 29). Pearson χ 2 test was used to perform bivariate analysis, and a decision tree was used to build a model. The decision model selected the frequency of desaturation > 4% per hour (DI4) as the indicator of OSA influence. The testing data accuracy of the C4.5 decision tree was 82.80%. External data were also used to validate the model reliability. The accuracy of the external data was 95.96%. Approximately one-third of patients with DI4 between 11 and 33 suffered from OSA. This population requires further diagnosis. Oximetry is an important and widely available screening method in Taiwan. This study proposes the need for PSG referral if DI4 is between 11 and 33.[[notice]]補正完