Projects in the construction industry are becoming increasingly large and complex, with construction technologies, methods, and the like developing rapidly. Various different types of information are generated by construction projects. Especially, a construction phase requires the input of many resources and generates a diverse set of information. While a variety of IT techniques are being deployed for information management during the construction phase, measures to create databases of such information and to link these various different types of information together are still insufficient. As such, this study aims to suggest a construction information database system based on BIM technology to enable the comprehensive management of site information generated during the construction phase. is study analyzed the information generated from construction sites and proposed a categorization system for structuring the generated information, along with a database model for storing such structured information. rough such efforts, it was confirmed that such a database system can be used for accumulating and using construction information; it is believed that, in the future, the continual accumulation and management of construction information will allow for corporate-level accumulation of knowledge as opposed to the individual accumulation of know-how.

The current method for diagnosing methamphetamine use disorder (MUD) relies on self-reports and interviews with psychiatrists, which lack scientific rigor. This highlights the need for novel biomarkers to accurately diagnose MUD. In this study, we identified transcriptome biomarkers using hair follicles and proposed a diagnostic model for monitoring the MUD treatment process. We performed RNA sequencing analysis on hair follicle cells from healthy controls and former and current MUD patients who had been detained in the past for illegal use of methamphetamine (MA). We selected candidate genes for monitoring MUD patients by performing multivariate analysis methods, such as PCA and PLS-DA, and PPI network analysis. We developed a two-stage diagnostic model using multivariate ROC analysis based on the PLS-DA method. We constructed a two-step prediction model for MUD diagnosis using multivariate ROC analysis, including 10 biomarkers. The first step model, which distinguishes non-recovered patients from others, showed very high accuracy (prediction accuracy, 98.7%). The second step model, which distinguishes almost-recovered patients from healthy controls, showed high accuracy (prediction accuracy, 81.3%). This study is the first report to use hair follicles of MUD patients and to develop a MUD prediction model based on transcriptomic biomarkers, which offers a potential solution to improve the accuracy of MUD diagnosis and may lead to the development of better pharmacological treatments for the disorder in the future.