UTILIZING ARTIFICIAL NEURAL NETWORKS AND RANDOM FORESTS TO FORECAST THE DYNAMIC AMPLIFICATION FACTORS OF NON-STRUCTURAL COMPONENTS

Utilizing Artificial Neural Networks and Random Forests to Forecast the Dynamic Amplification Factors of Non-Structural Components

Soft stories in buildings are well-known to present structural vulnerabilities during seismic events, and the failure of non-structural components (NSCs) has been evident in past earthquakes, along with structural damage.This study seeks to investigate how the presence of a soft story in a building affects the criteria for elastic floor acceleratio

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Cross- analyzing the opinions and experiences of nurses, physiotherapists, dentists, midwives, and pharmacists with respect to addictive disorder screening in primary care: A qualitative study.

Early addiction disorders screening is recommended in primary care.The goal of health system reform is to include allied health professionals in this screening.The appropriation of their new role has not yet been explored.The main aim of this study was to examine the perspective of allied health professionals in primary care on the screening of add

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Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data

The geological data of the mineral resource potential evaluation results (MRPERs) are diverse and extremely large; efficiently retrieving data remains a challenging problem.In this work, a new way of using the Hadoop platform is proposed.The Hadoop distributed file system Skimmers and Strainers is used to store the massive data and construct the da

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