• Journal of dental hygiene science
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• v.18 no.3
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• pp.164-171
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• 2018

To improve the oral health status of Korean people, it is necessary to encourage proper oral hygiene management habits, such as toothbrushing, through appropriate health promotion techniques. Therefore, the purpose of this study was to evaluate the removal of plaque and tooth abrasion using ultra-soft (filament 0.11~0.12 mm) and soft toothbrushes for toothbrushing. The plaque removal was performed using a dentiform and Arti-spray, and the Patient Hygiene Performance (PHP) index was calculated as the sum total score divided by the total number of surfaces. In the abrasivity experiment, according to the number of brushings, a micro Vickers hardness tester was used, and a sample in the range of 280~380 Vickers hardness number was selected. The number of toothbrushing stroke were 1,800 (2 months), 5,400 (6 months), 10,800 (12 months), and 21,600 (24 months). The tooth abrasion was measured using a scanning electron microscope. Statistical analysis was performed using IBM SPSS Statistics 22.0 and a p-value <0.05 was considered significant. According to the results, there was no statistically significant difference in the degree of plaque removal between ultra-soft and soft toothbrushes. The difference in tooth abrasion between before and after toothbrushing was found to be greater with the soft toothbrushes than with the ultra-soft toothbrushes. Therefore, the ultra-soft toothbrush not only lowers tooth damage by reducing tooth abrasion, but also shows a similar ability to remove plaque as soft toothbrushes.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.