• Research in Community and Public Health Nursing
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• v.12 no.3
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• pp.600-608
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• 2001

Accidents are the fourth-leading causal factor of death among the elderly, and fall is a major type of accident (53.17%). Many cases of falls in the elderly result in delayed discovery and loss of quality of life. As the number of the elderly grows, falls will be a more important health problem. Most previous research on falls investigated prevalence. mortality, and the related factors. There are many studies proving the effect of rhythmic movements. But few researches considered linking risk factors of fall with rhythmic movements. Purpose: We want to show the changes after performing rhythmic movement program, in risk factors of falls and mobility such as flexibility, balance, muscle power and persistency in the elderly, in order to provide basic information needed for the development of fall injury prevention program for the elderly. Method: The design of this study is quasi-experimental, the equivalent control group, pretest-posttest. The subjects consist of 124 people who lived in Do-Bong-Qu. Seoul, agreed to participate in this study, and were able to follow this rhythmic movement program. About 93 % of them are from 65 to 84 years (Mean${\pm}$sd: $73.7{\pm}5.7$): 64% are female. The rhythmic movement program was designed. and performed by two community health nurses working in the Do-Bong-Gu Public Health Center, regularly twice a week from May, 4 to December, 17. in 10 senior citizens' community centers. Risk factors of fall were measured with RAFS- II (Risk Assessment for Falls Scale II) by asking about each item: mobility was measured by observing their specific movements asked by investigators. Results: 1. After performing the program during 7 months, risk factors score of falls were decreased significantly (paired-t = 4.77. p<0.01). 2. After performing the program during 7 months, flexibility (paired-t = 2.26. p=0.03) and mobility were improved (paired-t = 4.98. p<0.01). but muscle power and persistency did not change (paired-t = 0.33. p=0.74). Overall, mobility affecting the occurrence of falls was improved significantly (paired-t = 5.15. p<0.01). Conclusions: A regular rhythmic movement program can be helpful in preventing falls in the elderly. Further. we can develop a fall injury prevention program using rhythmic movement.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.821-829
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• 2006

Traffic load comprise primary input to pavement design causing pavement damage. therefore it should be proceeded suitable traffic load distribution modeling for pavement design and analysis. Traffic load have been represented by equivalent single axle loads (ESALs) which convert mixed traffic stream into one value for design purposes. But there are some limit to apply ESALs to other roads because it is empirical value developed as part of the original AASHO(American Association of State Highway Officials) road test. There have been many efforts to solve these problems. Several leading country have implemented M-E(Mechanistic-Empirical) design procedures based on mechanical concept. As a result, they established traffic load quantification method using load distribution model known as Axle Load Spectra. This paper details Axle Load Spectra and presents axle load distribution model based on normal mixture distribution function using truck load data collected by WIM system installed in national highway. Axle load spectra and axle load distribution model presented in this paper could be useful for basic data when making traffic load quantification plan for pavement design, overweight vehicle permit plan and pavement maintenance cost plan.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.