• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.663-669
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• 2022

The content of fluoride in toothpaste commercially available in Korea has been increased to less than 1500 ppm. The purpose is to provide these results to consumers and to suggest alternatives to the safe use of toothpaste. This study was conducted on 1,300 people for 2 weeks from March 2021. As a research tool, general characteristics and oral health behaviors were surveyed. ConsExpo Web 1.0.2. It was used as an input variable for exposure evaluation. As a result of the study, when a toothpaste containing 1500 ppm of fluoride was used, the external dose on day of exposure was 2.3×10-2 mg/kg/day for males, 2.9×10-2 mg/kg/day for females, and children aged 2-3 years was estimated to be 7.3×10 -2 mg/kg/day. As a result of this study, it is thought that as the fluoride content of toothpaste distributed in the market increases, it will be used as a basic data to present standards for safe use by consumers.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.285-295
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• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.7
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• pp.105-112
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• 2021

Reduction of weld distortions and increase in productivity are some of the major goals of the shipbuilding industry. To address these issues, many researchers have attempted to apply new welding processes. In the shipbuilding industry, steel is the candidate material of choice owing to its good weldability. However, conventional welding techniques are not feasible for avoiding welding problems. Tip-rotating arc welding is one of the high-efficiency welding process that has several advantages, such as high welding speed, high melting rate, low heat input, and less distortion. The present study investigates the influence of the welding variables on the weld characteristics of tip-rotating arc welding. Welding was performed using EH36 as the base metal and SM-70s as the filler metal, which are widely used in shipbuilding. Basic experiments were conducted to understand the effects of the major welding variables, such as welding and tip-rotating speeds. The distortion and mechanical properties of the optimal welding conditions were used to evaluate the tip-rotating arc welding performance. Consequently, the feasibility of the tip-rotating arc welding process for joining steel components was investigated, so that the optimized welding conditions could be applied directly to ship body welding to enhance the quality of the welded joints.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.107-117
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• 2022

Purpose : The purpose of this study was to establish a model of the predictive factors for success or failure of examinees undertaking the Korean physical therapist licensing examination (KPTLE). Additionally, we assessed the pass/fail cut-off point. Methods : We analyzed the results of 10,881 examinees who undertook the KPTLE, using data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was the test result (pass or fail), and the input variables were: sex, age, test subject, and total score. Frequency analysis, chi-square test, descriptive statistics, independent t-test, correlation analysis, binary logistic regression, and receiver operating characteristic (ROC) curve analyses were performed on the data. Results : Sex and age were not significant predictors of attaining a pass (p>.05). The test subjects with the highest probability of passing were, in order, medical regulation (MR) (Odds ratio (OR)=2.91, p<.001), foundations of physical therapy (FPT) (OR=2.86, p<.001), diagnosis and evaluation for physical therapy (DEPT) (OR=2.74, p<.001), physical therapy intervention (PTI) (OR=2.66, p<.001), and practical examination (PE) (OR=1.24, p<.001). The cut-off points for each subject were: FPT, 32.50; DEPT, 29.50; PTI, 44.50; MR, 14.50; and PE, 50.50. The total score (TS) was 164.50. The sensitivity, specificity, and the classification accuracy of the prediction model was 99 %, 98 %, and 99 %, respectively, indicating high accuracy. Area under the curve (AUC) values for each subject were: FPT, .958; DEPT, .968; PTI, .984; MR, .885; PE, .962; and TS, .998, indicating a high degree of fit. Conclusion : In our study, the predictive factors for passing KPTLE were identified, and the optimal cut-off point was calculated for each subject. Logistic regression was adequate to explain the predictive model. These results will provide universities and examinees with useful information for predicting their success or failure in the KPTLE.

• Transactions of the Korean hydrogen and new energy society
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• v.33 no.4
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• pp.419-429
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• 2022

Effects of operating variables on temperature profile and performance of 3 MWth chemical looping combustion system were estimated by mass and energy balance analysis based on configuration and dimension of the system determined by design tool. Air reactor gas velocity, fuel reactor gas velocity, solid circulation rate, and solid input percentage to fluidized bed heat exchanger were considered as representative operating variables. Overall heat output and oxygen concentration in the exhaust gas from the air reactor increased but temperature difference decreased as air reactor gas velocity increased. Overall heat output, required solid circulation rate, and temperature difference increased as fuel reactor gas velocity increased. However, overall heat output and temperature difference decreased as solid circulation rate increased. Temperature difference decreased as solid circulation rate through the fluidized bed heat exchanger increased. Effect of each variables on temperature profile and performance can be determined and these results will be helpful to determine operating range of each variable.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.427-429
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• 2022

Recently, metaverse technology is being used in various service industries such as games, entertainment, manufacturing, distribution, advertising, and education. Studies on the correlation between the continuous use of devices used in metaverse content are still insufficient. In order to be more immersed in the metaverse, it is necessary to develop a natural movement and an easy-to-use input device. Based on flow, this study was conducted on the topic of continuous use of metaverse contents and devices. The constituent factors of Flow, an independent variable, were set as sense of reality, immersion, and interaction. We intend to use the data of 500 male and female metaverse users for research through a survey institution. Among the flow factors that increase the continuous use of metaverse contents and devices, the factors that have the greatest influence were studied. Through the results of this study, it is intended to help establish the direction of the next-generation metaverse content and device industry.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.617-632
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• 2022

In recent years, environmental pollution and determining the main factors causing this pollution have become an important issue. This study investigates the relationship between the agricultural sector and environmental pollution in Azerbaijan for 1992-2018. The dependent variable in the study is the agricultural greenhouse gas emissions (CO₂ equivalent). Eight variables were selected as explanatory variables: four agricultural inputs and four agricultural macro indicators. Unit root tests, ARDL boundary test, FMOLS, DOLS and CCR long-term estimators, Granger causality analysis, and variance decomposition analyses were used to investigate the effect of these variables on agricultural emissions. The results show that chemical fertilizer consumption, livestock number, and pesticide use positively and statistically significantly affect agricultural emissions from agricultural input variables. In contrast, agricultural energy consumption has a negative and significant effect. From agricultural macro indicator variables, it was found that the crop and animal production index had a positive and significant effect on agricultural emissions. According to the Granger causality test results, it was concluded that there are a causality relationship from chemical fertilizer consumption, livestock number, crop and livestock production index variables towards agricultural emissions. Considering all the results obtained, it is seen that the variables that have the most effect on the increase in agricultural emissions in Azerbaijan are the number of livestock, the consumption of chemical fertilizers, and the use of pesticides, respectively. The results from the research will contribute to the information on agricultural greenhouse gas emissions and will play an enlightening role for policymakers and the general public.

• Structural Engineering and Mechanics
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• v.86 no.1
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• pp.119-137
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• 2023

The purpose of this research is to assess the performance of CBN and ceramic tools during the dry turning of gray cast iron EN GJL-350. During the turning operation, the variable machining parameters are cutting speed, feed rate, depth of cut and type of the cutting material. This contribution consists of two sections, the first one deals with the performance evaluation of four materials in terms of evolution of flank wear, surface roughness (2D and 3D) and cutting forces. The focus of the second section is on statistical analysis, followed by modeling and optimization. The experiments are conducted according to the Taguchi design L₃₂ and based on ANOVA approach to quantify the impact of input factors on the output parameters, namely, the surface roughness (Ra), the cutting force (Fz), the cutting power (Pc), specific cutting energy (Ecs). The RSM method was used to create prediction models of several technical factors (Ra, Fz, Pc, Ecs and MRR). Subsequently, the desirability function approach was used to achieve a multi-objective optimization that encompasses the output parameters simultaneously. The aim is to obtain optimal cutting regimes, following several cases of optimization often encountered in industry. The results found show that the CBN tool is the most efficient cutting material compared to the three ceramics. The optimal combination for the first case where the importance is the same for the different outputs is Vc=660 m/min, f=0.116 mm/rev, ap=0.232 mm and the material CBN. The optimization results have been verified by carrying out confirmation tests.

• Journal of Korean Society on Water Environment
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• v.39 no.1
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Journal of Convergence for Information Technology
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• v.12 no.3
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• pp.116-125
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• 2022

The purpose of this study is to analyze whether investor anxiety caused by COVID-19 affects cryptocurrency prices in the COVID-19 pandemic, and to experiment with cryptocurrency price prediction based on a deep learning model. Investor anxiety is calculated by combining Naver's Corona search index and Corona confirmed information, analyzing Granger causality with cryptocurrency prices, and predicting cryptocurrency prices using deep learning models. The experimental results are as follows. First, CCI indicators showed significant Granger causality in the returns of Bitcoin, Ethereum, and Lightcoin. Second, LSTM with CCI as an input variable showed high predictive performance. Third, Bitcoin's price prediction performance was the highest in comparison between cryptocurrencies. This study is of academic significance in that it is the first attempt to analyze the relationship between Naver's Corona search information and cryptocurrency prices in the Corona phase. In future studies, extended studies into various deep learning models are needed to increase price prediction accuracy.