본 연구에서는 태백산국립공원 주요 탐방로 입구에 설치된 탐방객 자동 계수기 데이터를 이용하여 탐방로별 일간 탐방객수에 영향을 미치는 요인을 분석하고, 이를 바탕으로 탐방로를 유형화하였다. 일일 탐방객수를 종속변수로 다중회귀분석 실시한 결과, 개천절이나 눈축제 등 행사는 모든 탐방로의 탐방객수에 영향을 미쳐, 태백산국립공원의 일일 탐방객수를 결정하는 가장 큰 요인인 것으로 분석되었다. 3일 이상 휴일이 연속되는 연휴와 일반 공휴일도 대부분의 탐방로의 일일 탐방객수에 영향을 미쳤다. 강수량은 비산행 목적의 탐방객이 많은 탐방로의 탐방객수에는 부(-)의 영향을 미쳤으나, 산행 목적의 탐방객이 많은 탐방로의 탐방객수에는 유의미한 영향을 미치지 않아, 산행 목적 탐방객들은 날씨가 궂더라도 산행을 강행하는 것으로 나타났다. 기온은 산행 목적의 탐방객이 많은 탐방로의 탐방객수에는 정(+)의 영향을 미쳤으나, 눈축제가 열리는 당골광장 인근의 탐방로의 탐방객수에는 부(-)의 영향을 미쳐, 눈축제의 영향권인지 여부가 탐방로 관리에 결정적 요인이었다. K-mean clustering을 이용하여 18개 탐방로를 유형 분류한 결과, 태백산국립공원의 탐방로는 눈축제에 영향을 받는 유형(유형 1)과, 가까운 거리에 볼거리가 많아 비산행 탐방객이 많은 유형(유형 2), 그리고 산행을 목적으로 온 탐방객들이 대부분인 유형(유형 3)의 3개 유형을 분류할 수 있었다. 탐방로 유형별 탐방객 행태와 불법 행위 유형이 다르므로, 유형별 특성에 맞춰 탐방로 관리방안을 마련하여야 할 것이다.

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

Evaluating the operational efficiency of electric vehicle charging stations (EVCSs) is important to understand charging network evolution and the charging behavior of electric vehicle users. However, aggregation of efficiency performance metrics poses a significant challenge to practitioners and researchers. In general, the operational efficiency of EVCSs can be measured as a complicated function of various factors with multiple criteria. Such a complex aspect of managing EVCSs becomes one of the challenging issues to measure their operational efficiency. Considering the difficulty in the efficiency measurement, this paper suggests a way to measure the operational efficiency of EVCSs based on data envelopment analysis (DEA). The DEA model is formulated as constant returns of output-oriented model with five types of inputs, four of them are the numbers of floating population and nearby charging stations, distance of nearby charging stations and traffic volume as desirable inputs and the other is the traffic speed in congestion as undesirable one. Meanwhile, the output is given by the charging frequency of EVCSs in a day. Using real-world data obtained from reliable sources, we suggest operational efficiencies of EVCSs in Seoul and discuss implications on the development of electric vehicle charging network. The result of efficiency measurement shows that most of EVCSs in Seoul are inefficient, while some districts (Nowon-gu, Dongdaemun-gu, Dongjak-gu, Songpa-gu, Guro-gu) have relatively more efficient EVCSs than the others.

Game development costs a lot of money. Thus, in terms of cost-saving, game companies produce prototypes and undergo several tests and modifications during the development period. In this paper, we proposed a brainwave signal analysis system and linked it with the game to measure the player's emotion through brainwave analysis experiment and tried to solve these problems. The brainwave signal analysis system proposed in this paper is capable of various applications according to various development situations, such as the progress of the game, events, etc. in obtaining simple figures over time by linking games, and collecting the tester's responses according to development intentions, so there will have to be continuous additional research.

The purpose of this study was to analyze six English as a Foreign Language (EFL) learners’ trajectories of discriminating near-synonyms in a data-driven learning task. Since the learners find it considerably difficult to learn subtle meaning differences of near-synonyms, corpuscorpuscorpuscorpuscorpuscorpus-based data-driven learning may provide an opportunity for them to tackle their difficulties. The study materials guided the learners to identify the differences between the four pairs of near-synonyms, categorize the concordance lines based on their findings, and generalize the findings. The six participants had notably different trajectories of discriminating near-synonyms. The qualitative analysis of the trajectories showed a tendency that the intermediate learners focused on the meanings and found the correct answer without knowing the core meaning, and the advanced learners moved further to attend to structural differences and sometimes tested their previous knowledge on the concordance data. This study implies the need for careful guidance, collaborative group works, and strategy teaching in data-driven learning tasks.

The conversation analysis approach of using audio and video recordings of naturally occurring conversation contrasts with other methods of collecting data in the social and cognitive sciences. This paper intends to describe the ways in which conversation analysts collect their data suitable for analysis, including the steps involved in preparing to record, the recording of naturally occurring social interaction, and also the processing of recordings after they have been made. Before actually making the recordings, researchers must first choose what type of conversation they will collect and what device to use. Researchers also need to obtain consent for recording from the participants in a conversation analytic study. While making the recordings, researchers must figure out many practical issues such as who, when, how, what, how much, and where to record. After recording, researchers need to store data in audio or video formats. Researchers also archive it, using codes for identification that facilitate later finding, quoting and retrieving of any file. This paper focuses on these choices as they are made before, during, and after recordings and places specific emphasis on the effects these choices have on the recordings. It also illuminates how these techniques are closely related to the methodological and theoretical assumptions of the conversation analysis approach.

2017년 미국 DOE 주도하에 수행된 국제공동 복합모드운반시험 중 도로트럭운반시험, 연안항해시험, 대서양항해시험에서 측정된 가속도 및 변형률 데이터를 분석하였다. 먼저 각 운반모드 별로 발생한 하중이 전달경로에 따라 하중이 증폭되는지 감쇄되는지가 조사되었다. 그 결과 운반모드 및 하중경로 내 어떤 부분이냐에 따라 발생한 하중이 모의핵연료집합체에 전달되는 특성이 다름을 확인하였다. 그리고 변형률 데이터를 분석하여 육상 및 해상운반동안 발생한 변형률이 사용후핵연료에 건전성에 미치는 영향을 파악하였다. 그 결과 측정된 변형률은 사용후핵연료의 건전성에는 영향을 미치지 못하는 정도로 작은 크기임을 확인하였다. 본 연구에서 분석된 가속도와 피로평가 결과는 예정된 국내 정상운반시험조건에서의 운반시험에 유용한 기초자료로 활용될 것이다.

This study analyzed the health characteristics and comorbidity of adult men aged 40 years by dividing them into a control group of those without any disease related to fine dust and a patient group with one or more diseases related to fine dust in areas with high levels of fine dust pollution using the sixth and seventh Korea National Health and Nutrition Examination Survey (2013-2017). Among the general characteristics, the mean age of the patient group was significantly older than that of the control group (p<0.001), and in terms of the health-related characteristics, the frequency of breakfast consumption in the patient group was higher than in the control group (p<0.043). The body measurements were similar in the patient and control groups. Regarding the prevalence of comorbidity, the patient group showed a higher prevalence of hypertension, dyslipidemia, stroke, myocardial infarction, heart failure, and diabetes than the control group, but the differences were not statistically significant. On the other hand, the prevalence of other cancers (except stomach cancer, liver cancer, colorectal cancer, breast cancer, and cervical cancer) in the patient group was higher than in the control group (p<0.05). In terms of the clinical characteristics, the glycated hemoglobin levels in the patient group were significantly higher than in the control group (p<0.048). Information on nutrition and health in areas with frequent occurrences of fine dust was obtained through the study results, which can be used as basic data for measures of health and diet management against diseases that will increase in relation to fine dust.

Big data analysis methods are useful tools for sorting valuable data and products. Achyranthes Radix root extract (AR) is a well-known herbal medicine in East Asia due to its anti-osteoarthritis, pro-circulatory, and anti-osteoporosis effects. In this stud y, we investigated the liver- and kidney-protective effects of AR by applying big data analysis to traditional medicine. CDDP (cis-diamminedichloridoplatinum) is an effective cancer cell anti-proliferative agent used in the treatment of diverse types of tumors. However, it is clinically limited due to liver and kidney toxicity. The current study was designed to assess the potential protective effects of AR against CDDP-induced hepato-renal toxicity. For this purpose, male Sprague-Dawley (SD) rats were assigned to four groups, each consisting of four animals. Intravenous injection or oral administration of either saline or AR was performed daily for 14 days, whereas CDDP was injected intraperitoneally on day 3 following AR treatment. Serum biochemistry results revealed that CDDP induced clear hepatic and renal damage while the AR treatment groups showed less damage relative to controls. Next, we tested the pharmacokinetics of AR using 20-hydroxyecdysone (20-HE), which is the most abundant component of AR extract. After intravenous administration of AR, the plasma concentration of 20-HE rapidly declined with a terminal half-life (t1/2) of 0.99±0.47 h. The area under the plasma concentration vs. time curve was 24.96±3.5 h*ng/mL. The present study provides valuable tools for further verification studies of the classical herbal literature and its scientific relevance.

Google Trends is a useful tool not only for setting search periods, but also for providing search volume to specific countries, regions, and cities. Extant research showed that the big data from Google Trends could be used for an on-line market analysis of opinion sensitive products instead of an on-site survey. This study investigated the market share of tumor necrosis factor-alpha (TNF-α) inhibitor, which is in a great demand pharmaceutical product, based on big data analysis provided by Google Trends. In this case study, the consumer interest data from Google Trends were compared to the actual product sales of Top 3 TNF-α inhibitors (Enbrel, Remicade, and Humira). A correlation analysis and relative gap were analyzed by statistical analysis between sales-based market share and interest-based market share. Besides, in the country-specific analysis, three major countries (USA, Germany, and France) were selected for market share analysis for Top 3 TNF-α inhibitors. As a result, significant correlation and similarity were identified by data analysis. In the case of Remicade’s biosimilars, the consumer interest in two biosimilar products (Inflectra and Renflexis) increased after the FDA approval. The analytical data showed that Google Trends is a powerful tool for market share estimation for biosimilars. This study is the first investigation in market share analysis for pharmaceutical products using Google Trends big data, and it shows that global and regional market share analysis and estimation are applicable for the interest-sensitive products.

The purpose of this paper is to analyze factors affecting the net income of regional and industrial fisheries cooperatives in South Korea using panel data. This paper utilizes linear or GLS regression models such as pooled OLS model, fixed effects model, and random effects model to estimate affecting factors of the net income of regional and industrial fisheries cooperatives. After reviewing various tests, we eventually select random effects model. The results, based on panel data between 2013 and 2018 year and 64 fisheries cooperatives, indicate that capital and area dummy variables have positive effects and employment has negative effect on the net income of regional and industrial fisheries cooperatives as predicted. However, debt are opposite with our predictions. Specifically, it turns out that debt has positive effect on the net income of regional and industrial fisheries cooperatives although it has been increased. Additionally, this paper shows that the member of confreres does not show any significant effect on the net income of regional and industrial fisheries cooperatives in South Korea. This study is significant in that it analyzes the major factors influencing changes in the net income that have not been conducted recently for the fisheries cooperatives by region and industry.

The research model of panel data analysis in this study was used as the dependent variables and the business characteristics of the welding industry were reflected in the research model for systematic analysis of the effect of welding technology on the welding industry. As a result of the existing research, the domestic welding technology is seriously encroaching on the domestic welding industry between the United States, Japan and China. There is no quantitative statistical analysis on this aspect. In this study, the panel data analysis is used to indicate differences in explanatory power by numerical values of POLS model, fixed effect and random effect. And the prior studies on the current status of welding industry related to arc welding, special welding, multiple welding, welding and bonding technology are applied by the panel data analysis. Therefore, the problems of existing research are diagnosed while presenting the future research directions.