• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.27 no.1
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• pp.13-22
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• 2017

Objectives: The objective of this study was to evaluate the exposure levels of door-to-door deliverers to fine particulate matter (PM2.5). Another objective was to confirm the general working patterns of door-to-door deliverers via survey. Methods: In the city of Daegu, ten door-to-door deliverers who wished to join the study were recruited. The general working characteristics of door-to-door deliverers were surveyed using self-reported questionnaires. In the cabin of each car driven by a deliverer, a real-time PM2.5 sampler (Sidepak, Model AM510, TSI Inc., MN, USA) and a GPS device (GPS 741, Ascen, Korea) were installed. Each deliverer was monitored for four days per week so that each day could be monitored at least four times. Results: A total of 40 measurements of PM2.5 concentrations were taken during delivery of parcels. The average exposure levels of door-to-door deliverers to PM2.5 was $44.62{\mu}g/m^3$ ($7-9443{\mu}g/m^3$. Exposure levels to PM2.5 according to the day of the week and coverage areas were not significantly different (p>0.05). Door-to-door deliverers using trucks with older diesel engines manufactured before 2006 had significantly higher exposure levels to PM2.5 than in the case of trucks with diesel engines manufactured after 2006 (p<0.05). Many of the door-to-door deliverers reported the status of having windows open during the delivery task. During delivery services, the working hours spent in residential areas were higher than on roadsides, but exposure levels to PM2.5 in residential areas and on roadsides were $46.17{\mu}g/m^3$ and $49.90{\mu}g/m^3$, respectively. Real-time PM2.5 exposure levels were significantly different between roadways and residential areas (p<0.001). Conclusions: PM2.5 exposure levels of door-to-door deliverers were found to be affected by higher vehicle emissions from the roadsides near their vehicle during deliveries and while driving to other locations compared to by PM2.5 from the diesel engines of their own trucks. Particle concentrations from roadsides and emissions from nearby vehicles through open windows were the main source of PM2.5.

• The Mathematical Education
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• v.59 no.1
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• pp.63-80
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• 2020

This study analyzed 3,114 articles published in KCI journals and 1,636 articles published in SSCI journals from 2000 to 2019 in order to compare domestic and international research trends of mathematics education using a topic modeling method. Results indicated that there were 16 similar research topics in domestic and international mathematics education journals: algebra/algebraic thinking, fraction, function/representation, statistics, geometry, problem-solving, model/modeling, proof, achievement effect/difference, affective factor, preservice teacher, teaching practice, textbook/curriculum, task analysis, assessment, and theory. Also, there were 7 distinct research topics in domestic and international mathematics education journals. Topics such as affective/cognitive domain and research trends, mathematics concept, class activity, number/operation, creativity/STEAM, proportional reasoning, and college/technology were identified from the domestic journals, whereas discourse/interaction, professional development, identity/equity, child thinking, semiotics/embodied cognition, intervention effect, and design/technology were the topics identified from the international journals. The topic related to preservice teacher was the most frequently addressed topic in both domestic and international research. The topic related to in-service teachers' professional development was the second most popular topic in international research, whereas it was not identified in domestic research. Domestic research in mathematics education tended to pay attention to the topics concerned with the mathematical competency, but it focused more on problem-solving and creativity/STEAM than other mathematical competencies. Rather, international research highlighted the topic related to equity and social justice.

• Korean Journal of Clinical Laboratory Science
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• v.51 no.4
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• pp.495-503
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• 2019

This study explored the method of determining the appropriate size of the workforce according to the assessment of the workload of medical technologists (also called medical and clinical laboratory technologists, and medical and clinical laboratory scientists) in order to present a standard production model for the appropriate manpower in blood collection rooms. The eleven university hospitals selected for this study had between 600 and 2,000 beds. The 14-steps standard blood collection time was 4 minutes and 8 seconds for the outpatients aged between 20 to 60 years old (57%) except for children and the elderly (43%). Assuming that there were 8 hours per day for mechanically collecting blood, the maximum number of blood donations by one clinical laboratory scientist was analyzed to be 100 cases. In conclusion, it is appropriate to have fewer than 100 cases of daily blood collection by a medical technologist engaged in blood collection. Since the proper number of blood collection workers (100% of blood collection work)=the number of annual working days/(one day's work hours/time per case)×the number of working days per year, then the proper number of blood collection workers (one day's work hours)=the number of working days per year/100×the number of working days).

• Journal of Information Management
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• v.41 no.4
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• pp.227-249
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• 2010

Scholarly information has increased tremendously according to the development of IT, especially the Internet. However, simultaneously, people have to spend more time and exert more effort because of information overload. There have been many research efforts in the field of expert systems, data mining, and information retrieval, concerning a system that recommends user-expected information items through presumption. Recently, the hybrid system combining a content-based recommendation system and collaborative filtering or combining recommendation systems in other domains has been developed. In this paper we resolved the problem of the current recommendation system and suggested a new system combining collaborative filtering and Naive Bayes Classification. In this way, we resolved the over-specialization problem through collaborative filtering and lack of assessment information or recommendation of new contents through Naive Bayes Classification. For verification, we applied the new model in NDSL's paper service of KISTI, especially papers from journals about Sitology and Electronics, and witnessed high satisfaction from 4 experimental participants.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.12
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• pp.5287-5294
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• 2016

Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington. Materials and Methods: A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to 2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah, and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival times. The results of this study can be used to guide healthcare providers and researchers for further investigations into social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.1-11
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• 2014

Traffic congestion cost is more likely to occur in the inner city than interregional road, and it accounts for about 63.39% of the whole. Therefore, it is important to mitigate traffic congestion of the inner city. Traffic congestion in the urban could be divided into Recurrent congestion and Non-recurrent congestion. Quick and accurate detection of Non-recurrent congestion is also important in order to relieve traffic congestion. The existing studies about incident detection have been variously conducted, however it was limited to Uninterrupted Traffic Flow Facilities such as freeway. Moreover study of incident detection on the interrupted Traffic Flow Facilities is still inadequate due to complex geometric structure such as traffic signals and intersections. Therefore, in this study, incident detection model was constructed using by Artificial Neural Network to aim at urban arterial road that is interrupted traffic flow facility. In the result of the reliability assessment, the detection rate were 46.15% and false alarm rate were 25.00%. These results have a meaning as a result of the initial study aimed at interrupted traffic flow. Furthermore, it demonstrates the possibility that Non-recurrent congestion can be detected by using car navigation data such as car navigator system device.

• Korean Journal of Remote Sensing
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• v.35 no.5_2
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• pp.841-850
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• 2019

The purpose of this study is to strengthen the capability of rapid mapping for disaster through improving the positioning accuracy of mapping and fusion of multi-sensing point cloud data derived from Unmanned Aerial Vehicles (UAVs) and disaster investigation vehicle. The positioning accuracy was evaluated for two procedures of drone mapping with Agisoft PhotoScan: 1) general geo-referencing by self-calibration, 2) proposed geo-referencing with optimized camera model by using fixed accurate Interior Orientation Parameters (IOPs) derived from indoor camera calibration test and bundle adjustment. The analysis result of positioning accuracy showed that positioning RMS error was improved 2~3 m to 0.11~0.28 m in horizontal and 2.85 m to 0.45 m in vertical accuracy, respectively. In addition, proposed data fusion approach of multi-sensing point cloud with the constraints of the height showed that the point matching error was greatly reduced under about 0.07 m. Accordingly, our proposed data fusion approach will enable us to generate effectively and timelinessly ortho-imagery and high-resolution three dimensional geographic data for national disaster management in the future.

• Korean Journal of Soil Science and Fertilizer
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• v.40 no.1
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• pp.12-17
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• 2007

This study was conducted to evaluate the characteristics of nonpoint source pollutants discharge from a small rural watershed. A typical rural area in Gongju City, Korea, was selected as the research site. Water quality and quantity in streams and rainfall samples were analyzed periodically from May to October 2005. Pollutant loads were estimated from a nonpoint source pollution model (AvSWAT, Arcview Soil and Water Assessment Tool). During the rainy season, from June 26 to 30 September 2005 and the dry season, before 26 June and after 30 September 2005, biological oxygen demands and chemical oxygen demands accounted for 91.3% and 93.7% of annual load, respectively, while total-N and total-P were 97.1% and 91.1% of annual load, respectively. The observed stream flow was $66.5m^3sec^{-1}$, while simulation stream flow was $66.2m^3sec^{-1}$. That can be assumed that simulation can be used to estimate the stream flow without practical measurement. However, the runoff trend following the occurrence of a storm event was not recorded properly.

• Journal of the Korean Applied Science and Technology
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• v.38 no.4
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• pp.1117-1128
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• 2021

The purpose of the current study was to investigate the effects of 4-week health promotion intervention program consisting of high-intensity aerobic exercise to improve energy consumption and cardiorespiratory capacity, circuit training to strengthen muscular strength and endurance and education for lifestyle changes on the obesity level, physical fitness, insulin resistance and metabolic syndrome among overweight or obese elementary school students. Twenty three overweight or obese children as obese group and 15 normal body weight children as normal group were recruited. The health promotion program consisted of two exercise sessions and one education session, 3days/week in 4-week. Obesity level(body mass index, waist circumference, %body fat), physical fitness(muscular strength, muscular endurance, flexibility, cardiopulmonary fitness), insulin resistance(homeostasis model assessment of insulin resistance) and metabolic syndrome risk factors(blood pressure, fasting glucose, triglycerides, high density lipoprotein cholesterol) were measured. There was not significant decrease in obesity level; however, there were significant improvement in physical fitness, insulin resistance and metabolic syndrome risk factors after program participation among obese children. As a result, through participation in the summer camp consisting mainly of exercise, the improvement of the physical fitness level and the decrease of insulin resistance had an effect on the reduction of the metabolic syndrome frequency.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.769-777
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• 2021

This study investigated the change of the achievement rate of the target water quality conditioned on the occurrence of severe drought, to assess the effects of meteorological drought on the water quality management in the Nakdong River basin. Using three drought indices with difference time scales such as 30-, 60-, 90-day, i.e., SPI30, SPI60, SPI90, and three water quality indicators such as biochemical oxygen demand (BOD), total organic carbon (TOC), and total phosphorus (T-P), we first analyzed the relationship between severe drought occurrence water quality change in mid-sized watersheds, and identified the watersheds in which water quality was highly affected by severe drought. The Bayesian network models were constructed for the watersheds to probabilistically assess the relationship between severe drought and water quality management. Among 22 mid-sized watersheds in the Nakdong River basin, four watersheds, such as #2005, #2018, #2021, and #2022, had high environmental vulnerability to severe drought. In addition, severe drought affected spring and fall water quality in the watershed #2021, summer water quality in the #2005, and winter water quality in the #2022. The causal relationship between drought and water quality management is usufaul in proactive drought management.