고성능 분리막 제조기술과 더불어 새로운 분리막 다단공정 설계를 통해 용매사용량 감소 및 선택도 향상이 가능 하다. 본 연구에서는 내용매성 셀룰로스 나노분리막을 제조하여 용매에 따른 용질의 선택도 차이를 비교하였다. 제막한 셀룰 로스 막을 기반으로 비극성 용매의 선택도 평가를 진행하였으며, 비극성 용매에서 용질에 대한 음배제율이 관측되었다. 특히, 분자량이 클수록 음배제율이 높아지는 역선택도의 거동을 확인하였다. 이를 기반으로 설계한 공정에서는 기존 분획 공정 대 비 3배 이상의 용매저감이 가능한 것을 확인할 수 있었다.

In this study, the biogeochemistry management (BGC-MAN) model was applied to North and South Korea pine and oak forest stands to evaluate the Net Primary Productivity (NPP), an indicator of forest ecosystem productivity. For meteorological information, historical records and East Asian climate scenario data of Shared Socioeconomic Pathways (SSPs) were used. For vegetation information, pine (Pinus densiflora) and oak (Quercus spp.) forest stands were selected at the Gwangneung and Seolmacheon in South Korea and Sariwon, Sohung, Haeju, Jongju, and Wonsan, which are known to have tree nurseries in North Korea. Among the biophysical information, we used the elevation model for topographic data such as longitude, altitude, and slope direction, and the global soil database for soil data. For management factors, we considered the destruction of forests in North and South Korea due to the Korean War in 1950 and the subsequent reforestation process. The overall mean value of simulated NPP from 1991 to 2100 was 5.17 Mg C ha-1, with a range of 3.30-8.19 Mg C ha-1. In addition, increased variability in climate scenarios resulted in variations in forest productivity, with a notable decline in the growth of pine forests. The applicability of the BGC-MAN model to the Korean Peninsula was examined at a time when the ecosystem process-based models were becoming increasingly important due to climate change. In this study, the data on the effects of climate change disturbances on forest ecosystems that was analyzed was limited; therefore, future modeling methods should be improved to simulate more precise ecosystem changes across the Korean Peninsula through processbased models.

Although many attempts have been made to solve the atmospheric diffusion equation, there are many limits that prevent both solving it and its application. The causes of these impediments are primarily due to both the partial differentiation term and the turbulence diffusion coefficient. In consideration of this dilemma, this study aims to discuss the methodology and cases of utilizing a passive air sampler to increase the applicability of atmospheric dispersion modeling. Passive air samplers do not require pumps or electric power, allowing us to achieve a high resolution of spatial distribution data at a low cost and with minimal effort. They are also used to validate and calibrate the results of dispersion modeling. Currently, passive air samplers are able to measure air pollutants, including SO2, NO2, O3, dust, asbestos, heavy metals, indoor HCHO, and CO2. Additionally, they can measure odorous substances such as NH3, H2S, and VOCs. In this paper, many cases for application were introduced for several purposes, such as classifying the VOCs’ emission characteristics, surveying spatial distribution, identifying sources of airborne or odorous pollutants, and so on. In conclusion, the validation and calibration cases for modeling results were discussed, which will be very beneficial for increasing the accuracy and reliability of modeling results.

Odor is a type of air pollution where irritating substances enter through the olfactory system, causing discomfort. At present, the government is formulating various measures and policies to address this type of pollution. This paper seeks to analyze major research cases from both domestic and overseas settings in relation to odor management. In addition, it reviews the potential of addressing environmental issues using a living lab approach in conjunction with community mapping and citizen science. For example, in one domestic case, the Magok smart city living lab project, citizens’ data on community mapping of odor were collected for analysis using artificial intelligence (AI) to derive results. Additionally, in an overseas case in California, citizens directly participated in monitoring air quality using the Community-based monitoring (CBM) method, and both CBM and existing methods were used to assess the level of pollutants for effective data collection. In both of these cases, the potential to address environmental issues was seen to manifest through the development of citizens’ determination and ability to independently solve local problems. However, there are still problems in implementing citizen science, such as the lack of infrastructure and resources available, issues with data collection methodology, questions of objectivity regarding collected data, and concerns about sustainability and expertise in relation to civic participation. Addressing these problems would require an institutional foundation and systematic civic education. This study highlights the potential of addressing environmental problems inherent in the living lab system based on citizen science by analyzing two cases. In addition, this study suggests that if systematic civic education measures are introduced to address issues within existing citizen science research approaches, such measures would be valuable within the educational living lab framework in that they would become effective in tackling not only environmental problems but also social problems as well.

The limitation in deriving the species richness representing the entire country of South Korea lies in its relatively short history of species field observations and the scattered observation data, which has been collected by various organizations in different fields. In this study, a comprehensive compilation of the observation data for plants held by agencies under the Ministry of Environment was conducted, enabling the construction of a time series dataset spanning over 100 years. The data integration was carried out using minimal criteria such as species name, observed location, and time (year) followed by data verification and correction processes. Based on the integrated plant species data, the comprehensive collection of plant species in South Korea has occurred predominantly since 2000, and the number of plant species explored through these surveys appears to be converging recently. The collection of species survey data necessary for deriving national-level biodiversity information has recently begun to meet the necessary conditions. Applying the Chao 2 method, the species richness of indigenous plants estimated at 3,182.6 for the 70-year period since 1951. A minimum cumulative period of 7 years is required for this estimation. This plant species richness from this study can be a baseline to study future changes in species richness in South Korea. Moreover, the integrated data with the estimation method for species richness used in this study appears to be applicable to derive regional biodiversity indices such as for local government units as well.

현대사회에서 미술교육과 미술작품 제작 현장은 정보와 이미 지를 비롯한 폭발적인 성장 속에서 새롭게 전개되고 있다. 이 러한 사회·문화적 방향성의 모색이 필요하고 새로운 관점의 재정립이 요구되는 상황에서 인간과 교육을 둘러싼 사회에서의 미술교육에 대한 새로운 방법론적 접근이 필요하다. 현재 우리 나라 미술교육현장에서 수채화는 점점 도태되어 간다. 화랑가 의 유화 선호, 재료와 도구 사용에 대한 인식, 기법의 난이도 등으로 인해 다른 장르의 그림보다 소외되고 있다. 그 이유 중 가장 큰 문제점은 기초과정을 넘어선 작품 만들기 과정에서 드 러나는데 그것이 바로 보조재료 사용에 대한 미숙함이다. 수채화 제작은 기름을 사용하는 유화와는 달리 물을 사용함으 로 예술의 표현형식과 감상 효과에서 동양인의 체취에 더 친근 감을 주는 매체이다. 수채화 물감을 이용한 작업은 그 자체로 하나의 도전이라고 할 수 있을 정도로 어렵지만, 보조재료를 이 용한 기법을 활용한다면 더 수준 높은 작품성을 발휘할 수 있을 것이다. 수채화에서 중요한 점은 수채화 물감이 투명체 매체라 는 것인데 미세한 입자로 이루어져 있어 어두운색 위에 밝은색 을 덧칠할 수 없다는 것 때문에 작품제작 시 상당한 난관에 부 딪혀 쉽게 작업을 포기하게 되는 것이 현실이다. 본 연구에서는 그런 점을 개선하기 위해 현대미술에서 수채 화 채색작업을 중심으로 <마스킹 액(Masking fluid)> 보조재 료의 활용방안을 제시하고자 한다. 마스킹액을 이용한 여러 가 지 수채화 채색방법과 보조재료의 활용법을 제안함으로써 현장 에서 갖는 문제점에 대해 논의하고, 더욱 질 높은 미술교육과 작품제작에 이바지할 방안을 새롭게 모색하고자 한다.

PURPOSES : This study aims to perform a quantitative analysis of Forward Collision Warning and crash frequency using heavy vehicle driving data collected in expressway driving environments, and to classify the driving environments where Forward Collision Warnings of heavy vehicles occur for accident-prone areas and analyze their occurrence characteristics. METHODS : A bivariate Gaussian mixture model based on inter-vehicle distance gap and speed-acceleration parameters is used to classify the environment in which Forward Collision Warning occurs for heavy vehicles driving on expressways. For this analysis, Probe Vehicle Data of 80 large trucks collected by C-ITS devices of Korea Expressway Corporation from May to June 2022. Combined with accident information from the past five years, a detailed analysis of the classified driving environments is conducted. RESULTS : The results of the clustering analysis categorizes Forward Collision Warning environments into three groups: Group I (highdensity, high-speed), Group II (high-density, low-speed), and Group III (low-density, high-speed). It reveals a positive correlation between Forward Collision Warning frequency and accident rates at these points, with Group I prevailing. Road characteristics at sites with different accident incidences showed that on-ramps and toll gates had high occurrences of both accidents and warnings. Furthermore, acceleration deviation at high-accident sites was significant across all groups, with variable speed deviations noted for each warning group. CONCLUSIONS : The Forward Collision Warning of heavy vehicles on expressways is classified into three types depending on the driving environment, and the results of these environmental classifications can be used as a basis for building a road environment that reduces the risk of crashes for heavy vehicles.

Particulate matter is known to have adverse effects on health, making it crucial to accurately gauge its concentration levels. While the recent advent of low-cost air sensors has enabled real-time measurement of particulate matter, discrepancies in concentrations can arise depending on the sensor used, the measuring environment, and the manufacturer. In light of this, we aimed to propose a method to calibrate measurements between low-cost air sensor devices. In our study, we introduced decision tree techniques, commonly used in machine learning for classification and regression problems, to categorize particulate matter concentration intervals. For each interval, both univariate and multivariate multiple linear regression analyses were conducted to derive calibration equations. The concentrations of PM10 and PM2.5 measured indoors and outdoors with two types of LCS equipment and the GRIMM 11-A device were compared and analyzed, confirming the necessity for distinguishing between indoor and outdoor spaces and categorizing concentration intervals. Furthermore, the decision tree calibration method showed greater accuracy than traditional methods. On the other hand, during univariate regression analysis, the proportion exceeding a PM2.5/PM10 ratio of 1 was significantly high. However, using multivariate regression analysis, the exceedance rate decreased to 79.1% for IAQ-C7 and 89.3% for PMM-130, demonstrating that calibration through multivariate regression analysis considering both PM10 and PM2.5 is more effective. The results of this study are expected to contribute to the accurate calibration of particulate matter measurements and have showcased the potential for scientifically and rationally calibrating data using machine learning.

본 논문은 인간의 양면성에 대한 탐구를 바탕으로 예술적 언 어의 표현에 대한 연구이다. 철학에서 언급되는 논조의 이해를 바탕으로 회화, 도자 등 다양한 작품을 조사하였다. 이를 통해 각각의 예술가들이 시대별 환경 속에서 양면성에 대해 어떤 차 별적 관점으로 작업을 진행했는지 고찰하였다. 첫째, 동서양의 철학과 심리학의 기본이론과 함께 양면성에 대한 다양한 이해를 문헌 자료를 통해 고찰했다. 모든 사람이 내적, 외적 또는 이성과 감성의 양면성을 가지고 있음을 확인 하고, 인간의 내적 갈등과 복잡성을 탐구했다. 둘째, 미술작품 실례를 조사하고 형태와 특징을 분석했다. 예 술가는 개인의 내면적 감정이나 사회현상에 따른 사람들의 심 리상태를 작품형태로 표현한다. 인간의 양면성을 주제로 한 작 품들은 내면을 표현하고 주목하는 데에 중점을 두었다. 철학 적 이론과 작품 실례를 결합 연구한 후 연구자의 해석을 추가 했다. 셋째, 상이 연구를 바탕으로 철학 이론과 미학적 관점을 결합 하여 추상적인 개념을 도자공예매체로 구상화 및 시각화하였다. 이러한 표현방식을 매개체로 삼아 본인의 창작의도를 분석했 다. 다양한 시각화 과정과 일정한 철학을 가지고 진행하였고, 작품의 미학적 분석을 바탕으로 하여 도자조형을 통한 양면성 에 대한 표현기법 및 내포된 의미를 탐구했다. 사람에 대한 내적 사고와 관심을 불러일으키고, 작업을 통한 감성의 발전을 진전시켰다. 철학과 예술 분야의 논의를 통해 예술을 보다 심미적이고 철학적으로 만들고 현대예술에 새로운 영혼을 불어넣으려 한다.

Tomato is one of the major widely cultivated crops around the world. The leaf area is directly related to the total amount of photosynthesis, which affects the yield and quality of the fruit. Traditional methods of measuring the leaf area are time-consuming and can cause damage to the leaves. To address these problems, various studies are being conducted for measuring the leaf area. In this study, we introduced a model to estimate the leaf area using images of tomatoes. Using images captured by a camera, we measured the leaf length and width and used linear regression analysis to derive the leaf area estimation formula. Furthermore, we used a Neural Network (NN) for additional analysis to compare the accuracy of the models. Initially, to verify the reliability of the image data, we conducted a correlation analysis between the actual measurement data and the image data, which showed a high positive correlation. The leaf area estimation model presented 23 estimation formulas. We used regression analysis to estimate the coefficients of each model and also used employed an artificial neural network analysis to derive high R-squared (R2) values and low Root Mean Square Error (RMSE) values. Among the estimation formulas, the ninth model showed the highest reliability with an R-squared value of 0.863. We conducted a verification experiment to confirm the accuracy of the selected model, and the R-squared value was 0.925. This study confirmed the reliability of data measured from images and the reliability of the leaf area estimation model using image data. These methods are expected to be an important tool in agriculture, using imaging equipment for measuring and monitoring the crop growth.

본고는 표준 중국어 초급 학습자를 대상으로 ‘绕口令’ 자료를 활용하여 제3성과 제 2성에 중점을 둔 성조 교육 방안을 설계하고, 이를 직접 실제 교육 현장에 적용하여 그 효과를 검증하는 것에 그 목적을 두고 있다. 이를 위해 2장에서는 수업 설계 방 안을 제시하였고, 3장에서는 수업의 구체적인 운영 및 결과, 만족도에 대한 분석을 진행하였으며, 마지막 4장에서는 본고의 연구 성과를 종합하였다.

이 연구는 위성사진을 활용하여 건설지점의 기대풍속을 예측하기 위한 인공신경망 방법론을 제안한다. 제안된 방법은 기존 의 엔지니어의 판단을 대체하여, Auto-Encoder를 사용해 지형적 특성을 정량화하고, 이를 바탕으로 대상지점과 유사한 지역의 관측소 풍속 데이터를 선형 조합해 기대 풍속을 예측한다. 또한, 머신러닝과 인공신경망을 활용한 종단간 풍속 예측 모델을 제안하고, 성능을 비교 분석하였다. 그 결과 관측소의 풍속 데이터의 선형 조합보다는 종단간 모델을 구성하는 방법이 더 높은 정확도를 보였으며, 특히 Graph Neural Network (GNN)이 Multi-Layer Perceptron (MLP)에 비해 상당히 우수한 예측 성능을 나타내었다.

PURPOSES : This study is aimed to economic analysis of the ferronickel slag pavement method carried out to suggest the necessity of developing ferronickel slag pavement technology. METHODS : A life cycle cost analysis of the application of the Ferronickel Slag pavement method and the cutting + overlay pavement method was performed to compare the economic indicators and greenhouse gas emissions for each pavement method. RESULTS : As a result of the analysis, regardless of the Ferronickel Slag mixing rate, if the common performance of the Ferronickel Slag pavement method is the same or superior to the existing pavement method, it is more economical than the existing pavement method. Furthermore, the lower the maintenance cost of the Ferronickel Slag pavement method, the higher the economic feasibility due to the high Ferronickel Slag mixing rate. Greenhouse gas emissions can be reduced from at least 9% to up to 53% through the application of the Ferronickel Slag pavement method, except for some scenario analysis results. CONCLUSIONS : This study provided that the Ferronickel Slag pavement method was superior to the existing pavement method in terms of economic and environmental aspects. Therefore, it was found that the objective justification of developing road pavement technology using Ferronickel Slag was secured.

본고는 중국어 학습자를 대상으로 30개의 단어 연상에 대한 설문조사를 실시하여, 20대 대학생의 인지적인 측면의 특징을 살펴보고, 어휘 교육 방법에 적용하고자 하 였다. 사회가 변하면서 일부 단어 연상의 결과가 예전의 인식과 달라지고 있다는 것 을 알 수 있었다. 일부 추상적인 단어에서는 반응어 종류가 매우 다양한 것으로 나 타났다. 특히 형용사 ‘빨갛다’의 경우 예전의 설문조사에서는 출현하지 않았던 단어 들이 많이 출현한 것을 알 수 있었다. 반응어 종류가 가장 많은 것은 품사별로 명사 ‘책’과 ‘영화’, 동사 ‘좋아하다’, 형용사 ‘기쁘다’로 나타났다. 조사 결과를 토대로 적용 할 수 있는 어휘 교육 방법을 제안하였다.

This study aimed to explore nursing students' experience of learning cardiopulmonary resuscitation (CPR) in a web-based virtual simulation (vSim) through analysis of the reflection journals. Method: From June to July 2020, data were collected from 48 fourth-year nursing students who performed the simulation by reviewing prompt feedback on their CPR performance. The contents of the reflection journals were analyzed using NVivo qualitative data analysis software. Results: Nursing students experienced unfamiliarity with the English-based virtual environment as well as psychological pressure and anxiety about emergencies. Incorrect interventions were identified in the following order of frequency: violation of defibrillator guidelines, missing fundamental nursing care, error in applying an electrocardiogram monitor, inadequate initial response to cardiac arrest, insufficient chest compression, and inadequate ventilation. Lastly, the participants learned the importance of embodied knowledge, for knowing and acting accurately and reacting immediately, and their attitudes as nurses, such as responsibility, calmness, and attentiveness. Learning strategies included memory retention through repetition, real-time feedback analysis, pre-learning, and imagining action sequences in advance. The level of achievement, time required, CPR quality, and confidence improved with behavior-modification strategies developed through self-reflection. Conclusion: Educational interventions that are based on understanding accurate algorithms can strengthen selfawareness of mistakes to improve efficient imparting of CPR education.

Purpose: Even today, cancer remains a challenge to overcome. The purpose of this study is to understand the current status of lip-oral-pharyngeal cancer in Koreans by identifying the survival rate of lip-oral-pharyngeal cancer in Koreans through long-term big data. Material and Method: This study utilized 2023 KOSIS (Cancer Registration Statistics, Ministry of Health and Welfare) academically. The 5-year relative survival rates of lip-oral-pharyngeal cancer from 1996 to 2020 were compared and analyzed at 5-year intervals. Results: The 5-year relative survival rate for lip-oral-pharyngeal cancer was 47.4% from 1996 to 2000, 54.5% from 2001 to 2005, 61.1% from 2006 to 2010, 65.5% from 2011 to 2015, and 69.7% from 2016 to 2020. From 1996 to 2005, the 5-year relative survival rate for lip-oral-pharyngeal cancer was higher than the 5-year relative survival rate for all cancers. However, in the recent 15 years from 2006 to 2020, the 5-year relative survival rate for lip-oral-pharyngeal cancer was lower than for all cancers. Conclusions: In conclusion, this long-term big data showed that the 5-year relative survival rate of lip-oral-pharyngeal cancer in Koreans has increased further in modern times. However, in order to increase the overall survival rate of all human cancers, continuous efforts to improve the survival rate of lip-oral-pharyngeal cancer are needed in the future.

PURPOSES : Construction cost estimates are important information for business feasibility analysis in the planning stage of road construction projects. The quality of current construction cost estimates are highly dependent on the expert's personal experience and skills to estimate the arithmetic average construction cost based on past cases, which makes construction cost estimates subjective and unreliable. An objective approach in construction cost estimation shall be developed with the use of machine learning. In this study, past cases of road projects were analyzed and a machine learning model was developed to produce a more accurate and time-efficient construction cost estimate in teh planning stage. METHODS : After conducting case analysis of 100 road construction, a database was constructed including the road construction's details, drawings, and completion reports. To improve the construction cost estimation, Mallow's Cp. BIC, Adjusted R methodology was applied to find the optimal variables. Consequently, a plannigs-stage road construction cost estimation model was developed by applying multiple regression analysis, regression tree, case-based inference model, and artificial neural network (ANN, DNN). RESULTS : The construction cost estimation model showed excellent prediction performance despite an insufficient amount of learning data. Ten cases were randomly selected from the data base and each developed machine learning model was applied to the selected cases to calculate for the error rate, which should be less than 30% to be considered as acceptable according to American Estimating Association. As a result of the analysis, the error rates of all developed machine learning models were found to be acceptable with values rangine from 17.3% to 26.0%. Among the developed models, the ANN model yielded the least error rate. CONCLUSIONS : The results of this study can help raise awareness of the importance of building a systematic database in the construction industry, which is disadvantageous in machine learning and artificial intelligence development. In addition, it is believed that it can provide basic data for research to determine the feasibility of construction projects that require a large budget, such as road projects.

In this study, complaints from visitors in the science museum satisfaction survey were categorized into four primary factors and ten sub-factors. This categorization aimed to provide recommendations for enhancing the Geochang Wolseong Space Creative Science Museum as an illustrative case study. Futhermore, the study examimed the relative importance of each factor by surveying 90 science museum visitors. The following results are obtained. Firstly, it was evident that the Geochang Wolseong Space Creative Science Museum requires urgent improvement. Certainly, in the primary factors, issues related to the utilization environment emerged as the most significant sources of dissatisfaction, while in the sub-factors, the adequacy of fees was identified as the most prominent concern. Secondly, through the result of the IPA (Importance-Performance Analysis) of the complaint factors, four primary issues were identified as top priorities for consideration: fare adequacy, lack of convenient facilities, in sufficient publicity, and a lack of distinctiveness compared to similar facilities. Lastly, when analyzing the relationship between complaints and overall satisfaction, factors related to human service, environment, and educational content had negative effects. In contrast, factors associated with science museum operation exhibited a different trend, suggestinga structural impact relationship with the overall impact of complaints.

This study aims to verify the impact of a metaverse-based safety education program on the fire safety awareness of elementary school students. Utilizing the National Fire Agency's 119 metaverse program, an educational program was implemented for two months, and the experimental group was examined using paired-sample t-tests, while comparisons with the control group were made using independent-sample t-tests. The results revealed statistically significant differences between the experimental and control groups in the area of fire prevention. Although no statistically significant differences were observed over time within each group, the overall average scores for the entire class improved across all areas. Based on these findings, the effectiveness of metaverse-based safety education is suggested; however, a significant difference compared to traditional education was not detected. The study concludes by exploring implications for considerations in the implementation of metaverse safety education in the future.