This study presents the data analysis results of groundwater chemistry and the occurrence of fluoride in groundwater obtained from the groundwater quality monitoring network of Korea. The groundwater data were collected from the National Groundwater Information Center and censored for erratic values and charge balance (±10%). From the geochemical graphs and various ionic ratios, it was observed that the Ca-HCO3 type was predominant in Korean groundwater. In addition, water-rock interaction was identified as a key chemical process controlling groundwater chemistry, while precipitation and evaporation were found to be less important. According to a non-parametric trend test, at p=0.05, the concentration of fluoride in groundwater did not increase significantly and only 4.3% of the total groundwater exceeded the Korean drinking water standard of 1.5 mg/L. However, student t-tests revealed that the fluoride concentrations were closely associated with the lithologies of tuff, granite porphyry, and metamorphic rocks showing distinctively high levels. This study enhances our understanding of groundwater chemical composition and major controlling factors of fluoride occurrence and distribution in Korean groundwater.

Deep convolutional network is a deep learning approach to optimize image recognition. This study aimed to apply DCNN to the reading of mandibular cortical thinning in digital panoramic radiographs. Digital panoramic radiographs of 1,268 female dental patients (age 45.2 ± 21.1yrs) were used in the reading of the mandibular cortical bone by two maxillofacial radiologists. Among the subjects, 535 normal subject’s panoramic radiographs (age 28.6 ±7.4 yrs) and 533 those of osteoporosis pationts (age 72.1 ± 8.7 yrs) with mandibular cortical thinning were used for training DCNN. In the testing of mandibular cortical thinning, 100 panoramic radiographs of normal subjects (age 26.6 ± 4.5 yrs) and 100 mandibular cortical thinning (age 72.5 ± 7.2 yrs) were used. The sensitive area of DCNN to mandibular cortical thinning was investigated by occluding analysis. The readings of DCNN were compared by two maxillofacial radiologists. DCNN showed 97.5% accuracy, 96% sensitivity, and 99% specificity in reading mandibular cortical thinning. DCNN was sensitively responded on the cancellous and cortical bone of the mandibular inferior area. DCNN was effective in diagnosing mandibular cortical thinning.

Scientific and technological performances (e.g., patents and publications) made through R&D play a pivotal role for national economic growth. National governments encourage academia-industry cooperation and thereby pursue continuous development of science technology and innovation. Increasing R&D-related investments and manpower are crucial for national industrial development, but evidence of poor performance in business performance, efficiency, and effectiveness has recently been found in Korea. This study evaluates performance efficiency of the 6T sector (Information Technology, Bio Technology, Nano Technology, Space Technology, Environment Technology, Culture Technology), which is considered a high-potential promising industry for the next generation growth and currently occupies two thirds of the national R&D projects. The study measures the relative efficiency of R&D in a comparative perspective by employing the Data Envelopment Analysis (DEA) method. The result reveals overall low efficiency in basic R&D (0.2112), applied R&D (0.2083), development R&D (0.2638), and others (0.0641), confirming that economic performance and efficiency were relatively poor compared to production efficiency. Efficient R&D needs policy makers to create strategies that can increase overall efficiency by improving productivity performance and quality while increasing economic performance.

As a new entertainment and social way, online games now have a huge and increasing user group, so it is of great significance to identify the data stream of online games. Using the excellent nonlinear fitting ability of BP neural network and the advantages of global search of genetic algorithm, the initial weights and thresholds of BP neural network are optimized, and the BP neural network model optimized by genetic algorithm is established. The muti-dimensional input information is proposed to identify online game data streams. Through the experimental simulation, it shows that the selected muti-dimensional information and the established model can be well applied to online game stream recognition.

Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

PURPOSES : This study aims to investigate the factors that affect the practical condition of the convergence and convergence behaviors of the asymmetric transportation equilibrium problem (ATEP). METHODS : To achieve this objective, a real network experiment is critical because the crux of the problem associated with the ATEP is the difficulty of verifying the unique condition in real networks owing to asymmetric modeling. The study employed a numerical approach to deal with this problem because analytical derivation based on small networks has a limitation in extending its findings to practical applications. The study addressed the problems using large real networks and different types of interactions, including links and modes. An investigation of the factors that have the potential to affect the convergence of the problem was conducted with the solution algorithm, which is the double projection method. RESULTS : The study presented a partial answer to the question of whether the ATEP's convergence condition is too strong. In link interactions, demand intensities and symmetric features within the cost function along with the network configuration were determined to relax the convergence condition. In mode interactions, the degree of overlap of the route composition and the controlled asymmetric interactions in the cost function were determined to affect the convergence condition. CONCLUSIONS : The results suggested that the modeling of link interactions for a more complicated transportation system design enabled the modeling of complex asymmetric interactions as long as the demand intensity of the network was not strong. In the case of mode interactions, whereas it was not possible to control the route composition, it was considered possible to a degree where the use of a distinct route was observed for each class (for example, designated truck lanes).

본 연구는 SNS 중독 경향성 및 대인관계가 간호대학생의 대학생활적응에 미치는 영향을 파악하기 위한 서술적 조사연구이다. 본 연구는 2019년 10월 01일부터 10월 04일까지 G시 1∼3학년의 간호대학 재학생 228명을 대상으로 실시되었다. 결과분석은 기술통계, ANOVA, Pearson correlation coefficients, Multiple regression을 이용하였다. 연구결과, 대학생활적응은 성별(t=5.26, p<.001), SNS 일 평균 사용시간(F=8.07, p<.001), 현실에서의 교류 친구 수(F=2.87, p=.037)와 유의한 차이가 있었다. 변수 간의 상관관계로 대학생활적응은 SNS 중독 경향성(r=-.31, p<.001) 및 대인관계(r=.52, p<.001) 간에 유의한 상관관계가 있었으며, SNS 중독 경향성과 대인관계에서도 유의한 상관관계가 있었다(r=-.17, p=.011). 대학생활적응에 영향을 미치는 요인으로는 대인관계(β=.477, p<.001), 성별(β=-.198, p=.001), SNS 일 평균 사용시간(β=-.177, p=.003)으로 나타났으며 설명력은 37.8%였다. 본 연구결과를 토대로 간호대학생의 대학생활적응을 향상시키기 위해 긍정적인 SNS를 활용한 긍정적인 대인관계 형성을 위한 프로그램 개발이 필요하리라 사료된다.

An implementation of OSEK NM, a network management protocol for automotive embedded systems, has been defined. In this experiment, the NM ID of each ECU of the vehicle is reverse-engineered when the vehicle is parked with a discharged battery, and the power management status and error status of each ECU and node are identified through NM ID OPCODE analysis, Sleep status, and Wake-up status, LIMP HOME status, were analyzed. After analyzing the NM OPCODE to find the faulty ECU, remove the cause of the failure and analyze the sleep mode entry state.In order to prevent the vehicle battery from being discharged, additional research is needed on how to enter the forced sleep mode when a failure not related to vehicle security occurs.

이 연구는 W. B. 예이츠에 대한 연구 동향을 살펴보고 그에 대한 연구 방향을 설정하는데 필요한 정보를 제공하는 데 목적이 있다. 2021년 8월 KCI에서 ‘yeats’를 키워드로 910편의 논문이 검색되었고 최종 854개 논문의 제목을 연구대상으 로 선정하였다. Excel, TEXTOM 및 UCINET/NetDraw 프로그램을 활용하여 분석을 실시하였으며 예이츠 관련 연구의 연도별 및 학술지별 게재 현황, 예이츠 관련 연구의 주요 키워드, 주요 키워드 간의 연결중심성과 네트워크 분석을 시행하였다.

This study aimed to explore research trends of nursing ethics in Korea applying text network analysis and topic modelling. 306 articles published in KCI journals from 1998 to 2021 were identified and 516 author-provided keywords were collected. A co-occurrence matrix with 123 keywords, which appeared at least in two articles, were developed based on the Jaccard coefficient. Degree centrality and betweenness centrality were calculated and LDA topic modelling were performed using NetMiner software. The largest number of the articles (70, 23%) were published in Korean Journal of Medical Ethics. The most critical core-keywords, defined as the top 30 keywords in degree centrality and betweenness centrality, were ‘nursing students’ and ‘moral sensitivity’. The other core-keywords included ‘attitude,’ ‘awareness,’ ‘professionalism,’ ‘knowledge,’ and ‘critical thinking.’ related to ethical competence, ‘death,’ ‘hospice,’ ‘euthanasia,’ and ‘research ethics’ related to bioethical issues, and ‘job satisfaction,’ ‘burn out,’ ‘stress,’ ‘organizational culture,’ ‘ethical leadership,’ and ‘ethical climate’ related to organization and leadership. Five topics were identified and named as a) bioethics education for nursing students, b) knowledge and attitudes for bioethical issues, c) awareness and values of bioethics, d) ethical conflicts of RNs, and e) nursing ethics education. This study found that bioethics was the main topics in Korean nursing ethics research and suggested nursing research should focus on ethical issues RNs frequently experience in patient care. Also, research gaps were inferred in multiple topics including nurse-to-nurse relationships, theoretical perspectives of virtue ethics and care ethics, or witnessing healthcare professionals’ unethical behavior.

Even in an era where 8-meter class telescopes are common, small telescopes are considered very valuable research facilities since they are available for rapid follow-up or long term monitoring observations. To maximize the usefulness of small telescopes in Korea, we established the SomangNet, a network of 0.4{1.0 m class optical telescopes operated by Korean institutions, in 2020. Here, we give an overview of the project, describing the current participating telescopes, its scientic scope and operation mode, and the prospects for future activities. SomangNet currently includes 10 telescopes that are located in Australia, USA, and Chile as well as in Korea. The operation of many of these telescopes currently relies on operators, and we plan to upgrade them for remote or robotic operation. The latest SomangNet science projects include monitoring and follow-up observational studies of galaxies, supernovae, active galactic nuclei, symbiotic stars, solar system objects, neutrino/gravitational-wave sources, and exoplanets.

자기공명영상은 고해상도의 연부조직에 대한 영상정보를 제공하며, 뇌종양 등 연부조직 진단에 활용된다. 본 연구는 합성곱신경망 인공지능을 통해 뇌종양 자기공명영상 분류성능을 확인해 보고자 한다. 4개 종류로 구분된 3264 장의 MRI 데이터 세트(data set)를 이용하였으며, 인공지능 학습을 위해 훈련용 데이터와 시험용 데이터를 9 : 1, 훈련용 데이터의 10%를 검증용 데이터로 구분하였다. 합성곱신경망은 기본 CNN과 VGG16으로 구성하였으며, 학습 평가는 정확도와 손실율로 확인하였으며, 생성된 모델을 통해 분류성능 정확도를 확인하였다. 실험 결과 과적합은 없었으며, 분류성능은 기본 CNN과 VGG16 각각 67%와 80%의 분류성능을 보였다. 도출된 뇌종양 자기공명영상 분류 결과를 통해 자기공명영상과 인공지능 접목에 관한 기초 자료로 사용될 수 있을 것이라 사료된다.

국내 멸종위기 어류 25종의 먹이원을 문헌 조사한 결과, 먹이원은 20문, 31강, 58목, 116과, 154속으로 나타났다. 먹이원 중 가장 많은 어류가 섭식한 먹이원은 분류군에 따라 절지동물문, 곤충강, 파리목, 깔따구과로 조사되었으며, 식물류 먹이원은 돌말문, 윷돌말강, 반달돌말목, 반달돌말과로 조사되었다. 계층적 군집분석과 NMDS를 이용하여 멸종위기 어류 20종의 먹이원 유형화 결과, 어류를 주로 포식하는 충식성 어류와 식물플랑크톤을 섭식하는 어류 2가지 유형으로 나타났다. 네트워크 분석의 허브 점수가 높은 먹이원은 파리목, 하루살이목, 날도래목, 강도래목, 딱정벌 래목으로 나타났으며 식물류 먹이원 중 허브 점수가 높은 쪽배돌말목과 반달돌말목, 김발돌말목으로 조사되었다. 먹이원 폭이 큰 어류는 연준모치 (PP)와 열목어, 좀수수치, 가는돌고기, 꼬치동자개, 퉁사리, 묵잡자루, 미호종개로 Bi 지수 값이 0.3 이상으로 조사되어 다양한 먹이를 먹는 것으 로 조사되었다. 반면, 금강모치, 부안종개, 감돌고기, 흰수마자, 다묵장어, 돌상어, 얼룩새코미꾸리, 북방종개는 Bi 지수 값이 0.1 이하로 조사되어 먹이원 다양성이 낮게 조사되었다.

목적 : 본 연구는 대한작업치료학회지에 게재된 연구의 키워드를 분석대상으로 그 관계를 분석하여 작업치료 분야의 연구동향을 알아보는 데 목적이 있다. 연구방법 : 분석 대상은 대한작업치료학회지 1993년 창간호부터 2019년까지 게재된 총 721편의 논문이 며, 논문의 키워드를 분석 단위로 키워드 네트워크 분석을 실시하였고, 네트워크 분포의 지표로 밀도, 포괄성, 고립정도를 분석하였고, 중심성 지표로 연결 중심성, 매개 중심성, 위세 중심성을 분석하였다. 분석 기간은 1단계(1993-1999), 2단계(2000-2009), 3단계(2010-2019)로 10년 단위로 구분하여 분석하였다. 결과 : 분석 결과 중복 포함된 키워드는 총 2,835개, 독립적 개별 키워드는 총 1,453개로 나타났다. 네트 워크 분포 분석결과 밀도는 .182, 포괄성은 100%로 나타났으며, 고립된 노드는 나타나지 않았다. 중심 성 분석결과 연결 중심성이 높은 상위 키워드는 1단계 뇌졸중, 일상생활활동, 손기능, 뇌성마비, FIM, 2 단계 뇌졸중, 일상생활활동, 작업치료, 상지기능, 강제유도운동치료, 3단계 작업치료, 뇌졸중, 체계적 고 찰, 참여, 노인 순으로 나타났다. 매개 중심성이 높은 상위 키워드는 1단계 뇌졸중, 손기능, 스트레스, 뇌 성마비, 발달장애, 2단계 뇌졸중, 일상생활활동, 작업치료, 치매, 뇌성마비, 3단계 작업치료, 뇌졸중, 체계적 고찰, 인지기능, 참여 순서로 나타났다. 위세 중심성이 높은 상위 키워드는 1단계 그룹치료, 양육자. 정신사회적발달, 애착, 반응성 애착장애, 2단계 신뢰도, 타당도, 평가, 뇌손상, 뇌졸중, 3단계 신뢰도, 타당도, 참여, 우울, 인지기능 순으로 나타났다. 결론 : 본 연구는 키워드 네트워크 분석을 활용하여 작업치료 분야의 연구동향을 전체적으로 파악하였다는 것에 학문적 의의를 지니며, 이를 통해 작업치료 분야에 대한 이해와 연구 동향과 주제를 파악하는 기초 자료로 활용할 수 있을 것이다.