• Research in Community and Public Health Nursing
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• v.1 no.1
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• pp.172-173
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• 1989

본 연구는 전국의 보건진료원이 하는 업무를 분석하여 우리나라 보건진료원 제도 정착에 도움을 주는 기초자료를 제공하는 연구의 일환으로 강원도 지역에서 실시하였다. 연구대상은 강원도에 있는 보건진료원 전수로 하였고 이중 응답자수는 108명이었다. 연구도구는 문헌과 간호교육자들에 의해 작성 된 구조화된 설문지를 사용하였으며 수집된 자료는 SPSS를 이용하여 빈도, 백분율, 평균 및 피어슨 상관계수를 구하였고 유의성 검정을 위해서 t-test, ANOVA의 통계방법을 사용하였다. 본 연구결과를 통하여 얻은 결론은 다음과 같다. 1) 조사대상자인 보건진료원의 평균 연령이 31.5세이며 과반수의 보건진료원이 30세 미만 이었고 기혼자가 마혼자보다 약간 많았다. 보건진료원의 반수 이상이 현재 가족과 동거하고 있었고 학력은 3년제 간호전문대학 졸업자가, 경력은 3년 미만인 사람이 대부분이었으며 종교를 가진 사람이 대부분 이었으며 종교를 가진 사람이 안가진 사람보다 더 많았다. 또한 보건진료원의 근무지역 조건은 대부분이 을지에서 근무하고 있었고 대부분의 보건진료원이 신축된 보건진료소 시설에서 업무를 수행하고 보건진료소 내의 숙소에서 거주하는 것으로 냐타났다. 2) 보건진료원이 담당하는 평균 주민수는 1,660.8명 이었으며 과반수 정도의 보건진료원이 $501\sim1,000$명이 이상적인 적정 담당 주민수라고 생각하고 있었다. 강원도 주민의 연평균 보건진료소 이용자수는 4,099.3명 이었고 이용 주민수가 5,000명 이상인 보건진료소도 11개소 12.9 %나 되었다. 3) 보건진료소 사업대상지역 내에 있는 보건 의료기관은 약방 및 약종상이 62.1 %로 가장 많이 분포되어 있었고 보건지소도 16 %나 사업대상지역 내에 함께 있는 것으로 나타났다. 지역주민의 보건의료기관 이용은 보건진료소가 59.0 % 로 지역주민이 가장 많이 이용하고 있었고 보건 진료원이 가장 많이 이용하는 의뢰기관은 뱅 의원이 66 %, 보건소가 36.4 %로 나타났다. 또한 보건진료소의 보건의료기관과의 협조관계는 보건소와는 과반수 정도가 잘 협조하고 있다고 응답 한 반면 보건지소와 잘 협조하고 있다고 응답한 율은 37.6 % 밖에 안되었다. 4) 보건진료원이 업무영역 수행 정도를 살펴 보면 5점 만점에 통상질환관리가 3.69점, 사업 운영 관리 및 지도는 3.45점, 모자보건 및 가족계획은 3.28 점, 지역사회 조직 및 개발은 3.27 점, 보건정보체계 개발 및 수집은 3.17 점, 사업 계획 수립은 3.14 점, 지역사회 보건관리는 3.13 점의 순으로 나타났다. 보건진료원의 업무영역을 l 일 8 시간으로 하여 l 주 44 시간을 기준으로 측정하면 통상질환관리 18.56시간, 지역사회 보건관리 5.67 시간, 모자보건 및 가족계획 5.52 시간, 사업 운영관리 및 지도 4.10시간, 지역사회 조직 및 개발 3.05 시간, 보건정보체계 개발 및 수집 2.94 시간, 사업계획 수립 2.89시간의 순으로 나타났다. 5) 보건진료원의 업무영역별 수행 소요시간의 상판판계를 살펴보면 지역사펴 조직 및 개발을 위 해 소요한 시간은 사엽계획 수립 소요시간 및 보건정 보체계 관리 소요시간과 순상관관계를, 사업 계획 수립 소요시간은 지역사회 보건관리, 모자보건 및 가족계획 관리 소요시간 및 보건정보체제 관리 소요시간과 순상관관계를 나타냈다. 또한 통상질환관리 소요시간은 지역 사회 조직 및 개발, 사업계획 수립, 지역사회 보건관리와 모자보건 및 가족계획 관리, 사업운영 관리 및 지도, 보건정보체계 관리 소요시간과 역상관관계를 나타내었다. 6) 보건진료원의 총 업무수행 정도를 잘펴보면 업무수행 점수의 평균은 87.5점이었으며 보건진료원의 근무지가 병지이고 보건진료소의 시설상태가 나쁜 경우 업무수행 점수가 높은 것으로 나타났으며 업무수행 정도와는 별 차이가 없었다.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.7
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• pp.1003-1007
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• 2013

In this study, the antibacterial activities of selected barleys (UB, unhulled barley; PB, pearl barley; and NB, naked barley) and wheat (WG, wheat with germ and endosperm) extracts were evaluated against the food-borne pathogens Staphylococcus aureus KCTC 1927, Escherichia coli KCTC 2593, Salmonella Typhimurium KCTC 2054, and Bacillus cereus KCTC 1014. The amount of the antibacterial biomarker, 2,6-dimethoxy-1,4-benzoquinone (DMBQ), present in selected barleys and wheat, was measured by HPLC. Furthermore, antioxidant activity of samples was determined using the oxygen radical absorbance capacity (ORAC) assay. WG ($22.35{\pm}0.04mm$) was found to be highly inhibitory to Staphylococcus aureus followed by UB ($17.91{\pm}0.10mm$), PB ($16.87{\pm}0.05mm$), and NB ($15.69{\pm}0.20mm$). The antibacterial activity of the selected grains was correlated with antioxidant activities and the amount of DMBQ (Pearson's correlation coefficient, 0.7831). The antioxidant activity of the selected grains was also correlated with the total phenolic content (Pearson's correlation coefficient, 0.9934). WG extract showed significantly higher antibacterial activity, compared with barley extracts such as UB, PB, and NB. The results of this study suggest that barley has a potential in the development of natural antimicrobials and food preservatives for controlling food-borne pathogens.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.223-232
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• 2016

Drought is one of important environmental stress for plant. Drought has deleterious effect to plant growth including maize (Zea mays L.) such as vegetative and/or reproductive growth, root extension, photosynthesis efficiency, flowering, anthesis-silking interval (ASI), fertilization, and grain filling. In this study, we screened drought tolerant maize in 21 cultivars from different sources, sixteen NAM parent lines (B73, CML103, CML228, CML247, CML277, CML322, CML333, CML69, Ki11, Ki3, Ky21, M37W, Mo18w, NC350, Oh43 and Tx303), four Korean hybrids (Cheongdaok, Gangdaok, Kwangpyeongok and Pyeonganok) and one Southeast Asian genotype (DK9955). Drought stress (DS) index was evaluated with leaf rolling score at seedling stage and ASI at silking date. The leaf rolling scoring of CML228, DK9955 and Ki11 were determined 1.28, 1.85, 1.86, respectively. However, M37W, Kwangpyeongok, B73 and NC350 were determined over the 3. ASI analysis revealed that CML228, CML103, Cheongdaok, NC350, B73, CML322, Kwangpyeongok and Ki11 are represented less than 5 days under DS and less than 3 days of difference between DS and well-watered (WW), but CML69, Ki3, Pyeonganok, M37W, Mo18w and Gangdaok were represented more than 10 days under DS and more than 8 days of difference between DS and WW. Multi-Dimensional Scaling (MDS) analysis determined CML228, Ki11, and CML322 were regarded as drought tolerance cultivars. Eventually, Ki11 showed genetic similarity with Korean cultivars by QTL analysis and MDS analysis. Ki11 has a potential for development of drought tolerance maize with Korean cultivars.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.139-148
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• 2020

Infectious diseases have long plagued mankind, and predicting and preventing them has been a big challenge for mankind. For this reasen, various studies have been conducted so far to predict infectious diseases. Most of the early studies relied on epidemiological data from the Centers for Disease Control and Prevention (CDC), and the problem was that the data provided by the CDC was updated only once a week, making it difficult to predict the number of real-time disease outbreaks. However, with the emergence of various Internet media due to the recent development of IT technology, studies have been conducted to predict the occurrence of infectious diseases through web data, and most of the studies we have researched have been using single Web data to predict diseases. However, disease forecasting through a single Web data has the disadvantage of having difficulty collecting large amounts of learning data and making accurate predictions through models for recent outbreaks such as "COVID-19". Thus, we would like to demonstrate through experiments that models that use multiple Web data to predict the occurrence of infectious diseases through LSTM models are more accurate than those that use single Web data and suggest models suitable for predicting infectious diseases. In this experiment, we predicted the occurrence of "Malaria" and "Epidemic-parotitis" using a single web data model and the model we propose. A total of 104 weeks of NEWS, SNS, and search query data were collected, of which 75 weeks were used as learning data and 29 weeks were used as verification data. In the experiment we predicted verification data using our proposed model and single web data, Pearson correlation coefficient for the predicted results of our proposed model showed the highest similarity at 0.94, 0.86, and RMSE was also the lowest at 0.19, 0.07.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.393-404
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• 2022

Reliable terrestrial rainfall observations from satellites at finer spatial resolution are essential for urban hydrological and microscale agricultural demands. Although various traditional "top-down" approach-based satellite rainfall products were widely used, they are limited in spatial resolution. This study aims to assess the potential of a novel "bottom-up" approach for rainfall estimation, the parameterized SM2RAIN model, applied to the C-band SAR Sentinel-1 satellite data (SM2RAIN-S1), to generate high-spatial-resolution terrestrial rainfall estimates (0.01° grid/6-day) over Central South Korea. Its performance was evaluated for both spatial and temporal variability using the respective rainfall data from a conventional reanalysis product and rain gauge network for a 1-year period over two different sub-regions in Central South Korea-the mixed forest-dominated, middle sub-region and cropland-dominated, west coast sub-region. Evaluation results indicated that the SM2RAIN-S1 product can capture general rainfall patterns in Central South Korea, and hold potential for high-spatial-resolution rainfall measurement over the local scale with different land covers, while less biased rainfall estimates against rain gauge observations were provided. Moreover, the SM2RAIN-S1 rainfall product was better in mixed forests considering the Pearson's correlation coefficient (R = 0.69), implying the suitability of 6-day SM2RAIN-S1 data in capturing the temporal dynamics of soil moisture and rainfall in mixed forests. However, in terms of RMSE and Bias, better performance was obtained with the SM2RAIN-S1 rainfall product over croplands rather than mixed forests, indicating that larger errors induced by high evapotranspiration losses (especially in mixed forests) need to be included in further improvement of the SM2RAIN.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.