• ALGAE
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• v.18 no.1
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• pp.49-58
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• 2003

The short-term dynamics of the summer phytoplankton community structure were investigated in coastal waters around Namhae Island, the Southern Sea of Korea. The study was based on a comprehensive survey constituting 39 collections from 13 stations on July 18-22, August 1-2, 14-16 and 27-30, respectively. The community structure was analysed using cluster analysis and important environmental correlates of the assemblage structure were identified with canonical correspondence analysis (CCA). Water temperature, salinity, NO₂, NO₃, NH₄, PO₄, chlorophyll a and transparency were measured as physico-chemical environmental factors which may be associated with the phytoplankton community structure. Variations of salinity and concentrations of NO₃ and chlorophyll a were not significant. In addition to warmer water temperature, concentrations of NO₂, NO₄and PO₄ decreased at the beginning of August. And transparency was deeper and water column became very unstable after the middle of August. A wide taxonomic diversity was encountered during the survey, including a total of 121 taxa which was composed of 72 diatoms, 48 dinoflagellates and 1 euglenoid species. Cluster analysis showed that the Phytoplankton community could be divided into 4 distinct groups, indicating rapid changes of the community in the short course of this survey. These phytoplankton groups also showed distinctive dispersion patterns in 2-dimensional canonical space, indicating distinct groupings for stations at each survey. Dominant taxa of diatoms (Chaetoceros curvisetus, Chaetoceros spp., Leptocylindrus danicus, Leptocylindrus mediteraneus, Skeletonema costanum, and Pseudo-nitzschia pungen) clustered in region of CCA space corresponding to stations surveyed at the middle of July. Dominant taxa of dinoflagellates were tightly associated with stations surveyed at the middle (Karenia breve) and end (Cochlodinium polykrikoides and Polykrikos schwartzii) of August. The CCA also showed that the phytoplankton community compositions were highly associated with water temperature, transparency, NO₂, NH₄ and PO₄, suggesting that gradients in physical and nutrient conditions affect short-term changes in phytoplankton composition.

• Tuberculosis and Respiratory Diseases
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• v.65 no.2
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• pp.125-130
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• 2008

Endobronchial actinomycosis is a rare chronic suppurative granulomatous infection of the bronchus, and this is sometimes related with a foreign body or a broncholith. The traditional treatment of endobroncial actinomycosis is intravenous antibiotics for 2 to 6 weeks and then oral antibiotics therapy for 6 to 12 months. We report here on 2 cases of endobronchial actinomycosis that were associated with a broncholith and a foreign body, respectively. Surgery followed by short term antibiotics therapy for only 20 days and 34 days, respectively, was effective as treatment for the endobronchial actinomycosis in our cases. After treatment, there were no complications or recurrence during the following period. We suggest that short term antibiotics therapy combined with a surgical operation might be effective as treatment for primary endobronchial actinomycosis, and especially when this illness is combined with a foreign body or a broncholith, as compared with traditional long term antibiotic therapy.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.4
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• pp.878-890
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• 2013

The purpose of this study is to apply the PEOE class model that can enhance students' scientific creative problem-solving ability and self-directed learning ability in the middle school science subject and analyze the effects of it on students' long- and short-term retention, scientific creative problem-solving ability, and self-directed learning characteristics. And the paper has gained the following results: First, according to the result of analysis through the pre-test, post-test, and delay test to examine the effects of PEOE-based class on learners' long- and short-term retention, it is found to be statistically significant in the significant level of .05. In other words, the class using PEOE influences learners' short-term retention significantly, but it is even more effective in transmitting the concept that students acquire into their long-term memory. Second, according to the result of analysis through the pre-test and post-test to examine the effects of PEOE-based class on learners' scientific creative problem-solving ability, it is found to be statistically significant in the significant level of .05 in general. However, 'elaboration' and 'originality', the subfactors of scientific creative problem-solving ability, do not indicate significant effects. Third, according to the result of analysis through the pre-test and post-test to examine the effects of PEOE-based class on learners' self-directed learning characteristics, it is found to be statistically significant in the significant level of .05 as a whole. However, 'openness' and 'future-oriented self-understanding', the subfactors of self-directed learning characteristics, do not exert significant effects. Based on the above study results, it can be concluded that PEOE-based class is more effective for learners' academic achievement in science, scientific creative problem-solving ability, and self-directed learning characteristics than lecture-method instruction regarding the middle school science unit of 'The Properties of Air and Weather Change'.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.25 no.4
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• pp.196-202
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• 2014

Objectives : The purpose of this study is to evaluate the preliminary effects of school-based short term mental health awareness and school bullying prevention programs. Methods : From April to December 2013, the Child and Adolescent Mental Health Promotion team of Seoul National Hospital conducted school-based short term social skill training (N=56, 4 sessions), mental health awareness (N=84, 4 sessions) and school bullying prevention programs (N=171, 1 session) in elementary and junior high schools located in Seoul. The changes in the subjects before and after the program were assessed. Results : The social skills training program improved the 'helping' behaviors in boys (t=-2.355, p<.05) and 'sharing' (t=-3.223, p<.01), 'cooperation' (t=-2.235, p<.05), and 'comforting' (t=-2.830, p<.01) behaviors in girls, assessed using the Prosocial Behavior Questionnaire. The mental health awareness program improved 'general health awareness' (t=2.620, p<.05), measured using the Korean General Health Questionnaire. The school bullying prevention program resulted in decreased 'self esteem' (t=3.769, p<.01), measured using the Self Esteem Scale and decreased 'anger' (t=4.198, p<.01), assessed using the Novaco Anger Scale. Conclusion : The results of our preliminary analysis suggest that school-based mental health awareness and school bullying prevention programs may be effective even when conducted for a short term. Future investigation is necessary in order to validate the long term effects of these programs.

• Journal of Advanced Navigation Technology
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• v.23 no.6
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• pp.561-569
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• 2019

Solar photovoltaic can provide electrical energy with only radiation, and its use is expanding rapidly as a new energy source. This study predicts the short and long-term PV power generation using actual converter output data of photovoltaic system. The prediction algorithm uses multiple linear regression, support vector machine (SVM), and deep learning such as deep neural network (DNN) and long short-term memory (LSTM). In addition, three models are used according to the input and output structure of the weather element. Long-term forecasts are made monthly, seasonally and annually, and short-term forecasts are made for 7 days. As a result, the deep learning network is better in prediction accuracy than multiple linear regression and SVM. In addition, LSTM, which is a better model for time series prediction than DNN, is somewhat superior in terms of prediction accuracy. The experiment results according to the input and output structure appear Model 2 has less error than Model 1, and Model 3 has less error than Model 2.

• Health Policy and Management
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• v.12 no.4
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• pp.1-17
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• 2002

This study was attempted to identify the liquidity trends and determinants of private hospitals in Korea different. Data used in this study were collected from 98 hospitals with complete general data of present conditions as well as financial statements(balance sheets, income statements). They were chosen from hospitals that passed the standardization audit undertaken by the Korean Hospital Association from 1996 to 2000 for the purpose of accrediting training hospitals. The dependent variables in this study were used current ration and quick ratio as a proxy indicator for liquidity. The independent variables were ownership type, hospital type, location, bed size, period of establishment, short-term liabilities to total assets, long-term liabilities to total assets, borrowings to total assets, fixed asset ration, net profit to total assets, operating margin to gross revenue, growth rate of net worth to total assets, total asset turnover, and business risk(volatility of profit). The major findings of this study were as follows. Trends of liquidity(current ratio, quick ratio) had been continuously decreased. Especially, There were very distinct decreasing trends of personal hospitals and less than 300beds, which weakened liquidity. The factors had significant effect on current ratio were short-term debt to total assets(-), fixed asset ratio(-), business risk(+). High short-term debt to total assets, high fixed asset ratio and high business risk significantly decreased in liquidity. The factors that significantly affected on quick ratio were short-term debt to total assets(-), borrowings to total assets(+), fixed asset ratio(-), business risk(+).

• Journal of Digital Convergence
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• v.12 no.3
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• pp.211-218
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• 2014

Recently, wind energy is expanding to combination of computing to forecast of wind power generation as well as intelligent of wind powerturbine. Wind power is rise and fall depending on weather conditions and difficult to predict the output for efficient power production. Wind power is need to reliably linked technology in order to efficient power generation. In this paper, distributed power generation forecasts to enhance the predicted and actual power generation in order to minimize the difference between the power of distributed power short-term prediction model is designed. The proposed model for prediction of short-term combining the physical models and statistical models were produced in a physical model of the predicted value predicted by the lattice points within the branch prediction to extract the value of a physical model by applying the estimated value of a statistical model for estimating power generation final gas phase produces a predicted value. Also, the proposed model in real-time National Weather Service forecast for medium-term and real-time observations used as input data to perform the short-term prediction models.

• Journal of Korean Society for Quality Management
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• v.47 no.3
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• Journal of Animal Reproduction and Biotechnology
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• v.38 no.3
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• pp.177-187
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• 2023

Background: The grading of Hanwoo (Korean native cattle) is based on four economic traits, and efforts have been continuously made to improve the genetic traits associated with these traits. There is a technology to predict the expected grade based on the 4 economic genetic SNP characteristics of Korean cattle calves using single nucleotide polymorphism (SNP) technology. Selection of highly proliferative, self-renewing, and differentiating satellite cells from cattle is a key technology in the cultured meat industry. Methods: We selected the Hanwoo with high and low-scored of genomic estimated breeding value (GEBV) by using the Hanwoo 50K SNP bead chip. We then isolated the bovine satellite cells from the chuck mass. We then conducted comparative analyses of cell proliferation, immunocytochemistry, qRT-PCR at short- and long-term culture. We also analyzed the differentiation capability at short term culture. Results: Our result showed that the proliferation was significantly high at High scored GEBV (Hs-GEBV) compared to Low scored GEBV (Ls-GEBV) at short- and long-term culture. The expression levels of Pax3 were significantly higher in Hs-GEBV bovine satellite cells at long-term culture. However, there were no significant differences in the expression levels of Pax7 between Hs- and Ls-GEBV bovine satellite cells at short- and long- term culture. The expression levels of MyoG and MyHC were significantly high at Ls-GEBV bovine satellite cells. Conclusions: Our results indicated that selection of bovine satellite cells by Hanwoo 50K SNP bead chip could be effective selection methods for massive producing of satellite cells.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.76-83
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• 2020

Postal logistics organizations are characterized as having high labor intensity and short response times. These characteristics, along with rapid change in mail volume, make load scheduling a fundamental concern. Load analysis of major postal infrastructures such as post offices, sorting centers, exchange centers, and delivery stations is required for optimal postal logistics operation. In particular, the performance of mail traffic forecasting is essential for optimizing the resource operation by accurate load analysis. This paper addresses a traffic forecast problem of postal parcel that arises at delivery stations of Korea Post. The main purpose of this paper is to describe a method for predicting short-term traffic of postal parcel based on self-similarity analysis and to introduce an application of the traffic prediction model to postal logistics system. The proposed scheme develops multiple regression models by the clusters resulted from feature engineering and individual models for delivery stations to reinforce prediction accuracy. The experiment with data supplied by main postal delivery stations shows the advantage in terms of prediction performance. Comparing with other technique, experimental results show that the proposed method improves the accuracy up to 45.8%.