• Journal of the Korea Fashion and Costume Design Association
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• v.19 no.2
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• pp.129-144
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• 2017

Throughout the rich human history, patterns have developed as a symbolic sign and representation of the inner psychology of human beings. Thanks to its intrinsic beauty and emotional richness, the flower has been utilized as a one of the major materials for patterns used in everyday life and art. As a product of nature, floral patterns have played a key role in fashion trends as a Surface Design with other elements of fashion design such as silhouette, fabric and color. Therefore, this research sought to identify the trends of floral patterns of women's garments that appeared at the four major global fashion collections (Paris, Milano, New York and London), and to analyze how importantly the fashion magazines' prediction were applied in the actual collections. Furthermore, the research aimed to suggest possible methods to utilize trend magazines for collections in the future. As a main research method, the authors investigated professional fashion literature and internet websites to extract a total of 4,681 items presented by sixteen designers who participated in the four major global fashion collections each time during the period of the 2012 S/S~2017 S/S seasons. First View Korea and Samsung Design Net were used as major sources for the pattern extraction and analysis. According to the analysis, floral patterns account for 31%(1,454 items) among the total number of patterns appearing in the four major global fashion collections(4,681 items). For the reflection ratio, Samsung Design Net recorded a 4% higher ratio(52%) than First View Korea(48%). Based on the data and analysis of this research, the authors expect that floral patterns in various forms will be continuously presented in fashion collections, and conclude that utilizing fashion magazines is highly useful due to their appropriate predictions.

• The Journal of Korean Physical Therapy
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• v.25 no.5
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• pp.330-336
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• 2013

Purpose: Respiratory is an essential vital component for conservation of life in human, which is controlled by respiratory muscles and its related neuromuscular regulation. The purpose of this study is to assess lung capacity and respiratory pressure in healthy children, and to investigate relationship and predictability between respiratory pressure and other related respiratory functions. Methods: A total of 31 healthy children were recruited for this study. Demographic information and respiratory related factors were assessed in terms of body surface area (BSA), chest mobility, lung capacity, and respiratory pressure. Correlation between respiratory pressure and the rested variables was analyzed, and multiple regression using the stepwise method was performed for prediction of respiratory muscle strength, in terms of respiratory pressure as the dependent variable, and demographic and other respiratory variables as the independent variable. Results: According to the results of correlation analysis, respiratory pressure showed significant correlation with age (r=0.62, p<0.01), BSA (r=0.80, p<0.01), FVC (r=0.80, p<0.01), and FEV1 (r=0.70, p<0.01). In results of multiple regression analysis using the backward elimination method, BSA and FVC were included as significant factors of the predictable statistical model. The statistical model showed a significant explanation power of 71.8%. Conclusion: These findings suggest that respiratory pressure could be a valuable measurement tool for evaluation of respiratory function, because of significant relationship with physical characteristics and lung capacity, and that BSA and FVC could be possible predictable factors to explain the degree of respiratory pressure. These findings will provide useful information for clinical assessment and treatment in healthy children as well as those with pulmonary disease.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.7
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• pp.915-921
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• 2006

Various random regression models with different order of Legendre polynomials for permanent environmental and genetic effects were constructed to predict future milk yield of Holstein cows in Korea. A total of 257,908 test-day (TD) milk yield records from a total of 28,135 cows belonging to 1,090 herds were considered for estimating (co)variance of the random covariate coefficients using an expectation-maximization REML algorithm in an animal mixed model. The variances did not change much between the models, having different order of Legendre polynomial, but a decreasing trend was observed with increase in the order of Legendre polynomial in the model. The R-squared value of the model increased and the residual variance reduced with the increase in order of Legendre polynomial in the model. Therefore, a model with $5^{th}$ order of Legendre polynomial was considered for predicting future milk yield. For predicting the future milk yield of cows, 132,771 TD records from 28,135 cows were randomly selected from the above data by way of preceding partial TD record, and then future milk yields were estimated using incomplete records from each cow randomly retained. Results suggested that we could predict the next four months milk yield with an error deviation of 4 kg. The correlation of more than 70% between predicted and observed values was estimated for the next four months milk yield. Even using only 3 TD records of some cows, the average milk yield of Korean Holstein cows would be predicted with high accuracy if compared with observed milk yield. Persistency of each cow was estimated which might be useful for selecting the cows with higher persistency. The results of the present study suggested the use of a $5^{th}$ order Legendre polynomial to predict the future milk yield of each cow.

• Journal of Microbiology and Biotechnology
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• v.30 no.9
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• pp.1282-1289
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• 2020

Previously, we performed an in silico analysis of the Periplaneta americana transcriptome. Antimicrobial peptide candidates were selected using an in silico antimicrobial peptide prediction method. It was found that periplanetasin-5 had antimicrobial activity against yeast and gram-positive and gram-negative bacteria. In the present study, we demonstrated the anti-inflammatory activities of periplanetasin-5 in mouse macrophage Raw264.7 cells. No cytotoxicity was observed at 60 ㎍/ml periplanetasin-5, and treatment decreased nitric oxide production in Raw264.7 cells exposed to lipopolysaccharide (LPS). In addition, quantitative RT-PCR and enzyme-linked immunosorbent assay revealed that periplanetasin-5 reduced cytokine (tumor necrosis factor-α, interleukin-6) expression levels in the Raw264.7 cells. Periplanetasin-5 controlled inflammation by inhibiting phosphorylation of MAPKs, an inflammatory signaling element, and reducing the degradation of IκB. Through LAL assay, LPS toxicity was found to decrease in a periplanetasin-5 dose-dependent manner. Collectively, these data showed that periplanetasin-5 had anti-inflammatory activities, exemplified in LPS-exposed Raw264.7 cells. Thus, we have provided a potentially useful antibacterial peptide candidate with anti-inflammatory activities.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.184-193
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• 2021

Background: The radionuclides released by the Fukushima Daiichi Nuclear Power Plant (FDNPP) accident 9 years ago are still being monitored by various research teams and the Japanese government. Comparison of different surveys' results could help evaluate the exposure doses and the mechanism of radiocesium behavior in the urban environment in the area. In this study, we clarified the relationship between land use and temporal changes in the ambient dose rates (air dose rates) using big data. Materials and Methods: We set a series of 1 × 1 km² meshes within the 80 km zone of the FDNPP to compare the different survey results. We then prepared an analysis dataset from all survey meshes to analyze the temporal change in the air dose rate. The selected meshes included data from all survey types (airborne, fixed point, backpack, and carborne) obtained through the all-time survey campaigns. Results and Discussion: The characteristics of each survey's results were then evaluated using this dataset, as they depended on the measurement object. The dataset analysis revealed that, for example, the results of the carborne survey were smaller than those of the other surveys because the field of view of the carborne survey was limited to paved roads. The location factor of different land uses was also evaluated considering the characteristics of the four survey methods. Nine years after the FDNPP accident, the location factor ranged from 0.26 to 0.49, while the half-life of the air dose rate ranged from 1.2 to 1.6. Conclusion: We found that the decreasing trend in the air dose rate of the FDNPP accident was similar to the results obtained after the Chernobyl accident. These parameters will be useful for the prediction of the future exposure dose at the post-accident.

• Journal of Ecology and Environment
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• v.45 no.4
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• pp.264-276
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• 2021

Background: Salt marshes provide a variety of ecosystem services; however, they are vulnerable to human activity, water level fluctuations, and climate change. Analyses of the relationships between plant communities and environmental conditions in salt marshes are expected to provide useful information for the prediction of changes during climate change. In this study, relationships between the current vegetation structure and environmental factors were evaluated in the tidal flat at the southern tip of Ganghwa, Korea, where salt marshes are well-developed. Results: The vegetation structure in Ganghwa salt marshes was divided into three groups by cluster analysis: group A, dominated by Phragmites communis; group B, dominated by Suaeda japonica; and group C, dominated by other taxa. As determined by PERMANOVA, the groups showed significant differences with respect to altitude, soil moisture, soil organic matter, salinity, sand, clay, and silt ratios. A canonical correspondence analysis based on the percent cover of each species in the quadrats showed that the proportion of sand increased as the altitude increased and S. japonica appeared in soil with a relatively high silt proportion, while P. communis was distributed in soil with low salinity. Conclusions: The distributions of three halophyte groups differed depending on the altitude, soil moisture, salinity, and soil organic matter, sand, silt, and clay contents. Pioneer species, such as S. japonica, appeared in soil with a relatively high silt content. The P. communis community survived under a wider range of soil textures than previously reported in the literature; the species was distributed in soils with relatively low salinity, with a range expansion toward the sea in areas with freshwater influx. The observed spatial distribution patterns may provide a basis for conservation under declining salt marshes.

• Journal of Korean Society of Rural Planning
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• v.23 no.3
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• pp.163-174
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• 2017

This study developed the prototype of the system and implemented its main functions, which is the intelligent integrated agricultural water management information system and service (IaWAMISS). The developed system was designed to be able to collect, process and analyze the agricultural water information of spatially dispersed reservoirs in whole country and spatial geographic information distributed in various systems of other organizations. The system, IaWAMISS, is also possible to provide the reproduced information services in each reservoir and space units, such as agricultural water demand and supply analysis and drought prediction, to the people, experts, and policy makers. This study defined the 6 step modules to develop the system, which are to design the components of intelligent integrated information system, to derive the utilization contents of existing systems, to design the new development elements for IaWAMISS, to design the reservoir information system can be used by managers of city and county, to designate the monitoring reservoirs managed by city and county, and finally to prepare the sharing system between organizations with the existing information systems. In order to implement the prototype of the system, this study shows the results for three important functions of the system: spatial integration of reservoirs' information, data link integration between the existing systems, and intelligent analysis program development to assist decision support for agricultural water management. For the spatial integration with the reservoir water information of the Korea Rural Community Corporation, this study get IaWAMISS to receive the real-time reservoir storage information from the measurement facility installed in the municipal management reservoir. The data link integration connecting databases of the existing systems, was implemented by integrating the meteorological information of the Korea Meteorological Administration with IaWAMISS, so that the rainfall forecast data could be derived and used. For the implementation of the intelligent analysis program, this study also showed the results of analysis and prediction of agricultural water demand and supply amount, estimation of Palmer drought index, analysis of flood risk area in typhoon course region, and analysis of the storage status of reservoirs related to each storm. This study confirmed the possibility and efficiency of an useful system development through the prototype design and implementation of IaWAMISS. By solving the preliminary 6 step modules presented in this study, it is possible not only to efficiently manage water by spatial unit, but also to provide the service of information and to enhance the relevant policy and national understanding to the people.

• Journal of muscle and joint health
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• v.4 no.2
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• pp.197-220
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• 1997

The lack of a comprehensive theory describing the mechanism of adaptation scientifically has been one of the limiting factors for the development of nursing intervention of patients with chronic illness. Since Roy's theory provides the general conceptual framework depicting adaptation process with structural stimuli and control mechanism, it is appropriate to understand the process of adaptation. But in Roy's theory, the propositions about cognator and regulator as control mechanisms are not clearly defined. For this reason, most of the previous researches applying the Roy's theory have disregarded the study of cognator and regulator. For the patients with chronic illness such as rheumatoid arthritis, it was reported that adaptation states were different for the same stimuli due to the difference of the control mechanism. Moreover in nursing it is important to identify the control mechanism which can be and must be intervened by nurses. It was the Lazarus and Folkman's theory that proposed the control mechanism. They suggested that individual differences in the reaction against the perceived stress are due to the difference in appraisal and coping. Therefore, the synthesis of Roy's and Lazarus and Folkman's propositions might help to clearly understand the mechanism of adaptation. From this point, a theoretical framework has been developed and tested. The subjects were the 297 patients who had been diagnosed rheumatoid arthritis and attended the outpatient clinic. A hypothetical prediction model of adaptation was tested by the covariance structure analysis with PC-LISREL 7.13. As a result, the overall fit was good($x^2$=78.83, p=0.00 ; GFI=0.96 : AGFI=0.90 ; RMR=0.04) for the hypothetical model. In the final model added GA(5, 1), the overall fit was increased ($x^2$=57.82, p=0.003 ; GFI=0.97 ; AGFI=0.93 : RMR=0.036). Except the fact that illness symptoms affected physical adaptation directly, it was supported that focal and contextual stimuli affected physical and psychosocial adaptation through appraisal and coping. Therefore, it was asserted that the synthesis of two theory's propositions was appropriate. So this model would be useful for the theoretical framework in the nursing practice. And this study synthesizing and testing the theory might contribute to establish nursing's scientific base.