• Journal of Animal Science and Technology
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• v.49 no.2
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• pp.213-224
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• 2007

In this study, hatched male broiler chicks(Ross) were fed on a basal diet and LPS was administered via intraperitoneal injection three times every other day, on the 9th, 11th and 13th days of the experiment, and then PBMC and splenocytes were isolated on day 14. The degree of alama blue reduction was evaluated at 4, 24, 48, 96 and 120 h in the splenocytes, and at 4, 8, 12, 24 and 48 h for PBMC of incubation after the addition of alama blue solution to the media. The cell numbers used in this experiment were 103, 104 and 105 cells per well, and the con A levels were 0.0, 1.0, 5.0, and 10.0 ㎍ per ml of medium. 1. The degree of alama blue reduction was found to increase in a linear fashion with increasing incubation time and cell numbers, for both splenocytes and PBMC. 2. During acute phase response, the degree to which alama blue was reduced was significantly elevated (p<0.05) at an incubation time of 24 hr for the splenocytes, 4 hr for PBMC, and a cell number of 105 cells per well, respectively. 3. The raised reduction of alama blue to control was linear with Con A levels in medium, and higher reduction in Con A 10.0 ㎍ relative to 1.0 or 5.0 ㎍ in ml medium was shown 4. The medium with autologous serum evidenced a significantly (p<0.05) higher reduction of alama blue relative to FBS. 5. Splenocytes and PBMC from the LPS-injected birds evidenced significantly higher levels of alama blue reduction regardless of incubation time, number of cells, level of Con A added, or serum type, as compared with what was observed in normal birds. The results indicated that the assay conditions for proliferative activity using the alama blue method in birds in which the acute phase response had been activated via intraperitoneal LPS injection requires 4 hrs of incubation for PBMC, 24 hrs of incubation for splenocytes, and 10㎍ of Con A per ml of medium.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.1
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• pp.77-85
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• 2013

Purpose: The accurate movement of gantry rotation, collimator and correct application of dose rate are very important to approach the successful performance of Volumetric Modulated Arc Therapy (VMAT), because it is tightly interlocked with a complex treatment plan. The interruption and restart of dose delivery, however, are able to occur on treatment by various factors of a treatment machine and treatment plan. If unexpected problems of a treat machine or a patient interrupt the VMAT, the movement of treatment machine for delivering the remaining dose will be restarted at the start point. In this investigation, We would like to know the effect of interruptions and restart regarding dose delivery at VMAT. Materials and Methods: Treatment plans of 10 patients who had been treated at our center were used to measure and compare the dose distribution of each VMAT after converting to a form of digital image and communications in Medicine (DICOM) with treatment planning system (Eclipse V 10.0, Varian, USA). We selected the 6 MV photon energy of Trilogy (Varian, USA) and used OmniPro I'mRT system (V 1.7b, IBA dosimetry, Germany) to analyze the data that were acquired through this measurement with two types of interruptions four times for each case. The door interlock and the beam-off were used to stop and then to restart the dose delivery of VMAT. The gamma index in OmniPro I'mRT system and T-test in Microsoft Excel 2007 were used to evaluate the result of this investigation. Results: The deviations of average gamma index in cases with door interlock, beam-off and without interruption on VMAT are 0.141, 0.128 and 0.1. The standard deviations of acquired gamma values are 0.099, 0.091, 0.071 and The maximum gamma value in each case is 0.413, 0.379, 0.286, respectively. This analysis has a 95-percent confidence level and the P-value of T-test is under 0.05. Gamma pass rate (3%, 3 mm) is acceptable in all of measurements. Conclusion: As a result, We could make sure that the interruption of this investgation are not enough to seriously affect dose delivery of VMAT by analyzing the measured data. But this investigation did not reflect all cases about interruptions and errors regarding the movement of a gantry rotation, collimator and patient So, We should continuously maintain a treatment machine and program to deliver the accurate dose when we perform the VMAT for the many kinds of cancer patients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.162-174
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• 2017

This study is descriptive research conducted to determine the levels of depression, emotional intelligence, and psychological well-being of nurses employed in small-and medium-sized hospitals, as well as to identify the correlations of these variables, predict factors influencing nurses' psychological well-being, and finally, test the mediating effects of emotional intelligence in the relationship between depression and psychological well-being. The subjects of the study included 336 nurses employed in small-and medium-sized hospitals located in the Daegu-Gyeongbuk region. Using a structured questionnaire, a sample was taken from December 17, 2016 to January 8, 2017. The results that the nurses showed an average level of depression with a mean score of 1.55 points, while their mean scores of emotional intelligence and psychological well-being were above average (3.05 and 3.51 scores, respectively). Depression exhibited negative (-) correlations with emotional intelligence and psychological well-being, whereas emotional intelligence had a positive (+) correlation with psychological well-being. The significant predictors of psychological well-being were found to include sleep hours (${\beta}=0.111$), working department (${\beta}=0.236$), and depression (${\beta}=-0.245$). Moreover, evaluation of the mediating effects of emotional intelligence revealed significant relationships between depression and regulation of emotion (${\beta}=0.527$) and between depression and emotional utilization (${\beta}=0.167$). In conclusion, the work environment and depression were predicted to be major factors influencing psychological well-being, while emotional intelligence was found to be a partially mediating factor. Overall, these results demonstrate that easing depression and improving emotional intelligence can be very positive countermeasures in revitalizing the hospital organization, as well as in ensuring the happiness of individual nurses. Therefore, interventions aimed at improving work environments and easing depression are required to improve nurses' psychological well-being.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.46-57
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• 2017

This study examined the level of job satisfaction of rescue workers in accordance with the sociodemographic and health related characteristics, as well as job-related factors. Moreover, this study aimed to know the relationship between job satisfaction and violence experience, job stress, and burn-out. A total of 1,240 rescue workers, who works in 14 fire stations across the country, were surveyed. The survey was conducted by self-administered questionnaires during the period between March 1st and April 30, 2016. As a result, the score of job satisfaction according to the sociodemographic and health related characteristics were significantly lower in the younger-aged group, unmarried group, no-regular exercise group, and poor group of subjective sleep evaluation, unhealthy group of subjective health status than their respective counterparts. From the perspective of job-related characteristics, the job satisfaction scores were significantly lower in the groups of lower rank, lower job career, lower monthly income, hard group of physical burden of work, dissatisfaction group of sense of satisfaction in work, unfit group of the job, without group of consider quitting the job than their respective counterparts. The score of job satisfaction, in accordance with violence experience, job stress, and burn-out were significantly lower in groups with higher scores of violence experience, job stress, and burn-out. In a logistic regression analysis, the adjusted odds ratio of the low-risk job satisfaction were significantly increased in the very high group than in the low group of violence experience, in middle, high and very high group than in low group of job stress, in very high group than in low group of burn-out. The results suggest that the job satisfaction of rescue workers is significantly influenced by various factors, including socio-demographic characteristics, health-related behaviors, job-related characteristics, violence experience, job stress, and burn-out.

• Applied Biological Chemistry
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• v.48 no.1
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• pp.23-33
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• 2005

Agricultural sciences field in South Korea has many strong points such as numerous researchers, establishment of research infra-structure, excellence in research competitiveness and high technological level. However, there are also many weaknesses including insufficient leadership at related societies and institutes, deficiency of the next generation research group, and insufficiency in research productivity. There are many opportunities including increasing the importance of the biotechnological industry, activating international cooperation researches, and exploring the multitude of possible research areas to be studied. However, some threats still exist, such as pressure from the government of developed countries to open the agricultural market, the decrease of specialized farms, and intensification for researches to gratify economic and social demands. To encourage research activities in the agricultural sciences field in Korea, the following actions and systems are required: 1) formulation of a mid- and a long-term research master plan, 2) development of a database on the man power in related fields, 3) activation of top-down research topics, and associated increase of individual research grants, 4) development of special national programs for basic researches in agricultural sciences, 5) organization of a committee for policy and planning within the related societies, and 6) system development for the fair evaluation of the research results.

• Applied Biological Chemistry
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• v.29 no.2
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• pp.130-137
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• 1986

The five fish meal kojis which contained various ratios of barley were prepared and processed to produce six different fish-soy sauces. The chemical compositions including enzyme activities during fermentation were determined and sensory evaluation was done and changes of absorbance during heating process were also measured. The contents of reducing sugar increased until 12 hours, then slightly decreased and maintained constant level after 36 hours during koji making. The contents of total nitrogen were proportional to the amount of fish meal used in koji. The activities of amylase and protease were increased until 48 hours and then were not changed during koji making. The contents of reducing sugar were increased until 50 days and then were not much changed during koji making. The contents of nitrogen and amino nitrogen in sauces were increased gradually during fermentation. The total acid contents of sauces were increased until 70 days, after which it was constant during fermentation. The absorbances of sauces were increased with time during heating process. In sensory test, the fish-soy sauce the ratio of fish meal: barley of which was 10 : 16 received the highest score for flavor of sauce and the conventional soy sauce, for color and taste in a soup test. Fish-soy sauce resulted good quality when the ratio of fish meal to barley was 10 to 13 and 10 to 16.

• Clinical and Experimental Pediatrics
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• v.49 no.1
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• pp.24-28
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• 2006

Purpose : The purpose of this study was to investigate the health status and nutritional condition of children living in a low-income community through anthropometric, laboratory evaluation. Methods : A community-based survey identified children below 15 years living in a low-income community. Their weight, height, visual acuity, hearing level and dental status were measured. Blood sample were obtained on June and July, 2004. Hemoglobin, serum cholesterol, Hepatitis B antigen/antibody, AST and ALT were measured. Results : A total of 285 students(M : F=141 : 144) aged 6 to 14 years were included in this study. The heights and weights in some grades were smaller than controls. The prevalence of obesity was 10.6 percent in males and 10.7 percent in females. The prevalence of abnormal visual acuity, hearing impairments and dental carries were 20.5 percent, 0.3 percent and 69.4 percent. The prevalence of anemia was 10.1 percent. Serum total cholesterol was over 200 mg/dL in 7 percent. They complained of abdominal pain(22.1 percent) and headache(17.1 percent). Hyperthyroidism, cataract, neurofibromatosis, severe atopic dermatitis, ventricular septal defect, strabismus and inguinal hernia were newly diagnosed. Conclusion : Mean heights and weights of children in the low-income community were smaller than controls. The prevalence of abnormal visual acuity, hearing impairment and dental carries were higher than in the 2003 national health survey. Additional research is needed to evaluate the health status of the low-income community.

• Journal of the Korea Organic Resources Recycling Association
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• v.28 no.1
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• pp.5-14
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• 2020

The study was initiated to reduce production cost and environmental pollution with the evaluation of nutrient requirement of 'Nero' black chokeberry (Aronia melanocarpa) and optimum amount of oil cake application. 100% of a recommended amount (RA) of oil cake was designated as a 100-RA, with 0-RA, 25-RA, 50-RA, and 75-RA for 0%, 25%, 50%, and 75% RA, respectively. The oil cake was scattered around the black chokeberry at every year for two years from 2018 to 2019, with investigation conducted for the second year. Soil mineral nutrient concentrations were not significantly different among the treatments. Dry weight (DW) of root and leaves was low for 0-RA-treated black chokeberry, with no significant difference observed for the all treatments for the DW of stems. 75-RA increased the fruit DW of 615 g and yield efficiency of 45.3%. Top:root ratio was the highest of 4.7 for 75-RA. Increased amount of oil cake application expanded the tree volume. Tree volume had a strong positive relationship with the root DW (r²=0.977). Mineral nutrient uptake in the root was the highest on the 25-RA-treated black chokeberry, except for Fe uptake. Mineral nutrient uptake in the leaves were similar to all the black chokeberries, except for T-N and Fe uptake. 75-RA increased mineral nutrient uptake in the fruit, except for Cu, in particular, 7.45 g in fruit N, which was the highest level compared to those of the other organs. T-N and P uptake were evenly distributed in the leaves, stems, and fruit, with high K uptake for leaves and fruit. 75-RA maximized to 17.2 g of the total nutrient uptake in a black chokeberry, with 4.9 g for the 0-RA. All mineral nutrient uptake were overall higher on the black chokeberry treated with 50-RA, 75-RA, and 100-RA compared to those of 0-RA and 25-RA.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.