• Korean Journal of Animal Reproduction
• /
• v.26 no.2
• /
• pp.183-192
• /
• 2002

The present studies were carried out to examine the effects of exogenous oxytocin(OT) on plasma, uterine and placenta of estradiol-17$\beta$, progesterone, prostaglandin F$_2$$_{\alpha}$ (PGF$_2$$_{\alpha}$), Prostaglandin E$_2$(PGE$_2$) and OT receptor concentrations in pregnant rats. Pregnant rats received an injection of exogenous OT on days 14, 16, 18, 20, 22 of pregnancy and day 1 of postpartum. Concentrations of plasma estradiol-17 $\beta$ after OT injection started to increase after day 18 and peaked on day 22 of pregnancy but decreased on day 1 of postpartum. Plasma progesterone concentrations declined gradually from day 18 of pregnancy and decreased more rapidly until postpartum 1 day. Concentrations of estradiol-17$\beta$in uterine tissues after OT injection were sharply increased from day 20 to 22 of pregnancy and progestrone concentrations were peaked on day 16 and decreased rapidly from day 16 to 20 and maintained the same level until day 1 of postpartum. Uterine concentrations of PGF$_2$$_{\alpha}$ and PGE$_2$increased gradually until day 20 and peaked on day 22 of pregnancy but showed a marked decrease on day 1 of postpartum. Concentrations of PGF$_2$$_{\alpha}$ in placental tissues increased rapidly from day 14 of pregnancy and decreased sharply on day 1 of postpartum. Concentrations of PGE$_2$increased gradually after day 14 and peaked on day 20 of pregnancy. The concentration of OT receptor in uterus was significantly elevated from day 20 and rose to maximum on day 22 of pregnancy. These findings show that OT suppress the concentration of progestrone and stimulate productions of estradiol-17 $\beta$, PGF$_2$$_{\alpha}$, PGE$_2$ and oxytocin receptor concentrations in pregnant rats.

• Journal of Animal Science and Technology
• /
• v.46 no.2
• /
• pp.217-226
• /
• 2004

This study was canied out to evaluate the quality characteristics of the fermented pork with Korean traditional seasonings. The samples, outside muscle of pork ham were cut to cube(7 ${\times}$ 12 ${\times}$ 2cm) and five Korean traditional seasonings such as garlic paste(TI), pickled Kimchi(T2), pickled Kimchi juice(T3), soybean paste(T4), red pepper paste(T5) were seasoned by the proportions of meat to seasonings(1 : 1). The seasoned samples were fennented at - 1 ${\pm}$ 1$^{\circ}C$ for 20 days. According to proximate composition analysis, all pork samples contained protein 20 ${\sim}$ 22%, fat 3 ${\sim}$ 5%, moisture 64 ${\sim}$ 70% and ash 1.8 ${\sim}$ 2.0%. However, T5 had high crude fat level and relatively low moisture content. The highest pH among treatments was shown in TI whereas T3 showed the lowest. Water holding capacity(WHC) of T4 and T5 were higher, while those values were lower in T3 compared with other treatment. Shear force value was the highest in T5, while it was the lowest in T4. TBARS value of T3 was the highest, while that was the lowest in T4. Moreover the highest VBN value was observed in T4 due to fermentation of soy protein. However, the lowest VBN value shown in Tl indicated the inhibition of protein degradation by the garlic. The highest saccarinity was shown in T5 but it was the lowest of in T3. Salinity was shown to be high in T2 and low in T5. $L^*$ values of T4 was higher both at the surface and inner side of samples than the others but T5 showed the lowest value. T2 showed the highest $a^*$ value but T4 and T5 showed the lowest. In the result of sensory evaluation for cooked meat, T5 had the highest score in all item including overall acceptability, while T4 had the lowest score. Unsaturated fatty acid(UFA) ratio of T5 and n were 72.16 and 69.93 respectively, and the ratio of UFA/Saturated fatty acid(SFA) were higher in the order of T5 >T4> T3 >Tl >T2. Overall quality characteristics were higher in the order of T5 >T2 >Tl >T4 >T3.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.117-129
• /
• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.39-54
• /
• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.105-122
• /
• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.45 no.2
• /
• pp.23-39
• /
• 2017

This study began from the question, "is there a way to efficiently apply industrial demand in the university curriculum?" Research focused on how to actively accept and respond to the era of the NCS (National Competency Standards). In order to apply NCS to individual departments of the university, industrial personnel must positively participate to form a practical-level curriculum by the NCS, which can be linked to the work and qualifications. A valid procedure for developing a curriculum based on the NCS of this study is as follows: First, the university must select a specific classification of NCS considering the relevant industry outlook, the speciality of professors in the university, the relationship with regional industries and the prospects for future employment, and the need for industrial manpower. Second, departments must establish a type of human resource that compromises goals for the university education and the missions of the chosen NCS. In this process, a unique competency unit of the university that can support the basic or applied subjects should be added to the task model. Third, the task model based on the NCS should be completed through the verification of each competency unit considering the acceptance or rejection in the curriculum. Fourth, subjects in response to each competency units within the task model should be developed while considering time and credits according to university regulations. After this, a clear subject description of how to operate and evaluate the contents of the curriculum should be created. Fifth, a roadmap for determining the period of operating subjects for each semester or year should be built. This roadmap will become a basis for the competency achievement frame to decide upon the adoption of a Process Evaluation Qualification System. In order for the NCS to be successfully established within the university, a consensus on the necessity of the NCS should be preceded by professors, students and staff members. Unlike a traditional curriculum by professors, the student-oriented NCS curriculum is needed sufficient understanding and empathy for the many sacrifices and commitment of the members of the university.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.46 no.4
• /
• pp.61-75
• /
• 2018

This study aims to verify the effect of green buffers, built as urban planning facilities on the reduction of ultra-fine particulate($PM_{2.5}$) and analyze changes in ultra-fine particles by structure, green volume and planting types of wayside green buffers, thus drawing the factors that can be used when green buffers are built to reduce ultra-fine particulate based on the results. This study selected Songpa-gu, and investigated 16 sites on 5 green buffers adjacent to two of Songpa-gu's main roads, 'Yangjaedaero' and 'Songpadaero'. This study divided all the green spaces into three different types-slope type, plain type and mounding type, and analyzed the mean green volume. As a result of measuring the concentration of $PM_{2.5}$, this study found that it was $55.5{\mu}g/m^3$ on average in winter, which was a harmful level according to the integrated environmental index provided by Seoul City, saying that levels above $50{\mu}g/m^3$ may have a harmful effect on sensitive groups of people. Particularly, the concentration of $PM_{2.5}$ was $38.6{\mu}g/m^3$ on average in spring, which exceeded the mean concentration of $PM_{2.5}$ in Seoul City in 2015. The mean concentrations of $PM_{2.5}$ in every investigation spot were $46.6{\mu}g/m^3$ for sidewalks, $45.5{\mu}g/m^3$ for green spaces and $42.9{\mu}g/m^3$ for residential areas, all of which were lower than $53.2{\mu}g/m^3$ for roads, regardless of the season. The concentration of $PM_{2.5}$ for residential areas was the lowest. In the stage of confirming the effect of green buffers, this study analyzed the correlation between the green volume of vegetation and the fluctuated rate of ultra-fine particles. As a result, it was found that the green coverage rate of trees and shrubs was related to the crown volume in every investigation spot but were mutually and complexly affected by each other. Therefore, this study judged that the greater the number of layers of shrubs that are made, the more effective it is in reducing the concentration of $PM_{2.5}$. As for seasonal characteristics, this study analyzed the correlation between the concentration of $PM_{2.5}$ for residential areas in winter and the green coverage rate of each green space type. As a result, this study found that there was a negative correlation showing that the higher the shrub green coverage rate is, the lower the concentration value becomes in all the slope-type, plain-type and mounding-type green spaces. This study confirmed that the number of tree rows and the number of shrub layers have negative correlations with the fluctuated concentration rate of $PM_{2.5}$. Especially, it was judged that the shrub green volume has greater effect than any other factor, and each green space type shows a negative correlation with the shrub coverage rate in winter.

• Journal of radiological science and technology
• /
• v.28 no.3
• /
• pp.211-217
• /
• 2005

This study obtained the following conclusions by analyzing whether or not thyroid nodule, the number of nodules depending on age and gender, and the developed site of nodule, targeting 838 persons in their 30s-70s who were conducted the thyroid ultrasonography, without clinical symptoms, at the Health Promotion Center. 1. As for the general characteristics of 838 research subjects, men were 368 persons(44%), and women were 470 persons (56%), and the mean age was 51. 2. Among 838 whole subjects, a case, which was diagnosed to be normal, was 590 persons(70%), and persons with nodules findings were 248 persons (30%), thus it was indicated 30% on an average in having the thyroid nodules findings. 3. As for the frequency by age level in thyroid nodule, it was represented men with 10%-14% and women with $20{\sim}29%$ in their $30{\sim}40s$, and men with $27{\sim}33%$ and women with 37-52% in their 50-60s, and men with 46% and women with 50% in their 70s. 4. As a result of obtaining 248 persons, who have thyroid nodules findings, with the solitary nodule and the multiple nodule, it was indicated the solitary nodule of 50.5% with 125 persons and the multiple nodule of 49.5%, thereby representing the almost same ratio. 5. As for the size of thyroid nodule, the majority in all the age levels had the nodule in small size, and the size of $1{\sim}10\;mm$ was largest with 187 persons (75%) among 248 persons with abnormal findings, and it was 45 persons (18%) in $11{\sim}20\;mm$, 14 persons (5.6%) in $21{\sim}30\;mm$, and 2 persons in more than 31 mm. 6. As for the anatomically developed site in nodule, it was indicated the right lobe with 93 persons (38%), the left lobe with 67 persons (27%), both lobes with 75 persons (30%), and isthmus with 13 persons (5.3%), thereby representing the largest frequency in the right lobe, and it was discovered less in isthmus.

• Journal of radiological science and technology
• /
• v.36 no.2
• /
• pp.123-129
• /
• 2013

This study investigated the rate of setup variance by the rotating unbalance of gantry in image-guided radiation therapy. The equipments used linear accelerator(Elekta Synergy TM, UK) and a three-dimensional volume imaging mode(3D Volume View) in cone beam computed tomography(CBCT) system. 2D images obtained by rotating $360^{\circ}$and $180^{\circ}$ were reconstructed to 3D image. Catpan503 phantom and homogeneous phantom were used to measure the setup errors. Ball-bearing phantom was used to check the rotation axis of the CBCT. The volume image from CBCT using Catphan503 phantom and homogeneous phantom were analyzed and compared to images from conventional CT in the six dimensional view(X, Y, Z, Roll, Pitch, and Yaw). The variance ratio of setup error were difference in X 0.6 mm, Y 0.5 mm Z 0.5 mm when the gantry rotated $360^{\circ}$ in orthogonal coordinate. whereas rotated $180^{\circ}$, the error measured 0.9 mm, 0.2 mm, 0.3 mm in X, Y, Z respectively. In the rotating coordinates, the more increased the rotating unbalance, the more raised average ratio of setup errors. The resolution of CBCT images showed 2 level of difference in the table recommended. CBCT had a good agreement compared to each recommended values which is the mechanical safety, geometry accuracy and image quality. The rotating unbalance of gentry vary hardly in orthogonal coordinate. However, in rotating coordinate of gantry exceeded the ${\pm}1^{\circ}$ of recommended value. Therefore, when we do sophisticated radiation therapy six dimensional correction is needed.

• Korean Security Journal
• /
• no.61
• /
• pp.255-283
• /
• 2019

This study conducted a survey of 315 facility security workers in Daejeon, South Chungcheong Province and Gyeonggi Province for about a week from August 7 to August 13, 2019 to identify the impact of work environment and job stress on job satisfaction, and finally collected 293 of the total 315 parts of the data, excluding non-response and inappropriate responses. The STATA 14.2 Statistical Package Program was used for analysis of the collected data, frequency analysis was performed to determine the distribution ratio of the subjects, and reliability analysis and correlation analysis were performed with respect to the established key variables. Next, t-test and one-way ANOVA were conducted to verify differences in work environment, task stress and task satisfaction factors according to demographic characteristics, and the results were as follows: There were differences in work environment, job stress and job satisfaction recognition depending on demographic characteristics. In detail, the factors for the work environment indicated significant differences in age, academic background, number of years of service, wages and types of service in the recognition of the work environment. Job stress factors indicated significant differences in age, education, wages and types of service in job stress recognition. In job satisfaction factors, age, academic background, number of years of service and wages (monthly benefits) showed significant differences in job satisfaction recognition. In addition, the results of multiple regression analyses to identify the working environment, job stress, and job satisfaction are as follows. The working environment has had a positive impact on job satisfaction, and the better the job environment, promotion and organizational characteristics, the higher the job satisfaction. It has been shown that job stress has had a negative impact on job satisfaction, conflict of relationship (promoting colleagues). job autonomy increases job satisfaction, and job satisfaction decreases when job requirements and job insecurity increase. In terms of the impact of work environment on job satisfaction, the higher the work promotion, job environment and organizational characteristics, the higher the job satisfaction level, the report showed. According to these studies, the better the working environment, the lower the job stress, and the lower the job stress, the higher the job satisfaction. In addition, the better the working environment, the more satisfied the job was found to be, and the empirical research result was verified that improvement of the working environment of security workers can reduce job stress and improve job satisfaction through improvement of the working environment.