• Journal of agriculture & life science
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• v.51 no.6
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• pp.89-105
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• 2017

This study utilized 32,312 records of 17 linear type and 10 conformation traits(including final scores) of Hanwoo cows in the KAIA(Korea Animal Improvement Association) ('09~'10), with 60,556 animals in the pedigree file. Traits included stature, body length, strength, body depth, angularity, shank thickness, rump angle, rump length, pin bone width, thigh thickness, udder volume, teat length, teat placement, foot angle, hock angle, rear leg back view, body balance, breed characteristic, head development, forequarter quality, back line, rump, thigh development, udder development, leg line, and final score. Genetic and residual(co) variances were estimated using bi-trait pairwise analyses with EM-REML algorithm. Herd-year-classifier, year at classification, and calving stage were considered as fixed effects with classification months as a covariate. The heritability estimates ranged from 0.03(teat placement) to 0.42(body length). Rump length had the highest positive genetic correlation with pin bone width(0.96). Moreover, stature, body length, strength, and body depth had the highest positive genetic correlations with rump length, pin bone width, and thigh thickness(0.81-0.94). Stature, body length, strength, body depth, rump length, pin bone width, and thigh thickness traits also had high positive genetic correlations.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.3
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• pp.1-10
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• 2021

This study aimed to evaluate preferences of Bukhansan dulegil using sentiment analysis, a natural language processing technique, to derive preferred and non-preferred factors. Therefore, we collected blog articles written in 2019 and produced sentimental scores by the derivation of positive and negative words in the texts for 21 dulegil courses. Then, content analysis was conducted to determine which factors led visitors to prefer or dislike each course. In blogs written about Bukhansan dulegil, positive words appeared in approximately 73% of the content, and the percentage of positive documents was significantly higher than that of negative documents for each course. Through this, it can be seen that visitors generally had positive sentiments toward Bukhansan dulegil. Nevertheless, according to the sentiment score analysis, all 21 dulegil courses belonged to both the preferred and non-preferred courses. Among courses, visitors preferred less difficult courses, in which they could walk without a burden, and in which various landscape elements (visual, auditory, olfactory, etc.) were harmonious yet distinct. Furthermore, they preferred courses with various landscapes and landscape sequences. Additionally, visitors appreciated the presence of viewpoints, such as observation decks, as a significant factor and preferred courses with excellent accessibility and information provisions, such as information boards. Conversely, the dissatisfaction with the dulegil courses was due to noise caused by adjacent roads, excessive urban areas, and the inequality or difficulty of the course which was primarily attributed to insufficient information on the landscape or section of the course. The results of this study can serve not only serve as a guide in national parks but also in the management of nearby forest green areas to formulate a plan to repair and improve dulegil. Further, the sentiment analysis used in this study is meaningful in that it can continuously monitor actual users' responses towards natural areas. However, since it was evaluated based on a predefined sentiment dictionary, continuous updates are needed. Additionally, since there is a tendency to share positive content rather than negative views due to the nature of social media, it is necessary to compare and review the results of analysis, such as with on-site surveys.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.1
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• pp.17-25
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• 2021

Objectives : This study aimed to investigate quality of life, severity of depression, suicidality, subjective health and subjective stress of depression with subjective cognitive decline in Korean adults. Methods : We used the 7th KNHANES data to enroll 415 participants with a score of 10 or higher on Patient Health Questionnaire-9 (PHQ-9), aged 20-64. Depression was divided into two groups based on the presence/absence of subjective cognitive decline. Demographic and psychological characteristics were compared between two groups. Correlation analysis of subjective cognitive decline, quality of life, depression, suicidal idea was carried out. To detect which variables influenced quality of life, a multiple regression analysis was carried out. Results : Among the 415 participants, 98 had depression with subjective cognitive decline. We identified significant differences in age, marital status, education, employment between the two groups. After adjusting for these variables, depression with subjective cognitive decline had lower EuroQol-5D index scores, more severe depressive symptoms without cognition and worse subjective health than depression without cognitive decline. There was a significant correlation between subjective cognitive decline and quality of life (r=-0.236, p<0.001), suicidal idea (r=0.182, p<0.001), depression score without cognition (r=0.108, p=0.028). Through multiple regression analysis, subjective cognitive decline was predictor of reduced quality of life (β=-0.178, p<0.001). Conclusions : Depression with subjective cognitive decline has poor quality of life and severe depression. Cognitive decline should be considered to improve treatment result in depression.

• Korean Journal of Psychosomatic Medicine
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• v.29 no.1
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• pp.49-57
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• 2021

Objectives : This study evaluated the medical communication skills of trainee doctors and analyzed the relationship between medical communication skills, self-efficacy on clinical performance (SECP) and empathy. Methods : A total of 106 trainee doctors from a university hospital participated. The questionnaire comprised self-evaluated medical communication skills, modified SECP and the Korean version of the Jefferson Scale of Empathy-Health Professionals version. The mean difference in medical communication skills scores according to gender, age, division (intern, internal medicine group or surgery group) and position (intern, first-/second- and third-/fourth-year residents) were analyzed. Pearson correlation coefficients were determined between medical communication skills, modified SECP and empathy. The effects of each variable on medical communication skills were verified using the structural equation model. Results : There were no statistically significant mean differences in self-evaluated medical communication skills according to gender, age, division or position. Medical communication skills had a significant positive correlation with modified SECP (r=0.782, p<0.001) and empathy (r=0.210, p=0.038). Empathy had a direct effect on modified SECP (β=0.30, p<0.01) and modified SECP had a direct effect on medical communication skills (β=0.80, p<0.001). Empathy indirectly influenced medical communication skills, mediating modified SECP (β=0.26, p<0.05). Conclusions : Medical communication skills are an important core curriculum of residency programs, as they have a direct correlation with SECP, which is needed for successful treatment. Moreover, the medical communication needs a new understanding that is out of empathy.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.2
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• pp.143-154
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• 2020

Objectives : The purpose of this study was to examine cognitive and psychological characteristics of patients with military service suitability issues compared to the general psychiatric outpatients. Methods : 108 patients who visited psychiatric clinic center due to military service suitability issues and 80 general psychiatric patients were recruited from the Department of Psychiatry of university hospital. ANCOVA and chi-equare test were used to examine differences between two groups. Furthermore, we utilized paired t-test to compare the scrore within military group depending on when they performed the psychological assessment. Results : There were no significant differences between military group and general outpatient group in WAIS-IV scores. However, military group scored remarkably higher than control group on validity scales, F-r and Fp-r whereas they scored lower on validity scale, K-r. Furthermore, military group showed significantly higher on BDI and MMPI-2-RF, EID, RCd, RC2, RC3, COG, HLP, SFD, NFC, STW, SAVE, SHY, DSF, NEGE-r, INTR-r. As a result of comparison within the military group following the periods of assessment, military group did not show the significant differences on the overall scales of MMPI-2-RF. Conclusions : The present study showed that military group tends to report their psychological distress more exaggeratedly. In addition, they had significantly elevated not only emotional distress such as depression and anxiety but interpersonal problem. The implications and limitations were discussed along with some suggestions for the future studies.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.23-31
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• 2020

This study was carried out to generate various images of railroad surfaces with random defects as training data to be better at the detection of defects. Defects on the surface of railroads are caused by various factors such as friction between track binding devices and adjacent tracks and can cause accidents such as broken rails, so railroad maintenance for defects is necessary. Therefore, various researches on defect detection and inspection using image processing or machine learning on railway surface images have been conducted to automate railroad inspection and to reduce railroad maintenance costs. In general, the performance of the image processing analysis method and machine learning technology is affected by the quantity and quality of data. For this reason, some researches require specific devices or vehicles to acquire images of the track surface at regular intervals to obtain a database of various railway surface images. On the contrary, in this study, in order to reduce and improve the operating cost of image acquisition, we constructed the 'Defective Railroad Surface Regeneration Model' by applying the methods presented in the related studies of the Generative Adversarial Network (GAN). Thus, we aimed to detect defects on railroad surface even without a dedicated database. This constructed model is designed to learn to generate the railroad surface combining the different railroad surface textures and the original surface, considering the ground truth of the railroad defects. The generated images of the railroad surface were used as training data in defect detection network, which is based on Fully Convolutional Network (FCN). To validate its performance, we clustered and divided the railroad data into three subsets, one subset as original railroad texture images and the remaining two subsets as another railroad surface texture images. In the first experiment, we used only original texture images for training sets in the defect detection model. And in the second experiment, we trained the generated images that were generated by combining the original images with a few railroad textures of the other images. Each defect detection model was evaluated in terms of 'intersection of union(IoU)' and F1-score measures with ground truths. As a result, the scores increased by about 10~15% when the generated images were used, compared to the case that only the original images were used. This proves that it is possible to detect defects by using the existing data and a few different texture images, even for the railroad surface images in which dedicated training database is not constructed.

• Sleep Medicine and Psychophysiology
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• v.27 no.2
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• pp.56-66
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• 2020

Objectives: In this study, the clinical characteristics of OSA patients and the quality of life before and after CPAP use were compared to determine the degree of improvement in quality of life according to CPAP use. Methods: Age, sex, height, weight, body mass index, Epworth Sleepiness Scale, Modified Mallampatti Score, Montreal Cognitive Assessment-Korean, and Pittsburgh Sleep Quality Index were compared between men and women through medical records. To understand the degree of improvement in quality of life resulting from use of CPAP, a personal telephone call was made to compare the VAS scores for quality of life before and after CPAP use. Results: In height (HT) (Z = -4.525, p < 0.001), weight (BW) (Z = -2.844, p < 0.05), sleep quality (PSQI) (Z = -2.671, p < 0.05), and arousal index (AI) (Z = -2.105, p < 0.05), there was a difference between men and women (p < 0.05). There was no difference in the remaining variables. Cross-analysis (Chi-square test) confirmed a difference between severity and sex of OSA. It has been found that there is no statistically significant order in size according to level-specific severity of OSA for PreCPAP QOL, PostCPAP QOL, CPAPUse Months, and CPAP4Hr/d (%) (p > 0.05). The difference between AHI before and after CPAP was 36.48 ± 21.54 (t = 11.609, p < 0.001) and the difference between QOL before and after CPAP was -25.43 ± 22.06 (t = -7.901, p < 0.001), both of which were significant (p < 0.001). Conclusion: Among OSA patients, there were differences in height (HT), weight (BW), sleep quality (PSQI), arousal index (AI), and severity of OSA between men and women, but the quality of life before and after CPAP was different. However, there was no difference between men and women in quality of life before and after CPAP. In addition, quality of life in OSA patients improved after using CPAP.

• Nutrition Research and Practice
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• v.15 no.2
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• pp.213-224
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• 2021

BACKGROUND/OBJECTIVES: To evaluate the nutritional status and prevalence of malnutrition in hospitalized children at admission and during hospitalization in South Korea. SUBJECTS/METHODS: This first cross-sectional nationwide "Pediatric Nutrition Day (pNday)" survey was conducted among 872 hospitalized children (504 boys, 368 girls; 686 medical, 186 surgical) from 23 hospitals in South Korea. Malnutrition risk was screened using the Pediatric Yorkhill Malnutrition Score (PYMS) and the Screening Tool Risk on Nutritional status and Growth. Nutritional status was assessed by z-scores of weight-for-age for underweight, weight-for-height for wasting, and height-for-age for stunting as well as laboratory tests. RESULTS: At admission, of the 872 hospitalized children, 17.2% were underweight, and the prevalence of wasting and stunting was 20.2% and 17.3%, respectively. During hospitalization till pNday, 10.8% and 19.6% experienced weight loss and decreased oral intake, respectively. During the aforementioned period, fasting was more prevalent in surgical patients (7.5%) than in medical patients (1.6%) (P < 0.001). According to the PYMS, 34.3% and 30% of the children at admission and on pNday, respectively, had a high-risk of malnutrition, requiring consultation with the nutritional support team (NST). However, only 4% were actually referred to the NST during hospitalization. CONCLUSIONS: Malnutrition was prevalent at admission and during hospitalization in pediatric patients, with many children experiencing weight loss and poor oral intake. To improve the nutritional status of hospitalized children, it is important to screen and identify all children at risk of malnutrition and refer malnourished patients to the multidisciplinary NST for proper nutritional interventions.

• Journal of Nutrition and Health
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• v.54 no.4
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• pp.398-411
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• 2021

Purpose: The objective of this study was to investigate food and nutrition information utilization practices of adults aged between 20 and 30 years to provide the basic data for developing customized content. Methods: Statistical analyses were performed using the SPSS program (ver. 24.0) for the 𝛘²-test, t-test, one-way analysis of variance, and Duncan's multiple range test. Results: Of the 570 subjects surveyed, 45.4% were men, 54.6% were women, 66.3% were in their 20s, 33.7% were in their 30s, 41.4% were single-person households, and 58.6% lived with their families. On average, 14.2% of televisions (TVs), 26.0% of personal computers (PCs), and 63.7% of smartphones were used for more than three hours per day. 30.9% of respondents searched for food and nutrition information more than once a week. 70.0% of the respondents had then applied the information in real life and 54.7% of the respondents said they would share information with others. Information retrieval rate was in the order of 'restaurant (64.8%)', 'diet (57.5%)', and 'food recipes (55.7%)'. Overall satisfaction with food and nutrition information averaged 3.33 on a five-point scale. Satisfaction score was in the order of 'enough description and easy to understand (3.43)', 'matching title and content (3.35)', and 'providing new and novel information (3.22)'. Satisfaction scores were significantly higher in the group that searched for information (p < 0.001), the group that used the retrieved information in real life (p < 0.001), and the group that conveyed this information to others (p < 0.001). Conclusion: To improve information user satisfaction, it is necessary to provide customized information that fits the characteristics of information users. For this purpose, it is necessary to continuously conduct surveys and satisfaction evaluations for each target group.