• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.681-691
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• 2021

The estimated breeding value (EBV) and accuracy of Hanwoo steer (Korean cattle) is an indicator that can predict the slaughter time in the future and carcass performance outcomes. Recently, studies using pedigrees and genotypes are being actively conducted to improve the accuracy of the EBV. In this study, the pedigree and genotype of 46 steers obtained from livestock farm A in Gyeongnam were used for a pedigree best linear unbiased prediction (PBLUP) and a genomic best linear unbiased prediction (GBLUP) to estimate and analyze the breeding value and accuracy of the carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS). PBLUP estimated the EBV and accuracy by constructing a numeric relationship matrix (NRM) from the 46 steers and reference population I (545,483 heads) with the pedigree and phenotype. GBLUP estimated genomic EBV (GEBV) and accuracy by constructing a genomic relationship matrix (GRM) from the 46 steers and reference population II (16,972 heads) with the genotype and phenotype. As a result, in the order of CWT, EMA, BFT, and MS, the accuracy levels of PBLUP were 0.531, 0.519, 0.524 and 0.530, while the accuracy outcomes of GBLUP were 0.799, 0.779, 0.768, and 0.810. The accuracy estimated by GBLUP was 50.1 - 53.1% higher than that estimated by PBLUP. GEBV estimated with the genotype is expected to show higher accuracy than the EBV calculated using only the pedigree and is thus expected to be used as basic data for genomic selection in the future.

• Physical Therapy Korea
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• v.30 no.2
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• pp.136-143
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• 2023

Background: The International Classification of Functioning, Disability and Health (ICF) model, created by the World Health Organization, provides a theoretical framework that can be applied in the diagnosis and treatment of various disorders. Objects: Our research purposed to ascertain the relationship between structure/function, activity, and participation domain variables of the ICF and pain, pain-associated disability, activities of daily living (ADL), and quality of life in patients with chronic low back pain (LBP). Methods: Two-hundred patients with chronic LBP (mean age: 35.5 ± 8.8 years, females, n = 40) were recruited from hospital and community settings. We evaluated the body structure/function domain variable using the Numeric Pain Rating Scale (NPRS) and Roland-Morris disability (RMD) questionnaire. To evaluate the activity domain variable, we used the Oswestry Disability Index (ODI) and Quebec Back Pain Disability Scale (QBDS). For clinical outcome measures, we used Short-form 12 (SF-12). Pearson's correlation coefficient was used to ascertain the relationships among the variables (p < 0.05). All the participants with LBP received 30 minutes of conventional physical therapy 3 days/week for 4 weeks. Results: There were significant correlations between the body structure/function domain (NPRS and RMD questionnaire), activity domain (ODI and QBDS), and participation domain variables (SF-12), rending from pre-intervention (r = -0.723 to 0.783) and postintervention (r = -0.742 to 0.757, p < 0.05). Conclusion: The identification of a significant difference between these domain variables point to important relationships between pain, disability, performance of ADL, and quality in participants with LBP.

• Archives of Plastic Surgery
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• v.51 no.1
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• pp.94-101
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• 2024

Background For the small glabrous skin defect, Thenar and Hypothenar skin are useful donors and they have been used as a free flap. Because of similar skin characteristics, both flaps have same indications. We will conduct comparative study for the donor morbidity of the Free thenar flap and Hypothenar free flap. Methods From January 2011 to December 2021, demographic data, characteristics of each flap, and complications using retrospective chart review were obtained. Donor outcomes of the patient, who had been followed up for more than 6 months, were measured using photographic analysis and physical examination. General pain was assessed by Numeric Rating Scale (NRS) score, neuropathic pain was assessed by Douleur Neuropathique 4 Questions (DN4) score, scar appearance was assessed by modified Vancouver Scar Scale (mVSS), and patient satisfaction was assessed on a 3-point scale. Statistical analysis was performed on the outcomes. Results Out of the 39 survey respondents, 17 patients received Free thenar flaps, and 22 patients received Hypothenar free flaps. Thenar group had higher NRS, DN4, and mVSS (p < 0.05). The average scores for the Thenar and Hypothenar groups were 1.35 and 0.27 for NRS, 2.41 and 0.55 for DN4, and 3.12 and 1.59 for mVSS, respectively. Despite the Hypothenar group showing greater satisfaction on the 3-point scale (1.82) compared with the Thenar group (1.47), the difference was not significant (p = 0.085). Linear regression analysis indicated that flap width did not have a notable impact on the outcome measures, and multiple linear regression analysis revealed no significant interaction between flap width and each of the outcome measures. Conclusion Despite the limited number of participants, higher donor morbidity in general pain, neuropathic pain, and scar formation was noted in the Thenar free flap compared with the Hypothenar free flap. However, no difference in overall patient satisfaction was found between the two groups.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.19-38
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• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Journal of Hospice and Palliative Care
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• v.17 no.4
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• pp.216-222
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• 2014

Purpose: This study was performed to identify the symptoms and care needs of home-based cancer patients in Korea and to add to the scarce literature on this topic. Methods: Data were collected from patients who subscribed to home-based cancer care services in Jinju. Assessments were performed by nurses at the local public health center. The Edmonton Symptom Assessment System with a numeric rating scale (NRS) was used to identify symptoms, and a four-point Likert scale was used to assess psychological, social, and spiritual needs. Results: Cross-sectional data were collected in October 2013. A total of 209 patients participated and their median age was 65 years (range, 17~89 years). Most patients were diagnosed in the early stage of cancer (n=188); only 19 patients were diagnosed in the advanced stage. More than half the patients lived alone (n=115, 55.0%) and took care of themselves (n=128, 61.2%). Anorexia and fatigue were the most common symptoms (median NRS, 5 and 4, respectively). Patients needed economic support the most, whereas spiritual care was least needed (n=138 [67.3%] vs. n=128 [62.1%], respectively). Conclusion: Patients who signed up for home-based cancer care services in Jinju are struggling with a financial issue and physical symptoms. A customized approach is needed to improve the quality of the home-based care services.

• Journal of muscle and joint health
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• v.10 no.2
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• pp.142-155
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• 2003

The purpose of this study was to investigate the rate of utilization, kinds and effective complementary-alternative therapy in elder arthritics, and then utilize the results as basic data for nursing intervention for elder arthritics. Study subjects consisted of 157 elder arthritics over 60 years old, data were collected through a structured questionnaire and face to face interviews. Data collection was done from July 2001 to August 2001. Subjects were sampled out from outpatients of department of rehabilitation of a university hospital in S city, outpatients of a local hospital in D city, and outpatient at public heath center in K and S city. Sexual distribution of subjects showed male 19.1% and the female 80.9%. The diagnosis distribution showed degenerative arthritis at 91% and reumatoid arthritis at 8.9% Duration of arthritics was 10 years over by 46.5%, duration of hospital treatment was 1-5 years by 41% The degree of pain by arthritis pointed out a mean point of 3.37 on a 5-point numeric scale 94.2% of subjects have experience complementary-alternative therapies used. Of the kind the subjects used, physiotherapy occupied 38.2%, Oriental medicine 36.3%, physical exercise 35.7%, nutritional therapy 22.3%, animal diet 8.9%, herbal diet 3.8%. The hardest thing due to arthritis represented disability in daily life by 59.8% and the pain problem by 30.5%. In conclusion, results of the study reveal that elder arthritics have used physiotherapy, Oriental medicine, physical exercise. Concrete strategies for nursing intervention about these complementary-alternative therapy are required to the established soon.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Convergence Security Journal
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• v.17 no.5
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• pp.41-47
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• 2017

The Marine Environment Observation Information System supplies oceanographic information elements such as water temperature, chlorophyll, float, etc. based on satellite images to consumers. The data produced by the Korean marine environmental observatories are located in the coastal areas of Korea. But if the range is too far from a particular area of interest, due to distance or spatial constraints, the accuracy and up-to-dateness of the data can not be relied upon. Therefore, it is necessary to perform fusion and complex operation to solve the difference between the field observation and the marine satellite image. In this study, we develop a system that can process marine environmental information in the user's area of interest in the form of layered character (numeric) information considering the readability and light weight rather than the satellite image. In order to intuitively understand satellite image information, we characterize (quantify) the marine environmental information of the area of interest and we process the satellite image band values into layered characters to minimize the absolute amount of transmitted / received data. Also we study modular location-based interest information service method to be able to flexibly extend and connect interested items that diversify various observation fields as well as application technology to serve this.

• Women's Health Nursing
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• v.29 no.2
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• pp.137-145
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• 2023

Purpose: This non-randomized study was performed to evaluate the effects of music therapy on labor pain, the childbirth experience, and self-esteem in women during vaginal delivery. Methods: In total, 136 primiparous women over 37 weeks of gestation receiving epidural analgesia during vaginal delivery were recruited via convenience sampling. To minimize diffusion effects, data from the control group (n=71) were collected first (April 2020 to March 2021), followed by data from the music group (n=65; April 2021 to May 2022). Participants in the music group listened to classical music during labor, while the control group was offered usual care (no music). Labor pain was measured using a numeric rating scale (NRS), and self-esteem and childbirth experience were collected using self-report questionnaires. Data were analyzed using the independent t-test, chi-square test and Cronbach's α coefficients. Results: The overall pain level (NRS) at baseline was 0 in both groups. Mothers in the music therapy group had lower levels of latent pain (t=1.95, p=.005), active pain (t=3.69, p<.001) and transition-phase pain (t=7.07, p<.001) than the control group. A significant difference was observed between the two groups, and the music therapy group expressed more positive perceptions of the childbirth experience (t=-1.36, p=.018). For self-esteem, the experimental group's score was slightly higher, but without a statistically significant difference from the control group. Conclusion: Using music therapy during labor decreased labor pain and improved the childbirth experience. Music therapy can be clinically recommended as a non-pharmacological, safe, and easy method for nursing care in labor.