• Journal of KIISE:Software and Applications
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• v.30 no.12
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• pp.1158-1171
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• 2003

The UML is a widely accepted standard in object-oriented modeling. As the UML is semantically rich, we can describe in detail the system that will be developed, but we cannot guarantee the correctness and consistency of the designed model. Therefore, it is important to minimize the error by verifying user models in an early stage. In this paper, we propose a method for verifying the consistency of UML structural diagrams and behavioral diagrams using OCL verification rules and meta-metamodel. The consistency is a nature for checking whether the structural diagrams and behavioral diagrams are coherently designed according to a specific requirement. First we build meta-metamodels of the structural diagram and behavioral diagram that are described with the UML diagrams and the related elements, we derive rules for verifying the consistency from each meta-metamodels, and then formally specify with the language such as OCL for automatic verification. Finally, we verify the usefulness of the rule through a case study.

• Women's Health Nursing
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• v.25 no.3
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• pp.258-272
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• 2019

Purpose: The purpose of this study was to identify nursing interventions for the postpartum breast care of mothers and determine the effectiveness of interventions for breast pain and engorgement by systematic review. Methods: Eight national and international databases were reviewed to retrieve and collect randomized controlled trial and controlled clinical trial literature published up to March 2015. Two reviewers independently selected the studies and performed data abstraction and validation. The risk of bias was assessed using Cochrane criteria. A meta-analysis of the studies was performed to analyze the data. Results: The meta-analysis showed that breast massage, along with routine breast care, resulted in a 3.52-point reduction in pain on a 10-point visual analogue scale. Meta-analysis of therapy with cold cabbage leaves and routine breast care showed a pain reduction of 0.54 points. Meta-analysis of cold cabbage leaf application in the experimental group versus cold compress therapy in the comparison group showed a pain reduction of 0.44 points. Meta-analysis of cold cabbage leaf application and routine breast care showed an engorgement reduction of 0.67 points. Conclusion: The results of the analysis of 12 articles showed that hot and cold compresses, breast massage, and cabbage application were effective for postpartum breast pain and engorgement.

• Smart Structures and Systems
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• v.32 no.2
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• pp.123-133
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• 2023

This paper proposes a thermography-based coating thickness estimation method for steel structures using model-agnostic meta-learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured using an infrared (IR) camera. The measured heat responses are then analyzed using model-agnostic meta-learning to estimate the coating thickness, which is visualized throughout the inspection surface of the steel structure. Current coating thickness estimation methods rely on point measurement and their inspection area is limited to a single point, whereas the proposed method can inspect a larger area with higher accuracy. In contrast to previous ANN-based methods, which require a large amount of data for training and validation, the proposed method can estimate the coating thickness using only 10- pixel points for each material. In addition, the proposed model has broader applicability than previous methods, allowing it to be applied to various materials after meta-training. The performance of the proposed method was validated using laboratory-scale and field tests with different coating materials; the results demonstrated that the error of the proposed method was less than 5% when estimating coating thicknesses ranging from 40 to 500 ㎛.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.221-229
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• 2012

A model for expressing meta data syntax in the eXtensible Markup Language(XML) was developed to increase the portability of the Arden Syntax in medical treatment. In this model that is Arden syntax uses two syntax checking mechanisms, first an XML validation process, and second, a syntax check using an XSL style sheet. Two hundred seventy-seven examples of MLMs were transformed into MLMs in ArdenML and validated against the schema and style sheet. Both the original MLMs and reverse-parsed MLMs in ArdenML were checked using a Arden Syntax checker. The textual versions of MLMs were successfully transformed into XML documents using the model, and the reverse-parse yielded the original text version of MLMs.

• Journal of Dental Anesthesia and Pain Medicine
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• v.21 no.5
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• pp.397-411
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• 2021

This systematic review aimed to assess the effect of premedication on postoperative pain after root canal treatment in vital teeth. Five electronic databases were searched for randomized clinical trials, and two independent reviewers selected eligible studies, extracted data, and assessed the quality of studies using the Cochrane Risk of Bias tool. Meta-analysis was conducted using the random-effects model, and the pooled effect estimate of the standardized mean difference (SMD) between premedication and placebo was calculated. Subgroup analysis was conducted based on the class and route of the drug. Studies with a high risk of bias were excluded from the sensitivity analysis. Ten trials satisfied the inclusion criteria, of which eight were included in the meta-analysis. Premedication was more effective in reducing postoperative pain than placebo at 6 hours (SMD = -1.00; 95% confidence interval [CI] = -1.33 to -0.66), 12 hours (SMD = -0.80; 95% CI = -1.05 to -0.56), and 24 hours (SMD = -0.72; 95% CI = -1.02 to -0.43). The results of the sensitivity analysis confirmed the findings of the primary analysis. Based on these results, it can be concluded that premedication is effective in reducing postoperative pain in teeth with irreversible pulpitis. However, additional quality studies are required for further validation.

• Journal of Veterinary Clinics
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• v.40 no.4
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• pp.243-259
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• 2023

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

• The Korean Journal of Community Living Science
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• v.21 no.4
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• pp.499-507
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• 2010

본 연구의 목적은 최근 Hawk(2007)이 자녀 및 양육과 관련된 부모의 의도적 사고로 제시한 메타양육(MPQ) 척도를 한국 어머니들을 대상으로 타당화하는 것이다. 연구결과 첫째, 원래 MPQ에는 예상, 평가, 반성적 사고, 문제해결 등 4개의 요인이 있었으나 본 연구에서는 예상 요인과 반성적 사고 요인이 같은 요인으로 추출되었다. 둘째, 한국 어머니들은 미국 어머니보다 메타양육 수준이 높았으나 상대적으로 예상과 반성적 사고 요인에 더 높은 점수를, 그리고 문제해결에 더 낮은 점수를 나타내고 있었다. 그러나 한국 어머니들을 대상으로 한 K-MPQ는 상당히 안정적 구조로 높은 내적 일치도를 보였다. 셋째, 한국 어머니들의 메타양육 요인들은 대체로 양육신념 및 양육실제와 정적 상관이 있었다.

• Food Science of Animal Resources
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• v.43 no.2
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• pp.282-304
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• 2023

This study aimed to investigate the effectiveness of carotenoid supplementation on the performance, egg quality, and immunity of laying hens using a meta-analysis approach. The database was searched using Google Scholar and Scopus, from 2012 to 2022. The literature was published in English. 47 Articles were selected for meta-analysis. Analyses were performed using the Open Meta-analyst for Ecology and Evolution (OpenMEE) software. The heterogeneity and data validation against publication bias were analyzed using JASP 0.16.2 software. Overall, the results showed that carotenoid supplementation improved feed intake by 0.32 g/day/hen [95% confidence interval (CI)=0.02 to 0.61], final body weight by 0.33 g/hen (95% CI=0.05 to 0.60), egg production by 0.38% (95% CI=0.14 to 0.63), egg weight by 0.29 g (95% CI=0.09 to 0.5), yolk colour by 2.11 (95% CI=1.71 to 2.51), Haugh unit (HU) by 0.26 (95% CI=0.11 to 0.42), yolk carotenoids by 1.17 ㎍/kg (95% CI=0.59 to 1.75), immunoglobulin A (IgA) by 0.74 mg/L (95% CI=0.18 to 1.29), and lower yolk cholesterol by -0.38 mg/g (95% CI=-0.59 to -0.16). Feed conversion ratio (FCR), eggshell thickness, and white blood cells were unaffected by the application of carotenoids. The heterogeneity analysis showed variability in all studies (<0.05). In conclusion, carotenoid supplementation can elevate productivity, enhance egg quality, and improve immunity. However, based on Kendall's test, there was a publication bias in several parameters, namely FCR, egg weight, HU, yolk carotenoids, and IgA.

• Nutrition Research and Practice
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• v.16 no.sup1
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• pp.147-159
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• 2022

BACKGROUND/OBJECTIVES: Complementary and alternative medicines can be used to alleviate climacteric symptoms that significantly affect the quality of life of postmenopausal women. Isoflavones are the most common plant-based therapies for postmenopausal changes, but the results of previous studies have been controversial. MATERIALS/METHODS: To investigate whether isoflavones would affect menopausal symptoms as well as ovarian hormones, we performed a systematic review and meta-analysis. The PubMed and EMBASE databases were used to perform the systematic search. Included studies were limited to randomized controlled trials (RCTs) assessing the impact of isoflavone supplementation on menopausal symptoms. RESULTS: Eleven studies were included for the final quantitative assessment. Isoflavone intervention was varied between 49.3 and 135 mg of isoflavones per day for 12 wk-2 yrs. The meta-analysis showed that supplementation of isoflavones significantly increased the estradiol levels (standardized mean difference [SMD] = 0.615, P = 0.035) and Kupperman index (SMD = 3.121, P = 0.003) but had no significant effect on hot flashes, follicle-stimulating hormone, and luteinizing hormone. However, both estradiol and the Kupperman index showed significant heterogeneity among studies (I² = 94.7%, P < 0.001 and I² = 98.1%, P < 0.001, respectively). CONCLUSIONS: Although the results showed a significant SMD in estradiol and the Kupperman index, the results should be interpreted with caution due to the high heterogeneity. Further validation with a larger RCT will be necessary. Overall, isoflavone supplementation has distinct effects on the climacteric symptoms and hormonal changes in postmenopausal women.

• 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.