• Journal of The Korean Association of Information Education
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• v.12 no.4
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• pp.375-384
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• 2008

Computer programming has educational effect on improving high-level thinking abilities. However, students initially have to spend too much effort in learning the basic grammar and the usage model of programming languages, which negatively affects their eagerness in learning. To remedy this problem, we propose to apply the Scratch to a Game Developing Programming Class; Scratch is an easy-to-learn and intuitive Educational Programming Language (EPL) that helps improving the Meta-cognition and Self-efficacy of middle school students. Also we used the Demonstration-Practice instruction model with self-questioning method for activating the Meta-cognition. In summary, a game developing programming class using Scratch was shown to significantly improve the Meta-cognition of middle school students. However it was shown to insignificantly improve the Self-efficacy of girl students group.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.11
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• pp.6703-6707
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• 2013

Background: Numerous epidemiological studies have been conducted to evaluate the association between variants of the DNA repair gene XRCC3 and cancer risk. Here we focused on one XRCC3 polymorphism and development of cervical cancer, performing a meta-analysis. Methods: The pooled association between the XRCC3 Thr241Met polymorphism and cervical cancer risk was assessed by odds ratios (ORs) and their 95% confidence intervals (95%CIs). Results: A total of 5 case-control studies met the inclusion criteria. The pooled ORs for the total included studies showed no association among homozygotes TT vs. CC: OR=1.93, 95%CI=0.68-5.49, P=0.22; dominant model TT+TC vs. CC: OR=1.37, 95%CI=0.90-2.06, P=0.14; and recessive model TT vs. TC+CC: OR=1.76, 95%CI=0.68-4.55, P=0.25, but might be a slight risk factor for cervical cancer in heterozygote contrast TT vs. CT: OR= 1.33, 95%CI=1.04-1.71, P=0.02. In subgroup analysis, significant associations were found for Asians under all genetic models. Conclusions: Our meta-analysis suggested the XRCC3 Thr241Met polymorphism might not act as a cervical cancer risk factor overall. However, in subgroup analysis, a significant association was found in Asians under all genetic models. The association should be studied with a larger, stratified population, especially for Asians.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5655-5661
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• 2015

Purpose: To assess the role of two adjuvant chemotherapy regimens, anthracycline-based and CMF on disease free survival and overall survival breast cancer patients by meta-analysis approach in Eastern Mediterranean and Asian countries to determine which is more effective and evaluate the appropriateness and efficiency of two different proposed statistical models. Materials and Methods: Survival curves were digitized and the survival proportions and times were extracted and modeled to appropriate covariates by two multivariate mixed effects models. Studies which reported disease free survival and overall survival curves for anthracycline-based or CMF as adjuvant chemotherapy that were published in English in the Eastern Mediterranean region and Asia were included in this systematic review. The two transformations of survival probabilities (Ln (-Ln(S)) and Ln(S/ (1-S))) as dependent variables were modeled by a multivariate mixed model to same covariates in order to have precise estimations with high power and appropriate interpretation of covariate effects. The analysis was carried out with SAS Proc MIXED and STATA software. Results: A total of 32 studies from the published literature were analysed, covering 4,092 patients who received anthracycline-based and 2,501 treated with CMF for the disease free survival and in order to analyze the overall survival, 13 studies reported the overall survival curves in which 2,050 cases were treated with anthracycline-based and 1,282 with CMF regimens. Conclusions: The findings illustrated that the model with dependent variable Ln (-Ln(S)) had more precise estimations of the covariate effects and showed significant difference between the effects of two adjuvant chemotherapy regimens. Anthracycline-based treatment gave better disease free survival and overall survival. As an IPD meta-analysis in the Italy the results of Angelo et al in 2011 also confirmed that anthracycline-based regimens were more effective for survival of breast cancer patients. The findings of Zare et al 2012 on disease free survival curves in Asia also provided similar evidence.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6071-6074
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• 2014

Background: Evidence suggests that the rs11615 (C>T) polymorphism in the ERCC1 gene may be a risk factor for gynecological tumors. However, results have not been consistent. Therefore we performed this meta-analysis. Methods: Eligible studies were identified by search of PubMed, MEDLINE and Chinese National Knowledge Infrastructure (CNKI). Odds ratios (ORs) and 95% confidence intervals (CIs) were applied to assess associations between rs11615 (C>T) and gynecological tumor risk. Heterogeneity among studies was tested and sensitivity analysis was applied. Results: A total of 6 studies were identified, with 1,766 cases and 2,073 controls. No significant association was found overall between rs11615 (C>T) polymorphism and gynecological tumors susceptibility in any genetic model. In further analysis stratified by cancer type, significantly elevated ovarian cancer risk was observed in the homozygote and recessive model comparison (TT vs. CC: OR=1.69, 95% CI=1.03-2.77, heterogeneity=0.876; TT vs. CT/CC: OR=1.72, 95% CI=1.07-2.77, heterogeneity=0.995). Conclusion: The results of the present meta-analysis suggest that there is no significant association between the rs11615 (C>T) polymorphism and gynecological tumor risk, but it had a increased risk in ovarian cancer.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.10a
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• pp.169-170
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• 2012

The efforts for improving 'Maritime Traffic Safety Assessment Scheme(MTSA scheme)' have continued to the present since May 27th, 2009. But recently, there's a controversy about whether it has been performed properly or the results is significant. These new discussions were arose from lack of validity and appropriateness we had yet to find. At this point, it needs to establish sound MTSA scheme through the comprehensive review. This research developed a suitable meta-evaluation model for MSTA with applying theory of the teta-evaluation, that is the evaluation of evaluations and verified by using meta-evaluation methods like as literature studies, expert reviews, surveys and etc. The results of this study can be used to evaluate MTSA activities and it will contribute to improving MTSA scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.9
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• pp.1771-1780
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• 2007

The necessity of Information integration has emphasized by advancement of internet and change of enterprise environment. In enterprises, it usually integrates the multi-database constructing by M&A. For this integration of information it must guarantee interpretation and integration which is stabilized with solving heterogeneous characteristic problem. In this paper, we propose the method that change the global XML query to local XML query for interpretation. It is based on XMDR(eXtended Meta-Data Registry) which expresses the connection between the standard and the local for solve the interoperability problem in heterogeneous environment. Thus, we propose the legacy model that can search and modify by one Query with creating global XML Query by XMDR. and for his, we use the 2PC technique which is the distributed transaction control technique of existing.

• Journal of Preventive Medicine and Public Health
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• v.55 no.1
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• pp.37-48
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• 2022

Objectives: Depression is a frequent complication of type 2 diabetes mellitus. This study aimed to investigate the relationship between low social support and risk for depression in people with type 2 diabetes through a meta-analysis. Methods: PubMed, ProQuest, SpringerLink, ScienceDirect, Scopus, the Cochrane Library, Embase, and Google Scholar were searched for English-language articles published up to 2021. Pooled adjusted odds ratios (aORs) were calculated using a random-effect model with 95% confidence intervals (CIs). Heterogeneity was evaluated by using the Cochrane Q test and I² statistics. The risk of publication bias was estimated using a funnel plot, the Egger test, and the Begg test. The Joanna Briggs Institute Critical Appraisal Tools were used to assess the quality of evidence and the risk of bias. Results: Eleven studies were included in this meta-analysis, containing a total of 3151 people with type 2 diabetes mellitus. The pooled analysis showed that people with type 2 diabetes mellitus who had low social support had twice as high a risk of depression as those with high social support (aOR, 2.02; 95% CI, 1.51 to 2.70; p<0.001). A random-effect model was used because the heterogeneity was high (I² = 87%). Conclusions: Low social support was found to increase the risk of depression among people with type 2 diabetes mellitus. Further investigation into factors that may moderate this relationship is required.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.589-594
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• 2004

In design of suspension system for KHST, it was applied the approximated optimization method using meta-models which called Response Surface Model and Neural Network Model for 29 design variables and 46 performance index. These models was coded using correlation between design variables and performance indices that is made by the 66 times iterative execution through the design of experimental table consisted orthogonal array L32 and D-Optimal design table. The results show that the optimization process is very efficient and simply applicable for complex mechanical system such as railway vehicle system. Also it was compared with the sensitivity of some design variables in order to know the characteristics of two models. This paper describes the general method for dynamic analysis and design process of railway vehicle system applied to KHST development, and proposed the efficient methods for vibration mode analysis process dealing with test data and the function based approximation method using meta-model applicable for a complex mechanical system. This method will be able to apply to the other railway vehicle system in oder to systematize and generalize the design process of railway vehicle dynamic system.

• 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.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.5
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• pp.1857-1863
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• 2015

Background: Alpha-methylacyl-CoA racemase(AMACR) is thought to play key roles in diagnosis and prognosis of prostate cancer. However, studies of associations between AMACR gene polymorphisms and prostate cancer risk reported inconsistent results. Therefore, we conducted the present meta-analysis to clarify the link between AMACR gene polymorphisms and prostate cancer risk. Materials and Methods: A literature search was performed in PubMed, Embase, China National Knowledge Infrastructure (CNKI), Wanfang and Weipu databases. Odds ratios (ORs) and 95% confidence intervals (95%CIs) were calculated to assess the strength of any association between AMACR polymorphisms and prostate cancer risk. Subgroup analyses by ethnicity, source of controls, quality control and sample size were also conducted. Results: Five studies covering 3,313 cases and 3,676 controls on five polymorphisms (D175G, M9V, S201L, K277E and Q239H) were included in this meta-analysis. Significant associations were detected between prostate cancer and D175G (dominant model: OR=0.89, 95%CI=0.80-0.99, P=0.04) and M9V (dominant model: OR=0.87, 95%CI=0.78-0.97, P=0.01) polymorphisms as well as that in subgroup analyses. We also observed significant decreased prostate cancer risk in the dominant model (OR=0.90, 95%CI=0.81-0.99, P=0.04) for the S201L polymorphism. However, K277E and Q239H polymorphisms did not appear to be related to prostate cancer risk. Conclusions: The current meta-analysis indicated that D175G and M9V polymorphisms of the AMACR gene are related to prostate cancer. The S201L polymorphism might also be linked with prostate cancer risk to some extent. However, no association was observed between K277E or Q239H polymorphisms and susceptibility to prostate cancer.