• Journal of Industrial Convergence
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• v.18 no.5
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• pp.97-101
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• 2020

This study intended to analyze effects of education of python through meta-analysis. The researcher selected five primary studies reporting statistical data after implementing education of python in elementary classroom settings. Three research questions were stated. What is the total effect size of education of python? What are effect sizes of publication type, dependent variable, and etc.? What are results of meta-regression analysis by grade level, period, and etc.? Findings are as follows. The overall effect size was .598, which is medium. For categorical variables, the effect size of peer-reviewed journal articles was larger than theses. The effect size of affective domain was larger than student achievement and cognitive domain. For meta-regression analysis, education of python was more effective as the period and duration of the program increased. Finally, discussions and recommendations including qualitative investigation on affective domain and program management considering characteristics were presented regarding research findings.

• Journal of the Korea Convergence Society
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• v.10 no.9
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• pp.291-299
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• 2019

The purpose of this study is to identify the effects of laughter therapy on healthy life of human through meta-analysis of literature. Based on domestic and foreign academic databases, 495 foreign and 199 domestic literature were reviewed focusing on "quality of life," as the ultimate goal of health. The final seven literature were extracted. Analyses was performed in R version 3.5.1. Significant differences (SMD=0.23, p<0.01) were shown in the pre-post comparisons of experimental groups using the mean difference (effects size) analysis of the extracted samples. Significant results were not shown in meta regression analyses setting explanatory variables as 'treatment sessions and durations' and outcome variables as 'treatment effects of laughter therapy'. This study is meaningful in comparative analyses of pre-post experimental groups to establish 'laughter therapy' has a significant effect on improving 'quality of life' and is a useful intervention method, especially for older people, and older women with depression.

• Journal of The Korean Society of Integrative Medicine
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• v.10 no.3
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• pp.37-52
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• 2022

Purpose : The purpose of this meta-analysis was to evaluate the effects of electrical stimulation on patients with non-specific low back pain. Methods : Domestic databases were gathered from studies that conducted clinical trials associated with electrical stimulation and its impact on pain of non-specific low back patients. A total of 681 studies were identified, with 12 studies satisfying the inclusion data. The studies consisted of patient, intervention, comparison, outcome, and study design (PICO-SD). The search outcomes were items associated with low back pain. Cochrane risk of bias 2 (RoB 2) was used to evaluate the quality of 12 randomized controlled trials. Effect sizes (Hedges's g) in this study were computed as the corrected standard mean difference (SMD). A random-effect model was used to analyze the effect size because of the high heterogeneity among the studies. Egger's regression and 'trim-and-fill' tests were carried out to analyze the publication bias. Cumulative meta-analysis and sensitivity analysis were conducted to analyze the effect according to the sample size and the consistency of the effect size. Results : The following factors had a large overall effect size (Hedges's g=1.28, 95 % CI=.20~2.36) involving electrical stimulation on non-specific low back pain. The subgroup analysis all showed a statistical difference in the types of study design, electrical stimulation, and assessment tool. No statistically significant difference was found in the meta-regression analysis. Publican bias was found in the data. Conclusion : The findings in this study indicate that electrical stimulation interventions have a positive effect on patients with non-specific low back pain. However, due to the low quality of studies and publication bias, the results of our study should be interpreted cautiously.

• The korean journal of orthodontics
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• v.53 no.6
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• pp.374-392
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• 2023

Objective: Assess and evaluate the different indicators of oral health-related quality of life (OHRQoL) among patients treated with clear aligners (CAs) versus those treated with conventional fixed orthodontics (FAs). Methods: An electronic search was performed on the database is Web of Science, Scopus, and Embase databases. Randomized and non-randomized control trials, cross-sectional, prospective cohort and retrospective trials were included. Quality was assessed with risk of bias tool and risk of bias in non-randomised studies. Meta-analyses were performed with random effects models, estimating the standardized and non-standardized mean differences, odds ratio and risk ratio as the measure of effect. The effect on time was determined using a meta-regression model. Results: Thirty one articles were included in the qualitative synthesis and 17 in the meta-analysis. CAs had a significantly lower negative impact on QoL, with an "important" effect size, while the influence of time was not significant. Periodontal indicators plaque index (PI), gingival index (GI), probing depth (PD), and bleeding on probing show significantly better values in patients treated with CAs, with moderate to large effect sizes. PI and GI have a significant tendency to improve over time. In microbiological indicators, CAs present a lower biofilm mass without differences in the percentage of patients with high counts of Streptococcus mutans and Lactobacilli bacteria. The risk of white spot lesion onset is ten times lower in carriers of CAs. Conclusions: Patients wearing CAs show better periodontal indicators, less risk of white spot development, less biofilm mass and a better QoL than patients with FAs.

• Food Science of Animal Resources
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• v.44 no.2
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• pp.464-482
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• 2024

This study aimed to determine the effects of humectants on moisture content, water activity, tenderness, color, microbiological analysis, protein denaturation, and oxidation of jerky. A thorough search for papers published in scientific journals that examined the impacts of humectants on jerky was carried out using Web of Science, Google Scholar, PubMed, and Science Direct. Only 14 studies matched inclusion requirements. They were used in the meta-analysis to synthesise quantitative findings. In the current investigation, jerky produced with beef, poultry, goat, or pork was used. The standardised mean difference (SMD) between treatments with humectants and controls was examined to investigate the effects of humectants using random-effects models. Heterogeneity was investigated using meta-regression. A subgroup analysis was carried out for significant factors. Results revealed that the addition of humectants had no significant impact on water activity, pH, fat, ash, CIE L^*, or CIE a^* (p>0.05). However, humectant addition significantly increased moisture (SMD=1.28, p<0.05), CIE b^* (SMD=1.67, p<0.05), and overall acceptability (SMD=1.73, p<0.05). It significantly decreased metmyoglobin (SMD=-0.96, p<0.05), shear force (SMD=-0.84, p<0.05), and protein (SMD=-1.61, p<0.05). However, it was difficult to get a firm conclusion about how humectants affected the myofibrillar fragmentation index, total plate count, and 2-thiobarbituric acid-reactive substances because there were fewer than ten studies. To sum up, the proper use of humectants in jerky demands careful attention to both type and quantity, needing a delicate balancing act with other contributing factors.

• Journal of the Korean Data and Information Science Society
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• v.21 no.3
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• pp.447-459
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• 2010

The present study was carried out to summarize the effect of propolis in the diabetic rats by meta-analysis related studies. The association measure to test effect of propolis was Hedges's standardized mean difference between group of rats induced streptozotocin(STZ) or alloxan and group of rats induced STZ or alloxan treated with propolis about the considered 4 effect factors. In this particular fixed-effect model, blood glucose, Cholesterol, Triglyceride were significantly reduce. The case of heterogenous variable such as body weight, blood glucose, cholesterol, triglyceride, random-effect model was applied. In this model, blood glucose, triglyceride were decreased significantly in propolis treated group. According to the meta-regression analysis, period of injection was significant for body weight and blood glucose, cholesterol.

• 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.

• Korean Journal of Veterinary Research
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• v.44 no.4
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• pp.669-676
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• 2004

To evaluate the diagnostic accuracy of two commercial ELISA tests (Allied- and CSL-ELISA) for the diagnosis of Mycobacterium paratuberculosis in cattle, Meta-analysis using English language papers published during 1990-2001 was performed. Diagnostic odds ratios (DOR) were analyzed using regression analysis together with summary receiver operating characteristic (ROC) curves. The difference in diagnostic performance between the two ELISA systems was evaluated by using linear regression. Publication bias was assessed by funnel plot and linear regression. The pooled sensitivity and specificity were 44% (95% CI, 38 to 51) and 98% (95% CI, 96 to 99) for the random-effect model. The DOR between studies was heterogeneous. The area under the fitted ROC curve (AUC) was 0.72 for the unweighted and 0.77 for the weighted model. Maximum joint sensitivity and specificity for the unweighted and weighted model from their summary ROC curve were 70% and 75%, respectively. Based on the fitted model, at a specificity of 95%, sensitivity was estimated to be 52% for the unweighted and 57% for the weighted model. From the final multivariable model study characteristic, the country was the only significant variable with an explained component variance of 13.3%. There were no significant differences in discriminatory power, sensitivity, and specificity between the two ELISA tests. The overall diagnostic accuracy of two commercial ELISA tests was moderate, as judged by the AUC, maximum joint sensitivity and specificity, and estimates from the fitted model and clinical usefulness of the tests for screening program is limited because of low sensitivity and heterogeneous of DOR. It is, therefore, recommended to use ELISA tests as a parallel testing with other diagnostic tests together to increase test sensitivity in the screening program.

• Journal of Technology Innovation
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• v.19 no.3
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• pp.141-174
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• 2011

This paper presents the results of a meta-regression analysis on econometric evidence concerning the relationship between public funding of R&D and private R&D expenditure by reviewing literature and synthesizing existing results. The analysis on the effects of public financing on private investments in R&D has been the object of numerous studies, none of which having arrived at definite conclusion. A meta-analysis based upon a data-base including all relevant studies was carried out to examine whether the characteristics of the applied analysis influence the results. Three different empirical results are presented.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.413-421
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• 2011

The present study was carried out to summarize the effect of garlic in the hyperlipidemia rats by meta-analysis related studies. The association measure to test effect of garlic was the mean difference (MD). In this particular fixed-effect model of mean difference, body weight, liver weight, kidney weight and heart weight were significantly decreased (p < 0.05). Also, blood glucose, plasma total cholesterol, plasma triglycerides, LDL-cholesterol, liver cholesterol, liver triglycerides were significantly decreased. HDL-cholesterol was significantly increased. In this case of heterogeneous variable, random effect model was applied. In this model, liver weight, blood glucose, plasma total cholesterol, plasma triglycerides, LDL-cholesterol, liver cholesterol, liver triglycerides were significantly decreased. HDL-cholesterol was significantly increased. According to the meta-regression analysis, duration of injection was significantly for kidney weight, testis weight, plasma total cholesterol, plasma triglycerides, HDL-cholesterol, LDLcholesterol, liver cholesterol, liver triglycerides.