• Nutrition Research and Practice
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• v.15 no.3
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• pp.329-345
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• 2021

BACKGROUD/OBJECTIVES: Evidence has suggested an association between serum vitamin D and metabolic syndrome (MetS), but prospective studies are very limited. The objective was to assess the dose-response association between serum vitamin D concentration and MetS risk using a systematic review and meta-analysis of updated observational studies. MATERIALS/METHODS: Using MEDLINE, PubMed, and Embase, a systematic literature search was conducted through February 2020 and the references of relevant articles were reviewed. A random-effects model was used to estimate the summary odds ratio/relative risk and 95% confidence interval (CI). Heterogeneity among studies was evaluated with I² statistic. In total, 23 observational studies (19 cross-sectional studies, and four cohort studies) were included in the meta-analysis. RESULTS: The pooled estimates (95% CI) for MetS per 25-nmol/L increment in serum vitamin D concentration were 0.80 (95% CI, 0.76-0.84; I² = 53.5) in cross-sectional studies, and 0.85 (95% CI, 0.72-0.98; I² = 85.8) in cohort studies. Similar results were observed, irrespectively of age of study population, study location, MetS criteria, and adjustment factors. There was no publication bias for the dose-response meta-analysis of serum vitamin D concentrations and MetS. CONCLUSIONS: Dose-response meta-analysis demonstrated that a 25-nmol/L increment in the serum vitamin D concentration was associated with 20% and 15% lower risks of MetS in cross-sectional studies and cohort studies, respectively.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.5
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• pp.85-95
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• 2021

Structure design sensitivity was evaluated using the orthogonal array experimental method for passive-type deck support frame (DSF) developed for float-over installation of the offshore plant. Moreover, approximation characteristics were also reviewed based on various meta-models. The minimum weight design of the DSF is significantly important for securing both maneuvering performance and buoyancy of a ship equipped with the DSF and guaranteeing structural design safety. The performance strength of the passive type DSF was evaluated through structure analysis based on the finite element method. The thickness of main structure members was applied to design factors, and output responses were considered structure weight and strength performances. Quantitative effects on the output responses for each design factor were evaluated using the orthogonal array experimental method and analysis of variance. The optimum design case was also identified from the orthogonal array experiment results. Various meta-models, such as Chebyshev orthogonal polynomial, Kriging, response surface method, and radial basis function-based neural network, were generated from the orthogonal array experiment results. The results of the orthogonal array experiment were validated using the meta-modeling results. It was found that the radial basis function-based neural network among the meta-models could approximate the design space of the passive type DSF with the highest accuracy.

• Journal of Korean Neurosurgical Society
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• v.66 no.5
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• pp.525-535
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• 2023

Objective : We performed an expanded multi-ethnic meta-analysis to identify associations between inflammation-related loci with intracranial aneurysm (IA) susceptibility. This meta-analysis possesses increased statistical power as it is based on the most data ever evaluated. Methods : We searched and reviewed relevant literature through electronic search engines up to August 2022. Overall estimates were calculated under the fixed- or random-effect models using pooled odds ratio (OR) and 95% confidence intervals (CIs). Subgroup analyses were performed according to ethnicity. Results : Our meta-analysis enrolled 15 studies and involved 3070 patients and 5528 controls including European, Asian, Hispanic, and mixed ethnic populations. Of 17 inflammation-related variants, the rs1800796 locus (interleukin [IL]-6) showed the most significant genome-wide association with IA in East-Asian populations, including 1276 IA patients and 1322 controls (OR, 0.65; 95% CI, 0.56-0.75; p=3.24#x00D7;10^-9) under a fixed-effect model. However, this association was not observed in the European population (OR, 1.09; 95% CI, 0.80-1.47; p=0.5929). Three other variants, rs16944 (IL-1β), rs2195940 (IL-12B), and rs1800629 (tumor necrosis factor-α) showed a statistically nominal association with IA in both the overall, as well as East-Asian populations (0.01<p<0.05). Conclusion : Our updated meta-analysis with increased statistical power highlights that rs1800796 which maps on the IL-6 gene is associated with IA, and in particular confers a protective effect against occurrence of IA in the East-Asian population.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Clinical Nutrition Research
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• v.11 no.2
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• pp.133-145
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• 2022

The aim of this systematic review and meta-analysis was to summarize all the existing randomized controlled trials (RCTs) evidence and to evaluate the effects of magnesium supplementation on serum magnesium, calcium and urinary magnesium concentrations in patients with type 2 diabetes compared with the control. Two independent authors systematically searched online databases including Embase, Scopus, PubMed, and Web of Science from inception until 30th January 2022. RCTs complying with the inclusion criteria were included in this meta-analysis. The heterogeneity among the included studies was assessed using Cochrane's Q test and I-square (I²) statistic. Data were pooled using a random-effects model and weighted mean difference (WMD) was considered as the overall effect size. Sixteen trials were included in this meta-analysis. Serum magnesium (mean difference, 0.15 mg/dL; 95% confidence interval [CI], 0.06 to 0.23; p = 0.001) and urinary magnesium (WMD, 1.99 mg/dL; 95% CI, 0.36 to 3.62; p = 0.017) concentrations were significantly increased after magnesium supplementation when compared with the control group. However, magnesium supplementation did not have any significant effect on serum calcium (WMD, -0.09 mg/dL; 95% CI, -0.27 to 0.08; p = 0.294) level when compared with the control group. This meta-analysis demonstrated that magnesium supplementation significantly increased Serum magnesium levels which may have played an indirect role in improved clinical symptoms in patients with type 2 diabetes.

• Journal of Distribution Science
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• v.11 no.6
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• pp.13-19
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• 2013

Purpose - This study aims to predict the audit reports of listed companies on the Tehran Stock Exchange by using meta-heuristic algorithms. Research design, data, methodology - This applied research aims to predict auditors reports' using meta-heuristic methods (i.e., neural networks, the ANFIS, and a genetic algorithm). The sample includes all firms listed on the Tehran Stock Exchange. The research covers the seven years between 2005 and 2011. Results - The results show that the ANFIS model using fuzzy clustering and a least-squares back propagation algorithm has the best performance among the tested models, with an error rate of 4% for incorrect predictions and 96% for correct predictions. Conclusion - A decision tree was used with ten independent variables and one dependent variable the less important variables were removed, leaving only those variables with the greatest effect on auditor opinion (i.e., net-profit-to-sales ratio, current ratio, quick ratio, inventory turnover, collection period, and debt coverage ratio).

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.733-737
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• 2008

강우-유출(Rainfall Runoff, R-R)모형은 수자원계획과 관리를 위하여 가장 보편적으로 이용되는 도구로 홍수, 가뭄 등과 같은 극한 사상의 예측 또는 물수지 분석에 사용되고 있다. 오랜 기간 많은 수문학자들이 강우-유출 모형의 불확실성 개선 및 정량화하기 위하여 노력하였으나, 여전히 중요한 수문학적 과제로 남아있다. 이에 본 연구에서는 강우-유출 모형의 불확실성을 정량화하는데 목적을 두고 물리적 기반의 분포형 모형과 개념적 준 분포형 모형을 중랑천 유역에 적용하여 그 결과를 비교하였다.

• Journal of Family Resource Management and Policy Review
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• v.19 no.3
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• pp.1-16
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• 2015

The purpose of this study was to synthesize the results of studies on the effects of multi-cultural education program for Korean children. Using the author's own selection criteria, 17 studies were finally selected and 31 effect sizes were calculated from these studies and used for meta analysis. The overall effect size for all studies on the random effect model was .802, and it was positive and high. Given the heterogeneity among the effect size, subgroup analysis was conducted. According to the analysis, effect sizes significantly differed depending on program goal, concerned multi-cultural higher than others. Result also showed that the high scored effect sizes were the general family, pre-school age children group, and the program were 11-20 children group in size, and 11~20 times in frequency of education.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.169-181
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• 2011

The cooperation between universities and IT industry in producing IT manpower of quality is urgently called for to create the effective labor pool of supply and finally balance its supply and demand. Korean Government launched a program where industrial demand-driven curriculums are developed and applied to universities. This paper proposes a design technique of meta-modeling demand-driven curriculums and courses, based on the 3D software space and the software development process. This technique is proven to result in extensibility, flexibility and quality improvement in software design. Therefore, we expect that the proposed technique makes curriculums and courses possible to be continuously improved in many aspects.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.