• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.159-159
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• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.4
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• pp.145-153
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• 2014

This study examined the linkage between positive cognitive bias and entrepreneurial intention. Self-enhancement, unrealistic optimism, and illusion of control have been collectively referred as positive cognitive bias. We examined the effects of positive cognitive bias on attitudes toward success and failure. And we also examined the effect of attitudes toward success and failure on entrepreneurial intension. This study investigated these relationships using 240 high school students. The result of analysis indicated that the self-enhancement bias and unrealistic optimism bias had positive effects on attitude toward failure, but it had not any effect on attitude toward success. The illusion of control bias has positive effects on attitude toward success, but it had not any effect on attitude toward failure. The attitudes toward success and failure had positive effect on entrepreneurial intension. Then results of this study suggests that the cognitive biases showed a role of antecedents of attitudes toward success and failure. Finally, this study concluded with a discussion of the implications of the research findings and directions for future research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.10
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• pp.5268-5273
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• 2013

Technique for analyzing pile installed by vibratory pile driver was developed and results of analysis obtained from variation of bias weight were studied. It can be seen from load transfer curve for dynamic skin friction that load transfer curve shift to downward as bias weight increases. Shape of load transfer curve for dynamic skin friction becomes closer to shape of coil as the bias weight decreases. Magnitudes of toe resistances were not affected by the bias weight. Shape of load transfer curve for dynamic toe resistance shows the similar tendency as the load transfer curve for skin friction exhibits. Vertical displacement increases as the bias weight increases and the shape of vertical displacement with time shows more distinct shape of wave.

• Journal of Biomedical Engineering Research
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• v.19 no.2
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• pp.133-142
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• 1998

In Bayesian SPECT reconstruction, the incorporation of elaborate forms of priors can lead to improved quantitative performance in various statistical terms, such as bias and variance. In particular, the use of higher-order smoothing priors, such as the thin-plate prior, is known to exhibit improved bias behavior compared to the conventional smoothing priors such as the membrane prior. However, the bias advantage of the higher-order priors is effective only when the hyperparameters involved in the reconstruction algorithm are properly chosen. In this work, we further investigate the quantitative performance of the two representative smoothing priors-the thin plate and the membrane-by observing the behavior of the associated hyperparameters of the prior distributions. In our experiments we use Monte Carlo noise trials to calculate bias and variance of reconstruction estimates, and compare the performance of ML-EM estimates to that of regularized EM using both membrane and thin-plate priors, and also to that of filtered backprojection, where the membrane and thin plate models become simple apodizing filters of specified form. We finally show that the use of higher-order models yields excellent "robustness" in quantitative performance by demonstrating that the thin plate leads to very low bias error over a large range of hyperparameters, while keeping a reasonable variance. variance.

• Investigative Magnetic Resonance Imaging
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• v.23 no.2
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• pp.114-124
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• 2019

Purpose: We investigate biases in the assessments of left ventricular function (LVF), by compressed sensing (CS)-cine magnetic resonance imaging (MRI). Materials and Methods: Cardiovascular cine images with short axis view, were obtained for 8 volunteers without CS. LVFs were assessed with subsampled data, with compression factors (CF) of 2, 3, 4, and 8. A semi-automatic segmentation program was used, for the assessment. The assessments by 3 CS methods (ITSC, FOCUSS, and view sharing (VS)), were compared to those without CS. Bland-Altman analysis and paired t-test were used, for comparison. In addition, real-time CS-cine imaging was also performed, with CF of 2, 3, 4, and 8 for the same volunteers. Assessments of LVF were similarly made, for CS data. A fixed compensation technique is suggested, to reduce the bias. Results: The assessment of LVF by CS-cine, includes bias and random noise. Bias appeared much larger than random noise. Median of end-diastolic volume (EDV) with CS-cine (ITSC or FOCUSS) appeared -1.4% to -7.1% smaller, compared to that of standard cine, depending on CF from (2 to 8). End-systolic volume (ESV) appeared +1.6% to +14.3% larger, stroke volume (SV), -2.4% to -16.4% smaller, and ejection fraction (EF), -1.1% to -9.2% smaller, with P < 0.05. Bias was reduced from -5.6% to -1.8% for EF, by compensation applied to real-time CS-cine (CF = 8). Conclusion: Loss of temporal resolution by adopting missing data from nearby cardiac frames, causes an underestimation for EDV, and an overestimation for ESV, resulting in underestimations for SV and EF. The bias is not random. Thus it should be removed or reduced for better diagnosis. A fixed compensation is suggested, to reduce bias in the assessment of LVF.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.11
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• pp.1781-1794
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• 2018

Objective: This meta-analysis was conducted to evaluate the overall effect of direct-fed microbial (DFM) or probiotic supplementation on the log concentrations of culturable gut microbiota in broiler chickens. Methods: Relevant studies were collected from PubMed, SCOPUS, Poultry Science Journal, and Google Scholar. The studies included controlled trials using DFM supplementation in broiler chickens and reporting log concentrations of the culturable gut microbiota. The overall effect of DFM supplementation was determined using standardized mean difference (SMD) with a random-effects model. Subgroups were analyzed to identify pre-specified characteristics possibly associated with the heterogeneity of the results. Risk of bias and publication bias were assessed. Results: Eighteen taxa of the culturable gut microbiota were identified from 42 studies. The overall effect of DFM supplementation on the log concentrations of all 18 taxa did not differ significantly from the controls (SMD = -0.06, 95% confidence interval [-0.16, 0.04], p = 0.228, $I^2=85%$, n = 699 comparisons), but the 18 taxa could be further classified into three categories by the direction of the effect size: taxa whose log concentrations did not differ significantly from the controls (category 1), taxa whose log concentrations increased significantly with DFM supplementation (category 2), and taxa whose log concentrations decreased significantly with DFM supplementation (category 3). Category 1 comprised nine taxa, including total bacterial counts. Category 2 comprised four taxa: Bacillus, Bifidobacterium, Clostridium butyricum, and Lactobacillus. Category 3 comprised five taxa: Clostridium perfringens, coliforms, Escherichia coli, Enterococcus, and Salmonella. Some characteristics identified by the subgroup analysis were associated with result heterogeneity. Most studies, however, were present with unclear risk of bias. Publication bias was also identified. Conclusion: DFM supplementation increased the concentrations of some beneficial bacteria (e.g. Bifidobacterium and Lactobacillus) and decreased those of some detrimental bacteria (e.g. Clostridium perfringens and Salmonella) in the guts of broiler chickens.

• Korean Journal of Radiology
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• v.21 no.4
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• pp.450-461
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• 2020

Objective: We performed a meta-analysis to evaluate the agreement of cardiac computed tomography (CT) with cardiac magnetic resonance imaging (CMRI) in the assessment of right ventricle (RV) volume and functional parameters. Materials and Methods: PubMed, EMBASE, and Cochrane library were systematically searched for studies that compared CT with CMRI as the reference standard for measurement of the following RV parameters: end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), or ejection fraction (EF). Meta-analytic methods were utilized to determine the pooled weighted bias, limits of agreement (LOA), and correlation coefficient (r) between CT and CMRI. Heterogeneity was also assessed. Subgroup analyses were performed based on the probable factors affecting measurement of RV volume: CT contrast protocol, number of CT slices, CT reconstruction interval, CT volumetry, and segmentation methods. Results: A total of 766 patients from 20 studies were included. Pooled bias and LOA were 3.1 mL (-5.7 to 11.8 mL), 3.6 mL (-4.0 to 11.2 mL), -0.4 mL (5.7 to 5.0 mL), and -1.8% (-5.7 to 2.2%) for EDV, ESV, SV, and EF, respectively. Pooled correlation coefficients were very strong for the RV parameters (r = 0.87-0.93). Heterogeneity was observed in the studies (I² > 50%, p < 0.1). In the subgroup analysis, an RV-dedicated contrast protocol, ≥ 64 CT slices, CT volumetry with the Simpson's method, and inclusion of the papillary muscle and trabeculation had a lower pooled bias and narrower LOA. Conclusion: Cardiac CT accurately measures RV volume and function, with an acceptable range of bias and LOA and strong correlation with CMRI findings. The RV-dedicated CT contrast protocol, ≥ 64 CT slices, and use of the same CT volumetry method as CMRI can improve agreement with CMRI.

• Parasites, Hosts and Diseases
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• v.53 no.6
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• pp.689-697
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• 2015

The tapeworm Taenia solium is an important human zoonotic parasite that causes great economic loss and also endangers public health. At present, an effective vaccine that will prevent infection and chemotherapy without any side effect remains to be developed. In this study, codon usage patterns in the T. solium genome were examined through 8,484 protein-coding genes. Neutrality analysis showed that T. solium had a narrow GC distribution, and a significant correlation was observed between GC12 and GC3. Examination of an NC (ENC vs GC3s)-plot showed a few genes on or close to the expected curve, but the majority of points with low-ENC (the effective number of codons) values were detected below the expected curve, suggesting that mutational bias plays a major role in shaping codon usage. The Parity Rule 2 plot (PR2) analysis showed that GC and AT were not used proportionally. We also identified 26 optimal codons in the T. solium genome, all of which ended with either a G or C residue. These optimal codons in the T. solium genome are likely consistent with tRNAs that are highly expressed in the cell, suggesting that mutational and translational selection forces are probably driving factors of codon usage bias in the T. solium genome.

• Korean Journal of Occupational Health Nursing
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• v.25 no.3
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• pp.199-207
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• 2016

Purpose: The purpose of this study was to identify the obesity stress, weight bias and health care on Body Mass Index (BMI) in soldiers of non-combat area and to provide data for improving the quality of their life. Methods: This research involved 165 soldiers working in non-combat area. Data collection was conducted from November 1 to 20, 2015. Statistical analysis of the collected data were t-test and ANOVA, $Scheff{\acute{e}}$ method post hoc analysis, Pearson's correlation coefficients, and multiple liner regression using IBM SPSS 22.0. Results: The mean score of obesity stress was moderate ($19.05{\pm}5.28$). The mean score of weight bias was 69.03 and health care was 2.41 points. There are a positive correlation between obesity stress and BMI (r=.19, p<.05). Weight bias (r=-.19, p<.01) and health care (r=-.26, p<.01) among the subjects had negative correlations with BMI. In a multiple liner regression, obesity stress (${\beta}=.18$, p<.05), health care (${\beta}=-.18$, p<.05) were associated with BMI. Conclusion: Based on the findings that obesity stress and health care influence BMI, there is a need to control stress and to properly set proper guidelines on health care for soldiers.