• Nutrition Research and Practice
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• v.18 no.2
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• pp.165-179
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• 2024

BACKGROUND/OBJECTIVES: Mushroom consumption, rich in diverse nutrients and bioactive compounds, is suggested as a potential significant contributor to preventing cardiometabolic diseases (CMDs). This systematic review aimed to explore the association between mushrooms and cardiometabolic health outcomes, utilizing data from prospective cohort studies and clinical trials focusing on the general population, with mushrooms themselves as a major exposure. SUBJECTS/METHODS: All original articles, published in English until July 2023, were identified through searches on PubMed, Ovid-Embase, and google scholar. Of 1,328 studies, we finally selected 5 prospective cohort studies and 4 clinical trials. RESULTS: Existing research is limited, typically consisting of 1 to 2 studies for each CMD and cardiometabolic condition. Examination of articles revealed suggestive associations in some cardiometabolic conditions including blood glucose (both fasting and postprandial), high-density lipoprotein cholesterol related indices, high-sensitivity C-reactive protein, and obesity indices (body weight, body mass index, and waist circumference). However, mushroom consumption showed no association with the mortality and morbidity of cardiovascular diseases, stroke, and type 2 diabetes, although there was a potentially beneficial connection with all cause-mortality, hyperuricemia, and metabolic syndrome. CONCLUSION: Due to the scarcity of available studies, drawing definitive conclusions is premature. Further comprehensive investigations are needed to clarify the precise nature and extent of this relationship before making conclusive recommendations for the general population.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.

• The Research Journal of the Costume Culture
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• v.28 no.1
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• pp.76-95
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• 2020

Consumers want to express their original unique personality, and even are willing to endure high expenses in order to do this. One noticeable strategy in the market, used by companies to suit for this consumer sentiment, is that of employing limited edition marketing and limited free gifts. This study investigated the effects of limited free gifts on consumer response. Specifically, the present study examined how the need for uniqueness moderated the effects of limited free gifts on brand commitment and attitudes. The online survey method was used to gather the data and a total of 224 data were used to analyze data. The results of the research were as follows. The findings revealed four dimensions of limited free gifts: scarcity/specialty, not for sale, complementarity, and risk. Complementarity positively affected brand commitment, while all four dimensions of limited free gifts positively influenced brand attitude. In addition, the need for uniqueness was proven to be the strongest variable which positively influenced brand commitment and attitudes. Also, when the need for uniqueness was applied as a moderating variable, depending on the levels of the need for uniqueness, the effects of riskiness on the consumer's response were shown to be different. The findings of this study infer various academic and practical applications.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.292-302
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• 2007

Thousands of studies on predicting information and products that are suitable for customers' preference have been actively proceeding. In massive information, unnecessary information should be removed to satisfy customers' needs. This Information filtering has been proceeding with several methods such as content-based and collaborative filtering etc. These conventional filtering methods have scarcity and scalability problems. Thus, this paper proposes a recommendation system using BPN to solve them. Data obtained by survey questionnaire are used as training data of neural network. The recommendation system using neural network is expected to recommend suitable products because it creates optimal network. Finally, the prototype for recommendation system based on neural network is proposed to collect data and recommend appropriate methods through survey questionnaire. As a result, this research improved the problems of conventional information filtering.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.335-351
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• 2011

In order to resolve data scarcity problem related to crisis, Oh and Kim (2007) proposed to use stability oriented approach which focuses a base period of financial market, fits asymptotic stationary autoregressive model to the base period and then compares the fitted model with the current market situation. Based on such approach, they developed financial market instability index. However, since neural network, their major tool, depends on the base period too heavily, their instability index tends to suffer from inaccuracy. In this study, we consider linear asymptotic stationary autoregressive model and neural network to fit the base period and produce two instability indexes independently. Then the two indexes are combined into one integrated instability index via newly proposed combining method. It turns out that the combined instability performs reliably well.

• Smart Structures and Systems
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• v.31 no.4
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• pp.365-381
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• 2023

The existing vision-based techniques for inspection and condition assessment of civil infrastructure are mostly manual and consequently time-consuming, expensive, subjective, and risky. As a viable alternative, researchers in the past resorted to deep learning-based autonomous damage detection algorithms for expedited post-disaster reconnaissance of structures. Although a number of automatic damage detection algorithms have been proposed, the scarcity of labeled training data remains a major concern. To address this issue, this study proposed a semi-supervised learning (SSL) framework based on consistency regularization and cross-supervision. Image data from post-earthquake reconnaissance, that contains cracks, spalling, and exposed rebars are used to evaluate the proposed solution. Experiments are carried out under different data partition protocols, and it is shown that the proposed SSL method can make use of unlabeled images to enhance the segmentation performance when limited amount of ground truth labels are provided. This study also proposes DeepLab-AASPP and modified versions of U-Net++ based on channel-wise attention mechanism to better segment the components and damage areas from images of reinforced concrete buildings. The channel-wise attention mechanism can effectively improve the performance of the network by dynamically scaling the feature maps so that the networks can focus on more informative feature maps in the concatenation layer. The proposed DeepLab-AASPP achieves the best performance on component segmentation and damage state segmentation tasks with mIoU scores of 0.9850 and 0.7032, respectively. For crack, spalling, and rebar segmentation tasks, modified U-Net++ obtains the best performance with Igou scores (excluding the background pixels) of 0.5449, 0.9375, and 0.5018, respectively. The proposed architectures win the second place in IC-SHM2021 competition in all five tasks of Project 2.

• The Korean Journal of Franchise Management
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• v.7 no.2
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• pp.5-14
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• 2016

Purpose - Competitive industry structure and recent economic depression challenge a survival of Korean small- and medium-sized food franchising companies (SMFCs), albeit the explosive growth of the Korean food service industry for last few decades. Against this backdrop, it examines how these SMFCs overcome liabilities of smallness and resource scarcity to strengthen competitive advantage in the market. To tackle this, in this article we focus on corporate entrepreneurship and human resources as a knowledge-based asset for these SMFCs. Furthermore, the ratio of employee turnover is high in SMFCs. We view that such brain-drain may result in poor performance of the Korean SMFCs. As such, we pay attention to the role of organizational commitment to an organization as a solution for enhancing individual-level employees' loyalty toward their organization. Research design, data, and methodology - Our research question is to what extent corporate entrepreneurship (i.e., innovative organizational culture, organizational autonomy, and administrative innovation) affects an individual-level attitude toward the organization and, in turn, employee creativity and satisfaction in the Korean SMFCs context. We collected data from employees in SMFCs for three months. A total of 126 valid questionnaires were collected, and analyzed the data using partial least squares path modeling. Results - The reliable and valid measurement model feed into testing the structural model. Our findings suggest that innovative organizational culture and organizational autonomy positively affect employee commitment. Particularly, organizational autonomy has a greater effect than innovative culture on employee commitment. However, the relationship between administrative innovation and employee commitment is not significant. We also find that employee commitment positively affects both employee creativity and satisfaction. Conclusions - Our contribution to the existing franchising business and management literature is twofold. First, the conceptual model includes three antecedents in the organizational entrepreneurship dimension to organizational commitment. Second, we conceptualize organizational commitment as employee commitment, and validate its impact on employee creativity and job satisfaction at an individual performance level. Overall, this article suggests that it is critically important for the Korean SMFCs to develop corporate entrepreneurship in order to facilitate employees' positive attitudes toward their organizations.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.29-42
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• 2023

In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.353-366
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• 2022

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

• The Research Journal of the Costume Culture
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• v.16 no.5
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• pp.852-862
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• 2008

The purpose of this paper is to find how to do the width of skirt and the girth of the waist in the adequate pattern making for the obese women's skirt. Appearance test of the five experimental skirts was evaluated by the four experts in clothing construction. At the same time, 3D clothing air volume was observed for the five types of experimental skirt with different size specifications. The results from the appearance test were as follows; when the width of skirt pattern is set for(the shell girth/2), it was suggested w/4+1(front), w/4(back) for girth of the waist. On one hand, in case of (the shell girth of front)/2+(the shell girth of back)/2, it was suggested(the waist girth of front)/2 and(waist girth of back)/2 for obese women's skirt with the best appearance. As results, it was found that the width of skirt pattern for the obese women should be the greatest shell girth instead of hip girth. In the case of the hip girth, the amount of ease on hip was suggested 6cm. It was found that pattern with the wrinkle of ease was full of the gaps between body and skirt in 3D clothing air volume. In spreading out to 2D flat pattern from 3D scan data, when the width of skirt pattern was set for(the shell girth of front)/2+(the shell girth of back)/2, it was suggested(the waist girth of front/2)+(the waist girth of back/2) than the shell girth/2 in girth of the waist for the best appearance. And the conversion of 3D scan data into 2D flat pattern in curve shape of crosswise had to spread out of the plane in straight line. The obese women's clothing should be manufactured with systematical consideration of the diversity and scarcity of the obese women's body shape.