• Korean Journal of Community Nutrition
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• v.28 no.3
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• pp.181-191
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• 2023

Objectives: The incidence of inflammatory bowel disease (IBD) is increasing globally, and excessive added sugar consumption has been identified as one of the contributing factors. In the context of IBD, it is essential to explore functional sweeteners that can improve metabolic health and minimize the risk of IBD-related symptoms. This review article aims to shed light on the effects of natural mono- and di-saccharides as alternative sweeteners, specifically focusing on potential benefits for IBD. Methods: A comprehensive literature review was performed using PubMed and Google Scholar databases with articles published after the year 2000. The search terms 'IBD', 'added sugar', 'sweeteners', 'mono-saccharide', and 'di-saccharide' were combined to retrieve relevant articles. A total of 21 manuscripts, aligning with the objectives of the study, were selected. Papers focusing on artificial or high-intensity sweeteners were excluded to ensure relevant literature selection. Results: Multiple studies have emphasized the association between the high consumption of added sugars such as simple sugars and the increased risk of developing IBD. This is suggested to be attributed to the induction of pro-inflammatory cytokine productions and dysbiosis of the gut microbiota. Consequently, there is a growing demand for safe and functional sweeteners, in particular mono- and di-saccharides, that can serve as alternatives for IBD patients. Those functional sweeteners regulate inflammation, oxidative stress, and Intestinal barrier protection, and restore microbiome profiles in various IBD models including cells, animals, and humans. Conclusions: Understanding these mechanisms resolves the link between how sugar consumption and IBD, and highlights the beneficial effects of natural alternative sweeteners on IBD when they were administered by itself or as a replacement for simple sugar. Further, exploration of this relationship leads us to recognize the necessity of natural alternative sweeteners in dietary planning. This knowledge could potentially lead to more effective dietary strategies for individuals with IBD.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.4
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• pp.49-58
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• 2006

In the respect of the environmental protection viewpoint, $CO_2$ may be one of the most attractive alternative refrigerants for an automotive air-conditioning system. For the development of control algorithm of a $CO_2$ automotive air-conditioning system, characteristics of a $CO_2$ refrigerant should be considered. The high-side pressure of a $CO_2$ system should be controlled in order to improve the system efficiency. In this study, dynamic physical models of a $CO_2$ system were developed and dynamic behaviors of the system were predicted by using these models. Control algorithms of a $CO_2$ system were also developed and the effectiveness of these algorithm was verified by using dynamic models.

• Korean Journal of Construction Engineering and Management
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• v.1 no.4 s.4
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• pp.82-90
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• 2000

An integrated cost and schedule control is a noteworthy alternative of the separate control method which is commonly used in the domestic construction industry. In Korea, some cost and schedule integration models have been applied partly by construction managers. But, these models have not been customized yet because they did not reflect the characteristics of the domestic construction industry. This study investigates the characteristics of domestic integration models analyzed through interviews with experts who have the experiences in applying cost-schedule integration models. Based on these surveys, this study suggests the strategy for the improvement of work packaging model in the domestic construction industry.

• Geomechanics and Engineering
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• v.13 no.2
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• pp.237-254
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• 2017

Estimation of slope stability is a very important task in geotechnical engineering. However, its estimation using conventional and soft computing methods has several drawbacks. Use of conventional limit equilibrium methods for the evaluation of slope stability is very tedious and time consuming, while the use of soft computing approaches like Artificial Neural Networks and Fuzzy Logic are black box approaches. Multiple Regression (MR) analysis provides an alternative to conventional and soft computing methods, for the evaluation of slope stability. MR models provide a simplified equation, which can be used to calculate critical factor of safety of slopes without adopting any iterative procedure, thereby reducing the time and complexity involved in the evaluation of slope stability. In the present study, a multiple regression model has been developed and tested its accuracy in the estimation of slope stability using real field data. Here, two separate multiple regression models have been developed for dry and wet slopes. Further, the accuracy of these developed models have been compared and validated with respect to conventional limit equilibrium methods in terms of Mean Square Error (MSE) & Coefficient of determination ($R^2$). As the developed MR models here are not based on any region specific data and covers wide range of parametric variations, they can be directly applied to any real slopes.

• Journal of The Korean Astronomical Society
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• v.46 no.6
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• pp.235-242
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• 2013

We compare mass-loss rates of OH/IR stars obtained from radio observations with those derived from the dust radiative transfer models and IR observations. We collect radio observational data of OH maser and CO line emission sources for a sample of 1533 OH/IR stars listed in Suh & Kwon (2011). For 1259 OH maser, 76 CO(J=1-0), and 55 CO(J=2-1) emission sources, we compile data of the expansion velocity and mass-loss rate. We use a dust radiative transfer model for the dust shell to calculate the mass-loss rate as well as the IR color indices. The observed mass-loss rates are in the range predicted by the theoretical dust shell models corresponding to $\dot{M}=10^{-8}M_{\odot}/yr-10^{-4}M_{\odot}/yr$. We find that the dust model using a simple mixture of amorphous silicate and amorphous $Al_2O_3$ (20% by mass) grains can explain the observations fairly well. The results indicate that the dust radiative transfer models for IR observations generally agree with the radio observations. For high mass-loss rate OH/IR stars, the mass-loss rates obtained from radio observations are underestimated compared to the mass-loss rates derived from the dust shell models. This could be because photon momentum transfer to the gas shell is not possible for the physical condition of high mass-loss rates. Alternative explanations could be the effects of different dust-to-gas ratios and/or a superwind.

• Earthquakes and Structures
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• v.18 no.5
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• pp.555-565
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• 2020

The main objective of seismic codes is to prevent structural collapse and ensure life safety. Collapse probability of a structure is usually assessed by making a series of analytical model assumptions. This paper investigates the effect of finite element modeling (FEM) assumptions on the estimated collapse capacity of a reinforced concrete (RC) frame building and points out the modeling limitations. Widely used element formulations and hysteresis models are considered in the analysis. A full-scale, three-story RC frame building was utilized as the experimental model. Alternative finite element models are established by adopting a range of different modeling strategies. Using each model, the collapse capacity of the structure is evaluated via Incremental Dynamic Analysis (IDA). Results indicate that the analytically estimated collapse capacities are significantly sensitive to the utilized modeling approaches. Furthermore, results also show that models that represent stiffness degradation lead to a better correlation between the actual and analytical responses. Results of this study are expected to be useful for in developing proper models for assessing the collapse probability of RC frame structures.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.31-39
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• 2007

In this study we developed a turbidity management system to support the operation for effective turbid water management. The decision-making system includes various models for prediction of turbid water inflow, effective reservoir operation using the selective withdrawal facility, analysis of turbid water discharge in the downstream. The system is supported by the intensive monitoring devices installed in the upstream rivers, reservoirs, and downstream rivers. SWAT and HSPF models were constructed to predict turbid water flows in the Imha and Andong catchments. CE-QUAL-W2 models were constructed for turbid water behavior prediction, and various analyses were conducted to examine the effects of the selective withdrawal operation for efficient high turbid water discharge, turbid water distribution under differing amount and locations of turbid water discharge. A 1-dimensional dynamic water quality model was built using Ko-Riv1 for simulation of turbidity propagation in the downstream of the reservoirs, and 2-dimensional models were developed to investigate the mixing phenomena of two waters discharged from the Andong and Imha reservoirs with different temperature and turbidity conditions during joint dam operation for reducing the impacts of turbid water.

• Computers and Concrete
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• v.15 no.1
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• pp.89-101
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• 2015

In this paper, Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) models were discussed to determine the compressive strength of clinker mortars cured for 1, 2, 7 and 28 days. In the experimental stage, 1288 mortar samples were produced from 322 different clinker specimens and compressive strength tests were performed on these samples. Chemical properties of the clinker samples were also determined. In the modeling stage, these experimental results were used to construct the models. In the models tricalcium silicate ($C_3S$), dicalcium silicate ($C_2S$), tricalcium aluminate ($C_3A$), tetracalcium alumina ferrite ($C_4AF$), blaine values, specific gravity and age of samples were used as inputs and the compressive strength of clinker samples was used as output. The approximate reasoning ability of the models compared using some statistical parameters. As a result, ANN has shown satisfying relation with experimental results and suggests an alternative approach to evaluate compressive strength estimation of clinker mortars using related inputs. Furthermore MLR model showed a poor ability to predict.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.163-171
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• 2014

Epidemic models are used to analyze the spreading of epidemic diseases, estimate public health needs, and assess the effectiveness of mitigation strategies. Modeling scope of an epidemic model ranges from the regional scale to national and global scale. Most of the epidemic models developed in Korea are at the national scale using the equation-based model. While these models are useful for designing and evaluating national public health policies, they do not provide sufficient details. As an alternative, individual-based models at the regional scale are often used to describe disease spreading, so that various mitigation strategies can be designed and tested. This paper presents an individual-based epidemic spreading model at regional scale. This model incorporates 2005 census data to build the synthetic population in the model representing Daejeon in 2005. The model's capability is demonstrated by an example where we assess the effectiveness of several mitigation strategies using the model.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.310-317
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• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.