• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.2
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• pp.115-121
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• 2005

Using a general Saccharomyces cerevisiae as a model strain, continuous ethanol fermentation was carried out in a stirred tank bioreactor with a working volume of 1,500 mL. Three different gravity media containing glucose of 120, 200 and 280 g/L, respectively, supplemented with 5 g/L yeast extract and 3 g/L peptone, were fed into the fermentor at different dilution rates. Although complete steady states developed for low gravity medium containing 120 g/L glucose, quasi-steady states and oscillations of the fermented parameters, including residual glucose, ethanol and biomass were observed when high gravity medium containing 200 g/L glucose and very high gravity medium containing 280 g/L glucose were fed at the designated dilution rate of $0.027\;h^{-1}$. The observed quasi-steady states that incorporated these steady states, quasi-steady states and oscillations were proposed as these oscillations were of relatively short periods of time and their averages fluctuated up and down almost symmetrically. The continuous kinetic models that combined both the substrate and product inhibitions were developed and correlated for these observed quasi-steady states.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.265-273
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• 2007

This paper expands an analytical performance model of 802.11 to accurately estimate throughput and energy demand of 802.11-based wireless sensor network (WSN) when sensor nodes employ Reed-Solomon (RS) codes, one of block forward error correction (FEC) techniques. This model evaluates these two metrics as a function of the channel bit error rate (BER) and the RS symbol size. Since the basic recovery unit of RS codes is a symbol not a bit, the symbol size affects the WSN performance even if each packet carries the same amount of FEC check bits. The larger size is more effective to recover long-lasting error bursts although it increases the computational complexity of encoding and decoding RS codes. For applying the extended model to WSNs, this paper collects traffic traces from a WSN consisting of two TIP50CM sensor nodes and measures its energy consumption for processing RS codes. Based on traces, it approximates WSN channels with Gilbert models. The computational analyses confirm that the adoption of RS codes in 802.11 significantly improves its throughput and energy efficiency of WSNs with a high BER. They also predict that the choice of an appropriate RS symbol size causes a lot of difference in throughput and power waste over short-term durations while the symbol size rarely affects the long-term average of these metrics.

• Nutrition Research and Practice
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• v.13 no.6
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• pp.461-472
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• 2019

BACKGROUND/OBJECTIVES: Globally, it has been projected that there will be 2 billion overweight and 1 billion obese individuals by 2030. In Korea, the prevalence of adult obesity (BMI > 25) increased from 29.7% in 2009 to 32.4% in 2015. Moreover, childhood obesity, which leads to adulthood obesity, has increasingly become a social problem. The purpose of this review is to summarize the scientific basis for the development of effective models and policies aimed at preventing obesity over a lifetime based on research modeling obesogenic environments. MATERIALS/METHODS: The review focuses on the characteristics of obesity prevalence and trends in 3P analysis (papers, patents, and products) as well as government-funded projects in Korean obesity obesogenic environments over the last 10 years. RESULTS AND DISCUSSION: As a result of the 3P analysis, studies on obesity risk factors were frequently carried out, according to two data bases RISS (4.9%) and PubMed (24.7%). Since there were only 17% patents related to the mechanism of preventing obesity in 7,951 Korean patents related to obesity, new paradigms of technologies to dominate the global obesity markets are needed. After government-funded projects were analyzed, communication and cooperation in multi-governmental departments were suggested to elucidate the characteristics of Korean obesity. Government should also produce short- and long-term road maps to develop a practical, successful outcome. Although the rate of obesity in Korea is currently lower than in other developed countries according to WHO criteria, without adequate governmental intervention, obesity rates will approach those of the top countries with high incidence rates of obesity within the next 10 years.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.5
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• pp.785-793
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• 2021

The purpose of the research is to assess the factors affecting the liquidity of the commercial banks that are conducting mergers and acquisitions activities in Vietnam during the 2008-2018 period. This study employs samples based on 2-component data sets with cross-section and time-series data collected from the annual report of the State Bank and the audited acquisitions financial statements of nine commercial banks engaged in mergers and acquisitions activities. To carry out the research objectives, the authors conducted quantitative analysis through the Pooled OLS, REM, FEM and GMM models. The results shown that: (i) bank liquidity is positively affected by liquidity lagged, the return on equity (ROE) and economic growth; negatively affected by bank size, non-performing loan, short-run loan to deposit ratio; (ii) there is not enough evidence to conclude about the relationship between net profit margin, equity-to-assets ratio and inflation rate to bank liquidity; (iii) notably, we found evidence that, after the mergers and acquisitions, the liquidity of Vietnamese commercial banks decreased. The findings of this study suggest that bank managers take a more comprehensive view of the results of mergers and acquisitions and implications for banks to improve liquidity in the post-merger and acquisitions conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.5
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• pp.708-720
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• 1993

The new preventive control mechanisms for traffic management in BISDN/ATM networks can be divided into Connection Admission Control(CAC), Usage Parameter Control (UPC), and Priority Control. Of these mechanism, Usage Parameter Control continuously monitors the parameters admitted in the network's entry point to guarantee quality of service of connections already admitted. Upon detecting traffic that violates the negotiated parameter, it takes the necessary control measures to prevent congestion. Among these traffic control methods, this paper focuses on the Usage Parameter Control method, and proposes and designs GRACE-LB(Guaranteed Rate Acceptance & Control Element-using Leaky Bucket) which improves upon existing UPC models. GRACE-LB modifies the previous LB model by eliminating the cell buffer, dividing the token Pool into two pools, Long-term pool, Short-term pool, and changing the long-term token generating form using 'Cycle Token' into the same bursty form as the traffic source. Through this, GRACE-LB achieves effective control of the Average Bit Rate(ABR) and burst duration of bursty multimedia traffic which previous LB models found difficult to control. Also, since GRACE-LB can e implemented using only simple operations and there are no cell buffers in it, it has the merit of being easily installed at any place.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Proceedings of the KSME Conference
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• 2000.11b
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• pp.371-376
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• 2000

In this study, the computational method is presented to simulate the hemodynamics of the patients after the Fontan procedure. The short-term feedback control models are implemented to assess the hemodynamic responses of the patients exposed to the stresses such as gravitational effect or hemorrhage. To construct the base line of the Fontan model, we assume an increase in venous tone, in heart rates, and in systemic resistance that are based on the clinical observations. For the verification of the present method we simulate the LBNP (lower body negative pressure) test for the normal and the Fontan model and we compare these with experimental data. Computational results show that the diastolic ABP(arterial blood pressure) increases but the systolic ABP decreases during LBNP. The increase in heart rate is due to the control system activated by the decreased mean ABP and CVP(central venous pressure). In case of the Fontan model, the increased venous tone is the reason of the diminished CVP change during LBNP. We also simulate 20% hemorrhage stress to the patient after the Fontan procedure and these results are compared with the experimental and the existing computational one. Computational results on the hemodynamics of patients after the Fontan procedure show that the mean ABP and cardiac output decrease. Heart rate and systemic resistance increase to compensate for the decrease in ABP. The sensitivity analysis according to the conduit resistance is also presented to delineate the effects of the local blood flow resistance. The cardiac output decreases according to the increase of the conduit resistance. The 50% increase in the conduit resistance causes about 3% decrease of cardiac output.

• Biomedical Science Letters
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• v.21 no.3
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• pp.135-143
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• 2015

Electrocardiogram measures the electric impulses generated by the heart during its cycle. Recently genome-wide association studies on electrocardiogram traits revealed many relevant genetic loci. Therefore, these findings need to be validated and investigated to determine the underlying mechanisms using mouse models. Invasive radiotelemetry has been widely used to record the electrocardiogram in mice because it has several advantages over non-invasive measurements. However, radiotelemetry is expensive and requires complicated surgery. On the other hand, a non-invasive method using 3 electrodes (one for earth) for lead II is easy to establish and allows for rapid measurement. In this study, eleven mice were measured with this non-invasive method and no statistical difference among them was found in any ECG measurements. In addition, repeat measurement in the same mouse was performed in 9 sets of experiment and the results indicated that non-invasive method was reliable for reproducibility. Further it was shown that measurements for 1, 5, 10, and 15 minutes were not different so that a short recording such as 5 minutes was enough to estimate the ECG values including heart rate. Further this method was validated by measuring the ECG of Balb/c and FVB that were previously shown to differ in ECG values by radiotelemetry. Significant differences were found in heart rate, PR interval and corrected QT interval between these mouse strains. This study partially proved that non-invasive method also could provide the accuracy and reproducibility. Based on these results, the non-invasive ECG recordings of lead II is recommended as a useful method for quick test in mouse model.

• Proceedings of the Korean Nuclear Society Conference
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• 1995.05a
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• pp.707-712
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• 1995

Grain boundary diffusion plays significant role in the fission gas release, which is one of the crucial processes dominating nuclear fuel performance. Gaseous fission products such as Xe and Kr generated inside fuel pellet have to diffuse in the lattice and in the grain boundary before they reach open space in the fuel rod. In the mean time, the grains in the fuel pellet grow and shrink according to grain growth kinetics, especially at elevated temperature at which nuclear reactors are operating. Thus the boundary movement ascribed to the grain growth greatly influences the fission gas release rate by lengthening or shortening the lattice diffusion distance, which is the rate limiting step. Sweeping fission gases by the moving boundary contributes to the increment of the fission gas release as well. Lattice and grain boundary diffusion processes in the fission gas release can be studied by 'tracer diffusion' technique, by which grain boundary diffusion can be estimated and used directly for low burn-up fission gas release analysis. However, even for tracer diffusion analysis, taking both the intragranular grain growth and the diffusion processes simultaneously into consideration is not easy. Only a few models accounting for the both processes are available and mostly handle them numerically. Numerical solutions are limited in the practical use. Here in this paper, an approximate analytical solution of the lattice and stationary grain boundary diffusion in a polycrystalline solid is developed for the tracer diffusion techniques. This short closed-form solution is compared to available exact and numerical solutions and turns out to be acceptably accurate. It can be applied to the theoretical modeling and the experimental analysis, especially PIE (post irradiation examination), of low burn up fission. gas release.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.435-440
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• 2022

This study analyzed the impact of changes in real estate prices on the soundness of Korean banks using multiple regression models. As a result of the analysis, changes in real estate prices significantly increase the banks' non-performing loans through the increase in loans. Among macroeconomic variables, short-term interest rates were found to have a significant effect on all soundness indicators such as BIS capital adequacy ratio, non-performing loans ratio, and liquidity coverage ratio. Among the bank characteristics indicators, the loan growth rate had a significant negative effect on BIS capital adequacy ratio, and the real estate mortgage rate had a significant positive effect. In additional, it was found that non-performing loans ratio and liquidity coverage ratio had a negative effect on BIS capital adequacy ratio.