• Journal of Food Hygiene and Safety
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• 제32권1호
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• pp.8-13
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• 2017

This study was conducted to develop an agent-based computing platform enabling simulation of on-farm produce contamination by enteric foodborne pathogens, which is herein called PPMCS (Preharvest Produce Microbial Contamination Simulator). Also, fecal contamination of preharvest produce was simulated using PPMCS. Although Agent-based Modeling and Simulation, the tool applied in this study, is rather popular in where socio-economical human behaviors or ecological fate of animals in their niche are to be predicted, the incidence of on-farm produce contamination which are thought to be sporadic has never been simulated using this tool. The agents in PPMCS including crop, animal as a source of fecal contamination, and fly as a vector spreading the fecal contamination are given their intrinsic behaviors that are set to be executed at certain probability. Once all these agents are on-set following the intrinsic behavioral rules, consequences as the sum of all the behaviors in the system can be monitored real-time. When fecal contamination of preharvest produce was simulated in PPMCS as numbers of animals, flies, and initially contaminated plants change, the number of animals intruding cropping area affected most on the number of contaminated plants at harvest. For further application, the behaviors and variables of the agents are adjustable depending on user's own scenario of interest. This feature allows PPMCS to be utilized in where different simulating conditions are tested.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제19권8호
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• pp.353-359
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• 2018

In this study, the performance between subjective refraction and open-field/closed view autorefraction was estimated. We measured the refractive error of early adults aged 18 to 20 years who did not have eye disease. The differences between measurements obtained by subjective refraction and open-field autorefraction for SE, J0, and J45 were $-0.13{\pm}0.53D$ (p=0.17), $+0.33{\pm}0.68D$ (p=0.01), and $+0.13{\pm}0.68D$ (p=0.26), respectively, with only J0 differing significantly. The differences between the measurements of subjective refraction and closed-view autorefraction for SE, J0, and J45 were $-0.30{\pm}0.42D$ (p=0.00), $+0.30{\pm}0.71D$ (p=0.02), and $-0.02{\pm}0.63D$ (p=0.88), respectively, with only SE and J0 differing significantly. The coefficient of accuracy for SE, J0, and J45 components of open-field and closed-view autorefraction were 1.04, 1.33, and 1.34 and 0.83, 1.40, and 1.24, respectively. It is possible to predict the refractive error, which is necessary when deciding on subjective refraction, by measuring the objective refraction of open-field/closed view autorefractors.

• Proceedings of the Korean Nutrition Society Conference
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• 한국영양학회 1995년도 추계학술대회 초록
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• pp.11-34
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• 1995

Growth hormone (GH) plays a key role in regulating postnatal growth and can stimulate growth of animals by acting directly on specific receptors on the plasma membrane of tissues or indirectly through stimulating insulin-like growth factor (IGF)-I synthesis and secretion by the liver and other tissues. IGF-I and IGF-Ⅱ are polypeptides with structural similarity with proinsulin that stimulate cell proliferation by endocrine, paracrine and autocrine mechanisms. The initial event in the metabolic action of IGFs on target cells appears to be their binding to specific receptors on the plasma membrane. Current evidence indicates that the mitogenic actions of both IGFs are mediated primarily by binding to the type I IGF receptors, and that IGF action is also mediated by interactions with IGF-binding proteins (IGFBPs). Six distinct IGFBPs have been identified that are characterized by cell-specific interaction, transcriptional and post-translational regulation by many different effectors, and the ability to either potentiate or inhibit IGF actions. Nutritional deficiencies can have their devastating consequence during growth. Although IGF-I is the major mediator of GH's action on somatic growth, nutritional status of an organism is a critical regulator of IGF-I and IGFBPs. Various nutrient deficiencies result in decreased serum IGF-I levels and altered IGFBP levels, but the blood levels of GH are generally unchanged or elevated in malnutrition. Effects of protein, energy, vitamin C and D, and zinc on serum IGF and IGFBP levels and tissue mRNA levels were reviewed in the text. Multiple factors are involved in the regulation of intestinal epithelial cell growth and differentiation. Among these factors the nutritional status of individuals is the most important. The intestinal epithelium is an important site for mitogenic action of the IGFs in vivo, with exogenous IGF-I stimulating mucosal hyperplasia. Therefore, the IGF system appears to provide and important mechanism linking nutrition and the proliferation of intestinal epithelial cells. In order to study the detailed mechanisms by which intestinal mucosa is regulated, we have utilized IEC-6 cells, an intestinal epithelial cell line and Caco-2 cells, a human colon adenocarcinoma cell line. Like intestinal crypt cells analyzed in vivo or freshly isolated intestinal epithelial cells, IEC-6 cells and Caco-2 cells possess abundant quatities of both type Ⅰ and type Ⅱ IGF receptors. Exogenous IGFs stimulate, whereas addition of IGFBP-2 inhibits IEC-6 cell proliferation. To investigate whether endogenously secreted IGFBP-2 inhibit proliferation, IEC-6 cells were transfected with a full-length rat IGFBP-2 cDNA anti-sense expression construct. IEC-6 cells transfected with anti-sense IGFBP-2 protein in medium. These cells grew at a rate faster than the control cells indicating that endogenous IGFBP-2 inhibits proliferation of IEC-6 cells, probably by sequestering IGFs. IEC-6 cells express many characteristics of enterocyte, but do not undergo differentiation. On the other hand, Caco-2 cells undergo a spontaneous enterocyte differentiation. On the other hand, Caco-2 cells undergo a spontaneous enterocyte differentiation after reaching confluency. We have demonstrated that Caco-2 cells produce IGF-Ⅱ, IGFBP-2, IGFBP-3, and an as yet unidentified 31,000 Mr IGFBP, and that both mRNA and peptide secretion of IGFBP-2 and IGFBP-3 increased, but IGFBP-4 mRNA and protein secretion decreased after the cells reached confluency. These changes occurred in parallel to and were coincident with differentiation of the cells, as measured by expression of sucrase-isomaltase. In addition, Caco-2 cell clones forced to overexpress IGFBP-4 by transfection with a rat IGFBP-4 cDNA construct exhibited a significantly slower growth rate under serum-free conditions and had increased expression of sucrase-isomaltase compared with vector control cells. These results indicate that IGFBP-4 inhibits proliferation and stimulates differentiation of Caco-2 cells, probably by inhibiting the mitogenic actions of IGFs.

• Journal of the Korean Society of Hazard Mitigation
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• 제3권3호
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• pp.151-163
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• 2003

In this study, the algorithm of groundwater flow process was established for koreanized groundwater program development dealing with the geographic and geologic conditions of the aquifer have dynamic behaviour in groundwater flow system. All the input data settings of the 3-DFM model which is developed in this study are organized in Korean, and the model contains help function for each input data. Thus, it is designed to get detailed information about each input parameter when the mouse pointer is placed on the corresponding input parameter. This model also is designed to easily specify the geologic boundary condition for each stratum or initial head data in the work sheet. In addition, this model is designed to display boxes for input parameter writing for each analysis condition so that the setting for each parameter is not so complicated as existing MODFLOW is when steady and unsteady flow analysis are performed as well as the analysis for the characteristics of each stratum. Descriptions for input data are displayed on the right side of the window while the analysis results are displayed on the left side as well as the TXT file for this results is available to see. The model developed in this study is a numerical model using finite differential method, and the applicability of the model was examined by comparing and analyzing observed and simulated groundwater heads computed by the application of real recharge amount and the estimation of parameters. The 3-DFM model is applied in this study to Sehwa-ri, and Songdang-ri area, Jeju, Korea for analysis of groundwater flow system according to pumping, and obtained the results that the observed and computed groundwater head were almost in accordance with each other showing the range of 0.03 - 0.07 error percent. It is analyzed that the groundwater flow distributed evenly from Nopen-orum and Munseogi-orum to Wolang-bong, Yongnuni-orum, and Songja-bong through the computation of equipotentials and velocity vector using the analysis result of simulation which was performed before the pumping started in the study area. These analysis results show the accordance with MODFLOW's.

• Journal of Intelligence and Information Systems
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• 제17권4호
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of Intelligence and Information Systems
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• 제17권3호
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• Management & Information Systems Review
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• 제35권3호
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• pp.1-22
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• 2016

We analyzed the international comovements and structural changes in the quarterly real GDP by the Markov-switching vector autoregressive model (MS-VAR) from 1971(1) to 2016(1). The main results of this study were as follows. First, the business cycle phenomenon that occurs in the models or individual time series in real GDP has been grasped through the MS-VAR models. Unlike previous studies, this study showed the significant comovements, asymmetry and structural changes in the MS-VAR model using a real GDP across countries. Second, even if there was a partial difference, there were remarkable structural changes in the economy contraction regime(recession), such as 1988(2) ending the global oil shock crisis and 2007(3) starting the global financial crisis by the MS-VAR model. Third, large-scale structural changes were generated in the economic expansion and/or contraction regime simultaneously among countries. We found that the second world oil shocks that occurred after the first global oil shocks of 1973 and 1974 were the main reasons that caused the large-scale comovements of the international real GDP among countries. In addition, the spillover between Korea and 5 countries has been weak during the Asian currency crisis from 1997 to 1999, but there was strong transmission between Korea and 5 countries at the end of 2007 including the period of the global financial crisis. Fourth, it showed characteristics that simultaneous correlation appeared to be high due to the country-specific shocks generated for each country with the regime switching using real GDP since 1973. Thus, we confirmed that conclusions were consistent with a number of theoretical and empirical evidence available, and the macro-economic changes were mainly caused by the global shocks for the past 30 years. This study found that the global business cycles were due to large-scale asymmetric shocks in addition to the general changes, and then showed the main international comovements and/or structural changes through country-specific shocks.

• Journal of Environmental Policy
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• 제13권1호
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• pp.69-93
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• 2014

The aim of this research was to develop a climate change vulnerability index at the district level (Si, Gun, Gu) with respect to the health care sector in Korea. The climate change vulnerability index was esimated based on the four major causes of climate-related illnesses : vector, flood, heat waves, and air pollution/allergies. The vulnerability assessment framework consists of six layers, all of which are based on the IPCC vulnerability concepts (exposure, sensitivity, and adaptive capacity) and the pathway of direct and indirect impacts of climate change modulators on health. We collected proxy variables based on the conceptual framework of climate change vulnerability. Data were standardized using the min-max normalization method. We applied the analytic hierarchy process (AHP) weight and aggregated the variables using the non-compensatory multi-criteria approach. To verify the index, sensitivity analysis was conducted by using another aggregation method (geometric transformation method, which was applied to the index of multiple deprivation in the UK) and weight, calculated by the Budget Allocation method. The results showed that it would be possible to identify the vulnerable areas by applying the developed climate change vulnerability assessment index. The climate change vulnerability index could then be used as a valuable tool in setting climate change adaptation policies in the health care sector.

• Journal of the Korean Ceramic Society
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• 제34권2호
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• pp.145-156
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• 1997

ZrO2 phase transformations depending on the type and amount of dopants and the sintering temperatures were studied for the 2 components (CaO-, Y2O3-, MgO-ZrO2) and the 3 components(MgO-ZrO2-Al2O3)ZrO2 powder by X-ray diffraction and Raman spectroscopy. In the CaO- and Y2O3-ZrO2 systems, as the CaO and Y2O3 contents increased to 6~15mol% and 3~15mol% respectively, we were not able to identify between tetragonal and cubic in the X-ray diffraction patterns. On the other hand, all Raman modes shifted to lower wavenumbers, decreasing in intensity and the number of bands, markedly. These phenomena were caused by tetragonallongrightarrowcubic phase transformation and interpreted by the breakdown of the wave vector selection rule(k=0) and the structural disorder associated with the formation of oxygen sublattice which was caused by the substitution between Zr4+ ion and Ca2+ or Y3+ ion in ZrO2 matrix. The monoclinic to cubic phase transformation occurred in 10mol% MgO-ZrO2 system. As the Al2O3 content increased from 0 to 20mol% in the MgO-ZrO2-Al2O3 systems, cubic phase transformed to monoclinic phase, this is because the MgO didn't play a role in a stabilizer because of the formation of the spinel(MgAl2O4) by the reaction between MgO and Al2O3, Also, the ZrO2 phase transformation was explained by the change of it's lattice parameters depending on the type and amount of dopants. Namely, as the amount of dopant increased to 10~13mol%, the axial ra-tio c/a came close to unity with increasing the lattice parameter a and decreasing the lattice parameter c. At that time, the tetragonallongrightarrowcubic phase transformation occurred.

• Tuberculosis and Respiratory Diseases
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• 제51권2호
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• pp.122-134
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• 2001

Background : Some chemotherapeutic drugs induce NF-${\kappa}B$ activation by degrading the $I{\kappa}B{\alpha}$ protein in cancer cells which contributes to anticancer drug resistance. We hypothesized that inhibiting $I{\kappa}B{\alpha}$ degradation would block NF-${\kappa}B$ activation and result in increased tumor cell mortality in response to chemotherapy. Methods : The "superrepressor" form of the NF-${\kappa}B$ inhibitor was transferred by an adenoviral vector (Ad-$I{\kappa}B{\alpha}$-SR) to the human lung cancer cell lines (NCI H157 and NCI H460). With a MIT assay, the level of sensitization to cisplatin and paclitaxel were measured. To confirm the mechanism, an EMSA and Annexin V assay were performed. Results : EMSA showed that $I{\kappa}B{\alpha}$-SR effectively blocked the NF-${\kappa}B$ activation induced by cisplatin. Transduction with Ad-$I{\kappa}B{\alpha}$-SR resulted in an increased sensitivity of the lung cancer cell lines to cisplatin and paclitaxel by a factor of 2~3 in terms of $IC_{50}$. Annexin-V analysis suggests that this increment in chemosensitivity to cisplatin probably occurs through the induction of apoptosis. Conclusion : The blockade of chemotherapeutics induced NF-${\kappa}B$ activation by inducing Ad-$I{\kappa}B{\alpha}$-SR, increased apoptosis and increasing the chemosensitivity of the lung cancer cell lines tested, subsequently. Gene transfer of $I{\kappa}B{\alpha}$-SR appears to be a new therapeutic strategy of chemosensitization in lung cancer.