• Journal of Food Hygiene and Safety
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• v.35 no.1
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• pp.68-74
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• 2020

This study investigated the inactivation and synergistic efficacy of combined low-temperature heating (LT) and atmospheric plasma (AP) against Escherichia coli in red pepper powder. A cocktail of two strains of E. coli (ATCC 11229, KCCM 11234) was inoculated onto red pepper powder and then treated with LT (60℃ for 5-20 min) and AP (atmospheric plasma for 5-20 min). The counts of E. coli in the red pepper powder were significantly (P<0.05) reduced with the increase in treatment time using LT and AP. The reduction of E. coli levels in red pepper powder when treated with LT alone for 5, 10, 15, and 20-min were 0.25, 1.45, 2.54, and 2.85 log₁₀ CFU/g, respectively. Also, the reduced levels of E. coli on red pepper powder when treated with AP alone for 5, 10, 15, and 20 min were 0.19, 0.32, 0.54, and 0.96 log₁₀ CFU/g, respectively. The synergistic effects were not dependent on the treatment time with AP, but were dependent on the LT treatment time. Synergistic reduction values for combined LT and AP treatment against E. coli in red pepper powder were -0.13 to 2.91 log₁₀ CFU/mL, respectively. Especially, the largest synergistic values (2.91-2.82 log₁₀ CFU/mL) of E. coli in red pepper powder were revealed by the combination of a 20-min treatment with LT and a 15-20-min treatment with AP. All sensory parameters (color, off-odor, taste, texture, and overall acceptability) were not significantly (P>0.05) different between non-treated and all combination-treated samples. Therefore, these results suggest that the combination of LT and AP can be potentially utilized in the food industry to effectively inactivate E. coli without incurring quality deterioration in red pepper powder.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Korea Technology Innovation Society
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• v.19 no.3
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• pp.465-482
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• 2016

Previous researches have introduced various ways to analyze the impact of standardization on innovation while the works are not only small in number but based on interview or case study. This paper addresses the impact of standardization activities within South Korean industries on technological innovation applying an empirical analysis of standardization activities and technological innovation. Drawing on Korean Industrial Standards Classification from panel data of 2003 to 2012, we employed corresponding data of each industrial classification: Number of standards, Accumulated number of standards, Number of patents applied in Korea, Sales, Operational profit, Intangible asset, and R&D invest. In the first model, we run panel data models employing the number of patents applied in Korea as an independent variable, and the number of standards, accumulated number of standards, sales, and operational profit as dependent variables to observe industrial impacts upon the relationship between standards and patents, along with time lagged consideration. The result shows that number of standards are revealed to have a negative influence on patent applications in the year of research, and no significant effect appears for the next two years while positive effect shows up on the third year. Meanwhie, accumulated number of standards turned out to have positive effects on patent applications in Korea. This implies it takes time for innovation subjects to embrace newly established standards while having a significant amount of positive effect on technological innovation in the long term. In the second model, we use canonical correlation analysis to find industrial-wide characteristics. The result of this model is equivalent to the result of panel data analysis except in a few industries, where some industry specific characteristics appear. The implications of our results present that Korean policy makers have to take account of industrial effects on standardization to promote technological innovation.

• Journal of Korean Society of Environmental Engineers
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• v.22 no.1
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• pp.33-42
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• 2000

Adsorption onto the surfaces of solid particles is a well known phenomenon that causes the retardation effect of heavy metals in soils. For adequate remediation of soil and groundwater contamination, it is important to investigate the mobility of heavy metals that largely depends on pH conditions in the soil water since adsorption of heavy metals is pH-dependent. In this study, we investigated the transport of Zn ion under various pH conditions in a sandy soil by conducting batch and column tests. The batch test was performed using the standard procedure of equilibrating fine fractions collected from the soil with eleven different initial $ZnCl_2$ concentrations, and analysis of Zn ion in the equilibrated solutions using ICP-AES. The column test consisted of monitoring the concentrations of soil solutions exiting the soil column with time known as a breakthrough curve (BTC). We injected respectively $ZnCl_2$ and KCl solutions with the concentration of 10 g/L as a tracer in a square pulse type under three different pH conditions (7.7, 5.8, 4.1) and monitored the flux concentration at the exit boundary using an EC meter and ICP-AES. The resident concentration was also monitored at the 10cm-depth by Time Domain Reflectometry (TDR). The results of batch test showed that ion exchange process between Zn and other cations (Ca, Mg) was predominant. The retardation coefficients obtained from adsorption isotherms (Linear, Freundlich, Langmuir) resulted in the various values ranging from 1.2 to 614.1. No retardation effect but ion exchange was found for the BTCs under all pH conditions. This can be explained by the absence of other cations to desorb Zn ion from soil exchange sites under the conditions of ETC experiment imposing blank water as leachate in steady-state flow. As pH decreased, the peak concentration of Zn increased due to the competition of Zn with hydrogen ions ($H^+$) and the concentrations of other cations decreased. The peak concentration of Zn was increased by 12.7 times as pH decreased from 7.7 to 4.1.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Life Science
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• v.19 no.4
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• pp.520-525
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• 2009

Early onset of Batten disease (EBD), one of the most lethal neurodegenerative storage disorders of childhood, is caused by inactivating mutations in the Ceroid Lipofuscinosis, Neuronal (CLN1) gene. Neurogranin, a calmodulin-binding protein, is expressed in the brain and participates in the protein kinase C (PKC) signaling pathway. While oxidative stress is the suggested cause of neurodegeneration in EBD, its molecular mechanism(s) remains obscure. In this research, we examined the levels of neurogranin in the brain mRNA of wild-type (WT) mice and EBD knockout (KO) mice, as well as the proteins. We also performed neuronal cultures to measure the expression levels of neurgranin and phosphorylated-neurogranin with or without oxidative stress inducers and anti-oxidants. Results showed that neurogranin in both EBD KO mice brain mRNA and protein extracts decreased in an age dependent manner. However, high amounts of phosphorylated-neurogranin were detected in the 6-month brain. This pattern was also confirmed by cultured neurospheres samples. Moreover, neurospheres treated with $H_2O_2$, an oxidative stress inducer, showed increased phosphorylated-neurogranin patterns. Interestingly, this pattern returned to normal status when treated with N-acetyl-L-cystein, an anti-oxidant, after $H_2O_2$ treatment was performed. Our results suggest that the phosphorylation of neurogranin is affected by oxidative stress status in EBD, and appropriate anti-oxidant treatment will relieve hyper-phosphorylation of neurogranin.

• International Journal of Industrial Entomology and Biomaterials
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• v.19 no.2
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• pp.237-241
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• 2009

The objective of our study was the evaluation of pathogenicity of a local strain of Mamestra brassicae nucleopolyhedrovirus-K1 (MabrNPV-K1) derived from a diseased larva of M. brassicae found in Korea. The effect of temperature and larval instar on the pathogenicity and production of MabrNPV-K1 was determined under laboratory conditions. The median lethal concentration ($LC_{50}$) values of MabrNPV-K1 for 3rd instar larvae were $3.7\times10^4$ PIBs/larva at $20^{\circ}C$, $9.9\times10^2$ PIBs/larva at $25^{\circ}C$ and $3.8\times10^2$ PIBs/larva at $30^{\circ}C$, respectively. The $LC_{50}$ for the 4th instar larvae was similar to that for the 3rd instar larvae. However, the pathogenicity to the 3rd instar larvae was higher than that to the 4th instar larvae. The median lethal time ($LT_{50}$) values of MabrNPV-K1 were 11.4 to 5.0 days at $30^{\circ}C$ and 18.3 to 5.5 days at $25^{\circ}C$ for the 3rd instar larvae. The $LT_{50}$ value was lowered as temperature went up to $30^{\circ}C$ and dependent on viral concentration. In production efficiency of MabrNPV-K1 using M. brassicae larvae, the mortality of the 3rd instar larvae was 100% when inoculated with $1.0\times10^5$ PIBs/larva and the yield of MabrNPV-K1 was maximal. Regarding the mortality, yield of polyhedra, inoculation doses and required time, the $1.0\times10^4$PIBs/larva at $30^{\circ}C$ was determined as optimal conditions producing polyhedra efficiently.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.12
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• pp.1647-1653
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• 2008

This study used response surface methodology (RSM) in an effort to optimize the water extraction conditions of Hypsizigus marmoreus in order to increase cytotoxicity activity of the extract. A central composite design was applied to investigate the effects of independent variables, which included the extraction temperature ($X_1$), extraction time ($X_2$), the ratio of solvent to sample ($X_3$) on dependent variables of the extracts, including extraction yield ($Y_1$) and protein content ($Y_2$). The estimated optimal conditions were as follows: $51.3^{\circ}C$ extraction temperature, 8.2 hrs extraction time, and 46.7 mL/g of solvent per sample. The extract (CE) was extracted at optimal condition and crude polysaccharides (CPS) were obtained from CE by ethanol precipitation, dialysis, and freeze drying. Neutral (NPS) and acidic (APS) fraction of polysaccharides were seperated from CPS by ion chromatography. The growth inhibitory effects of the APS (0.5 mg/mL) on AGS human cancer cells were 73.97%. CPS showed the highest growth inhibitory effects on HepG2 human cancer cell at 0.5 mg/mL. However all fraction polysaccharides from Hypsizigus marmoreus showed lower than 20% growth inhibition on SW480 human cancer cell.

• Journal of Life Science
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• v.27 no.8
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• pp.879-887
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• 2017

It is well established that brain-derived neurotrophic factor (BDNF) is expressed not only in the brain but also in skeletal muscle, and is required for normal neuromuscular system function. Histone deacetylases (HDACs) and insulin-like growth factor-I (IGF-I) are potent regulators of skeletal muscle myogenesis and muscle gene expression, but the mechanisms of HDAC and IGF-I in skeletal muscle-derived BDNF expression have not been examined. In this study, we examined the effect of IGF-I and suberoylanilide hydroxamic acid (SAHA), a pan-HDAC inhibitor, on BDNF induction. Proliferating or differentiating C2C12 skeletal muscle cells were treated with increasing concentrations (0-50 ng/ml) of IGF-I in the absence or presence of $5{\mu}M$ SAHA for various time periods (3-24 hr). Treatment of C2C12 cells with IGF-I resulted in a dose- and time-dependent decrease in BDNF mRNA expression. However, inhibition of HDAC led to a significant increase in the expression of BDNF mRNA levels. In addition, immunocytochemistry revealed high BDNF protein levels in undifferentiated C2C12 skeletal muscle cells, whether untreated, IGF-I-treated, or exposed to SAHA. These results represent the first evidence that IGF-I can suppress the mRNA and protein expression of BDNF; conversely, SAHA attenuates the effects of IGF-I. Consequently, SAHA upregulates BDNF expression in C2C12 skeletal muscle cells.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.4
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• pp.1041-1054
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• 2004

Electroacupuncture (EA) has been reported to increase pain threshold, and to enhance the NK cell activity by up-regulation of IFN-γ and endogenous β-endolphin. For the purpose of understanding the molecular mechanism of EA stimulation, we analyzed the gene expression profile of rat hypothalamus, treated on Zusanli (ST36) with EA, in comparison with control group by oligonucleotide chip microarray (Affymetrix GeneChip Rat Neurobiology U34 Array) and real-time RT-PCR. Sprague-Dawley (S-D) male rats were stimulated at the Zusanli (ST36) acupoint in restriction holder. Simultaneously the control group was given only holder stress without EA stimulation. In order to prove the appropriateness of EA treatment, we measured spleen NK cell activity with standard 51Cr release assay. NK cell activity of EA group was significantly increased comparing to control group. The microarray and PCR results show that EA treatment up-regulates expression of genes associated with 1) nerve growth such as NGF induced factor A and VGF, 2) signal transduction such as 5HT3 receptor subunit, AMPA receptor binding protein and Na-dependent neurotransmitter transporter, and 3) anti-oxidation such as superoxide dismutase and glutathione S-transferase. In addition, the activity of the anti-oxidative enzyme, SOD of hypothalamus, liver and RBC was enhanced compared to that of control. The list of differentially expressed genes may implicate further insight on the mechanism of acupuncture effects.