• Journal of Food Hygiene and Safety
• /
• v.33 no.5
• /
• pp.325-329
• /
• 2018

This study developed a rapid detection method for Bacillus cereus in fresh-cut cabbages. Fresh-cut cabbage samples were inoculated at 1-, 2- and 3-Log CFU/g, and pathogens were enriched in tryptic soy broth containing 0.15% polymyxin B at $30^{\circ}C$, $37^{\circ}C$, and $42^{\circ}C$ to determine the detection limit and appropriate enrichment temperature for multiplex PCR detection. Enriched bacterial cells in enrichment broth were collected in a hydrophobic filter prior to DNA extraction for multiplex PCR. Filters were resuspended in distilled water, and DNA was extracted from the suspension. DNA samples were further analyzed by multiplex PCR. Detection limit of multiplex PCR was 5-Log CFU/mL. B. cereus cell counts were higher (P < 0.05) at $42^{\circ}C$ than other temperatures. Detection rate of 1-, 2-, and 3-Log CFU/g inoculated samples were 60%, 80%, and 100% after enrichment respectively. However, when enriched samples were filtered with hydrophobic membrane filter, detection rates became 100%, regardless of inoculation level. Results indicate a combination of enrichment with hydrophobic filtration improves rapid detection efficiency of B. cereus in fresh-cut cabbage by multiplex PCR.

• Maxillofacial Plastic and Reconstructive Surgery
• /
• v.28 no.5
• /
• pp.375-395
• /
• 2006

Purpose: Lingual nerve (LN) damage may be caused by either tumor resection or injury such as wisdom tooth extraction, Although autologous nerve graft is sometimes used to repair the damaged nerve, it has the disadvantage of necessity of another operation for nerve harvesting. Moreover, the results of nerve grafting is not satisfactory. The nerve growth factor (NGF) is well-known to play a critical role in peripheral nerve regeneration and its local delivery to the injured nerve has been continuously tried to enhance nerve regeneration. However, its application has limitations like repeated administration due to short half life of 30 minutes and an in vivo delivery model must allow for direct and local delivery. The aim of this study was to construct a well-functioning $rhNGF-{\beta}$ adenovirus for the ultimate development of improved method to promote peripheral nerve regeneration with enhanced and extended secretion of hNGF from the injured nerve by injecting $rhNGF-{\beta}$ gene directly into crush-injured LN in rat model. Materials and Methods: $hNGF-{\beta}$ gene was prepared from fetal brain cDNA library and cloned into E1/E3 deleted adenoviral vector which contains green fluorescence protein (GFP) gene as a reporter. After large scale production and purification of $rhNGF-{\beta}$ adenovirus, transfection efficiency and its expression at various cells (primary cultured Schwann cells, HEK293 cells, Schwann cell lines, NIH3T3 and CRH cells) were evaluated by fluorescent microscopy, RT-PCR, ELISA, immunocytochemistry. Furthermore, the function of rhNGF-beta, which was secreted from various cells infected with $rhNGF-{\beta}$ adenovirus, was evaluated using neuritogenesis of PC-12 cells. For in vivo evaluation of efficacy of $rhNGF-{\beta}$ adenovirus, the LNs of 8-week old rats were exposed and crush-injured with a small hemostat for 10 seconds. After the injury, $rhNGF-{\beta}$ adenovirus($2{\mu}l,\;1.5{\times}10^{11}pfu$) or saline was administered into the crushed site in the experimental (n=24) and the control group (n=24), respectively. Sham operation of another group of rats (n=9) was performed without administration of either saline or adenovirus. The taste recovery and the change of fungiform papilla were studied at 1, 2, 3 and 4 weeks. Each of the 6 animals was tested with different solutions (0.1M NaCl, 0.1M sucrose, 0.01M QHCl, or 0.01M HCl) by two-bottle test paradigm and the number of papilla was counted using SEM picture of tongue dorsum. LN was explored at the same interval as taste study and evaluated electro-physiologically (peak voltage and nerve conduction velocity) and histomorphometrically (axon count, myelin thickness). Results: The recombinant adenovirus vector carrying $rhNGF-{\beta}$ was constructed and confirmed by restriction endonuclease analysis and DNA sequence analysis. GFP expression was observed in 90% of $rhNGF-{\beta}$ adenovirus infected cells compared with uninfected cells. Total mRNA isolated from $rhNGF-{\beta}$ adenovirus infected cells showed strong RT-PCR band, however uninfected or LacZ recombinant adenovirus infected cells did not. NGF quantification by ELISA showed a maximal release of $18865.4{\pm}310.9pg/ml$ NGF at the 4th day and stably continued till 14 days by $rhNGF-{\beta}$ adenovirus infected Schwann cells. PC-12 cells exposed to media with $rhNGF-{\beta}$ adenovirus infected Schwann cell revealed at the same level of neurite-extension as the commercial NGF did. $rhNGF-{\beta}$ adenovirus injected experimental groups in comparison to the control group exhibited different taste preference ratio. Salty, sweet and sour taste preference ratio were significantly different after 2 weeks from the beginning of the experiment, which were similar to the sham group, but not to the control group.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.163-178
• /
• 2014

Following research proposes "Virtualization of Smart Cards (ViSCa)" which is a security system that aims to provide a multi-device platform for the deployment of services that require a strong security protocol, both for the access & authentication and execution of its applications and focuses on analyzing Virtualization of Smart Cards (ViSCa) platform-based mobile payment service by comparing with other similar cases. At the present day, the appearance of new ICT, the diffusion of new user devices (such as smartphones, tablet PC, and so on) and the growth of internet penetration rate are creating many world-shaking services yet in the most of these applications' private information has to be shared, which means that security breaches and illegal access to that information are real threats that have to be solved. Also mobile payment service is, one of the innovative services, has same issues which are real threats for users because mobile payment service sometimes requires user identification, an authentication procedure and confidential data sharing. Thus, an extra layer of security is needed in their communication and execution protocols. The Virtualization of Smart Cards (ViSCa), concept is a holistic approach and centralized management for a security system that pursues to provide a ubiquitous multi-device platform for the arrangement of mobile payment services that demand a powerful security protocol, both for the access & authentication and execution of its applications. In this sense, Virtualization of Smart Cards (ViSCa) offers full interoperability and full access from any user device without any loss of security. The concept prevents possible attacks by third parties, guaranteeing the confidentiality of personal data, bank accounts or private financial information. The Virtualization of Smart Cards (ViSCa) concept is split in two different phases: the execution of the user authentication protocol on the user device and the cloud architecture that executes the secure application. Thus, the secure service access is guaranteed at anytime, anywhere and through any device supporting previously required security mechanisms. The security level is improved by using virtualization technology in the cloud. This virtualization technology is used terminal virtualization to virtualize smart card hardware and thrive to manage virtualized smart cards as a whole, through mobile cloud technology in Virtualization of Smart Cards (ViSCa) platform-based mobile payment service. This entire process is referred to as Smart Card as a Service (SCaaS). Virtualization of Smart Cards (ViSCa) platform-based mobile payment service virtualizes smart card, which is used as payment mean, and loads it in to the mobile cloud. Authentication takes place through application and helps log on to mobile cloud and chooses one of virtualized smart card as a payment method. To decide the scope of the research, which is comparing Virtualization of Smart Cards (ViSCa) platform-based mobile payment service with other similar cases, we categorized the prior researches' mobile payment service groups into distinct feature and service type. Both groups store credit card's data in the mobile device and settle the payment process at the offline market. By the location where the electronic financial transaction information (data) is stored, the groups can be categorized into two main service types. First is "App Method" which loads the data in the server connected to the application. Second "Mobile Card Method" stores its data in the Integrated Circuit (IC) chip, which holds financial transaction data, which is inbuilt in the mobile device secure element (SE). Through prior researches on accept factors of mobile payment service and its market environment, we came up with six key factors of comparative analysis which are economic, generality, security, convenience(ease of use), applicability and efficiency. Within the chosen group, we compared and analyzed the selected cases and Virtualization of Smart Cards (ViSCa) platform-based mobile payment service.

• Journal of Intelligence and Information Systems
• /
• v.22 no.4
• /
• pp.177-192
• /
• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Korean Business Review
• /
• v.17 no.2
• /
• pp.63-87
• /
• 2004

To create and maintain comparative supremacy as a strategic tool of business, many organizations have introduced informational technology and system. By using this system, Some companies got a beneficial value for achieving organizational goals but others could not obtain their effectiveness and efficiency. In particular, a lot of organizations that tried to make strategic supremacy with e-commercial trade are under hard condition because of poor profit. It implies that it is essential to identify and analyse the consumer who uses e-commercial trade. This paper, therefore, focusing on internet shopping malls between business and consumer as one of areas of e-commercial trades, shows the difference between consumer expectation and performance. The results of this study are as follows: First, as for the significant difference of influencing factors to consumer satisfactions according to the types of internet shopping malls, there is a meaningful difference in consumer anxiety and internet usefulness, but not in consumer service. Prior to verify the differences in detail on consumer's anxiety and internet usefulness, we examined that there is any difference between expectation and performance. T-test was used for the variants of consumer anxiety and internet usefulness, and its meaningful probability was 0.000, which means that both showed statistically significant difference. Based on the results, we also found that regardless of the types of internet shopping malls, consumer expectation was greater than performance. although the difference between expectation and performance was not equal according to the internet shopping malls. Second, a regression analysis was performed to understand the relation between consumer service, internet usefulness, consumer anxiety, and consumer satisfaction, it was found that consumer service, internet usefulness, consumer anxiety had significantly effected on consumer satisfaction. Third, To verify the relation between consumer satisfaction and repurchase-intentions, intentions to spread out, Pearson correlation analysis was used. it was found that consumer satisfaction had positive effect on both intentions. This study has some limitations because of the shorts of money and time. since the sample of this study was consumers who have ever bought one or more products via internet shopping mall, this sample was appropriate. but the major parts of sample were college students, and the sample size was so small. therefore this results should carefully be generalized. For further study, it is required to select more precise samples and to include more variables.

• Journal of Korean Academy of Nursing
• /
• v.25 no.3
• /
• pp.581-596
• /
• 1995

Clinical practice in nursing education provides an opportunity for students, through the process of ap-plying theoretical knowledge to practice, and to learn nursing skills as well as being socialized into nursing and as such decrease the reality shock of actual nursing practice. Because of a shortage of nursing faculty, the job of achieving the objectives of the clinical practice had been turned over to the head nurses. This resulted in many problems, such as, unclear location of responsibilities and inadequate feedback from head nurses. Therefore this study was done to introduce and evaluate the use of preceptors as a way to minimize the above problems, and to maximize the achievement of the clinical practice objectives. Using an adaptation of Zerbe's (1991) three-tiered team model, clinical practice was done using a preceptor, a head nurse and a clinical instructor, each with different and well defined roles. The subjects of this study were 67 senior students of the College of Nursing of Y University in Seoul whose clinical practice in adult nursing was carried out between May 1, 1994 and December 8, 1994. There were 22 preceptors who had at least two years of clinical experience and who were recommended by their head nurses. They were given additional education on the philosophy and objectives of the College of Nursing, on communication skills, on the theory and practice of education, and on nursing diagnosis and education evaluation. The role of the preceptor was to work one-to-one with students in their practice. The role of the head nurse was to supervise and evaluate the preceptors. The role of the clinical instructor was to provide the education program for the preceptors, to provide ad-vice and suggestions to the preceptors and to maintain lines of communication with the college. With each of these roles in place, it was thought that the effectiveness and efficiency of the clinical practice could be increased significantly. To evaluate the effectiveness of the preceptorship, the three - tiered model, Lowery's Teacher Evaluation Opinion Form translated and adapted to Korea was used to measure student statisfaction. The Clinical Practice Compentency Evaluation Tool developed by Lee et ai was also used to measure student competencies. The results of this study are as follows 1. The satisfaction with clinical practice was higher with the introduction of the perceptors than it was before they were used. (t=-5.96, p=<.005) 2. The clinical practice competencies were higher with the introduction of the preceptors than it was before they were used(t=-5.l3, p<.005) 3. In order to analyze areas not measured by the quantitative tools additional analysis of the open questions was done. The results of this analysis showed that : 1) The students felt positive about their sense of security, confidence, handling of responsbility, and being systematic. They also felt positive about improvements in knowledge, opportunities for direct care, and socialization. 2) The students felt negative about the technical part of their role, lack of knowledge by the preceptor, unprofessional attitudes on the part of the preceptor, difficulty in the role of the professional nurse(student). 3) The preceptors felt positive about their responsibility, motivation, and relationship with the college. 4) The preceptors felt negative about their bur-den. Introduction of the preceptorship model will lead to change and improvement in the negative factors discussed above, solve problems in the present clinical education system, increase continuity in the education of the students, help with socialization of the students and motivation of the preceptors to up-grade their education and increase their confidence. These objectives must be obtained to further the development of professional nursing, and thus, making the preceptorship a reality is our job for the future.

• Korean Journal of Microbiology
• /
• v.52 no.2
• /
• pp.192-201
• /
• 2016

Recently, Psychrobacter sp. ArcL13 strain showing the extracellular lipase activity was isolated from the Chuckchi Sea of the Arctic Ocean. However, due to the low expression levels of the enzyme in the natural strain, the production of recombinant lipase is crucial for various applications. Identification of the gene for the enzyme is prerequisite for the production of the recombinant protein. Therefore, in the present study, a novel lipase gene (ArcL13-Lip) was isolated from Psychrobacter sp. ArcL13 strain by gene prospecting using PCR, and its complete nucleotide sequence was determined. Sequence analysis showed that ArcL13-Lip has high amino acid sequence similarity to lipases from bacteria of some Psychrobacter genus (84-90%) despite low nucleotide sequence similarity. The lipase gene was cloned into the bacterial expression plasmid and expressed in E. coli. SDS-PAGE analysis of the cells showed that ArcL13-Lip was expressed as inclusion bodies with a molecular mass of about 35 kDa. Refolding was achieved by diluting the unfolded protein into refolding buffers containing various additives, and the highest refolding efficiency was seen in the glucose-containing buffer. Refolded ArcL13-Lip showed high hydrolytic activity toward p-nitrophenyl caprylate and p-nitrophenyl decanoate among different p-nitrophenyl esters. Recombinant ArcL13-Lip displayed maximal activity at $40^{\circ}C$ and pH 8.0 with p-nitrophenyl caprylate as a substrate. Activity assays performed at various temperatures showed that ArcL13-Lip is a cold-active lipase with about 40% and 73% of enzymatic activity at $10^{\circ}C$ and $20^{\circ}C$, respectively, compared to its maximal activity at $40^{\circ}C$.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.8 no.6
• /
• pp.1572-1578
• /
• 2007

The specified wastes consist of waste acid, waste alkali, waste oil, waste organic solvent, waste resin, dust, sludge, infectious waste, and others. Among these specified wastes, a great portion is liquid phase wastes. The purpose of this study is to develop the high temperature and high pressure (HTHP) treatment system for decomposition of the liquid phase specified waste (LPSW). For this, we analyzed the physical and chemical properties of the LPSW such as density, proximate analysis, ultimate analysis, heating values, and designed 0.3 ton/day HTHP treatment system. The LPSW tested in this experiment were prepared by adding TCE(trichloroethylene) and toluene to liquid phase waste which was brought into the commercial waste treatment company. The average density of waste oil (25 samples), waste resin (5 samples), and waste solvent (12 samples) was 0.99 g/mL, 0.91 g/mL, and 0.93 g/mL, respectively. And the average lower heating value of waste oil, waste resin, and waste solvent was 8,294 kcal/kg, 5,809 kcal/kg, and 7,462 kcal/kg, respectively. The DRE (Destruction & Removal Efficiency) of TCE and toluene were 99.95% and 99.73% at atmospheric pressure conditions and that were 99.99% and 99.82% at pressurized conditions, respectively. These results showed that TCE/toluene mixtures were properly decomposed over about 99.73% of DRE by the HTHP treatment system and pressurized conditions were more effective to destroy those pollutants than atmospheric pressure conditions. Also these systems could be directly applied to industries which try to treat the liquid phase specified waste within the regulation limit.

• KIPS Transactions on Computer and Communication Systems
• /
• v.1 no.3
• /
• pp.143-150
• /
• 2012

A simulation methodology and corresponding program based on it is to be discussed for analyzing the effects of the networking operation of existing DHC system in connection with CHP system on-site. The practical simulation for arbitrary areas with various building compositions is carried out for the analysis of operational features in both systems, and the various aspects of thermal energy grids with connecting operation are highlighted through the detailed assessment of predicted results. The intrinsic operational features of CHP prime movers, gas engine, gas turbine etc., are effectively implemented by realizing the performance data, i.e. actual operation efficiency in the full and part loads range. For the sake of simplicity, a simple mathematical correlation model is proposed for simulating various aspects of change effectively on the existing DHC system side due to the connecting operation, instead of performing cycle simulations separately. The empirical correlations are developed using the hourly based annual operation data for a branch of the Korean District Heating Corporation (KDHC) and are implicit in relation between main operation parameters such as fuel consumption by use, heat and power production. In the simulation, a variety of system configurations are able to be considered according to any combination of the probable CHP prime-movers, absorption or turbo type cooling chillers of every kind and capacity. From the analysis of the thermal network operation simulations, it is found that the newly proposed methodology of mathematical correlation for modelling of the existing DHC system functions effectively in reflecting the operational variations due to thermal energy grids with connecting operation. The effects of intrinsic features of CHP prime-movers, e.g. the different ratio of heat and power production, various combinations of different types of chillers (i.e. absorption and turbo types) on the overall system operation are discussed in detail with the consideration of operation schemes and corresponding simulation algorithms.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.161-177
• /
• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.