• Journal of Microbiology and Biotechnology
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• v.15 no.4
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• pp.844-854
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• 2005

We constructed a defined physical and genetic map of H. pylori strain 51, previously isolated from a Korean patient with a duodenal ulcer, by combining a restriction analysis by pulse-field gel electrophoresis with the construction of a BAC library. A Notl-digest of H. pylori strain 51 genome yielded seven fragments, from which the genomic size was estimated to be 1,698$\pm$24 kb. The BAC library was constructed from 50 to 200 kb fragments of HindIII-digested genomic DNA. From 700 BAC clones, an ordered overlapping maxi-set of 82 BAC clones was assembled that covered the entire genome. The positions of 15 genes were localized in the strain 51 genome with 4-22 kb of resolution and were compared with their orthologues in strain 26695 and strain J99. The arrangement of the 15 genes was identical in strain 51 and strain J99, except for flaA and hpaA. The plasticity zone of strain 51, like that of strain J99, was located in the single region, and was shorter than those of strain 26695 and strain J99. The strain 51 plasticity zone consisted of ORFs common only to strain 51 and J99 or to strain 51 and 26695, as well as strain 51-specific ORFs. Three genetic translocations and/or inversions were found between orthologue ORFs in strain 51 and strain J99. These results show that the chromosomal organization of strain 51 differs from Western strains such as strain 26695 and strain J99.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.891-903
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• 2017

In recent days, school violence has been rising as a significant social problem. The occurrence of school violence can be fueled by a wide range of social and environmental contexts, thus it is very difficult to come up with the ultimate solution. Community CPTED program is one of the more comprehensive set of efforts that has been developed to prevent crime in general, and the primary focus of the study is to investigate suitable components of CPTED to prevent school violence based on the physical conditions of communities as well as the perception of residents outside of school grounds, in the vicinity of schools. Three sets of analyses in series were employed in the research. First, Ward's minimum-variance cluster analysis was used to classify the places where school violence can occur outside of school grounds based on the physical conditions recognized by local residents. Next, Importance-Performance Analysis was performed to analyze the perception of local residents about the importance and the effectiveness of CPTED components in preventing school violence. Subsequently, Ordered Logit Model was used to analyze the local residents' awareness on safety regarding school violence in their community space. Combining the results of the analyses, the priority of the community CPTED applications to prevent school violence outside of school was derived. Reflecting the local residents' perception on safety of students in their community, the sense of security in communities against school violence can be reinforced by the communities' sensible efforts in creating safer environment for their students.

• The Korean Journal of Franchise Management
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• v.12 no.4
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• pp.41-54
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• 2021

Purpose: Delivery food orders are on the rise due to the COVID 19 pandemic. Many customers are ordering food through delivery apps rather than visiting restaurants to eat out. Delivery application platforms are growing due to the development of O2O. Most of the people who provide gig worker for delivery applications are rider. Rider provides labor on their own terms and have more work flexibility and autonomy than ordinary workers. Trust can be transferred from a well-known entity to an unknown entity. From the customer's point of view of using the delivery application, trust can be seen through the third-party trust of the delivery application platform-rider-customer. Therefore, this study intends to investigate the effect on delivery application trust and rider trust through the well-known characteristics of delivery applications. Research design, data, and methodology: This study was conducted on Korean consumers over 20 years of age who have ordered food through a delivery application for the past month. After educating 5 investigators about the purpose of this study, 60 copies of the survey were conducted per person. During the investigation period, from September 2 to September 26, 2021, 322 copies were collected over 25 days. Among the collected questionnaires, 37 were excluded from insincere or partially unanswered, and 285 were used for analysis. In addition, the collected data were analyzed using SPSS 25.0 and AMOS 25.0. Result: As a result of the study, convenience, price, and variety of restaurants were found to have a significant positive (+) effect on app trust, but design did not have a significant effect on app trust. Also, it was found that convenience had a significant positive (+) effect on trust in rider, but design, price, and variety of restaurants did not have a significant effect. App trust was found to have a significant positive (+) effect on rider trust and intention to use, and it was found to have a significant positive (+) effect on rider trust and intention to use. Conclusions: First, this study established a structural framework between delivery application characteristics-delivery-app trust-rider trust-intention to use. Second, in this study, it was found that customer trust in well-known delivery applications was transferred to less-known rider trust. Third, the delivery application should increase the convenience of use. Fourth, delivery application should set the delivery fee appropriately. Fifth, delivery application must continuously train the rider.

• The Journal of the Korean Society for Microbiology
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• v.34 no.6
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• pp.519-532
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• 1999

Helicobacter pylori is a causative agent of type B gastritis and plays a central role in the pathogenesis of gastroduodenal ulcer and gastric cancer. To elucidate the host-parasite relationship of the H. pylori infection on the basis of molecular biology, we tried to evaluate the genomic diversity of H. pylori. An ordered overlapping bacterial artificial chromosome (BAC) library of a Korean isolate, H. pylori 51 was constructed to set up a genomic map. A circular physical map was constructed by aligning ApaI, NotI and SfiI-digested chromosomal DNA. When the physical map of H. pylori 51 was compared to that of unrelated strain, H. pylori 26695, completely different restriction patterns were shown. Fifteen known genes were mapped on the chromosome of H. pylori 51 and the genetic map was compared with those of strain 26695 and J99, of which the entire genomic sequences were reported. There were some variability in the gene location as well as gene order among three strains. For further analysis on the genomic diversity of H. pylori, when comparing the genomic structure of 150 H. pylori Korean isolates with one another, genomic macrodiversity of H. pylori was characterized by several features: whether or not susceptible to restriction digestion of the chromsome, variation in chromosomal restriction fingerprint and/or high frequency of gene rearrangement. We also examined the extent of allelic variation in nucleotide or deduced amino acid sequences at the individual gene level. fucT, cagA and vacA were confirmed to carry regions of high variation in nucleotide sequence among strains. The plasticity zone and strain-specific genes of H. pylori 51 were analyzed and compared with the former two genomic sequences. It should be noted that the H. pylori 51-specific sequences were dispersed on the chromosome, not congregated in the plasticity zone unlike J99- or 26695-specific genes, suggesting the high frequency of gene rearrangement in H. pylori genome. The genome of H. pylori 51 shows differences in the overall genomic organization, gene order, and even in the nucleotide sequences among the H. pylori strains, which are far greater than the differences reported on the genomic diversity of H. pylori.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.995-1006
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• 2003

It is well know that in the wireless communication systems the transmitted signals can suffer from multipath fading due to the wave propagation characteristics and the obstacles over the paths, resulting in serious reduction in the power of the received signals. However, it is possible to take advantage of the inherent diversity imposed in the multipath reception if the underlying channel can be properly estimated. One of the diversity reception methods in this case is Rake processing. In this paper we study the Rake receivers for the direct-sequence spread-spectrum communication systems utilizing PN (pseudo noise) sequences to achieve spread spectrum. A conventional Rake receiver can use the finite-duration impulse (FIR) filter followed by the PN sequence demodulator, where the FIR filter coefficients are the reverse-ordered complex conjugate values of the fading channel impulse response estimates. Here, we propose a new Rake processing method by replacing the aforementioned PN code sequence with a new set of optimum demodulator coefficients. More specifically, the concept of the new optimum Rake processing is first introduced and then the optimum demodulator coefficients are theoretically derived. The performance obtained using the new optimum Rake processing is also calculated. The analytical results are verified by computer simulation. As a result, it is shown that the new optimum Rake processing method improves the MSE performance more than 10 dB over the conventional one using the fixed PN sequence demodulator. It is also shown that the new optimum Rake processing method improves the MSE performance about 10 dB over the Adaptive Correlator that performs the combining of the multipath components and PN demodulation concurrently. And finally, the MSE performance of the optimum Rake demodulator is very close to the MSE performance of OPSK demodulator under the AWGN channel.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.