• Journal of Microbiology and Biotechnology
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• v.29 no.4
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• pp.596-606
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• 2019

N-acyl-homoserine lactone quorum sensing (AHL-QS) has been shown to regulate many physiological behaviors in Serratia marcescens MG1. In the current study, the effects of AHL-QS on the biosynthesis of acid and neutral products by S. marcescens MG1 and its isogenic ${\Delta}swrI$ with or without supplementing exogenous N-hexanoyl-L-homoserine lactone ($C_6-HSL$) were systematically investigated. The results showed that swrI disruption resulted in rapid pH drops from 7.0 to 4.8, which could be restored to wild type by supplementing $C_6-HSL$. Furthermore, fermentation product analysis indicated that ${\Delta}swrI$ could lead to obvious accumulation for acidogenesis products such as lactic acid and succinic acid, especially excess acetic acid (2.27 g/l) produced at the early stage of fermentation, whereas solventogenesis products by ${\Delta}swrI$ appeared to noticeably decrease by an approximate 30% for acetoin during 32-48 h and by an approximate 20% for 2,3-butanediol during 24-40 h, when compared to those by wild type. Interestingly, the excess acetic acid produced could be removed in an AHL-QS-independent manner. Subsequently, quantitative real-time PCR was used to determine the mRNA expression levels of genes responsible for acidogenesis and solventogenesis and showed consistent results with those of product synthesis. Finally, by close examination of promoter regions of the analyzed genes, four putative luxI box-like motifs were found upstream of genes encoding acetyl-CoA synthase, lactate dehydrogenase, ${\alpha}$-acetolactate decarboxylase, and Lys-like regulator. The information from this study provides a novel insight into the roles played by AHL-QS in switching from acidogenesis to solventogenesis in S. marcescens MG1.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.6
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• pp.43-55
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• 2021

Recently, as the domestic job problem has become serious, government ministries are investing a lot of budgets to encourage startups by prospective entrepreneurs. What is important to the success of startups is the recognition of various startup opportunities before starting a startup, and the experience through trial. However, in reality, prospective entrepreneurs recognize and seek various startup opportunities through support such as startup education and initial commercialization funds through various government supported projects, but it is difficult to actually start a business. Previous studies have revealed that the recognition of entrepreneurial opportunities affects entrepreneurial intentions by various variables such as gender, but research is insufficient on what kind of black box exists between the recognition of entrepreneurial opportunities and entrepreneurial intentions. The purpose of this study is to emphasize the importance of positive psychological capital as a major method for improving the entrepreneurial intention, and to analyze exploratorily whether positive psychological capital plays a mediating role between the recognition of entrepreneurial opportunities and the entrepreneurial intention. As a result of the study, it was confirmed that the recognition of startup opportunities affects the intention to start a business, and positive psychological capital has a mediating effect between the recognition of the startup opportunity and the intention to start a business. This means that positive psychological capital is important in order to lead to actual entrepreneurial intentions after recognizing a startup opportunity. Therefore, in order to revitalize the startups of prospective entrepreneurs in the current startup ecosystem, it is necessary to prepare a plan to improve the recognition of startup opportunities and positive psychological capital.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.