• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.361-381
• /
• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Food Science of Animal Resources
• /
• v.30 no.1
• /
• pp.133-140
• /
• 2010

The pork hams and bacon comprising the most popular processed pork were treated with autoclave to investigate application of hypoallergenic pork. Among pork hams and bacon, two products with the highest binding ability were selected for experiments. The results of ci-ELISA on pork hams treated with autoclave showed that the binding ability of p-IgG and pigallergic patient's sera (P2) to PSA (porcine serum albumin) from pork ham samples by autoclave treatment at $121^{\circ}C$ for 30 min was slightly decreased. The binding ability to p-IgG of b and c bacon treated with autoclave was declined to below 16% and 11% as compared with control sample that showed 60% and 91% binding ability. The binding ability to P2 of b and c bacon treated with autoclave decreased to below 22% and 34% as compared with control sample that showed 95% and 126% binding ability. A result of immunoblotting on bacon showed that p-IgG as well as pig patient's sera did not recognize PSA well in autoclave treatment. The results obtained from this work indicated that autoclave treatment was effective for a reduction of allergenicity of pork hams and bacon. Therefore the autoclave treatment may be applied to development of hypoallergenic pork.

• Korean Chemical Engineering Research
• /
• v.44 no.1
• /
• pp.65-72
• /
• 2006

Recent technical advances in the biorecognition engineering and the microparticle fabrication may enable us to develop the single step purification using magnetic particle, because of its simplicity, efficacy, ease of automation, and process economics. In this study, we used commercial magnetic particles from Seradyn, Inc. (Indianapolis, USA). It was ca. 2.8 micron in diameter, consisted of polystyrene core and magnetite coating, and its surface had carboxyl groups. The model, capture protein was IgG and anti-IgG was used as the ligand molecule. We studied the different surfaces ('nude', ester-activated, and anti-IgG coated) for their biorecognition of IgG. At a high pH condition, we could reduce non-specific binding. Also anti-IgG immobilized magnetic particle could capture IgG more selectively. We attempted 'oriented immobilization' of anti-IgG, in which the polysaccharides moiety near the C-terminus was selectively oxidized and linked to the hydrazine-coated MP, to improve the efficacy of biorecognitive binding. Using this method, the IgG capturing ability was improved by ca. 2 fold. From the binary mixture of the IgG-insulin, IgG could be more selectively captured. In summary, the oriented immobilization of oxidized anti-IgG proved to be as effective as the streptavidin-biotin system and yet simpler and cost-effective. This immobilization method can find its applications in protein biochips and biotargeting.

• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.48 no.4
• /
• pp.365-372
• /
• 2022

Cellobiose is a dissacharide constituted by two glucose units joined by a β-('1,4') glycosidic bond that is produced by the decomposition of cellulose. This product exists naturally in plants and has been utilized in different industries as a food sweetener, and as a cosmetic and pharmaceutical material. In this study, the potential of cellobiose as a whitening cosmetic product was evaluated by analyzing autophagy induction and the inhibition of melanin production. A cytotoxicity test conducted in the human melanin-producing cell line MNT-1 with increasing concentrations of cellobiose revealed that this compound did not cause cytotoxicity at 20 mg/mL or less. Based on this, autophagy was firstly evaluated by immunostaining with the autophagy marker microtubule-associated protein 1 light chain 3 (LC3) after treatment with 20 mg/mL of cellobiose. The subsequent confocal microscopy analysis revealed an increase in LC3 puncta, indicating induction of autophagy. In addition, autophagy was further confirmed by western blot analysis, which demonstrated that cellobiose converted LC3-I to LC3- ∏ in a concentration- and time-dependent manners. An analysis of melanin contents after cellobiose treatment at a concentration of 20 mg/mL during 7 days revealed that melanin production was reduced by more than 50%. Additionally, the expression levels of melanogenesis-related proteins TYR and TYRP1 were markedly decreased after cellobiose treatment. Based on these studies, a cosmetic cream formulation containing cellobiose was prepared and the change in formulation was tested for 4 weeks, and it was confirmed that the appearance changed to liquid form at high temperature, but the pH did not change. In conclusion, the present research demonstrated that cellobiose activates autophagy and inhibits melanin production, and showed the potential of this product as a material for whitening cosmetics.

• Journal of Service Research and Studies
• /
• v.13 no.4
• /
• pp.97-112
• /
• 2023

Background: In modern society, the importance of Public Design has become increasingly significant in contributing to the enhancement of urban functionality and the quality of life of citizens. Smart Public Facilities have played a pivotal role in enriching user experience by improving accessibility, convenience, and safety, and in elevating the value of the city. This research recognizes the importance of Public Facilities and explores the potential of Smart Public Facilities in solving urban challenges and progressing towards sustainable and Inclusive cities. Method: The literature review comprehensively examines existing theories and research results on Smart Public Facilities. The case study analyzes actual examples of Smart Public Facilities implemented in cities both domestically and internationally, drawing out effects, user satisfaction, and areas for improvement. Through analysis and discussion, the results of the case studies are evaluated, discussing the potential development of Smart Public Facilities. Results: Smart Public Facilities have been found to bring positive changes in various aspects such as urban management, energy efficiency, safety, and information accessibility. In terms of urban management, they play a crucial role in optimization, social Inclusiveness, environmental protection, fostering citizen participation, and promoting technological innovation. These changes create a new form of urban space, combining physical space and digital technology, enhancing the quality of life in the city. Conclusion: This research explores the implications, current status, and functions of Smart Public Facilities in service and design aspects, and their impact on the urban environment and the lives of citizens. In conclusion, Smart Public Facilities have brought about positive changes in the optimization of urban management, enhancement of energy efficiency, increased information accessibility, User-Centric design, increased interaction, and social Inclusiveness. Technological innovation and the integration of Public Facilities have made cities more efficient and proactive, enabling data-based decision-making and optimized service delivery. Such developments enable the creation of new urban environments through the combination of physical space and digital technology. The advancement of Smart Public Facilities indicates the direction of urban development, where future cities can become more intelligent, proactive, and User-Centric. Therefore, they will play a central role in Public Design and greatly contribute to improving the urban environment and the quality of life of citizens.

• Journal of Food Hygiene and Safety
• /
• v.21 no.2
• /
• pp.100-106
• /
• 2006

The use of underarm and body care cosmetics with oestrogenic chemical excipients (particularly the parabens) and the hypothesized association with breast cancer incidence, particularly in women. It is noted that the type of cosmetic product is irrelevant (e.g. antiperspirant/deodorant versus body lotion, moisturizers or sprays versus creams) and attention must focus on issues of actual exposure to chemicals through continued dermal application of body care products and the endocrine/hormonal activity and toxicity of the chemicals in the formulations. To evaluate the estrogenic activities of parabens such as ethylparaben, butylparaben, propylparaben, isobutylparaben and isopropylparaben, we used recombinant yeasts containing the human estrogen receptor [Saccharomyces cerevisiae ER+LYS 8127], human breast cancer MCF-7 cell lines and human estrogen receptor ${\alpha}\;and\;{\beta}$. In E-screen assays, isopropylparaben is the most estrogenic paraben, and in ER competition assay, isobutylparaben is the most estrogenic paraben. We evaluated isopropylparaben was most active in the recombinant yeast assay, followed by propylparaben, ethylparaben, isobutylparaben and butylparaben. Results from this study demonstrate that parabens are observed in human endocrine system. Therefore, we have shown that the parabens is induced the estrogenic activities similar to $17{\beta}$-estradiol and Bisphenol-A.

• Journal of Intelligence and Information Systems
• /
• v.24 no.2
• /
• pp.221-241
• /
• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Development and Reproduction
• /
• v.14 no.4
• /
• pp.287-295
• /
• 2010

It was recently reported that nesfatin-1/NUCB2, which is secreted from the brain, controls appetite and energy metabolism. The purpose of this research was to confirm whether or not the protein and its binding site should have been expressed in the mouse reproductive organs and to know the possible effects of nesfatin-1 on the reproductive function. Using the ICR female mouse ovary and uterus, the expression of NUCB2 mRNA was confirmed with the conventional PCR and the relative amount of NUCB2 mRNA in the tissues was analyzed with real-time PCR. Immunohistochemical staining was performed using the nesfatin-1 antibody to investigate the nesfatin-1 protein expression and the biotin conjugated nesfatin-1 to confirm the binding site for nesfatin-1 in the ovary. Furthermore, in order to examine if the expression of NUCB2 mRNA in the ovary and uterus is affected by gonadotropin, its mRNA expression was analyzed after PMSG administration into mice. As a result, the expression level of NUCB2 mRNA in the ovary and the uterus was as much as the expression level in hypothalamus. As a result of the immunohistochemical staining, nesfatin-1 proteins were localized at the theca cells, the interstitial cells, and some of the luteal cells. However, the granulosa cells in the follicles did not stain. Interestingly, the oocytes in the some follicles were stained with nesfatin-1. On the other hand, nesfatin-1 protein binding sites were displayed at the theca cells and the interstitial cells near the tunica albuginea. After PMSG administration the expression level of NUCB2 mRNA was increased in the ovary and the uterus. These results demonstrate that for the first time the nesfatin-1 and its binding site were expressed in the ovary and NUCB2 mRNA expression was controlled by gonadotropin, suggesting an important role in the reproductive organs as a local regulator. Therefore, further study is needed to elucidate the functions of nesfatin-1 on the reproductive organs.

• Microbiology and Biotechnology Letters
• /
• v.34 no.1
• /
• pp.7-14
• /
• 2006

Feather, generated in large quantities as a byproduct of commercial poultry processing, is almost pure keratin, which is not easily degradable by common professes. Four strains, SMMJ-2, FL-3, NO-4 and RM-12 were isolated from soil for production of extracellular keratinolytic protease. They were identified as Bacillus sp. based on their morphological and physiological characteristics. They shown high protease activity on 5.0% skim milk agar medium and produced a substrate like mucoid on keratin agar medium. Bacillus sp. SMMJ-2 had a faster production time for producing keratinolytic protease than other strains. This strain did not completely degrade whole chicken feather for five days in basal medium but completely degraded whole chicken feather when supplied with nitrogen source for 40hours in keratinolytic producing medium ($0.7%\;K_{2}HPO_{4},\;0.2%\;KH_{2}PO_{4},\;0.1%$ fructose, 1.2% whole chicken feather, $0.01%\;Na_{2}CO_3$, pH 7.0). When supplied with chicken feather as nitrogen source, keratinolytic protease activity was 89 units/ml/min. When soybean meal was used as nitrogen source, the keratinolytic protease production reached a maximum of 106 units/ml/min after 48 hours under $30^{\circ}C$, 180 agitation. To isolate the keratinolytic protease, the culture filtrate was precipitated with $(NH_4)_{2}SO_4$ and acetone. The recovery rate of keratinolytic protease was about 96% after treatment with 50% acetone. The enzyme was stable in the range of $30{\sim}50^{\circ}C$ and pH $6.0{\sim}12.0$.

• The Korean Journal of Nuclear Medicine
• /
• v.38 no.5
• /
• pp.338-343
• /
• 2004

Purpose: The purpose of this study was to develop a small animal PET using dual layer phoswich detector to minimize parallax error that degrades spatial resolution at the outer part of field-of-view (FOV). Materials and Methods: A simulation tool GATE (Geant4 Application for Tomographic Emission) was used to derive optimal parameters of small PET, and PET was developed employing the parameters. Lutetium Oxyorthosilicate (LSO) and Lutetium-Yttrium Aluminate-Perovskite(LuYAP) was used to construct dual layer phoswitch crystal. $8{\times}8$ arrays of LSO and LuYAP pixels, $2mm{\times}2mm{\times}8mm$ in size, were coupled to a 64-channel position sensitive photomultiplier tube. The system consisted of 16 detector modules arranged to one ring configuration (ring inner diameter 10 cm, FOV of 8 cm). The data from phoswich detector modules were fed into an ADC board in the data acquisition and preprocessing PC via sockets, decoder block, FPGA board, and bus board. These were linked to the master PC that stored the events data on hard disk. Results: In a preliminary test of the system, reconstructed images were obtained by using a pair of detectors and sensitivity and spatial resolution were measured. Spatial resolution was 2.3 mm FWHM and sensitivity was 10.9 $cps/{\mu}Ci$ at the center of FOV. Conclusion: The radioactivity distribution patterns were accurately represented in sinograms and images obtained by PET with a pair of detectors. These preliminary results indicate that it is promising to develop a high performance small animal PET.