• Journal of Nutrition and Health
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• v.47 no.2
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• pp.113-123
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• 2014

Purpose: Vitamin D has been known to play an important role in the central nervous system and brain functions in the human body, and cumulative evidence has shown that vitamin D deficiency might be linked with various mental health conditions. Epidemiologic studies have shown that vitamin D deficiency may be associated with higher risk of depression in the US and European populations. However, limited information is available regarding the association between vitamin D status and depression in the Korean population. The objective of this study was to examine the associations between vitamin D levels and prevalence of depression. Methods: We conducted a cross-sectional analysis using nationally representative data from the 2008-2010 Korean National Health and Nutrition Examination Survey from which serum 25-hydroxyvitamin D concentrations were available. A total of 18,735 adults who had available demographic, dietary, and lifestyle information were included in our analysis. We defined "depression" with a diagnosis by a physician. "Depressed condition" was defined as having feelings of sadness or depression without diagnosis by a physician. Results: The prevalence of depression was 1.63% and 5.43% in Korean men and women, respectively; 12.5% of men and 26.1% of women were defined as the group having depressed conditions. In multivariate logistic regression models, no significant associations were observed between vitamin D status and prevalence of depression or depressed conditions in Korean men and women. Conclusion: We found no association between vitamin D insufficiency and depression/depressed conditions in Korean adults. Future large prospective studies and randomized controlled trials are needed to confirm this relationship.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.27-35
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• 1986

This experiment was carried out to elucidate the factor of nitrogenase formation and to establish the nitrogen fixation system in mixed culture of cultured cells and rhizobia through tissue culture technique using three soybean varieties, Hwangkeum, Namcheon and D 68-0099 as host plants. The results obtained were as follows; The callus was induced in embryo and radicle, but not in hypocotyl. The most favorable callus induction was caused by the individual application of 2,4-D and NAA at the concentration of 2mg/1 and 4mg/1, respectively, but in case of treating both 2,4-D and kinetin, that was done at the concentration of 0.2mg(2,4-D)/0.05mg(kinetin)per liter. The growth of cultured cell was good at the concentration of 2.0mg(2,4-D)/1 and 0.2mg(2,4-D)/0.05mg(kinetin)per liter. When cultured cells were inoculated with R. japonicum 019 and 011, their growthes were considerably inhibited. The addition of single amino acid inhibited the growth of cultured cells. Hwangkeum was inhibited considerably by methionine and leucine. The inhibition of growth by single amino acid can be abolished by the addition of certain amino acids. The differentiation of adventitious root was good at the concentration of 2.0mg 2,4-D and 0.2mg 2,4-D/0.05mg kinetin per liter. Of three host plants tested with 25 R. japonicum strains, Hwangkeum had affinity for 10 strains, Namcheon for 7 strains and D68-0099 for none. The nitrogen fixing abilities of Hwangkeum and Namcheon caused by cultured cell-Rhizobium association were high in strain 019, 007, and in 007 mixed with 119, respectively.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.77-86
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• 1986

The purpose of this study is to estimate an optimum formula of rainfall intensity on basis of the characteristics for short period of rainfall duration in Kyungpook province for the design of urban sewerage and small basin drain system. Results studied are as follows; 1. The optimum method for Taegu and Pohang, Iwai's and Gumbel-Chow's method are recommended respectively. 2. The opotimum type of rainfall intensity for these area, $I=\frac{a}{\sqrt{t}+b}$ (Japanese type), is confirmed with 2.52~4.17 and 1.86~4.54 as a standard deviation for Taegu and Pohang respectively. The optimum formula of rainfall intensity are as follows. Taegu : T : 200 year - $I=\frac{824}{\sqrt{t}+1.5414}$ T : 100 year - $I=\frac{751}{\sqrt{t}+1.4902}$ T : 50 year - $I=\frac{678}{\sqrt{t}+1.4437}$ T : 30 year - $I=\frac{623}{\sqrt{t}+1.4017}$ T : 20 year - $I=\frac{580}{\sqrt{t}+1.3721}$ T : 10 year - $I=\frac{502}{\sqrt{t}+1.3145}$ T : 5 year - $I=\frac{418}{\sqrt{t}+1.2515}$ Pohang : T : 200 year - $I=\frac{468}{\sqrt{t}+1.1468}$ T : 100 year - $I=\frac{429}{\sqrt{t}+1.1605}$ T : 50 year - $I=\frac{391}{\sqrt{t}+1.1852}$ T : 30 year - $I=\frac{362}{\sqrt{t}+1.2033}$ T : 20 year - $I=\frac{339}{\sqrt{t}+1.2229}$ T : 10 year - $I=\frac{299}{\sqrt{t}+1.2578}$ T : 5 year - $I=\frac{257}{\sqrt{t}+1.3026}$ 3. Significant I.D.F. curves derived should be applied to estimate a suitable rainfall intensity and rainfall duration.

• Journal of Distribution Science
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• v.10 no.6
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• pp.27-37
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• 2012

Many studies have focused on the importance of organizational integration when companies try to achieve growth through mergers and acquisitions (M&A). However, there has been little research that focuses on the new branding or customer base integration of the M&A process, despite the fact that this integration is very important for achieving M&A goals and business performance in industries such as retail. The purpose of this study is to provide an M&A case study of the retail industry, focused especially on the new branding and customer integration of two department stores. This study examined key integration processes in terms of brand name and membership systems of both companies by examining how the merged company achieved its new branding and the integration of its membership systems. The methodology of this research is the case study, which is used in both normative and empirical studies for distribution research in Korea. This research analyzes the case of both new branding and customer membership systems of the two companies. The new branding initiatives of this case centered on decision making including brand extension and brand naming. The customer membership integration of the two companies is analyzed on the basis of the customer reward programs that include both financial and service rewards. This study shows the success factors of new branding and customer integration in the M&A process in terms of achieving marketing goals and business performance as follows: First, companies should identify the integration areas by analyzing the brand and membership of both companies and make a balanced decision for both the customer and company. Second, the goals of new branding and membership integration in the M&A process should not emphasize business efficiency from a short-term perspective but rather should consider brand power and business synergy from a long-term perspective. Third, the post-merger integration process of the brand or customer areas requires not only the organized execution of integration tasks but also follow-up programs for changes in business strategy and marketing-related programs to realize the synergy effects of integrated organization. Although this study provides a detailed review and analysis of the new branding and customer integration processes in post-merger integration and in identifying the primary decision-making areas of these processes, there are some limitations requiring further research that may overcome or compensate for these limitations. The suggested future research areas are as follows: First, since this research is a case study of only one M&A, it makes few theoretical contributions such as new propositions or theories or possibilities for generalization. This limitation can be overcome through further research using multiple cases, which may lead to new propositions. Second, the methodology of this study lacks sufficient rigor in terms of its analytic approach because this case study was developed and analyzed descriptively. Further research is needed to compensate for these limitations, such as using a theory-based approach or comparative analysis approach that makes case analysis more systematic.

• Journal of Distribution Science
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• v.10 no.6
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• pp.39-53
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• 2012

It is widely known that knowledge management plays a facilitating role that contributes to upgrading organizational performance. Knowledge management systems (KMS), especially, support the knowledge management process including the sharing, creating, and using of knowledge within a company, and maximize the value of knowledge resources within an organization. Despite this widely held belief, there are few studies that describe how companies actually develop, share, and practice their knowledge. Companies in the domestic small franchise sector, which are in the early stages in terms of knowledge management, need to improve their KMS to manage their franchisees effectively. From this perspective, this study uses a qualitative approach to explore the actual process of knowledge management implementation. This article presents a case study of PB (Paris Baguette) company, which is the first to build a KMS in the franchise industry. The study was able to confirm the following facts through the analysis of target companies. First, the chief executive's support is a critical success factor and this support can increase the participation of organization members. Second, it is important to build a process and culture that actively creates and leverages information in knowledge management activities. The organizational learning culture should be one where the creation, learning, and sharing of new knowledge is developed continuously. Third, a horizontal network organization is needed in order to make relationships within the organization more close-knit. Fourth, in order to connect the diverse processes such as knowledge acquisition, storage, and utilization of knowledge management activities, information technology (IT) capabilities are essential. Indeed, IT can be a powerful tool for improving the quality of work and maximizing the spread and use of knowledge. However, during the construction of an intranet based KMS, research is required to ensure that the most efficient system is implemented. Finally, proper evaluation and compensation are important success factors. In order to develop knowledge workers, an appropriate program of promotion and compensation should be established. Also, building members' confidence in the benefits of knowledge management should be an ongoing activity. The company developed its original KMS to achieve a flexible and proactive organization, and a new KMS to improve organizational and personal capabilities. The PB case shows that there are differences between participants perceptions and actual performance in managing knowledge; that knowledge management is not a matter of formality but a paradigm that assures the sharing of knowledge; and that IT boosts communication skills, thus creating a mutual relationship to enhance the flow of knowledge and information between people. Knowledge management for building organizational capabilities can be successful when considering its focus and ways to increase its acceptance. This study suggests guidelines for major factors that corporate executives of domestic franchises should consider to improve knowledge management and the higher operating activities that can be used.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.32 no.4
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• pp.501-506
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• 1999

This study was conducted to evaluate the purification efficiency in rearing water of the land based fish farm by screen filter and ultra violet (UV) irradiation. Purification efficiency for rearing seawater has been examined with screen filter of 60 $\mu$m pore size and UV irradiation at dose of 0.5 $mWS/cm^2$ for 5 months. Purification efficiency by changing of temperature, salinity, pH, DO, total bacteria and Vibrio species in rearing seawater by filtering and UV irradiation were not significant during 5 months, However, the removing rate of suspended solid and turbidity of rearing seawater were $43.8\~45.6\%$ (average, $44,7\%$) and $29.2\~33.2\%$ (average, $31,3\%$) by filtering, respectively. Also, Purification efficiency for the $NO_3^{-}-N,\;NO_2^{-}-N,\;NH_4^{+}-N$ and $PO_4^{3-}-P$ were $21.3\~21.9\%$ (average, $21.6\%$), $24.1\~25.2\%$ (average, $24.7\%$), $17.6\~17.8\%$ (average, $17.7\%$) and $19.0\~20.4\%$ (average, $19.7\%$) respectively by the system used on this study.

• Resources Recycling
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• v.4 no.1
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• pp.38-45
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• 1995

The objective of this study was to investigate the effect of temperature on the formation of CaCO, polymorphs(i.e.,calcite, aragonite, vaterite) and on the crystal shape of CaCO,.The reaction systems were rnvestigated at the temperature range of 2.0%-85.3r, at the fixed cmditions ofconcentration and pressure, 2X10-' M, atomospheric pressure, respectively.The reaction systems studied include a Ca(HCO.,),-Air bubble, O Ca(OH)s-CO,, @ Ca(OH),-H,CO, ,Ca(OH1,-Na>CO,, O Ca(OH),-K,CO,, @ Ca(OH),-(NH,),CO,, D CaC1,-Na,CO,, CaC1,-K3C03, 8 CaC1,-(NH,,),CO,, 0 Ca(N0,X-Na,CO,, 03 Ca(N0,X-QCO,. 0 Ca(NO,),-(NH,XCO,. The results obtained are summarizedas follows:Calcite is formed at the temperature range of 2t-80"C and the highest calcite yield was obtained at 30%.Aragonite begins to be formed at the temperature range of 41.0%-53.0%. and the higher temperature is thehigher yield is obtained. pH of the reaction system affect the yield of aragonite, and the yield reaches the highestpercentage at the pH range of 10.0-11.0, and at the conditions of pH 12.3 or over, aragonite is scarcely formed.Vaterlle is fnrmed at the temperature range of 40.0% or less, and transites utterly to calcite within 10-60mmutes in the case of bemg residenced in mother liqmd which C1 is not contained, and within 140hours inthe case of containing CI-.s in the case of containing CI-.

• Journal of the Korean Arthroscopy Society
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• v.4 no.2
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• pp.159-165
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• 2000

Purpose : Endoscopic carpal tunnel release technique was developed and has being used to decrease postoperative morbidity and complications. The purpose of this study was to evaluate the clinical results and clinical usefulness of endoscopic carpal tunnel release using single portal technique. Methods and Materials : 18 carpal tunnel syndrome patients who were diagnosed by means of clinical symptoms, physical examination, and electrodiagnostic study had endoscopic carpal tunnel release using single portal technique with about 1 cm oblique wrist incision on 30 hands. And then they were followed-up and reviewed in the same way. Late results of operation were analysed by grading system according to patient's own assessments of relief of symptoms at the final fellow-up. The follow-up period ranged 6 to 13 months from surgery. Results : There were postoperative improvements with respect to clinical symptoms, physical examination, and electrodiagnostic study. 23 of 30 hands$(76.7\%)$ had complete resolution of symptoms. 27 hands$(90\%)$ were able to return to normal activities and work within 6 weeks, and 30 hands$(100\%)$ returned within 8 weeks. In grip strength study, 29 hands$(96.6\%)$ regained preoperative strength in 6 months. 12 of 22 hands$(55\%)$ had improvement with respect to thenar atrophy within 6 months. Late results were as follows . 23 hands$(76.7\%)$ was graded as excellent, 6 hands$(20\%)$ graded as good and 1 hand$(3.3\%)$ graded as fair, and there was no poor result. Conclusion : We think that endoscopic carpal tunnel release with single portal technique is technically safe and simple, if the surgeon takes step to stay within the safety zone based on local anatomy and selects an appropriate patient and that endoscopic carpal tunnel release does have advantages over open release. We agree that the surgeon must be prepared to perform an open technique, if technical difficulties arise, difficulty in introducing the device into the carpal tunnel is encountered, or the transverse fibers of the transverse carpal ligaments are not clearly seen.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.33-56
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• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.