• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.117-129
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• 2015

As the Internet becomes ubiquitous, a large volume of information is posted on the Internet with exponential growth every day. Accordingly, it is not unusual that investors in stock markets gather and compile firm-specific or market-wide information through online searches. Importantly, it becomes easier for investors to acquire value-relevant information for their investment decision with the help of powerful search tools on the Internet. Our study examines whether or not the Internet helps investors assess a firm's value better by using firm-level data over long periods spanning from January 2004 to December 2013. To this end, we construct weekly-based search volume for information technology (IT) services firms on the Internet. We limit our focus to IT firms since they are often equipped with intangible assets and relatively less recognized to the public which makes them hard-to measure. To obtain the information on those firms, investors are more likely to consult the Internet and use the information to appreciate the firms more accurately and eventually improve their investment decisions. Prior studies have shown that changes in search volumes can reflect the various aspects of the complex human behaviors and forecast near-term values of economic indicators, including automobile sales, unemployment claims, and etc. Moreover, search volume of firm names or stock ticker symbols has been used as a direct proxy of individual investors' attention in financial markets since, different from indirect measures such as turnover and extreme returns, they can reveal and quantify the interest of investors in an objective way. Following this line of research, this study aims to gauge whether the information retrieved from the Internet is value relevant in assessing a firm. We also use search volume for analysis but, distinguished from prior studies, explore its impact on return comovements with market returns. Given that a firm's returns tend to comove with market returns excessively when investors are less informed about the firm, we empirically test the value of information by examining the association between Internet searches and the extent to which a firm's returns comove. Our results show that Internet searches are negatively associated with return comovements as expected. When sample is split by the size of firms, the impact of Internet searches on return comovements is shown to be greater for large firms than small ones. Interestingly, we find a greater impact of Internet searches on return comovements for years from 2009 to 2013 than earlier years possibly due to more aggressive and informative exploit of Internet searches in obtaining financial information. We also complement our analyses by examining the association between return volatility and Internet search volumes. If Internet searches capture investors' attention associated with a change in firm-specific fundamentals such as new product releases, stock splits and so on, a firm's return volatility is likely to increase while search results can provide value-relevant information to investors. Our results suggest that in general, an increase in the volume of Internet searches is not positively associated with return volatility. However, we find a positive association between Internet searches and return volatility when the sample is limited to larger firms. A stronger result from larger firms implies that investors still pay less attention to the information obtained from Internet searches for small firms while the information is value relevant in assessing stock values. However, we do find any systematic differences in the magnitude of Internet searches impact on return volatility by time periods. Taken together, our results shed new light on the value of information searched from the Internet in assessing stock values. Given the informational role of the Internet in stock markets, we believe the results would guide investors to exploit Internet search tools to be better informed, as a result improving their investment decisions.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Restorative Dentistry and Endodontics
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• v.34 no.3
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• pp.177-183
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• 2009

The aim of this study was to evaluate the marginal and internal gaps in CEREC3 CAD/CAM inlays of three different preparation designs. CEREC3 Inlays of three different preparation designs (n=10) were fabricated according to Group I-conventional functional cusp capping/shoulder preparation, Group II-horizontal reduction of cusps and Group III-complete reduction of cusps/shoulder preparation. After cementation of inlays. the bucco-lingual cross section was performed through the center of tooth. Cross section images of 20 magnifications were obtained through the stereomicroscope. The gaps were measured using the Leica application suite software at each reference point. Statistical analysis was performed using one-way ANOVA and Tukey's test (${\alpha}<0.05$). The marginal gaps ranged from 80.0 to $97.8{\mu}m$ for Group I, 42.0 to $194.8{\mu}m$ for Group II, 51.0 to $80.2{\mu}m$ for Group III. The internal gaps ranged from 90.5 to $304.1{\mu}m$ for Group I, 80.0 to $274.8{\mu}m$ for Group II, 79.7 to $296.7{\mu}m$ for Group III. The gaps of each group were the smallest on the margin and the largest on the horizontal wall. For the CEREC3 CAD/CAM inlays, the simplified designs (groups II and III) did not demonstrate superior results compared to the traditional cusp capping design (group I).

• Journal of radiological science and technology
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• v.33 no.4
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• pp.327-334
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• 2010

Exposure during childhood results in higher risk for certain detrimental cancers than exposure during adulthood. We measured entrance skin dose (ESD) under 7-year children undergoing chest imaging and compared the relationship between ESD and age, height, weight, chest thickness. Though it is important to measure chest thickness for setting up the exposure condition of chest examination, it is difficult to measure chest thickness of children. We set up exposure parameters according to age because chest thickness of children has correlation with age. In the exposure parameters, for chest A-P examination under 2 year-children, tube voltage (kVp) in hospital A was higher than that in hospital B while tube current (mAs) was higher in hospital B, thus the ESD values were about 1.7 times higher in hospital B. However, for chest P-A examination over 4 year-children, the tube voltage was 7 kVp higher in hospital B, the tube current were same in all two systems, and focus to image receptor distance (FID) in hospital B (180 cm) was longer than that in hospital A (130 cm), thus the ESD values were 1.4 times higher in hospital A. For same ages, the ESD values for chest A-P examinations were higher than those for chest P-A examinations. Comparing ESD according to age, ESD values were $154{\mu}Gy$, $194{\mu}Gy$ and $138{\mu}Gy$ for children under 1 year, 1 to under 4 years and 4 to under 7 years of age, respectively. These values were lower than reference level ($200{\mu}Gy$) recommended in JART (japan association of radiological technologists), however these were higher than reference values recommended by EC (european commission), NRPB (national radiological protection board) and NIFDS (national institute of food & drug safety evaluation). In conclusion, the values of ESD were affected by exposure parameters from radiographer's past experience more than x-ray system. ESD values for older children were not always higher than those for younger children. Therefore we need to establish our own DRLs (diagnostic reference levels) according to age of the children in order to optimize pediatric patient protection.

• Journal of radiological science and technology
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• v.39 no.1
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• pp.1-11
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• 2016

The purpose of this study was to provide resources for medical exposure reduction through evaluation of organ dose and image resolution for lumbar spine around according to the size of the collimator in DR system. The size of the collimator were varied from $8^{\prime\prime}{\times}17^{\prime\prime}$ to $14^{\prime\prime}{\times}17^{\prime\prime}$ by 1" in AP and lateral projection for the lumbar spine radiography with RANDO phantom. The organ dose measured for liver, stomach, pancreas, kidney and gonad by the glass dosimeter. The image resolution was analyzed using the Image J program. The organ dose of around lumbar spine were reduced as the size of the collimator is decreased in AP projection. There were no significant changes decreasing rate whenever the size of the collimator were reduced 1" in the gonad. The organ dose showed higher on liver and kidney near the surface in lateral projection. There were decreasing rate of less than 5% in liver and kidney, but decreasing rate was 24.34% in the gonad whenever the size of the collimator were reduced 1". Organ dose difference for internal and external of collimator measured $549.8{\mu}Gy$ in the liver and $264.6{\mu}Gy$ in the stomach. There were no significant changes organ dose difference that measured $1,135.1{\mu}Gy$ in the gonad. Image Quality made no difference because SNR and PSNR were over than 30 dB when the collimator size is less than $9^{\prime\prime}{\times}17^{\prime\prime}$ on AP projection and $10^{\prime\prime}{\times}17^{\prime\prime}$ on lateral projection. Therefore, we are considered that the recommendations criterion for control of collimator were suggested in order to reduce unnecessary X-ray exposure and to obtain good image quality because lumbar spine radiography contains a lot of peripheral organs rather than other area radiography.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.19 no.1
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• pp.17-27
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• 2013

The effects of chlorine dioxide gas ($ClO_2$) treatments between high-concentration-short-time and low-concentration-long-time on maintaining the quality of cherry tomatoes (Lycopersicon esculentum Mill. cv 'unicorn') were investigated. Tomatoes were treated with 5 ppm for 10 min and 10 ppm for 3 min as high-concentration-short-time $ClO_2$ gas treatment conditions and 1 ppm for once a day interval in terms of low-concentration-long-time $ClO_2$ gas treatment condition, respectively. After $ClO_2$ gas treatments, tomatoes were storage at 5 and $23^{\circ}C$ for 7 days. Weight loss, changes in tomato color, firmness, soluble solids content, pH, growth of total microorganism, and decay rate were evaluated. On day 7, tomatoes treated with chlorine dioxide gas showed low values of respiratory rate, total microbial growth, and decay rate compared to those of tomato without chlorine dioxide gas treatment. Additionally, tomatoes treated the chlorine dioxide were kept the values of firmness and soluble solids content during storage. However, chlorine dioxide gas treatment on tomatoes had no direct effect on weight loss, pH, and color. Results showed that both $ClO_2$ concentration and treatment time played the important roles for keeping the quality of tomatoes during storage. Tomatoes with chlorine dioxide gas treatment of low-concentration-long-time had more effective values of firmness, the total microbial growth, and decay rate than those with two chlorine dioxide gas treatments of high-concentration-short-time. Results suggest the potential use of chlorine dioxide gas treatment of low-concentration-long-time as an highly effective method for keeping the freshness of cherry tomato.

• Journal of Chest Surgery
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• v.31 no.5
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• pp.488-493
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• 1998

In the preoperative evaluation before coronary artery bypass surgery, review of the coronary arteriogram is the most important step. Expected "normal" lumen diameter at a given coronary anatomic location is a basis for quantative estimation of coronary disease severity that could be more useful than the traditional "percent stenosis". The distribution and number of major coronary artery branches are determinants of number of bypass grafts needed. We reviewed the coronary artery anatomy in 174 adult patients who revealed no coronary pathology in angiographic studies done from September 1994 to June 1996. Quantative analysis was done in all cases by a single person using a Computerized System (Arripro 35ⓡ). The results were follows; 1) The mean diametre of left main coronary artery was 4.45 mm(range 2.74~6.72). The pattern of branching was bifurcation in 67.24%, trifurcation in 28.74% and quadrifurcation in 4.02% of the patients. 2) The mean diametre of left anterior descending artery was 3.17 mm(range 2.10~5.85), 2.79 (range 1.55~5.59) and 2.17 mm(range 1.37~3.81) in the proximal, mid, and the distal portions, respectively. The number of diagonal branches of left anterior artery was from one to four(mode=2). 3) The mean diametre of proximal and distal left circumflex artery were 3.17mm(range 1.74~4.89) and 2.19 mm(range 1.21~4.46). The number of obtuse marginal branches of left circumflex artery is from one to six(mode 2). 4) The mean diametre of proximal and distal right coronary artery, the posterior descending artery and the largest posterolateral branch were mean 3.51 mm(range 2.07~5.67), 2.09 mm (range 1.42~3.60), 2.09 mm(range 1.02~3.60) and 2.30 mm(range 1.39~4.39). 5) The right coronary artery dominant was 163 cases(93.68%) of the total 174 cases. 6) The large significant acute marginal artery was visualized in more than half of the people. half of the people.

• The Korean Journal of Nuclear Medicine Technology
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• v.24 no.1
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• pp.20-26
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• 2020

Purpose It is intended to figure out the errors derived from changes in depth and volume when measuring the Standard source and ^99mTc-pertechnetate by using a Dose calibrator. Then recommend appropriate measurement depth and volume. Materials and Methods As a Dose calibrator, CRC-15βeta and CRC-15R (Capintec, New Jersey, USA) was used, and the measurement sources were ⁵⁷Co, ¹³³Ba, ¹³⁷Cs and ^99mTc-pertechnetate was also adopted due to its high frequency of use. The Standard source was respectively measured the changes according to its depth without changing the volume, in a range of 0 cm to 15 cm from the bottom of the ion chamber. ^99mTc-pertechnetate was measured at each depth by changing the volume with 0.1 mL, 0.3 mL, 0.5 mL, 0.7 mL and 0.9 mL Respectively. And the depth range was from 0 cm to 15 cm at the bottom of the ion chamber. Results In the case of Standard source ⁵⁷Co, ¹³³Ba, ¹³⁷Cs and ^99mTc-pertechnetate, there were significant differences according to the measurement depth(p<0.05). ^99mTc-pertechnetate has a negative correlation coefficient according to the depth, and the error of the measured value was negligible at a depth from 0 cm to 7 cm at 0.3 mL and 0.5 mL, and the range of error increased as the volume increased. Conclusion In clinical practice, it is sometimes installed differently than the Standard depth recommended by the equipment company. If it's measured at the recommended depth and volume, it could be thought that unnecessary exposure of the operator and the patient will be reduced, and more accurate radiation exams will be possible in quantitative analysis.

• Current Research on Agriculture and Life Sciences
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• v.32 no.4
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• pp.225-230
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• 2014

This study investigates the effect of organic certification of apples on consumer preference in China as a way to support the expanded export of Korean apples to China. A choice experiment was designed to analyze the apple consumption in China. A total of 298 Chinese consumers answered the survey, and multinomial logit models were used to analyze the results. Organic certification was identified as an important determinant of consumer preference for apples in China, affecting both the evaluation and choice of country of origin. The results also indicated that Korean organic certification significantly increased the probability of Chinese consumers choosing Korean apples. Thus, organic certification by the Korean government should be strengthened to promote apple exports to China, plus the results of this study may provide useful information to promote agricultural product exports and improve the organic certification system.

• 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.