• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1235-1249
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• 2022

This study applied the web crawling technique for extracting big data news on water quality accidents in the water supply system and presented the algorithm in a procedural way to obtain accurate water quality accident news. In addition, in the case of a large-scale water quality accident, development patterns such as accident recognition, accident spread, accident response, and accident resolution appear according to the occurrence of an accident. That is, the analysis of the development of water quality accidents through key keywords and sentiment analysis for each stage was carried out in detail based on case studies, and the meanings were analyzed and derived. The proposed methodology was applied to the larval accident period of Incheon Metropolitan City in 2020 and analyzed. As a result, in a situation where the disclosure of information that directly affects consumers, such as water quality accidents, is restricted, the tone of news articles and media reports about water quality accidents with long-term damage in the event of an accident and the degree of consumer pride clearly change over time. could check This suggests the need to prepare consumer-centered policies to increase consumer positivity, although rapid restoration of facilities is very important for the development of water quality accidents from the supplier's point of view.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.936-949
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• 2023

On August 3, 2023, a brutal incident of unprovoked violence, termed as "Abnormal motivated crime," occurred in a multi-use facility, where retail and transportation facilities converge, near Seohyeon Station. The assailant drove onto the sidewalk, hitting pedestrians, and then entered a department store where a knife rampage ensued, resulting in a total of 14 victims. In the aftermath of this incident, numerous murder threats were posted on social media, causing widespread anxiety among the public. This fear was further exacerbated by the emergence of a "Terrorless.01ab.net" service. Purpose: This research aims to explore necessary institutional improvements for private security personnel who protect customers and employees in multi-use facilities, to enable them to perform their duties more effectively. Method: To assess the risk of Abnormal motivated crime, a time series analysis using the ARIMA model was conducted to analyze the domestic trends of such crimes. Additionally, Result: the study presents suggestions for improvements in the domestic security service law and emergency manuals for multi-use facilities. Conclusion: This is informed by a legal analysis of the indemnity rights for weapon use by private security guards abroad and their operational authority beyond weapon usage.

• Journal of the Korea Society of Computer and Information
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• v.29 no.9
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• pp.137-144
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• 2024

Digital advertising, both indoors and outdoors, is evolving from traditional 2D formats to more immersive 3D forms. 3D advertising involves creating 3D content and displaying it through large LED installations on two sides of a building's corner, or using 3D hologram projectors indoors. This study examines the production process of 3D hologram projectors used indoors and LED-based 3D content used outdoors, analyzing potential issues and considerations when creating 3D digital advertising content. The findings reveal that while indoor hologram projector content provides 3D effects, the low resolution of the devices makes it challenging to implement complex content. However, they are cost-effective and easy to operate. On the other hand, LED-based 3D advertising content, produced in high resolution, requires more time for content creation and incurs higher hardware installation costs. Despite this, it effectively represents complex content and maximizes visibility due to its enhanced 3D effects. In conclusion, it is crucial to create tailored content that matches the resolution of the display device to maximize 3D effects in advertising. Specifically, when producing 3D billboard-style outdoor advertising content, the structure of the building on which it will be installed must be carefully considered.

• Journal of Nutrition and Health
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• v.48 no.1
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• pp.9-18
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• 2015

Purpose: Poncirus trifoliata has been reported to have anti-inflammatory, antioxidant, and immune activities. However, its anti-obesity activity and the mechanism by which the water extract of dried, immature fruit of Poncirus trifoliata (PF-W) acts are not clear. This study suggests a potential mechanism associated with the anti-obesity activity of PF-W. Methods: We measured the effect of PF-W on lipoprotein lipase (LPL) regulation using enzyme-linked immunosorbent assay (ELISA) and an activity assay. The LPL regulation mechanism was examined by reverse transcription polymerase chain reaction (RT-PCR) to measure the mRNA expression of biomarkers related to protein transport and by western blot for analysis of the protein expression of the transcription factor CCAAT-enhancer-binding protein ($C/EBP{\beta}$). Results: The total polyphenol and flavonoid content of PF-W was $52.15{\pm}4.02$ and $6.56{\pm}0.47mg/g$, respectively. PF-W treatment decreased LPL content in media to $58{\pm}5%$ of that in control adipocyte media, and increased LPL content to $117{\pm}3.5%$ of that in control adipocytes, but did not affect the mRNA expression of LPL. PF-W also increased the mRNA expression of sortilin-related receptor (SorLA), a receptor that induces endocytosis and intracellular trafficking of LPL, in a concentration- and time-dependent manner. Finally, cell fractionation revealed that PF-W treatment induced the expression of $C/EBP{\beta}$, a SorLA transcription factor, in the nuclei of 3T3-L1 adipocytes. Conclusion: The LPL secretion and activity assay showed PF-W to be an LPL secretion inhibitor, and these results suggest the potential mechanism of PF-W involving inhibition of LPL secretion through $C/EBP{\beta}$-mediated induction of SorLA expression.

• Journal of Nutrition and Health
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• v.46 no.3
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• pp.250-260
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• 2013

The purposes of this study were 1) to investigate eating behaviors and patterns in breast cancer patients using a newly developed food frequency questionnaire and 2) to examine perception and use of nutritional information about breast cancer treatment among cancer patients treated with radiation therapy. Sixty breast cancer patients (case group) undergoing radiation therapy in Ajou University Hospital, Suwon, South Korea and 79 healthy women (control group) participated in this study. Mean age of subjects in the control group was $46.00{\pm}7.88$ years and BMI was $23.12{\pm}2.85kg/m^2$, and that of the case group was $50.06{\pm}11.64$ years and $22.32{\pm}3.24kg/m^2$. The results of eating behaviors showed several significant differences between control and case groups. Breast cancer patients ate meals on a more regular basis, on time, and more frequently compared to control subjects. In addition, they preferred more salty or spicy and bland food compared to healthy women. According to answers from the food frequency questionnaire, breast cancer patients consumed significantly lower amounts of boiled white rice, meats and processed food, fish and shellfish, coffee, milk, and cheese, whereas they consumed a significantly large amount of boiled multigrain rice, vegetable, seaweeds, soybean and processed food, and yoghurt compared to healthy women. This study also observed the way in which cancer patients and healthy control subjects obtain information about breast cancer treatment and its reliabilities. Results showed that healthy women did not hesitate to obtain information from mass media, while breast cancer patients would obtain nutritional information from specialists rather than mass media. Results of this survey confirmed that breast cancer patients avoided intake of red meat protein, even though they already recognized the importance of dietary protein intake for recuperation and treatment of the disease. These results could be used for future diet and nutrition guidelines for breast cancer patients.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.5
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• pp.482-488
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• 2014

This paper aims to analyze user's emotion automatically by analyzing Twitter, a representative social network service (SNS). In order to create sentiment analysis models by using machine learning techniques, sentiment labels that represent positive/negative emotions are required. However it is very expensive to obtain sentiment labels of tweets. So, in this paper, we propose a sentiment analysis model by using self-training technique in order to utilize "data without sentiment labels" as well as "data with sentiment labels". Self-training technique is that labels of "data without sentiment labels" is determined by utilizing "data with sentiment labels", and then updates models using together with "data with sentiment labels" and newly labeled data. This technique improves the sentiment analysis performance gradually. However, it has a problem that misclassifications of unlabeled data in an early stage affect the model updating through the whole learning process because labels of unlabeled data never changes once those are determined. Thus, labels of "data without sentiment labels" needs to be carefully determined. In this paper, in order to get high performance using self-training technique, we propose 3 policies for updating "data with sentiment labels" and conduct a comparative analysis. The first policy is to select data of which confidence is higher than a given threshold among newly labeled data. The second policy is to choose the same number of the positive and negative data in the newly labeled data in order to avoid the imbalanced class learning problem. The third policy is to choose newly labeled data less than a given maximum number in order to avoid the updates of large amount of data at a time for gradual model updates. Experiments are conducted using Stanford data set and the data set is classified into positive and negative. As a result, the learned model has a high performance than the learned models by using "data with sentiment labels" only and the self-training with a regular model update policy.

• 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.

• Journal of Animal Science and Technology
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• v.49 no.2
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• pp.213-224
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• 2007

In this study, hatched male broiler chicks(Ross) were fed on a basal diet and LPS was administered via intraperitoneal injection three times every other day, on the 9th, 11th and 13th days of the experiment, and then PBMC and splenocytes were isolated on day 14. The degree of alama blue reduction was evaluated at 4, 24, 48, 96 and 120 h in the splenocytes, and at 4, 8, 12, 24 and 48 h for PBMC of incubation after the addition of alama blue solution to the media. The cell numbers used in this experiment were 103, 104 and 105 cells per well, and the con A levels were 0.0, 1.0, 5.0, and 10.0 ㎍ per ml of medium. 1. The degree of alama blue reduction was found to increase in a linear fashion with increasing incubation time and cell numbers, for both splenocytes and PBMC. 2. During acute phase response, the degree to which alama blue was reduced was significantly elevated (p<0.05) at an incubation time of 24 hr for the splenocytes, 4 hr for PBMC, and a cell number of 105 cells per well, respectively. 3. The raised reduction of alama blue to control was linear with Con A levels in medium, and higher reduction in Con A 10.0 ㎍ relative to 1.0 or 5.0 ㎍ in ml medium was shown 4. The medium with autologous serum evidenced a significantly (p<0.05) higher reduction of alama blue relative to FBS. 5. Splenocytes and PBMC from the LPS-injected birds evidenced significantly higher levels of alama blue reduction regardless of incubation time, number of cells, level of Con A added, or serum type, as compared with what was observed in normal birds. The results indicated that the assay conditions for proliferative activity using the alama blue method in birds in which the acute phase response had been activated via intraperitoneal LPS injection requires 4 hrs of incubation for PBMC, 24 hrs of incubation for splenocytes, and 10㎍ of Con A per ml of medium.

• The Korean Journal of Mycology
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• v.1 no.2
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• pp.15-23
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• 1973

Mold producing cellulase were isolated from rotten woods, and identified as the three species: Aspergillus niger van Tieghem, Aspergillus schiemanni Thom and Trichoderma viride Pers. In this paper, culture conditions in the media and characteristics of these strains were investigated. Using these strains, we have conducted a research concerning the utilization of farm product waste materia's. 1. Optimum conditions for the cellulase formation were as follows. KM 10-1; pH 5.2-5.5, $35^{\circ}C$, incubation time 6 days. OL 11-1; pH 5.5, $30-35^{\circ}C$, incubation time 6 days. SH 9-2; pH 5.5, $30^{\circ}C$, incubatoin time 6 days. 2. Their cellulase activities in their optimum condition were as follows: KM 10-1; CMC-LP 78.5% CMC-SP 4.0 glucose mg/gm of the cultures/min. OL 11-1; CMC-LP 89.9%, CMC-SP 4.9 glucose mg/gm of the cultures/min. SH 9-2; CMC-I.P 77.4%, CMC-SP 3.9 glucose mg/gm of the cultures/min. 3. Hydrolysis of animal feed containing a large quantity (23-30%) of cellulose by means of the crude enzyme in the selected strains resolved 30% of the cellulose contained in the animal feed.

• Journal of the Korean Society of Radiology
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• v.9 no.1
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• pp.9-16
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• 2015

The purpose of this study is finding optimum contrast medium quantity during abdominal CT using dual energy technique. The study subjects are 30 patients who had received general single energy abdominal CT and received double energy technique follow-up abdominal CT. dual energy technique abdominal CT images were obtained after setting contrast medium quantities at 30%, 40%, 50%, 60% and 70% of contrast medium quantity at the time of single energy technique. Then the contrast enhancement (Hounsfield Unit; HU) was estimated by setting-up the regions of interest at aorta, inferior vena cava, hepatic portal vein and hepatic parenchymal. The obtained values were compared to the values of the same parts measured during single energy technique abdominal CT. The results of the study were as following. The 60% set up group had HU in aorta : $210.80{\pm}13.609$, IVC : $190.40{\pm}25.215$, hepatic portal vein : $198.40{\pm}21.232$ and hepatic parenchymal : $119.20{\pm}7.98$, The single energy abdomianl CT images had HU in aorta : $205.40{\pm}16.426$, IVC : $188.20{\pm}21.476$, hepatic portal vein : $195.40{\pm}22.744$ and hepatic parenchymal : $121.00{\pm}6.595$. Therefore, it is possible to obtain contrast enhancement by dual energy technique abdominal CT similar to the same by single energy technique abdominal CT by setting-up the quantity of contrast medium at 60% of contrast medium at the time of single energy technique abdominal CT. Based on the result of this study, it is possible to decrease existing quantity of contrast medium by _% and the injection velocity can be also decreased. Accordingly, it is believed that the result of study would be quite useful for patients who have renal function disorder, weak vein or side effect of contrast medium in the past.