• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1526-1531
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• 2024

¹³¹I is a fission product produced in a nuclear reactor by irradiating tellurium dioxide, with a half-life of 8.02 day. The most important and widely used method for making ¹³¹I is irradiation using a nuclear reactor and post-irradiation followed by dry distillation. The advantage of the dry distillation process is that the process and the equipment are relatively simple, namely TeO₂ (m.p. 750 ℃), which can withstand heating during reactor irradiation. Based on TeO₂ irradiation by neutron following the technique of dry distillation was explained for production of ¹³¹I on a large scale. A dry distillation followed the radioisotope production operation using the 30 MW GA Siwabessy nuclear reactor to meet national demand. TeO₂ targets are 25 and 50 g irradiated for 87-100 h. The resulting ¹³¹I activity is 20.29339-368.50335GBq. According to the requirements imposed on the radionuclide purity of the preparation, the contribution of ¹³¹I training in the resulting preparation was not less than 99.9 %

• Information Systems Review
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• v.20 no.4
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• pp.1-23
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• 2018

A smart phone has been widely spread around world and makes people enjoy online shopping in any time and any place. Recently it also changes the distribution environment. O2O (Online-to-Offline) service becomes new normal due to its convenience of ease shopping of product and services. O2O service market shows steady and steep growth, It is reported that, however, 80% of the businesses has been discontinued within the first year because of unstable business models, customer dissatisfaction and distrust of service. Therefore, it is very important research issue to find out influential factors promoting continuous usage intention of O2O service. Previous study shows that it only considers online characteristics and lack of analysis about offline characteristics and social impact factors. The purpose of this paper is to find out continuous usage intention factors of O2O services by literature review, case analysis, and empirical test. A comprehensive research model and related hypothesis are developed and tested by using a structural equation, Survey was carried out among users who have used O2O service including payment service for at least once. Finally 611 samples are selected out of total 813 surveys. The result shows that the model is theoretically proved and 12 out of 17 hypotheses are accepted. The contribution of this paper is that it provides a new theoretical research model about continuous usage intention factors as well as practical guidelines about promoting continuous usage and growth strategies of O2O service.

• Journal of the Korean Recycled Construction Resources Institute
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• v.12 no.1
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• pp.8-16
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• 2024

Limestone (CaCO₃, calcium carbonate), which is used as a raw material in the portland cement and steel industry, emits CO₂ through decarbonation by high temperatures in the manufacturing process. To reduce CO₂ emissions by the use of raw materials like limestone, it has been proposed to replace limestone with various industrial by-products that contain CaO but less or none of the carbonated minerals, that cause CO₂ emissions. Loss of Ignition (LOI), Thermogravimetric analysis (TG), and Infrared Spectroscopy (IR) are used to quantitative the amount of CO₂ emission by using these industrial by-products, but CO₂ emissions can be either over or underestimated depending on the characteristics of by-product materials. In this study, we estimated CO₂ contents by LOI, TG, IR and DTG(Differential Thermogravimetric analysis) of calcite(CaCO₃) and samples that contain CO₂ in the form of carbonate and whose weight increases by oxidation at high temperatures. The test results showed for CaCO₃ samples, all test methods have a sufficient level of reliability. On the other hand, for the CO₂ content of the sample whose weight increases at high temperature, LOI and TG did not properly estimate the CO₂ content of the sample, and IR tended to overestimate compared to the predicted value, but the estimated result by DTG was close to the predicted valu e. From these resu lts, in the case of samples that contain less than a few percent of CO₂ and whose weight increases during the temperature that carbonate minerals decompose, estimating the CO₂ content using DTG is a more reasonable way than LOI, TG, and IR.

• Journal of Ginseng Culture
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• v.6
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• pp.135-151
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• 2024

In order to compare the ginseng consumption culture of Korean and Chinese college students, their purchase status of ginseng products, attitudes toward ginseng, and satisfaction with ginseng products were examined, and the purchase and recommendation intention of ginseng products was investigated. It targeted 267 Korean college students and 318 Chinese college students who had experience eating ginseng products. As a result of the survey, in the case of Korean college student consumers, interest in ginseng products increased compared to before COVID-19, and the intention to purchase and recommend ginseng products increased. In addition, the higher the satisfaction with ginseng, the higher the frequency of ginseng purchase experience, the higher the social benefit attitude toward ginseng, and the higher the age, the higher the intention to purchase and recommend ginseng products. Chinese college student consumers had higher parental purchases than Korea, higher positive intentions to purchase and recommend social and psychological benefits, and their 20s are already more interested and friendly than Korea. What Korean college students and Chinese college student consumers have in common is that interest in health, safety, and environment has increased since before COVID-19, and interest in ginseng-related products has changed in individual experiences, indicating that individual experiences are important and Chinese college student consumers are influenced by parents. In particular, COVID-19 is an opportunity to recognize the importance of health, which is important to those in their 20s, and is actually related to purchase intention. Focusing on these results, it seems that expansion to preferred products for college student consumers and differentiation of marketing strategies according to family influence and consumption culture should be made, and these new changes due to COVID-19 seem to be a timely opportunity. At a time when interest in health and safety has increased, strategic preparations are needed for the future consumersociety to respond to changesin product diversity and convergence, changes in marketing media to meet consumer consumption values, and changesin consumer family types, such assingle households.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.392-401
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• 2023

Corn (Zea mays L.) is one of the world's most important crops, along with wheat and rice, with a global corn production expected to reach 1,154.5 million tons in 2023. Considering this grain production, The generation of corn cob is expected to reach approximately 207.8 million tons in 2023. However, as an agricultural by-product, corn cobs are often considered waste and remain underutilized. Phytosterols, which are abundant in vegetable oils such as corn oil, provide a number of health benefits, including liver health, cholesterol reduction, and protection against chronic diseases such as diabetes. In this study, we investigated the potential of Kwangpyeongok ears, which are commonly used as grain and silage corn in Korea. We also examined the variation in phytosterol content with the maturity of corn ears to identify the optimal time for utilization. At the beginning of physiological maturity, corn cobs had 113.3 mg/100g DW of total phytosterols, which was highest phytosterol abundance during the growth stage. Corn kernels also had the highest phytosterol content at the beginning of physiological maturity. While previous studies on corn bioactive compounds have mainly focused on the kernels, the results of this study highlight that cobs are an excellent source of these compounds. Furthermore, phytosterol levels were influenced by genetic factors and developmental stages, suggesting the to increase the use of cobs as a source of bioactive compounds.

• Journal of Applied Biological Chemistry
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• v.66
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• pp.416-423
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• 2023

Figs has known to have antioxidant, whitening, anti-inflammatory, and antibacterial effects in their leaves, roots, stems, latex, and fruits. In order to develop cosmetic materials based on natural products, we have studied on the skin activity of the ficin in latex as well as the whitening function of the fruit extract with 70% ethanol, and used it as a raw material for released cosmetic product. However, there is little research on the demand for the development of new eutectic solvent extraction methods and its ability to control skin inflammation and psoriasis regulation. Thus, in this study, we evaluated the effectiveness of fig fruit extracts and fractions using eutectic solvent extraction for skin inflammation control and psoriasis. First, fig fruits were extracted under optimal eutectic solvent conditions and fractionated with n-hexane, dichloromethane, ethyl acetate, and butanol. First, the antioxidant activity and inhibition of nitric oxide (NO) production were confirmed in mouse macrophage RAW264.7 cells. In addition, as a result of observing the mRNA expression through RT-PCR, pro-inflammatory cytokines such as TNF-α, IL1α, and IL-1β were suppressed significantly in the hexane, dichloromethane, and ethyl acetate fractions. In addition, it was confirmed in TNF-α stimulated HaCaT keratinocyte model. Finally, chemokine CC motif ligand 20 (CCL20), marker gene of human psoriasis skin disease, was significantly suppressed in the hexane, dichloromethane, and ethyl acetate fractions. These results suggested its anti-inflammatory and skin soothing effect and the possibility of development as an excellent skin soothing natural cosmetic material in the future through future clinical trials.

• Journal of the Korea Organic Resources Recycling Association
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• v.17 no.3
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• pp.27-39
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• 2009

The main objective of this study is to investigate the characteristics of combustion by analyzing fuel gases from a combustion equipment with various combustion conditions for refuse-derived fuels (RDFs). CO gas is a parameter for indicating of incomplete combustion during a combustion process. The lowest CO gas was produced when the experiment conditions were m=2 under air-fuel condition and $800^{\circ}C$. $CO_2$ gas is a final product after complete combustions. The highest amount of $CO_2$ gas was produced when the experiment conditions were m=2 under air-fuel condition and $800^{\circ}C$. The highest level of $SO_2$ gas was produced in S.1 sample containing the highest sulfur. The highest level of NOx gas was produced in S.1 sample with the highest nitrogen content and air-fuel condition of m=2 under temperature of $800^{\circ}C$. HCl gas that is generated by reacting with metals catalyst through oxygen catalyst reaction during combustion process is a precursor of dioxin formation. The higher level of HCl gas was produced in the sample with higher chlorine content. The lowest level of HCl gas was produced when the experiment conditions were air-fuel condition of m=2 and $800^{\circ}C$. The lowest level of $NH_3$ gas was generated when the experiment condition was m=2 under air-fuel condition and after 3 minutes. Air-fuel condition is more important to create $NH_3$ gas than operating temperatures. Higher level of $H_2S$ gas was generated in S.1 sample with the higher sulfur content and was created in RDFs that contain higher mixture ratios of sewage sludge and food wastes. A result of combustion, gases and gases levels from the combustion of S.1 and S.2 were very similar to the combustion of a stone coal. As results of this research, when evaluating the feasibility of the RDFs, the RDFs could be used as auxiliary and main fuels.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.351-361
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• 2024

Satellite imagery contains various elements such as clouds, cloud shadows, and terrain shadows. Accurately identifying and eliminating these factors that complicate satellite image analysis is essential for maintaining the reliability of remote sensing imagery. For this reason, satellites such as Landsat-8, Sentinel-2, and Compact Advanced Satellite 500-1 (CAS500-1) provide Usable Data Masks(UDMs)with images as part of their Analysis Ready Data (ARD) product. Precise detection of clouds and their shadows is crucial for the accurate construction of these UDMs. Existing cloud and their shadow detection methods are categorized into threshold-based methods and Artificial Intelligence (AI)-based methods. Recently, AI-based methods, particularly deep learning networks, have been preferred due to their advantage in handling large datasets. This study aims to analyze the applicability of constructing UDMs for high-resolution satellite images through deep learning-based cloud and their shadow detection using open-source datasets. To validate the performance of the deep learning network, we compared the detection results generated by the network with pre-existing UDMs from Landsat-8, Sentinel-2, and CAS500-1 satellite images. The results demonstrated that high accuracy in the detection outcomes produced by the deep learning network. Additionally, we applied the network to detect cloud and their shadow in KOMPSAT-3/3A images, which do not provide UDMs. The experiment confirmed that the deep learning network effectively detected cloud and their shadow in high-resolution satellite images. Through this, we could demonstrate the applicability that UDM data for high-resolution satellite imagery can be constructed using the deep learning network.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.19-41
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• 2016

Recently, the proliferation of mobile devices such as smartphones and tablet personal computers and the development of information communication technologies (ICT) have led to a big trend of a shift from single-channel shopping to multi-channel shopping. With the emergence of a "smart" group of consumers who want to shop in more reasonable and convenient ways, the boundaries apparently dividing online and offline shopping have collapsed and blurred more than ever before. Thus, there is now fierce competition between online and offline channels. Ever since the emergence of online shopping, a major type of multi-channel shopping has been "showrooming," where consumers visit offline stores to examine products before buying them online. However, because of the growing use of smart devices and the counterattack of offline retailers represented by omni-channel marketing strategies, one of the latest huge trends of shopping is "webrooming," where consumers visit online stores to examine products before buying them offline. This has become a threat to online retailers. In this situation, although it is very important to examine the influencing factors for switching from online shopping to webrooming, most prior studies have mainly focused on a single- or multi-channel shopping pattern. Therefore, this study thoroughly investigated the influencing factors on customers switching from online shopping to webrooming in terms of both the "search" and "purchase" processes through the application of a push-pull-mooring (PPM) framework. In order to test the research model, 280 individual samples were gathered from undergraduate and graduate students who had actual experience with webrooming. The results of the structural equation model (SEM) test revealed that the "pull" effect is strongest on the webrooming intention rather than the "push" or "mooring" effects. This proves a significant relationship between "attractiveness of webrooming" and "webrooming intention." In addition, the results showed that both the "perceived risk of online search" and "perceived risk of online purchase" significantly affect "distrust of online shopping." Similarly, both "perceived benefit of multi-channel search" and "perceived benefit of offline purchase" were found to have significant effects on "attractiveness of webrooming" were also found. Furthermore, the results indicated that "online purchase habit" is the only influencing factor that leads to "online shopping lock-in." The theoretical implications of the study are as follows. First, by examining the multi-channel shopping phenomenon from the perspective of "shopping switching" from online shopping to webrooming, this study complements the limits of the "channel switching" perspective, represented by multi-channel freeriding studies that merely focused on customers' channel switching behaviors from one to another. While extant studies with a channel switching perspective have focused on only one type of multi-channel shopping, where consumers just move from one particular channel to different channels, a study with a shopping switching perspective has the advantage of comprehensively investigating how consumers choose and navigate among diverse types of single- or multi-channel shopping alternatives. In this study, only limited shopping switching behavior from online shopping to webrooming was examined; however, the results should explain various phenomena in a more comprehensive manner from the perspective of shopping switching. Second, this study extends the scope of application of the push-pull-mooring framework, which is quite commonly used in marketing research to explain consumers' product switching behaviors. Through the application of this framework, it is hoped that more diverse shopping switching behaviors can be examined in future research. This study can serve a stepping stone for future studies. One of the most important practical implications of the study is that it may help single- and multi-channel retailers develop more specific customer strategies by revealing the influencing factors of webrooming intention from online shopping. For example, online single-channel retailers can ease the distrust of online shopping to prevent consumers from churning by reducing the perceived risk in terms of online search and purchase. On the other hand, offline retailers can develop specific strategies to increase the attractiveness of webrooming by letting customers perceive the benefits of multi-channel search or offline purchase. Although this study focused only on customers switching from online shopping to webrooming, the results can be expanded to various types of shopping switching behaviors embedded in single- and multi-channel shopping environments, such as showrooming and mobile shopping.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.