• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• The Journal of Internal Korean Medicine
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• v.44 no.4
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• pp.726-740
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• 2023

Objectives: This review investigated the research on warm herbal foot bath therapy (WHFT) for insomnia. Methods: A search was conducted on the China National Knowledge Infrastructure (CNKI) database to collect relevant studies published up to August 29, 2023. Randomized controlled trials (RCTs) comparing WHFT and sleeping pills in patients with insomnia were included. The methodological quality of the included studies was assessed using the Cochrane risk-of-bias assessment tool. The results of the meta-analysis were presented as risk ratios (RRs) or mean differences (MDs) and their 95% confidence intervals (CIs). Results: A total of 11 RCTs were included. WHFT as monotherapy resulted in a significantly higher total effective rate (TER) (RR, 1.25; 95% CI, 1.15 to 1.36; I²=25%) and an improved Pittsburgh Sleep Quality Index (PSQI) global sore (MD, -3.10; 95% CI, -4.24 to -1.95; I²=73%) compared to benzodiazepines. Additionally, WHFT as a combined therapy with benzodiazepines resulted in a significantly higher TER (RR, 1.15; 95% CI, 1.04 to 1.27; I²=0%) and an improved PSQI global score (MD, -2.23; 95% CI, -4.09 to -0.38; I²=80%) compared to benzodiazepines alone. In network analysis visualizing the components of HWFT, four clusters were discovered, and Polygoni Multiflori Ramuls and Ziziphi Spinosae Semen were the key herbs used in WHFT. Overall, the methodological quality of the included studies was poor. Conclusions: There was limited evidence that WHFT as a monotherapy or combined therapy was effective in improving insomnia. The findings can be used as basic data for future WHFT research in South Korea.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.35-52
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• 2023

Recently, as the non-face-to-face environment has developed due to COVID-19 and environmental pollution, the importance of online digital healthcare is increasing, and venture start-ups and activities such as health care, telemedicine, and digital treatments are also actively underway. This study conducted the impact on the acceptability of digital healthcare smartwatches with an integrated approach of the expanded integrated technology acceptance model (UTAUT2) and the behavioral inference model (BRT). The most advanced integrated technology acceptance model for innovative technology acceptance research was used to identify major factors such as utility expectations, social effects, convenience, price barriers, lack of alternatives, and behavioral intentions. For the study, about 410 responses from ordinary people in their teens to 60s across the country were collected, and based on this, the hypothesis was verified using structural equations after testing reliability and validity of the data. SPSS 23 and AMOS 23 were used for research analysis. Studies have shown that personal innovation has a significant impact on the reasons for acceptance (use value, social impact, convenience of use), attitude, and non-use (price barriers, lack of alternatives, and barriers to use). These results are the same as the results of previous studies that confirmed the influence of the main value of innovative ICT on user acceptance intention. In addition, the reason for acceptance had a significant effect on attitude, but the effect of the reason for non-acceptance was not significant. It can be analyzed that consumers are interested in new ICT products and new services, but purchase them more carefully and selectively. This study has evolved from the acceptance analysis of general-purpose consumer innovation technology to the acceptance analysis of consumer value in smartwatch digital healthcare, which is a new and important area in the future. Industrially, it can contribute to the product's purchase and marketing. It is hoped that this study will contribute to increasing research in the digital healthcare sector, which will play an important role in our lives in the future, and that it will develop into in-depth factors that are more suitable for consumer value through integrated approach models and integrated analysis of consumer acceptance and non-acceptance.

• The Journal of Korean Society for School & Community Health Education
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• v.25 no.2
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• pp.59-70
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• 2024

Objectives: The purpose of this study is to analyze each country's infectious disease response capacities and, based on this, find areas for improvement in Korea's infectious disease management response. Methods: First, the capacity to respond to the COVID-19 infectious disease was analyzed by country using the SPAR scores of 96 countries around the world released by WHO in 2022. Second, we analyzed each country's specific COVID-19 quarantine performance using Our World in Data and the Global Health Security Index (GHSI). Results: First, the quarantine intensity index on January 24, 2021 was the highest in the Southeast Asia branch at 67.6, which had strong quarantine measures, and the lowest at 44.5 in the Africa branch. As of December 31, 2022, the quarantine intensity index in Europe was significantly lowered to 11.6. Second, the factor that influenced the SPAR indicator on the total number of patients per million population was national laboratory (C4), p=.027, and the factor that influenced the total number of deaths per million population was infection prevention and control (C9), p=.005., Risk Communication and Community Participation (C10) p=.040. The influential factor on GDP per capita was infection prevention and control (C9) p=.009, and the influential factor on GHSI was infection prevention and control (C9) p=.002. Conclusion: The research findings indicate that it was difficult to find a correlation between the SPAR, which is each country's self-assessment of their infectious disease capacities, and the number of COVID-19 cases or the intensity of pandemic responses. However, mortality rates, as well as factors such as the Global Health Security Index (GHSI) and national income, appear to be somewhat influenced. For future improvements in infectious disease management and response in our country, it is necessary to develop pandemic strategies that can reduce socio-economic costs based on more scientific and reliable data like JEE or GHSI, especially in preparation for potential unknown emerging infectious diseases. Based on this, proactive decision-making led by a control tower of experts and effective health communication are also required to respond to public health crises at a national level.

• Journal of the Korean Society of Radiology
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• v.85 no.3
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• pp.566-578
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• 2024

Purpose This study investigated whether the respiratory phase during pleural puncture in CT-guided percutaneous transthoracic needle biopsy (PTNB) affects complications. Materials and Methods We conducted a retrospective review of 477 lung biopsy CT scans performed during free breathing. The respiratory phases during pleural puncture were determined based on the table position of the targeted nodule using CT scans obtained during free breathing. We compared the rates of complications among the inspiratory, mid-, and expiratory respiratory phases. Logistic regression analysis was performed to control confounding factors associated with pneumothorax. Results Among the 477 procedures, pleural puncture was performed during the expiratory phase in 227 (47.6%), during the mid-phase in 108 (22.6%), and during the inspiratory phase in 142 (29.8%). The incidence of pneumothorax was significantly lower in the expiratory puncture group (40/227, 17.6%; p = 0.035) and significantly higher in the mid-phase puncture group (31/108, 28.7%; p = 0.048). After controlling for confounding factors, expiratory-phase puncture was found to be an independent protective factor against pneumothorax (odds ratio = 0.571; 95% confidence interval = 0.360-0.906; p = 0.017). Conclusion Our findings suggest that pleural puncture during the expiratory phase may reduce the risk of pneumothorax during image guided PTNB.

• Tunnel and Underground Space
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• v.34 no.4
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• pp.393-412
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• 2024

In selecting a disposal site for high-level radioactive waste, the hydrogeological research of the site is very important, and the hydraulic conductivity and the storage coefficient are key parameters. In this study, the hydraulic conductivity obtained by two different types of field hydraulic test equipment and methods was compared and analyzed for the deep granite rockmass in the Wonju area to understand the hydraulic characteristics of the deep granite rockmass. One was to perform the lugeon test, constant pressure injection test, and slug test at a maximum depth of 602.0 m by using the auto pressure/flow injection system, and the calculated hydraulic conductivity ranged from 1.26E-9 to 4.16E-8 m/s. In the overall depth, the maximum and minimum differences of the hydraulic conductivity were found to be about 33 times, and in the same test section, the difference by test method or analysis method was 1.13 to 8.25 times. In the other, the hydraulic conductivity calculated by performing a constant pressure injection test and a pulse test at a maximum depth of 705.1 m using the deep borehole hydraulic testing system was found to be 1.60E-10 to 2.05E-8 m/s, and the maximum and minimum differences were found to be about 130 times. In the constant pressure injection test, the difference depending on the analysis method was found to be 1.02 to 2.8 times. The hydraulic conductivity calculated by the two test equipment and methods generally showed similar ranges as E-9 and E-8 m/s, and no clear trend was observed according to depth. It was found that the granite rockmass in the Wonju area where the field hydraulic test was conducted showed low or very low rockmass permeability, and although there are differences in the range of hydraulic conductivity and the depth of application that can be measured depending on the applied test equipment and test method, it is generally believed that reliable results were presented.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.6
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• pp.934-947
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• 2011

The purpose of this study is to analyze products for good taste and convenience, which become an engine to constantly create customers. In addition, this study is aimed at investigating the relationship between the selective attributes of Home Meal Replacement, the perceived utilitarian value, and the repurchase intention, and drawing new suggestions on the Home Meal Replacement market from a new marketing perspective. Based on a total of 215 samples, this study reviewed the reliability and fitness of the research model and verified a total of 5 hypothesized using the Amos program. The result of study modeling was GFI=0.905, AGFI=0.849, NFI=0.889, CFI=0.945, and RMR=0.0.092 at the level of $x^2$=230.22 (df=126, p<0.001). First, the food quality (${\beta}$=0.221), convenience (${\beta}$=0.334), packing (${\beta}$=0.278), and employee service (${\beta}$=0.204) of home meal replacement consideration attributes had a positive (+) influence on perceived utilitarian value. Second, perceived utilitarian value (${\beta}$=0.584) had a positive (+) influence on repurchase intention. The factors to differentiate one company from other competitors in terms of the utilitarian value are the quality of food, convenience, wrapping, and services by employees. This study has illustrated the need to focus on the development of a premium menu to compete with other companies and to continue to research and develop nutritious foods that are easy to cook. Moreover, the key factors to have a distinct and constant competitive edge over other companies are the alleviation of consumer anxiety over wrapping container materials, the development of more designs, and the accumulation of service know-how. Therefore, it is necessary for a company to strongly develop the key factors based on its resources as a core capability.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.

• Journal of Korean Home Economics Education Association
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• v.24 no.2
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• pp.117-134
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• 2012

The purpose of this study was to identify the stage of concern, the level of use, and the innovation configuration of Home Economics teachers regarding creativity and personality education in Home Economics(HE) classes. The survey questionnaires were sent through mails and e-mails to middle-school HE teachers in the whole country selected by systematic sampling and convenience sampling. Questionnaires of the stages of concern and the levels of use developed by Hall(1987) were used in this study. 187 data were used for the final analysis by using SPSS/window(12.0) program. The results of the study were as following: First, for the stage of concerns of HE teachers on creativity and personality education, the information stage of concerns(85.51) was the one with the highest response rate and the next high in the following order: the management stage of concerns(81.88), the awareness stage of concerns(82.15), the refocusing stage of concerns(68.80), the collaboration stage of concerns(61.97), and the consequence stage of concerns(59.76). Second, the levels of use of HE teachers on creativity and personality education was highest with the mechanical levels(level 3; 21.4%) and the next high in the following order: the orientation levels of use(level 1; 20.9%), the refinement levels(level 5; 17.1%), the non-use levels(level 0; 15.0%), the preparation levels(level 2; 10.2%), the integration levels(level 6; 5.9%), the renewal levels(level 7; 4.8%), the routine levels(level 4; 4.8%). Third, for the innovation configuration of HE teachers on creativity and personality education, more than half of the HE teachers(56.1%) mainly focused on personality education in their HE classes; 31.0% of the HE teachers performed both creativity and personality education; a small number of teachers(6.4%) focused on creativity education; the same number of teachers(6.4%) responded that they do not focus on neither of the two. Examining the level and type of performance HE teachers applied, the average score on the performance of creativity and personality education was 3.76 out of 5.00 and the mean of creativity component was 3.59 and of personality component was 3.94, higher than standard. For the creativity education, openness/sensitivity(3.97) education was performed most and the next most in the following order: problem-solving skill(3.79), curiosity/interest(3.73), critical thinking(3.63), problem-finding skill(3.61), originality(3.57), analogy(3.47), fluency/adaptability(3.46), precision(3.46), imagination(3.37), and focus/sympathy(3.37). For the personality education, the following components were performed in order from most to least: power of execution(4.07), cooperation/consideration/just(4.06), self-management skill(4.04), civic consciousness(4.04), career development ability(4.03), environment adaptability(3.95), responsibility/ownership(3.94), decision making(3.89), trust/honesty/promise(3.88), autonomy(3.86), and global competency(3.55). Regarding what makes performing creativity and personality education difficult, most HE teachers(64.71%) chose the lack of instructional materials and 40.11% of participants chose the lack of seminar and workshop opportunity. 38.5% chose the difficulty of developing an evaluation criteria or an evaluation tool while 25.67% responded that they do not know any means of performing creativity and personality education. Regarding the better way to support for creativity and personality education, the HE teachers chose in order from most to least: 'expansion of hands-on activities for students related to education on creativity and personality'(4.34), 'development of HE classroom culture putting emphasis on creativity and personality'(4.29), 'a proper curriculum on creativity and personality education that goes along with students' developmental stages'(4.27), 'securing enough human resource and number of professors who will conduct creativity and personality education'(4.21), 'establishment of the concept and value of the education on creativity and personality'(4.09), and 'educational promotion on creativity and personality education supported by local communities and companies'(3.94).