• Journal of Industrial Convergence
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• 제20권10호
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• pp.25-32
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• 2022

The purpose of this study was to identify the degree of empathy, communication competence and person-centered care of nursing students to identify the factors that influence person-centered care. Data were collected by using questionnaires from 207 students in the 3^rd and 4^th grades of the nursing college who experienced clinical practice in G city, from May 2 to 20, 2022. Data were analyzed by t-test, ANOVA, Games-Howell test, Scheffe test, Pearson's correlation, and multiple regression. Statistically, person-centered care showed a significantly positive correlation with empathy(r=.57, p<.001) and communication competence(r=.63, p<.001). Influencing factors on person-centered care were communication competence(β=.44, p<.001), empathy(β=.30, p<.001) and clinical practice satisfaction(β=-.13, p=.028) accounting for 49.8% of the total change. Based on this results, in order to improve person-centered care of nursing students, it is necessary to develop education programs to increase communication competence and empathy ability and to provide active feedback through pre and post evaluation to increase clinical practice satisfaction.

• BMB Reports
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• 제55권12호
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• pp.609-614
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• 2022

Mutation of the gene for adenomatous polyposis coli (APC), as seen in Apc^Min/+ mice, leads to intestinal adenomas and carcinomas via stabilization of β-catenin. Transmembrane 4 L six family member 5 (TM4SF5) is involved in the development of non-alcoholic fatty liver disease, fibrosis, and cancer. However, the functional linkage between TM4SF5 and APC or β-catenin has not been investigated for pathological outcomes. After interbreeding Apc^Min/+ with TM4SF5-overexpressing transgenic (Tg^TM4SF5) mice, we explored pathological outcomes in the intestines and livers of the offspring. The intestines of 26-week-old dual-transgenic mice (Apc^Min/+:Tg^TM4SF5) had intramucosal adenocarcinomas beyond the single-crypt adenomas in Apc^Min/+ mice. Additional TM4SF5 overexpression increased the stabilization of β-catenin via reduced glycogen synthase kinase 3β (GSK3β) phosphorylation on Ser9. Additionally, the livers of the dualtransgenic mice showed distinct sinusoidal dilatation and features of hepatic portal hypertension associated with fibrosis, more than did the relatively normal livers in Apc^Min/+ mice. Interestingly, TM4SF5 overexpression in the liver was positively linked to increased GSK3β phosphorylation (opposite to that seen in the colon), β-catenin level, and extracellular matrix (ECM) protein expression, indicating fibrotic phenotypes. Consistent with these results, 78-week-old Tg^TM4SF5 mice similarly had sinusoidal dilatation, immune cell infiltration, and fibrosis. Altogether, systemic overexpression of TM4SF5 aggravates pathological abnormalities in both the colon and the liver.

• The Journal of the Convergence on Culture Technology
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• 제9권1호
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• pp.777-786
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• 2023

Generative AI systems are expected to be more widely utilized. However, relatively little attention has been paid to understanding how users perceive and accept generative AI results. To identify strategies for increasing the future use of generative AI and prepare for potential issues, we organized design workshop for the general user group and the designer group. They created artwork utilizing Novel AI and semi-structured interview was followed to evaluate their attitudes toward generative AI and its copyright. Results indicate that the general public views generative AI positively, while the design-related group views it quite negatively. The participants expressed concerns as to the misuse the system, specifically related to copyright issues. People who are likely to utilize generative AI outcomes have insisted more strongly that copyrights should be their own. Those working in the design field highly evaluated the possibility of using generative AI in their work. Copyright perceptions were not significantly influenced by users' satisfaction or their level of involvement in the creation process. We discuss design implications for interfaces using generative AI based on the findings.

• The Journal of the Convergence on Culture Technology
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• 제9권3호
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• pp.299-304
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• 2023

Recently, drones have been used as a major means of attack drones in major wars around the world, and it seems likely that they will evolve into game changers in the future. Recently, drones have been used as a major means of attack drones in major wars around the world, and it seems likely that they will evolve into game changers in the future. In the major wars of the 21century, attack drones are used for precision fire-guided or self-destruct attacks, For the purpose of cognitive warfare, its territory is expanding not only to land and air, but also to sea and water. These attack drones will perform multi-domain operations, and for this purpose, the level of autonomy will be improved and High-Low Mix We will continue to develop by strengthening concept-based scalability. North Korea has also been making considerable efforts to operate attack drones for a long time, and activities such as third-country-level self-explosive drones, artificial intelligence-based clustered self-explosive drones, and self-destructive stealth unmanned semi-submersible are expected. In addition to North Korea's provocations and attacks, it is hoped that there will be a need for active follow-up research on our military's countermeasures and utilization plans.

• Journal of Industrial Convergence
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• 제21권12호
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• pp.83-90
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• 2023

The purpose of this study is a descriptive research study to confirm the difference in health empowerment according to the current status and general characteristics of North Korean women defectors residing in South Korea. The subjects of the study were 201 North Korean women defectors living in the local community, and data were collected from September 16 to September 21, 2020 using a structured questionnaire. The collected data were analyzed by descriptive statistics, t-test, ANOVA, pearson's correlation coefficients using the SPSS/WIN 23.0 program. As a result of the study, the domain with the lowest score in the health empowerment of North Korean women defectors was "I know a positive method to cope with stress related to my health care." in the stress management question. Statistically significant differences were found in North Korean repatriation experience(t=3.77, p<.001), education(F=4.56, p=.012), economic status(t=-2.95, p=.004), subjective health status(t=-4.75, p<.001) and subjective stress(t=-2.47, p=.015). Based on the results of this study, alternatives should be prepared to strengthen the health empowerment of North Korean women defectors according to their individual characteristics.

• Journal of Intelligence and Information Systems
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• 제19권3호
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• Journal of Intelligence and Information Systems
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• 제21권1호
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• pp.1-13
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• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.