• Knowledge Management Research
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• v.23 no.4
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• pp.315-338
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• 2022

In 1986, Korea established legal systems to support small and medium-sized start-ups, which becomes the main pillars of national development. The legal systems have stimulated start-up ecosystems to have more than 1 million new start-up companies founded every year during the past 30 years. To analyze the trend of Korea's start-up ecosystem, in this study, we collected 1.18 million news articles from 1991 to 2020. Then, we extracted news articles that have the keywords "start-up", "venture", and "start-up". We employed network analysis and topic modeling to analyze collected news articles. Our analysis can contribute to analyzing the government policy direction shown in the history of start-up support policy. Specifically, our analysis identifies the dynamic characteristics of government influenced by external environmental factors (e.g., society, economy, and culture). The results of our analysis suggest that the start-up ecosystems in Korea have changed and developed mainly by the government policies for corporation governance, industrial development planning, deregulation, and economic prosperity plan. Our frequency keyword analysis contributes to understanding entrepreneurial productivity attributed to activities among the networked components in industrial ecosystems. Our analyses and results provide practitioners and researchers with practical and academic implications that can help to establish dedicated support policies through forecast tasks of the economic environment surrounding the start-ups. Korean entrepreneurial productivity has been empowered by growing numbers of large companies in the mobile phone industry. The spectrum of large companies incorporates content startups, platform providers, online shopping malls, and youth-oriented start-ups. In addition, economic situational factors contribute to the growth of Korean entrepreneurial productivity the economic, which are related to the global expansions of the mobile industry, and government efforts to foster start-ups. Our research is methodologically implicative. We employ natural language processes for 30 years of media articles, which enables more rigorous analysis compared to the existing studies which only observe changes in government and policy based on a qualitative manner.

• Journal of Life Science
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• v.32 no.3
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• pp.189-195
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• 2022

Kinesin-2 comprises two subfamilies of the heterotrimeric or homodimeric motors found in mammalian cells. Heterotrimeric kinesin-2 consists of kinesin superfamily proteins (KIFs) 3A and 3B and kinesin-associated protein 3 (KAP3), which is a molecular motor protein that moves along microtubules. It plays diverse roles in cargo transport, including anterograde trafficking in cilia, and interacts with many different cargoes and proteins, but their binding proteins have not yet been fully identified. In this study, the yeast two-hybrid assay was used to identify the proteins that interact with the cargo-binding domain (CBD) of KIF3A, and an interaction between KIF3A and brain expressed X-linked 2 (Bex2) was found. Bex2 bound to the CBD-containing C-terminal tail region of KIF3A but did not interact with the same region of KIF3B or KIF5A (a motor protein of kinesin-1). KIF3A interacted with another isoform, Bex1, but did not interact with Bex3. In addition, glutathione S-transferase (GST) pull-downs showed that KIF3A specifically interacts with GST-Bex1 and GST-Bex2 but not with GST alone. When co-expressed in HEK-293T cells, Bex2 co-localized with KIF3A and co-immunoprecipitated with KIF3A and KIF3B but not KIF5B. In combination, these results suggest that Bex2 is capable of binding to heterotrimeric kinesin-2 and may serve as an adaptor protein that links heterotrimeric kinesin-2 with cargo.

• Journal of Korean Society of Forest Science
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• v.111 no.1
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• pp.115-124
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• 2022

This study was conducted to obtain basic data that could help prevent damage caused by slow-moving landslides (land-creep). Specifically, the geological, topographic, and physical characteristics of land-creep were analyzed in Jiphyeon-myeon, Jinju-si. The first and second analyzed land-creeps occurred in 1982 and 2019, respectively. The area damaged in the second land-creep was about 11.5-fold larger than that damaged in the first land-creep. The dominant constituent rock in the land-creep area was sedimentary rock, which seems to be weakly resistant to weathering. The areas that collapsed due to land-creep were related to the presence of separated rocks between the bedding plane in the estimated activity surface over the slope direction and the vertically developed joint surface. Thus, surface water and soil debris were introduced through the gaps of separated rocks. Additionally, the areas collapsed due to the combination of the bedding plane and joint surface shale and sandstone showed an onion structure of weathered outcrop from the edge to inner part caused by weathering from ground water. Consequently, core stones were formed. The study area was a typical area of land-creep in a mountain caused by ground water. Land-creep was classified into convex areas of colluvial land-creep. The landslide-risk rating in the study area was classified into three and five classes. The flow of ground water moved to the northeast and coincided with the direction of the collapse. Soil bulk density in the collapsed area was lower than that in ridge area, which was rarely affected by land-creep. Thus, soil bulk density was affected by the soil disturbance in the collapsed area.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.131-154
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• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.

• Korean Journal of Plant Taxonomy
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• v.52 no.1
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• pp.54-63
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• 2022

Prunus subg. Cerasus is the most planted street and landscaping tree in South Korea, but it is difficult to identify species according to their macro-morphologies, leading to problems when attempting properly to manage species quantities. The purpose of this study is to understand the current status of plant types and species compositions in Bundang Central Park in Bundang-gu of Seongnam City and to discuss the necessity of the management of landscaping tree planting. In April of 2021, during the cherry blossom season, a total of 5,866 planted cherry trees were investigated within an area of 6 km² of Bundang-gu in Seongnam City. As a result, 5,744 trees were sorted into eleven taxa, but the remaining 122 trees were not determined due to their complex morphologies. Prunus ×yedoensis Matsum. accounted for the highest proportion (52.1%), followed by P. serrulata Lindl. var. pubescens Nakai and P. jamasakura Siebold ex Koidz. P. ×nudiflora (Koehne) Koidz., a plant native to Jejudo Island, was not found in this survey. In order to help identify cherry trees based on micro-morphologies, an identification key was presented for the eleven taxa planted as major landscaping trees. It is known that cherry trees frequently form interspecific hybrids in nature. In order to prevent a loss of the genetic originality of native species due to hybridization and gene introgression from foreign cherry trees, it is necessary to manage planting species near the habitats of native taxa and track their origins.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.329-352
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• 2022

It is reported that particulate matter(PM) penetrates the lungs and blood vessels and causes various heart diseases and respiratory diseases such as lung cancer. The subway is a means of transportation used by an average of 10 million people a day, and although it is important to create a clean and comfortable environment, the level of particulate matter pollution is shown to be high. It is because the subways run through an underground tunnel and the particulate matter trapped in the tunnel moves to the underground station due to the train wind. The Ministry of Environment and the Seoul Metropolitan Government are making various efforts to reduce PM concentration by establishing measures to improve air quality at underground stations. The smart air quality management system is a system that manages air quality in advance by collecting air quality data, analyzing and predicting the PM concentration. The prediction model of the PM concentration is an important component of this system. Various studies on time series data prediction are being conducted, but in relation to the PM prediction in subway stations, it is limited to statistical or recurrent neural network-based deep learning model researches. Therefore, in this study, we propose four transformer-based models including spatiotemporal transformers. As a result of performing PM concentration prediction experiments in the waiting rooms of subway stations in Seoul, it was confirmed that the performance of the transformer-based models was superior to that of the existing ARIMA, LSTM, and Seq2Seq models. Among the transformer-based models, the performance of the spatiotemporal transformers was the best. The smart air quality management system operated through data-based prediction becomes more effective and energy efficient as the accuracy of PM prediction improves. The results of this study are expected to contribute to the efficient operation of the smart air quality management system.

• Journal of the Korean Orthopaedic Association
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• v.54 no.4
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• pp.309-316
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• 2019

Purpose: This study examined the residential environment and accessibility of rehabilitation for cerebral palsy (CP) to identify the problems with residential laws pertaining to the disabled and provide basic data on the health legislation for the rights of the disabled. Materials and Methods: The literature was searched using three keywords: residence, rehabilitation, and accessibility. Two items were selected: residential environment and rehabilitation accessibility. The questionnaire included 51 items; 24 were scored using a Likert scale and 27 were in the form of multiple-choice questions. Results: This study included 100 subjects, of which 93 lived at home and seven lived in a facility. Of these 93 subjects, 65% were living in apartments, usually two or more floors above ground, and 40% of them were living without elevators. According to the Gross Motor Function Classification System, subjects with I to III belonged to the ambulatory group and IV, V were in the non-ambulatory group. Subjects from both groups who lived at home found it most difficult to visit the rehabilitation center by themselves. In contrast, among those who lived at the facility, the ambulatory group found it most difficult to leave the facility alone, while the non-ambulatory group found it most difficult to use the toilet alone. Moreover, 83% of respondents thought that rehabilitation was necessary for CP. On the other hand, 33% are receiving rehabilitation services. Rehabilitation was performed for an average of 3.6 sessions per week, 39 minutes per session. Conclusion: There is no law that ensures secure and convenient access of CP to higher levels. Laws on access routes to enter rooms are insufficient. The disabled people's law and the disabled person's health law will be implemented in December 2017. It is necessary to enact laws that actually reflect the difficulties of people with disabilities. Based on the results of this study, an investigation of the housing and rehabilitation of patients with CP through a large-scale questionnaire will necessary.

• Journal of Popular Narrative
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• v.25 no.3
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• pp.303-345
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• 2019

This article analyzes the multi-layered communication in the Webtoon Princess Pari, released on the Daum portal site, created (written and illustrated) by Kim Naim, through analyzing the narrative structure and comments with the qualitative / quantitative methodology. The webtoon Princess Pari is structured in an omnibus style in which unit narratives are intermittently articulated, multi-lined, and interconnected. As integrated narratives which link with unitary narratives, Pari's growth story as a shaman and a romance narrative are structured. The classical original story of the shaman was used as a prehistory corresponding to the prequel of the webtoon through a preview, and the writer restructured the narrative to overcome the contradictions of the gender asymmetry and the patriarchal ideology of the original text. The viewer then creates a conversational space by giving critical and reflective comments. According to a statistical analysis conducted through sampling, the types of comments can be classified as follows: Appreciation and criticism of the contents ≫ Emotional response ≫ Intuitive overall review ≫ Knowledge and reflection ≫ Comments on comments. In the process of creation and acceptance of the Webtoon, a multi-layered dialogue between classical and modern, content and audience, acceptance and creation has been at play. In the creation dimension, the writer used a device to fill the gap of mythical symbols of the contents. At the level of the audience, they formed a culture of sharing information, knowledge, and reflection about tradition/folk/culture through comments. This corresponds to classical and modern dialogue through the webtoon. The viewers form a sympathetic bond, attempt hermeneutical coordination, supplement the information, and search for a balanced angle through controversial conversation. In addition, by commenting on attitudes, views, and perspective, the commentators showed a behavioral pattern corresponding to meta-criticism in literature. The viewers' comments acted as feedback on the creation of the webtoons, so that the creation and acceptance itself influenced the production of the content of the webtoon. The webtoon Princess Pari, which was based on Korean classical narrative, has been reorganized onto 'moving and dynamic' content, which leads to sense, thinking, criticism and reflection through the formation of various dialogues.

• Journal of Popular Narrative
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• v.25 no.3
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• pp.349-383
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• 2019

This paper examined the meaning and task of romance research with a focus on Reading the Romance(1984) by Janice A. Radway. This book, which analyzes romance texts by examining the situation and meaning of reading romance by women readers integrating between cultural studies and literary studies, is one of the most popular studies on the romance genre. Radway scrutinized the practical significance of reading romance in a community of women readers. Through a study involving questionnaires and in-depth interviews, she found that for women, romance reading is a 'compensatory fiction' that brings happiness and emotional redemption through a sense of liberation achieved by escaping from patriarchal daily life. The romance that women prefer is composed of 4 stages and 13 divisions: 'Encounter → Attest → Recovery → Happy End'. It also maintains a formula that begins with an immature female character's identity crisis and ends with a blissful union that recognizes the intrinsic value of the main character, who has turned into a man who is considerate of the women. Therefore, romance plays the role of pursuit of the 'female utopian fantasy' and at the same time a reconciliation of women to patriarchy. Feminist critics of the day criticized this argument. However, reading romance is a 'feminine reading', and romance is literature about the functional relationship between women's lives and patriarchy. Yet the interpretation could differ depending on the different viewpoints and definitions of the women's utopian fantasy. In recent years, the conditions of female reader's lives, awareness and imagination have been changing rapidly. As a result, the female utopian fantasy has also changed significantly. Nevertheless, women's lives in the real patriarchal system are still contradictory, and their adventurous imagination is spreading in alternative spaces such as the subculture. In this regard, the question is about the definition of romance and the meanings of romance research are still important task.