• Journal of Digital Convergence
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• v.19 no.3
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• pp.401-414
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• 2021

The purpose of this study was to examine the associations of Korean older adults' use of Social Network Service (SNS) and text messages with frequency of contact with 1) non-coresident adult children, 2) siblings and relatives, or 3) friends, neighbors, and acquaintances. Data were drawn from the 2017 Survey of Living Conditions and Welfare Needs of Korean Older Persons 65+ (N=8,392), and older adults were categorized into 4 groups depending on their familiarity with use of SNS and text messages. Ordinary Least Squares regression models were estimated for analyses. Results revealed that older users of both types of communication media reported frequent exchanges of calls, text messages, etc. with both family and friends. However, using SNS and text messages was consistently related to more face-to-face contact with non-family members. To conclude, older adults' familiarity with communication media could be key to exchanges of emotional and instrumental support with informal social network members and quality of life in the community. Overall, our results highlight the importance of information communication education targeting older adults for continued involvement with their informal social network members.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.3
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• pp.197-212
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• 2022

This study was conducted to empirically analyze the effects and interactions among beneficial bacteria, commensal bacteria, and acne bacteria, which are factors in the skin microbiomes, on detrimental bacteria by 289 people, who are 20 to 49 years old among Koreans. As a result of multiple regression models using bio big data of skin microbiomes, when the difference in skin microbiomes according to the sex and age of the subjects was controlled, the beneficial bacteria showed a negative (-) effect on the detrimental bacteria, while the commensal and acne bacteria showed a positive (+) effect. Particularly, the negative (-) effect of beneficial bacteria on detrimental bacteria was different through interaction with acne bacteria according to the level of commensal bacteria. These results demonstrate that the activation of beneficial bacteria inhibits detrimental bacteria, and the effect of skin microbiomes on detrimental bacteria is balanced with skin microbiomes through interaction with independent influence. Therefore, it is suggested that when studying skin microbiomes products to help the proliferation of beneficial bacteria and to create a skin environment that inhibits detrimental bacteria in the personalized cosmetics manufacturing industry, it is necessary to consider the independent effects and interactions among skin microbiome factors together.

• Journal of Industrial Convergence
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• v.20 no.1
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• pp.23-33
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• 2022

The study was conducted to develop an implementation model for digital transformation (DX) of manufacturing companies. To this end, previous studies on the process of management innovation and digital transformation were reviewed. The DX process model was derived based on the NEBIC theory and innovation theory applied in the innovation process of the Internet business. In addition, a research model including the factors of the will of the top management class (TMT) was constructed and confirmed through empirical data. The research hypothesis were verified based on data collected from members of mid-sized manufacturing companies promoting digital transformation. Through regression analysis, the influence relationship of each stage of the research model (technical knowledge, TK → opportunity perception, OR → performace expectation, PE and → Intention to execute, IE) was confirmed. Hierarchical regression analysis was conducted to understand the mediating effect of the members' perception of the top management's willingness to promote DX in the process. As a result of checking the Sobel test, it was confirmed that the management's perception of DX promotion partially mediated the relationship at each stage. This study is meaningful in that it presented a model applicable to the digital transformation of the mid-sized manufacturing industry. It is also valuable in providing an empirical basis for innovative research and NEBIC expansion. Longitudinal studies are required to overcome the limitations of empirical data for process models with dynamic characteristics whereas extended empirical studies are required in various fields other than manufacturing to generalize research results.

• Journal of Digital Convergence
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• v.20 no.2
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• pp.241-250
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• 2022

Due to the COVID-19 pandemic, with increased 'untact' services and with unstable household economy, the bike insurance fraud is expected to surge. Moreover, the fraud methodology gets complicated. However, the fraud detection model for bike insurance is absent. we deal with the issue of skewed class distribution and reflect the criterion of fraud detection expert. We utilize a balanced random-forest algorithm to develop an efficient bike insurance fraud detection model. As a result, while the predictive performance of balanced random-forest model is superior than it of non-balanced model. There is no significant difference between the variables used by the experts and the confirmatory models. The important variables to detect frauds are turned out to be age and gender of driver, correspondence between insured and driver, the amount of self-repairing claim, and the amount of bodily injury liability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.108-117
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• 2021

The educational approach using Capstone Design is gradually expanding with the change in social and educational paradigms. Course development utilizing the Capstone Design in models in the department of fashion can create a positive effect in that it enhances the practical capabilities of the fashion model and expands the perspective of various fields related to fashion shows. This study proved the educational efficacy by applying the Capstone Design to the model work presentation course and investigating the implications of the design from the instructor's perspective. The research methods used to guide this course utilizing the Capstone Design were theatrical research and model development research. This study showed that learners' satisfaction for this course combined with Capstone Design was very high, and students were very satisfied with the progress of the class. The instructor's point of view in progressing this course showed the need for education from an in-depth and convergence perspective related to fashion, improvement of temporal and spatial utilization of space, concerns about establishing connections with experts and various industries, and expanding the scope of education through continuous exchanges and cooperation with industry.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Journal of Industrial Convergence
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• v.20 no.8
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• pp.1-13
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• 2022

Although the number of venture start-ups has increased significantly, it is difficult to judge the success or failure based on short-term performance alone. The survival of a company cannot be guaranteed if it does not show sustainable growth prospects. As a growth factor for venture companies, the level of technology commercialization capability and competitive strategies are considered important. Recently, the emergence of innovative business models is creating new opportunities and driving the growth of numerous venture start-ups. This study tried to investigate the mediating effect of business model innovation in the relationship between technology commercialization capability, competitive strategy and the growth prospects of venture companies. For this, empirical analysis was conducted using the original data of the Research on the Precision Status of Venture Firms 2021. As a result, production, manufacturing, marketing capability, cost leadership and product differentiation had a positive(+) effect on growth prospects. The mediating effect of business model innovation between all factors except for manufacturing capacity and growth prospects was verified. This study expanded the scope of research by shedding new light on the factors influencing the long-term growth prospects of venture companies and revealing business model innovation as a new mediating variable. In future research, it is necessary to develop an objective measurement tool and to identify differences according to industrial characteristics.

• BMB Reports
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• v.56 no.3
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• pp.196-201
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• 2023

Renal fibrosis is the final manifestation of chronic kidney disease (CKD) regardless of etiology. Hypoxia-inducible factor-2 alpha (HIF-2α) is an important regulator of chronic hypoxia, and the late-stage renal tubular HIF-2α activation exerts protective effects against renal fibrosis. However, its specific role in progressive renal fibrosis remains unclear. Here, we investigated the effects of the long-term tubular activation of HIF-2α on renal function and fibrosis, using in vivo and in vitro models of renal fibrosis. Progressive renal fibrosis was induced in renal tubular epithelial cells (TECs) of tetracycline-controlled HIF-2α transgenic (Tg) mice and wild-type (WT) controls through a 6-week adenine diet. Tg mice were maintained on doxycycline (DOX) for the diet period to induce Tg HIF-2α expression. Primary TECs isolated from Tg mice were treated with DOX (5 ㎍/ml), transforming growth factor-β1 (TGF-β1) (10 ng/ml), and a combination of both for 24, 48, and 72 hr. Blood was collected to analyze creatinine (Cr) and blood urea nitrogen (BUN) levels. Pathological changes in the kidney tissues were observed using hematoxylin and eosin, Masson's trichrome, and Sirius Red staining. Meanwhile, the expression of fibronectin, E-cadherin and α-smooth muscle actin (α-SMA) and the phosphorylation of p38 mitogen-activated protein kinase (MAPK) was observed using western blotting. Our data showed that serum Cr and BUN levels were significantly lower in Tg mice than in WT mice following the adenine diet. Moreover, the protein levels of fibronectin and E-cadherin and the phosphorylation of p38 MAPK were markedly reduced in the kidneys of adenine-fed Tg mice. These results were accompanied by attenuated fibrosis in Tg mice following adenine administration. Consistent with these findings, HIF-2α overexpression significantly decreased the expression of fibronectin in TECs, whereas an increase in α-SMA protein levels was observed after TGF-β1 stimulation for 72 hr. Taken together, these results indicate that long-term HIF-2α activation in CKD may inhibit the progression of renal fibrosis and improve renal function, suggesting that long-term renal HIF-2α activation may be used as a novel therapeutic strategy for the treatment of CKD.