• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.472-483
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• 2019

Why can't we be free from diverse crises threatening our lives in a high-tech future society? Disasters interrupt habitual and institutionalized patterns of behavior and bring about a kind of social shock to make people follow social and individual changes. An interesting fact revealed in the study finding is that the role of disaster management control tower was proper during the Great Flood ofthe Eulchuk Year(1925) and the unified disaster management system facilitated smooth cooperation with relevant authorities. Also, motivating disaster management organizations positively influenced organizational commitment. This implies that if we constantly ask to improve current institutions by introspecting and learning, based on historical records, we may be able to find insights for a safe society of the future.

• Healthcare Informatics Research
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• v.24 no.4
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• pp.253-262
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• 2018

Objectives: We reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method. Methods: We reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies. Results: We identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses. Conclusions: Digital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

• Research in Community and Public Health Nursing
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• v.30 no.3
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• pp.295-306
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• 2019

Purpose: There has been a growing recognition that person-centered care enhances the quality of life of nursing home residents with dementia. This study was conducted to develop a person-centered dementia care online education program for direct care staff in long-term care facilities. Methods: Delphi method with expert group was used to validate contents. We developed 61 draft items based on literature review. Twenty experts participated in consecutive three round surveys including 5-point Likert scale questions and open-ended questions. Based on experts' opinions, the content validity ratio for content validity and the coefficient of variation for stability were calculated. Results: Three-round Delphi surveys and additional feedback from the expert panel established a consensus of core contents: 1) dementia (7 categories), 2) person-centered care (6 categories), 3) communication (8 categories), and 4) behavioral and psychological symptoms of dementia (6 categories). Specific sub-categories in each category were differentiated according to the job qualifications (65 sub-categories for registered nurses, 64 sub-categories for nursing aids, and 41 sub-categories for personal care workers). Conclusion: This delphi study identified person-centered dementia education curricula, in which the person-centered approach should be a key policy priority in Korean long-term care system. Now it is urgently needed to develop education programs utilizing online platforms that enable efficient and continuous learning for long-term care staff, which can contribute to behavior changes in the person-centered dementia care approach and improvement of care quality in long-term care facilities.

• Biomolecules & Therapeutics
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• v.27 no.2
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• pp.160-167
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• 2019

Depression is a major mood disorder. Abnormal expression of glial glutamate transporter-1 (GLT-1) is associated with depression. Schisantherin B (STB) is one bioactive of lignans isolated from Schisandra chinensis (Turcz.) Baill which has been commonly used as a traditional herbal medicine for thousands of years. This paper was designed to investigate the effects of STB on depressive mice induced by forced swimming test (FST). Additionally, we also assessed the impairment of FST on cognitive function in mice with different ages. FST and open field test (OFT) were used for assessing depressive symptoms, and Y-maze was used for evaluating cognition processes. Our study showed that STB acting as an antidepressant, which increased GLT-1 levels by promoting PI3K/AKT/mTOR pathway. Although the damage is reversible, short-term learning and memory impairment caused by FST test is more serious in the aged mice, and STB also exerts cognition improvement ability in the meanwhile. Our findings suggested that STB might be a promising therapeutic agent of depression by regulating the GLT-1 restoration as well as activating PI3K/AKT/mTOR pathway.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.35-42
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• 2019

Recently, as the Internet and various wearable devices have appeared, Internet technology has contributed to obtaining more convenient information and doing business. However, as the internet is used in various parts, the attack surface points that are exposed to attacks are increasing, Attempts to invade networks aimed at taking unfair advantage, such as cyber terrorism, are also increasing. In this paper, we propose a feature selection method to improve the classification performance of the class to classify the abnormal behavior in the network traffic. The UNSW-NB15 dataset has a rare class imbalance problem with relatively few instances compared to other classes, and an undersampling method is used to eliminate it. We use the SVM, k-NN, and decision tree algorithms and extract a subset of combinations with superior detection accuracy and RMSE through training and verification. The subset has recall values of more than 98% through the wrapper based experiments and the DT_PSO showed the best performance.

• Smart Structures and Systems
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• v.23 no.1
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• pp.1-14
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• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• Journal of Engineering Education Research
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• v.24 no.4
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• pp.21-29
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• 2021

The objective of this research is to devise a BARS evaluation system as a performance evaluation plan for non-face-to-face capstone design and to verify the validity through the expert FGI as the remote education is highlighted as a new normal standard in the post corona epoch. The conclusion of this research is as follows. First, the non-face-to-face capstone design is a competency centered subject that allows you to develop the engineering and majoring knowledge and its function and attitude, and the achievement of program outcome is the objective competency, and the researcher proposes the BARS method evaluation, one of competency evaluation method, as a new performance evaluation plan. Second, for the evaluation of PO achievement of non-face-to-face capstone design, the researcher deduced 20 behavior identification standard(anchor) of BARS evaluation system, and developed the achievement standard per 4 levels. Third, as the evaluation tool of non-face-to-face capstone design, the presentation data(PPT), presentation video, product such as trial product(model), non-face-to-face class participation video, discussion participating video, team activity report, and result report for the evidential data of BARS evaluation were appeared as proper. Finally, the BARS evaluation plan of non-face-to-face capstone design would be efficiently made through the establishment of evaluation plan, the establishment of grading standard of BARS evaluation scale, the determination of evaluation subject and online BARS evaluation site.

• Journal of Microbiology and Biotechnology
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• v.31 no.1
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• pp.51-62
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• 2021

Here, we investigated the prebiotic and antioxidant effects of Actinidia arguta sprout water extract (AASWE) on lipopolysaccharide (LPS)-induced cognitive deficit mice. AASWE increased viable cell count, titratable acidity, and acetic acid production in Lactobacillus reuteri strain and showed a cytoprotective effect on LPS-induced inflammation in HT-29 cells. We assessed the behavior of LPS-induced cognitive deficit mice using Y-maze, passive avoidance and Morris water maze tests and found that administration of AASWE significantly improved learning and memory function. The AASWE group showed antioxidant activity through downregulation of malondialdehyde levels and upregulation of superoxide dismutase levels in brain tissue. In addition, the AASWE group exhibited activation of the cholinergic system with decreased acetylcholinesterase activity in brain tissue. Furthermore, AASWE effectively downregulated inflammatory mediators such as phosphorylated-JNK, phosphorylated-NF-κB, TNF-α and interleukin-6. The major bioactive compounds of AASWE were identified as quercetin-3-O-arabinopyranosyl(1→2)-rhamnopyranosyl(1→6)-glucopyranose, quercetin-3-O-apiosyl(1 → 2)-galactoside, rutin, and 3-caffeoylquinic acid. Based on these results, we suggest that AASWE not only increases the growth of beneficial bacteria in the intestines, but also shows an ameliorating effect on LPS-induced cognitive impairment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• Korean Journal of Community Nutrition
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• v.25 no.6
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• pp.451-463
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• 2020

Objectives: The purpose of this study was to develop and evaluate booklets and video clips to prevent children from picky eating. Methods: Based on a survey conducted on food preferences of preschool children aged 2 to 5 years, 14 kinds of less preferred vegetables were selected. Accordingly, educational videos, activity books, and teaching-learning guides were produced for preschool children using the 'food bridge' theory, and the educational materials were named "Friendly vegetables". Educational materials were distributed to childcare institutions, and their effectiveness was investigated for preschool children who were instructed on these materials once every 30 days from March to November, 2019. The children were examined for changes in their knowledge of names, colors, taste/texture, methods of cultivation, and preferences for vegetables before and after the instructional course. Results: The awareness of vegetables increased significantly in younger children and the picky eating group. When the assessment was carried out in terms of vegetable knowledge, it was observed that the younger the age or the pickier the in eating food, the more effective the education is compared to the counter part. The preference for vegetables also increased after the instruction compared to the pre-instruction period, but significant changes were seen only in the 2~3 year age group for boys and girls. Also, only the picky group of girls showed changes in preference. The children's average interest in the education materials was 3.85 points out of 5 points. Conclusions: Through this study, we have developed educational materials for standalone use in childcare facilities and confirmed that they have a significant effect on improving awareness and preferences related to vegetables. In summary, the younger the age or the pickier the child in eating food, the more effective the education. It is believed that additional education on mealtime guidance is needed which can alter the eating behavior of preschool children and improve their diet. It is proposed to widen the scope of use of the materials by collecting diverse opinions from child care teachers.