• Journal of Popular Narrative
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• v.27 no.2
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• pp.45-77
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• 2021

The aftermath of the COVID-19 virus continues. There are two potential fears behind the various preventive and quarantine measures. : the fear that "I may be infected" and the fear that "someone may infect me". This subconscious is built on the 'imagination of infection'. This paper attempted to analyze science fiction(SF) narratives and zombie narratives that influenced our imagination of infection. And this paper attempts to examine how SF novels and movies understand and express "infection", and how zombie narratives reveal "infection" and its horror. Mary Shelley's novel "The Last Man" revealed the paradox that the fear of an infectious disease gave humanity an opportunity for reflection. The films and showed that fear and aversion to infectious diseases can lead to riots and conflict. Zombie narrative is a genre that most dramatically expresses the horror of infection. Director Yeon Sangho's zombie trilogy, including , reveals that people around you can turn into the most dangerous source of infection. Through SF and zombie narratives, we can realize that humanity must have a humble sense of solidarity, ethics, and empathy in the face of infectious diseases. Through this narrative texts, we can realize the importance of the imagination of infection. Imagination of infection is the basis for understanding the causes and consequences of the spread of infection, the process and future prospects.

• Korean small business review
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• v.42 no.2
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• pp.43-67
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• 2020

Studies on technological innovation of SMEs have made remarkable growth both qualitatively and quantitatively, but each study has a limitation to generalize due to lack of data, diversity of methodologies and variables. Therefore, a systematic and comprehensive statistical approach to obtain generalized conclusions through numerous empirical studies can help both the strategic decision making of SMEs and the government's innovation policies. The purpose of this study is to comprehensively analyze the technological innovation process of SMEs through meta-analysis. For this, the antecedents of technological innovation, the relationship between technological innovation and management performance of SMEs were analyzed. The results of using 62,512 samples from 111 domestic empirical studies were as follows; First, to improve the technological innovation of SMEs, internal cooperation, innovation culture, dynamic capabilities, and absorptive capacity were important antecedents. Second, in terms of IP performance, which was introduced as a proxy for technological innovation, human resource management, technological opportunities, commercialization capabilities, financial resources, and R&D expenditure. Third, technological innovation has a medium-sized effect on financial performance, however the effect of IP performance has a small effect size. Lastly, in the relationship between technological innovation and financial performance, the method of measurement and publication type showed statistically significant moderating effects.

• Pediatric Infection and Vaccine
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• v.28 no.3
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• pp.144-148
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• 2021

Purpose: Nonpharmaceutical measures, such as social distancing, have resulted in unintended consequences, including a decrease in the incidence of childhood diseases. This study aimed to estimate the impact of social distancing on the incidence of febrile seizure (FS) in Korea using nationally representative data. Methods: We used claims data from the Health Insurance Review and Assessment Service, a single-payer database capturing >95% of the Korean population. The database included any inpatient encounter with a FS diagnosis from January 2010 to September 2020 for those aged 0-5 years old. We aggregated the monthly number of cases to estimate the incidence per 100,000 patient-years in 2020 (January 1 to September 30) for the same periods in 2010-2019. Results: The incidence of FS in 2020 ranged from 113 per 100,000 (95% confidence interval [CI], 108-118 per 100,000) in January to 27 per 100,000 (95% CI, 25-30 per 100,000) in September, whereas the average FS incidence in 2010-2019 ranged from 116 per 100,000 (95% CI, 112-121 per 100,000) in January to 101 per 100,000 (95% CI, 97-106 per 100,000) in September. Conclusions: The incidence of FS decreased by -38% in 2020, suggesting that social distancing contributed towards decreasing the incidence of FS.

• Journal of Korean Elementary Science Education
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• v.41 no.1
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• pp.44-64
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• 2022

This study explored the elementary school teachers' experiences while teaching students with low achievement in science based on the grounded theory. In-depth interviews and analysis were conducted on 13 teachers with experiences in teaching students with low achievement in science within the last three years and more than five years of field experience until the theoretical saturation of data on the teaching experiences for students with low achievement in science. The analysis results were as follows. First, the teaching experiences of elementary school teachers for underachievers in science were classified into 119 concepts, 41 subcategories, and 17 categories. Based on the paradigm model, the categories were structured and presented as causal conditions, contextual conditions, intervening conditions, action/interaction strategies and consequences based on the central phenomenon of 'difficulty in teaching students with low achievement in science'. Second, the core category of elementary school teachers' teaching underachievers in science was assumed to be 'overcoming difficulties and teaching underachievers in science'. And according to the properties and dimensions of the core category, teachers who teaching students with low achievement in science were divided into four types: 'compromising-', 'overcoming-', 'accepting-', and 'conflicting-reality type'. Third, a conditional matrix was presented to summarize and integrate the results of this study by classifying the teaching experience of elementary school teachers for underachievers in science into educational providers and educational demanders. On the basis of these findings, educational implications for teaching students with low achievement in science were discussed.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.1
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• pp.175-183
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• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• Korean Journal of Culture and Social Issue
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• v.28 no.1
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• pp.43-59
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• 2022

This study conducted a multiple mediation analysis using sub-factors of basic psychological needs (BPNs) as mediators in the relationship between problem gambling and stress of gambling addicts to confirm that BPNs and stress, which affect gambling addiction, may be the result of problem gambling and to find effective intervention strategies. A total of 206 adults gambling addicts were screened by using CPGI. Descriptive statistics, correlation, hierarchical regression, and mediation analysis were conducted. Hierarchical regression analysis results yielded that problem gambling and sub-factors of BPNs were significant predictors of stress when controlling for gender and debt. The sub-factors of BPNs mediated the relationship between problem gambling and stress. These results indicated that BPNs and stress are not only the causes of gambling addiction but also the results from the harmful consequences of gambling addiction. The study supported the possibility of the psychological process of "Deficits of BPNs (of gambling users) → stress (of gambling users) → problem gambling → gambling addiction (of gambling addicts) → problem gambling → Deficits of BPNs (of gambling addicts) → stress (of gambling addicts)" among the variables and provided clinical implications for problem gambling counseling. Lastly, the limitations of this study and suggestions for further study were discussed.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.57-64
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• 2022

This scientific article is devoted to the study of the legal significance of such a category of legal status of the purchaser of another's thing, as its good faith. The essence of this phenomenon has been studied, it has been established that the criterion of good faith attaches significant importance to the claims of the participants of these relations for the acquisition or preservation of private property rights. The paper emphasizes that, in addition to the importance of good conscience at the time of possession of another's thing, which gives legal certainty the possibility of registration of the title and is part of the actual composition for the acquisition of property or the right of ancient possession, bona fides also characterizes the behavior of the occupier. In this case, good conscience only has some legal consequences when it is opposed to subjective law. Under such conditions, it acquires direct legal significance, including as a condition for the acquisition and protection of rights. Good faith possession of another's property is an internal indicator of the subject's awareness of a certain property status. This sense, the article assesses this status from the standpoint of the scientific concept of the visibility of law. According to this theory, prescription is also considered as a consequence of the appearance of law, however, because it arises and lasts against the will of the parties and despite their awareness of this fact. Therefore, bona fide continuous and open possession of property as one's own, during the acquisition period, was most significantly associated with the appearance of property. Therefore, the concept of good faith, in the sense of personal perception of real values, is closely related to the principle of protection of the appearance of law, as it is aimed at understanding it by third parties. The paper notes certain differences in the application of the theory of the appearance of the right in the acquisition of property by a bona fide purchaser from an unauthorized alienator and the acquisitive prescription. It is emphasized that such a mechanism must be used in presuming the attitude to the thing as its own, by the holder of movable property. But there should be exceptions to the rule, in particular, if the owner has grounds for vindication of the thing.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.5
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• pp.780-790
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• 2022

Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.

• Korean Journal of Culture and Social Issue
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• v.28 no.4
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• pp.577-595
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• 2022

The purposes of the study is to classify the psychological characteristics of gamblers using by latent profile analysis and to identify the consequences according toof the latent profiles. The subjects of the study are 473 adults gamblers who responded to a online survey about gambling patterns, basic psychological needs(BPNs), and mental health status(MHS) such as stress, depression, and anxiety. Using latent profile analysis known as the person-centered analysis, the results showed that psychological characteristics of gamblers were classified into three groups: 'Lower MHS-BPNs', 'Middle MHS-BPNs', and 'Upper MHS-BPNs'. Also, the as outcome variable, levels of problematic gambling(KCPGI) showed significant differences across the latent profiles such as Problem gambling(M=11.393) on 'Lower MHS-BPNs', Moderate-risk gambling(M=4.277) on 'Middle MHS-BPNs' and Low-risk gambling (M=1.718) on 'Upper MHS-BPNs'. Overcoming the limitations of variable-centered analysis in the existing studies, this study providesreveals new insights onin the psychological characteristics of gamblers and how different latent profiles of gamblers may be in theirdistinct levels of problematic gambling. Finally, limitations of the study and future directions for research on gambling problems are discussed.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.89-103
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• 2023

Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders that can lead to serious consequences, including hypertension and/or cardiovascular diseases, if not treated promptly. Continuous positive airway pressure (CPAP) is widely recognized as the most effective treatment for OSA, which needs the proper titration of airway pressure to achieve the most effective treatment results. However, the process of CPAP titration can be time-consuming and cumbersome. There is a growing importance in predicting personalized CPAP pressure before CPAP treatment. The primary objective of this study was to optimize the CPAP titration process for obstructive sleep apnea patients through EEG feature engineering with machine learning techniques. We aimed to identify and utilize the most critical EEG features to forecast key OSA predictive indicators, ultimately facilitating more precise and personalized CPAP treatment strategies. Here, we analyzed 126 OSA patients' PSG datasets before and after the CPAP treatment. We extracted 29 EEG features to predict the features that have high importance on the OSA prediction index which are AHI and SpO2 by applying the Shapley Additive exPlanation (SHAP) method. Through extracted EEG features, we confirmed the six EEG features that had high importance in predicting AHI and SpO2 using XGBoost, Support Vector Machine regression, and Random Forest Regression. By utilizing the predictive capabilities of EEG-derived features for AHI and SpO2, we can better understand and evaluate the condition of patients undergoing CPAP treatment. The ability to predict these key indicators accurately provides more immediate insight into the patient's sleep quality and potential disturbances. This not only ensures the efficiency of the diagnostic process but also provides more tailored and effective treatment approach. Consequently, the integration of EEG analysis into the sleep study protocol has the potential to revolutionize sleep diagnostics, offering a time-saving, and ultimately more effective evaluation for patients with sleep-related disorders.