• Research in Community and Public Health Nursing
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• v.32 no.4
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• pp.415-429
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• 2021

Purpose: The purpose of this study was to develop the Self-Care Non-adherence Risk Assessment Scale (SCNRAS) for patients with chronic illness in South Korea. Methods: This study was conducted from April to July, 2020 and utilized a convenience sampling method to recruit 336 patients with chronic illness from three hospitals located in South Korea. The content, factorial structure, item-convergent/discriminant validity, convergent validity, internal consistency reliability, and test-retest reliability of the scale were evaluated. The data were analyzed using exploratory and confirmatory factor analyses, Pearson's correlation coefficient, Cronbach's α, and intra-class correlation coefficient. Results: The exploratory and confirmatory factor analyses yielded six-factors. Convergent validity was demonstrated using measures of defining issues. Internal consistency reliability and test-retest reliability were found to be acceptable, as indicated by a Cronbach's α of .65~.81 and an intra-class correlation coefficient of .93~.98. The Self-Care Non-adherence Risk Assessment Scale for patients with chronic illness is a new instrument that comprehensively measures the knowledge, skill, physical function status, access to health care, social support, motivation, and confidence. It comprises 18 items scored on a 5-point Likert scale. The validity and reliability of the scale were verified. Conclusion: The scale developed through this study is expected to screen those who need nursing intervention early by predicting the self-care non-adherence risk group.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.158-165
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• 2022

Journal of Convergence for Information Technology. This study explores how native speakers (NSs) and non-native speakers (NNSs) of English negotiate meanings during conversational interactions to achieve successful communication. This study involved 40 participants: 20 native English speakers and 20 Korean university students. The participants were divided into 20 pairs, with each pair consisting of one NS and one NNS. Tasks for conversation were given and the execution recorded in order to collect data. 37 recorded conversations were transcribed and used for analysis, including statistical analyses. Results showed that both NSs and NNSs mutually put in effort for successful communication. While NSs mostly played the role of leading the natural flow of the conversation, encouraging their non-native interlocutors to speak, NNSs used various strategies to compensate for their lack of linguistic competence in the target language. NNSs employed a wide range of communicative strategies to keep the conversation going. The results of this study contribute to a better understanding of interactions between NSs and NNSs and yield pedagogical implications.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.6
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• pp.67-87
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• 2021

Provisioning service, which is one of the ecosystem service functions, means goods and services such as food and fuel that people get from ecosystem. Provisioning functions are closely related to the primary industry, a sector of economy. Excessive demand and use of human society can cause trade-offs among regulation, cultural, and supporting services. Therefore, it is important to perform evaluation ecosystem services periodically and to monitor the time series fluctuations to identify the impact of provisioning services on other ecosystem services (trade-off) and to maintain sustainable provisioning service. When it comes to the precise assessment of provisioning service, it is necessary to get the statistical data and standardize indicators and methods. In this study, indicators and methods, which are applicable to the spatial level of national-local-protected areas, were derived through literature analysis and expert survey. The result of this study implies that provisioning services measurement by spatial level improve the efficiency of the establishment of environmental conservation plans by whose purpose.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.81-94
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• 2022

In the service sectors, the qualitative evaluation method in the form of a survey is widely used as a major assessment tool to evaluate the quality of service. However, the results obtained from a survey can involve the subjective judgment of the respondent. In this study, we propose a method to secure objectivity by excluding subjectivity that may be included in the qualitative evaluation results. In particular, we deal with a situation where the same type of qualitative evaluation tool is used repeatedly by several service providers. To this end, by utilizing both the Ordered Probit model and third-party evaluation results, we determine whether subjectivity is involved in the results. After correcting subjectivity, the final results are obtained through statistical analysis. The application analyzed in this study is the medical service area. With the actual evaluation results supplied by the service providers, we explain how objectivity can be secured from the assessment data by applying our proposed approach.

• Atmosphere
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• v.31 no.5
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• pp.489-510
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• 2021

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

• The Korean Journal of Food And Nutrition
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• v.34 no.6
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• pp.576-583
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• 2021

The purpose of this study was to estimate the influence of the determinants for home meal replacement (HMR) purchase frequency before and after COVID-19. Multinomial logistic regression was applied to the 2018~2020 Consumer Behavior Survey for Food data from the Korea Rural Economic Institute (KREI). Gender, age, number of households, monthly income, use of eating out, delivery and takeout order service, HMR food safety concern, the frequency of cooking at home, grocery shopping, and eating alone were applied as the explanatory variables to explain HMR purchase frequency. The results are as below. Compared to the previous year, the growth rate of HMR purchase frequency in 2020 was relatively high, indicating that the COVID-19 outbreak acted as a catalyst. Unlike in 2018 and 2019, there was no statistical difference in the HMR purchase frequency between single- and multi-person households in 2020, with indicating multi-person households began to emerge as one of the major HMR consumption groups. Unlike 2018, the 2020 HMR purchase frequency showed a statistically positive relationship with those of grocery shopping and eating alone. There was a positive relationship between the frequency of eating out/food delivery orders and HMR purchases. The more often cooking at home occurred, the less HMR food was purchased.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

• Journal of the Korean Society of Physical Medicine
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• v.16 no.4
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• pp.13-21
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• 2021

PURPOSE: This study examined the effects of action observational training with acoustic stimulation (AOTA) on the balance and gait ability in stroke patients. METHODS: Forty-five chronic stroke patients were divided into three groups. The AOTA group (n = 15) received training via a video that showed a normal gait with the sound of footsteps. The action observation training (AOT) group (n = 15) received AOT without acoustic stimulation. The control group (n = 15) received physical training. Each intervention was applied once per day, three times per week for six weeks. The participants in the AOTA and AOT groups had five minutes of AOT. The participants in the all group had 20 minutes of physical training. All participants were measured using the Berg Balance Scale, the Timed Up and Go Test, the Functional Reaching Test, 10 Meter Walk Test, six Minute Walk Test, and Dynamic Gait Index. The collected data were analyzed using SPSS version 20.0 for Windows. The between- and within-group comparisons were analyzed using the one-way analysis of variance (ANOVA) test and a paired t-test, respectively. For all statistical analyses, the significance level was set to .05. RESULTS: The one-way ANOVA test identified significant differences among the measurement results of the three groups (p < .05). Post hoc analyses indicated the AOTA group to undergo more significant balance and gait changes than the control group (p < .05). CONCLUSION: The gait and balance abilities could be improved effectively for patients with stroke when action observation training and acoustic stimulation were applied simultaneously.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.384-394
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• 2022

This study aimed to examine how COVID-19 affected life and health of welfare recipients by comparing groups associated with sex, living with children, and household type and suggest directions for welfare policy and services. Researchers collected a random sample of 500 recipients from 𐩒𐩒gu, Seoul who are registered in Haengbok E-eum, the social security information system and conducted a survey on overall life domains and physical and mental health. For data analysis, t-test, Anova and chi-square test were used. The results showed that factors of sex, living with children, and household type made significant differences in difficulties of employment status, child care, leisure and cultural activities and overall social activities. In terms of health status, mental health status and life satisfaction have been decreased after COVID-19. There were statistical differences in COVID blue and thinking about death among different groups. Based on these findings, this study suggested directions for welfare services for the future New Normal society.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.631-644
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• 2022

This paper considers robust estimation of the sparse vector autoregressive model (sVAR) useful in high-dimensional time series analysis. First, we generalize the result of Xu et al. (2008) that the adaptive lasso indeed has robustness in sVAR as well. However, adaptive lasso method in sVAR performs poorly as the number and sizes of outliers increases. Therefore, we propose new robust estimation methods for sVAR based on least absolute deviation (LAD) and Huber estimation. Our simulation results show that our proposed methods provide more accurate estimation in turn showed better forecasting performance when outliers exist. In addition, we applied our proposed methods to power usage data and confirmed that there are unignorable outliers and robust estimation taking such outliers into account improves forecasting.