• The Journal of Korean Society for School & Community Health Education
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• v.23 no.1
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• pp.1-16
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• 2022

Background & Objectives: Self-rated health has been widely used to evaluate health status and accepted as a subjective measurement of quality of life. This study aimed to analyze the associations between self-rated health, health promotion behaviors, and mental health factors and suggest the approaches to improve health status among university students. Methods: Two thousand six hundred seventy-seven students who had stayed at dormitories on campus participated in the DU health survey by self-reported questionnaire from April 10 to 14, 2017. Multivariate logistic regression analysis was performed to estimate the odds ratios and 95% confidence intervals of association of self-rated health with health-related factors among male and female students. Results: 38.6% of the respondents reported good self-rated health. Male and first-year students were more likely to report good self-rated health than female and third-year students. There were significant differences in sex, grade, health problems, BMI, sleeping hours, eating breakfast, consumption of fruits and vegetables, physical activity (regular walking, strength exercise, moderate exercise, vigorous exercise), perceived stress, depression, and suicide thought (p<0.05). Conclusion: Although health promotion programs for university students are essential to support their adaptation to campus life and academic achievement, evidence-based health programs to encourage their participation are still insufficient. Therefore, it should establish a campus-based health policy and develop health promotion programs to increase self-rated health levels and prevent mental health problems for university students.

• Women's Health Nursing
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• v.28 no.4
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• pp.307-316
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• 2022

Purpose: This study investigated the vulnerability to human immunodeficiency virus (HIV) infection and associated factors among married women in northwest Ethiopia. Methods: A community-based cross-sectional survey (n=657) was conducted from April 1 to 15, 2020, in Metema District, northwest Ethiopia, in four randomly selected kebele administrations (the lowest level of local government). The inclusion criteria were married women aged ≥18 years residing with their husbands. Logistic regression analysis was conducted to identify factors associated with married women's vulnerability to HIV infection. Results: Participants were on average 33.70±9.50 years and nearly one-fourth (n=148, 22.5%) were identified as vulnerable to HIV infection (i.e., experienced sexually transmitted disease symptoms or an extramarital affair of either spouse within the past 12 months). Only 18.9% reported sexual communication with their husband. Respondents who did not discuss the risk of HIV infection with their husbands had fivefold odds of vulnerability (adjusted odds ratio [AOR], 5.02; 95% confidence interval [CI], 1.43-17.5). Those who did not have premarital sex (AOR, 0.20; 95% CI, 0.05-0.77) had no worries about HIV infection (AOR, 0.27; 95% CI, 0.08-0.94), sufficient income (AOR, 0.56; 95% CI, 0.16-0.86), and less than four children (AOR, 0.69; 95% CI, 0.50-0.97) had decreased odds of being vulnerable to HIV than their counterparts. Conclusion: Not discussing risk of HIV infection with husband was a major factor of vulnerability to HIV infection as was premarital sex, worry about HIV, income, and number of children. Measures to strengthen couple's sexual communication and support economical stability is important for decreasing HIV vulnerability.

• Research in Community and Public Health Nursing
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• v.33 no.4
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• pp.396-407
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• 2022

Purpose: The purpose of this study was to assess insomnia and the quality of sleep, investigate the concordance between objective and self-report sleep patterns, and identify physiological, psychological, and situational factors influencing insomnia and sleep quality among firefighters. Methods: A descriptive, cross-sectional study was conducted with 103 firefighters in Korea. The collected data were analyzed using SPSS 23.0. Descriptive statistics, the independent t-test, and hierarchical logistic regression analysis were performed. Results: Insomnia was found in 66 (64.1 %) of the total subjects, and the average quality of sleep (PSQI) was 5.65 (SD=2.57). Total sleep time (401.00 minutes) and sleep latency (21.60 minutes) measured using self-reported scales were longer than the ones measured using objective measurements by approximately 48.70 and 17.10 minutes, respectively. Factors related to insomnia included the role as a paramedic (OR=4.28, 95% CI: 1.02~17.92), anxiety (OR=1.12, 95% CI: 1.01~1.24), and sedentary lifestyle (OR=0.85, 95% CI: 0.78~0.94), and factors related to sleep quality were physical illness status (OR=5.17, 95% CI: 1.53~17.51) and social support (OR=0.86, 95% CI: 0.78~0.95). Conclusion: The results show a high prevalence of insomnia, poor quality of sleep and the discrepancy between objective and subjective sleep patterns among firefighters. To promote sleep quality and health, early screening and treatment of anxiety and physical illness are required. It is necessary to conduct further studies examining the relationship between physical activity level and sleep.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.135-147
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• 2023

Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.

• Journal of Korea Trade
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• v.26 no.6
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• pp.118-135
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• 2022

Purpose - The purpose of this study is to investigate the relationships among Chinese state-owned enterprises (SOEs), previous M&A experience, and the probability of deal completion in cross-border mergers and acquisitions (CBMAs). Since Chinese SOEs tend to be recognized by host countries as agents of their home country government, this study argues that SOEs will face difficulties in completing CBMA deals. However, the study expects that these difficulties may vary depending on whether the firm has previous M&A experience because firms can gain the knowledge and capabilities necessary to implement subsequent M&As successfully from past M&A experience. Design/methodology - To investigate our argument, we conduct a logistic regression using a sample of 363 CBMA deals from 304 Chinese publicly listed firms during 2007 to 2017. We used SOEs as an independent variable, experience of domestic and foreign M&As as moderating variables, respectively, and CBMA deal completion as the dependent variable. Findings - The study shows a negative and significant relationship between Chinese SOEs and the completion likelihood of CBMA deals. We find that this negative relationship is strengthened when the firm had prior domestic M&A experience, whereas foreign M&A experience alleviated the negative relationship. Originality/value - The issue of government ownership has remained unclear since government intervention has both advantages and disadvantages in pursuing CBMAs. Our findings support literature that argues Chinese SOEs face legitimacy concerns in the host countries, thereby lowering their CBMA deal completion likelihood. Furthermore, the study enriches the literature by identifying different moderating effects of domestic and foreign M&A experience on the negative relationship between SOEs and CBMA deal completion.

• Journal of Technologic Dentistry
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• v.44 no.1
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• pp.15-23
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• 2022

Purpose: The purpose of this study was to identify the experiences of depression, suicidal thoughts, and habitual drug use in middle and high school students and examine their relationship with the oral symptoms experiences. Methods: The participants of this study were 54,948 middle and high school students who took the screening and health survey at the 16th "Youth Health Behavior Survey" (2020). The SPSS statistical software (IBM SPSS 23.0 for Windows; IBM) was used for data analysis. The significance level was set to 0.05. Results: Complex-sample logistic regression analysis was performed to confirm the relationship between the experiences of depression, suicidal thoughts, and habitual drug use and oral symptom experienced. The results indicated that the absence of depression, suicidal thoughts, or habitual drugs had a significant effect on oral symptom experience. Conclusion: A systematic counseling program for early detection of oral symptoms and oral health promotion as well as strategies for practicing correct oral hygiene are required. Additionally, it is necessary to develop a customized education program to promote health education in middle and high school students. It can be used as the basis for an integrated support system that students can use to grow healthy. A differentiated program on the topic of mental health promotion for each grade can be planned and its effects can be monitored.

• Health Policy and Management
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• v.33 no.2
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• pp.118-128
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• 2023

Background: Personal socio-economic abilities are crucial as it affects health inequalities. These multidimensional inequalities across the regions have been structured and fixed. This study aimed to analyze health vulnerabilities by regional cluster and identify regional health disparities of self-rated health, using nationally representative cross-sectional data. Methods: This study used personal and regional data. Data from the Community Health Survey 2021 were analyzed. K-means cluster analysis was applied to 250 si-gun-gu using administrative regional data. The clusters were based on three areas: physical environment, health-related behaviors and biological factors, and the psychosocial environment through the conceptual framework for action on the social determinants of health. And binary logistic regression analyses were conducted to examine the differences in self-rated health status by the regional clusters, controlling human biology, environment, lifestyle, and healthcare organization factors. Results: The most vulnerable group was group 3, the moderate vulnerable group was group 1, and the least vulnerable group was group 2. The group 2 was more likely to have high self-rated health status than the moderate vulnerable group (odds ratio [OR], 1.023; p<0.001). And the group 3 showed low self-rated health status than the moderate vulnerable group (OR, 0.775; p<0.001). However, the moderate vulnerable group had significantly higher self-rated health status than the most vulnerable group (group 2: OR, 1.023; p<0.001; group 3: OR, 0.775; p<0.001). Conclusion: These results demonstrate that community members' health status is influenced by regional determinants of health and individual levels. And these contribute to understanding the importance of specific and differentiated interventions like locally tailored support programs considering both individual and regional health determinants.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.482-491
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• 2022

The purpose of this study was to classify latent profiles based on satisfaction of student by the campus-life&educational-experiences and to identify factors affecting satisfaction according to each type. For this study, data from the survey of the A univ(1,952 data) were used. To analyze this, a latent profiles analysis was applied to identify subgroups, in which the students by the campus-life&educational-experiences satisfaction, and a multinomial logistic regression model was applied to verify factors affecting group classification. As a result of the analysis, first four groups were classified in the order of 'average·class·highest·relationship satisfaction type'. Second the factors affecting the classification into the remaining three types with 'the average satisfaction type' as a reference group were found to be significant influencing factors(gender, grade, admission process, GPA grade). Based on these results, this study suggested implications for planning and promoting student-tailored education and student support policies at the university level.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.241-246
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• 2023

The purpose of this study was to suggest the direction of university employment support through analysis of employment success factors for young college graduates and comparison of determining factors in metropolitan and non-metropolitan areas. For this purpose, the factors were analyzed using SPSS 25.0 statistical package binary logistic regression analysis using the 2019 'College Graduate Occupational Movement Path Survey' data provided by the Korea Employment Information Service. As a result of the study, among the personal characteristics of college graduates in Seoul and the metropolitan area, age, parental assets, and language training experience were (+) factors for employment success, and in terms of college characteristics, 2-3 year college graduates were more likely to succeed in employment than 4-year college graduates or education college graduates. In addition, among the personal characteristics of college graduates from non-metropolitan areas, age and parental assets were (+) factors in employment success, and 2-3 year college graduates were more likely to succeed in employment than 4-year college graduates.