Purpose: This study was conducted to identify the factors affecting nursing students' resilience. Methods: The subjects were 192 nursing students from a college in G city. Data were collected from september 23 to 26, 2019 and analyzed using SPSS 22.0 and descriptive statistics, t-test, ANOVA, Sheffé test, Pearson's correlation coefficients, and multiple regression. Results: Resilience showed a statistically significant difference according to gender, grade, personal relation, motive for application, major satisfaction, grade point in general characteristics. Academic engagement and resilience showed apparent positive correlation (r=.37, p<.001), stress and resilience showed weak negative correlation (r=-.23, p=.001). In multiple regression analysis, the most affecting factor was the academic engagement (𝛽=.24), poor of subjective health status (𝛽=-.21), female (𝛽=-.19), junior of grade (𝛽=.13). These variables explained 33.0% of the total variance in resilience. Conclusion: To strengthen resilience in nursing students, learning atmosphere creation through intrinsic motivation in the regular class. Also, a variable academic engagement program should be provided to be able to positive thinking about academic study and achievement.
Due to COVID-19 and soaring participation of individual investors, large-scale transactions exceeding system capacity limits have been reported frequently in the capital market. The capital market IT systems, which the impact of system failure is very critical, have encountered unexpectedly tremendous transactions in 2020, resulting in a sharp increase in system failures. Despite the fact that many companies maintained large-scale system capacity planning policies, recent transaction influx suggests that a new approach to capacity planning is required. Therefore, this study developed capital market IT system capacity planning models using machine learning techniques and analyzed those performances. In addition, the performance of the best proposed model was improved by using sentiment index that can promptly reflect the behavior of investors. The model uses empirical data including the COVID-19 period, and has high performance and stability that can be used in practice. In practical significance, this study maximizes the cost-efficiency of a company, but also presents optimal parameters in consideration of the practical constraints involved in changing the system. Additionally, by proving that the sentiment index can be used as a major variable in system capacity planning, it shows that the sentiment index can be actively used for various other forecasting demands.
This study aims to present implications for the appropriate establishment and development of a science-authorized textbook system through an understanding of the process of selecting science-authorized textbooks and analyzing the perception of teachers. Toward this end, this study conducted interviews with five elementary school teachers who participated in the science textbook selection process, surveys on 32 teachers, and analysis on the authors of the textbooks. The result demonstrated, first, that the "opinion gathering" stage was the most important one, and a council was formed in consideration of career and major. Moreover, the evaluation standard was reorganized and used according to the situation of the school. Second, in the process of opinion gathering, the teachers used a method for reviewing the entire textbook for each teacher. Inquiry activities and textbook composition (readability) were crucially considered as internal factors, and teaching and learning materials, such as videos, were deemed extremely important as external factors. The variable of the author, which is an indicator of the reliability and expertise of textbooks, was also recognized as vital. Third, the deliberation by the School Committee and the report by the principal were recognized as the administrative final step after selection. Finally, selecting the most suitable textbooks for each grade group was recognized as more important than arbitrarily unifying textbooks for the third and fourth grades and for the fifth and sixth grades.
Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model's performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.
The purpose of this study was to explore the role of professional identity as mediating variable in the relationship between job- and organization- related factors and job satisfaction. This study surveyed social workers who worked at 24 senior welfare facilities in Daegu·Gyeoungbuk province from Aug. 1. to Aug. 30. 2006. A total of 137 questionnaires were collected using on-site survey (response rate 76.7%). Statistical analyses were performed using SPSS 12.0 for Windows. Descriptive analysis and frequency analysis were performed on overall measurement items and hierarchical regression analysis was conducted to test the mediating effect of professional identity. The reliability of statements was acceptable since the coefficient alphas were > .70. Results of hierarchical regression showed that professional identity was verified as a partial mediator in the relationship between factors related with job and organization and job satisfaction. As the population ages, there will be an increasing need for professional social workers effectively to work with and help care for the elderly. This study highlighted that job- and organization- related factors, namely self-regulations and social supports, are significantly related with job satisfaction of social workers. Especially, such effect was more significantly apparent in high professional identity which is playing a partial mediator. This result implies that there is potential to change work environments of social workers ensuring a delegation of power and responsibility. Therefore, efforts should be made to improve the promotion system and connect social worker as servant with community through diverse service learning programs.
Purpose: The trend of highway traffic accidents shows a repeating pattern of increase and decrease, with the fatality rate being highest on highways among all road types. Therefore, there is a need to establish improvement measures that reflect the situation within the country. Method: We conducted accident severity analysis using Random Forest on data from accidents occurring on 10 specific routes with high accident rates among national highways from 2019 to 2021. Factors influencing accident severity were identified. Result: The analysis, conducted using the SHAP package to determine the top 10 variable importance, revealed that among highway traffic accidents, the variables with a significant impact on accident severity are the age of the perpetrator being between 20 and less than 39 years, the time period being daytime (06:00-18:00), occurrence on weekends (Sat-Sun), seasons being summer and winter, violation of traffic regulations (failure to comply with safe driving), road type being a tunnel, geometric structure having a high number of lanes and a high speed limit. We identified a total of 10 independent variables that showed a positive correlation with highway traffic accident severity. Conclusion: As accidents on highways occur due to the complex interaction of various factors, predicting accidents poses significant challenges. However, utilizing the results obtained from this study, there is a need for in-depth analysis of the factors influencing the severity of highway traffic accidents. Efforts should be made to establish efficient and rational response measures based on the findings of this research.
The 22q11.2 duplication syndrome is an extremely variable disorder with a phenotype ranging from normal to congenital defects and learning disabilities. Recently, the detection rate of 22q11.2 duplication has been increased by molecular techniques, such as array CGH. In this study, we report a familial case of 22q11.2 duplication detected prenatally. Her first pregnancy was terminated because of 22q11.2 duplication detected incidentally by BAC array CGH. The case was referred due to second pregnancy with same 22q11.2 duplication. We perfomed repeat amniocentesis for karyotype and FISH analysis. Karyotype analysis from amniocytes and parental lymphocytes were normal, while FISH analysis of interphase cells presented a duplication of 22q11.2 in the fetus and phenotypically normal mother. The fetal ultrasound showed grossly normal finding. After genetic counseling about variable phenotype with intrafamilial variability with 50% recurrence rate, the couple decided to continue the pregnancy. The newborn had no apparent congenital abnormalities until 2 weeks after birth. We recommend that family members of patients with a 22q11.2 duplication be tested by the interphase FISH analysis. Also, we point out the importance of genetic counseling and an evaluation of the clinical relevance of diagnostic test results.
Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.
Inquiry-centered education is important in science education, but in the actual education field, scientific research is being done in a uniform manner due to realistic difficulties. In this study, we use RS (Reverse Science) as a secondary chemistry class to provide opportunities for students to engage in inquiry learning and scientific thinking through process-oriented activities. In this study, we developed and applied it to explore the effects on the scientific inquiry abilities of middle school students and checked the students' perception of it. For the application of the program, 128 students were selected from 6 classes of the 2nd grade in D district middle school, 64 from the experimental group and 64 from the comparative group. The experimental group taught RSP-based the chemistry inquiry programs and the comparative group taught instructor-led classes and verification experiments on the same topic over the seventh hour with three themes. In addition, we analyzed the results of the pre- and post-test by using the science inquiry ability test, and discussed the effects of the program based on the students' perceptions through class observation, student activity area, questionnaire and interview. As a result, the class using the program showed statistically significant changes in the science inquiry ability of secondary school students. Specifically, the experimental group was found to be significant in its prediction among the subcomponents of basic exploration ability compared to the comparative group. The differences have also been shown to be significant in terms of data translation, hypothesis setup and variable control, which are subcomponents of integrated exploration capabilities (p <. 05). In addition, students became interested in the process of creating the theory of science, and were highly interested in collaborating with their friends. It also provided students with opportunities to experience scientific thinking through process-oriented inquiry. Finally, based on the positive impact of the RSP-based chemistry inquiry program on students, we were able to identify the potential use of the program.
We define $21^{st}$ century as an amalgamation of globalization and localization, or Glocalization. Additionally, due to the increasing supply of smart phones and wide usage of social networking services, the ability to utilize such global and regional information has increased a coperation's competitiveness in its market, and even the business models have evolved from the conventional "production and distribution" to E-commerce, through which either a direct or a non-direct transaction is possible. My hypothesis is that the ability to adapt to this trend is possible through transfer of learning, and consequently, this will have an impact on company's performance. Thus, this thesis analyzes the mid- to the long-term impact of such ability and environmental factors on the performance and technology innovation of Korean companies in China. Ultimately, this study intends to engender a basic foundation for a corporation's management strategy in China. Finally this research focuses on those Korean companies in China only and on the proof of influential factors' impact on technological innovation and technological innovation's impact on those corporations' future performances. Section I is an abstract and section II, the case examines the uniqueness and current status of Korean companies in China identifies the concept and the definition of influential factors such as education-orientation, technological innovation, and performance, and then scrutinizes each factors through a closer look at their past researches. Section III explains the thesis model, the survey's method and target, the thesis, variable factors, the content, and the method of analysis. In section IV, the thesis is proved based on the outcome of the survey. The result in Section V highlights the high comprehension of technological innovation: both education-orientation and technological innovation prove to have a positive (+) correlation with the performance. The vision on education orientation proves to have a positive (+) influence on technological innovation. The vision on education-orientation and technological innovation prove to have a positive (+) influence individually on company's performance.
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