Journal of the Korea Academia-Industrial cooperation Society
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v.20
no.4
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pp.293-302
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2019
This study systematically analyzed computer-based driving intervention programs for seniors, to provide the academic background for driving intervention for seniors. Articles published from January 2009 till December 2018 were researched and analyzed. 'PubMed, Google Scholar, and Science Direct' were used to search articles published overseas, and 'RISS, KERIS, and KISS' searched for articles published in Korea. Based on the inclusion and exclusion criteria, totally 359 papers were retrieved, and 10 articles were finally analyzed; 8 articles (80%) were evidence level I, and 2 articles (20%) were evidence level III. Amongst the computer-based interventions, driving simulators (70%) were the most common, followed by two video image training (20%) and one Nintendo Wii program (10%). In most studies, driving simulators trained the cognitive and visual abilities of seniors and enhanced their abilities to cope with risk situations under various simulated circumstances. Other interventions were also reported to have a positive effect. For evaluating elderly drivers, the driving performance evaluation using a driving simulator was the most common; in addition, evaluations of attention, space-time ability, cognitive function, risk perception, depression and anxiety were also commonly used. We believe that it is appropriate to employ computer-based driving intervention programs for seniors to train and evaluate various domains. We expect that these interventions can be used as an effective tool for safe driving.
In recent years, research on shipping market forecasting with the employment of non-linear AI models has attracted significant interest. In previous studies, input variables were selected with reference to past papers or by relying on the intuitions of the researchers. This paper attempts to address this issue by applying the stepwise regression model and the random forest model to the Cape-size bulk carrier market. The Cape market was selected due to the simplicity of its supply and demand structure. The preliminary selection of the determinants resulted in 16 variables. In the next stage, 8 features from the stepwise regression model and 10 features from the random forest model were screened as important determinants. The chosen variables were used to test both models. Based on the analysis of the models, it was observed that the random forest model outperforms the stepwise regression model. This research is significant because it provides a scientific basis which can be used to find the determinants in shipping market forecasting, and utilize a machine-learning model in the process. The results of this research can be used to enhance the decisions of chartering desks by offering a guideline for market analysis.
Objective: This study is a case study on mother's perceived the adaptation process and program of a one-year-old toddlers in daycare center. Methods: This study is a case study of four mothers who live in Seoul, South Korea and whose first child enters in daycare center at 18 or 20 months. The data collection method was applied to group interview and the individual in-depth interview method. Results: After the interview data was analyzed, first, they decided to enroll their child to a daycare center after two mothers were pregnant with the second child or the others received a phone call for admission. The first impression of the teachers had a great influence on the choice of admission when mother first visited the daycare center. mother, as well as toddlers who exhibited anxiety, experienced an adaptation process. Through communication between mother and teacher, mothers could feel the toddler's adaptation. Second, as for the difference of the adaptation program according to the daycare center type through the adaptation process of the one-year-old toddlers, there was a big difference in the program guide, the period, the content and the proceeding method. Conclusion/Implications: Based on the results of this study, it is expected that it will be a basic data for the development of the adaptation program for one-year-old toddlers in daycare center at the national level.
The purpose of this study is to present implications for revitalizing start-ups and contribute to enhancing the success rate of start-ups by clarifying factors and processes for converting workers with knowledge, experience and networks in related fields into entrepreneur. Based on the Shapero's Entrepreneurial Event Model, this study demonstrated whether the job insecurity and entrepreneurship of the workers were precipitating events of the entrepreneurial intention and whether the perceived desirability and feasibility of the entrepreneurial behaviour mediated between them. According to the results of the study, first, it was confirmed that job insecurity, innovativeness, and risk-taking of workers are factors that increase the entrepreneurial intention. Second, the indirect effect of perceived desirability between all components of job insecurity and entrepreneurial intentions was not significant, but all components of entrepreneurship appeared to improve entrepreneurial intention through perceived desirability. Third, it has been confirmed that job insecurity, innovativeness, and risk-taking strengthen the entrepreneurial intention through the perception of feasibility for entrepreneurial behavior. Through this study, it is confirmed that in order to convert workers into entrepreneur, it is necessary to strengthen entrepreneurship education and support for internal ventures for workers to increase their positive attitude and confidence in implementation. Therefore, it is expected to help solve job problems and revive the sluggish economy by contributing to boosting start-ups.
The Journal of the Convergence on Culture Technology
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v.8
no.6
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pp.49-58
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2022
The purpose of this study is to find out how on-line counseling modalities (phone, video, and video counseling using digital mask) differ from face-to-face counseling in terms of clients' perception of working alliance, depth and smoothness of each session, satisfaction, and their qualitative counseling experience. 40 university students participated in the experiment, divided into 4 groups, received 3 personal counseling sessions per person. The quantitative data revealed no significant difference among the four counseling groups in working alliance. Also, the "depth" of the session was similar in the four groups, but phone and video with mask counseling group who did not expose their faces showed higher "smoothness" in the first and second sessions than face-to-face counseling group, indicating that anonymity was helping the clients' inhibition overcome. Through the post-interview data, subtle differences in experience of each counseling method were identified by the participants. The results are expected to provide primary information for developing and implementing various online counseling modalities in the future.
KSCE Journal of Civil and Environmental Engineering Research
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v.42
no.3
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pp.379-389
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2022
The number of public transportation users has dropped drastically due to COVID-19. In this work, my survey was conducted to uncover the factors that influence citizens' travel patterns. Data were collected and logistic regression analysis on the shifts in transportation was undertaken. Additionally, an importance-performance analysis was carried out to investigate how to effectively operate public transportation systems and improve facilities. The main research findings were as follows: First, the more individuals were concerned about COVID-19 (+) and being infected when using public transportation (+), the greater the tendency to switch to private transportation modes. Secondly, when it came to personal traits, respondents who could drive a car (+) or owned a car (+)or did more online shopping (+) or used public transportation for trips (+) tended to switch over, compared with respondents who could not drive or did not own a caror used public transportation to commute. In addition, respondents who were vaccinated (-) or had more household members tended not to switch transportation modes, compared with those who were not vaccinated or had fewer household members. Third, it is important to continue the following efforts to safeguardhygiene linked to public transportation: wearing masks, disinfecting hands, controlling diseases, and general cleaning. The conclusion was that it is important to put traffic congestion and ventilation issues first, especially in regards public transportation, which was not rated as satisfactory enough compared to its importance. The research findings can provide useful basic data when establishing countermeasures to the current COVID-19 circumstances in the areas of public transportation operation and management and in the event of an infectious disease outbreak in the future.
In this study, the effect of repeated traffic vibration on the long-term stability of mine openings is analyzed for re-utilization of abandoned mine galleries. The research mine in this study is an underground limestone mine which is developed by room-and-pillar mining method, and a dynamic numerical analysis is performed assuming that the research mine will be utilized as a logistics warehouse. The actual traffic vibration generated by the mining vehicles is measured directly, and its waveform is used as input data for dynamic numerical analysis, As a results of dynamic numerical analysis, after 20,000 repetitions of traffic vibration, the mine openings is analyzed to be stable, but an increase in the maximum principal stress and an additional area of plastic zone are observed in the analysis section. As shown in the changes of displacement, volumetric strain, and maximum principal stress which are measured at the mine opening walls. It is confirmed that if the repeated traffic vibration is continuously applied, the instability of the mine openings can be increased. Authors expect that the results of this study can be used as a reference for basic study on utilization of abandoned mine.
As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.
KSCE Journal of Civil and Environmental Engineering Research
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v.30
no.2A
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pp.93-102
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2010
The numerical analysis and safety assessment by construction stages were considered the essential examination particular in order to solving the unstability of long-span bridges in the middle a construction. When estimating structural response characteristics by the construction stage analysis of long-span bridges, the influence of the near-field ground motion (NFGM) would be evaluated as a critical factor for the seismic design because it indicates clearly different aspects from the existing input earthquake motion data. Therefore, this study re-examined the response aspect of long-span bridges considering NFGM characteristics based on the response spectrum result, and advanced the presented numerical analysis program by the related research for conducting the construction stage analysis and reliability assessment of long-span bridges efficiently. The excellency of various construction schemes was assessed using the time history analysis result of critical member considering NFGM characteristics. For evaluating quantitative safety level, the reliability analysis was conducted considering the influence of external uncertainties included in random variables, and presented the safety index and failure probability of the critical construction stage by NFGM characteristics. In addition, the reliability result was examined the influence of internal uncertainties using monte carlo simulation (MCS), and assessed the distribution aspect of the essential analysis result. It is expected that this study will provide the basic information for the construction safety improvement when performing seismic design of long-span bridges considering NFGM characteristics.
When it comes to explaining the relationship between inventory investment and business fluctuations, the production smoothing theory and the stock-out avoidance theory take contradictory stances. Decision-making related to inventory investments of corporations is thought to be influenced by both motives, but the relative sizes or directions of their respective influences can differ depending upon the phase of the business cycle. Against this backdrop, this paper differs from existing studies in that it theoretically tests the relative significances of the production smoothing and stock-out avoidance motives in the inventory investment dynamics, while placing its analytical focus on determining the existence and patterns of the asymmetric dynamics of inventory investment over the business cycle phases. To this end this paper sets up a non-linear model that is expanded from the existing linear inventory investment model, and checks whether its predictive power is better than that of the existing model. The results of analysis confirm the nature of the asymmetric dynamics of inventory investment over the business cycle phases. A stock-out avoidance motive appears but there is no significant production smoothing motive in boom times. In downturns, in contrast, the stock-out avoidance motive is insignificant, but a quality of asymmetric dynamics in which changes in inventory cause the deepening of recessions, due to the non-convexity of production costs proposed by Ramey (1991), is detected. This paper confirms that a model considering the asymmetric dynamics of inventory investment can have better predictive power than one that does not consider it, through within-sample and out-of-sample predictions and various predictive power tests. These research results are expected to be useful for economic forecasting, through their enhancement of the understandings of the inventory investment dynamics and of the nature of its business cycle destabilization.
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