• Computers and Concrete
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• v.25 no.6
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• pp.565-574
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• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1369-1378
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• 2017

The present paper reports the development of a computational code based on the Adaptive Group of Ink Drop Spread (AGIDS) for reconstruction of the neutron noise sources in reactor cores. AGIDS algorithm was developed as a fuzzy inference system based on the active learning method. The main idea of the active learning method is to break a multiple input-single output system into a single input-single output system. This leads to the ability to simulate a large system with high accuracy. In the present study, vibrating absorber-type neutron noise source in an International Atomic Energy Agency-two dimensional reactor core is considered in neutron noise calculation. The neutron noise distribution in the detectors was calculated using the Galerkin finite element method. Linear approximation of the shape function in each triangle element was used in the Galerkin finite element method. Both the real and imaginary parts of the calculated neutron distribution of the detectors were considered input data in the developed computational code based on AGIDS. The output of the computational code is the strength, frequency, and position (X and Y coordinates) of the neutron noise sources. The calculated fraction of variance unexplained error for output parameters including strength, frequency, and X and Y coordinates of the considered neutron noise sources were $0.002682{\sharp}/cm^3s$, 0.002682 Hz, and 0.004254 cm and 0.006140 cm, respectively.

• Journal of Korean Academy of Nursing
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• v.46 no.5
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• pp.697-709
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• 2016

Purpose: This study was conducted to develop and test a structural model of evidence-based practice (EBP) implementation among clinical nurses. The model was based on Melnyk and Fineout-Overholt's Advancing Research and Clinical Practice through Close Collaboration model and Rogers' Diffusion of Innovations theory. Methods: Participants were 410 nurses recruited from ten different tertiary hospitals in Korea. A structured self-report questionnaire was used to assess EBP knowledge/skills, EBP beliefs, EBP attitudes, organizational culture & readiness for EBP, dimensions of a learning organization and organizational innovativeness. Collected data were analyzed using SPSS/WINdows 20.0 and AMOS 20.0 program. Results: The modified research model provided a reasonable fit to the data. Clinical nurses' EBP knowledge/skills, EBP beliefs, and the organizational culture & readiness for EBP had statistically significant positive effects on the implementation of EBP. The impact of EBP attitudes was not significant. The dimensions of the learning organization and organizational innovativeness showed statistically significant negative effects on EBP implementation. These variables explained 32.8% of the variance of EBP implementation among clinical nurses. Conclusion: The findings suggest that not only individual nurses' knowledge/skills of and beliefs about EBP but organizational EBP culture should be strengthened to promote clinical nurses' EBP implementation.

• Child Health Nursing Research
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• v.23 no.2
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• pp.199-206
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• 2017

Purpose: The purpose of this study was to explore perception of the clinical learning environment, resilience and perfectionism in relation to practice burnout and to identify factors influencing practice burnout in student nurses. Methods: A descriptive correlational study was conducted. The participants were 313 student nurses from three universities in B and U city. Data were analyzed using t-test, ANOVA, Pearson correlation coefficient, $Scheff{\acute{e}}s$ test and multiple regression analysis. Results: Mean score for practice burnout in student nurses was 2.92 out of 5 points. Practice burnout explained 44.7% of the variance in perfectionism (${\beta}=.245$, p<.001), satisfaction with college life (${\beta}=.232$, p<.001), resilience (${\beta}=-.228$, p<.001), clinical learning environment (${\beta}=-.193$, p<.001), satisfaction with major (${\beta}=.180$, p=.001), practical relationships with peers (${\beta}=.128$, p=.005), and satisfaction with clinical practice (${\beta}=.124$, p=.039). Conclusion: Research results suggest that the important variable for student nurses' practice burnout is perfectionism. Therefore education is needed to develop strategies to manage perfectionism and reduce practice burnout.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.443-452
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• 2020

Worker's awkward postures and unreasonable physical load can be corrected by monitoring construction activities, thereby increasing the safety and productivity of construction workers and projects. However, manual identification is time-consuming and contains high human variance. In this regard, an automated activity recognition system based on inertial measurement unit can help in rapidly and precisely collecting motion data. With the acceleration data, the machine learning algorithm will be used to train classifiers for automatically categorizing activities. However, input acceleration data are extracted either from designed experiments or simple construction work in previous studies. Thus, collected data series are discontinuous and activity categories are insufficient for real construction circumstances. This study aims to collect acceleration data during long-term continuous work in a construction project and validate the feasibility of activity recognition algorithm with the continuous motion data. The data collection covers two different workers performing formwork at the same site. An accelerator, as well as portable camera, is attached to the worker during the entire working session for simultaneously recording motion data and working activity. The supervised machine learning-based models are trained to classify activity in hierarchical levels, which reaches a 96.9% testing accuracy of recognizing rest and work and 85.6% testing accuracy of identifying stationary, traveling, and rebar installation actions.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.131-140
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• 2024

The outbreak of Covid-19 has forced teachers of special education in Saudi Arabia to keep to themselves to live in a technology-infused society throughout the virtual teaching and learning process. This study set out to explore the competence, self-efficacy, and autonomy in using information communication technology (ICT) of special education teachers in Saudi Arabia. A total of 244 special education teachers in Saudi Arabia participated in this study. This study adopted the New General Self-Efficacy Scale developed and validated by Chen, Gully, and Eden (2001), as well as the Basic Psychological Needs in Exercise Scale (BPNES) developed and validated by Vlachopoulos and Michailidou (2006). Confirmatory factor analysis (CFA) and multivariate analysis of variance (MANOVA) were used as the main data analysis in this study. The findings showed that special education teachers in Saudi Arabia possessed competence, self-efficacy, and autonomy in using ICT in their teaching and learning process. All the factor loadings in each factor were.75 or higher, indicating good factor loadings. The results of the MANOVA indicated that special education teachers in Saudi Arabia do not report different perceptions of their competence, self-efficacy, and autonomy despite their different gender, age group, academic background, and teaching experiences.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.1-12
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• 2020

As one of the essential resources in the agricultural process, soil moisture has been carefully managed by predicting future changes and deficits. In recent years, statistics and machine learning based approach to predict soil moisture has been preferred in academia for its generalizability and ease of use in the field. However, little is known that machine learning based soil moisture prediction is applicable in the situation of South Korea. In this sense, this paper aims to examine 1) whether publicly available weather data generated in South Korea has sufficient quality to predict soil moisture, 2) which machine learning algorithm would perform best in the situation of South Korea, and 3) whether a single machine learning model could be generally applicable in various regions. We used various machine learning methods such as Support Vector Machines (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting Machines (GBM), and Deep Feedforward Network (DFN) to predict future soil moisture in Andong, Boseong, Cheolwon, Suncheon region with open source weather data. As a result, GBM model showed the lowest prediction error in every data set we used (R squared: 0.96, RMSE: 1.8). Furthermore, GBM showed the lowest variance of prediction error between regions which indicates it has the highest generalizability.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.626-635
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• 2017

This study aimed to identify the factors affecting confidence in performance of fundamental nursing skills in nursing students. Participants were 318 nursing students who haven't had clinical practice experiences to the point of data collection in two universities in B and U cities. Data were analyzed using t-test, ANOVA, Scheff? test, Pearson's correlation coefficients, and stepwise multiple regression. The mean scores of self-directed learning ability, satisfaction with practicum, and confidence in performance of fundamental nursing skills were $3.38{\pm}0.40$, $3.93{\pm}0.59$, and $3.40{\pm}0.61$, respectively. Correlations were found between confidence in performance of fundamental nursing skills and self-directed learning ability (r=.289, p<.001) and satisfaction with practicum (r=.353, p<.001), and between self-directed learning ability and satisfaction with practicum (r=.393, p<.001). Factors influencing the confidence in the performance of fundamental nursing skills were satisfaction with practicum (${\beta}=.24$, p<.001), self-directed learning ability (${\beta}=.15$, p=.010), and attitude to practicum participation (${\beta}=.13$, p=.027). These factors explained 15.6% of the variance in the participants' confidence in performance of fundamental nursing skills. Therefore, effective nursing educational programs need to be developed in order to foster confidence in the performance of fundamental nursing skills of nursing students by enhancing self-directed learning ability, satisfaction with practicum and active attitude to practicum participation.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.445-452
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• 2020

The purpose of this study was to identify the relationship between Nunchi and learning flow among nursing students and to investigate the factors influencing communication skills. Method: The participants were 260 nursing students in K city, who were surveyed between March 5 and April 17, 2019, using self-report questionnaire. Data were analyzed by frequencies, t-test, ANOVA, Pearson's correlation, multiple regression using SPSS Win 21.0. Result: There were positive correlation between Nunchi of participants and learning flow(r=.502, p<.001). There were positive correlation between Nunchi and communication skills(r=.619, p<.001) and between learning flow and communication skills(r=-.567, p<.001). In the multiple regression, Nunchi(β=.381, p<.001), learning flow(β=.243, p<.001) and satisfaction of clinical practice(β=.107, p=.028) were associated with communication skills. These factors accounted for 47.4% of the total variance in communication skills. Based on these results, it will be necessary to develop educational programs and strategies related with the Nunchi and learning flow disposition to improve communication skills of nursing students.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.267-274
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• 2021

The purpose of this study is to investigate differences in learning flow and teaching presence according to the lecturer's appearance and the lecturer's speech rate. For this experiment, 183 freshman students from Xingtai University in China were selected as subjects of the experiment, and a total of four types of lecture videos were developed to test the lecturer's appearance and their speech rates. Data was analyzed through multivariate analysis of variance. According to the results of the analysis, first, learning flow and teaching presence of groups who experienced the presence of the lecturer appeared were significantly higher than the groups who learned without the appearance of the lecturer. Second, the groups who learned from videos with a fast speech rate showed higher learning flow and teaching presence than the group who learned at a slow speech rate. Third, there were no significant differences in both learning flow and teaching presence according to the lecturer's appearance and speech rate. This result provides a theoretical and practical basis for developing customized videos according to learners' characteristics.