• Journal of agricultural medicine and community health
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• v.49 no.1
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• pp.37-49
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• 2024

Objectives: This study aimed to explore whether there are differences in smoking status between two regions of Wonju-City and Chuncheon-City, Gangwon State, and to determine whether the experience of smoking cessation programs in the region affects quit attempts. Methods: The study design was a cross-sectional study in which adults aged 19 and older living in two cities were surveyed using a pre-developed mobile app to investigate social capital for smoking cessation, and a total of 600 citizens were participated, including 310 in Wonju-City and 290 in Chuncheon-City. The statistical analysis was conducted using chi-square test and logistic regression analysis. Results: Wonju-City had a higher prevalence of current smoking than Chuncheon-City. Among smoking cessation programs operated by local public health centers, Wonju-City had a lower odds ratio for experience with smoking cessation education than Chuncheon-City (OR=0.52, 95% CI=0.33 to 0.81). When examining the effect of smoking cessation program experience on quit attempts, in Wonju-City, citizens who had completed smoking cessation education and used a smoking cessation clinic were more likely to attempt to quit than those who had not (OR=2.31 and OR=2.29, respectively). In Chuncheon-City, citizens who were aware of smoking cessation support services were 2.26 times more likely to attempt to quit smoking than those who were not, but statistical significance was not reached due to the small sample size. Conclusion: Therefore, healthcare organizations in both regions should develop more practical intervention strategies to increase smokers' quit attempts, reduce smoking rates in the community, and address regional disparities.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.91-110
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• 2024

In this study, we compared and analyzed the influence of self-leadership on innovative behavior and the mediating effect of job satisfaction among R&D practitioners in manufacturing and information communication technology (ICT) industries. To accomplish this, we conducted an online survey using random sampling methods and collected data from 209 respondents. We employed exploratory factor analysis, reliability analysis, correlation analysis, multiple regression analysis, and mediation analysis using SPSS 20.0 software to analyze the data and to compare differences between the manufacturing and ICT sectors. The research findings are as follows: Firstly, both in manufacturing and ICT sectors, self-leadership showed significant positive correlations with job satisfaction and innovative behavior. Secondly, in the analysis of the impact of self-leadership on innovative behavior, in the manufacturing sector, only natural reward strategy and constructive thought strategy showed significant positive effects, while in the ICT sector, behavioral-oriented strategy, natural reward strategy, and constructive thought strategy all showed significant positive effects. Thirdly, in the analysis of the impact of self-leadership on job satisfaction, in the manufacturing sector, only natural reward strategy and constructive thought strategy showed significant positive effects, while in the ICT sector, behavioral-oriented strategy and natural reward strategy showed significant positive effects. Fourthly, in the analysis of the impact of job satisfaction on innovative behavior, significant positive effects were observed in both manufacturing and ICT sectors, with manufacturing sector having relatively greater impact than ICT sector. Lastly, the results of the analysis on the mediating effect of job satisfaction indicate that in the manufacturing sector, only a constructive thinking strategy significantly influences, showing partial mediating effects. However, in the ICT sector, no mediating effects of job satisfaction were observed for any sub-factors of self-leadership. These research findings highlight differences in the mechanisms of action of self-leadership on innovative behavior and its mediating effects between the manufacturing and ICT sectors. Furthermore, the results suggest the importance of improving organizational strategies and culture towards promoting leadership, job design, and job satisfaction, considering the characteristics of each industry and research and development organization.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.703-711
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• 2024

This study attempted to verify the effect of SBAR-based simulation practice on reporting confidence, communicative competence, nursing competence and debriefing satisfaction of nursing students. This study included 46 students who took the simulation practice course for third-year nursing students at one universities located in one region, The data were collected from October 30 to December 22, 2023 using a self-report questionnaire before and after simulation practice, and is a one group pretest-posttest design study. Data analysis was performed using SPSS/WIN version 26.0 program using frequency analysis, descriptive statistics, Shapiro-Wilk test, and Paired t-test. As a result of the study, the average of the reporting confidence was 5.79±1.47 before the training and 7.13±1.56 after the training, the communicative competence was 3.62±0.44 before the training and the average after the training was 4.34±0.67, the nursing competence was 2.64±0.39 before the training and 3.26±0.51 after the training, and the debriefing satisfaction was 3.57±0.51 before the training and 4.18±0.58 after the training. There was a statistically significant difference in reporting confidence(t=2.84, p=.006), communicative competence(t=-3.28, p=.001), nursing competence(t=-8.16, p<.001), debriefing satisfaction(t=2.72, p<.001) before and after SBAR-based simulation practice. Based on the results of this study, it is thought that communication education using SBAR to nursing students should be systematically carried out from the lower grade curriculum, and it is necessary to strengthen and expand reporting education using SBAR communication in various practice situations as well as simulation practical education to improve nursing competency.

• Journal of The Korean Society of Grassland and Forage Science
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• v.44 no.2
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• pp.64-70
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• 2024

The experiment was conducted to determine the changes in seed productivity of Italian ryegrass (Lolium multiflorum Lam.) according to nitrogen fertilization levels in the southern region of Korea. Italian ryegrass (IRG) variety 'Green Call' was sown in the fall of 2021 in Jinju, Gyeongsangnam-do. The experiment consisted of three nitrogen fertilizer levels (100, 120, and 140 N kg/ha) with three replications using a randomized complete block design. Harvesting was done approximately 30 days after heading on May 18th. There was no difference in heading date among treatments, which occurred on April 18th. The longest IRG was observed in the 140 N kg/ha treatment, but there was no significant difference. No significant differences were observed in lodging, disease resistance, and cold tolerance among treatments, but lodging was severe in all treatments. The length of the spike averaged 44.95 cm, with no difference among treatments, and the number of seeds per spike was highest in the 120 N kg/ha treatment. Seed yield increased with increasing nitrogen fertilizer levels, averaging 3,707 kg/ha (as-fed basis). DM content of seed and straw averaged 76.95% and 62.19%, respectively, with no significant differences among treatments. The remaining straw after harvesting averaged 6,525 kg/ha on a dry matter basis, with the highest value observed in the 140 N kg/ha treatment. Overall, considering the results, the optimal nitrogen fertilizer application rate for seed production of Italian ryegrass in the southern region when sown in autumn was found to be 120 N kg/ha.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.260-276
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• 2024

The purpose of this study is to explore whether pre-service teachers majoring in earth science improve their perception of computational thinking through STEAM classes focused on engineering-based wave power plants. The STEAM class involved designing the most efficient wave power plant model. The survey on computational thinking practices, developed from previous research, was administered to 15 Earth science pre-service teachers to gauge their understanding of computational thinking. Each group developed an efficient wave power plant model based on the scientific principal of turbine operation using waves. The activities included problem recognition (problem solving), coding (coding and programming), creating a wave power plant model using a 3D printer (design and create model), and evaluating the output to correct errors (debugging). The pre-service teachers showed a high level of recognition of computational thinking practices, particularly in "logical thinking," with the top five practices out of 14 averaging five points each. However, participants lacked a clear understanding of certain computational thinking practices such as abstraction, problem decomposition, and using bid data, with their comprehension of these decreasing after the STEAM lesson. Although there was a significant reduction in the misconception that computational thinking is "playing online games" (from 4.06 to 0.86), some participants still equated it with "thinking like a computer" and "using a computer to do calculations". The study found slight improvements in "problem solving" (3.73 to 4.33), "pattern recognition" (3.53 to 3.66), and "best tool selection" (4.26 to 4.66). To enhance computational thinking skills, a practice-oriented curriculum should be offered. Additional STEAM classes on diverse topics could lead to a significant improvement in computational thinking practices. Therefore, establishing an educational curriculum for multisituational learning is essential.

• Journal of Korean Elementary Science Education
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• v.43 no.3
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• pp.415-432
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• 2024

This study developed and applied the teacher education model and its principles for science classes using Virtual and Augmented Reality (VR/AR) content and analyzed preservice elementary teachers' feedback on the teacher education model and the changes in their perceptions as to the use of VR/AR content. First, existing Technological Pedagogical Content Knowledge (TPACK) teacher education models and prior studies on the use of the VR/AR contents were reviewed to derive the teacher education model to cultivate the VR/AR-TPACK and set the key principles for each of its stages. The developed teacher education model has five stages: exploration, mapping, collaborative design, practice, and reflection. Second, to examine the appropriateness of the model's five stages and principles, we applied it within the regular course of instruction at the university of education, which was attended by 25 preservice elementary teachers. This study collected data from surveys on the perception of the usage of VR/AR contents before and after the course, as well as the group lesson plans prepared by the preservice teachers, and their feedback on the teacher education model. The feedback on the teacher education model and the survey conducted by the preservice teachers before and after the course were analyzed through open coding and categorization. As a result, most preservice teachers expressed positive opinions about the activities and experiences at each stage of the implementation of the teacher education model. Perceptions related to the usage of the VR/AR content changed in three aspects: first, the vague positive perception of the VR/AR content has changed to a positive perception based on specific educational affordance. Second, they recognized the need for preparedness by anticipating potential problems associated with the use of the VR/AR content. Third, they came to view the VR/AR contents as a useful instructional resource that the teachers could use. Based on these results, we discussed the implications for the VR/AR-TPACK teacher education model and assessed the limitations of the research.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.65-72
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• 2024

To faithfully represent the intention of the work in the exhibition space, lighting that provides high color reproduction like natural light is required. Thus, many lighting technologies have been introduced to improve CRI, but most of them only evaluated the general color rendering index (CRI Ra), which considers eight pastel colors. Natural light provides excellent color rendering performance for all colors, including red and blue, expressed by color rendering index of R9 and R12, but most artificial lighting has the problem that color rendering performance such as R9 and R12 is significantly lower than that of natural light. Recently, lighting technology that provides CRI at the level of natural light is required to realistically express the colors of works including primary colors but related research is very insufficient. Therefore this paper proposes exhibition hall lighting that fulfills CRI with a focus on CRI Ra, R9, and R12 based on the characteristics of natural light. First reinforcement wavelength bands for improving R9 and R12 are selected through analysis of the actual measurement SPD of natural and artificial lighting. Afterward virtual SPDs with a peak wavelength within the reinforcement wavelength band are created and then SPD combination conditions that satisfy CRI Ra≥95, R9, and R12≥90 are derived through combination simulation with a commercial LED light source. Through this, after specifying two types of light sources with 405,630nm peak wavelength that had the greatest impact on the improvement of R9 and R12, the exhibition hall lighting applied with two W/C White LEDs is designed and a control Index DB of the lighting is constructed. Afterward experiments with the proposed method showed that it was possible to achieve high CRI at the level of natural light with average CRI Ra 96.5, R9 96.2, and R12 94.0 under the conditions of illuminance (300-1,000 Lux) and color temperature (3,000-5,000K).

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.

• Journal of the Korea Organic Resources Recycling Association
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• v.17 no.1
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• pp.58-72
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• 2009

In this study, the organic matter and biomass was characterized by using respirometry based on ASM No.2d (Activated Sludge Model No.2d). The activated sludge models are based on the ASM No.2d model, published by the IAWQ(International Association on Water Quality) task group on mathematical modeling for design and operation of biological wastewater treatment processes. For this study, OUR(Oxygen Uptake Rate) measurements were made on filtered as well as non-filtered wastewater. Also, GC-FID and LC analysis were applied for the estimation of VFAs(Volatile Fatty Acids) COD(S_A) in slowly bio-degradable soluble substrates of the ASM No.2d. Therefore, this study was intended to clearly identify slowly bio-degradable dissolved materials(S_S) and particulate materials(X_I). In addition, a method capable of determining the accurate time to measure non-biodegradable COD(S_I), by the change of transition graphs in the process of measuring microbial OUR, was presented in this study. Influent fractionation is a critical step in the model calibrations. From the results of respirometry on filtered wastewater, the fraction of fermentable and readily biodegradable organic matter(S_F), fermentation products(S_A), inert soluble matter(S_I), slowly biodegradable matter(X_S) and inert particular matter(X_I) was 33.2%, 14.1%, 6.9%, 34.7%, 5.8%, respectively. The active heterotrophic biomass fraction(X_H) was about 5.3%.