• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.269-283
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• 2023

Based on daily data from January 4, 2016 to September 27, 2022, the impact of extreme movements of BDI on shipping companies' network connectivity was analyzed using CoVaR network connectivity. The main results and policy implications are as follows. First, according to the copula model results, the Student-t copula was selected as the most suitable model for COSCO, HMM, HRAG, MAERSK, and WAN. EVER was selected as a time-varying Gumbel copula, and YANG was selected as a time-varying rotated-Gumbel copula. Second, as a result of analysis using the TVP-VAR model, the linkage between shipping companies tended to increase when the BDI turned into an extreme risk state. In the comparison of net connectivity, the roles of COSCO and EVER changed. In addition, in the analysis of net pairwise connectivity, it was found that the change in the extreme risk state of BDI also affected the connectivity of shipping companies. In particular, EVER, WAN, and COSCO showed large changes. Taken together, the extreme fluctuations in BDI changed the role of Asian shipping companies, intensifying competition among shipping companies and strengthening risk delivery. It was confirmed that BDI has a great influence on the network connectivity of shipping companies and has an important influence on the stability of the stock market network. Therefore, the results of this study should consider not only the connectivity of shipping companies according to market conditions, but also the connectivity in extreme situations.

• Journal of Life Science
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• v.34 no.8
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• pp.531-539
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• 2024

Biogenic amines in food, produced through amino acid decarboxylation, can cause allergies, digestive issues, and neurological symptoms. Recently, various studies have been conducted to reduce the levels of biogenic amines in food. This study analyzes the impact of salt concentration in Ganjang on microbial diversity and biogenic amine production. Results show a statistically significant correlation between salt concentration in Ganjang and species richness and species abundance indices. Although the alpha-diversity index did not significantly correlate with biogenic amine levels, higher salt concentration resulted in a statistically significant decrease in histamine and tyramine levels. An analysis of the correlation between microbial distribution and biogenic amines based on the salt concentration in Ganjang revealed that the distribution of Lactobacillus sp. and Bacteroides sp. increased as the salt concentration increased while the levels of biogenic amines decreased. On the other hand, the distribution of Tetragenococcus sp., Chromohalobacter sp., and Halomonas sp. decreased with increasing salt concentration, accompanied by an increase in biogenic amine levels. The results of this study suggest that within the salt concentration ranges in Ganjang, an increase in salt concentration is associated with an increase in the distribution of Lactobacillus sp., Bacteroides sp., Streptococcus gallolyticus, and Pseudomonas sp. This increase in microbial distribution is presumed to be related to a reduction in biogenic amine production or an enhancement in biogenic amine degradation.

• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.245-253
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• 2024

Blue-green algal bloom, or harmful algal bloom has a negative impact on the aquatic ecosystem and purified water supply system due to oxygen depletion in the water body, odor, and secretion of toxic substances in the freshwater ecosystem. This Blue-green algal bloom is expected to increase in intensity and frequency due to the increase in algae's residence time in the water body after the construction of the Nakdong River weir, as well as the increase in surface temperature due to climate change. In this study, in order to respond to the expected increase in green algae phenomenon, an algal bloom forecast system based on hydro-meteorological factors was presented for preemptive response before issuing a algal bloom warning. Through polyserial correlation analysis, the preceding influence periods of temperature and discharge according to the algal bloom forecast level were derived. Using the decision tree classification, a machine learning technique, Classification models for the algal bloom forecast levels based on temperature and discharge of the preceding period were derived. And a algal bloom forecast system based on hydro-meteorological factors was derived based on the results of the decision tree classification models. The proposed algae forecast system based on hydro-meteorological factors can be used as basic research for preemptive response before blue-green algal blooms.

• Journal of The Korean Association For Science Education
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• v.44 no.4
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• pp.301-311
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• 2024

We developed a small-group training program for in-service teachers focused on scientific modeling. We collected the discourses of the teachers who participated in the activity and analyzed them by type. The training program employed a collaborative approach in which a small group completed tasks and produced outputs based on the theme of 'galaxies and the Universe' to enable practical application in classes. Three in-service science teachers participated in the training program. Their discourses were recorded, transcribed, and classified into types based on individual turns and interaction units. The language expressions of the teachers reflected the unique characteristics of the teaching profession, with each participant having preferred language expression types, albeit with a generally low prevalence of specific language expression types across the participants. Differences in discourse characteristics related to the modeling theme, task presentation method, and model types, revealed that variations in the proportion of interaction unit types during the modeling design, build, and evaluation stages were primarily influenced by the teachers' familiarity with the modeling theme. While the task presentation method also influenced interaction types, model types had little impact on the distribution of interaction types. Considering these findings, training programs on modeling for in-service teachers should include a checklist to encourage sufficient interaction between participants as well as propose proper questions that can be effectively addressed through collaboration.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.121-132
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• 2024

This study attempted to determine the effects of job stress, nursing problem-solving ability, nursing work performance, and job stress and nursing problem-solving ability on nursing work performance according to the MBTI type of nurses. The study subjects were 141 nurses working at a medical institution in G Province, and data collection was conducted from March 01 to March 31, 2024. The collected data were analyzed using correlation and multiple regression analysis. Among the psychological function types of MBTI, the SF type (sympathetic and friendly type) was the most common, and among the psychological temperament types, the SP type (sensuous and open type) was the most common. Nursing work performance had a negative correlation with job resource stress, a positive correlation with nursing problem-solving ability, and a positive correlation with problem recognition, information collection, planning ability, and evaluation. The variable that had a significant impact on nursing work performance was job resources, and problem recognition, a subfactor of nursing problem-solving ability, was found to be the best predictor of nursing work performance, followed by planning ability. The explanatory power was 17.8%. The results of this study are expected to be used as basic data to develop efficient nursing management guidelines by not only improving understanding of the personality of nurses but also investigating factors related to nurses' work performance. Through the development of programs and measures to improve nursing performance, it is necessary to revitalize programs, provide educational opportunities, and provide institutional support from hospital organizations to enable high-quality nursing care through skilled nursing work.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.159-168
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• 2024

This study was conducted to identify factors influencing clinical performance among nursing students who experienced clinical practice due to COVID-19 and provide basic data to improve clinical performance. This study collected data from October 10 to October 27, 2023, targeting 144 students at two nursing schools in G Province. The collected data were analyzed using descriptive statistics, difference analysis (t-est, on-way ANOVA), correlation, and hierarchical regression analysis. As a result of the study, in Model 1, extrovert and mixed personality types were found to be significant predictive factors explaining clinical performance. The goodness of fit of Model 1 was statistically significant, and the explanatory power was 9.2% (F=8.256, p<.001). In Model 2, interpersonal skills and confidence in nursing skills appeared as significant predictive factors explaining clinical performance ability. Confidence in nursing skills was the best predictor of clinical performance, followed by interpersonal skills. The explanatory power of the model was 50.1%, an increase of 41.3% compared to Model 1. Model fit was also statistically significant. Simulation education that reproduces various situations should be strengthened to increase opportunities to perform direct nursing and improve nursing students' nursing capabilities. If you improve your confidence in nursing skills and interpersonal skills through simulation education, your clinical performance will ultimately improve, and this will be able to be demonstrated as a nursing competency when employed as a nurse.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.443-449
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• 2024

This study aims to analyze the complex impact of technological changes in the Fourth Industrial Revolution era and demographic shifts in Korea on military personnel management, and to explore innovative strategies for the Korean military's workforce operations. The research findings indicate that changes in future battlefield environments and the introduction of advanced technologies necessitate a fundamental restructuring of military personnel, emphasizing a shift towards a highly specialized and elite workforce. Key research findings are as follows: First, the military application of cutting-edge technologies, such as unmanned systems, autonomous weapon systems, and AI-based decision support systems, is expanding. Second, this technological advancement requires a restructuring of personnel to foster a technology-intensive elite force, including optimizing troop size, reorganizing unit structures, and increasing the utilization of civilian expertise. Third, strategies for securing high-tech talent include strengthening internal technology talent development programs, establishing systems to attract civilian experts, and building a talent development system through industry-academia-research cooperation. The significance of this study lies in providing a theoretical and practical foundation for building a future-oriented and efficient Korean military organization by presenting innovative measures for military human resource management systems suitable for the Fourth Industrial Revolution era. For these changes to be successfully implemented, cooperation among relevant stakeholders, including the military, government, academia, and industry, is essential, supported by comprehensive national-level planning and support.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.319-341
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• 2024

The utilization of multispectral imaging systems (MIS) in remote sensing has become crucial for large-scale agricultural operations, particularly for diagnosing plant health, monitoring crop growth, and estimating plant phenotypic traits through vegetation indices (VIs). However, environmental factors can significantly affect the accuracy of multispectral reflectance data, leading to potential errors in VIs and crop status assessments. This paper reviewed the complex interactions between environmental conditions and multispectral sensors emphasizing the importance of accounting for these factors to enhance the reliability of reflectance data in agricultural applications.An overview of the fundamentals of multispectral sensors and the operational principles behind vegetation index (VI) computation was reviewed. The review highlights the impact of environmental conditions, particularly solar zenith angle (SZA), on reflectance data quality. Higher SZA values increase cloud optical thickness and droplet concentration by 40-70%, affecting reflectance in the red (-0.01 to 0.02) and near-infrared (NIR) bands (-0.03 to 0.06), crucial for VI accuracy. An SZA of 45° is optimal for data collection, while atmospheric conditions, such as water vapor and aerosols, greatly influence reflectance data, affecting forest biomass estimates and agricultural assessments. During the COVID-19 lockdown,reduced atmospheric interference improved the accuracy of satellite image reflectance consistency. The NIR/Red edge ratio and water index emerged as the most stable indices, providing consistent measurements across different lighting conditions. Additionally, a simulated environment demonstrated that MIS surface reflectance can vary 10-20% with changes in aerosol optical thickness, 15-30% with water vapor levels, and up to 25% in NIR reflectance due to high wind speeds. Seasonal factors like temperature and humidity can cause up to a 15% change, highlighting the complexity of environmental impacts on remote sensing data. This review indicated the importance of precisely managing environmental factors to maintain the integrity of VIs calculations. Explaining the relationship between environmental variables and multispectral sensors offers valuable insights for optimizing the accuracy and reliability of remote sensing data in various agricultural applications.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.389-395
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• 2024

This study aims to deeply analyze the military strategies and tactics used in the battles between Israel and Hamas, to understand the military approaches, technical capabilities, and their impact on the outcomes of the conflict. To achieve this, methodologies such as literature review, data analysis, and case studies were utilized. The research findings confirm that Hamas employed asymmetric tactics, such as rocket attacks and surprise attacks through underground tunnels, to counter Israel's military superiority. On the other hand, Israel responded to Hamas's attacks with the Iron Dome interception system and intelligence-gathering capabilities, but faced difficulties due to Hamas's underground tunnel network. After six months of fighting, the casualties in the Gaza Strip exceeded 30,000, and more than 1.7 million people became refugees. Israel also suffered over 1,200 deaths. Militarily, neither side achieved a decisive victory, resulting in a war of attrition. This study suggests that the Israel-Hamas war exemplifies the complexity of modern asymmetric warfare. Furthermore, it recommends that political compromise between the two sides and active mediation efforts by the international community are necessary for the peaceful resolution of the Israel-Palestine conflict.