• Journal of Industrial Convergence
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• 제21권11호
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• pp.117-124
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• 2023

This study is a descriptive research study to determine the relationship between nursing students' emotional intelligence, communication competence, interpersonal competence, and adaptation to college life, and to identify factors that adaptation to college life. The subjects of the study were 173 nursing students attending a university in G City, and data collection was conducted from September 10 to October 10, 2022 Descriptive statistics, t-test, ANOVA, Pearson correlation coefficient, and multiple regression analysis were performed on the collected data using SPSS 25.0 program. The results showed a significant positive correlation between emotional intelligence(r=.760, p<.001), communication competence(r=.600, p<.001), and interpersonal competence(r=.451, p<.001). Factors that have a significant impact on adaptation to college life include emotional intelligence(𝛽=.543, p<.001), communication competence(𝛽=.433, p=.001), and interpersonal competence(𝛽=.283, p<.031). The total explanatory power of these variables on adaptation to college life was 59.9%, and emotional intelligence was confirmed to be the main influencing factor.

• The Journal of the Convergence on Culture Technology
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• 제9권6호
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• pp.85-96
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• 2023

In a reality where negative perceptions and social prejudices towards individuals with mental illnesses persist, the absence and lack of social support systems acquired through personal intimate social networks can be considered a significant hindrance to the quality of life for those with mental disorders. Therefore, this study examined the impact of the characteristics of social networks and social support on the life satisfaction of individuals with mental illnesses. A survey was conducted with 180 patients from seven mental health treatment facilities in the Daegu area. The data were analyzed using MANOVA, hierarchical multiple regression analysis, and Sobel test for mediation analysis with SPSS 25 software. The key findings of the study revealed that the intimacy aspect of social networks had a positive and significant effect on social support and life satisfaction. On the other hand, the size of the social network showed a negative influence on social support. Furthermore, social support partially mediated the relationship between the intimacy of social networks and life satisfaction, and it fully mediated the relationship between the size of social networks and life satisfaction. Based on these research outcomes, practical and policy-related recommendations are provided to enhance life satisfaction through increased social support for this population.

• Korean Journal of Culture and Social Issue
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• 제10권spc호
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• pp.177-209
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• 2004

This paper provides a theoretical and conceptual framework for understanding adolescent culture and educational achievement in Korea. In the first part of the paper, the authors outline a research paradigm in cultural psychology and adolescent culture. In the second section, the traditional family structure, the role of parents, and how they have been changed by modernization are outlined. In the third section, socialization practices and parent-child relationship are reviewed. In the fourth section, Western theories that have been developed to explain educational achievement and their limitations are examined. In the fifth section, factors that contribute to educational success of Korean students are presented. In the final section, the impact of centralized, standardized, and rigid educational system that is imposed on adolescents is discussed. The highly regulated and centralized bureaucracy restricts educational and career opportunities for adolescents and it is responsible for the high rate of violence, delinquency, and bullying in Korea. The need for encouraging civil society that allows for diversity of ideas and skills and at the same time maintaining strong relational bonds are discussed.

• Tunnel and Underground Space
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• 제33권6호
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• pp.594-609
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• 2023

TBM (Tunnel Boring Machine) method is gaining popularity in urban and underwater tunneling projects due to its ability to ensure excavation face stability and minimize environmental impact. Among the prominent models for predicting disc cutter life, the NTNU model uses the Cutter Life Index(CLI) as a key parameter, but the complexity of testing procedures and rarity of equipment make measurement challenging. In this study, CLI was predicted using multiple linear regression analysis and tree-based machine learning techniques, utilizing rock properties. Through literature review, a database including rock uniaxial compressive strength, Brazilian tensile strength, equivalent quartz content, and Cerchar abrasivity index was built, and derived variables were added. The multiple linear regression analysis selected input variables based on statistical significance and multicollinearity, while the machine learning prediction model chose variables based on their importance. Dividing the data into 80% for training and 20% for testing, a comparative analysis of the predictive performance was conducted, and XGBoost was identified as the optimal model. The validity of the multiple linear regression and XGBoost models derived in this study was confirmed by comparing their predictive performance with prior research.

• Phonetics and Speech Sciences
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• 제15권4호
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• pp.91-100
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• 2023

Reverberation time (T60) is a typical acoustic parameter that provides information about reverberation. Since the impacts of reverberation vary depending on the frequency bands even in the same space, frequency-dependent (FD) T60, which offers detailed insights into the acoustic environments, can be useful. However, most conventional blind T60 estimation methods, which estimate the T60 from speech signals, focus on fullband T60 estimation, and a few blind FDT60 estimation methods commonly show poor performance in the low-frequency bands. This paper introduces a modified approach based on Attentive pooling based Weighted Sum of Spectral Decay Rates (AWSSDR), previously proposed for blind T60 estimation, by extending its target from fullband T60 to FDT60. The experimental results show that the proposed method outperforms conventional blind FDT60 estimation methods on the acoustic characterization of environments (ACE) challenge evaluation dataset. Notably, it consistently exhibits excellent estimation performance in all frequency bands. This demonstrates that the mechanism of the AWSSDR method is valuable for blind FDT60 estimation because it reflects the FD variations in the impact of reverberation, aggregating information about FDT60 from the speech signal by processing the spectral decay rates associated with the physical properties of reverberation in each frequency band.

• Journal of the Korean Association of Geographic Information Studies
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• 제26권4호
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• pp.80-96
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• 2023

The expansion of human activities and road development has led to the loss and fragmentation of ecological spaces, which is a negative factor for biodiversity. In particular, urban areas where land use and land cover have rapidly changed into urbanization zones are regions where ecological spaces are lost and isolated, making it difficult for wildlife to inhabit. Furthermore, the loss and fragmentation of ecological spaces due to urbanization can have a negative impact on ecosystem services. Therefore, to enhance biodiversity and ecosystem services in urban and national land, it is necessary to establish a practical ecological axis that reflects the current status of the city. Thus, this study analyzed the connectivity of ecological spaces and forest axis that can be used for spatial planning related to urban ecological axis of local governments in Daegu and Gyeongsangbuk-do. The ecological connectivity was analyzed by dividing the Daegu-Gyeongbuk region into 31 local government units, distinguishing between forests and natural areas using land cover data. Subsequently, the study area was divided into 20,483 hexagonal grids of 1 square kilometer each, and the restoration effects for ecological fragmentation within 100 meters were spatially clustered to visualize priority restoration areas. The forest axis was derived by considering regional conditions such as land cover, building area, slope, and others to connect 1,534 forests of 100 hectares or more. The research results are expected to be used as fundamental data for spatial planning, goal setting, and the selection of restoration areas for improving ecological connectivity.

• Journal of Marine Bioscience and Biotechnology
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• 제15권2호
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• pp.82-89
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• 2023

Microalgae, as photosynthetic organisms, possess the ability to produce a diverse array of bioactive compounds. This study focused on the transformant Chlamydomonas reinhardtii dZL and subjected it to cultivation under varying light intensities (60, 120, 180, and 240 µmol/m²/s). Our aim was to assess the impact of light intensity on both microalgal biomass and carotenoid production. The cultivation took place in 80 mL bubble column photobioreactors, specifically the Multi-cultivator. Notably, the culture exposed to 240 µmol/m²/s exhibited the most rapid cell growth, surpassing even the cell concentration achieved at 180 µmol/m²/s by day 8. A detailed analysis of the specific irradiance rate over time unequivocally revealed a sharp decline in growth rates when the rate fell below 2 × 10^-10 µmol/cell/s. Although the culture with 60 µmol/m²/s yielded the highest carotenoid content (1.2% of dry weight), the culture exposed to 240 µmol/m²/s recorded the highest carotenoid concentration at 8.9 mg/L owing to its higher biomass. Our findings reveal the critical importance of maintaining a specific irradiance rate above 2 × 10^-10 µmol/cell/s to enhance biomass and carotenoid productivity. This study lays the groundwork for defining optimal light intensity conditions applicable to mass culture systems, with the objective of augmenting C. reinhardtii biomass and optimizing carotenoid productivity.

• Journal of the Korea Institute of Building Construction
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• 제23권6호
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• pp.663-671
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• 2023

This research experimentally evaluated the recyclability of four varieties of lignocellulosic fly ash(FA), a by-product from three power plants employing lignocellulosic biomass(Bio-SRF, wood pellets) as a fuel source. Comprehensive analyses were conducted on FA, encompassing both physical parameters (particle shape, size distribution, fineness, and density) and chemical properties(chemical composition and heavy metal content). Mortar test specimens, with FA mixing ratios ranging from 5 to 20%, were produced in compliance with KS L 5405 standards, and their flow and compressive strength were subsequently measured. The test results indicated that the four types of FA exhibited particle sizes approximately between 20~30㎛, densities around 2.3~2.5g/cm³, and a fineness range of 2,600~4,900cm²/g. The FA comprised approximately 50~90% of components such as SiO₂, Al₂O₃, Fe₂O₃, and CaO, displaying characteristics akin to type-II and type-III FA of KS L 5405 standards, albeit with differences in chlorine and SiO₂ content. From the mortar tests, it was observed that the compressive strength of the mortar ranged between 34~47MPa when the pellet combustion FA was mixed in proportions of 5~20%. FA, produced exclusively from the combustion of 100% lignocellulosic fuel, is assessed to possess high recyclability potential as a substitute for conventional admixtures.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제16권6호
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• 제18권1호
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• pp.1-11
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• 2023

Big tech companies are further strengthening its status against the background of data accumulation, price competitiveness by the platform, and competitive advantage due to the network effect. The competition subcommittee of the European Union(EU) imposed a huge fine on Google for antitrust violations, which was interpreted as an attempt to collect Google's unpaid taxes. In fact, taxation efforts in the form of 'Google tax' are underway, targeting expedient tax avoidance by global platforms. It has power and has a considerable influence on the startup ecosystem. The domestic sales and tax scale of global platforms, which have a great impact on domestic content startups and small and medium-sized venture companies, are not accurately measured. In the case of Google, according to research literature, sales in Korea were estimated at about 2 trillion to 3 trillion won in 2017, but Google Korea reported sales of 290 billion won in 2021 and paid 13 billion won in taxes. This study aims to verify the economic effect of the global platform that has a great influence on Korea, and specifically to quantitatively estimate the annual domestic sales and taxes of Google, a representative global platform. As a result of estimating Google's annual domestic sales and taxes based on the figures presented in the document related to Google's economic effect published by Google, the result was 4 to 9 trillion won in annual sales and 390.6 to 913.1 billion won in taxes. This study is meaningful in that it provides basic data on the direction of national and tax policies in the future digital economy era by estimating the problem of tax authority by country of global platform companies with a specific example of Google.