• Proceedings of the Plant Resources Society of Korea Conference
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• 2021.04a
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• pp.37-37
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• 2021

Calystegia soldanella L. is a perennial herbaceous halophyte belonging to the convolvulaceae family, which mainly grows in coastal sand dunes in Korea. Shoots and rhizomes are edible, and roots called 'Hyoseon Chogeun' are known to have medicinal effects such as antipyretic, sterilization, and diuretic. In addition, physiological activities of antioxidant, anti-inflammatory, antiviral, antifungal and PTP-1B (protein tyrosine phosphate-1B) inhibition have been reported. In this study, in vitro induction cell lines of C. soldanella L. collected from the coastal sand dunes in Jeju island was redifferentiated into adventitious roots that can be used as medicinal resources. Also the biomass of mass-proliferated adventitious roots using a bioreactor were evaluated. Plants of C. soldanella L. were collected from the crevice of the seashore in the coastal area of Taeheung 2-ri, Namwon-eup, Seogwipo-si. Then, it was separated into leaves, stems, rhizomes, and roots, and surface sterilized with 70% ethyl alcohol and 2% NaOCl (sodium hypochlorite). After washing with sterilized water, each organ section was cultured in Hormone-free MS medium (Murashige & Skoog Medium). As a result, the induction response rates were evaluated at 85% and 55%, respectively, in terms of callus formation and shoot generation in the rhizome segment. In the case of the adventitious roots morphological characteristics induced by single-use treatment of auxin-based plant growth regulators IBA and NAA from redifferentiated shoots were compared. Most efficient adventitious root culture method as a rooting rate, number, length, and biomass proliferation in the bioreactor system was confirmed when treated by culturing in MS salts, Sucrose 30 g·L^-1, and IBA 1mg·L^-1 for 4 weeks. In this study, the medium composition and culture period were confirmed using a bioreactor system to mass-proliferate adventitious roots derived from C. soldanella L. in Jeju island. Also this adventitious root line developed a new medicinal material could increase value of the bio-industry ingredient through quantitative and qualitative screening of phyto-bioactive compounds.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.93-98
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• 2023

In paddy rice farming, water management is a critical task. To suppress weed emergence during the early stages of growth, fields are deeply flooded, and after transplantation, the water level is reduced to promote rooting and stimulate stem generation. Later, water is drained to prevent the production of sterile tillers. The adequacy of water supply is influenced by various factors such as field location, irrigation channels, soil conditions, and weather, requiring farmers to frequently check water levels and control the ingress and egress of water. This effort increases if the fields are scattered in remote locations. Automated irrigation systems have been considered to reduce labor and improve productivity. However, the net income from rice production in 2022 was about KRW 320,000/10a on average, making it financially unfeasible to implement high-cost devices or construct new infrastructure. This study focused on developing an IoT-Based irrigation valve that can be easily integrated into existing agricultural infrastructure without additional construction. The research was carried out in three main areas: Firstly, an irrigation valve was designed for quick and easy installation on existing agricultural pipes. Secondly, a power circuit was developed to connect a low-power Cat M1 communication modem with an Arduino Nano board for remote operation. Thirdly, a cloud-based platform was used to set up a server and database environment and create a web interface that users can easily access.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.59-73
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• 2023

In the era of the 4th Industrial Revolution, manufacturing is the implementation of smart factories through digital transformation, and refers to consumer-centered intelligent factories that combine next-generation digital new technologies and manufacturing technologies beyond the existing factory automation level. In order to successfully settle such a smart factory, it is necessary to train professionals. However, education for smart factories is difficult to have actual field mechanical facilities or overall production processes. Therefore, there is a need for a system that can visualize and control the flow and process of logistics at the actual production site. In this paper, the logistics flow of the actual site was implemented as a small FMS, a physical system, and the production process was implemented as a digital system. In real-time synchronization of the physical system and the digital system, the location of AGV and materials, and the process state can be monitored to see the flow of logistics and process processes at the actual manufacturing site. The developed digital twin system can be used as an effective educational system for training manpower in smart factories.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.

• Economic and Environmental Geology
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• v.57 no.1
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• pp.83-91
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• 2024

According to the IEA (2022), global rechargeable battery demand is expected to reach 1.3 TWh in 2040. EV batteries will account for about 80% of this demand, and used EV batteries are expected to be discharged after 30 years. Used EV batteries can be recycled and reused to create new value. They can also resolve one of the most vulnerable parts of the battery supply chain: raw material insecurity. In this study, we analyzed the amount of used batteries generated by EV in Korea and their potential for reuse and recycling. As a result, it was estimated that the annual generation of used batteries for EV began to increase to more than 100,000 in '31 and expanded to 810,000 in '45. In addition, it was found that the market for recycling EV batteries in '45 could be expected to be equivalent to the production of 1 million batteries, and the market for reuse could be expected to be equivalent to the production of 36 Gwh of batteries. On the other hand, according to the plan standard disclosed by the recycling company, domestic used EV batteries can account for 11% of the domestic recycling processing capacity (pre-treatment) ('30). So it will be important to manage the import and export of used batteries in terms of securing raw materials.

• Information Systems Review
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• v.22 no.4
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• pp.75-92
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• 2020

As population and generation structures change, more and more customers tend to avoid facing relation due to the development of information technology and spread of smart phones. This phenomenon consists with efficiency and immediacy, which are the consumption patterns of modern customers who are used to information technology, so offline network-oriented distribution companies actively try to switch their sales and services to untact patterns. Recently, untact services are boosted in various fields, but beauty products are not easy to be recommended through untact services due to many options depending on skin types and conditions. There have been many studies on recommendations and development of recommendation systems in the online beauty field, but most of them are the ones that develop recommendation algorithm using survey or social data. In other words, there were not enough studies that classify segments based on user information such as skin types and product preference. Therefore, this study classifies customer segments using machine learning technique K-prototypesalgorithm based on customer information and search log data of mobile application, which is one of untact services in the beauty field, based on which, untact marketing strategy is suggested. This study expands the scope of the previous literature by classifying customer segments using the machine learning technique. This study is practically meaningful in that it classifies customer segments by reflecting new consumption trend of untact service, and based on this, it suggests a specific plan that can be used in untact services of the beauty field.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.73-82
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• 2024

In 2017, the United Nations reported that the population aged 60 and above was increasing more rapidly than all younger age groups worldwide, projecting that by 2050, the population aged 60 and above would constitute at least 25% of the global population, excluding Africa. The world is experiencing a decline in the rate of increase in the working-age population due to global aging, and the younger generation tends to avoid difficult and challenging occupations. Although theoretically, AI equipped with artificial intelligence can replace humans in all fields, in the realm of practical information security, human judgment and expertise are absolutely essential, especially in ethical considerations. Therefore, this paper proposes a method to retrain and reintegrate IT professionals aged 50 and above who are retiring or seeking career transitions, aiming to bring them back into the industry. For this research, surveys were conducted with 21 government/public agencies representing demand and 9 security monitoring companies representing supply. Survey results indicated that both demand (90%) and supply (78%) unanimously agreed on the absolute necessity of such measures. If the results of this research are applied in the field, it could lead to the strategic development of senior information security professionals, laying the foundation for a new market in the Korean information security industry amid the era of low birth rates and longevity.

• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1521-1532
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• 2023

This study was done to identify the face-to-face and non-face-to-face student counseling experiences of nursing students during the COVID-19 period. Data were collected through interviews with 10 students at S Women's University in Gyeonggi-do from December 2022 to April 2023. All recorded data were analyzed using an inductive content analysis method. As a result of the study, two themes, four categories, and eight subcategories were found. Themes were 'factors promoting counseling' and 'Non-facilitating factors in counseling'. The first theme category was 'adaptation to new counseling methods' and 'pursuing convenience as the MZ generation', and the second theme category was 'resistance to counselling' and 'discrepancy from the desire for mutual relationships'. Through this study, it was helpful to understand that student counseling is essential for nursing students and what the most appropriate and useful counseling method is. Additionally, this study provided important evidence to lay the foundation for comparative research on face-to-face and non-face-to-face counseling among research participants according to gender and major. Further suggests research to develop a student counseling program for nursing students and confirm its effectiveness.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.36 no.2
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• pp.37-49
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• 2024

To date, numerous empirical formulas have been proposed through hydraulic model experiments to predict the wave breaker index, including wave height and depth of wave breaking, due to the inherent complexity of generation mechanisms. Unfortunately, research on the characteristics of wave breaking and the prediction of the wave breaker index for gravel beaches has been limited. This study aims to forecast the wave breaker index for gravel beaches using representative linear-based machine learning techniques known for their high predictive performance in regression or classification problems across various research fields. Initially, the applicability of existing empirical formulas for wave breaker indices to gravel seabeds was assessed. Various linear-based machine learning algorithms were then employed to build prediction models, aiming to overcome the limitations of existing empirical formulas in predicting wave breaker indices for gravel seabeds. Among the developed machine learning models, a new calculation formula for easily computable wave breaker indices based on the model was proposed, demonstrating high predictive performance for wave height and depth of wave breaking on gravel beaches. The study validated the predictive capabilities of the proposed wave breaker indices through hydraulic model experiments and compared them with existing empirical formulas. Despite its simplicity as a polynomial, the newly proposed empirical formula for wave breaking indices in this study exhibited exceptional predictive performance for gravel beaches.

• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.510-520
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• 2024

Objective : Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases, characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. The treatment of PD aims to alleviate motor symptoms by replacing the reduced endogenous dopamine. Currently, there are no disease-modifying agents for the treatment of PD. Zebrafish (Danio rerio) have emerged as an effective tool for new drug discovery and screening in the age of translational research. The neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is known to cause a similar loss of dopaminergic neurons in the human midbrain, with corresponding Parkinsonian symptoms. L-type calcium channels (LTCCs) have been implicated in the generation of mitochondrial oxidative stress, which underlies the pathogenesis of PD. Therefore, we investigated the neuro-restorative effect of LTCC inhibition in an MPTP-induced zebrafish PD model and suggested a possible drug candidate that might modify the progression of PD. Methods : All experiments were conducted using a line of transgenic zebrafish, Tg(dat:EGFP), in which green fluorescent protein (GFP) is expressed in dopaminergic neurons. The experimental groups were exposed to 500 μmol MPTP from 1 to 3 days post fertilization (dpf). The drug candidates : levodopa 1 mmol, nifedipine 10 μmol, nimodipine 3.5 μmol, diethylstilbestrol 0.3 μmol, luteolin 100 μmol, and calcitriol 0.25 μmol were exposed from 3 to 5 dpf. Locomotor activity was assessed by automated tracking and dopaminergic neurons were visualized in vivo by confocal microscopy. Results : Levodopa, nimodipine, diethylstilbestrol, and calcitriol had significant positive effects on the restoration of motor behavior, which was damaged by MPTP. Nimodipine and calcitriol have significant positive effects on the restoration of dopaminergic neurons, which were reduced by MPTP. Through locomotor analysis and dopaminergic neuron quantification, we identified the neuro-restorative effects of nimodipine and calcitriol in zebrafish MPTP-induced PD model. Conclusion : The present study identified the neuro-restorative effects of nimodipine and calcitriol in an MPTP-induced zebrafish model of PD. They restored dopaminergic neurons which were damaged due to the effects of MPTP and normalized the locomotor activity. LTCCs have potential pathological roles in neurodevelopmental and neurodegenerative disorders. Zebrafish are highly amenable to high-throughput drug screening and might, therefore, be a useful tool to work towards the identification of disease-modifying treatment for PD. Further studies including zebrafish genetic models to elucidate the mechanism of action of the disease-modifying candidate by investigating Ca2+ influx and mitochondrial function in dopaminergic neurons, are needed to reveal the pathogenesis of PD and develop disease-modifying treatments for PD.