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To transform tall goldenrods, which are invasive alien plant that destroy the ecosystem of South Korea, into useful materials, cellulose fibers isolated from tall goldenrods are applied as EMI shielding materials in this study. The obtained cellulose fibers were blended with CNTs, which were used as additives, to improve the electrical conductivity. TGCF/CNT papers prepared using a facile paper manufacturing process with various weight percent ratios and thickness were carbonized at high temperatures and investigated as EMI shielding materials. The increase in the carbonization temperature, thickness, and CNT content enhanced the electrical conductivity and EMI SE of TGCF/CNT carbon papers. TGCF/CNT-15 papers, with approximately 4.5 mm of thickness, carbonized at 1300 °C exhibited the highest electrical conductivity of 6.35 S cm−1, indicating an EMI SE of approximately 62 dB at 1.6 GHz of the low frequency band. Additionally, the obtained TGCF/CNT carbon papers were flexible and could be bent and wound without breaking. [ABSTRACT FROM AUTHOR]

In South Korea, the rapid proliferation of smartphones has led to an uptick in messenger phishing attacks associated with electronic communication financial scams. In response to this, various phishing detection algorithms have been proposed. However, collecting messenger phishing data poses challenges due to concerns about its potential use in criminal activities. Consequently, a Korean phishing dataset can be composed of imbalanced data, where the number of general messages might outnumber the phishing ones. This class imbalance problem and data scarcity can lead to overfitting issues, making it difficult to achieve high performance. To solve this problem, this paper proposes a phishing messages classification method using Biased Discriminant Analysis without resorting to data augmentation techniques. In this paper, by optimizing the parameters for BDA, we achieved exceptionally high performances in the phishing messages classification experiment, with 95.45% for Recall and 96.85% for the BA metric. Moreover, when compared with other algorithms, the proposed method demonstrated robustness against overfitting due to the class imbalance problem and exhibited minimal performance disparity between training and testing datasets. [ABSTRACT FROM AUTHOR]

To prevent regulation from becoming an obstacle to healthcare technological innovation, regulation should evolve as new healthcare technologies are developed. Although regulation is closely related to healthcare technology development, there are few studies that view healthcare technological advances from the multi-layered perspective of papers, patents, and clinical research and link this with regulatory evolution. Therefore, this study tried to develop a new method from a multi-layer perspective and draw regulatory implications based on it. This study applied this method to intraocular lens (IOLs) for cataract treatment and detected four major healthcare technologies and two recent healthcare technologies. Moreover, it discussed how current regulations evaluate these technologies. The findings provide implications for healthcare technological advances and the evolutionary direction of regulation through the example of IOLs for cataract treatment. This study contributes to the development of theoretical methods for co-evolution with regulations based on healthcare technology innovation. [ABSTRACT FROM AUTHOR]

The relationship between natural cycles and human actions in causing climate change and its impacts on society have been a subject of debate. This paper explores the responses of pre-modern Koreans to climate change and natural disasters. The focus of this paper is on the societal reactions to these natural calamities and disasters during the Koryŏ dynasty (936–1392 A.D.) and their influence on Korea afterwards. This paper investigates the reasons behind the retention of primal religious worldviews in the face of ecological crises while other major imported religions were abandoned. The research method involved in this paper is archival research, and the literature sources include ancient records and other ecotheological resources. Through an analysis of the archival research and ecotheological resources, this study sheds light on the interpretation of climatic events in pre-modern Korea. Furthermore, based on the research findings, it presents missional implications for the Korean church in the post-pandemic era. This study underscores the importance of the church's responses to environmental awareness, collective responsibility, and spiritual support in addressing the challenges of climate change and provides missional implications for the Korean Church in response to the post-COVID-19 era based on the results of the research. [ABSTRACT FROM AUTHOR]

Foreign bodies (FBs) in the nasal cavity are commonly encountered in otorhinolaryngology. This retrospective study was conducted between January 2009 and December 2020. The nasal FB frequencies were investigated according to age and sex. The type, shape, and location of the FBs, onset, and clinical features were obtained. In a total of 1228 cases, the average age was 3.9 ± 5.7 years. We found a higher incidence in patients 2–4 years old. The most frequent nasal FBs were beads (24.0%), toys/plastics (17.8%), and beans/corn (15.8%). However, there were some differences in the nasal FB types according to the age group. There was no difference in the location of the nasal FBs according to age group, but nasal FBs were found more frequently in the right nasal cavity in the 1–3-years-old groups. Most patients (97.5%) visited the hospital the same day when the nasal FB insertion was suspected, and most were asymptomatic. After the removal of the nasal FBs, most patients were free of complications. Of the nine cases involving button batteries, septal perforation was observed in two patients who visited the hospital one or two days after the FB insertion. There were specific age-based characteristics of the nasal FBs that may serve as a basis for specific precautions. [ABSTRACT FROM AUTHOR]

Christianity does not have as long of a history as other monotheistic religions and traditional ideologies in Korea, but—especially from the end of the 19th century—its new concepts have had a huge impact on the basic thoughts of Korean society. This paper focuses on the effects of Christianity and the activity of Christian communities on women's lives. According to my preliminary findings, in the late 1800s, the Christian missionaries and their newly formed communities offered opportunities for girls and women to get education, a profession, access to better health care, and learn self-care. After the complicated decades between 1910 and 1950, South Korea was experiencing remarkable changes, and Christians were an active part of this rebuilding, helping the lives of those who were struggling with poverty, lack of daily necessities, education, and health care. This paper aims to examine the thoughts and actions of Korean Christian communities towards decisions on childbirth, children out of wedlock, adoption, and single motherhood. This study investigates the approaches of South Korean Christian communities towards women related to the above-mentioned circumstances, focusing on the early examples and the last five to six decades. It is assumed that even though South Korea is now considered a modernized country, the government sometimes fails to cope with current problems, and traditional notions are still strong in familial matters. Therefore, not obeying conventional forms may cause social conflicts, or the decisions are hidden because of taboos and stigmas. This research is based on the publications of Christian communities and involves documentaries and case studies, including the baby box operation and adoption. This paper contributes to the expanding studies on Korean Christian communities, highlighting the social norms and their changes generated by new religious thoughts, while giving an insight into the daily struggles of Korean women's lives when it comes to decision-making about their motherhood. [ABSTRACT FROM AUTHOR]

The relationship between modernization and religion is contested, with the literature differing in how and in what ways religion helps or hinders countries' social, economic, and political development. This paper draws upon the history of Christianity in South Korea to critically explore the links between religion and modernization. It makes two arguments. First, discussions of the link between religion and modernization frequently employ static definitions of religion, but Christianity is characterized by oscillations between worldly (institutionalizing) and unworldly (countercultural) impulses that theoretically make very different contributions to social, economic, and political development. Second, in the case of South Korea, it is shown that both impulses have made vital contributions to the country's modernization at different times. This suggests that the dynamic tug-of-war between the institutional and countercultural facets of Korean Christianity, although problematic for individual believers and religious leaders, helped it become an important contributor to the country's success story. However, this paper concludes on a cautionary note by warning that extreme instances of these impulses have caused cleavages between Christianity and the Korean state and society and could undermine its future contributions. This suggests that diversity and toleration—a hallmark of Korean Christianity—will continue to be the best pathway forward. [ABSTRACT FROM AUTHOR]

The adoption of recommender systems in business-to-business (B2B) can make the management of companies more efficient. Although the importance of recommendation is increasing with the expansion of B2B e-commerce, not enough studies on B2B recommendations have been conducted. Due to several differences between B2B and business-to-consumer (B2C), the B2B recommender system should be defined differently. This paper presents a new perspective on the explainable B2B recommender system using the knowledge graph attention network for recommendation (KGAT). Unlike traditional recommendation systems that suggest products to consumers, this study focuses on recommending potential buyers to sellers. Additionally, the utilization of the KGAT attention mechanisms enables the provision of explanations for each company's recommendations. The Korea Electronic Taxation System Association provides the Market Transaction Dataset in South Korea, and this research shows how the dataset is utilized in the knowledge graph (KG). The main tasks can be summarized in three points: (i) suggesting the application of an explainable recommender system in B2B for recommending potential customers, (ii) extracting the performance-enhancing features of a knowledge graph, and (iii) enhancing keyword extraction for trading items to improve recommendation performance. We can anticipate providing good insight into the development of the industry via the utilization of the B2B recommendation of potential customer prediction. [ABSTRACT FROM AUTHOR]

By analysing the childcaring experiences of female skilled workers from South Korea (hereafter, Korea) in Germany, this paper maintains that the challenges in labour market participation for highly skilled women, and especially those with children, should be understood in the context of their encounters with similar and different care and employment regimes between their home and host countries. On the theoretical level, this research confirms the argument that the migration of highly skilled workers should be contextualized not from a neoclassical perspective in which the maximization of economic profits takes priority, but from an institutional point of view in which social and cultural norms, practices, and policies in both the home and host societies are taken into consideration. Specifically, through a series of in-depth interviews conducted with skilled female migrants from Korea, this paper highlights the significance of taking the function of similar and different caring and employment regimes into account in explaining the challenges faced by highly skilled migrant women in labour market participation. On the empirical level, this paper sheds light on the migration experiences of skilled women from Asia as well as the (dis)integration processes of newcomers from third-national countries in Germany, with a focus on female migrants from Korea. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources into a power system increases, the significance of precise short-term forecasts for wind power generation becomes paramount. However, the erratic and non-periodic nature of wind poses challenges in accurately predicting the output. This paper presents a comprehensive investigation into forecasting wind power generation for the following day, using three machine learning models: long short-term memory (LSTM), convolutional neural network-bidirectional LSTM (CNN-biLSTM), and light gradient boosting machine (LGBM). In addition, this paper proposes a method to improve the prediction performance of LGBM by separating data according to the distribution of features, and training and testing each separated dataset with a distinct model. This study includes a comparative analysis of the performance of the proposed models in predicting wind turbine output, offering valuable insights into their respective efficiencies. The results of this investigation were analyzed for two geographically distinct wind farms (Korea and the UK). The findings of this study are expected to facilitate the selection of efficient prediction models within the forecast accuracy auxiliary service market and assist grid operators in ensuring reliable power supply for the grid. [ABSTRACT FROM AUTHOR]

Through an investigation of two recently discovered paper sheets of the Incantation of Wish-Fulfillment from the Silla kingdom, this paper reveals that early Korea had more diverse forms of dhāraṇī practices than previously assumed. Through analyses of these incantation sheets, this paper contributes toward filling the gap in our current understanding of the material practice pertaining to the Incantation of Wish-Fulfillment of medieval East Asia. Previously, all known traces of material dhāraṇīs from early Korea, with just a few exceptions, were related to the Sūtra of the Pure Light Incantation enshrined in the relic crypts of pagodas—a practice that has little connection to contemporaneous Chinese dhāraṇī practice. However, the newly discovered Incantation of Wish-Fulfillment sheets, whose date this paper infers to be between the eighth and ninth century, show that Unified Silla had a dhāraṇī practice closely linked to coeval Chinese practice. The Incantation of Wish-Fulfillment sheets from Silla show the modification and continuation of Chinese dhāraṇī practice. Unlike the Chinese amulet sheets of the Incantation of Wish-Fulfillment that were buried in tombs, the Silla amulet sheets were likely enshrined in one of the pagodas erected on Mount Nam in Silla's capital. At the same time, they were placed in the pagoda to wish for good afterlives of the soldiers who died at the battle, suggesting that they had a mortuary function similar to those buried in Chinese tombs. [ABSTRACT FROM AUTHOR]

In South Korea, Renewable Energy Sources (RES) have been increasing with the application of energy policies, such as Feed in Tariff (FIT) and the Renewable Portfolio Standard (RPS). However, a rapid increase in RES supply leads to an uncertain power supply due to the intermittent output of RES. A representative example is the curtailment of Wind Turbines (WT), which frequently occurs in Jeju Island, South Korea. The proportion of RES power on Jeju Island is 67%, and there are cases where WT is curtailed among the operable sections to maintain the balance of power supply and demand. This paper applies Power-to-Gas (P2G) technology to hydrogenate, store, and utilize unused power to solve this problem. In this paper, Aewol-eup in Jeju Island is selected as a target site for case study. An Integrated Energy System (IES) for various energy operations is designed to control RES output. This paper proposes the optimal facility configuration and finally drives the optimal design and operation solution of IES by analyzing the objective functions and focusing on the Independent Power Producer (IPP) perspective. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic air-to-ground (A2G) channel model selection method based on machine learning (ML) using digital surface model (DSM) terrain data. In order to verify whether a communication network for a new non-terrestrial user service such as Urban Air Mobility (UAM) satisfies the required performance, it is necessary to perform a simulation reflecting the characteristics of the corresponding terrain environments as accurately as possible. For this simulation, A2G channel models corresponding to various terrain environments and a method of automatically classifying the terrain type of the simulation area must be provided. Many A2G channel models based on actual measurement results exist, but the practical automatic topography classification method still needs to be developed. This paper proposes the first practical automatic topography classification method using a two-step neural network-based classifier utilizing various geographic feature data as input. Since there is no open topography dataset to evaluate the accuracy of the proposed method, we built a new dataset for five topography classes that reflect the characteristics of Korea's topography, which is also a contribution of our study. The simulation results using the new data set show that the proposed ML-based method could increase the selection accuracy compared to the technique for direct classification by humans or the existing cross-correlation-based classification method. Since the proposed method utilizes the DSM data, open to the public, it can easily reflect the different terrain characteristics of each country. Therefore, the proposed method can be effectively used in the realistic performance evaluation of new non-terrestrial communication networks utilizing vast airspace such as UAM or 6G mobile communications. [ABSTRACT FROM AUTHOR]

This work investigates the possibility of isolating cellulose nanofibers from pulps of tall goldenrod plant, which are invasive plants in Korea, by a convenient method, without strong acids or high-pressure homogenization, using electron beam irradiation (EBI). The obtained cellulose nanofibers were characterized by scanning electron microscopy (SEM), ultraviolet–visible (UV–vis) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA), and in terms of their mechanical properties. SEM showed that the initially isolated 10-μm-diameter cellulose fibers became more finely separated with increasing EBI dose, and that cellulose fibers treated with 300 kGy of EBI were separated into long cellulose nanofibers of around 160 nm in diameter. In addition, the paper samples prepared from more finely separated fibers generated by using higher doses of EBI had enhanced UV–vis transmittance. Via the XRD analysis, we observed that cellulose I in the EBI-treated cellulose fibers were gradually converted into a different type of cellulose similar to cellulose type II, as the EBI dose increased. Meanwhile, the TGA demonstrated that the finely separated cellulose fibers observed after administering the high EBI dose had lowered thermal stability due to the reduction of cellulose I but higher char yield. In addition, tensile strengths of paper samples increased with decreasing the diameters of their constituent fibers that result from the different EBI doses used in the preparation of the paper pulp. [ABSTRACT FROM AUTHOR]

In 2019, the National Archives of Korea (NAK) developed a blockchain recordkeeping platform to conduct R&D on recordkeeping approaches. This paper introduces two types of R&D studies that have been conducted thus far. The first is the use of blockchain transaction audit trail technology to ensure the authenticity of audiovisual archives, i.e., the application of blockchain to a new system. The second uses blockchain technology to verify whether the datasets of numerous information systems built by government agencies are managed without forgery or tampering, i.e., the application of blockchain to an existing system. Government work environments globally are rapidly shifting from paper records to digital. However, the traditional recordkeeping methodology has not adequately kept up with these digital changes. Despite the importance of responding to digital changes by incorporating innovative technologies such as blockchain in recordkeeping practices, it is not easy for most archives to invest funds in experiments on future technologies. Owing to the Korean government's policy of investing in digital transformation, NAK's blockchain recordkeeping platform has been developed, and several R&D tasks are underway. Hopefully, the findings of this study will be shared with archivists around the world who are focusing on the future of recordkeeping. [ABSTRACT FROM AUTHOR]

In accordance with South Korea's recent 2030 Carbon Neutral Plan, an 8GW offshore wind farm is planned for construction in the South-west Sea. Therefore, it is expected that large-scale wind turbines will be installed, and these turbines must operate stably, even when there are instantaneous voltage fluctuations in the power system. The grid code is described for the low-voltage-ride-through (LVRT) and high-voltage-ride-through (HVRT) functions, and a test facility that can perform both LVRT and HVRT tests is essential. In the case of LVRT/HVRT test facilities developed by the existing RLC (impedance component) method, it may be difficult to test large-scale wind turbines due to problems such as power quality, frequent failures and narrow short-circuit capacity ranges. Therefore, to solve such problems, this paper proposes an LVRT/HVRT test facility of the autotransformer type, which is capable of outputting the desired voltage range by changing the wiring method and tap position. Specifically, in order to implement the test facility of the autotransformer type, which is able to output the desired voltage range by changing the wiring method and tap according to the LVRT/HVRT test status, this paper presents an impedance determination algorithm (two-step layer impedance determination algorithm) of auto-transformer based on the fault-current analysis and operation strategy at a real LVRT/HVRT testing evaluation facility. [ABSTRACT FROM AUTHOR]

Franchising occurred in Vietnam during the implementation of government policy for economic reform and openness in the mid-1990s. The Vietnam-specific franchise law was passed in January 2006 to create a stable growth in the franchise sector in Vietnam. The legal framework in Vietnam generally follows international legal frameworks. However, the legal framework of franchising businesses is still uncertain. This paper explores Vietnam's franchising law and displays a comparison to the legal framework of Thai franchising law and the Republic of Korea franchising law. The paper concludes that while Vietnam's franchising law generally conforms to international standards, some legal issues should be addressed for further development of the Vietnamese franchising sector. [ABSTRACT FROM AUTHOR]

A number of accidents at sea are primarily caused by low visibility due to sea fog. Therefore, it is important to estimate sea fog intensity and visibility in real-time in the ocean environment. Visibility meters utilize optical sensors rather than visional information, so that the measured visibility data occasionally includes errors. Moreover, visibility meters have significant costs so that it is not viable to install them at various locations. Therefore, this paper proposes an algorithm called RDCP (Reduced Dark Channel Prior), which provides reliable estimation at a low cost by processing images captured from cameras, since they receive identical information as human eyes. For the estimation, the RDCP algorithm firstly acquires dark channels from an ocean image, then applies an optimized threshold value to the dark channels and crops out the sky region in the ocean image. For the estimation performance evaluation, 320 raw images captured from cameras at four different ports in Republic of Korea are used. Considering facilities in oceans, which are usually operated in power-limited environment, the processing time performance of PDCP is also examined. The experiments demonstrate that the RDCP algorithm provides reliable estimation performance in real-time. [ABSTRACT FROM AUTHOR]

Environmental, social, and governance (ESG) criteria, currently used as a set of standards by socially conscious investors to evaluate a company's operations before investing, are becoming an important global trend today. In particular, environmental and ecological crises are increasingly being seen as issues that will determine the sustainability of human civilization. Scholars of religion have been paying more attention to the issue as well. In fact, religion and environmentalism have emerged as sub-disciplines in, among others, religious ethics, religious studies, the sociology of religion, and theology. In view of this development, this paper aims to reexamine Minjung theology, literally meaning "the people's theology", which arose as a form of liberation theology in South Korea in the 1970s, from an ecological perspective, particularly focusing on the former's view on the relationship and interrelationship between the individual and the environment. The paper pays special attention to the work of Ahn Byung-Mu, a founding scholar of Minjung theology, shedding light on the connection between his concept of gong, literally meaning "publicness", and ecology, the characteristics of his ecological thoughts and their relevance to his view of god, and his views on bapsanggongdongche, literally meaning "the table community". [ABSTRACT FROM AUTHOR]

With apartment buildings representing a rapidly growing share of the residential market in South Korea, the effect of construction defects throughout the life cycle of construction projects, and particularly during the occupancy stage, has emerged as a significant social issue that may ultimately lead to an increase in defect disputes between new occupants and general contractors. An important step toward mitigating the likelihood of these defect disputes is to identify and address the factors that give rise to occupant dissatisfaction during the defect repair process. However, a reliable method by which to identify these factors has yet to be developed. In this respect, the main objective of the research presented in this paper is to develop a method for identifying occupant dissatisfaction factors in the construction defect repair stage. The developed method comprises the following procedures: (i) text pre-processing, which involves data cleaning, normalization, tokenization, morphological analysis, and removal of stopwords; (ii) term frequency–inverse document frequency for keyword extraction; and (iii) semantic network analysis to recognize relationships between words. The method was implemented using a dataset of 12,874 comments in Korean text format obtained from apartment building occupants. Based on the processing and analysis of this dataset, the occupant dissatisfaction factors were found to be: (i) inaccurate and inadequate repair work (represented by such keywords as "Repair", "Visit", and "Accuracy"); (ii) failure to keep promises (e.g., "Fulfillment", "Promise", and "Change"); and (iii) unprofessional conduct on the part of representatives in the repair service center (e.g., "Response", "Attitude", and "Receipt"). [ABSTRACT FROM AUTHOR]

Recently, the generalization of P2P (peer-to-peer) technology with enhanced security due to blockchain technology and the expansion of renewable energy-based distributed energy resources have led to blockchain technology being applied in power transactions, thus giving the potential to become a new platform for DC microgrid operation. Meanwhile, the voltage of a DC microgrid represents the balance of energy supply and demand and also serves as a stability index. The balance is represented as a steady state; the stability is represented during and after events. This paper examines the stability of the DC microgrid built on a university campus in Korea and, in particular, the blockchain technology-based power transactions performed in the DC microgrid. The test is based on the pre-planned transaction schedule applied in the DC microgrid. The transaction schedule has used day-ahead and real-time bidding data. Although many technologies are included in the project, this paper focuses on the voltage stability of the DC microgrid. In addition, the DC protection is applied and evaluated. To consider general DC protection, the DC breaker was simplified with several IGBTs, diodes, capacitors, and arrestors and was designed to interrupt the fault current within five milliseconds. The stability was evaluated using a PSCAD/EMTDCTM. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability to effectively learn unstable environmental variables and their complex interactions. However, NNs are limited in their practical industrial application in the energy sector because the optimization of the model structure or hyperparameters is a complex and time-consuming task. This paper proposes a two-stage NN optimization method for robust solar PV power forecasting. First, the solar PV power dataset is divided into training and test sets. In the training set, several NN models with different numbers of hidden layers are constructed, and Optuna is applied to select the optimal hyperparameter values for each model. Next, the optimized NN models for each layer are used to generate estimation and prediction values with fivefold cross-validation on the training and test sets, respectively. Finally, a random forest is used to learn the estimation values, and the prediction values from the test set are used as input to predict the final solar PV power. As a result of experiments in the Incheon area, the proposed method is not only easy to model but also outperforms several forecasting models. As a case in point, with the New-Incheon Sonae dataset—one of three from various Incheon locations—the proposed method achieved an average mean absolute error (MAE) of 149.53 kW and root mean squared error (RMSE) of 202.00 kW. These figures significantly outperform the benchmarks of attention mechanism-based deep learning models, with average scores of 169.87 kW for MAE and 232.55 kW for RMSE, signaling an advance that is expected to make a significant contribution to South Korea's energy industry. [ABSTRACT FROM AUTHOR]

The use of renewable energy sources to achieve carbon neutrality is increasing. However, the uncertainty and volatility of renewable resources are causing problems in power systems. Flexible and low-carbon resources such as Energy Storage Systems (ESSs) are essential for solving the problems of power systems and achieving greenhouse gas reduction goals. However, ESSs are not being installed because of Korea's fuel-based electricity market. To address this issue, this paper presents a method for determining the optimal investment timing of Battery Energy Storage Systems (BESSs) using the Least Squares Monte Carlo (LSMC) method. A case study is conducted considering the System Marginal Price (SMP) and Capacity Payment (CP), which are electricity rates in Korea. Revenue is calculated through the arbitrage of a 10 MW/40 MWh lithium-ion BESS, and linear programming optimization is performed for ESS scheduling to maximize revenue. The ESS revenue with uncertainty is modeled as a stochastic process using Geometric Brownian Motion (GBM), and the optimal time to invest in an ESS is determined using an LSMC simulation considering investment costs. The proposed method can be used as a decision-making tool for ESS investors to provide information on facility investments in arbitrage situations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a tremendous influence on many aspects of life in Korea. Some people have had to relocate their workplaces from factories or offices to their homes in order to stop the spread of the virus. This paper examines the effects of home production on the Korean macroeconomy during the COVID-19 pandemic. Then, the impulse response function is used to perform an empirical analysis. The results show that total output, market goods consumption, investment, capital, and market work hours all decline as a consequence of a home productivity shock, while home goods consumption, wages, transfer payments, and home work hours all increase. Moreover, using fiscal policies such as lowering the capital tax rate and increasing the fiscal deficit, the effect of the COVID-19 pandemic on the Korean macroeconomy can be improved. Robustness tests are carried out in light of the uneven economic development and different COVID-19 pandemic scenarios inside and outside the Seoul circle. The conclusions of this paper are accurate and reliable, as shown by the results of the robustness test. [ABSTRACT FROM AUTHOR]

Recently, the craze of K-POP contents is promoting the development of Korea's cultural and artistic industries. In particular, with the development of various K-POP contents, including dance, as well as the popularity of K-POP online due to the non-face-to-face social phenomenon of the Coronavirus Disease 2019 (COVID-19) era, interest in Korean dance and song has increased. Research on dance Artificial Intelligent (AI), such as artificial intelligence in a virtual environment, deepfake AI that transforms dancers into other people, and creative choreography AI that creates new dances by combining dance and music, is being actively conducted. Recently, the dance creative craze that creates new choreography is in the spotlight. Creative choreography AI technology requires the motions of various dancers to prepare a dance cover. This process causes problems, such as expensive input source datasets and the cost of switching to the target source to be used in the model. There is a problem in that different motions between various dance genres must be considered when converting. To solve this problem, it is necessary to promote creative choreography systems in a new direction while saving costs by enabling creative choreography without the use of expensive motion capture devices and minimizing the manpower of dancers according to consideration of various genres. This paper proposes a system in a virtual environment for automatically generating continuous K-POP creative choreography by deriving postures and gestures based on bidirectional long-short term memory (Bi-LSTM). K-POP dance videos and dance videos are collected in advance as input. Considering a dance video for defining a posture, users who want a choreography, a 3D dance character in the source movie, a new choreography is performed with Bi-LSTM and applied. For learning, considering creativity and popularity at the same time, the next motion is evaluated and selected with probability. If the proposed method is used, the effort for dataset collection can be reduced, and it is possible to provide an intensive AI research environment that generates creative choreography from various existing online dance videos. [ABSTRACT FROM AUTHOR]

This study reviewed the papers that studied the effect of simulation nursing education in the nursing field and examined the trend of simulation nursing education for nursing college students in Korea. Background: Simulation-based education started receiving attention as a pedagogical method in order to provide medical service of high quality in an ethical and safe environment. This has been of great importance during the coronavirus disease 2019 global pandemic. This literature review was conducted to suggest a direction for simulation-based nursing education in Korea. Methods: For literature searches, the authors used the following search terms in the Web of Science, CINAHL, Scopus, PubMed—'utilization', 'simulation,' 'nursing student', 'nursing education'. A final search was conducted on 6 January 2021. The materials for this study were collected through literature searches according to the PRISMA guidelines. Results: 25 papers were selected as the final literature for analysis. The study was conducted for 48 percent of senior students in nursing college students in Korea (N = 12). High fidelity (HF) as the simulation type was 44 percent (N = 11). The simulation education subjects were composed of 52 percent adult health nursing (N = 13). According to educational goals described by Benzamine Bloom (1956), 90% in the psychomotor domain is considered a positive learning achievement. Conclusions: Effectiveness in the psychomotor domain through simulation-based training is correlated with expert nursing. It is essential to develop a systematic debriefing model and methods to evaluate performance and learning in the short- and long-term to expand the effectiveness of simulation-based education in nursing. [ABSTRACT FROM AUTHOR]

This paper presents economic value estimation of improved biogas utilization systems of public wastewater treatment plants in Republic of Korea. Since a large amount of biogas produced at digestion facilities is being wasted as a by-product, the biogas energy utilization system needs to be enhanced. In this paper, three operating options able to utilize the produced biogas are proposed, and then their monetary benefits are estimated by means of net present value calculation. Real operational data from the public wastewater treatment plant located in Sejong city, Republic of Korea, is used to reflect a variation of the rated daily gas production and its concentration according to the weather and seasons, resulting in calculating more reliable results. Additionally, to minimize the estimation errors due to uncertainties of the gas concentration and the gas selling price, a Monte Carlo simulation considering the variation of critical input data is carried out. As a result, the proposed approach can lead to better decisions in selecting the suitable biogas utilization system by forecasting the ranges of possible economic values. [ABSTRACT FROM AUTHOR]

To supply stable and high-quality power according to the advancement of industrial growth, electric power companies have performed maintenance of power facilities using various methods. In the case of domestic power distribution facilities, there are limitations in performing diagnostic management on all facilities owing to the large number of facilities; therefore, old facilities are managed using the health index assessment method. The health index assessment comprises only facility operation data and external environmental data and is managed only for four types of distribution facilities including overhead/underground transformers and switchgears. In the case of high voltage overhead lines, there are a large number of wires such as transformers and switchgears connected to the lines, and the ripple effect of power outages is large. However, in Korea, there is no overhead line health index standard. In overseas cases, a health index for overhead lines was developed, but only the material characteristics and surrounding environment of the overhead lines were considered and economic feasibility was not considered. Therefore, in this paper, we developed a health index evaluation methodology for ultra-high voltage overhead lines that considers economic feasibility. In this paper, unlike the existing health index evaluation method that uses only operational data and external environmental data to determine facility performance evaluation and aging replacement standards, we developed an economic health index evaluation methodology that additionally considers failure probability and risk costs. Using the health index assessment methodology developed in this paper, it is possible to expect a reduction in facility operating costs and investment costs from the perspective of the electric power companies through the replacement of old extra-high voltage overhead cables. In addition, from the perspective of consumers, it is expected to increase power reliability and reduce the ripple effect of failure by preferentially replacing equipment with a high probability of failure. [ABSTRACT FROM AUTHOR]

This paper aims to compare the sustainability assessment methods of DGNB and G-SEED, the two domestic green building rating systems, focusing on indoor environmental evaluation and reviewing recent laws, certifications systems, and guidelines from Germany and South Korea. According to existing studies, Korea's green building certification has similar evaluation items, environmental (62.6%), social aspects (25.4%), and economic aspects (3.0%) compared with DGNB. Continuing research comparing DGNB and G-SEED, this paper will present an insight into the DGNB and G-SEED green building rating systems' aspects and motives; furthermore, we intend to equate the sustainability assessment methods of the DGNB and G-SEED certification systems, in order to improve certification systems by comparing and finding solutions to differences, thereby making international systems more adaptable to each country. This analysis will unify property categorization systems and will promote green building and sustainable development globally. Findings: Regarding similarities and differences, the certification criteria for air, thermal, visual/light, and acoustic quality are based on domestic laws in both countries. DGNB and G-SEED are similar in terms of the requirements but differ in methods and domestic laws. Some findings regarding differences are that DGNB requires low VOC-emitting products, while G-SEED focuses on natural ventilation. Thermal comfort is measured through design-phase simulation in DGNB, while G-SEED lighting is evaluated on the basis of saving light energy. Finally, in G-SEED, acoustic quality is measured via indoor soundproofing performance, while DGNB takes room size into account. [ABSTRACT FROM AUTHOR]

An SMR (small modular reactor) is expected to be able to operate flexibly in conjunction with a high renewable energy penetration grid as a result of improved safety and easy power control compared to large nuclear power plants. SMRs, such as South Korea's System-integrated Modular Advanced ReacTor (SMART), are designed to use canned motors (CMs) for their reactor coolant pumps (RCPs) to enhance their safety. CMs passively enhance the reactor's safety by preventing the leakage of radioactive reactor coolant. However, with motor sizes designed to be as small as possible, and the increased air gap of CMs between the stator and rotor, the starting torque may be insufficiently low compared to that of similar-sized induction motors (IMs). Thus, CMs may require variable frequency drives (VFDs) to start. This paper compares the torque characteristics of CMs with those of IMs for SMART's RCPs. ETAP is then used to perform a motor-starting analysis for CMs activated with and without VFDs. The results are presented and analyzed to find out if VFDs can deal with the CM starting torque issue. [ABSTRACT FROM AUTHOR]

Every year, diverse types of safety accidents cause major damage to human life and property. In particular, failure to suppress safety accidents caused by fires during the early stages can lead to large-scale accidents, which in turn can cause more serious damage than other types of accident. Therefore, this paper presents an analysis of the prevailing research trends and future directions for research on preventing safety accidents due to fire. Since fire outbreaks can occur in many types of places, the study was conducted by selecting the places and causes involved in frequent fires, using fire data from Korea. As half of these fires were found to occur in buildings, this paper presents an analysis of the causes of building fires, and then focuses on three themes: fire prevention based on fire and gas detection; fire prevention in electrical appliances; and fire prevention for next-generation electricity. In the gas detection of the first theme, the gas referred to does not denote a specific gas, but rather to the gas used in each place. After an analysis of research trends for each issue related to fire prevention, future research directions are suggested on the basis of the findings. It is necessary to evaluate the risk, select a detection system, and improve its reliability in order to thoroughly prevent fires in the future. In addition, an active emergency response system should be developed by operating a fire prevention control system, and safety training should be developed after classifying the targets of the training targets appropriately. [ABSTRACT FROM AUTHOR]

With the increase in both rainfall and intensity due to climate change, the risk of debris flows is also increasing. In Korea, the increasing damage caused by debris flows has become a social issue, and research on debris-flow response is becoming increasingly important. Understanding the rainfall that induces debris flows is crucial for debris-flow response, and methods such as the I-D method have been used to evaluate and predict the risk of debris flows. However, previous studies on debris flow-induced rainfall analysis have been limited by the subjective decision of the researcher to select the impact meteorological stations, which greatly affects reliability. In this paper, in order to establish an objective standard, various maximum allowable distances between debris-flow disaster areas and meteorological stations were adjusted to 1, 3, 5, 7, 9, 11, 13, and 15 km using the CTRL-T automatic calculation algorithm, and the optimal maximum allowable distance suitable for Korean terrain was derived through parameter sensitivity analysis. Based on this, we developed a nomogram for sediment disaster risk prediction and warning in Gangwon-do, and applied it to past disaster cases, and found that, although the prediction time for each stage varies depending on the maximum allowable distance, on average, it is possible to predict the risk of sediment flows 4 to 5 h in advance. It is believed that the results of this study can be used to reduce sediment flow damage in advance. [ABSTRACT FROM AUTHOR]

This paper investigates how historical inter-firm syndication networks influence venture capitalists' (VCs) propensity to invest in startups pursuing novel, uncertain technologies, with a focus on artificial intelligence (AI). We theorize that VCs' positional attributes within cumulative syndication networks determine their access to external expertise and intelligence that aid AI investment decisions amidst informational opacity. Specifically, reachability to prior AI investors provides referrals and insights transmitted across short network paths to reduce ambiguity. Additionally, VC brokerage between disconnected industry clusters furnishes expansive, non-redundant information that is pivotal for discovering and assessing AI opportunities. Through hypotheses grounded in social network theory, we posit network-based mechanisms that equip VCs to navigate uncertainty when engaging with ambiguous innovations like AI. We test our framework, utilizing comprehensive historical records of global venture capital investments. Analyzing the location information of VC firms in this database, we uncovered a history of 14,751 investments made by Korean and Japanese firms. Using these data, we assembled an imbalanced panel dataset from 1984 to 2022 spanning 230 Korean and 413 Japanese VCs, with 4508 firm-year observations. Negative binomial regression analysis of this dataset reveals how historical relational patterns among venture capital firms foster readiness to evaluate unfamiliar innovations. [ABSTRACT FROM AUTHOR]

In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage. [ABSTRACT FROM AUTHOR]

(1) Background: Among Korean research papers there have been studies on the correlation between tuberculosis-hypertension and diabetes and the correlation between dementia-hypertension and diabetes, but there were no analysis data specifically on tuberculosis and dementia. (2) Methods: A total of 2992 tuberculosis patients in the Gyeongbuk region were analyzed through a final analysis of integrated disease and health management system data collected from 2021 to 2022. In this selection, patients with tuberculosis under 50 years of age and 368 people diagnosed with tuberculosis were excluded. (3) Results: From 2021 to 2022, among the 2992 tuberculosis patients in Gyeongsangbuk-do aged 50 or older, 2722 (91.0%) belonged to the general tuberculosis patient group, while 270 (9.0%) belonged to the dementia–tuberculosis patient group. The average age in the dementia–tuberculosis group was 81.4 years, significantly higher than the general group's average of 75.7 years. Within the dementia–tuberculosis patient group, 235 patients (87.0%) had underlying medical conditions in addition to dementia and tuberculosis. The tuberculosis treatment cure rate was 56.3% (1477 patients) in the general group and 38.9% (105 patients) in the dementia–tuberculosis patient group. (4) Conclusions: The cure rate was notably higher in the general group. Similarly, the mortality rate (deaths due to tuberculosis) was significantly higher in the dementia–tuberculosis patient group (7.0%, 19 patients) compared to the normal group (3.0%, 81 patients). The mortality rate in the dementia group was more than twice that of the general group. [ABSTRACT FROM AUTHOR]

Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth services to connected clients. In this paper, we present highly optimized parallel implementations of Lightweight Secure Hash (LSH), a hash algorithm developed in Korea, on server sides. To optimize LSH performance, we leverage two parallel architectures: AVX-512 on high-end CPUs and NVIDIA GPUs. In essence, we introduce a word-level parallel processing design suitable for AVX-512 instruction sets and a data parallel processing design appropriate for the NVIDIA CUDA platform. In the former approach, we parallelize the core functions of LSH using AVX-512 registers and instructions. As a result, our first implementation achieves a performance improvement of up to 50.37% compared to the latest LSH AVX-2 implementation. In the latter approach, we optimize the core operation of LSH with CUDA PTX assembly and apply a coalesced memory access pattern. Furthermore, we determine the optimal number of blocks/threads configuration and CUDA streams for RTX 2080Ti and RTX 3090. Consequently, in the RTX 3090 architecture, our optimized CUDA implementation achieves about a 180.62% performance improvement compared with the initially ported LSH implementation to the CUDA platform. As far as we know, this is the first work on optimizing LSH with AVX-512 and NVIDIA GPU. The proposed implementation methodologies can be used alone or together in a server environment to achieve the maximum throughput of LSH computation. [ABSTRACT FROM AUTHOR]

This study investigates the impact of digital textbooks, which play an important role in post-COVID-19 digitalized education, on the development of elementary school students' affective and social competencies. The study quantitatively analyzed three years of student panel data collected from 1,418 students in the third and fourth grades of a digital textbook pilot elementary school in South Korea. This study examined differences in the development of affective and social competencies between the treatment group (n = 708), who used digital textbooks for three years, and the control group (n = 710), who used digital textbooks for two years and returned to paper textbooks. Results showed that the affective competencies of self-efficacy, learning motivation, and learning attitudes were higher in the treatment group than in the control group, and the social competencies of communication, collaboration, and sociability were also higher. Implications for digital education research and related policies are provided, and limitations and suggestions for future research are discussed. [ABSTRACT FROM AUTHOR]

This paper proposes a method for forecasting surface solar irradiance (SSI), the most critical factor in solar photovoltaic (PV) power generation. The proposed method uses 16-channel data obtained by the GEO-KOMPSAT-2A (GK2A) satellite of South Korea as the main data for SSI forecasting. To determine feature variables related to SSI from the 16-channel data, the differences and ratios between the channels were utilized. Additionally, to consider the fundamental characteristics of SSI originating from the sun, solar geometry parameters, such as solar declination (SD), solar elevation angle (SEA), and extraterrestrial solar radiation (ESR), were used. Deep learning-based feature selection (Deep-FS) was employed to select appropriate feature variables that affect SSI from various feature variables extracted from the 16-channel data. Lastly, spatio-temporal deep learning models, such as convolutional neural network–long short-term memory (CNN-LSTM) and CNN–gated recurrent unit (CNN-GRU), which can simultaneously reflect temporal and spatial characteristics, were used to forecast SSI. Experiments conducted to verify the proposed method against conventional methods confirmed that the proposed method delivers superior SSI forecasting performance. [ABSTRACT FROM AUTHOR]

Recognizing how human activities affect hydrological systems is vital for the sustainable preservation and effective management of water resources in the watershed. Hence, this paper focuses on the hydrological response to land use and land cover (LULC) change scenarios in the Anyang watershed, South Korea. We obtained LULC data maps for the years 2000, 2013, and 2022 from the local government, revealing significant changes over the years. Agricultural lands experienced a 6.2% increase from 2000 to 2022, and pastureland expanded by 8.67% over two decades. The SWAT model was utilized to assess the impact of LULC on the hydrological components of the study watershed. Model calibration and validation for each LULC change were carried out using the SWAT-CUP program, considering the recorded streamflow information of the region. An excellent agreement was reached between the simulated and measured streamflow in both the calibration and validation stages under various LULC conditions. The Nash–Sutcliffe model efficiency (NSE), the objective function, demonstrated values of 0.9, 0.89, and 0.89 during the calibration for 2000, 2013, and 2022, respectively, in the LULC scenario, while for the validation, we obtained values of 0.82, 0.78, and 0.80 for 2000, 2013, and 2022, respectively. Our findings indicate that the surface runoff rise contributed much to the water yield increase over the two decades compared to the other components in terms of the water yield, while the contribution of evapotranspiration (ET) to the watershed hydrological cycle declined by 1.66% from 2000 to 2022. The southeastern sub-basin part showed a high groundwater recharge distribution due to agricultural land, rice area, and forest area changes. [ABSTRACT FROM AUTHOR]

This paper proposes a practical framework for developing a net-zero electricity mix scenario (NEMS), which considers detailed conditions for supply of each energy. NEMS means a path scenario for power generation amount by year of each generation resource required to achieve carbon neutrality in 2050. NEMS framework refers to a methodological framework that contains procedures and requirements to continuously update the NEMS by comprehensively reflecting policy changes. For evaluation of NEMS, indicators such as a system inertia resource ratio (SIRR) and a fuel conversion rate (FCR) are proposed. The proposed framework and indicators are applied for the 2050 NEMS in Korea's electricity sector. The SIRR, indicating the ratio of inertial resources to total resources, projects values of 49% and 15% for the years 2030 and 2050, respectively. Furthermore, the FCR, reflecting the ratio of fuel conversion for resources undergoing this process, predicts that all targeted resources will have completed conversion by the year 2043. [ABSTRACT FROM AUTHOR]

As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module's determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation. [ABSTRACT FROM AUTHOR]

Recently, the demand for citizen-sensible service solutions such as traffic, crime prevention, and disasters in smart cities is increasing. In order to provide technology-based smart city services, local government control centers could be utilized. Accordingly, this paper presented a method for selecting a control center-based dynamic object information utilization service model through in-depth interviews with 26 related local government control center operation personnel. A comparative analysis according to the size of the local government to which the evaluator belongs was also performed. As a methodology, Fuzzy AHP was used, which can support rational decision-making by mathematically expressing ambiguous phenomena such as subjective and uncertain judgments. The summary of the research results is as follows. Services related to recent incidents in South Korea (e.g., school zone traffic accidents and lowland inundation) were identified as very important. These social issues are significant factors in policy decisions. In comparing the results for each urban scale, the importance of pedestrian safety services on backside roads and main road traffic services was found to be important in the metropolitan area and regional, medium, and small cities, respectively. This was attributed to metropolitan cities with high population density, and medium and small cities experiencing alienated traffic information. In metropolitan areas, new services are highly important owing to the demand for a more scientific control service and future mobility based on a sound control infrastructure. In medium and small cities, facility management services were assessed relatively highly owing to the poor conditions of regional cities with a lack of supervising personnel in the field and a lack of surveillance system infrastructure. This paper was able to confirm the difference in service preference by city size, and it is necessary to select the optimal service model considering these results. [ABSTRACT FROM AUTHOR]

This paper tackles the dominant views of Augustine's notion of 'love' in South Korea which have been described as the puritan pathos of distance from civic commonality. A complete guide to the reception and transmission of Augustine's philosophy in South Korea would be almost unmanageable. However, the essential key to understanding the place of Augustine's philosophy in South Korea can be found in the interpretations of Augustine's notion of love. In all its complexity in these interpretations, the legacy of Augustine in South Korea turns out to consist exclusively of anti-political or non-communal eschatological longings for salvation. In a similar vein, the dominant views of Augustine's notion of love have been convoluted with their emphasis on the superiority of love of God over love of neighbor. Based on these observations, this paper suggests Ahn Changho's Confucian reappraisal of Christian love as an alternative to the dominant views of Augustine's notion of love in South Korea, by investigating his view of filial piety as mutual love with respect to the possible implications of Augustine's notion of love for shaping or consolidating civic friendship beyond brotherly commonality in Christianity. [ABSTRACT FROM AUTHOR]

This paper demonstrates the use of software-defined networking (SDN) orchestration to integrate regionally separated networks in which different network parts use incompatible key management systems (KMSs) managed by different SDN controllers to ensure end-to-end QKD service provisioning to deliver the QKD keys between geographically different QKD networks. The study focuses on scenarios in which different parts of the network are managed separately by different SDN controllers, requiring an SDN orchestrator to coordinate and manage these controllers. In practical network deployments, operators often utilize multiple vendors for their network equipment. This practice also enables the expansion of the QKD network's coverage by interconnecting various QKD networks equipped with devices from different vendors. However, as coordinating different parts of the QKD network is a complex task, this paper proposes the implementation of an SDN orchestrator which acts as a central entity to manage multiple SDN controllers, ensuring end-to-end QKD service provisioning to address this challenge. For instance, when there are multiple border nodes to interconnect different networks, the SDN orchestrator calculates the path in advance for the end-to-end delivery of keys between initiating and target applications belonging to different networks. This path selection requires the SDN orchestrator to gather information from each SDN controller managing the respective parts of the QKD network. This work shows the practical implementation of SDN orchestration for interoperable KMS in commercial QKD networks in South Korea. By employing an SDN orchestrator, it becomes possible to coordinate multiple SDN controllers and ensure the efficient and secure delivery of QKD keys between different QKD networks with varying vendor equipment. [ABSTRACT FROM AUTHOR]

This article aims to understand the global and longitudinal trends of research on Panax ginseng. We used bibliometrics to analyze 3974 papers collected from the Web of ScienceTM Core Collection database during 1959--2016. The number of publications showed a steady growth before 2000 and exponentially increased in stage III (2000--2016, about 86% of the papers were published). Research on P. ginseng was conducted in 64 countries, mainly in Asia; in particular, 41% and 28% of the publications were from South Korea and China, respectively. The institutions from South Korea and China had high publication output and close cooperation and provided the majority of financial support. All top 10 authors and four of the top 20 journals in terms of number of publications originated from South Korea. The leading research subjects were pharmacology (39%), plant science (26%), and integrative complementary medicine (19%). The hotspot of P. ginseng research transformed from basic science to application, and multidisciplinary sciences will play a substantial role in the future. This study provides a comprehensive analysis to elucidate the global distribution, collaboration patterns, and research trends in the P. ginseng domain. [ABSTRACT FROM AUTHOR]

The management of product quality is a crucial process in factory manufacturing. However, this approach still has some limitations, e.g., depending on the expertise of the engineer in evaluating products and being time consuming. Various approaches using deep learning in automatic defect detection and classification during production have been introduced to overcome these limitations. In this paper, we study applying different deep learning approaches and computer vision methods to detect scratches on the surface of microfasteners used in rechargeable batteries. Furthermore, we introduce an architecture with statistical quality control (SQC) to continuously improve the efficiency and accuracy of the product quality. The proposed architecture takes advantage of the capability of deep learning approaches, computer vision techniques, and SQC to automate the defect detection process and quality improvement. The proposed approach was evaluated using a real dataset comprising 1150 microfastener surface images obtained from a factory in Korea. In the study, we compared the direct and indirect prediction methods for predicting the scratches on the surface of the microfasteners and achieved the best accuracy of 0.91 with the indirect prediction approach. Notably, the indirect prediction method was more efficient than the traditional one. Furthermore, using control charts in SQC to analyze predicted defects in the production process helped operators understand the efficiency of the production line and make appropriate decisions in the manufacturing process, hence improving product quality management. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power can be a reasonable alternative as a carbon-free power source in a global warming environment. However, when many PV generators are interconnected in power systems, inaccurate forecasting of PV generation leads to unstable power system operation. In order to help system operators maintain a reliable power balance, even when renewable capacity increases excessively, an incentive program has been introduced in Korea. The program is expected to improve the self-forecasting accuracy of distributed generators and enhance the reliability of power system operation by using the predicted output for day-ahead power system planning. In order to maximize the economic benefit of the incentive program, the PV site should offer a strategic schedule. This paper proposes a PV generation scheduling method that considers incentives for accurate renewable energy forecasting. The proposed method adjusts the predicted PV generation to the optimal generation schedule by considering the characteristics of PV energy deviation, energy storage system (ESS) operation, and PV curtailment. It then maximizes incentives by mitigating energy deviations using ESS and PV curtailment in real-time conditions. The PV scheduling problem is formulated as a stochastic mixed-integer linear programming (MILP) problem, considering energy deviation and daily revenue under expected PV operation scenarios. The numerical simulation results are presented to demonstrate the economic impact of the proposed method. The proposed method contributes to mitigating daily energy deviations and enhancing daily revenue. [ABSTRACT FROM AUTHOR]

Rain attenuation based on the drop size distribution (DSD) with different rainfall rates (R) at low THz frequencies is investigated in this paper. The rain attenuation is calculated using the DSD measured for one year and the extinction cross-section (ECS) by the Mie scattering theory. Moreover, the obtained specific rain attenuation is verified by the empirical model using the measurement system consisting of a transmitter, a receiver, and weather measurement units. We measured the received power against the uniform transmitted power at 240, 270, and 300 GHz on the rooftop of the National Radio Research Agency (RRA) in Korea during the same period as the DSD measurement period. After curve fitting by regression analysis, we compared both rain attenuations obtained in two methods with the recommendation International Telecommunication Union Radiocommunication Sector (ITU-R) P.838-3. The root mean square errors (RMSEs) of the DSD model are 2.8977, 2.8646, and 2.8331 at 240, 270, and 300 GHz, respectively. The calculated result using the Mie scattering and the measured DSD methods shows the best fit to the data of the ITU-R recommendation for a rainfall rate of up to 5 mm/h. On the other hand, the empirical results using the T/Rx antenna system are slightly higher compared to the data of the ITU-R recommendation. As the rainfall rate increases, the difference between our results and ITU-R recommendation increases. This study will be useful for predicting rain attenuation for terrestrial wireless links operating at low THz frequencies. [ABSTRACT FROM AUTHOR]

Coastal erosion is caused by various factors, such as harbor development along coastal areas and climate change. Erosion has been accelerated recently due to sea level rises, increased occurrence of swells, and higher-power storm waves. Proper understanding of the complex coastal erosion process is vital to prepare measures when they are needed. Monitoring systems have been widely established around a high portion of the Korean coastline, supported by several levels of governments, but valid analysis of the collected data and the following preparation of measures have not been highly effective yet. In this paper, we use a drone to obtain bed material images, and an analysis system to predict the representative grain size of beach sands from the images based on artificial intelligence (AI) analysis. The predicted grain sizes are verified via field samplings. Field bed material samples for the particle size analysis are collected during two seasons, while a drone takes photo images and the exact positions are simultaneously measured at Jangsa beach, Republic of Korea. The learning and testing results of the AI technology are considered satisfactory. Finally, they are used to diagnose the overall stability of Jangsa beach. A beach diagnostic grade is proposed here, which reflects the topography of a beach and the distribution of sediments on the beach. The developed beach diagnostic grade could be used as an indicator of any beach stability on the east coast of the Republic of Korea. When the diagnostic grade changes rapidly at a beach, it is required to undergo thorough investigation to understand the reason and foresee the future of the beach conditions, if we want the beach to function as well as before. [ABSTRACT FROM AUTHOR]

As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model's performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption. [ABSTRACT FROM AUTHOR]