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• Development of a prediction model for airborne chloride depositions with surrounding environmental information. • Bayesian regularization-based artificial neural network considering the high data variance. • Average 35% performance improvement with Bayesian regularization compared to conventional ANN. This paper suggests an artificial neural network model combining Bayesian regularization (BRANN) to estimate concentrations of airborne chlorides, which would be useful in the design of reinforced concrete structures and for estimating environmental effects on long-term structural performance. Meteorological and topographical data were collected, and airborne chlorides were measured at 19 areas all over Korea. Data were classified for the three major coasts, and then prepared for training. To investigate the relationship between each input feature and output and then construct a model for estimating airborne chlorides with only meteorological and topographical information, both the standard artificial NN (ANN) and BRANN models were examined. The 3 or 4-layered BRANN model with 64 nodes at each layer showed the best and most robust performance. This BRANN model successfully predicted airborne chloride content with reasonable values of MSE and R-square although the input data and the airborne chlorides had quite low correlation. The results showed that the BRANN was better able to solve this problem than ANN. It is expected to be broadly applicable for predicting the penetration of chlorides into concrete and even the evaluation of concrete durability. [ABSTRACT FROM AUTHOR]

• A data-driven optimization algorithm is developed for reliable PtX design. • Constraint of grid utilization prevents overuse of grid for sustainable production. • The trade-off between variations and means of performance indicators is revealed. • Results offer insights into design decision-making under renewable uncertainty. Designing a reliable power-to-X (PtX) process that uses renewable energy and grid electricity as backup power is challenging due to the uncertain nature of renewables and potential limits on grid usage. To address this challenge, this paper proposes a data-driven, reliability-based optimization approach for designing a grid-assisted PtX process and understanding how the uncertainty of time-series renewable profile affects its design and optimal sizing. This approach uses generative adversarial networks (GANs) to capture the complex patterns of renewables in temporal and spatial dimensions, rather than simply treating uncertainty as a probabilistic range as in conventional methods. The optimization considers both the mean and variance of production cost as objectives, while also taking into account the level of grid energy penetration as a probabilistic constraint through the reliability analysis. A power allocation and performance evaluation model is developed and used in conjunction with the optimization to assess uncertainty propagation through the PtX process and evaluate the impact of renewable uncertainty on process performance. A case study involving the production of methanol on Jeju Island in South Korea demonstrates how the mean and variance of production cost and other performance indicators can be balanced in the reliable design of PtX processes under renewable energy uncertainty, providing insights for decision-making in such situations. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicle (FCEV) is a disruptive technology for traditional vehicles. High cost and inadequate infrastructure are the primary shortcomings of adopting FCEV, which prevents the commercialization of FCEV technology. It is an exciting issue whether a new wave of developing FCEV is present. The paper explores FCEV technology sources globally by combing multiple patent information. Our study finds the development of FCEV technology has been reviving slowly since 2014. The U.S., Japan, Germany, China, and South Korea are the core sources of FCEV technology, but FCEV technology sources are being reconstructed. Moreover, dominant incumbents, including large automobile manufacturers and component suppliers, are essential technology sources of FCEV development. Our research enriches the literature on the wave of developing FCEV technology and helps policy-makers or managers consider whether they should develop FCEV technology in the face of FCEV development reviving. • A method of combing multiple patent information to detect technology sources. • The development of Fuel cell electric vehicles has been reviving slowly since 2014. • The U.S., Japan, Germany, China, and South Korea are the core technology sources. • Large car manufacturers and component suppliers are the essential technology sources. [ABSTRACT FROM AUTHOR]

Since November 2021, gradual changes in non-pharmaceutical interventions (NPI) had begun in consideration of the socio-economic cost and the high rate of COVID-19 vaccination. As an effort to monitor the COVID-19 situation and the effect of NPIs, regular risk assessment for COVID-19 has been conducted based on the input from the external advisory committee. This paper examines the correlation between the risk assessment results and other indicators, such as the incidence of COVID-19, public perception, community mobility, and the government policy from November 2021 to May 2022. For weekly regular risk assessment of COVID-19, the data were collected from the National Infectious Disease Surveillance System website, which included incidence, mortality, and % of severe cases. Other indicators were from regular surveys on perceived risk among the public, community mobility from the Google website, and the government's response policy from The Oxford Stringency Index. The level of risk was classified into five levels from very low to very high. The level of COVID-19 risk and its correlation with the COVID-19 incidence, risk perception, community mobility, and government policy were examined. The result of the correlation analysis showed the highest positive correlation between the risk level and risk perception (r=0.86, CI 0.72-0.94). This indicates that the actual risk level and the risk perceived by the public are similar. Among the incidence-related indicators, the number of new severe cases of COVID-19 per week showed the highest correlation with the risk level (r=0.62, CI 0.33-0.80). The high correlation between the weekly severe cases and the overall COVID-19 risk level suggests that Korea's COVID-19 policy priority was mainly at protecting the high-risk population [ABSTRACT FROM AUTHOR]

Event-based surveillance (EBS) and rapid risk assessment (RRA) are essential tools for the early warning system against outbreaks, especially in the initial stages of an incident of potential public health threat. Since the Middle East respiratory syndrome outbreak in 2015, Korea Disease Control and Prevention Agency (KDCA) established EBS and RRA systems. This paper described KDCA's implementation of EBS and RRA to support the decision-making process for acute public health events. The process of EBS and RRA systems for potential public health threats occurring outside of Korea is described and potential barriers and challenges are discussed. EBS and RRA systems have been constantly utilized in Korea. One of the recent examples is the EBS and RRA activities for acute hepatitis of unknown etiology in children and the global monkeypox outbreak which provided the basis to establish timely surveillance and response systems. EBS tends to rely heavily on access to timely information. The quality of information needs to be ensured through a verification process by trained personnel as the quality of information determines the credibility of EBS results. Efforts to strengthen the information coverage and enhance the quality of information are key elements of EBS and RRA. Standardized manuals and systematic implementation of the EBS and RRA enable a more accurate evaluation of the risk. The process of RRA also needs flexibility in the way the risk is estimated. Public health threats are more likely to be imported than before as exchanges among countries grow frequent due to the eased border control measures for COVID-19 in recent days. The EBS and RRA systems are essential tools for all countries to ensure a timely response. Efforts to improve access to timely information and to ensure the quality of information should be continuously made to improve EBS and RRA systems. [ABSTRACT FROM AUTHOR]

• The Open Phase Condition (OPC) is defined as an open circuit of one or two of the three phases of any power circuit needed for the normal operation of electrical systems. • The OPC of the nuclear power plant (NPP) may cause station blackout (SBO) and pose a great risk to the integrity of the reactor core. • It was confirmed that there was a valid difference in OPC detection between the types of Korea's standard NPPs. • The goal of this study is to secure the integrity of the reactor core by detecting the OPC of the NPP's power system early. Open Phase Condition (OPC) is a failure in which one (or two) of the three-phases of the power system is maintained open, and the OPC of the nuclear power plant (NPP) may cause station blackout (SBO) and pose a great risk to the integrity of the reactor core. Recently, OPCs have occurred in power systems at NPPs around the world, but many problems have been reported that failures are not detected by the current protection systems. Accordingly, the NPPs in Korea, which have a power system similar to that of overseas NPPs, also need to review the risks to OPCs. In this paper, the occurrence of OPCs was simulated for Korea's standard NPPs using ETAP, and the current protective relays were analyzed and compared for each scenario. In addition, based on these analysis results, countermeasures against OPCs are proposed in order to secure reactor core integrity. It is considered that this paper can be used as a technical background material necessary for improving the on/off-site power protection system of NPPs and establishing related detailed regulatory guidelines in the future. [ABSTRACT FROM AUTHOR]

Extreme weather events such as tropical cyclones (TC) are considered the most significant weather events causing severe damage to the power distribution grid. However, few or no studies have been performed at the perspectives of the power system although it is necessary, but a relevant study has recently received attention in South Korea. Accordingly, the motivation of this paper addresses the need to present a study of the impact of TCs, focusing on the analysis and the estimation of TC-induced damage. The study uses three sets of historical data: TC data, power distribution grid damage data, and local weather data from 2008 to 2018. Using these data, first, this paper provides an overview of the damage characteristics to understand power system damage. TC, local weather, and TC-induced damage data are analyzed to find their correlated characteristic. Then, TCs are classified by their track patterns associated with the damage characteristic. The importance of TC track is proved by the significance test and comparing the damages by TC tracks. Furthermore, the underlying key hazard variables (i.e., TC and local weather variables) associated with TC-induced damages are identified. Lastly, this paper proposes a multi-stage damage estimation methodology to evaluate the intensity of TC-induced damages using identified major TCs and local weather variables as well as TC tracking information. Therefore, this study will provide useful insights to assess TC-induced damages from the power system perspective in South Korea. • The impact of tropical cyclone (TC) on the power grid in South Korea is analyzed. • The correlated characteristics between local weather and damages are found. • TC classified by track patterns and significance is proved by statistical analysis. • The key hazard variables associated with TC-induced damages are identified. • A multi-stage damage estimation method is proposed to evaluate TC-induced damages. [ABSTRACT FROM AUTHOR]

The Korean utility, KHNP (Korea Hydro and Nuclear Power Co.), carried out a three-year research study in July 2016 (named the Korean SOARCA: K-SOARCA), which is similar with the United State (US) SOARCA (State-of-the-Art Reactor Consequence Analysis) study. One of the two reference reactors designated for the study was Advanced Power Reactor of 1400 MWe (APR1400). To consider the integral response of the reactor, steam generators, and containment to severe accidents initiated by the representative accident sequences selected from the plant-specific Probabilistic Safety Assessment (PSA) results, dedicated modeling was performed for a broad spectrum of plant safety/mitigation systems and relevant phenomena influencing the evolution of the accident. The influence of dedicated accident mitigation strategies on the evolution of accident progression and fission product source terms was also investigated to assess their effectiveness in coping with relevant severe accident sequences. Especially, relating to the APR1400 SOARCA, the latest versions of two severe accident analysis codes, MELCOR 2.2 and MAAP5.04, were used to compare the analysis results of interest. This paper presents best-practice analysis results for the evolution of key accidents and plant response including the environmental release of radiological fission product Cs (cesium), as implemented through the APR1400 SOARCA study, and relevant insights. • The SOARCA study was carried out for the Korean APR1400. • The latest versions of MELCOR and MAAP5 were employed for the study. • Best-practice modeling was performed for plant safety/mitigation systems. • Key results & relevant insights were summarized in terms of points of interest. [ABSTRACT FROM AUTHOR]

• This work focuses on the PV power output forecasting using time series algorithms. • The input datasets are selected PV data from north region of South Korea. • Feature selection for time series algorithms is based on different seasons and climate changes. • PV power generation forecasting by different AI models has carried out on an hourly basis to test efficacy. • LSTM model shows the lowest error rate for quick PV power generation forecasting. The present photovoltaic (PV) power generation systems are globally facing the irregularity problem in the distribution of PV generation. In particular, the exact PV power forecasting is critical for grid-connected photovoltaic (PV) systems under unwanted changes in environmental circumstances. The grid energy management, grid operation and scheduling are important factors to forecast the PV power output. Time series analysis is one of the most important aspects of PV output prediction, especially in places (in South Korea) where past solar radiation data or other weather parameters have not been recorded. In this paper, a variety of time-series methods including deep-learning algorithm and machine learning algorithms was used to predict the PV power generation output for quick respond to equipment and panel defects. For designing AI models, the input data were characterized by dividing seasons and choosing the multiple parameters from seasons. In this study, the photovoltaic power generation data was collected from Ansan city, South Korea during January 2017 to June 2021 and the weather data was collected from Suwon city, South Korea during January 2017 to June 2021. In this work, approx. 40,000 hours of operation data from 1.5 MW grid-connected PV system in South Korea was used. PV power generation forecasting was carried out on an hourly basis to test efficacy of various models. Among all models (Holt-Winters, Multivariate Linear Regression, ARIMA, SARIMA, ARIMAX, SARIMAX), LSTM model presented the lowest error rate as compared to other models for quick PV power generation forecasting. [ABSTRACT FROM AUTHOR]

• Spectral noise reduction using the forecasting characteristics of the LSTM network for ultrasonic signals. • Data reduction without compromising the spectral density amplitude of the existing nonlinear modulation components. • Experimental validation and performance evaluation using the signals obtained from an aluminum plate, attachment lug specimen, and real bridge in South Korea. This paper presents a spectral noise and data reduction technique based on long short-term memory (LSTM) network for nonlinear ultrasonic modulation-based fatigue crack detection. The amplitudes of the nonlinear modulation components created by a micro fatigue crack are often very small and masked by noise. In addition, the collection of large amounts of data is often undesirable owing to the limited power, data storage, and data transmission bandwidth of monitoring systems. To tackle the issues, an LSTM network was applied to ultrasonic signals to reduce the noise level and the amount of data. The proposed technique offers the following benefits: (1) spectral noise reduction using the LSTM network for ultrasonic signals and (2) data reduction without compromising the spectral density amplitude of the existing nonlinear modulation components. Finally, the performance evaluation was conducted using the data obtained from complex geometry and real structure under external noises, indicating that the proposed method can be applied to various structures. [ABSTRACT FROM AUTHOR]

With an intention to reduce greenhouse gas emissions, the South Korean government decided to retire thirty coal-fired power plants early by 2034. Rather than dismantling all of them, some of them are proposed to be used as cold reserves. This paper seeks to explore the public preference and cost bearing for utilization of early retiring coal-fired power plants as a cold reserve by adopting a contingent valuation. To this end, the public willingness to pay (WTP) for the utilization was elicited through a survey of 1000 South Korean households. The payment vehicle was set to be an increase in monthly electricity bill. The method of inducing the WTP was determined as one-and-one-half-bounded questioning. The spike model was selected to reflect the WTP observations with zeros. The average monthly household WTP was estimated as KRW 1596 (USD 1.32), which was worth 3.6% of the average monthly electricity bill (KRW 43,950 or USD 36.32) per household. If the average WTP is re-calculated for all households in the country, it will reach KRW 263.52 billion (USD 217.80 million) a year. Therefore, the utilization can be introduced to the extent that this level of cost is incurred. Several issues that need to be solved in order for the utilization to succeed are also discussed. [Display omitted] • Utilization of early retiring coal-fired power plants as cold reserves is proposed. • Public preference and cost bearing for the utilization were examined. • Data on public willingness to pay (WTP) for the utilization were gathered. • Monthly mean WTP was KRW 1596 (USD 1.32), which was 3.6% of the electricity bill. • Its national value reached KRW 263.52 billion (USD 217.80 million) a year. [ABSTRACT FROM AUTHOR]

The Wolsung Nuclear Power Plant in Korea is under IAEA safeguards. During IAEA physical inventory verifications, the Optical Fiber radiation Probe System has been used since 2007 as the official instrument for verification of spent fuel. Its low sensitivity requires excessive time and labor to perform the IAEA safeguard activities and increases the operator load for preparing a dangerous and time-consuming secondary inspection. The objective of this paper is to develop a superior new optical fiber-based scintillation detector to deal with such issues. To increase the sensitivity of the instrument, a new optical fiber-based scintillation detector was developed by combining a p-terphenyl scintillator with a new type of optical fiber. A field test was carried out in the CANDU Wolsung Unit 1 to characterize the performance of the new detector. The field test showed that the OFPS using the new optical fiber-based scintillation detector has at least 100-times higher sensitivity and can also measure even very weak signals not measurable by the existing detector. From the experimental results, we can conclude that the newly developed OFPS detector would increase the effectiveness and efficiency of nuclear safeguards for verification of spent fuel in wet storage pools of CANDU reactors. • A new optical fiber-based scintillation detector has been assessed for spent-fuel safeguards. • The detector is made of a p-terphenyl organic scintillator to enhance the light yield and response time. • The signals of spent-fuel bundles with low activity were observed with the new detector. [ABSTRACT FROM AUTHOR]

District 2-2 (or Saerom-dong), located in Sejong smart city, is designated as a 'Specially Designed Zone for Women'. The purpose of this paper is to investigate the gendered dimensions of this smart city district through gender analysis. This paper analyzes the planning, implementation, and post-occupancy stages of the district through a review of guidelines of gender-mainstreaming policies of Korean cities, interviews of experts involved, and a survey of 100 residents to gain an insight into how users perceive the spatial aspects of the women-friendly walking environment. Results show that, at the planning stage, little interprofessional communication occurs between smart city experts and 'women-friendly city' experts. At the implementation stage, this leads to incomplete gender-mainstreaming of the district. Post-occupancy survey results reveal, however, that despite these shortcomings, both women and men perceive the 'Specially Designed Zone for Women' to be safe. Even so, there is a gender difference in the perception regarding smart installations. CCTVs are more important to men than women, while on-site emergency bells are more significant to women than men. This indicates that women need more active safety systems than passive surveillance systems. This paper argues that a gendered perspective is needed for the more inclusive planning of smart cities. • The gendered dimensions of Sejong's smart city district are investigated in three stages. • There is little interprofessional communication at the planning stages. • This led to incomplete gender-mainstreaming of the district at the implementation stage. • Post-occupancy survey results reveal that both women and men perceive the 'Specially Designed Zone for Women' to be safe. • A gendered perspective is needed as women need more active safety systems than passive surveillance systems. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic has caused the death of millions of people, and PCR testing is widely used as the gold standard method to detect the infections to restrict the outbreak. Through the interviews conducted with people from the field in South Korea, the UK, and Turkey, we have found that there are numerous testing strategies worldwide. Those testing strategies include drive-through and home delivery testing capabilities, local test sites, and mobile test centers. Our primary motivation is to propose a generic model based on the best practices in the UK and South Korea. Also, we aim to present a case study on Turkey for the implementation of vital procedures and increase their availability. This paper represents a study on how to construct a temporary testing logistics system during the initial phases of pandemics to increase the availability of PCR testing with the primary objective of maximizing total sample collection. The design also considers minimizing the maximum walking distance to increase the convenience of sample collection for the people living in the neighborhoods. The proposed system consists of temporary testing centers and a central laboratory. Temporary testing centers perform direct tours to the potential areas to collect samples and bring the collected sample to the designated central laboratories located at central hospitals. Moreover, to represent the non-linear inheritance of the pandemic progress within a population, we consider diminishing sample potentials over time and coverage. This new problem is defined as an extension of the Selective Vehicle Routing Problem and Covering Tour Problem. We propose a mathematical model and four two-stage math-heuristic algorithms to determine the location and routing of the temporary testing centers and their lengths of stay at each visited location. The performances of the proposed solution methodologies are tested on two data sets. The first set is constructed by the confirmed cases of the districts of Seoul, Korea, and by the interview of health personnel of H+ Yangji Hospital COVID-19 semi-mobile booth application, and the second set is constructed by 99 hospital/health centers from distinct neighborhoods of 22 districts of Istanbul, Turkey. The Pareto set of optimum solutions is generated based on total sample collection and maximum walking distance. Finally, sensitivity analyses on some design parameters are conducted. • We present a variant of Covering Tour Problem and Selective Vehicle Routing Problem. • We propose exact and heuristic algorithms for this problem. • We use the mathematical model to design a mobile testing system for Turkey. • We test the performance of the solution methods developed with small and large data sets. [ABSTRACT FROM AUTHOR]

This paper attempts to appraise the public value of expanding the introduction of a smart water metering and monitoring system (SWMMS) nationwide by adopting a contingent valuation approach of asking about a consumer's preference for a good using a survey of people. Randomly chosen 1000 households were interviewed using the one-and-one-half-bounded question. The monthly public value was obtained as KRW 862 (USD 0.77) per household, equivalent to 6.6% of a water bill. The monthly related cost was calculated as KRW 425 (USD 0.38) per household. Thus, the introduction of an SWMMS would be socially profitable. • The introduction of a smart water metering and monitoring system is considered. • The public value of the introduction is appraised, applying contingent valuation. • The mean willingness to pay is obtained as KRW 862 per household per month. • The related cost incurred by the introduction is KRW 425 per household per month. • The public value is about two times greater than the cost. [ABSTRACT FROM AUTHOR]

• Prospects of biofuels to replace fossils fuels in thermal power plants is discussed. • Status of power generation facilities that can support this growth was analyzed. • Use of biofuels in existing thermal power plants is proposed. • Biomass from unused forest resources is ideal to meet wood pellet production demand. Since the Renewable Portfolio Standards (RPS) policy was introduced in 2012, the use of biomass and its import has significantly increased in the power generation sector of Korea. Currently, biofuel-fired electricity production is fifty-fold compared to before the implementation of the RPS. However, the rapid growth of bioenergy at the initial stage of the RPS and institutional changes for equity with other renewable energy industries brought about significant changes in the bioenergy market and related industries in a short period. For example, the effect of the policy amendment to reduce dependence on foreign bioenergy and expand the use of domestic biomass had an immediate effect on the market. As a result, the imported biomass and bio-SRF began to shrink, and these markets had −1 % and −36 % CAGR between 2018 and 2020, respectively. Meanwhile, the policy contributes to revitalizing the domestic bioenergy industry, and it is predicted that the domestic biomass utilization will be more than 6 million tons in 2050. This paper reports the impact of changes in Korea's bioenergy policy on the power generation sector over the past ten years. When carbon neutrality is essential in all energy fields, the result can be a good reference for establishing bioenergy policies. [ABSTRACT FROM AUTHOR]

• Provide a comprehensive overview of the solidarity in health and long-term care sectors. • The overall direction of reforms is developing toward universal coverage. • Reforms might be possible without overall consensus on solidarity. • The pay-as-you-go financial arrangements will require strong intergenerational solidarity. • The capacity of informal caregiving of family could be considered. Different from the development of health sector, there are significant variations in the underpinnings and formation timings of solidarity-based long-term care (LTC) systems. This paper compares the historical developmental trajectories and identifies factors of the transitions of the actual practices of solidarity - the scope of community, the scope of interdependence, and the costs of joint action - in the health and LTC sectors in four East Asian countries: Japan, South Korea, Taiwan, and Singapore. Comparative policy analysis. Healthcare tends to be considered a universal value such that its boundary should include all residents, while LTC is closer to an entitlement of citizens. The differing solidarity reflects path dependency as well as historical legacies and policy diffusion between the health and LTC sectors. In both health and LTC sectors, the costs involved in collective service provision are not distributed evenly between generations. Centralized governance is a prevailing feature of health and LTC sectors in East Asia, allowing solidarity-based institutional arrangements to be established without an overall sense of solidarity. The relationship between solidarity and health and LTC systems is complex and dynamic. [ABSTRACT FROM AUTHOR]

• Developed a Neural Network foreign exchange trading system using CCS-IRS basis. • The NN foreign exchange trading model outperforms XGBoost and LR model in USDKRW. • The NN trading model outperforms a buy-n-hold and sell-n-hold strategies in USDKRW. • The CCS-IRS basis replaces the CDS premium as a new leading indicator in USDKRW. This study aims to develop a foreign exchange trading model with a new leading indicator based on cross-currency swap (CCS) and interest rate swap (IRS) rates. We derive the correlation dimension from the chaos analysis, use Neural Networks (NNs) to optimize foreign exchange trading, and then compare the performance of the NNs model with XGBoost and Logistic Regression model (LR). The CCS-IRS basis is the spread between the cross-currency swap (CCS) rate and interest rate swap (IRS) rate and is often used as an indicator for foreign exchange soundness along with the CDS premium in Korea. Based on the phenomenon in which the USDKRW exchange rate rises (falls) when the CCS-IRS basis widens (tightens), this study proposes to use the CCS-IRS basis to build a foreign exchange trading model of the USDKRW exchange rate. The experimental results show that our NNs trading model of the USDKRW exchange rate outperforms XGBoost and LR model, generating an annual return of 4.888%∼8.464%, whereas the buy-and-hold and sell-and-hold strategies in the USDKRW exchange rate market produces an annual return of 2.2% and −1.15%, respectively. The foreign exchange trading model established in this paper not only enables foreign exchange traders to obtain sustainable economic profits, but also contributes to the efficiency of the financial market, thereby contributing to sustainable economic growth. [ABSTRACT FROM AUTHOR]

Growing international trade is likely to become a major obstacle in reducing carbon emissions. Research that estimates trade-related emissions and analyzes the sources will be important to overcoming this obstacle. With this in mind, the current paper investigates carbon emissions embodied in South Korea's trade during 2000–2014. Using multi-regional input-output analysis, we find large increases in emissions embodied in exports (production-based emissions) and imports (consumption-based emissions) which drove increases in Korean emissions. Over time, Korea's net carbon exports grew alongside its net value-added exports, implying a greater trade-off between the environmental costs and economic benefits of trade. However, emission intensities of exports and imports both fell which suggests improvements in emission efficiencies at home and abroad. Analysis of bilateral trade shows that growth in trade with China accounted for the largest share of increases in Korea's embodied emissions. Analysis at the sectoral level shows high levels of emissions relative to value added in basic materials and transportation services but low levels of emissions relative to value added in electronic and mechanical equipment and transportation equipment. Lastly, structural decomposition analysis indicates that improvements in emission intensities limited growth in emissions, while rising levels of final demand expanded growth in emissions. As the first extensive study to analyze the drivers of changes in Korea's carbon emissions embodied in multilateral exports and imports, this paper recommends various abatement policies to effectively reduce emissions. [Display omitted] • CO 2 emissions embodied in Korea's trade during 2000–2014 were estimated. • Production-based and consumption-based CO 2 emissions were measured. • Multi-regional input-output and structural decomposition methods were applied. • Korea's net CO 2 emissions exports increased considerably during 2007–2014. • The scale of final demand contributed most to the rise in Korea's emissions. [ABSTRACT FROM AUTHOR]

Building energy consumption and carbon emissions must be reduced to decrease the rate of global warming. To improve the energy efficiency of historic buildings, energy retrofitting can serve as an effective strategy. Retrofitting historic buildings is difficult due to the conflict between the historical value and usefulness of the building, but a modern scientific approach can help ensure that valuable historical records and artifacts remain protected and available. In addition, the retrofit of historic buildings can contribute, although a small part, to reducing carbon emissions nationally, and can have a symbolic meaning as a combination of history and modernity. This paper proposes a package of appropriate measures to improve energy efficiency, reduce the energy consumption of buildings, and preserve regularly used historic buildings for 100-year-old wooden truss and masonry composite structured historic buildings in Korea. The proposed measures are applied to most historic buildings in Korea built in the 1920s and have the potential to improve building performance. A new approach to applying energy technology to historic buildings is proposed. The energy application suitability analysis process was established by integrating three analysis techniques: the energy-saving effect analysis technique, historical value impact evaluation technique, and economic analysis technique. To achieve an appropriate retrofit plan, the condition of a building was evaluated through on-site diagnosis. Thermal transmittance rate assessments, temperature, and humidity monitoring, and infrared thermal imaging analyses were performed to evaluate the building's condition. Consequently, thermal transmittance rates for the wall and roof of the historic building were 1.18 W/m2K and 1.67 W/m2K, respectively, which indicated a risk of excessive energy consumption and condensation. Then, the suitability of each energy technology was evaluated by analyzing its economic feasibility and impact on the historical value and by simulating the reduction in energy consumption of the building. Thereafter, based on the analyses, the energy technologies deemed suitable were integrated to form an energy-efficient retrofit package, and an appropriate solution was elucidated. In conclusion, the package with the lowest impact on the historical value and the highest energy reduction decreased the total energy consumption by 15.9%. According to the results of this study, the process and package solutions proposed in this study are appropriate measures for historic buildings built in the 1920s in Korea, and can be a strategy with the potential to reduce carbon emissions and energy. • A retrofit plan for a historic building in the 1920s in Korea was studied. • Retrofit packages were applied to reduce energy consumption. • A suitability assessment integrating three analysis methods has been proposed. • The appropriate retrofit scenario was derived in consideration of the historical value. • Applying the optimal retrofit scenario could realize an energy saving of 15.9%. [ABSTRACT FROM AUTHOR]

• We examine the impact of social infrastructure, defined as expenditure in education, childcare, health and social care. • Higher social infrastructure expenditure increases female employment more than male employment in the short run. • Higher social expenditures raise both male and female employment in the medium run due to increasing output. • South Korean economy is gender equality-led, although the impact of gender pay gaps on growth is relatively small. • Sustainable equitable development requires a mix of both labour market and fiscal policies. This paper examines the short-run and medium-run impact of spending in social infrastructure, defined as expenditure in education, childcare, health and social care, wages and gender pay gap on output and employment of men and women for the case of South Korea. Based on a gendered post-Kaleckian feminist macroeconomic theoretical model, we estimate the macroeconomic effects of social expenditure, wages and gender pay gap using a structural vector autoregression (SVAR) analysis for the period of 1970–2012. The results show that an increase in the public social infrastructure significantly increases the total non-agricultural output and employment in South Korea both in the short and medium run. Moreover, we find that higher social infrastructure expenditure increases female employment more than male employment in the short run and raises both male and female employment in the medium run due to increasing output. Finally, the results show that South Korean economy is gender equality-led in the medium run, although the effects are economically small in comparison to the strong effects of increases social infrastructure spending. The results indicate that sustainable equitable development and a substantial increase in employment requires a mix of both labour market and fiscal policies. [ABSTRACT FROM AUTHOR]

• Development of a nuclear power plant decommissioning cost estimation program algorithm based on the CANDU structure. • Developed by supplementing the disadvantages of the established nuclear power plant decommissioning cost estimation program, such as repetitive work and difficulty in correcting errors. • Some input items for cost estimation can be automatically entered, and the user can select the desired work object. As part of the safety evaluation during the decommissioning process of Kori Unit 1, candidate scenarios were selected using the risk matrix method. Since Korea has no experience in dismantling commercial nuclear power plants, systematic research on how to select accident scenarios in risk assessment is performed. In this paper, the priority of 11 candidate scenarios selected by the risk matrix method is determined using the AHP method to facilitate the selection of additional scenarios. [ABSTRACT FROM AUTHOR]

• Performance analysis of a commercial pressurized water reactor core loaded with accident-tolerant fuel. • Development of accident-tolerant fuel containing Mo/Cr metallic microcell UO 2 pellets and a CrAl coating on the cladding. • The metallic microcell UO 2 pellet can increase the thermal conductivity of fuel pellets. • Systematic reactor core performance and safety analysis of the reactor core with accident-tolerant fuels from a neutronic perspective. This paper presents a performance analysis of a commercial pressurized water reactor (PWR) core loaded with accident-tolerant fuel (ATF) containing Mo/Cr metallic microcell UO 2 pellets and a CrAl coating on the cladding. Metallic microcell UO 2 pellets using additive materials, such as Mo and Cr, with coated cladding have been developed by the Korea Atomic Energy Research Institute (KAERI) for an OPR-1000 reactor. As demonstrated by the fuel pellet developed at KAERI, a metallic microcell UO 2 pellet can increase the thermal conductivity of fuel pellets by forming a metallic film that encapsulates the UO 2 to connect the metallic microcell and UO 2 pellets; however, there has been no systematic core performance and safety analysis of the core with ATFs. In this study, core analysis with ATFs was conducted from a neutronic perspective. 1) The fuel assembly (FA) containing Mo/Cr metallic microcell UO 2 pellets was analyzed using the lattice code STREAM developed by Ulsan National Institute of Science and Technology in terms of the Mo depletion chain and the impact of resonance treatment. 2) The initial and equilibrium cores were analyzed using STREAM/RAST-K 2.0 with respect to the impact of the additives and CrAl coating on the cycle length, power peaking, and fuel temperatures, etc. The reductions in the equilibrium core cycle length were 61, 44, and 65 effective full power days for each material type. 3) Finally, a rod ejection accident (REA) analysis was performed on the core using a Mo metallic microcell UO 2 pellet and CrAl-coated ATF. Moreover, the maximum fuel centerline temperature of the ATF core was determined to be approximately 200 ℃ lower than that of the reference UO 2 core. Furthermore, the presence of additional thermal margins in ATF cores for steady-state and accident transient scenarios was verified through a systematic neutronic analysis, and future studies will focus on the optimization of the ATF-loaded core by exploiting the extra margins achieved by ATFs and increasing the cycle length. [ABSTRACT FROM AUTHOR]

In order to address the continuing growth of demands on airspace capacity, various navigation methods have been developed such as Area Navigation (RNAV), which allows pilots and air traffic controllers to have a higher degree of freedom in the airspace, but at the same time, the airspace becomes more complex than ever, and maintaining the safety and efficiency becomes challenging. To develop assistant tools for such situation, this paper proposes (i) a trajectory pattern identification framework that can identify complex and diverse trajectory patterns in the RNAV terminal airspace, and (ii) a recurrent neural network-based real-time trajectory pattern classification framework that is necessary for real-time air traffic applications. The proposed frameworks are tested with the real air traffic data recorded at Incheon International Airport, South Korea, in 2019, and evaluated by predicting estimated time of arrival in a real-time manner. • Area navigation terminal airspace has complex operations. • Trajectory patterns in the area navigation terminal airspace can now be identified. • Real-time trajectory pattern classification is achieved. • Automated air traffic assistant tools can be better developed and implemented. [ABSTRACT FROM AUTHOR]

• We suggest a method for automatically finding potential business partners. • Deep learning is applied to capture transaction patterns between companies. • The proposed model is effective to identify potential business partners. • Recommendation performances of the proposed model superior to conventional ones. Potential business partner (BP) recommendation is one of the most important issues for companies to increase sales opportunities by discovering new candidate buyers. Recommendation at a low cost and automation is especially essential for small and medium businesses. However, identifying potential BPs has been regarded as a challenging task since not only an analysis of business characteristics for each candidate company by human experts is required but also an investigation of all the possible combinations of their matchings is necessary. Therefore, in this paper, we propose novel BP recommendation models, called deep business partner recommendation (DBR) models, that aim to automatically suggest potential BPs. Specifically, deep learning technique is applied to understand hidden transaction patterns between companies with various industrial sectors and product properties through the two-phases involving i) BP relation representation phase and ii) training and testing phase. In the former, for each company, its features including the industrial sector, product property, relative transaction volume, and geographical distance are embedded into a vector for utilizing as the input of the proposed models. In the latter, the proposed DBR models repeatedly use the input values to capture the hidden transaction patterns between companies in the training. In the testing phase, the extensive experiments conducted to evaluate the BP recommendation performances of the proposed DBR models using a real-world dataset consisting of transaction records among companies in South Korea. The experiment results show that the suggested DBR models significantly outperform the conventional models, in terms of the accuracy for the BP recommendation tasks. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermal potential estimation of water source and their energy systems are reviewed. • Practical potential mainly depends on the geographical and temporal potential. • Pumping-well system requires high installation costs but can use high temperature. • Critical parameters for improving performance of entire system were investigated. In recent years, the utilization of a water source heat pump system (WSHP) has been proposed in South Korea as a solution to reduce energy consumption of buildings. To practically utilize the thermal energy of a water source, it is necessary to estimate the thermal potential of water sources and apply suitable energy conversion systems. However, there are extremely limited studies that conduct in-depth analyses to estimate the thermal potential of water sources and the energy conversion systems of water source heat pumps. In this context, this paper focuses on the review of the main issues in thermal potential estimation and energy conversion systems of water source heat pump systems. The estimation of thermal potential was reviewed in terms of theoretical, geographical, environmental, technical, and temporal potentials. The components constituting the system were investigated by dividing into heat pumps, water intake systems, and air handling units. In addition, the critical parameters for improving the performance of the entire system, such as building type, water source, and water intake distance, were investigated. It is expected that this study will be a valuable supplement to enable the thermal energy utilization of water sources to meet the growing energy demands. [ABSTRACT FROM AUTHOR]

A Radioactive Waste Information Management System (RAWINGS) currently in operation mainly manages the inventory and history of the operating waste. The system has the disadvantages of the entered information needing to be transferred manually from the site to the system, information getting incorrectly entered during the process or information going missing. Recently, the Nuclear Safety and Security Commission (NSSC) and Korea Radioactive Waste Agency (KORAD) called for the development of a digital system that can show information transparently in real-time regarding the preliminary inspections of RAdioactive Waste (RAW) and the assessment of its suitability for disposal before the radioactive waste is delivered to the disposal site. A Digital Twin (DT)system is being developed for the safety management of radioactive waste to address the problems that these systems have and meet the needs of disposal operators. This paper introduces the DT technology that uses Augmented Reality (AR) technology enabling users to check the contents of small-packaged wastes in radioactive waste drums without opening them, Internet of Things (IoT) sensor technology that checks the status of the drums in the radioactive waste storage and the RAWINGS system. Based on the performance of a prototype Digital Twin consisting of three modules (AR, IoT and RAWINGS), the augmented reality enables users to see the shape information and filling rate of small-packaged wastes in the radioactive waste drums and includes Quick Response (QR) code management. The basic data of the radioactive waste used in the augmented reality, as well as small packaged wastes and repackaged drums, were processed in conjunction with RAWINGS. In addition, real-time monitoring of radioactive waste drums loaded in the designated space (Y zone: an area where combustible waste is loaded within radioactive waste storage and TEST area: a section where drums scheduled to be transported to the disposal site are loaded) of the radioactive waste storage was possible by transmitting IoT sensor signals attached to the drum to the digital twin. Currently, augmented reality has an important role in enhancing the visibility and intuitiveness of radioactive waste information for radioactive waste managers and workers by overlapping digital information about radioactive waste storage. Due to the nature of radioactive waste, it is difficult to know what waste is inside the enclosed drum. However, the results of this study confirmed that waste contained in radioactive waste drums can be identified in real time in the Digital Twin rather than in the radioactive waste storage. This technology will be useful in determining the conformity of the radioactive waste acceptance criteria required by KORAD before the delivery of radioactive waste drums to disposal sites. [ABSTRACT FROM AUTHOR]

With the frequent coastal activities of human beings, tens of thousands of tons of floating marine plastics pose a serious threat to marine ecosystems and human survival. This paper studies the feasibility of regional marine plastic garbage management, taking the Northwest Pacific as the research object. According to the Cobb-Douglas utility function of the costs and benefits of plastic processing in China, Japan and South Korea, the Shapley value method was used to analyze it. The study found that the data were substituted into the Shapley value model of cooperative game for calculation, and the results showed that any country joining the triple alliance U would create greater benefits, and obtain higher benefits than the sub alliance. It is a triple win situation for China, Japan and the South Korea to jointly manage marine plastic in the Northwest Pacific. The results of the model confirm that the three countries are willing to maintain long-term cooperation on marine plastic governance, this method can be used to solve the trans-national marine governance innovatively and promote regional ocean cooperation governance. At the same time, by comparing the Northwest Pacific Plan with other action plans, China, Japan and South Korea should abandon political controversy, seeking common ground while putting aside differences, to cooperate fully to curb the growing trend of plastic waste problem in the Northwest Pacific, and contribute to the sustainable development of regional marine. [ABSTRACT FROM AUTHOR]

Due to high operation cost of dedicated inspection vehicles, conventional pavement management systems (PMS) suffer from limited data quantity collected from periodic inspections. However, increasing market penetration of connected autonomous vehicles (CAVs) offers opportunities to monitor pavement conditions more frequently through sensors, including vision cameras and accelerometers, originally installed for autonomous driving. In this paper, we proposed an autonomous condition monitoring-based pavement management system (ACM-PMS) with real-time data collection using CAVs traveling voluntarily. We presented a novel mathematical framework to evaluate potential benefits of ACM-PMS in reducing social costs for both users and agency, systematically accounting for its unique three advantages: (i) large amount of condition data increases prediction model accuracy; (ii) aggregated measurement of current facility condition improves inspection accuracy; (iii) agency can perform maintenance activities at optimal timings, achieving continuous-time and condition-based policies. Results of numerical examples confirm that ACM-PMS significantly reduces the social cost of conventional PMS. • Propose an initial concept of an autonomous condition monitoring-based pavement management system (ACM-PMS) • Identify and evaluate potential benefits of the ACM-PMS • Compare between a conventional pavement management system and the ACM-PMS • Measure information values according to the accuracies of prediction and inspection • Analyze a real-world expressway in South Korea [ABSTRACT FROM AUTHOR]

• Immersed tunnel in a seismic sensitive area consisting of 18 tunnel elements. • Total length of 3.6 km, maximum depth of the tunnel is 48 m below sea level. • Founded on a sea bottom varying from weak marine clay to bedrock. • Long term wave-weather forecast system, 10 cm wave height accuracy 4 days in advance. • Fine positioning of TE's by an External Positioning System (EPS). The Busan Geoje Fixed Link project is one of the landmark projects in South Korea, providing a road connection between Gaduk and Geoje Island near to the city of Busan at the Southern tip of the Korean peninsula. The project consists of two Cable Stayed Bridges, an Immersed Tunnel, rock tunnels and it has a total length is over 8 km. This paper will focus on the immersed tunnel part of the link and the particular challenges that the project faced in designing and constructing the tunnel, these challenges required a significant evaluation in immersed tunnel technology. The tunnel consists of 18 tunnel elements and has a total length of 3.6 km. The tunnel location was characterised by the following conditions: • a large depth, maximum depth of the tunnel is 48 m below sea level. • the tunnel is located in a seismic sensitive area. • the tunnel is founded largely on weak marine clay, but parts also on bedrock. • part of the tunnel was constructed on a sub sea embankment which is also founded on a weak marine clay. • the tunnel has been constructed in a location that is facing to the Sea of japan and is exposed to a severe wind and wave environment with large long period swell waves present for part of the year. Because for only a few months a year the wave- and weather circumstances were suitable for immersion, extensive numerical and physical model tests were carried out to predict the behaviour of the immersion system for the expected conditions. These model tests were used to determine the wind and wave climate that the immersion system could cope and a go, no-go range of conditions. Additionally, an accurate, sophisticated short and long term wave and weather forecast system was developed. This system enabled forecasts to be made within a 10 cm wave height accuracy, for periods leading up to the element transport and immersion operations which allowed the immersion operations to be possible within acceptable risks. The tunnel elements (TE) were built in batches in a pre-cast yard (PC). After inundation of the dock the elements were transported to a mooring location close to the PC-yard for fitting out and to wait for favourable immersion weather. The elements were immersed using two catamaran pontoons and placed in a previous dredged trench on the sea bed. A taut mooring configuration was used in order to reduce the wave influenced motions to a minimum. Anchor points were created by pre-installed plate anchors. Due to the total length and installation depth of the tunnel the traditional survey systems using towers and total stations were not suitable. Therefore new survey methods were developed, these included a tautwire system and an Ultra Short Base Line (USBL) acoustic system were used for the positioning of the elements during the immersion operation. Fine positioning of the TE's under swell wave influence was done using a special designed External Positioning System (EPS) and placed on the pre-laid screeded gravel bed. [ABSTRACT FROM AUTHOR]

South Korea is suffering from high concentrations of particulate matters in the air caused by domestic and foreign influences, and the government is attempting to reduce nitrogen oxide (NOx) emissions in the transportation sector, which could be causing secondary generation of these emissions. This paper presents a life cycle assessment (LCA) on the six base fuels and finally produced results for WTW NOx emissions for internal combustion engine vehicles (ICEVs) (gasoline), ICEVs (diesel), battery electric vehicles (BEVs) and fuel-cell electric vehicles (FCEVs) through LCA based on electric power generation sources and hydrogen production methods. For the tailpipe emissions of ICEVs, the real driving emission (RDE) NOx emission data from previous studies supported by the Ministry of Environment in South Korea were considered, in addition to the NOx emissions based on the New European Driving Cycle (NEDC) test procedure. As a result, the WTW NOx emissions of ICEVs (gasoline), ICEVs (diesel-euro6), and ICEVs (diesel-euro6d-temp) were calculated at 0.113, 0.776 and 0.154 g/km, respectively, when considering the RDE NOx emissions. In addition, for BEVs, coal, natural gas and power generation mixes were calculated at 0.113, 0.059, 0.065 g/km, respectively, depending on the power generation source. In the case of FCEVs, naphtha cracking (NC), steam methane reforming (SMR with natural gas), SMR (with landfill gas), electrolysis, and production mixes were calculated as 0.088, 0.080, 0.030, 0.254, 0.088 g/km, respectively, according to the hydrogen production method. It was noted that the WTW NOx emissions from the most recent diesel-fueled ICEVs (euro6d-temp) was similar to those of FCEVs and ICEVs (gasoline), although pre-euro6d-temp diesel vehicles suffer from significantly high WTW NOx emissions, mostly from tailpipe emissions under real driving conditions. In this study, it is possible to analyze the life cycle from the actual operational data in a uniform perspective for all vehicle types. In addition, it is able to derive WTW results for alternative fuel vehicles according to various power generation sources and hydrogen production methods and to understand the effect of upstream energy sectors. • Well-to-wheel (WTW) NOx emissions are conducted for ICEVs and AFVs. • Real driving emission and facilities' actual data are used. • Various electric power generation fuels and hydrogen production pathways are considered. • BEVs have the lowest WTW NOx emissions, and FCEVs and ICEVs (gasoline) are on a similar level. • Pre-euro6d-temp diesel vehicles suffer from high WTW emissions under real driving conditions. [ABSTRACT FROM AUTHOR]

This paper tested the relation among macroeconomic variables such as economic growth, current account balance, energy imports and non-economic variable as militarization for the selected countries by employing Markov Switching-Bayesian Vector Auto Regressive approach for China, Israel and South Korea. These countries have high rate of export and import of weapons, the current account surplus, and higher rate of energy imports. The results showed that the relation between the selected variables differs in these countries with respect to their energy and weapon import levels. According to Markov-Switching Bayesian Granger Causality results, if the governments determine the polices in the context of the results of the traditional causality test, it will be impossible to obtain the estimated impacts from the policies. Results determined that Militarization, energy imports, economic growth and current account balance relate to each other for all countries. Energy imports increase in the effect of militarization races and economic growth. The current account of the selected countries are vulnerable to the negative energy shocks and militarization. • Markov-Switching Bayesian Granger Causality test and traditional Granger causality test were applied. • Militarization, energy imports, economic growth and current account balance relate to each other. • Energy imports increase in the effect of militarization races and economic growth. • The current account balances are vulnerable to the negative energy shocks and militarization. [ABSTRACT FROM AUTHOR]

To improve PM 2.5 predictions in Northeast Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM 2.5 observations. In contrast to the conventional method of BEC estimation that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. By taking the emission uncertainty into account, we found that the standard deviations in the BEC were significantly increased. The standard deviations became around three times larger than those in the conventional method at the surface. The impacts of the new BEC were then tested for the prediction of surface PM 2.5 over Northeast Asia using the Community Multiscale Air Quality (CMAQ) model initialized by three-dimensional variational method (3D-VAR). The surface PM 2.5 data measured at 154 sites in South Korea and 1535 sites in China were assimilated every 6 h during the campaign period of the Korea-United States Air Quality Study (KORUS-AQ) (1 May–14 June 2016). The data assimilation with the new BEC showed better agreement with the surface PM 2.5 observations than with the BEC from the conventional method. Our method was also more consistent with the observations in 24-h PM 2.5 predictions than the conventional method (specifically, with a ∼44% reduction of negative biases). We concluded that increased standard deviations, together with updated horizontal and vertical length scales in the new BEC, improved the data assimilation and short-term predictions of the surface PM 2.5. This paper also suggests several research efforts to further improve the BEC for better short-term PM 2.5 predictions in Northeast Asia. [Display omitted] • A new background error covariance (BEC) for PM 2.5 predictions was developed. • The new BEC considers the uncertainties in anthropogenic emissions. • Two different emission inventories were used to account for the uncertainty. • 24-h PM 2.5 predictions were improved with ∼44% fewer negative biases. [ABSTRACT FROM AUTHOR]

The most common reaction to suggesting that we could learn valuable lessons from the way the current pandemic has been/ is being handled, is to discourage the attempt; as it is suggested that it can all be done more accurately and authoritatively after the inevitable Public Inquiry (Slater, 2019). On the other hand, a more constructive approach, is to capture and understand the work that was actually done.This would include normal activities, as well as positive adaptations to challenges and failures that may have occurred. Such an approach aimed at improving what worked, rather than blaming people for what went wrong, has the potential to contribute more successfully to controlling the consequences of the current crisis. Such an approach should thus be aimed at detecting and feeding back lessons from emerging and probably unexpected behaviours and helping to design the system to adapt better to counter the effects. The science and discipline of Human Factors (HF) promotes system resilience. This can be defined as an organisation's ability to adjust its functioning before, during or after significant disturbances (such as a pandemic), enabling adaptation and operation under both anticipated and unanticipated circumstances. A "functional" approach methodology enables the identification of where the system and its various interdependent functions (an activity or set of activities that are required to give a certain output), could be improved and strengthened; if not immediately, at least for the future. Along these lines, suggestions for adding key resilience functions are additionally identified and outlined. The application and insights gained from this functional approach to the 2015 MERS-Cov pandemic in South Korea has been seen as contributing substantially to the effective response to the current crisis in that country (Min, submitted for publication). In this paper, we present an overarching framework for a series of projects that are planned to carry out focussed systems-based analysis to generate learning from key aspects of the COVID-19 pandemic response in the United Kingdom. [ABSTRACT FROM AUTHOR]

This paper aims to develop the mathematical model of electricity based on renewable energy and large-scale utility (eRELU) networks to achieve a low-carbon economy. Huge petrochemical industries allocated in South Korea are considered to evaluate the proposed model by techno-economic and environmental assessment subject to Korean renewable energy policy. The suggested mathematical model consists of two parts: the deterministic model to optimize industrial-scale utility networks and the stochastic model to construct clean electricity networks using variable renewable energy coupled with energy storage systems to provide feasible quantities of renewable electricity required from the optimized utility network. The resulting model is complemented by carbon capture and storage (CCS) systems in doing so the inevitable amount of greenhouse gases from boilers in utility networks can be significantly captured. Diverse scenarios under the uncertain parameters such as facility investment/operating costs and capacity factors of renewable energy are applied to the developed model, and the results show that the best scenario-based eRELU-CCS network reduces 16% of the total costs and capture/mitigate 114 tCO 2 /d comparing to the base case. It is expected that the proposed model will play an essential role in advancing the country's energy transition. [Display omitted] • Mathematical model developed for renewable energy and large-scale utility network. • CCS technology is combined with the optimized utility network for low-carbon economy. • A case study of huge petrochemical industries in South Korea were applied. • Scenario-based techno-economic and environmental assessment was conducted. • 16% of total economic costs and 114 tCO 2 /d of GHG emissions were reduced. [ABSTRACT FROM AUTHOR]

• Development of long term cooling safe shutdown analysis methodology using passive safety system. • Long term cooling safe shutdown performance analysis for the SMART reference plant. • Sensitivity analyses of the passive system failures on the long term cooling safe shutdown performance. Passive safety systems have been adopted in reactor design to enhance safety especially in SMRs after the TMI-2 accident in 1979. The passive system has high operational reliability with natural driving forces such as gravity, natural circulation and stored energy. In recent years, development of innovative SMRs has emerged as a global R&D initiative for sustainable energy to challenge the carbon neutrality of climate change. The passive safety system makes reactor design simple and thus enhances the reliability of the reactor. As advanced reactors adopt passive safety systems, regulatory requirements and guides are required. Thus, development of regulatory guide and detailed regulatory review methodology for the passive safety systems is needed. In this paper, long term cooling safe shutdown acceptance criteria and performance analysis methodology are proposed for the regulatory standard design approval for the SMART100 integral reactor with fully passive system which has been jointly developed by KAERI in Korea and K.A.CARE in Saudi Arabia. The long term cooling safe shutdown performance analyses are performed until 72 hours to evaluate the methodology, and sensitivity analyses are also performed to provide a technical basis for the regulatory independent review for the long term cooling performance of the SMART100 passive safety systems. [ABSTRACT FROM AUTHOR]

The Korea fourth-generation storage ring (Korea-4GSR), with 4-GeV beam energy and 58-pm-rad emittance has been designed by the Pohang Accelerator Laboratory. The evaluated brilliance and coherence are approximately 100 times higher in the hard X-ray region in Korea-4GSR than in the third-generation storage ring at PLS-II. The coherence properties and preservation of coherence through beamline optical elements were studied using SPECTRA code and hybrid method. Result of this paper show essential information for planning experiments and designing beamlines using ultimate low emittance storage ring. [ABSTRACT FROM AUTHOR]

The experience of Singapore and South Korea makes it clear that under certain circumstances massive testing is an effective way for containing the advance of the COVID-19. In this paper, we propose a modified SEIR model which takes into account tracing and massive testing, proving theoretically that more tracing and testing implies a reduction of the total number of infected people in the long run. We apply this model to the spreading of the first wave of the disease in Spain, obtaining numerical results. After that, we introduce a heuristic approach in order to minimize the COVID-19 spreading by planning effective test distributions among the populations of a region over a period of time. As an application, the impact of distributing tests among the counties of New York according to this method is computed in terms of the number of saved infected individuals. • A SEIR model is introduced to analyse the efficiency of test distributions. • It is proved that massive testing helps reducing the number of infected people. • A heuristic method is developed to increase the number of saved infections. • Extensive computational experience quantifies the number of saved infections. [ABSTRACT FROM AUTHOR]

Based on the resource dependence theory and the resource-based view, this study examined the impact of the resources and capabilities of government-funded research institutes (GRIs) on technology transfer. Panel analysis was performed on 21 GRIs in South Korea representing three mission types—basic future leading, public infrastructure, and industrialization—for the 2015–2019 period. The analysis confirmed that the factors affecting technology transfer performance differed among GRIs depending on their mission type. For basic future leading GRIs, the number of technology transfer cases was strongly associated with the number of research personnel, while there was a negative relationship between technology transfer and the total budget, the number of research publications, and the number of patent registrations. None of the variables affected the revenue from technology fees. Researchers at these GRIs appear to have a strong motivation for technology transfer, but the priority for resource allocation at the institutional level is the production of papers and patents rather than technology transfer. For public infrastructure GRIs, the number of patents held and the number of technology licensing office (TLO) personnel had a positive impact on the number of technology transfer cases, while none of the variables affected the revenue from technology fees. Thus, the number of patents is more favorable for technology transfer at this type of GRI compared to those that pursue a mission of basic future leading, possibly because their research focus is more related to engineering than to basic science. For industrialization GRIs, the number of TLO personnel affected the number of cases of technology transfer, and the number of patent registrations and TLO personnel affected the revenue from technology fees. The speed of technology development and industrial application is thus much faster in industrialization GRIs than in the other GRI types. The results of this analysis show that mission attributes are important drivers of technology transfer performance. This study thus offers policy implications by illustrating those different resources should be provided to different types of GRI to optimize their technology transfer performance. [ABSTRACT FROM AUTHOR]

• A 3D nuclear reactor safety analysis code, CUPID-RV, was developed. • Validation calculations related to a LBLOCA were conducted. • A 3D LBLOCA calculation was accomplished for APR1400 PWR. As the need for simulations of multi-dimensional behavior in the safety analysis is increasing, efforts have been devoted to developing a three-dimensional module for the conventional system analysis codes. After having developed a one-dimensional system analysis code, MARS, the Korea Atomic Energy Research Institute (KAERI) began to develop the CUPID code to address the need for a multi-dimensional analysis. Recently, the reactor vessel module, such as a package of interfacial heat and momentum transfer model, wall friction and wall-to-fluid heat transfer model, were implemented in the CUPID and named CUPID-RV module. The final goal of this study was an analysis of the Large Break Loss of Coolant Accident (LBLOCA) at a rod-by-rod level, and the study focused on the validation of LBLOCA thermal-hydraulics phenomena and the application to a three-dimensional LBLOCA simulation. In this paper, we introduce the validations using Edward pipe, UPTF, and FLECHT-SEASET experiments related to the LBLOCA blowdown, refill, and reflood phases and a three-dimensional LBLOCA simulation for the APR1400 PWR reactor pressure vessel using simple boundary conditions at an assembly-by-assembly level as an application. [ABSTRACT FROM AUTHOR]

The evaluation of maritime waterway risk is required to take further advance measure for reduction of ship collision accident. In general, a risk assessment of ship collision can be estimated by analyzing the level of risk for navigation to prevent collision. This paper proposes a framework of real-time multi-ship collision risk assessment, named Habour Traffic Hazard Map to identify encounter ships, using Marine Traffic Hazard Index from the large amounts of monitored vessels in a surveyed sea area. The methodology uses the influence of a ship to the neighborhood to detect high-risk area in busy waterway from a AIS data. The proposed index is validated by comparison with traffic congestion rate of surveyed area. To illustrate the effectiveness of the framework proposed, an experimental case study has been carried out within Busan Port, Korea. The result is shown as hazard map that is effective and efficient at showing the dynamic level of risk and predicting the potential collision locations. Therefore, this model can improve the safety and security of maritime waterway. [Display omitted] • Suggest Marine Traffic Hazard Index to present dynamic risk level. • Demonstrate the degree of risk in a moment of encounter situation. • Detect high-risk area in busy waterway. • Generate the Habour Traffic Hazard Map for risk evaluation. [ABSTRACT FROM AUTHOR]

• Harmonization and quality control of originally heterogenous observational databases. • Large-scale application of machine learning methods in a distributed data network. • Transparent use of open-source software tools and publicly shared analytical code. As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g. , by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world. [ABSTRACT FROM AUTHOR]

• The main functions and design concepts of the HCCR breeding blanket are to be verified in ITER through the HCCR Test Blanket Module (TBM) program. • R&D activities for the HCCR TBM program in the fields of structural and breeder material, TBM set fabrication technology, tritium extraction technology, and helium cooling technology were introduced. • The maximum operating temperature and pressure conditions and thermal properties of Li 2 TiO 3 were determined by investigating the sintering phenomena and pebble degradation. • The capability of absorption and desorption of hydrogen isotopes was investigated in the cryogenic molecular sieves adopted in the TES.. • The performance curves (characteristics) of the circulator obtained with the pressure ratio data were well grouped according to the corrected speed and the corrected mass flow rate under the reference conditions of 80 bar and a 50 °C helium flow. Development of the Helium Cooled Ceramic Reflector (HCCR) breeding blanket is underway according to the development strategy of core technology for K-DEMO under the fusion energy development roadmap in Korea. The main goals are to validate integrated design tools and to develop core technologies in the field of materials, manufacturing and joining technologies, helium cooling and tritium technologies, system integration, and safety technologies. A unique graphite reflector concept was adopted to save cost by reducing the amount of beryllium neutron multiplier. The main functions and design concepts of the HCCR breeding blanket are to be verified in ITER through the HCCR Test Blanket Module (TBM) program. Not only breeder modules are to be tested in ITER but also the technologies of cooling, coolant purification, tritium extraction, etc. are to be tested or proved in ITER before employed in DEMO. This paper introduces the current updates of the design and R&D activities of the HCCR TBM systems in the preliminary design stage. [ABSTRACT FROM AUTHOR]

Although the existing literature on green innovation has little focus on the mediation role of open innovation, we aimed to explore how intellectual property rights and government support affect green innovation through open innovation. Drawing upon open innovation literature, this paper aims to investigate, at the firm level, the structural causality between internal and external resources and examine the role of open innovation on green innovation divided into green process and green product innovation. We attempt to suggest a structural model comparing open innovation's direct and mediating role using data from manufacturing sectors in South Korea from 2014 to 2016. The methodology was based on the partial least square structural equation method (PLS-SEM) with 1203 samples. The results revealed that a firm's intellectual property rights and government support significantly affect open innovation, green process innovation, and green product innovation while open innovation played a mediating role between each. Additionally, we provide a supplementary analysis using the sample-split test by comparing the sensitivity to partner selection and the multigroup test by firm size. This study proposes several implications emphasizing the mediating role of open innovation in enhancing green process innovation along with the direct and indirect effect of intellectual property rights and government support on green innovation. [ABSTRACT FROM AUTHOR]

• A method of considering rocking of rigid foundation is proposed for NLSSI analysis of base-isolated nuclear facility based on two-step ASCE4-16 method. • A refined time-domain NLSSI analysis to a design earthquake in Korea has been performed for base-isolated nuclear structure using BRM and ANSYS software. • Responses obtained using the proposed technique compares very well with those of the refined time-domain NLSSI analysis. Seismic Analysis of Safety-Related Nuclear Structures (ASCE 4-16) offers a two-step nonlinear soil-structure interaction analysis procedure for seismically base-isolated structures based on the seismic isolation design response spectrum. The two steps are (I) development of seismic isolation design response spectrum at the foundation level of an isolated structure using frequency domain soil–structure interaction (SSI) analysis for an equivalent linear SSI system and (II) nonlinear time domain analysis of the isolated structure using foundation input motions, which is consistent with the seismic isolation design response spectrum. In step II, the rocking component of the structure shall be considered when its effects are significant. However, the ASCE 4-16 does not present how to consider the rocking input. In this study, we propose a method of taking account the rocking component of a base-isolated structure with rigid basemat. Specifically, this paper presents a method of developing rocking input motion that is fully coupled with horizontal input motion at the base of the isolated structure. The method was verified by comparing numerical results using the rocking input for a base-isolated nuclear power plant with rigid basemat on a compliant soil medium with those of a refined nonlinear response history analysis. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of catching and convergence at the level of firms, and investigates whether Korean firms tend to converge toward mature firms represented by the US firms in terms of their behavior and performance as well as firm-level innovations systems. It conducts regression analyses of several behavior and performance variables, using the data of the Korean and US firms during the 1990s, the 2000s, and 2010s. It finds some evidence of convergence, such that Korean firms become more profitability- rather than growth-oriented, borrowing and investing less, and thus being less indebted. However, they have not changed much in terms of their behavior toward firm values and dividend tendencies. Further analyses, using the patent-derived, innovation system variables, also confirm some aspects of convergence, compared with the early results, for which self-citations become significant and positive for firm values; furthermore, the variable of cycle time of technology is no longer significant for profitability, which is consistent with the results from the US firms. Meanwhile, changes in corporate governance associated with the rise of foreign shareholder are also shown to have resulted in higher profitability but insignificant change in firm values. An emerging conclusion is an ongoing but partially completed process of convergence. [ABSTRACT FROM AUTHOR]

• This study presents hydrological forecasts over South Korea for one to seven days. • We utilize a stacking ensemble framework to improve the forecast skill. • Base-model diversity has a primary role in enhancing the forecast abilities. This study presents the potential of hydrological ensemble forecasts over South Korea for medium-range forecast lead times (1–7 days). To generate hydrological forecasts, this study utilizes a framework based on stacking ensemble learning, an emerging machine learning technique that includes a two-level structure: base-learner and meta-learner models. In particular, the present research contributes to hydrological post-processing techniques by: (1) introducing a penalized quantile regression-based meta-learner to generate probabilistic predictions, (2) considering modeled climate predictions and antecedent hydrologic conditions simultaneously for regional hydrological forecast development, and (3) quantifying the skill enhancements from the multi-model forecasts under the stacking generalization. The proposed model is evaluated in massive 473 grid cells along with nine additional simpler models to test the specific hypotheses introduced in this study. Results indicate that our proposed forecasts can be used for relatively short lead times. In addition, results demonstrate that utilizing a penalized probabilistic meta-learner and antecedent conditions contributes to the forecast skill improvements. Lastly, we find that base-model diversity outperforms increased ensemble size alone in enhancing the forecast abilities under the stacking ensemble generalization. We conclude this paper with a discussion of possible forecast model improvements from an adaptation of additional information from input and model structures under the stacking generalization. [ABSTRACT FROM AUTHOR]

The increasing integration of wind energy generation into energy systems has led to difficulties in power flow calculations due to uncertainty and variability, which significantly affect the stability and reliability of power grids. Consequently, it is critical to evaluate the energy security limit of power grids based on probabilistic approaches. In this paper, we propose the probabilistic modeling of wind energy potential for power grid expansion planning. The proposed probabilistic model estimates wind energy potential through Weibull distribution, Monte Carlo Simulation (MCS), and the enhanced spatial modeling based on universal kriging. It can be used to establish the expansion plan for transmission and distribution facilities to resolve the variability and uncertainty issues of wind generation resources, which is expected to increase the penetration levels of renewable energy. To validate the proposed model, the empirical data from the Jeju Island's wind farms are considered in South Korea. [ABSTRACT FROM AUTHOR]

This paper presents a thermochemical process design and techno-economic analysis for the production of methanol (MeOH) from Kenaf cultivated in Korea. The proposed process combines three main subsystems to allow for the processing of 2000 tons of kenaf a day: kenaf-to-syngas, syngas-to-MeOH, and a combined heat and power cycle. In the first subsystem, clean syngas is produced from kenaf via a two-step carbonization and steam gasification process. The clean syngas is then catalytically upgraded to MeOH using a commercial Cu/ZnO/Al 2 O 3 catalyst. The third subsystem allows for the generation of heat and electricity to close the energy balance by combusting the biomass residues. Energy analysis and optimization of the heat exchanger network allowed for all energy requirements to be satisfied internally, high overall energy efficiency (60.6%), and the selling of 3.3 MW of excess electricity. Furthermore, an economic feasibility analysis of the process showed that the minimum selling price of MeOH (99.9 wt%) was 0.413 US$/kgMeOH. Importantly, the process economics could be in the range of the market price with respect to incorporating different types of H 2 production technology into the process. • Green methanol production is a promising technology to convert Kenaf. • A process including a two-step of gasification and catalytic upgrading is designed. • Economic feasibility is evaluated by considering process scale. • Sensitivity analysis identify the relative significance of the major key parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • Column and sieve tests can be used for preliminary sizing purposes (14–33% error). • Further calibration against pilot data considerably improves sizing precision (5–9% error). • Estimation of model coefficients from pilot data requires 8 h testing (15 min frequency). • Model structure can be calibrated against pilot data from Canada, Korea, USA, and Australia. Microsieving by rotating belt filters is an engineered fractionation method for particle separation from wastewater that can be used as an alternative to primary clarification due to its smaller footprint and lower total cost of ownership. Because of the multiscale nature of the filtration processes involved, and the interdependent variables controlling performance and the system design, the availability of verified and comprehensively validated models that, based on fundamental principles, can explicitly capture the effect of particle size, mesh size and polymer dose are essential for performance optimization and scale-up. In this paper, a detailed microsieving filtration model was derived by extending Darcy's law to a dual-layer dynamic framework whose predictions were validated against batch (column) and continuous flow (RBF pilot) experimental data. The ability of the microsieving model to accurately capture observed trends confirmed that the model formulation, derived from column experiments, were sufficiently accurate and flexible to capture the relevant physics involved, with a relative error in performance scale up (from column to pilot) of 33% and 14% for filter capacity and effluent total suspended solids, respectively. Upon further calibration against pilot data, the relative error was reduced to 9% and 5%, respectively, indicating the suitability of the model structure in further adapting to filtration process conditions occurring at pilot scale. Finally, the calibrated model was used to derive guidance for future pilot studies. It was determined that, under naturally varying influent conditions, at least 8 h of continuous pilot data, with sampling frequency of at least 15 min for filter capacity and suspended solids concentration, were necessary for a satisfactory estimate of the model parameters. [ABSTRACT FROM AUTHOR]